The analysis of Multiple Indicator Cluster Survey data · PDF fileThe analysis of Multiple Indicator Cluster Survey data ... The analysis of Multiple Indicator Cluster Survey data

The analysis of Multiple IndicatorCluster Survey data

25%50%

Rural/Urban Disparitiesin the Situation of

Children and Women

2015

AB

03

Rural/Urban Disparities in the Situation of

Children and WomenThe analysis of Multiple Indicator

Cluster Survey data

2015

Preface and Acknowledgments

T he Republic of Serbia is one of the few countries in which all five rounds of the Multiple Indicator Cluster Survey (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 urban and rural disparities present in the everyday life of children and women in Serbia, and was conducted by Mr. Dragan Stanojevic.

We owe special thanks to Professor Marija Babovic, 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.


25%50%

Gender Aspectsof Life Course in Serbia

Seen through MICS Data

2015

AB

Contents

1. Introduction 5

1.1 Theoretical and methodological framework 6

1.1.1 Spatial aspects of the approach 6

1.1.2 Life course aspects of the approach 8

1.2 Structure of the study 10

2. Children’s household environment 12

2.1 Access to water 13

2.1.1 Gaps in access to water 13

2.1.2 Access to water in Roma settlements 14

2.2 Access to sanitation 15

2.2.1 Urban-rural gap in access to sanitation 15

2.2.2 Access to sanitation in Roma settlements 16

2.3 Household environment 16

2.3.1 Wealth status 17

2.3.2 Cultural capital of households with children 20

2.4 Child health 23

2.4.1 Immunization 23

2.4.2 Nutrition 24

2.4.3 Early child development 29

2.5 Upbringing 31

2.5.1 Support for learning 31

2.5.2 Child discipline 35

2.6 Education 37

2.6.1 early childhood education 37

2.6.2 School readiness 40

2.6.3 Net intake rate in primary education 41

2.6.4 gender parity index 42

2.6.5 Out-of-school children 42

2.7 Early labour engagement 44

2.8 Attitudes toward children with disabilities 48


25%50%



2015

AB

3. Women: disparities and gaps 51

3.1 Household environment 52

3.1.1 Regional differences - - - - - - - - - - - - - - - - - - - - - - - - - - - - 52

3.2 Education and activity status 55

3.2.1 Regional differences 55

3.3 Marriage/union and family 60

3.3.1 Partnership and early marriages 60

3.3.2 Family planning — Contraception 63

3.3.3 Early childbearing and abortion 65

3.3.4 Unmet need 67

3.3.5 Antenatal care 69

3.3 Risks of domestic violence 71

4. Conclusions 73

4.1 Children 73

4.1.1 Life conditions for children 0-17 years old 73

4.1.2 Children 0-4 years old 74

4.1.3 Children 5-17 years old 75

4.2 Women 76

5. Recommendations 77

5.1 Recommendations regarding research methodology 77

5.2 Policy recommendations 78

References 79

Appendix 83

The

Ana

lysis

of M

ICS

da

ta

5


25%50%



2015

AB

1IntroductionT he subject of this study are children and women from Serbia, while its main objective is to look at their well-

being, as well as their practices and attitudes in the period from 2005 to 2014, in relation to the areas in which they live. The analysis is based on the Multiple Indicator Cluster Survey (MICS) framework, while areas of residence are classified according to the degree of urbanization (defined in terms of population density).

The specific aims of this paper are: 1) descriptive — to identify in which aspects of their well-being key differences emerge among children and among women living in urban and rural areas; and 2) analytical — to recognize key causes of these differences1.

Bearing in mind the data available from the MICS study, the main idea is to recognise, by conceptualizing the life course of children and women, differences and similarities in relation to location in the following dimensions of 1) children’s life: family characteristics and material conditions of the life of children, care and protection, child health, child development, education, early labour engagement, and attitudes toward children with disabilities; and 2) women’s life: family characteristics and material conditions of life, education, marriage/union, family planning, and risks of domestic violence.

The MICS is a sufficiently comprehensive and adequate framework for such research. The analysis based on MICS data provides the trend analysis, considering that the last three waves of the surveys have comparable almost all indicators relevant to this analysis (2005, 2010 and 2014). The indicators are very sensitive to issues of the growth and development of children, as well as issues related to the position of women. Large samples provide sufficient freedom to disaggregate the majority of indicators not only by areas of residence, but also additionally by other characteristics of women and children (such as region, education, gender, etc.). One very important advantage of the last three rounds of MICS surveys is related to the fact that they also covered the population of Roma settlements. This is especially important, considering that the Roma population is underrepresented in most of the surveys carried out by the Statistical Office of the Republic of Serbia (SORS).

An especially salient aspect of this study is the use of the dual division of areas according to the degree of urbanization. The importance of introducing a new tripartite division (densely populated area, intermediate populated area and thinly populated area) and comparative presentation of the data in accordance with the dichotomous division into urban and other (rural) provides insight into a more differentiated picture of the life of women and children.

