B A

The analysis of Multiple Indicator Cluster Survey data 25% 50% 05

Education in in Light of the MICS Data

2015

Education in Serbia in Light of the MICS Data

The analysis of Multiple Indicator Cluster Survey data

2015 Preface and Acknowledgments

he Republic of Serbia is one of the few countries in which all five rounds of the Multiple Indicator Cluster T Surveys (MICS) have been implemented. The third (2005), fourth (2010) and fifth round (2014) of the MICS included two surveys: a standard one, representative at the national level; and another representative of the population from Roma settlements – with the aim to close the data gap for this very vulnerable population group. Implementation of all rounds of the MICS has allowed for observation of trends in the selected areas. The latest 2014 Serbia MICS and 2014 Serbia Roma Settlements MICS were carried out by the Statistical Office of the Republic of Serbia with technical and financial support provided by the United Nations Children’s Fund (UNICEF). In addition to the traditional MICS indicators, country-specific indicators were included. The standard outputs of the 2014 surveys include a key findings report and the final MICS report. Both reports are of a descriptive nature and present important social and development indicators by selected background characteristics of the households and individuals. In order to utilize the full potential of the surveys, UNICEF supported secondary data analyses in different domains (early childhood development, education, rural/urban disparities, gender equality, child protection and poverty). The analysis presented in this report examines the issue of education in Serbia and was conducted by Dejan Stankovic. We owe special thanks to Professor Sasa Baucal, who provided valuable guidance and peer reviewed the analysis. Thanks are also due to all contributors for their commitment and efforts to bring new knowledge that will inform policymaking and the advancement of child rights. B A

Contents

1. Introduction 5 The analysis of Multiple Indicator 2. Context of the study 6

3. Multiple Indicator Cluster Surveys (MICS) programme 10 Cluster Survey data

4. Goals of the study 11

5. Methodology 12

6. MICS data on education in Serbia 25% 14

6.1 Preparation Programme (PPP) attendance 14

6.2 adjusted net attendance rate 18

6.3 Net primary school completion 21

6.4 Gross intake ratio to the last grade of 24 50%6.5 adjusted net attendance rate 27 6.6 Early school leaving 30

7. Secondary analysis 33

7.1 Serbia 37

7.1.1 Secondary school attendance 37 7.1.2 Early school leaving 38

7.2 Children from Roma settlements 40

7.2.1 Primary school attendance 40 7.2.2 Secondary school attendance 41 7.2.3 Early school leaving 42

8. Conclusion 43

9. Recommendations 46

References 49

Gender Aspects of Life in Serbia Seen through MICS Data

2015

E purpose withinthisrealm. its finds data (MICS) SurveysCluster IndicatorMultiple the of analysissecondary This decision-makers. and policy- forguidance reliable and valid secureto analysis and data research, is this all for Essential evidence. ‘justified’,isit howitbepreventedcan soeradicated,theand – programmespolicies etc.and on based be can our education and our schools so they can accommodate all children, not just those whom they favour more. (e.g. educational attainment)(Bourdieu, 1997) we knowshould this and waysfind to restructure and reculture them in doing so. theIf reason is more subtle than insufficient material means, such as lacking cultural capital fortheir children, we knowmeansthistoshould establishhelp and effectivesocial and financial efficient and mealsand/or clothing books, buytohave money not parentsdo that fact theto dueis this If schools.attend actions,and implement measuresthem and newfollow proposethem through. Weit, analyse must stories),understand the reasonsand whyfigures ourthe childrenexamine may and not (gather affairs of state current seek to reproduce the same distribution powerof (Bourdieu &Passeron, 1990). groupswho of servicein is byand formed mobility.issystem educationsocial The ratherthan reproduction, activelysocialsystem tocontributeseducation whole the because is this thatsay would Bourdieu education. of levels elementaryeven finish orattend enrol, not do still childrendisadvantagedmany how of figures the for a quality life. view of education as a tool for social mobility, enabling individuals to improve their social status and prospects power toincrease both the quantity(participation) and quality educational provisions.of Thisis inline with a a disability, etc. Therefore, one would expect that every state would prioritize education and do everything in its educational disadvantage manyof children, often because they are poor, belong to a certain ethnic group, have However,theright. regularlywitnesshumanwe basic Moreover,awell-being. is and developmenteducation Introduction Of course,Of much more needs tobe known – where and when exclusion occurs, whyit happens how,and how Yet,the struggle for amorejust society more and equitable education oughtnotto stop. We must assessthe becomesNevertheless,ittherethatobviousforces are this developmentstrong opposing looksatwhen one time, education is also vital for individuals from the perspective of their own personal and professional and personal owntheir perspectiveof the from individuals for vital also is education time, isducation a vital condition anation’s of prosperity as an economysociety and atlarge. At the same 1 5 The Analysis of MICS data 6 The Analysis of MICS data 2 P Context Study the of 2 1 indicators: established been have 2020 for goals strategic following the education useof of all education resources (Government theof Republic Serbia, 2012). of education to lifelong learning; 3) preschool fromachieve levels, and educational maintain all the at relevancepopulation the of coverageeducation; of the and increase4) increase2) the education; efficiency of outcomes and processthe of quality the increase 1) goals: long-term several set Strategy Firstly,the indicators. system and the least have a (less than 1%)(Radovanović &Knežević, 2014). haveeducationlowerorfewer onlylevel significantlyof havea primary(87%), a(11.5%), [SORS], 2013). Educational attainment indicators are the least favourable for Roma population: most members Serbia Republic theof Officeof higher(Statistical hasattainededucation 16% onlyand secondaryeducation; has (49%) population the of majority the lower; or education primary has over and 15 aged population the of 34% aroundthat showed dataCensus 2011 TheCensus. 2011 and 2002 thebetween progress observed the achievements and final exam results (Eurydice, 2015). school primary of basis the toareonschoolsadmitted entertheyand secondary education when years old 15 or14areStudentsusuallylevels. 3 years,ISCED tocorresponding4 or 3 lasts secondaryvocationaleducation secondary and vocationalyears 4 lasts education secondary schools), General types. frequent other, (grammarless and schools, art schools, education schools education secondary general of form the in available is — all children have to be enrolled in primary school between the ages of 6.5 and 7.5 years. Secondary education grade (corresponding to ISCED level 2). Public primary education is free and the only admission criterion is age based teaching from 1 children 5.5-6.5 years old. Primary education is compulsory and lasts 8 years, divided into two long preschoolcycles: preparatory programmeclassroom-isthe first part compulsory of education and provides servicesfor 3 years old), preschool (3-5.5 years old) and a preschool preparatory programme (). The 9-month- start with primary education (corresponding to ISCED level 0

Furthermore, the Strategy stipulates that in order to increase the coverage, relevance and efficiency of of efficiency and relevance coverage, the increase to order in that stipulates Strategy the Furthermore, key unsatisfactory these addressed 2012 in adopted was that 2020 Strategy Development Education The The educational structure of the population of the Republic of Serbia is considered to be unfavourable, despite Here we present only the goals pertaining up to the level of the first cycle of higher education. higher of cycle first the of level the to up pertaining goals the only present we Here 2011. (ISCED) Education of Classification Standard International secondary education. Preschool education and care is intended for children from 6months old until they re-universitySerbiaisorganizedin preschoolprimaryeducationwithincare,and education and education st to 4 to th grade (corresponding to ISCED level 1), and subject-based teaching from 5 1 ). It comprises three levels: nursery (6 months to 2 , together with specific quantitative specific with together , th to 8 to th

3 target groups (SORS,2015). risk of poverty, after social transfers, are children (up to the age of 18) and youth (aged 18-24) compared to persons wholiveother inand villages towns and thinlyin populated persons,Among groupsSerbia. areas inthat are atself-employedhighestthe and unemployed education, no or low very with persons among spread widely ispoverty of risk The 16.9%). averagethe(where israteStates Member Union European 28 theto compared Living Conditions (SILC) policy needs to be formulated and implemented. According to the European Union Statistics on Income and Roma), people with disabilities, and women (European Commission, 2010). as (such minorities ethniccertain background, migrant a with people dependents, with households parents, single people, young children, poverty: of risk the to exposed particularly as emergedhave that population the in groupscertainhighlighted Exclusion Social Povertyagainstand Platform European its in Commission European the example, For context. to contextfrom vary vulnerable considered Groups n.d.). Bank, ( characteristics that make it at higher risk of falling into poverty than others living in areas targeted by a project’ in poverty terms. For example, the World Bank defines a vulnerable group as ‘a population vulnerablethat groups,has a somefact that specific will be described in the following chapters. Vulnerable groups are usually defined