1 Given the significant differences in the structure of the population (based on education and wealth status) and the access to infrastructure in urban and rural areas, a part of the differences can be explained by these factors. Other differences may be influenced by culture, value systems and specific lifestyles in urban and rural areas.

The

Ana

lysis

of M

ICS

da

ta

6

1.1 Theoretical and methodological frameworkThe global MICS programme was developed by UNICEF in the 1990s as an international household survey

programme to collect internationally comparable data on a wide range of indicators on the situation of children and women. MICS surveys measure key indicators that allow countries to generate data for use in policies and programmes, and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

Serbia is one of the few countries in which all four previous rounds of MICS were implemented. The MICS surveys implemented in 2005, 2010 and 2014 were conducted on Roma settlements samples as well and proved to be sensitive enough to measure disparities and bring a wealth of data about groups that are difficult to reach; indeed, they have remained the only source for many indicators that reveal the status of Roma children and women. Bearing in mind the ongoing process of reforms, the key precondition for development of new, adequate social inclusion policies will be the availability of recent, reliable data on the general population, but even more importantly on socially excluded and deprived groups. The MICS provides up-to-date and comparable data that will enable decision-makers within the government and all other stakeholders to critically assess progress made and to put additional efforts in areas that require more attention.

However, as in the case of most post hoc analysis, there are certain limitations in terms of data availability and lack of indicators that would fully frame the desired image of a phenomenon. The limitations of the MICS framework are as follows: 1) limited comparability of some indicators from three rounds of surveys due to changes in indicator definitions; and 2) the general limitations of every post hoc analysis in which a researcher overtakes the indicators already used in another research and has no influence on their operationalization.

The analysis is divided into two parts: 1) cross-sectional and 2) trend analysis. The data of the 2014 MICS will be used in the first case and the data from 2005, 2010 and 2014 in the second one. The analysis is mainly descriptive. However, in the analysis of cross-sectional data from 2014, logistic regression models were used in order to identify key predictors of outcome variables.

Bearing in mind the aforementioned advantages and limitations of such an endeavour, in the analysis, two levels of social reality will be directly related: 1) the spatial framework, and 2) the life course of women and children.

1.1.1 Spatialaspectsoftheapproach

Intra- and inter-spatial disparities (urban and rural) represent an important framework for explaining differences in life practices and life chances of the population of women and children. Disparities between urban and rural living areas depend largely on the level of productivity in agriculture compared to other industries (service, in particular). These disparities increase along with a lower level of development in a country, creating the preconditions for a larger gap between the countryside and the city (Gollin, Lagakos, & Waugh, 2014). Data in Serbia indicate that the rural population of Serbia is particularly at risk of poverty, especially the households with children (Babović, 2011). Research at the global level confirms the existence of disparities based on place of residence in different facets of life: the financial situation of families; living

The

Ana

lysis

of M

ICS

da

ta

7

conditions2; family arrangements; the health of women and children3; child-rearing practices; access to institutions of education and social protection; the transition to marriage/partnership and parenthood; working arrangements of women; and subjective perceptions of life satisfaction.

In addition to the differences that exist between urban and rural contexts, results indicate a high poverty rate and exclusion that children in urban contexts in developing countries are facing. This situation brings greater risks while growing up, and reduces the chances of a successful transition through education and to the labour market, namely to integration into society4. In the Serbian context, the Roma represent an especially vulnerable population in urban areas, who, by the level and complexity of exclusion, differ significantly from the general population5.

After the Second World War in Serbia, significant changes occurred in the urban-rural population ratio — from 1948, when the percentage of the rural population amounted to 74%, to 2011, when this share had declined to about 40%. In this process, Belgrade became the most important destination of all migrations (from rural and urban living areas to Belgrade). Favouring urban living areas affected the increase of an urban-rural gap, while the rural population was perceived as a reactionary element (part of the ancien régime) during the whole period of socialism6. With the EU accession processes, the importance of rural living areas and the rural population is recognized almost exclusively through international commitments and projects, while there are very few domestic initiatives to improve living conditions in rural living areas.

As mentioned above, the place of residence represents a good predictor for explanations of variations in the populations of children and women. The spatial framework in this study encompasses two modes of operationalization. First, the division of settlements into three categories according to population density will be the main form used (and the only one for the analysis of the current situation), based on the data of the MICS 2014 survey: 1) densely populated areas (DPAs) or high-density clusters (or city centres) include fields of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum settlement population of 50,000 inhabitants; 2) intermediate populated areas (IPAs) or urban clusters include fields of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000 in a settlement; and 3) thinly populated areas (TPAs) or village clusters comprise all values not falling in the first and second category. The second form of operationalization will include official statistical classification into ‘urban’ and ‘other’7 areas. This analysis will be used to examine the trends where there are relevant conditions for it.

There are methodological, theoretical and practical reasons for such a decision. The methodological justification of using two forms of operationalization of the same phenomenon is reflected in the fact that

2 In developing countries, significant disparities appear within the urban population, as do significant differences between urban and rural contexts, in terms of accessing the most basic resources such as water and the most basic sanitation. (World Health Organization & UNICEF, 2006; United Nations, 2013).

3 Children’s health, mortality and food also significantly vary depending on the urban and rural context (Sastry, 1997; Paciorek, Stevens, Finucane, & Ezzati, 2013). 4 UNICEF (2002, November), Poverty and exclusion among urban children, Innocenti digest, No. 10 — November 2002. 5 Serbia Multiple Indicator Cluster Survey (MICS) and 2014 Serbia Roma Settlements Multiple Indicator Cluster Survey, 2005, 2010, 2014. 6 Bogdanov (2007) depicts the period of socialism where ‘the mechanisms for the implementation of policies related to rural and balanced territorial development were

not sufficiently coherent, stable and sustained. Thus their combined effect failed to create a significant impact. Generally speaking, this developmental dimension was pushed to the margins, being viewed as an auxiliary, rather than a constitutive part of other policies and development programs. Rural areas were always viewed as a problem, rarely as a resource’ (p. 67).

7 Official statistics in Serbia do not include a specific definition for rural settlements. Instead, an ‘administrative-legal’ criterion is applied that designates settlements as 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’. For the purpose of this analysis the term ‘rural’ will be used to represent ‘other areas’.

The

Ana

lysis

of M

ICS

da

ta

8

tripartite classification has not been used in domestic research, while the need for detecting significant statistical differences between different features (in the lives of children and women) in accordance with the level of population density would justify its future use. At the next level, theoretical questions are open, namely on how to explain the differences representing not only the urban-rural discrepancy, but also differences within urban settlements (DPA and IPA). At the third level, which is a practical one, the combination of the two approaches allows us to: 1) keep track of trends and identify where the differences increase, where they reduce, and where they remain constant; and 2) observe the nuances in the latest wave of research.

In order to take advantage of the trend analysis as well as of more diversified observation of the level of urbanization, analysis of each of the features starts with a depiction of the current state by the criteria of the tripartite division of the level of urbanization, and then (in the boxed sections of the text) continues with the presentation of the trends on the basis of the binomial division of settlements (urban/rural). The relationship of the two operationalizations of the level of urbanization should also be taken into account. Looking at the entire population, the following table indicates the method of the population’s classification, where DPA entirely falls under the urban, IPA is divided into a majority of urban and minority of rural areas, and TPA in most cases (89%) coincides with rural areas.

Table 1

Per cent of population in MICS survey by two-area criteria and degree of urbanisation

Urban Rural TotalDensely populated area (DPA) 100.0 0.0 100.0Intermediate area (IPA) 78.6 21.4 100.0Thinly populated area (TPA) 11.5 88.5 100.0

Analytical concepts to be used for explaining the spatial differences are: 1) intra-urban and intra-rural disparities, and 2) an urban-rural gap (or urban-rural divide). The first pair of concepts is used for explaining differences among groups of children and women within the urban and rural living areas, while the second one serves to express differences in the characteristics between populations living in cities and in the countryside.

1.1.2 Lifecourseaspectsoftheapproach

The spatial analysis will be combined with the conceptualization of the life course for children and women. The life course approach represents a conceptual tool used for understanding the life paths of individuals and groups within a specific social context which may be structurally and culturally limiting or stimulating for some life paths. The life course is the sequence of roles, activities and events in different domains of the life of a person. These events can come one after another or can occur at the same time (e.g. marriage and parenthood). However, what makes this perspective so fertile is its contextualization of the lives of individuals within the social and cultural milieu. Elder et al. (2002) list the following dimensions meant to generate difference between the term ‘life course’ and similar terms (such as life span, life history, life cycle). Firstly, a person’s development does not end with mental and physical maturation, but rather continues throughout life, so that adults too are under the influence of social, psychological and biological changes. Furthermore, individuals are active, as they

The

Ana

lysis

of M

ICS

da

ta

9

make decisions and act within historical and social frameworks, which may be limiting or stimulating. The kind of reaction to certain events and the meanings attributed to them depend on the moment when they occur in his/her life (parenting by those who became parents in their adolescent period differs from those by parents in their forties) (Elder et al., 2002).

The aim of this approach is to describe and explain ‘the synchronic and diachronic distribution of individual persons into social positions across [a] lifetime’, while representing a significant aspect in mapping ‘internal temporal ordering, i.e. the relative duration times in given states as well as the age distributions at various events or transitions’ (Mayer, 2002, p. 2). This analytical perspective explains individuals’ life trajectories through the action of at least four mechanisms: institutional, social (economic, cultural), personal, and group (cohort). While personal perspective eludes the possibilities of our analysis, we will focus on the institutional and social context as well as the specifics of age cohorts. Considering that the aim of the analysis is the identification of variations in two spatial frameworks, the variations will be interpreted within the analytical vocabulary of the urban and rural life course of children and women. Bearing in mind that social structures (the institutional and social contexts) exercise restrictive and encouraging powers, we expect that life courses will f low in different ways depending on different opportunities and constraints women and children face in rural and urban living areas in various domains of life.