It becomes increasingly obvious that if those targets are to be met in due time, a comprehensivepro-poora time, due in met be to aretargets those if that obviousincreasingly becomes It and poor considering especially targets, those from far are rates completion and attendance Current The EU-SILC is considered the most relevant instrument for monitoring poverty, social inclusion, inequality and living standards in Europe. in standards living and inequality inclusion, social poverty, monitoring for instrument relevant most the considered is EU-SILC The 6) 5) 4) 3) 2) 1) Seventyper cent enrolledstudents of completehigher ( orbasic academicstudies) those Of who completed4-year secondary vocationalthe schools(15%–18.5% of generation), 40%–50% A minimum of 95% of the enrolled (37% of the generation) complete 4-year secondary vocational schools; secondary enter will generation) the of (88% school primary complete who those of 95% least At All children of statutory school age (a minimum of 98% of the generation), regardless of social, economic, For children aged 6 months to 3 years, access to diversified programs and services has been increased and 2020 onwards is at least 35%, most probably 38.5%. givenfromgenerationa of highereducation in1-year proportionadelay,the withinor that time so on The total that enters institutions of higher education is at least 50%, most probably 55% enrol in higher of education, theas do 95% those of generation.who completed grammar schools(35% theof generation). the same number goes for grammar schools (37% theof generation). generation, while general secondary and art education enrol 39% theof generation. school. Vocational 4-year schools enrol 39% theof generation; other vocational schools enrol 10% theof 93% theof generation). bycompleted is school (primary 5% thanhigher no isratedropout the which from education primary qualitybycovered are characteristics, other and religious ethnic, linguistic, national, regional, health, achieved. programswaskindergarten of formspreparatoryhalf-day coverage and full-dayfull through old, years year.Forschool one programs during educational quality (half-day) short the of chargeuse free of providedare years old 4-5.5 children All 30%. isagethat coverage the of children of 3 , the at-risk-of-poverty rate in Serbia was 24.6% in 2012, which is the highest ratehighest the is which 2012, in 24.6% was Serbia inat-risk-of-poverty ratethe , children from 5.5-6 from children .5 .5 7 The Analysis of MICS data 8 The Analysis of MICS data implemented by civil society and/or donor organizations. instruments combined throughout and the measureseducation and social welfareof array system wide is in aplace, and missing, well complemented is by measures background poverty a from children for support and provision education for policy targeted and comprehensivea although that is conclusions its of One 2013). study recently addressed those questions (Kovač Cerović, Lakićević Dobrić, Cenerić, Venalainen, & Mladenović, excellentimproved.An be canthey how were theyand efficient howeffectiveand is keyquestion One years. social measuresbythesocial centres forwork.theIt social pro-poormeasures policies directlythatseemsand of all childreneducationfromsocial assistance of from financial vulnerablethe central groups,budget, one-off and preschool supporting benefits allowance,child assistance, social financial namely: education, torelevantare families that have during the beenpast influential decade. Kovač Cerović et al. four(2013) identified national policies related to the education of children from low-SES A number measuresof to support students from vulnerable groups have been implemented in the previous On the otherhand, the authorshave severalidentified social welfare pro-poor policies and/ormeasures that 4. 3. 2. 1. sports program. parent participation program, an active role in social protection, and a free of charge extracurricular and have givennewschools responsibilities:the provisionmeals, aschool dropoutof prevention program, a werepoliciesschool inclusive on education and violenceprevention. Recent developmentsinthe system fromstudentsmeasuresvulnerableimplement supporting For groups. fewamostprominentyears, the Nascent school development policies. Education, Science and Technological Development, 2015). provided for students of all eight grades in primary school coming from 11 vulnerable groups (Ministry of weretextbooksfree when 2015/2016, yearacademic in forceinto camechangesrecent most The etc.). workbooks, and textbooks both included provision the not or whether targeted, were grades (which Free textbook provision policy. and an anti-discrimination school policy. for inclusive education, the establishment of a network of experts for the support of inclusive trainingteacher education); projects, inclusion Romafor grants municipal projects, inclusiveeducation for grants (school systemssupport of mobilization dormitories; in placementfor actionaffirmative with coupled into secondary and and enrolment of students withRoma disabilities of intoenrolment the fortertiary policy education, action affirmative an schools; in assistants personal of presencethe subject; adjustments in the assessment policy ; introducing pedagogical assistants for Roma students and policy; the introduction of individual education plans; studying Roma language and culture as an optional Broad inclusive education andRoma integration policy. 2009 expanded this to a 9-month provision (Law on the Foundations theof Education System). in LFES theto Changes 2006. in started implementation its requirementand 6-month minimum a as hours daily. This was introduced by the Law on the Foundations of the Education System (LFES) in 2003 outis theintroduction anof obligatory preschool yearfor all children, with ainstruction4 minimum of preparatory education forstarting school. Preschool expanding access policy and requiring attendance obligatory in the last preschool year of Since the implementation of this policy in 2009 it has seen several changes col ae nraigy euse t etbih oiis and policies establish to requested increasingly are Schools In this preschool policy, out of five policy lines, one that stands Thispolicy consisted anew of: school enrolment 4 in terms of outcomes for children, including ones in education. also MICS the 2014 includes information on of a cashas benefit module that nowthat enables researchmention into theto impact theseof importantprogrammes is It vulnerable. most the of inclusion social at aimed between different population groups withinboth sample surveys, and allowsfor evidence-based policymaking The disaggregatedMICS datanature(gender, of area, education, wealth, etc.)provides insightinto disparities collectsinternationally comparable data on a widerange indicators onthe children of situation of andwomen. exclusion. Moreover, UNICEF’s Multiple Cluster Indicator Survey (MICS) makes an important contribution as it Serbiabegan theSurvey onIncome LivingConditionsand (SILC), amajor source dataof on poverty and social inclusion indicators have been the Household Budget Survey (HBS) and the Labour Force Survey (LFS). In 2013 poorvulnerableand remains studentshighpriority.a far,Sosocialfor sources datamain the calculationof of of educationthe comprehensivesystemof aupmonitoring forsetting need the that and 61), (p. assessed’ be suggest that ‘the efficiency and effectiveness of the set of available measures, both national and local can hardly and Social Affairs’ information system, or emerging monitoring of social inclusion), Kovač Cerović et al. (2013) (e.g. monitoring financial social assistance and child allowance via the Ministry of Labour, Employment, Veteran age 18 receive child allowance (Matković, Mijatović &Stanić,2013). 1.88 child allowancesDataper household.have from2011 shown childrenthat28.3% of and adolescentsup to 380,000 childrenhouseholds2012,from aroundreceived200,000 childallowance, which gives an average of attendanceregulartoschool relatedtransfer, is cash conditionalto aitsimilar sinceis Serbia in measure children in the family and for children/youth up to age 19 (children with disabilities up to 26). The child allowance 17.6% (Matković, Mijatović, &Stanić,2013). thattheirmind isshareinthe intotalpopulationonly assistance social bearing users, financial populationof the in above-averagepresence thean have Children of assistance. social 38% financial nearly of beneficiaries is of number which total assistance, social financial received that households in lived children 86,000 total the of 2012, In tripled. yearsitalmost (3.1% 12 in hascontinuallyand since2000, usersrisen number of The people population). 224,000 almost with households, 87,000 over by received was assistance social financial 2012, yearthefor administrativetoAccording datawork. social centresof the by administered and budgetcentralthe from funded program benefit cashpro-poor a is assistance social financial The exclusion. assistance and child allowances, both of which we describe. shall briefly most important pro-poor cash benefit programs that aim to fulfil the basic needs of children are financial social Serbia’s(KovačRomafamilies Cerović2013). frompoorchildren and etal., indirectly or supportof education

Although there are developments in the field of monitoring pro-poor educational and social welfare measures Financial social assistance Child allowance Applicant and family members, in addition to the adequate housing in which they live, may not hold other real estate or agricultural land exceeding 2 hectares per per hectares 2 exceeding land agricultural or estate real other hold not may live, they which in housing adequate the to addition in members, family and Applicant household, nor money or other assets worth more than the amount of 30 children’s allowances per family member at the time of application. The allowance is granted granted is allowance The application. threshold. of the exceed time not does the made at was request member the which in month family the per preceding 3months the in allowances realized member children’s 30 of family per income amount monthly the total the than if more worth assets other or money nor household, is the largest program targeting poor children in Serbia. It can be provided for the first four is considered the most important state measure against poverty and social 4 . In 9 The Analysis of MICS data 10 The Analysis of MICS data 3 T Multiple IndicatorMultiple 5 assess progress made and to invest additional efforts in areas that require more attention. comparable data that will enable decision-makers within the andgovernment up-to-date provides and MICS all Theother policies. stakeholders inclusion to social criticallyadequate new of developmentfor precondition generalbut— evenpopulation more importantly, onsocially excluded and deprivedbe thegroupskey— will recentthereliablehavingand dataonreforms, process the ongoingof mind in Bearing women.childrenand theseindeed, reach; surveys have remained the only sourcefor many indicators that reveal the Roma status of tobesensitive enoughto measure bring a disparities andwealth data of about groupsthat areto difficult surveys implemented in 2005, 2010 and 2014 were conducted on Roma settlement samples as well, and proved other internationally agreed-upon commitments. and (MDGs) GoalsDevelopment Millennium monitortowardsprogress tothe programmes,and and policies children and women. TheMICS surveysmeasure key indicators that allow countries to generate data for usein (MICS) Programme Surveys Cluster

Serbiaisthefew one countries of in whichfour all previous MICS wererounds implemented. of TheMICS This section is based on the description given in the final report for MICS 2014 (SORS & UNICEF, 2014). &UNICEF, 2014 (SORS MICS for report final the in given description the on based is section This programmeto collectinternationally comparable data on a widerange indicators onthe of situation of surveyhousehold international 1990sasanthe in programme MICS UNICEF bywashedeveloped global

5 B social assistance. allowancefinancial child i.e. programmes,and benefit focuscashon rates, leavingspecial awith early school to discern the effects individual, household of and geographical characteristics on attendance, completion and Goals Study the of Specific research questions that will be dealt with in the study are: 3) 2) 1) aig n id ht h MC hs rvdd sre o rlal dt o ky dcto idctr, the indicators, education key years,10 orderanalysetrends tooverlastindetect on and thedata tothis examine is study this data of purpose reliable of series a provided has MICS the that mind in earing What are the effects of cash benefit programmes (i.e. financial social assistance and child allowance cash) geographicalcharacteristicsand attendance, household on individual,different Whataretheeffectsof What are the trends in the pre- education attendance rates, primary education completion rates on attendance, completion and early school leaving rates (both nationwide and in Roma settlements)? completion and early school leaving rates (both nationwide and in Roma settlements)? (both nationwide and in Roma settlements)? characteristicsgeographical and household individual, byrates disaggregatedleaving school earlyand 4 11 The Analysis of MICS data 12 The Analysis of MICS data 5 Methodology T 8 7 6 thereferstotheagein school primary group,theappropriateafterwards.Forborn first age startof thethe at thoseearlier,or and 1998 in born childrenfor wereseparateapplied calculations 2006, in Serbia in changed legislative changesreflect regarding those age eligibilitySince criteria. the criteriafor primaryschool starting toorderattendanceinrates school secondary as well rates,as completion attendance and school primaryfor accountageto behad done eligibility newMICS criteriafor calculations for 2010theschool, primary starting intotaketo adjusted issinceage Forexample, done. be to had calculationshowever, new domainscertain in education mother’s area, calculating different education indicators and disaggregating data by the following characteristics: sex, region Roma settlements). By using sample weights, the model was adjusted to Census data. approximately 1%, so usage of sample weightswas populationfor generalthe the wholein population Roma of Serbia loweredof proportion weightedthe numbers data, forCensus 2002 the to (accordingweights sample and over-sampling to due numbers, unweighted than lower significantly are settlements Roma for numbers weighting. For reporting of the national level results, sample weights were used. In the 2005 MICS, samplesarenotself-MICS The Serbia.the Roma in settlementsin samplerepresentativeliving the population weightedof Romasettlementsa on based out carried was MICS SettlementsRoma Serbia the while Serbia, of population Roma Serbia the and MICS SettlementsSerbiaTheMICS. MICS was carriedoutbased onsamplenational arepresentative Serbiathe whole of the — samples different two using Serbia in out carried were 2010 and learn about its basic indicators . topublicgeneral the and policymakers of need the programmereflecting preparationattendance,preschool wascycles MICS twolast the in indicatorsurvey-specific such One indicators. MICS basic theto were added reason that for and Serbia forimportance critical of be to found werethat indicators i.e. indicators, specific survey-involvedSerbia in studies MICS the indicators, MICS calculating for used data collecteduniversally

In order to detect and analyse trends over the last 10 years in Serbia, the first part of our analysis included analysisour of part first the Serbia, in years10 last overtrendstheanalyse and detecttoorder In 2014 from surveys MICS surveys. MICS 2014 and 2010 2005, the from come analysis this in used Data It should be noted that when referring to someone’s educational level, it actually refers to the level that person has attended, not necessarily meaning that the person person the that meaning necessarily not attended, has person that level the to refers actually it level, educational someone’s to referring when that strata. noted be sample as should It used not were they since MICS Settlements Roma Serbia the for regions by done not was data education down Breaking attendance; school primary entrance; school primary attendance; Programme Preparation Preschool readiness; school are: MICS in produced indicators the of Some completed that level of education. level that completed gender education and attendance; school secondary school; secondary to transition completion; school primary parity.school; primary of grade last the reaching children of different levels of education, including data on children’s progress within these levels. In addition to addition In levels. these withinchildren’s progresson data including education, of levels different of MICS collectshe fundamentalto datanumber apertaining education of 8 n wat idx unie Tee aa r aray rsne i ofca reports; official in presented already are data These quintile. index wealth and 6 , mainly with respect to attendance 7 , is found inthe isfoundsection devotedto eachparticular analysis. level)educationto and discernthe effectsthefactors.More sizes each of of onthemodels and variables chosen a binary logistic regression to control the interaction between characteristics (e.g. wealth index and mother’s programmes — affect attendance, completion and early school leaving rates, a second part of our analysis used settlements, as these were not separately reported in Romathe forreport. official MICS 2005 the for done was calculations of set new a Furthermore, 2009. February of end the at years)completed (in child the ageof the is ageadjusted thegroup, second theyear,calendarfor 2009while Inorder togeographical benefitand household determine cashhowincluding individual, characteristics — 13 The Analysis of MICS data 14 The Analysis of MICS data 6 I Education Serbia in Data on MICS having attended the preparatory programme as shown by the 2014 MICS. studies lowered, with a decrease from 70.6% in 2010 to a little less than 63% of children of PPP age attending or indicators are not as favourable for children from Roma settlements. The percentage between the two MICS However,2014. in 98.1% and 2010 in 92.4% twosurveys: these in high verywasSerbia attendancein of level the 1, Figure the in shown As years. 5-7 agechildren of number total the PPP,percentage aof a as expressed MICS and 2014 MICS in Serbia. It represents the number of children age 5-7 years attending or having attended 6.1 Preschool Programme (PPP) Preparation attendance 2014; SORS,2011; SORS&Strategic Marketing Research Agency, 2006). UNICEF,& (SORS reports official in consultedbemaycharacteristicsrelated indicators and education of list Preschool preparation programme (PPP) attendance rate was a specific indicator introduced into the 2010 the into introducedindicator specific a attendancewasrate(PPP) programme preparation Preschool madein such waythat it gives a good, comprehensive picturethe of situationin Amorethe field. complete n this text only a portion theof MICS data on education will be presented and discussed, but its selection is 20 10 Serb Preschool preparationprogrammeattendance 92.4

ia 20 14 Serb 98.1

Figure 1 ia settlemen 20 10 Roma 70.6

ts settleme 20 14 Roma 62.9 nt s from the poorest quintile were attending a PPP in 2014, and only 44.1% of children whose mother does not does mother whose children of 44.1% only and 2014, in PPP a attendingwere quintile poorest the from children of 46.6% only that seeWe education. secondary or education primary with those and attainment better-off (morequintiles betweenpronounced2010), in and children whohave mothersno with educational tocomparison in quintilepoorest the from comingPPP attended having orattending children of number in Southern and Eastern Serbia, and for children who have mothers with primary education only. an increase between the two surveys can be noticed in attendance rates in Sumadija and Western Serbia and not get significant differences whenbreaking downPPP attendance databy other variables(Table However,1). With such a high PPP attendance rate in Serbia, especially in 2014 as recorded by the MICS, expectedly we did The situation is quite different in Roma settlements. Data in Table 2 show that there is a noteworthy difference Note: Data for 2014 MICS are taken from MICS reportofficial for Serbia and data for 2010 MICS were calculated by the author. education Mother’s quintile Wealth index Area Region Sex Total Preschool PreparationProgrammeattendanceinSerbia determined Cannot be Higher Secondary Primary None Richest Fourth Middle Second Poorest Other Urban Serbia and Eastern Southern Serbia and Western Sumadija Female Male (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Attendance Attendance of PPP 77.2 96.9 89.3 99.1 85.8 89.7 97.6 88.5 86.4 90.3 99.5 93.6 94.0 90.7 92.4 96.4 96.1 (*) (*) Attendance ofPreschoolPreparationProgramme Serbia 2010 Table 1 children of children of Number of PPP age 148 128 119 107 226 42 32 40 42 54 40 50 99 58 61 60 46 0 4 Attendance Attendance of PPP (94.7) 100.0 100.0 99.5 98.4 95.3 99.4 99.5 94.6 96.8 98.8 99.7 94.1 97.9 99.0 97.3 98.1 (*) (*) Serbia 2014 children of children of Number of PPP age 117 126 107 198 55 23 45 43 49 33 28 72 49 66 47 36 91 2 0 15 The Analysis of MICS data 16 The Analysis of MICS data between urban and rural have education. In the last MICS cycle, no differences between boys and girls were found, but some differences 9 organized transport, 6.5% walks, 5.1% uses public transportationhas and 9.2% 1.1% facility,goesthe gobyto bicycle. vehicle privateIn Roma a uses settlements(78.1%) them of majority large A Serbia. in kindergarten a kmaway livechildren2 from morethan of 16.7% PPPfacilities,for network thestandard of legal is a km 2 of This is also in reflected the thatfinding 31.6% of children attend PPP in a nearby school. Although the distance isless developedin rural areas, wherekindergartens are twice as far away asin urban areaskm(2 versuskm).1 whiletheaveragekm, 1.4 betoPPPSerbianetworkThefound was km. distance forRoma settlements1.9 was the urban-rural dimension into consideration (SORS & UNICEF, 2014). The average distance to a PPP facility in

It is worth noting what the 2014 MICS says about the preschool network in Serbia, especially when taking whenespecially Serbia, in network preschool the aboutsays MICS 2014 the what noting worthis It Official statistics in Serbia do not include a specific definition for rural settlements. Instead, an ‘administrative-legal’ criteria is applied that designates settlements as as settlements designates that applied is criteria ‘administrative-legal’ an Instead, settlements. rural for definition specific a include not do Serbia in statistics Official either ‘Urban’ or ‘Other’. Urban settlements are recognized as such by an act of the local self-government, with all other settlements falling into the category of ‘Other’. ‘Other’. of category the into falling settlements other all with self-government, local the of act an by such as areas. ‘rural’ as recognized presented be are will areas ‘other’ report, this of settlements purpose the For Urban ‘Other’. or ‘Urban’ either + 2014 MICSRoma settlements data pertaining to mothers with secondary and higher education are calculated together due to small Note: Data for 2014 MICS are taken from MICS reportofficial for Serbia and data for 2010 MICS were calculated by the author. education Mother’s quintile Wealth index Area Sex Total Preschool PreparationProgrammeattendanceinRomasettlements 9 areas were preserved from 2010. determined Cannot be Higher Secondary Primary None Richest Fourth Middle Second Poorest Other Urban Female Male (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Attendance Attendance numberunweighted of cases. of PPP (88.9) (92.1) (80.2) (75.4) (69.0) 73.5 70.6 73.1 53.1 45.8 65.1 73.6 67.9 n/a - Roma settlements n/a —Not applicable. Attendance ofPreschoolPreparationProgramme Table 2 2010 children of children of Number of PPP age n/a 105 217 135 140 112 30 51 46 31 44 39 57 77 - Attendance Attendance of PPP (64.0) (68.3) 63.7 62.9 68.7 44.1 62.8 46.6 57.8 65.1 62.0 (*) (*) (*) Roma settlements + 2014 children of children of Number of PPP age 111 194 142 136 44 17 47 33 51 45 58 82 0 7 10 for their child’s not attending PPP. tonotknowing that PPP isUNICEF, & compulsory(SORS 2014) is also a group of reasons categorized as ‘parental attitudes’ (17.0%) — here the most frequent answer pertained respondents claimed access problems, mainly because the kindergarten institution is too far from home. There thesewereamong problemshygiene relatedto oftenfoodand financial Secondly, expenses.clothes, of 28.8% Serbia Roma Settlements MICS(Figure 2). settlements not attending PPP, given its mandatory nature. Here we present some of the findings from the 2014 transportation, 5% go by private vehicle, 4.9% go by bicycle, and 18.1% use organized transport to a facility. method of travel is fairly different: 27.4% of children walk (more than 2 km) to a PPP facility, 44.5% take public almost every fourth child (23.1%) lives more than 2 km away from a PPP facility. However, the distribution of the

TheMICS also offersinsightinto the reasons whythere wassuch ahighpercentage Roma childrenin of h ms feun nwr (05)gvn yrsodns al no h aeoyo fnnilpolm — problems financial of category the into fall respondents by given (30.5%) answers frequent most The clothes, food, and hygiene expenses. Access problems include: overcrowded facility, institution being too far, child not registered, and no one available to take the child child the take to available one no and registered, not child far, too being institution facility, attention, of PPP. lack a attend to is there compulsory hence is it know not did overcrowded overcrowded are parents and groups treatment, disabled, include: is inadequate child have PPP, the in children learn to problems much not is there Access opinions: certain involves attitude expenses. PPP. to Parental hygiene and food, clothes, Each of the three main groups of reasons for not attending PPP consists of several specific reasons. Financial reasons include costs of: transportation, school supplies, supplies, school transportation, of: costs include reasons Financial reasons. specific several of consists PPP attending not for reasons of groups main three the of Each 31.8% Reasons fornon-attendanceofPPP 30.5%

17.0% 28.8%

Figure 2

Parental attitudes Other reason Financial problems Access problems 10 . Finally,. 32% parents of stated otherreasons s 17 The Analysis of MICS data 18 The Analysis of MICS data school, i.e. almost every seventh child is not in school when he/she should be. This ageschool primary also childrenRomafrom 2014 of of settlements in were84.9% attending presentsprimaryor secondary a slight decrease very stable at98%. Yet again, whenlooking only atRoma settlements,the data arenot that encouraging.Only indicate very high, almost universal attendance school of among children primaryof school age — the data are currently attendingprimary or secondary schools. Thefromfigures thelast three SerbiaMICS studiesin clearly 6.2 ewe wat qitls r dcto lvl o cide’ mtes ecp fr hs wt n educational no with those attainment for at all, whose (except children are least likely mothers to children’s attend school). of levels education or quintiles wealth between no groups childrenthat of significantly differfrom thosein othersterms.Differences arenot evenpronounced from 88.3%in the 2010 MICS. Again, with such a high level of school attendance of children of primary school age in Serbia overall, there are Thenetprimaryschool adjusted attendance rate refers to thepercentage primary childrenschool age of of

Primary school rate net attendance adjusted Primary

20 05 Serb 98.4 Pe

ia rcen 20 ta Primary schoolnetattendancerate(adjusted) 10 Serb ge 98.1 of

ch ia ildr 20 en or 14 Serb of 98.5 se pr co

imar Figure 3 ia ndar y scho settlemen 20 y scho 05 Roma 73.6 ol ol

ag ts e at settleme 20 te 10 Roma 88.3 ndin

nt g pr s imar settleme 20 14 Roma 84.9 y

nt s attending primary or secondary school, in comparison to 97.2% from the richest quintile. werequintilepoorest the fromage school primary of children of 66.3% 2014, In wealth. of increase the with with secondary orhigher educationis95%. A very similartrend appliesto wealth: attendance percentage rises whose mother does not have education is attending primary or secondary school, while this number for mothers this is a stable trend throughout the surveys. For example, in 2014, about 75% of children of primary school age mother’s The attendance. school education is an to important feature: regardsas educational attainment in rises, the matterattendance topercentages rise seem as well, notand does distinction urban-rural the MICS, When considering children from Roma settlements, with the exception theof 2010 Serbia Roma Settlements quintile Wealth index education Mother’s Area Region Sex Total Note: Data for 2014 MICS were taken from MICS reportofficial for Serbia; data for 2010 MICS and total and regions for 2005 MICS Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Serbia and Eastern Southern Serbia and Western Sumadija Vojvodina Belgrade Female Male 1 Primary schoolnetattendancerate(adjusted)inSerbia attendance (adjusted) 99.3 98.9 99.8 99.0 95.5 99.4 99.3 95.6 98.5 98.4 97.9 99.4 98.6 97.4 98.4 98.5 98.4 rate rate n/a n/a Net Net 1 Data for 2005 for primary education include primary or less. 2005 Serbia (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. of children Number wereby calculatedthe author. 1589 1262 1407 1309 1361 2669 n/a n/a 524 533 521 522 569 438 642 739 733 747 450 n/a —Not applicable. Table 3 attendance (adjusted) (81.9) 98.1 99.7 96.9 98.8 97.4 98.1 97.1 99.6 98.2 99.6 95.8 99.2 97.9 98.5 98.0 98.2 97.5 rate rate n/a Net Net 2010 Serbia of children Number 1619 n/a 466 404 280 800 819 334 329 327 339 291 332 975 293 757 862 469 20 attendance (adjusted) (74.0) 98.0 98.5 97.7 97.9 99.1 98.5 98.4 99.5 99.9 98.6 95.4 98.5 98.9 98.7 97.6 99.1 99.7 rate rate Net Net (*) 2014 Serbia of children Number 1570 446 455 308 809 762 326 352 356 279 278 343 939 248 646 924 361 21 19 19 The Analysis of MICS data 20 The Analysis of MICS data quintile Wealth index education Mother’s Area Sex Total + 2014 MICSRoma settlements data pertaining to mothers with secondary and higher education are calculated together due to small percentage. Note: Data for 2014 MICS are taken from MICS reportofficial for Serbia and data for 2010 MICS and 2005 MICS were calculated by the author. Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Female Male Primary schoolnetattendancerate(adjusted)inRomasettlements attendance (adjusted) 2005 Romasettlements (98.3) (98.3) 87.8 87.8 88.7 69.0 97.8 72.5 72.5 74.2 71.1 76.0 73.6 rate rate n/a Net Net (*) - (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. of children Number n/a 65 23 45 34 34 68 53 1 1 5 8 3 - n/a —Not applicable. Table 4 attendance (adjusted) 2010 Romasettlements 90.0 78.8 82.6 90.8 86.4 90.2 88.3 97.1 93.3 88.5 88.4 78.2 99.1 rate rate n/a Net Net (*) of children Number 1297 n/a 895 284 387 909 641 656 224 243 242 270 318 111 7 attendance (adjusted) 2014 Romasettlements 95.2 88.1 74.7 86.4 84.4 85.2 84.5 84.9 97.2 93.7 83.9 86.7 66.3 rate rate Net Net (*) + of children Number 1018 1194 1524 402 300 819 705 277 281 309 325 332 11 93 thesmaller numberthe children appropriate of of ageinthe samples. trend, with the highest peak in 2014 at 37.3%. ratesinrecent years.Figures are muchworse for children fromRoma but settlements,they do show a positive completionthe of stabilitythe indicates actually which same, thenearly remains cycles MICS twolast the in the gross intake ratio to the last grade of primary education (see below) and net primary school completion rate 90%, but it went down in the 2014 Serbia MICS to 80.4%. However, the difference in percentage points betweentheat, again, expected graduation age.Net primary school completionSerbiaininthe previous decade was at primaryschool completion — whether children who enterschool at an expected age do completeprimaryschool ratepreviousThiscycles).representsinyears14 one way‘on-time’measure toand MICS, 2014 the inyears 13 graduation age(age appropriateinto grade accordanceprimary school thefinal of withrelevant —legislation school primary of children of number the to time, first the for school primary of gradelast the enteringage 6.3 The netTheprimaryschool completion rate istheprimaryschoolratio graduationthenumber students total of of of Estimatesfor primaryschool completion for differentsubgroups are generallynotreliable enoughbecause of Net primary school completion rate school completion Net primary 20 05 Serb 90.7 Pe rcen ia ta ge 20 10 Serb of 90.0 ch

ildr Net primaryschoolcompletion th ia e last en 20 of 14 Serb gr 80.4 pr imar ad