Although the approach has been widely applied, primarily in qualitative studies, it has also been utilized when it comes to quantitative data. In these approaches, age-specific groups and/or specific events are considered as milestones. The basic idea is to identify specific paths, i.e. choices that individuals make in life, as well as to understand and explain them. Bearing in mind that the choices available to individuals are influenced, and even bounded, by structural and cultural conditions, it is understandable that different types of available resources (material, social, cultural) as well as cultural patterns (values) guiding individuals through life would limit or encourage them to undertake certain steps in life. Several important structures are almost always present when we talk about different paths of individuals. The first one is gender: the paths of growing up and life course significantly differ for boys and for girls, i.e. men and women (Tomanović et al., 2012). Namely, amongst other things, the education of girls and boys will not be equally considered in all cultures.

Expectations are based on cultural patterns. It is culturally embedded when and how women should become mothers and have a child. Another structure is related to stratification — social status that can be marked by class strongly related to the level of education. Expectations that children should be well positioned within the social structure lead, if there are needed conditions, to extended education and postponement of parenthood and employment. A higher level of education is often accompanied by easier employment and safer jobs (Stanojević, 2012).

Studies on the transition to adulthood of young people in Serbia indicate differences in the pace of growing up in relation to educational paths, so that each successive level of education leads to a ‘thickening of events’ (moving from one to another becomes faster), in spite of the fact that the paths of the majority of young people in Serbia are usually standard ones (from education through employment, to the establishment of their own households and families) (Tomanović, 2012). Gender and stratification differences in a local context are also still mediated by place of residence, where urban–rural differences prove to be significant when it comes to different practices of children, youth and adults (Babović & Vuković, 2008). Children engage in the adult world

The

Ana

lysis

of M

ICS

da

ta

10

more often in rural living areas than in urban ones, which implies that they start to do household chores, engage in economic activities, have less time for extracurricular activities (they also have less opportunities to engage in these activities, since they are less available), and enter into marriage and establish their own family at an earlier age. On the other hand, men and women who grew up in an urban environment are more likely to stay longer in the education system or, for example, enter into cohabitation (Tomanović, 2012). Urban centres enable easier overcoming of traditional patterns and pressures that can come from social communities; therefore, in many ways these centres represent the framework for individualization.

In the analysis, we divided the phases of life course of children and women in accordance with the developmental stages and the educational cycle so that this population will be divided into three phases of development:

1. Early childhood (up to 59 months) is the phase in life full of survival and developmental risks. As per the recommendations of the WHO and UNICEF, physical and psychosocial development of children should be under under constant monitoring by parents/guardians.

2. Kindergarten and primary school age (6-14 years old) is the phase that begins with formal compulsory education and ends with its completion. During this period children acquire general knowledge and social skills to prepare them for the next phase in life, during which a more significant diversification of their individual paths starts.

3. Adolescence (15-17 years old) is the phase of diversified social dimensions in which children evolve, take on roles, and gain competences. This phase usually involves the continuation of formal education (which may be general/grammar schools, vocational or trade), inclusion in the sphere of labour (whether through a permanent or temporary employment), early marriage/cohabitation, parenting, etc. During this phase, one can recognize more clearly the choices made as well as structural and cultural frameworks that have shaped them.

To identify differences in the patterns of life course of women — namely the course encompassing the domains of education, marriage/union and motherhood — we will follow three age cohorts of women whenever this division is adequate and where the indicators enable it: young women (15-24 years old), prolonged youth (25-35 years old) and middle age women (36-49 years old). We make this division bearing in mind the national characteristics of the transition to adulthood of young people who, compared the youth in societies of Western Europe, acquire the attributes of adulthood somewhat later, at about 35 years of age (Tomanović & Ignjatovic, 2006).

1.2 Structure of the studyThe study is organized considering the developmental needs of the child and relevant events (in the spheres

of education, employment, marriage and family formation) representing milestones in the life of adult women in different areas (DPA/IPA/TPA or urban/rural for trend analysis). The first part of the study is devoted to the lives of children up to 18 years of age, and the second one to women aged 15-49. Each chapter begins with the background of a household where an individual grows up in order to successively move to age-specific situations and problems.

The

Ana

lysis

of M

ICS

da

ta

11

The study is divided into two major parts: 1) the lives of children, and 2) the lives of women. We have organized the study in such a way that certain dimensions (e.g. education, etc.) are presented and then, within them, specifics of subjects at different stages of life courses. Therefore, the first chapter first describes the living conditions of households and families with children (under the age of 5 and from 5 to 17 years), and then covers the following dimensions: child health, early child development, upbringing, child education, child discipline and attitudes toward children with disabilities. The second chapter presents differences in the following dimensions of women’s lives: the living conditions of households and families in which women live, education and activity status, marriage/union, and family and risks of domestic violence. At the end of the study, there is an appendix with all the tables that complement the picture of the life of children and women.