Figure 4 e of ia y scho pr settlemen 20 imar 05 Roma 28.1 ol co y scho

mple ts ol settleme 20 tion 10 Roma 33.5 ag e at

nt s te ndin settleme 20 14 Roma 37.3 g

nt s 21 The Analysis of MICS data 22 The Analysis of MICS data thosepercentages forboys andgirls were very closeto each other. cycleswhere markedlya higherpercentage girlsRomafrom of settlements 2014 incompleted school, primary thelesslikely that they will completechildren, primaryOne isschool). interestingthat thefinding afterpoorertwo previous(theMICS way expected an in school primary completing for critical is wealth that presume completion ageto have fully reliable estimatesforthe subgroups. From allof the available data wemight As on the level of Serbia, the Roma settlements survey does not contain enough children of primary school primary not Romacontainsettlementsenoughchildrensurveytheof does Serbia, levelof the on As quintile Wealth index education Mother’s Area Region Sex Total Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Serbia and Eastern Southern Serbia and Western Sumadija Vojvodina Belgrade Female Male 1 Net primary Net primary completion completion school (95.1) (97.5) 95.2 94.6 91.8 77.7 96.1 73.1 89.9 91.2 88.7 94.5 91.4 85.7 90.7 90.7 90.7 rate n/a Net primaryschoolcompletioninSerbia 1 Data for 2005 for primary education include primary or less. 2005 Serbia (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Note:All calculation were donebythe author. completion completion Number of Number of of primary of primary children school age n/a 102 168 155 322 184 132 190 62 67 64 69 51 71 58 80 67 92 n/a —Not applicable. Table 5 Net primary Net primary completion completion (100.0) school (94.5) (93.5) (95.4) (96.4) (69.1) (90.5) (74.5) (79.8) (95.2) 92.5 87.8 90.0 95.6 86.6 92.6 rate (*) (*) (*) 2010 Serbia completion completion Number of Number of of primary of primary children school age 187 109 108 52 38 90 97 49 36 34 28 40 34 40 79 56 40 0 3 Net primary Net primary completion completion school (76.5) (85.5) (87.1) (51.1) (83.0) (79.1) (95.5) 82.8 78.9 80.4 78.3 81.8 79.3 rate (*) (*) (*) (*) (*) (*) 2014 Serbia completion completion Number of Number of of primary of primary children school age 152 48 20 59 93 30 20 40 40 22 36 89 22 69 83 42 41 2 3 quintile Wealth index education Mother’s Area Sex Total + 2014 MICSRoma settlements data pertaining to mothers with secondary and higher education are calculated together due to small percentage. Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Female Male Net primaryschoolcompletioninRomasettlements Net primary Net primary completion completion school 2005 Romasettlements (15.5) 26.6 26.3 33.1 32.6 23.1 28.1 rate n/a n/a (*) (*) (*) (*) (*) (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Note:All calculation were donebythe author. completion completion Number of Number of of primary of primary children school age n/a n/a 0 0 0 1 6 0 7 2 5 4 4 7 n/a —Not applicable. Table 6 Net primary Net primary completion completion school 2010 Romasettlements (59.8) (37.3) (42.4) (28.0) (11.9) (12.4) 13.2 43.6 38.9 28.4 33.5 39.4 rate n/a (*) (*) completion completion Number of Number of of primary of primary children school age n/a 103 154 51 75 79 27 32 20 40 36 12 97 46 0 Net primary Net primary completion completion school 2014 Romasettlements (56.9) (62.1) (23.8) (6.0) (7.8) 33.8 37.8 36.8 37.3 40.8 rate (*) (*) (*) (*) + completion completion Number of Number of of primary of primary children school age 132 156 112 24 76 80 45 19 28 32 31 33 2 9 23 The Analysis of MICS data 24 The Analysis of MICS data relevantindicatorThislegislation). typicallyincludes children whoinprimaryschool either enrolled early or of children of primaryschool graduation agenumber(the age appropriatetheinto grade accordance primaryschool thefinal of to with time, first the for school primary of grade last the entering age, of regardless education primary of 6.4 girls thanfor boys. surveys. What can be observed from Table 7 is that from 2010 there is a tendency higherof completion rates for reliablethe enoughbecause smaller unweightednumber of of the children cases appropriate of of agethein population. generalthe in overagethanmore students much having typically settlements,Roma from childrenfor grade percentage27 isalmostitThis clearlypoints. shows widermuch MICS age aaingiven2014 dispersionthe in intake is at the level 64%. of There is a notable difference between the gross intake and the net completion, e.g. As for children from Roma settlements, a significant increase occurred from 2005 to 2010, and in 2014 the gross Serbia as found in the 2010 MICS (Figure 5). The latest data from the 2014 MICS in caseshowthe was Such theratecompletion 100%. exceedsprimarygiven ayearthe grossin that intakehappen may it atand late, 93.4%. As in the case of net primary school completion, these estimates for different subgroups are generally notgenerally aresubgroups different for estimates these completion, school primary net of case the in As students, of number total the of ratio the is education primary of grade last the to ratio intake gross The Gross intake ratio to the last grade last tothe Gross intake ratio 20 Pe 05 Serb rcen 96.7 firs t time ta Gross intakeratiotothelastgradeofprimaryeducation

ia ge to of 20 th ch 10 Serb 104.1 e number ildr en

ia en te 20 of ri 14 Serb ng ch 93.4 th ildr Figure 5

e last en ia of settlemen 20 gr pr 05 Roma ad imar 40.4 e of y scho

ts pr imar settleme 20 ol 10 Roma gr y educ 62.7 aduation

nt

at s io settleme 20 n fo ag 14 Roma 64 e r th

e nt s quintile Wealth index education Mother’s Area Region Sex Total Note: Data for 2014 MICS were taken from MICS reportofficial for Serbia; data for 2005 MICS and 2010 MICS were calculated by the author. Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Serbia and Eastern Southern Serbia and Western Sumadija Vojvodina Belgrade Female Male Gross intakeratiotothelastgradeofprimaryeducationinSerbia Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade (95.7) (98.2) 103.9 102.3 104.6 98.6 97.4 89.9 98.7 85.1 92.8 97.7 95.5 85.8 96.9 96.4 96.7 (*) 1 Data for 2005 for primary education include primary or less. 2005 Serbia (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. completion completion Number of Number of of primary of primary children school age 184 132 190 102 168 155 322 67 64 69 51 71 58 80 67 92 62 0 Table 7 Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade (108.2) (104.7) (115.2) (103.0) (100.0) (109.9) (105.4) (113.5) (94.6) (93.3) 108.3 100.3 104.1 100.4 104.1 104.2 (*) (*) (*) 2010 Serbia completion completion Number of Number of of primary of primary children school age 187 109 108 52 38 90 97 49 36 34 28 40 34 40 79 56 40 0 3 Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade (102.3) (101.2) (95.5) (89.4) (66.3) (92.1) (87.3) 97.9 90.5 93.4 89.4 95.2 91.9 (*) (*) (*) (*) (*) (*) 2014 Serbia completion completion Number of Number of of primary of primary children school age 152 48 20 59 93 30 20 40 40 22 36 89 22 69 83 42 41 2 3 25 The Analysis of MICS data 26 The Analysis of MICS data between males and females in 2014 decreased significantly in comparison to 2010 data. When it comes to children from Roma settlements (Table 8), it is only reliable to conclude that the difference quintile Wealth index education Mother’s Area Sex Total Note: Data for 2014 MICS were taken from MICS reportofficial for Serbia; data for 2005 MICS and 2010 MICS were calculated by the author. Gross intakeratiotothelastgradeofprimaryeducationinRomasettlements Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Female Male Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade 2005 Romasettlements (29.5) 33.0 35.5 44.7 39.7 41.3 40.4 n/a (*) (*) (*) (*) (*) (*) 1 Data for 2005 for primary education include primary or less. (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. completion completion Number of Number of of primary of primary children school age n/a 4 7 0 0 0 1 6 0 0 7 2 5 4 n/a —Not applicable Table 8 Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade (128.6) (100.5) 2010 Romasettlements (57.8) (34.1) (28.6) (26.3) 51.0 62.7 62.3 28.7 79.5 74.9 (*) (*) (*) completion completion Number of Number of of primary of primary children school age 154 103 79 27 32 20 40 36 12 97 46 51 75 0 0 Gross intake Gross intake ratio to the ratio tothe education of primary of primary last grade last grade (105.3) 2014 Romasettlements (73.2) (57.7) (22.5) (50.3) 65.1 64.0 56.1 56.5 62.8 (*) (*) (*) (*) completion completion Number of Number of of primary of primary children school age 156 112 132 80 45 19 28 32 31 33 24 76 2 9 6.5 11 althoughthe differences cannotbe accountedforby any governmenttopolicy expandsecondary inschools 7-8 percentage points higher than other areas. In 2014 the rates for urban and other areas were nearly the same, area: the secondary school adjustednet attendance rate for urban areasin the2005 2010and Serbia MICS was the regarding trends the in changesdetectto interesting is It 2014. in ratesthese in first region this making Serbia, Eastern and Southern in attendanceratesin yearstheover increasesteady a observe canwe regions, forAs2010. inpercentage(withnot a but7 difference theAlmost 2005,same ratio points). in of noticed was 2014 MICS and 11.9%in the 2010 MICS). be noted, however, that more than 10% of children of secondary school age attend primary school (13.6% in the only appropriate every childof fifth agefromRoma settlements attendssecondary school or higher. It should words,otherIn 20%. around remain figuresrecent twothese nevertheless MICS, 2005 theto comparison in atlevel.thisAlthough attendanceratios for childrenfromRoma settlements theintwo last cycles diddouble other hand, the gap between the situation in Serbia as a whole and in Roma settlements increases dramatically theOn 84%. almostwasfigure thewhen 2005, from increaseobserved the stabilizing thus 2010), and (2014 currently attending secondary school or higher. The ratio for the whole Serbiaof was 89% in the two last cycles

Thesecondary netschool adjusted attendancerate isthepercentage childrensecondary of school age of We can observe in Table 9 that in Serbia in 2014 more girls attended secondary education or higher than boys In Serbia this is 14-18 years of age. age. of 14-18 is years this Serbia In