The

Ana

lysis

of M

ICS

da

ta

12

O ne of the Millennium Development Goals (MDG) implies that between 1990 and 2015 the proportion of the world population without access to safe drinking water and adequate sanitation should be halved8.

According to current estimates, on a global scale this goal has been achieved when it comes to access to water, but not to basic sanitation (it was estimated that 75% of the predefined target would be achieved) (UNICEF, 2015). Although since 1990 the number of residents with no access to basic infrastructure has decreased significantly at the global level, some 700 million people still lack access to safe drinking water and about 2.5 billion people have no access to improved sanitation facilities (WHO & UNICEF, 2014).

Serbia was one of the few European countries that were not on track to reach the MDG drinking water target by 2015. On the other hand, Serbia was on track to reach the MDG sanitation target by 2015 (WHO & UNICEF, 2014). Global and national reports indicate that despite the increase in coverage of infrastructure, inequalities in access remain. The differences appear between urban-rural populations, households of different wealth levels, people with different levels of education, etc. In the domestic context, inequalities occur between the urban and rural populations. In addition, intra-urban and intra-rural inequalities are also indicated, and to a large extent these are differences between the general and the Roma population (SORS & UNICEF, 2014).

We examine general differences in the material conditions of the life of the population (households with children up to 18 years of age) in different living areas through the main source of drinking water and access to sanitation infrastructure, namely through availability of basic infrastructure. Although Serbia is not among the countries with major disadvantages to basic infrastructure, there are visible differences between settlements with different levels of urbanization. For optimal health and development of children, access to safe water is necessary, namely to bacteriologically and chemically clean water. Improved sources of water do not always imply healthier water, but do include the control of its quality, which at least provides users with the information to seek some other appropriate source in case of contamination.

8 Goal 7.C

2Children’s Household Environment

The

Ana

lysis

of M

ICS

da

ta

13

2.1 Access to water2.1.1 Gapsinaccesstowater

Basic infrastructure is not equally available in urban and in rural living areas9. When it comes to improved sources of water, in DPAs there is a higher per cent of the population in households with children that are directly connected to the water supply system, in comparison to less densely populated areas (Figure 1); in TPAs there are more of those who receive water from a tube well or protected well when compared to living areas with a higher level of population density (Figure 2). In DPAs, there are no children living in households that use water from unimproved sources, while such children in IPAs and TPAs amount to 0.7% and 0.8%, respectively (Table 1 in Appendix).

Figure 1

Per cent distribution of population from households with children that are directly connected to the water supply system, in three areas

Figure 2

Per cent distribution of population from households with children using water from tube well, in three areas

9 The MICS framework recognizes use of water (as a main source) as improved and unimproved sources. Improved sources include water: piped into dwelling; piped into compound, yard or plot; piped to neighbor; accessed from public tap/standpipe, tube well, borehole, protected well, or protected spring; and bottled water. Unimproved sources include: unprotected well, unprotected spring, water from tanker truck, surface water and bottled water.

84.9

80.1

78.4

Densely populated area

Intermediate area

Thinly populated area

Piped into dwelling

0.4

0.5

3.6


Intermediate area


Tube well, Borehole

The

Ana

lysis

of M

ICS

da

ta

14

DPA gap. When it comes to access to drinking water within a DPA, regional disparities are indicated. The region of Southern and Eastern Serbia has the most ramified water supply network — 96.8% of population from households with children use water from the public water supply. The same is used the least in Vojvodina (66.5%). It is used by 87.4% of the population from households with children in Belgrade and 88.8% in Šumadija and Western Serbia. The bad quality of drinking water from water supplies is compensated by usage of bottled water in Vojvodina (25.4%) and Belgrade (12.1%). The issue of inadequate drinking water from the public water supply systems in Vojvodina is particularly alarming. On the aggregate level, there are no major differences between regions in terms of access to improved sources of water.

On the other hand, there are certain regional differences when it comes to the population in households with children living in TPAs. Water from a water supply system is used in central Serbia mostly (Šumadija and Western Serbia, 84.9%; and Southern and Eastern Serbia, 79.9%), and less in Belgrade (78.8%) and Vojvodina (70.1%). Non-usage of water from the public drinking water supply system is usually compensated by buying bottled water in Vojvodina (17.7%) and other improved sources of water in other regions. Yet in the total sum, in central Serbia there is a somewhat higher per cent of the population that uses unimproved sources (Southern and Eastern Serbia, 0.8%; Šumadija and Western Serbia 1.5%; only 0.1% in Vojvodina; and not at all in Belgrade). Bearing in mind that the rural population in these regions is more prevalent than in Vojvodina and Belgrade, the importance of improving their infrastructure becomes evident.