Secondary school rate netSecondary attendance adjusted

20 Pe 05 Serb rcen 83.8 ta

ge ia Secondary schooladjustednetattendancerate of 20 ch 10 Serb 89.3 ildr en

ia of se 20 co 14 Serb 89.1 ndar Figure 6

or y scho ia higher settlemen 20 ol 05 Roma 10.2 ag e at

ts te settleme ndin 20 10 Roma 19.3 g se

nt co ss ndar 20 ettlem 14 Roma y scho 21.6 en

ol ts

11

27 The Analysis of MICS data 28 The Analysis of MICS data the pooresttheimproved,thiscategoryfrom74%childrenquintilehigher, or attending withsecondary school of difference was around 35the percentage 2010 points, in and in 2005 cycles: around 30 percentageMICS previous points. Moreover,in observed was prospects what forthan less is This quintile). richest the favourof (in wealth index, the difference in attendance between the poorest and the richest quintile is 23 percentage points theseindicatorsSerbia2010SerbiainMICS, the which 2005MICS and were75%. aroundWith respect to the of values to compared latter the for increase an represents this Nevertheless, education. primary only with had higher education were in secondary education or higher, compared to 83.6% of children who have mothers rural areas. Mothers’ education still matters: in 2014, 98.9% of children of secondary school age whose mothers from 2010 to 2014 for the middle and fourth quintiles (7 and 5 percentage points respectively). comparedinto theSerbia 61% 2005201064% respectively.MICS and and However, attendancerates lowered Sex Total quintile Wealth index education Mother’s Area Region Note: Data for 2014 MICS were taken from MICS reportofficial for Serbia; data for 2010 MICS and total and regions for 2005 MICS None Other Urban Serbia and Eastern Southern Serbia and Western Sumadija Vojvodina Belgrade Female Male Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary Secondary schooladjustednetattendancerateinSerbia attendance attendance ratio 75.6 79.5 87.3 79.3 86.9 84.2 86.3 87.0 80.6 83.8 93.9 91.3 90.1 78.7 64.4 74.7 96.7 96.4 Net Net 1 Data for 2005 for primary education include primary or less. 2005 Serbia (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Number of children wereby calculatedthe author. 1264 236 248 287 248 244 107 143 504 232 561 703 357 340 368 198 649 615 Table 9 attendance attendance ratio 95.2 95.9 97.0 91.4 60.6 83.0 98.7 98.7 73.9 85.3 92.7 87.1 89.7 88.3 93.4 90.3 88.3 89.3 Net Net (*) 2010 Serbia Number of children 90.3 88.3 191 209 197 153 151 132 339 133 410 491 242 263 228 169 901 81 6 attendance attendance ratio 97.3 90.4 89.9 89.1 74.0 81.1 98.9 95.6 83.6 87.4 90.3 93.3 87.3 87.0 88.6 93.0 86.0 89.1 Net Net (*) 2014 Serbia Number of children 202 179 162 223 134 339 106 373 379 521 245 268 217 170 402 498 900 78 6 and the poorest one is around 35 percentage points (data do not differ between the two latest MICS surveys). intolooking wealth thequintiles: difference inattendanceschool betweenthe childrenfrom therichest quintile when largest are differences The 2010. from improvement slight a marks Thishigher. or education secondary children whosemother hasno education, and oneinthree children whosemother has primary education attend tenin one2014, Inreliable estimates. numberof the limits perchildrencategory again numberof the although Tablein10, presenteddata the in visibleis wealth of effectThe MICS. 2014 the inpercentage points5 to MICS secondary education or higher than from other areas. The difference ranges from 14 percentage points in the 2010 in areareasurbanfrom children higherpercentage a forof area: exists trend similarA 15%). versus(28 forgirls Moreover,agehighergirls.orthanattendsecondary school percentagethe2014 boysinfor thatalmostdoubles In RomaIn settlements we can observe astablein trend which ahigher percentage boyssecondary school of of quintile Wealth index education Mother’s Area Sex Total Note: Data for 2014 MICS and 2010 MICS were taken from MICS reportofficial for Serbia; data for 2005 MICS were calculated by the + 2014 MICSRoma settlements data pertaining to mothers with secondary and higher education are calculated together due to small Richest Fourth Middle Second Poorest determined Cannot be Higher Secondary Primary None Other Urban Female Male Secondary schooladjustednetattendancerateinRomasettlements attendance attendance 2005 Romasettlements (25.2) ratio 25.3 11.7 12.4 14.1 10.2 n/a Net Net 5.5 4.0 4.4 5.9 (*) (*) (*) 1 Data for 2005 for primary education include primary or less. (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Number of children n/a 24 18 23 15 17 32 0 1 2 5 6 0 9 n/a —Not applicable. Table 10 percentage. author. attendance attendance 2010 Romasettlements (68.8) ratio 42.5 27.9 12.8 10.9 26.8 10.2 24.7 16.5 22.8 19.3 n/a n/a Net Net 6.5 6.5 6.4 Number of children n/a 103 114 118 127 123 133 209 219 366 328 257 585 44 60 attendance attendance 2014 Romasettlements ratio 39.6 35.9 21.4 17.2 31.0 17.7 23.0 14.9 28.0 21.6 Net Net (*) 7.9 4.7 9.3 + Number of children 145 120 111 127 152 290 224 122 174 481 319 336 655 19 29 The Analysis of MICS data 30 The Analysis of MICS data accommodate thenational education context and theMICS Firstly,data. wehave inincluded the definition all (18-24) group age same the of population total the of consists rate leaving school early the calculate to groupreference survey.The the preceding weeks 4 short 3c or 2 1, 0, ISCED is attained training or education of levelhighest the (1) twofollowingconditions:the fulfilling 24 to 18 agedpersonsas aredefined 6.6 14 13 12 leavers come more often from non-urban areas, though the latest data show that this gap is shrinking. education, the percentage ESLof is 1% or less. Yet another clear, and again expected, pattern is that early school Leaving rates rise as the education level theof household headlowers — where the household headhas higher poorest the from peoplequintile between 18 24youngand years old of leavedid school 28% early,almost comparedyet to less than1% fromsmallest, the richestthe quintile. is difference this MICS, Serbia 2014 the In Roma settlements, which decreased to 80.4%in 2014 — still an overwhelming percentage. frompopulationleaversthe in early school there of were200587.2% arenumbersInstrikingly the different. completionrates mustbe approached with caution. orrate attendance net of measures current with comparison direct measure, retrospectivea is rate ESL the (89%),whereasrateESL thosetheincludes who latecompleted(overage secondaryschool Becausechildren). overageof children attendingsecondary thatschool arenotbeing countedinthe net adjusted attendancerate betweennetprimary completion rate andgrossprimary completion rate indicatesthere arenumberssignificant lower than 90%) and data from the 2011 Census and completion rates in Serbia (e.g. the MICS shows that the secondary school adjusted net attendance rates are attendanceondata MICS the of light in startling somewhatarefigures These2014. in 7.8% to 2005 in 11.9% and training it takes into account is limited to participation in formal education only. time during the current school year. In a sense our definition is more time-wise; flexible however, the education precedingthesurvey. Rather, providesMICS informationtheon whether hassomeoneattended at school any weeks4 the in receivedtrainingprovidehas notwhether onanydatasomeoneand educationdoes MICS the Secondly, qualifications. havesecondary not do people youngthose that meaning system, education Serbian ESLlevel. atisThis theEU linewiththe alsoin of withthe definition provision secondary inthe education of line in is which education, secondary yearsof 2 aftercompletinghaveschool years leftwho 18-24 agedthose

Expectedly, the largest difference in the various categories is between the poorest quintile and other quintiles. ESLratesThe decreasing trend is of for observable youngpeoplefrom Roma settlements as well,but overall percentagesfromarei.e. decreasing, positive, is leaving earlyschool in trend the thatshowSerbia for Data leavers school early Union, European the In ways.different in defined be may (ESL) leaving school Early Data from an unpublished further analysis of Census data that was available to the author. the to available was that data Census of analysis further unpublished an from Data Definition used in Eurostat (http://ec.europa.eu/eurostat). Eurostat in used Definition Early school leavers are therefore those who have only achieved pre-primary, primary or a short upper secondary education of less than 2years. than less of education secondary upper ashort or primary pre-primary, achieved only have who those therefore are leavers school Early Early school leaving Early 14 . However, it should also be noted that the difference in numbers 13 12 . However, we have slightly altered the definition so it can it so definition the altered slightly have However, we . ; and (2) no education or training has been received in the in receivedbeen has training or education no (2) and ; quintile Wealth index head household Education of Area Region Sex Total Richest Fourth Middle Second Poorest Missing/DK Higher Secondary Primary None Other Urban Eastern Serbia Southern and Western Serbia Sumadija and Vojvodina Belgrade Female Male 20 05 Serb 11.9

ia leavers ratio 20 Early school Early school 10 Serb 15.2 38.6 28.0 17.1 17.3 10.3 12.8 11.5 12.3 11.9 1.1 4.1 7.5 0.8 6.6 7.7 6.9 8.7 -

2005 Serbia (*) Figures are based on less than 25 unweighted cases. Pe ia Early schoolleavinginSerbia () Figures are based on 25-49 unweighted cases. rcen Note:All data wereby calculatedthe author. 20 people age people age Early schoolleaving ta of young of young 14 Serb Number Number 18-24 18-24 ge 1233 1186 2419 1310 1072 1347 7.8 527 504 516 491 381 403 706 547 677 688 507 - of

Table 11 Figure 7 ia ea rl settlemen 20 y scho leavers ratio Early school Early school 05 Roma 87.2 35.3 19.4 13.0 1.1 3.8 3.1 8.8 0.5 5.2 5.6 9.6 9.6 9.6 5.5 9.6 7.7 8.7 (*) (*) ol

leav 2010 Serbia ts settlemen er 20 s 10 Roma people age people age 81.5 of young of young Number Number 18-24 18-24 1113 1892 426 394 390 410 272 383 981 503 779 460 561 451 420 954 938 21 4

ts settleme 20 14 Roma leavers ratio 80.4 Early school Early school 27.8 17.8 0.9 2.5 4.4 8.4 0.5 5.3 9.7 6.6 7.5 5.9 9.9 8.2 6.2 9.3 7.8 (*) (*)