2.1.2 AccesstowaterinRomasettlements

When it comes to the population from households with children in Roma settlements, the situation regarding access to water is significantly different from the situation described in Serbia. In DPAs, Roma settlements’ main source of drinking water does not differ from the rest of the urban population’s: as much as 99.7% of them use improved sources of water. On the other hand, among them there are far fewer who use bottled water (1.8% compared to 11.6% of the general DPA population). This indicates the risks of using water in cases when the water supply network does not provide high-quality drinking water. Inhabitants in DPA Roma settlements are more often connected to the water network of their neighbours (3.1% versus 0.3% in the general DPA population), or have water piped into a compound, yard or plot (12.3% versus 0.2% in the general DPA population), which indicates the high material deprivation of this population.

The TPA Roma children are significantly more disadvantaged when compared both to the general population and the population of the DPA and IPA Roma. Less than half of the Roma population from households with children uses drinking water that is piped into their dwelling (44.5%), and as much as 9.7% of the population receives water from unimproved sources (mostly from a tanker truck [6%] and unprotected well [1.3%]).

The

Ana

lysis

of M

ICS

da

ta

15

2.2 Access to sanitation2.2.1 Urban-ruralgapinaccesstosanitation

Access to sanitation infrastructure indicates that there are clear differences in the use of improved sanitation facilities and considerably more unimproved sanitation facilities in use in TPAs than in DPAs and IPAs (Figure 3). In TPAs, a significantly lower percentage of the population from households with children is connected to piped sewer system than those in DPAs. Also, residents of these living areas more often use a septic tank. In TPAs, 6.6% of the population uses a pit latrine with slab, while the same is used by 2.12% in DPAs.

Figure 3

Per cent distribution of population from households with children with unimproved sanitation facilities, in three areas

Figure 4

Type of toilet facility used by household members (from households with children) in three areas

DPAgap. Regional differences of DPAs are differences between central Serbia, on the one hand, and Belgrade and Vojvodina on the other. Belgrade and Vojvodina DPA inhabitants use sanitation facilities with septic tanks significantly more than in other regions: in Belgrade 14.9% and Vojvodina 21.2%, in contrast to 0.5% in Šumadija and Western Serbia and 4.4% in Southern and Eastern Serbia.TPAgap.Although DPAs of central Serbia are generally well equipped with a sewage system, TPAs generally

lack it, exposing their population to higher risks in connection to the structure of improved and unimproved

0.5

0.9

5.3


Intermediate area


Unimproved sanitation facility

87.9

72.3

21.3

9.9

25.3

63.3

Densely populatedarea

Intermediate area


Flush to septic tank Flush to piped sewer system

The

Ana

lysis

of M

ICS

da

ta

16

sanitation facilities. In Southern and Eastern Serbia, as much as 6.6% of the population uses unimproved sanitation facilities; 6.0% in Šumadija and Western Serbia; and only 0.2% in Belgrade and 2.1% in Vojvodina. In Southern and Eastern Serbia, every sixth citizen from a household with children (16.4%) uses a pit latrine with slab.

2.2.2AccesstosanitationinRomasettlements

Urban-ruralgap.The population from households with children in Roma settlements is significantly different from the general population. When it comes to this population, the best access to sanitary infrastructure is in IPAs and the worst in TPAs (Figure 5). It is significant to say that every fifth inhabitant from households with children in DPAs has no access to improved sanitation facilities. As for improved sanitation facilities, they use the types that carry more health risks significantly more often, such as a pit latrine with slab (15.3% in DPAs, 18.1% in IPAs and 32.2% in TPAs).

Figure 5

Per cent distribution of population from households with children with unimproved sanitation facilities, in three areas — Roma settlements

2.3 Household environment For the optimal development of children, optimum conditions of the household in which they live are

necessary. In addition to access to basic infrastructure (water and sanitation), other material and non-material conditions are important for healthy growth and proper development. Unambiguous evidence worldwide proves the importance of adequate material resources for the development of the child. Children from poorer families with parents with low educational status have higher risks of negative child outcomes while growing up (Hanson, McLanahan, & Thomson, 1997; McLoyd, 1998; Pickett, 2007; and Davis-Kean, 2005), especially if children are under significant risk of poverty during preschool years (Brooks-Gunn & Duncan, 1997). Domestic research indicates that family background is a very important predictor of children’s achievement. Children whose parents have completed only primary education have 300 times lower chances of finishing university than children whose parents have tertiary education. The odds ratio of children whose parents have three-year secondary education is 17 and of those with parents with four-year secondary school is 2.4 (Stanojevic, 2013).

33.4

8.7

21.9


Intermediate area

Thinly- populated area

Rom

ase

ttlem

ents

Unimproved sanitation facility

The

Ana

lysis

of M

ICS

da

ta

17

In addition to material conditions, other social conditions are also necessary for the development of the child. Much research points to the importance of the family environment for child outcomes (family structure, atmosphere and relationship between the spouses/partners, and between children and parents). Children growing up in single-parent families are at greater risk, as are those in families with second (third, etc.) marriages, families without developed networks of informal support, families with conflict relations between members, etc. (McLanahan & Sandefur, 1999; Thomson, Hanson, & McLanahan, 1994; Tomanovic, Stanojevic, & Ljubicic, 2014; Milić, 2010; UNICEF, 2013).

The MICS methodology uses a wealth index as a composite index of a household’s material situation10. This measure is relational and divided into five quintiles, out of which, in our analysis, we will use division of 60% poorest.11

2.3.1Wealthstatus

2.3.1.1 Urban-rural gap in wealth statusMaterial conditions of children’s life are significantly different in less populated areas. Such a gap is very large

both when it comes to children up to 5 years of age and to children aged 5-17 years. In TPAs, three quarters of children under the age of 5 and children aged 5-17 years belong to the category of the poorest 60% (Figures 6 and 7)12.

Figure 6

Poorest 60% of children under 5 years of age in three areas

10 In the MICS methodology, the basic research unit is a household defined as ‘a community of persons whose members live together, prepare food and spend earned income jointly, or as a single person, living, preparing food and spending income on his/her own’. While all children grow up in households, they usually grow up in family households, so that the family framework within which a child grows up is actually a significant framework for the explanation of the difference in opportunities for the well-being of a child growing up and through life.

11 For the method of calculating this index for the general and Roma population see SORS & UNICEF, (2014, p.12-22). 12 When looking at the participation of the poorest (first quintile — 20% of the poorest), the gap is even larger: in TPAs, there are three times more poor children aged

up to 5 years compared to in DPAs, and twice as many among the age group 5-17.

63.5

64.2

84.2

32.6

44.6

74.3

DPA

IPA

TPA

DPA

IPA

TPA

Rom

a se

ttlem

ents

Serb

ia

Poorest 60 percent

The

Ana

lysis

of M

ICS

da

ta

18

Figure 7

Poorest 60% of children 5-17 years old in three areas

We note the following interesting data: the differences in wealth between living areas are higher in the population of children up to 5 years of age than in the population of older children; when compared to younger children, older ones in IPAs and DPAs more often belong to the poorest 60% of the population. One possible explanation is that these data indicate a gradual retraction of parenting by the poorer population of urban living areas, due to high costs of raising children and, particularly, challenges in the reconciliation of family and work life.

Figure 8

Regional differences of poorest 60% of children under 5, in three areas

Poorest 60 percent

60.9

60.5

75.4

34.9

52.4

78.5

DPA

IPA

TPA

DPA

IPA

TPA

Rom

a se

ttlem

ents

Serb

ia

Poorest 60 percent

19.6

32.0

73.0

48.9

45.3

76.6

45.1

46.4

76.6

30.0

51.5

71.2

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

Belg

rade

Voj

vodi

naŠ&

WSe

rbia

S&ES

erb

ia

The

Ana

lysis

of M

ICS

da

ta

19

When we compare regions (children under the age of 5), we clearly see that Vojvodina and Šumadija and Western Serbia have the smallest differences, while Belgrade has the greatest differences in the percentage of children by wealth status. In the more urbanized parts of the Belgrade region, children live in better conditions, primarily due to the better conditions that the capital offers (Figure 8).

2.3.1.2 Wealth status of children from Roma settlementsThe child population in Roma settlements under the age of 5 lives in even worse conditions than the general

population in Roma settlements. There is a larger share of children in the 60% and 20% of the lower part of the wealth index. On the other hand, fewer children aged 5-17 years fall below this limit (compared to the general population of Serbia) and this share decreases with the level of urbanization. The explanation for these differences in comparison to the general population of children in Serbia may be related to earlier involvement of Roma children in the sphere of labour in more urbanized living areas, leading to a relative improvement of their material situation compared to younger children and children from TPAs.

Trendsinthewealthstatus

Changes in the last 10 years indicate a second effect of the economic crisis. Austerity measures has begun to affect families in the city increasingly more than those in the countryside. Freezing and reduction in public sector wages, fewer jobs in the labour market, price increases, and the like, have probably affected the urban population to a somewhat greater extent. Earlier research also indicates that, in times of crisis, the relative position of the agricultural population improves (Lazić & Cvejić, 2004).

Figure 1

Trends in wealth of children under five

Figure 2

Trends in wealth of children 5-17 years old

41.4

31.0 36.5

81.7 84.9

72.8

2005 2010 2014 2005 2010 2014

Poorest 60% Urban Poorest 60% Rural Poorest 60% Urban Poorest 60% Rural

37.4 37.3 41.1

87.4 87.8

80.8

Children age 5-17 in urban and other areasChildren under five in urban and other areas

The

Ana

lysis

of M

ICS

da

ta

20

Roma settlements: Urban-rural gap. The facts regarding the wealth of the population from Roma settlements confirm the trends characteristic of the general population, but also point to the specifics of this population. When it comes to the wealth of children under the age of 5 in the period of 4 years (between 2010 and 2014), relatively the same level of material conditions was maintained in rural areas, while there was a further increase of the number of people in poverty in urban living areas (from 58% to 68.6% of the poorest 60%). The situation of families of older children (aged 5-17) follows the changes characteristic of the general population. Therefore, there has been a decline in the number of the poorest 60% (from 81.8% to 72.6%) in rural living areas and an increase of the same in urban ones (from 55% to 60%).