nt s 2014 Serbia people age people age of young of young Number Number 18-24 18-24 1577 345 332 311 336 253 330 892 329 616 961 405 450 402 320 756 821 25 0 31 The Analysis of MICS data 32 The Analysis of MICS data leavers.school early are quintile poorest the from coming people young of 95% around 2014, in indicators: wealth fromcomesagain data Finally, striking vicemostversa.and the ESL, percentagethe smaller theof educated, of education theof household headis correlated with early school leaving: the more the household headis level the that observe easily can we Again, narrowing. is gap the that indication some with decade, past the isthat larger a percentage young of womenthanleaves menyoung school early. Itbeenhas a stable trend over When it comes to early school leaving in Roma settlements, the first clear pattern that is visible in Tablein isvisible12 clearpattern that first the Roma settlements, in leavingitcomesto early Whenschool quintile Wealth index head household Education of Area Sex Total + 2014 MICSRoma settlements data pertaining to mothers with secondary and higher education are calculated together due to small Richest Fourth Middle Second Poorest Higher Secondary Primary None Other Urban Female Male leavers ratio Early school Early school 2005 Romasettlements (69.3) 78.5 73.8 92.1 66.2 89.6 89.2 86.3 93.0 81.9 87.2 Early schoolleavinginRomasettlements (*) (*) (*) Figures are based on less than 25 unweighted cases. () Figures are based on 25-49 unweighted cases. Note:All data wereby calculatedthe author. people age people age of young of young Number Number 18-24 33 41 15 31 22 24 46 0 1 3 7 0 4 Table 12 percentage. leavers ratio Early school Early school 2010 Romasettlements 62.2 77.7 82.9 83.9 96.7 50.0 88.1 88.3 91.4 77.2 86.9 76.3 81.5 (*) people age people age of young of young Number Number 18-24 1027 176 201 215 214 221 156 728 133 311 716 507 520 10 leavers ratio Early school Early school 2014 Romasettlements 64.7 67.3 75.3 82.7 85.6 94.6 81.8 89.9 79.8 80.6 83.9 76.7 80.4 + people age people age of young of young Number Number 18-24 1071 252 221 196 191 212 160 754 156 295 777 552 519 T case dueto the small sample size. notdid fit the observed data well, thereforeit wasnot inincluded thefollowing text. We assume this tobe the that model a in resultedsettlements Roma from attendancechildrenkindergartenfor for analysisregression 98.5% and respectively98.1% — so the— lack of variabilityhigh in thevery dataare makescountry anywhole such the analysis forunproductive. rates Moreover,attendance the sincethe level, national the on analysis leaverschool = early0). However, not 1, kindergarten = leaverattendance school and (earlyprimary school leaving attendanceschool earlywere and left out0); from the= regressionattendingor not school 1, secondary= attending(attending higherage school secondary of children 0); = attending not 1, = (attending school attending age PPP of children settlements): Roma to kindergarten (attending = 1, pertaining not attending = 0); children dataof primary school age forattending primary or secondary and data nationwide for (both futuremeasures and developments. aboutconclusions drawing for relevant most the environment, societal and educational ever-changing an in educationindicators. Moreover, we todecided emphasize datafrom the most recentMICS since round of itis, policy-relevantrangethepredictors measuresof broadeningwereof thuscollected,benefit pro-poor cashon (Field, 2009). regression we can predict which of two categories a person is likely to belong to, based on a set of characteristicsnot attendthe primaryschool) analysis choicelogisticmight of regressionbebinary analysis.logisticWith does or attends someone (e.g. dichotomous is variable (dependent) outcome the since case, particular this differentmultivariate datamethods of analysis — oftenthe differentmultipleregressionuse of analysis.In notclearly usuallyrequiresuseshowdoof Thisthe effectsthesizes.level)and mother’s education and index Nevertheless,these comparisons notdo take into account the controlling for other characteristics (e.g.wealth distribution rate how of overviewbehaves useful within each aof them, e.g. offering what the children, primary school attendance of rates characteristics are background across keydifferent wealth several quintiles. by Secondary Analysis Secondary Outcomevariables: In this analysis we place our focus on the2014 MICS data for the reason thatin this round theof survey, data in Serbia mainly in terms of attendance rates in different levels of education. Moreover, data was segregated basiche analysisthe indicators selectedhas shown of the overalltrends overthepast in decade education Logisticregression wasinitiallytoplanned be performed onthese outcome variables 7 33 The Analysis of MICS data 34 The Analysis of MICS data Economic Co-operation and Development (OECD) (OECD, 2012). Moreover, 2014 MICS data for Serbia alsoSerbia for data Moreover,MICS 20142012).(OECD, (OECD) Development and Co-operation Economic women are increasingly better educated than young men in many member countries theof Organisation for Consequently,young education. secondary of out dropto likely more areboys where trend a witnessing are girls arelikelyless still evento in enrolsecondary Onthehigh-income in education. other hand, countries we secondary the school at level especially remain, and discriminationamong the and most disparities gendermarginalized around children bottlenecks (UNICEF, and 2015). barriers Inimproved, many developing countries and yetrelevant enoughfor ourthe estimating powerpurposes of households’ cultural capital. we have chosen to include education of the household head, as this was the variable with the least missing data fathers’ educationattainment data werehighermothers’ and isboth(if on collected).Inthe case our of analysis educational parent’swhicheveraccount into take researchers sometimes outcomes; educational different of moreoftenmother’sused is Theas2009).homeapredictor education (Hattie, across variables 0.80) all = (d achievement with relationshipstrongest the has which achievement, children’seducational for expectations Moreover,personnel. teachersschool withand isalsolevelrelatedto parents’educationtheir aspirations and arenotfamiliar. makesThis itfor difficult tothemhelp their children attend engageand learning,inor speak itsintroducesnorms withwhichparentslowercultural Schooling witha set of education has influence. of kind ownwhich 1997), (Bourdieu, capital cultural of form a as seen particularly is education Parents’ (OECD, 2013b). schools and students between disparities socio-economic by explained be can schools different in students across observed achievement student in differences the of half than more countries, OECD across averageon that show (PISA) Assessment Student International forProgramme 2012 the from data example, is a well-known and well-researched factor with a powerful on influence students’ educational attainment.For from to 13% 19%. Therefore, it seems worthwhile to inspect the effects of age on school attendance. less than 7-9% of children ages 7, 8 and 9 do not attend school, while the percentage for children ages 10-13 goesimportanceintheface some reliable of dataDataprovidedforinthe 2014MICS. Roma settlements show that older,droptheyprobabilitiestheThesethatthefromwill increase.systemoutvery general assumptionsgain otherrelated childrenneeds different of of ages.These often combineforces insuch a waythat as children get theand educationsystem mayhave differentpractices cateringtopossibilities and of the educationaland direction, i.e. raising educational aspirations or opting out theof education process. Last but not least, schools otheror one in developchoices and Further,viewschildren’s own time. over changemay education their of value and for prospects the regarding members family other and parents own their of expectationsFirstly, education, especially in urban areas. secondaryin disadvantagedaregirlssettlements Roma in thatshow data MICS 2014 this, toContrary 2014). UNICEF,& (SORS householdspoorestthefromchildren Belgrade among region the and particularlyinlevel, showthatthere arenotable differences in gender Serbiafavourininparity girls of atthe secondary school Education ofhousehold head. Age. Sex. Predictors:

The gender gapinis educationstill amatter concern of aroundthe globe.While genderhasparity Students’ experiencesStudents’ theinwithschools,and education system in general, change theyas grow older. Parents’ education is often used as a measure of socioeconomic status, which

of teachersof (OECD, 2013c). have more and better resources, greater autonomy in how they allocate those resources, and an adequate supply consequence both of economic conditions students (of and community) and school conditions. Urban schools ais (UNESCO,This 2015).America in surveys proficiency reading in or(OECD, 2013c) surveysPISA in inurban areastendto perform athigher levels than studentsfromnon-urban settings, as shown,for example, schools attend who students economies, and countries mostMoreover, in 2013). UNFPA, & UNICEF, China, of Statistics percentageBureauof (National16 by pointsurbanchildren lower childrenruralwasthatof than school-aged secondary senior of rateattendance school the China in e.g. countries; many in trends similar show data recentMore 2000). (UNICEF,attendance school primary in gap urban-rural pointpercentage 10 aleast at wasthere countries 34 in 1999, and 1990 between conductedcountries 54 in surveystoAccording access to schooling, as well as family organization and cultural roles, etc. (Maralani, 2008).and production, of modes development, of level society’s a including context-dependent,more be to seems amount of resources that parents can invest in children’s education was the primary explanation. However, this tonegative, depending onthe context(Maralani, 2008). Where anegative relationship wasfound,the limited whileresults from developing countries arenot conclusivethe— associationranges from positive to neutral Powell, Werum,Carter,& 2002); however, many studies donein developed countries still show such a pattern, with better results than those with more siblings. This conventional wisdom has been challenged (Steelman correlated— childrennumber withthesiblings fewer with of brothers sisters and more obtain schooling and with the lowest 20% theof population defined as the poorest and the upper 20% as the richest. quintiles,in presentedis wealthdistributionthe MICS the In 2013). Steendijk, & (Smits lifecomfortable and material needs, to one in which a household possesses all assets broadly considered necessary for living an easy well-being’. It ranges from a situation in which a household has no possessions at all that may help satisfy their ‘material i.e. satisfaction, needs material thatof is MICS thein used indexthewealth centralof dimensionA countriesin enrolment and educational attainmentthe richpoor. and of poor the of differ profilesacross enrolmentcountries but thefall into thatdistinctive regional alia, patterns, interand that therefound, are They enormous households.differences pooracross and rich from children of attainment and enrolment the in patternsdifferent document to countries 35 in (DHS) Surveys Health and relationship is not so linear and straightforward. They used household survey data from 44 of the Demographic percentile) was roughly 1.25 standard deviations. On the other hand, Filmer and Pritchet (1999) found that the 90 the (at families the last three decades — now the gap in standardized scores in reading achievement between high-income Recently, Reardon (2013) found that the income achievement gap in the United States has grown significantly in Area (urbanization level). Numberchildrenof thein household. Wealth indices have become widely used instruments for measuring the economic status households.of Wealth.

A family’s economic status has a well-known strong effect on the educational prospects of a child.a prospectsof educational the well-knownoneffecta stronghas family’s economic statusA th percentile of the income distribution) and those from low-income families (at the 10 the (at families low-income from those and distribution) income the of percentile

h ubnrrl iie n col tedne s wl-nw phenomenon. well-known a is attendance school in divide urban-rural The Until recently, it has been widely agreed that schooling is negatively th

35 The Analysis of MICS data 36 The Analysis of MICS data within countries may be large, some having regions where more than one third of young people are NEET (Mardin inare Turkey, people young Sicily in Italy,of third Central Greece, oneand Ceuta in Spain) (OECD,than Observing 2013a). datamore from thewhere regions having some large, be may countries within however, 13.2%; isdifferencesUnion European the age theingroup inTheirshare involvededucation. norin 2013a). Another example relates to so-called NEET population — young people 18-24 that are neither employed differences(more 1 thanyear were schooling) Mexico,of infoundAustralia Canada, Spain, (OECD,Brazil and in Veneto was 93 points higher than in Calabria, which is the equivalent to 2 years of schooling. Major regional students of 2012 PISA in performancemathematics Italythe in Forexample,systems. educational unitary in 0.333 (the odds would be 1:3). likely to attend primary school, but if the base category were female, then the odds ratio would have a value of likely to attend primary school than boys (the odds are 3:1). It is the same if we say thatthe femaleoddsis aratioIf rather hasmeansitchild that aboysmale). girls3, than value are threeof more times are three times less will happen (e.g. attending primary school) if a child has a new value in comparison to the base value (e.g. if mostinformative intablesfigurepresented all below isthe oddsratio. It says whatthe odds arethat an event attendingprimaryof school when wealthisthe andeverything elseinthemodel same for allchildren.The constant. For example, analysis will provide estimates about whether receiving child allowance raises the odds the variables is its effect on the indicator (dependent variable) when everything else in the model is held category weused base aas small, veryis education levelsof higher with those of number thesince — head household the of thewithdata, regionexclusion as of a predictor aand different basethecategory use of for level educational education; comes from a densely populated area in Belgrade. The same applies for analyses on theRoma richest quintile settlementwhich does not use financial social assistance, and where the head has the highest level of in household afrom comesallowance; child of user a not male; combination: this included model baseline the data, national the on done analysesregression all in rule a As variables. dummyinto turned werethey The categoricalpredictors were eitherbinary variables(e.g. sex) or,they consistedif more than twoof levels, receivingthis assistancehelpsprospectspoor educational of children. howinvestigate and whetherto opportunity an offers MICS 2014 the transfers, cash these on data collecting areimportant most them, among children; of needs basic for Education Quality and Evaluation, 2014). averageBackaSouthlevel,districtsperform Nis while aboveBelgrade, and average threeon testsall (Institute level): over half of the districts perform below scale average (500 points), one third of the districts achieve atSerbia examin last atprimaryfinal school also the shows endof theregional differences(atthe Cash benefit programmes Cash benefit Region. All categorical predictors with their levels used in the binary logistic regression are presented in Table in13. presentedregressionarelogistic binary the in used levelstheir predictors categoricalwith All Regional differences within a countrytopertaining mayeducation be strikinglylarge, existing even none (no education). However, it should be noted that what we see as a result for each of of eachfor result a as seewewhat that noted beHowever, should education). (noit that arerelated to Serbia childrenin arethefamilies to aimedathelping fulfil financial social assistance financial and child allowance okrug , or, district . By . everything else in the model is held constant, e.g. wealth). When it comes to financial social assistance, patterns attendanceof than those who donot receiveit (once again, we bearshould in mind that thisis the result when children wherethe attainment washigher Users education. child allowance of alsohave highertimes6.5 the odds than higher or school secondary attending of oddslower times 13.3 haveonly education primary has or educational attainment theof household head. Children from households where the headhas no education isattendancepredictor the best The Table14. in passed shown aspredictors has of set the model includes and test new fit Theof goodness age). and level urbanization (region, model the of iteration next the from weakestthebe predictorsshowedtoThreethat werevariables data. excludedobservedthe to well fit not did 7.1 Serbia The initial model formodel predicting Theinitial attendance secondary school or of higher childrensecondary school of age of 7.1.1 Secondary school attendance Note:RomaAnalysis settlement of datanotincluderegion did as apredictor since they werenotused as sample strata. Levels ofcategoricalpredictorsandassignedvalues Child allowance Financial socialassistance Region Area Wealth Education ofhouseholdhead Sex Predictor 1 Analysis of RomaAnalysis settlement of no database(none) asused education category. (bc) Used as(bc) abase category. Table 13 Fourth quintile Middle quintile Second quintile Poorest quintile Higher (bc) Secondary Primary None Female Male (bc) Level Non-user (bc) User Non-user (bc) User Southern andEasternSerbia Sumadija andWesternSerbia Vojvodina Belgrade (bc) Thinly populatedarea Intermediate Densely populatedarea(bc) Richest quintile(bc) 1 37 The Analysis of MICS data 38 The Analysis of MICS data Roma population. receive child allowance, but do receive social financial assistance. A stronger effect in this respect is seen in the attending secondary school or higher posits that they are boys with a low educated household head; they do not thenumberAn childreninthehousehold. approximate of themost profile vulnerable of childreninterms of higherInterestingly,orsecondarythanschool girls. predictor,tobenotaoutwealthsignificant turned asdid financial social assistance. Further, boys of secondary school age have almost two times lower odds of attending lower attending odds of secondary school or higher thentheir counterpartsfromhouseholdsthat donotreceive times 7 almost have assistance social financial receivethat households from coming Children reversed. are leaverstheirthan counterpartsfromhouseholds where hassecondary headthe or higher Analysis education. householdsin wherehasprimarythe head or no education arethree moretimes likely to become earlyschool denselypopulated area.The same oddshead. Youngratiofor household isfound educationpeople of wholive inintermediate anliving populated areahave almostthree highertimesESLthan young oddspeoplefrom of a Youngpeopleone. richest theto comparedtoo, quintile, middle and second thefor substantially rise ESL of an earlyleaverschool highertimes29 forthan those youngtopeoplebelonging the richest Thequintile. odds The best predictor for ESL in the model is wealth. Belonging to the poorest quintile raises the odds of becoming 7.1.2 Early school leaving Wealth index quintile Wealth indexquintile Secondary Primary None Education ofhouseholdhead(Higher=0) Female Sex (Male=0) Included Attending secondaryschool=1 Constant User (Non-user =0) Child allowance User (Non-user =0) Financial assistance Children age1-17inhousehold Richest q. Fourth q. Middle q. Second q. (Poorest quintile=0) Model forpredictingsecondaryschoolattendance,Serbia Note: R 2 = .094 (Cox &Snell),.200 (Nagelkerke). Model χ²(8) =14.390, *p <.05,**p <.01 -2.596* (.1.009) -2.096** (.571) -1.935** (.529) 1.876** (.479) -1.185* (.541) -0.093 (.403) .696** (.264) 1.136 (.551) 0.169 (.357) .040 (.125) .012 (.408) Table 14 2.947* -2.947* (0.639) B (SE) Lower 2.55 1.06 .455 .413 .588 1.19 .05 .81 .11 .04 .01 95% CIforoddsratio Odds ratio 19.05 6.53 1.04 3.12 1.01 .911 1.18 2.00 .14 .31 .12 .07 Upper 16.70 .407 1.33 9.12 2.25 2.00 2.39 .883 .377 .539 3.36 in a denselypopulated area. leavers suggests they come from the poorest quintile, with a low educated household head, and live in Belgrade effects were found for receivingNo financial age.social child’s assistancea in orincrease thechild allowance,with also orand for1-17, agedsex. childrenA morehaveprofile householdsof earlyas riseschool ESL of odds Finally,theBelgrade.thatTablefromtheir counterpartsfrom seewe than15 can ESL lower timesof odds 2.5 also shows that young people from Sumadija and Western Serbia and Southern and Eastern Serbia have about Constant Southern andEasternSerbia Sumadija andWesternSerbia Vojvodina Region (Belgrade=0) Thinly populatedarea Intermediate area (Densely populatedarea=0) Degree ofurbanization Child allowance Financial assistance Children age1-17inhousehold Fourth q. Middle q. Second q. Poorest q. (Richest quintile=0) Wealth indexquintile None &Primary (Secondary orhigher=0) Education ofhouseholdhead Age Female Sex (Male=0) Included Early schoolleaver=1 Note: R Model forpredictingearlyschoolleaving,Serbia 2

= .135 (Cox &Snell),.328 (Nagelkerke). Model χ²(8) =7.982, *p <.05,**p <.01 -1.069** (.362) 1.776** (.660) 2.541** (.643) 3.376** (.643) 1.066** (.234) 1.015**(.360) -1.459 (.992) .378** (.120) -.844* (.346) 1.020 (.700) .135* (.059) -.252 (.339) -.462 (.280) -.433 (.232) Table 15 .701 (.385) 7.108** -7.108** (1.418) B (SE)

Lower .40.17 1.15 .703 1.62 3.60 8.29 1.84 1.02 .22 .36 .18 .03 .95 .41 95% CIforoddsratio Odds ratio .78.34 12.70 29.26 .001 2.02 1.46 2.77 5.90 2.90 1.14 .43 .63 .36 .23 .65 1.51.70 103.22 Upper 10.94 21.51 44.73 1.09 1.62 4.29 1.85 4.59 1.29 1.02 .85 .73 39 The Analysis of MICS data 40 The Analysis of MICS data transfer— a maychild receiveit onlyher/she if attends Still, school. thisis a goodindication that themeasure his/ istimes.Thisexpected,her attendingsincerise 12 benefit, schoolodds almost allowanceof child isa conditional cash cash this receives child a If allowance. child is predictor best the settlements, Roma from age 7.2 Roma from settlements Children 15 come from the poorest quintile and whose household headhas no education. whobut assistance, social receive notallowancefinancial dowhochild attendanceortherefore,children are, No1.8. effect was forobserved sex or age. mostThe vulnerable group childrenin relationto of primaryschool Table16 also shows that receiving social assistancefinancial raises the odds school of attendanceby afactor of attendingoddsprimary of or secondary thethannoschool has children education.head whosehousehold highertimes 10.5 havethanmoresecondaryeducationleast at has head wherehouseholdsthefrom children odds attendingof primary or secondary school than their counterparts from the richest Furthermore,quintile. is effective. Wealthis also a good predictor: childrenfrom the poorest quintilehave more timesthan9.5 lower

In the model thatInthemodel was to used predict primary orsecondary school attendance 7.2.1 Primary school attendance From the initial model, level of urbanization was excluded in order to have a good fit to the observed data. observed the to fit agood have to order in excluded was urbanization of level model, initial the From Constant User Child allowance(Non-user=0) User (Non-user =0) Financial socialassistance Children age1-17inhousehold Fourth q. Middle q. Second q. Poorest q. (Richest quintile=0) Wealth indexquintile Secondary orhigher Primary Education ofhouseholdhead(None=0) Age Sex (Male=0) Included Primary schoolattendance=1 Model forpredictingprimaryschoolattendance,Romasettlements Note: R 2 = .241(Cox &Snell), .421 (Nagelkerke). Model χ²(8) =13.76, *p <.05,**p <.01 -1.844** (.437) -1.680** (.438) -2.262** (.423) 2.474** (.187) 2.353** (.665) -.129** (.046) 1.355* (.609) .614** (.182) -.918 (.470) Table 16 .028 (.216) .068 (.038) .065 (.176) B (SE)

.672.86 Lower 8.23 1.29 .80 .16 .07 .08 .04 .99 .76 95% CIforoddsratio Odds ratio 1.0310.52 11.87 15 1.85 1.07 1.07 .88 .40 .16 .19 .10 for primary childrenschool of 1.5738.69 Upper 17.11 2.64 1.00 1.15 1.51 .96 .37 .44 .24 coming from the poorest quintile, having more siblings and not receiving child allowance. assistance.Therefore,social lowest thefinancial attending secondaryoddsto or higherof school belong girls, orlevelfor urbanizationnorage, head, household Surprisingly,the getnot forany of weeffecteducation did children.fewer have who households from children than school secondary attend to likely less are children higher odds of attending secondary school than girls. Further, children coming from households that have more almost 7 times more likely than children from the poorest quintile. Analysis also shows that boys have 2.5 times 8 times more likely to attend secondary school or higher than their counterparts from the second quintile, and those who donot receivethis Wealth cashbenefit. with follows comparisonnext, with childrenin from the richestit receivequintile almostwho those for times 16 school attending of odds the raises it allowance: child The best predictor in the model for predicting attendance of secondary school or higher is, again, receivingagain, is,higher orfor attendancesecondarypredictingschool model thepredictorof bestin The 7.2.2 Secondary school attendance Constant Thinly populatedarea Intermediate area (Densely populatedarea=0) Degree ofurbanization User Child allowance(Non-user=0) Financial socialassistance Children age1-17inhousehold Fourth q. Middle q. Second q. Poorest q. (Richest quintile=0) Wealth indexquintile Secondary orhigher Primary (None =0) Education ofhouseholdhead Age Female Sex (Male=0) Included Secondary schoolattendance=1 Table forpredictingsecondaryschoolattendance,Romasettlements Note: R 2 = .327 (Cox &Snell),.497 (Nagelkerke). Model χ²(8) =4.803, *p <.05,**p <.01 -1.389** (.385) -2.074** (.451) -1.914** (.512) -1.983 (1.989) 2.790** (.301) -.319** (.100) -.924** (.260) -.566 (.370) -.033 (.288) -.290 (.286) .860 (.545) .502 (.481) .085 (.114) Table 17 B (SE) Lower 9.02 .27 .55 .43 .60 .12 .05 .05 .81 .64 .87 .24 95% CIforoddsratio Odds ratio 16.27 2.36 1.65 1.09 .14 .57 .97 .75 .73 .25 .13 .15 .40 Upper 29.35 1.17 1.70 1.31 6.88 4.24 1.36 .88 .53 .30 .40 .66 41 The Analysis of MICS data 42 The Analysis of MICS data higher. Where the educational attainment is primary school, the odds becomingof an early orschool leaver education secondary has are 2.2 head household whose those than ESL of greaterodds times 2.7 haveattainment therichest Likewise, quintile. youngpeople comingfrom households whoseheads donothave any educational approach those of the richest group, until there is no effect on outcome whether someone belongs to higher the oddsfourth of ESLor than their counterparts from the richest quintile. As material well-being rises, the odds of ESL looking at the odds ratios from Table 18 we can see that the young people from the poorest quintile have 6.1 times early school leavers. Regarding the number of children age 1-17 in a household, we found no effect. peoplefromthinly populatedrural areasintermediate and populatedurban areashave lessbecoming chance of the odds leavingof school early are 13 times higher for those who do not receive this type of support. Also, young fromhouseholdsthat donotreceive The effectbenefit. theseis cashtypes very of prominent for child allowance: assistancereceivethatsocial orallowance child financial likelyless aretoleave thoseearlycomingschoolthan Results also show that, keeping all other characteristics equal, young people aged 18-24 coming from households Furthermore, our analyses showthat with increasean in age youngpeople’s increasesESLlikelihood slightly.of highertimes thanthe reference group.Girls aremoremoretimes1.5 likely than boysthan to leave school early. Among the best predictors for ESL in the model are wealth quintiles and education of household head. By head. household of education and quintiles wealthare model the in ESL for predictors best the Among 7.2.3 Early school leaving Constant Thinly populatedarea Intermediate area area =0) Degree ofurbanization(Denselypopulated User Child allowance(Non-user=0) User (Non-user =0) Financial socialassistance Children age1-17inhousehold Fourth q. Middle q. Second q. Poorest q. (Richest quintile=0) Wealth indexquintile Primary None (Secondary orhigher=0) Education ofhouseholdhead Age Sex (Male=0) Included Early schoolleaver=1 Model forpredictingearlyschoolleaving,Romasettlements Note: R 2 = .139 (Cox= .139 &Snell),.222 (Nagelkerke). Model *pχ²(8) =8,488, <.05,**p <.01

-2.316* (1.045) -2.563** (.529) 1.068** (.287) 1.068** (.287) 1.820** (.379) -.544** (.249) -.605** (.211) -.544** (.184) .772** (.264) .807** (.214) .990** (.348) .135** (.049) .515** (.177) Table 18 .062 (.058) .317 (.232) B (SE) Lower 1.29 1.66 2.94 1.47 1.36 1.04 1.18 .36 .36 .03 .40 .95 .87 95% CIforoddsratio Odds Ratio 1.06 1.37 2.16 2.91 6.17 2.24 2.69 1.14 1.67 .10 .58 .55 .08 .58 Upper 12.98 1.19 2.16 3.63 5.10 3.40 5.32 1.26 2.36 .95 .83 .22 .83 W of of the Strategy on Roma. Research shows that the policy tools aimed at increasing coverage, reducing dropout have since been improved and adopted were the Strategy on Roma and the Action Plan for the implementation institutional responsibilityof for the education Roma. of Among the most important strategic documents that Moresanctioning discrimination. theseissuesforregulationprovided detailed funding the of establishmentand action, affirmative grade, first the in policies admission inclusive assistants, teaching of introduction Education, whichof created System conditionsfor systemic This action.the particularly appliesto theregulations concerningof the Foundations the on Law new the of adoption the by to contributed certainly priority issue for the signatory states wasa discriminationwhich in education. withinAt the national levelInclusion, this processRoma is of Decade the of presidency Serbian the wasprocess this of trigger initial The all. for education quality to access equal ensure that policies educational implementing and creating for mechanisms institutional build to begun has Serbia 2009 Since 2010). Stojanović, & (Baucal practices pedagogicalinchanges tosubstantial lead torequirednor binding neitherareintentions, good reasons and their implementation were the recommendations given by the Ministry Education, of which, regardless of the was project-oriented. Positive measures were undertaken with an ad hoc methodology and the instruments for initiativetheotherand Educationgovernment2009, until institutionsof Ministry the thewasagenda on of Romathateducationfactthe and 2005, in whichstarted Inclusion, Roma Decadeof Despitethe education. many different national programmes and initiatives to promote inclusion Romaof students into mainstream Theprogress becouldattributedto Romaintegration the(Kovač policy is inCerović thatvisible 2013) et al., school primary in observed 2014). in be 84.9% and 2010 in could 88.3% to2005 in trend 73.6% (fromRomasettlements attendancefrom forchildren linear no hand, other the On school. primary attending not This analysisnot couldprovide reliable aboutfindings thefactors the affectingpercentage small children of organized transport. than 2 km from a PPP facility. Moreover, more than 27% themof walk to the facility, compared to 18% whomore livesuse settlements Roma from child fourth everyalmost facility, and school a in PPPattends population generalthe from child third everyalmost since network, preschool the with problemsare there thatsuggest and access problems,but there is also a group reasons of categorized as parental attitudes.Moreover, data also preparatory the programmeattending agewas PPPobserved in the last two MICS of cycles. Results childrenshow that respondents of see obstacles in 62.9% financial to 2010 in 70.6% from decrease A years. recent in Conclusion Primary school adjusted attendance remains steadily veryhigh inthe general population(more than98%). (98.1% in the 2014 Serbia MICS), the situation with children from Roma settlements has grownhasRomasettlementsworse fromchildren with situation the MICS), Serbia2014 the in (98.1% hile the level of attendance of the Preschool Preparatory Programme in Serbia is very high at the moment 8 43 The Analysis of MICS data 44 The Analysis of MICS data taking theinclusivetaking first steps,the absorption capacities schoolshave of weakened. After opening up schools support to and schools measuresare needed, systemicsince the However, furtherreduction in attendance 2009). rates Evaluation, shown in this and study indicates Quality that, after Education for (Institute parents the attendancerate, number studentsin of extracurricular activities, and schools’ cooperation with students’ favourtheir positive of effects onthe educationalachievementRomathe minority. of The sameis visiblefor inspeaksSerbia, Republic theof in schoolsprimary 22 inpedagogical assistants introductiontheof suchas toInled schools addition, analysis their2013). and first effects(Jovanović, on the measures,policy effects of in implemented successfully been have students Roma of success school increasing and absenteeism, and 16 but settlements, Roma from people young for observable is trend same The 2014. in 7.8% to 2005 in 11.9% did not show up in Roma settlements. parents (and their children, naturally). Interestingly, the effect of the household head’s educational attainment population; this suggests, again, that more efforts are needed from the education system and schools to adjusttheof household’s tochildren. The level of education in the household is an important factor in the general thesocial assistance to caseisthatused financial caterto evenmoreneedsbasicneedsthethan educational householdsthat receive social assistancefinancial However, arein the worstsettlements). economic Roma situation, so it might very in wellbeattendance school secondary of predictor strong a is poverty that findings account into taking when (especially assistance social financial with needs educational children’stargeting good direction that was taken with the conditional child allowance measure, and the room that exists for better assistancehas anegative effect(generalpopulation) orsocial no effect(Roma settlements). This underlines againthe financial while settlements, Roma and population general the for both attendance school secondary attends settlements Roma from agesecondary school or higher appropriate of child fifth every only nevertheless, MICS, Roma SerbiaSettlements 2005 the to comparison in double did cycles last two the in settlements Roma from children settlementsincreases atlevel educationthis of dramatically.Although secondary school attendance rates for MICS cycles(2014 2010).However,and the gapbetween childrenfrom the general population fromand Roma afactor. significant childrenfrom the richest households.Analysis also suggestedthat levelthe household iseducation of quite than school primaryattending lower timesof have oddsRomasettlements9.5 showed:from poorchildren attend andcompleteisprimaryThis education. even moreimportant considering whatthe analysis also room for thinking about how this social protection measure could be more upaligned opens with which ensuring effect, positiveall large children a suchhave not does assistance social However, recipients.financial for times 12 increase school primary attending of odds the — settlements Roma from children on effects theirschool completion rates and achievementlevels. for Roma students, more efforts are needed to sustain the trend, to keep them attending school, and to elevate

The secondary school adjusted net attendance rate for the whole of Serbia stabilized at 89% in the last twolast the in 89% atSerbiastabilized attendancenet rateadjustedwholetheforof school secondary The Logisticregression suggested that child allowance as a conditional cashtransfer doesshow verypositive Trends in early school leaving are positive in the general population, i.e. percentages are decreasing, from decreasing,percentages are i.e. population, general the in positiveare leaving school early inTrends It should be noted that 13.6% of children from Roma settlements of secondary school age attend primary school. primary attend age school secondary of settlements Roma from 13.6% children of that noted be should It 16 . Logisticregression . showed that againthe childallowanceis good predictor of inthe sector educational the and social care system to reduce the percentage earlyleavers. of school findings suggest that this support system is only partly effective, and that additional measures are needed both Romasettlements thisrecipientsbenefit dohave cash of higher inthestaying odds education of system. These in butgeneral, in Serbia inhave effectnotan do benefits Cash head. household the of education wealthand arefactors strongest the among populations both In percentage.overwhelming an still — 2014 in 80.4% to decreasedwhich 87%, toleaversamounted schoolearly 2005 In strikinglydifferent.arenumbers overallthe 45 The Analysis of MICS data 46 The Analysis of MICS data 9 Recommendations T room for improvement, and authorities should consider how to better align this cash benefit with children’swith benefit cash this betteralign to how consider should authorities and improvement,for room is there whole, a asineffective measure assistance social financial the deem not do we although that means social assistance notfinancial demonstrate did fullits effectiveness inrelation to educational attainment.This tothe fact that the cashtransfer is conditionalupon regular school attendance.However, currentresults show termsin assistingchildren’s of attendance differentlevels of This education. canprobably of bepartly attributed Onepositive education. of forsign the is authoritiesthat child allowancehasshown to be an effectivemeasure measuresin thisfield. being introduced in Belgrade deserves thorough monitoring and evaluation in order to inform potential system but alsoparentssubsidizing tobe able to their enrol childreninprivate Suchfacilities.initiativenew a currently privateinstitutions that with cooperationoffer throughkindergarten. orThis PPPmeans not for only intended giving premises permits schoolto allow of the remodelling service substantial to throughbe provided there, Unfortunately, this alsomeans thatfunds in the systemneed to be allocated toinsure quality service, of either thisis donein a place thatmay be unsuitablefor such a purpose, asis oftenoffered the iscase with theservice buildings.school the when even that and underdeveloped, remains network PPP the that fact the to speaks closer to the place beneficiaries’ residence.of relocating or all, for services transport providing in especially authorities, educational among action an for callsThiskindergarten. in PPP,child atoavoidtoaenrolling reason displacementadditional givesaansuch fact they may want to. On the other hand, for those Roma parents that may be PPP,reluctant thetothe despiteto bring their child childrentheir bring not do thus and service transport theprovide to unable areparents many as system,coverage the within influences directly homespeople’s from facilities the of displacementa havefacility.themtheaccesstoaway,transport toSuchkm organized number of 2 onlya thanand more are programmes. As the results of our study show, for many children from Roma settlements kindergarten facilities the population andwouldof serve as asource evidence of for policiesthe educational formulation and of sector social protection. of Afully operationalinformation system wouldbe ableto provide reliable parameters Serbianthetheeducationsystemsystemisinin thatdirectly information linkedsystem the functioning with recommendation actuallypertainsto to firstneed the establish an effectivethe information andefficient education Therefore, size. in small be might that groups vulnerable to related those including interest, of be Poverty proved once again tohave strong effects on the educational attainment childrenof at differentlevels Furthermore, thethatfinding almost a third all of children attendingkindergarten do soin buildingsschool However, it is a sample-based study and sometimes it cannot provide enough data for all analyses that may he MICS studies underline the importance havingof reliable data on a number keyof education indicators. inclusive cultureininstitutions. education an build to strive level system the at efforts if effective be only may these However,programmes.all sports charge extracurricularfreeof and protection;and programs;active social parentparticipation inengagement efforts of schools and civil society organizations in the provision of school meals; dropout prevention programs; completion of a specific education level. Central initiatives at the system level should be complemented by inclusiveon Roma educationpolicyintegration and thatpolicyfoster children’stheinstaying system and actionssustainable feasibleand formulated, well of setais needed still iswhat Jovanović, 2013), (e.g. studies not guarantee their staying in the system. Although many theof measures have doesproven this effective inpolicy), several school anti-discrimination an and projects, inclusion Roma for grants municipal projects, educationinclusive for grants school Roma, for assistants pedagogical introducing policy,enrolment school system has been focusing its efforts on facilitating entry moreof Roma children in the system (e.g. a new that facilitates their child in attending and staying in the system. Despite the fact that since 2005 the education attainmentirrespective whetherof theybelong to themainstream orRoma population, and providing support asystem especially entailsreaching outto thoseto parentsbelonging groupslower with levels educational of children’s needs but also reaching out towards parents has yet to be fully installedin our education system. Such educational attainment.meansThisthat amoreinclusive educationsystem not only cateringto the range of and other school materials, free clothing, school meals and different types subsidiesof and scholarships. support for the additional provideservices to accompanyingas so aconsidered bechild’s also attendanceshould assistance of financial school, such of asmeans otherproviding that, transport, Beyondfree age.textbooks orby automaticallyproviding childallowancesocial assistance for financial recipients with children aspecific of needs. Thiseducational may be donethrough theintroduction conditions of asinthe case child allowance, of ute, idns ugs ta te dcto o te oshl ha hs srn efc o children’s on effect strong a has head household the of education the that suggest findings Further, 47 The Analysis of MICS data

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