2.3.2 Culturalcapitalofhouseholdswithchildren

Cultural capital in this study is seen as the achieved level of education, namely as objectified cultural capital, in accordance with Pierre Bourdieu’s theory. Bourdieu (1986) assumes that the position that an individual will occupy in a society depends to a large extent on the cultural capital of the family of origin (in addition to economic and social family capital). The educational system and family are key institutions in the reproduction of social position. Highly educated parents are more likely to value the education system highly; to be involved as a support in their children’s school work; and to provide a family atmosphere that stimulates the learning process, for example by owning a home library and a variety of teaching aids, making family trips with an educational character and the like (Kraaykamp & Van Eijck, 2010). Educational profiles of households in which children grow up also differ in living areas with different levels of population density. Every fifth child in the general population under the age of 5 grows up in a family in which the mother has only completed (or attended) primary school or has an even lower level of education (19.3%), whereas such a situation applies to every tenth child in DPAs (9.7%). The situation is also similar when it comes to the share of children with highly educated mothers. In DPAs, more than half of the children under 5 have mothers who attended higher education, while in TPAs this share accounts for only 15.2% (Figure 9). The situation is somewhat different if we look at the education of mothers of older children (5-17). In IPAs and TPAs, there has been a shift towards a slight improvement of the educational level of mothers. However, the change is more present in DPAs (Figure 10). These findings on the one hand indicate an improvement of the educational structure of women in general, but also possible recognition of the importance of resources for making decisions about parenthood.

DPA differences appear to be differences between Belgrade and the rest of urban Serbia. In the educational structure of mothers in the Belgrade region, there are as many as 54.7% of children under 5 with mothers who have higher education, while in other regions there are fewer of them (in Vojvodina 36.2%, Southern and Eastern Serbia 34%, and Šumadija and Western Serbia 26.5%). This indicates a disproportion between micro-environments in which children are growing up.

The

Ana

lysis

of M

ICS

da

ta

21

Figure 9

Education of mother — children under 5, in three areas

Figure 10

Education of mother — children 5-17 years old, in three areas

1.9 0.8 0.5

7.8 7.4

18.5

33.9

59.1

65.8

56.4

32.8

15.2

Densely populated area Intermediate area Thinly populated area

None Primary Secondary Higher

Densely populated area Intermediate area Thinly populated area

None Primary Secondary Higher

1.7 0.3 0.9 7.9 9.9

20.8

48.3

60.7 63.9

40.4

25.4

12.3

The

Ana

lysis

of M

ICS

da

ta

22

TPA differences are expressed differently. Children in Belgrade TPAs have mothers with the most favourable educational structure (7.1% have mothers with primary school and 13.5% with university education), followed by Vojvodina (31.1% with primary school or less and 9.4% with university education), Šumadija and Western Serbia (20.1% with primary school and 15.5% with university education) and Southern and Eastern Serbia (23.8% with primary school or less and 9.4% with university education). Data on gaps indicate a concentration of children with well-educated mothers in Belgrade in both urban and rural living areas, which distinguishes this region as a privileged one in terms of educational achievement of children.

Roma settlements: Differences between the urban and rural population of children from Roma settlements are significantly smaller. Although the educational level of mothers is well below the average of the general population, differences between DPAs, IPAs and TPAs are small, which indicates a balance in dearth of cultural capital (educational structure).

Trendsintheeducationofthemother

Although the educational structure of the entire population improves both in rural and urban living areas, the pace of these changes is not the same in these two. While the share of children with low-educated (primary and no primary education) mothers decreases in rural and urban living areas at almost the same rate, the percentage of children with highly educated urban mothers increases faster than in rural living areas (Figure 3). In this way, the educational gap between urban and rural living areas actually deepens.

Figure 3

Children under 5 who have mothers with higher education in urban and rural areas

2005 2010 2014

Urban Rural

24.0

36.9

48.7

9.4 13.5 16.2

The

Ana

lysis

of M

ICS

da

ta

23

Roma settlements: Data on educational trends reveal an unfavourable position of children, especially in urban living areas. Between 2010 and 2014, there has been a decline of the share of children who have mothers that attended secondary education in urban areas (from 12.9% to 9.3%), while there has been an increase in the percentage of those who have mothers without and with primary education (from 87.1% to 90.7%). In rural living areas, there has been a slight improvement of the educational structure at the level of secondary or higher education (increasing from 5.1% to 6.3%). A significant finding of longitudinal analysis shows that urban living areas with all their educational opportunities in the last decade have failed to generate changes in the population of Roma children (Tables a.1.0.1-a1.05 in Appendix 2).

2.4 Child health2.4.1 Immunization

UNICEF and WHO’s recommendation (Burton et al., 2009) is that children under the age of two should receive a ‘BCG vaccination to protect against tuberculosis, three doses of DPT containing [a] vaccine to protect against diphtheria, pertussis, and tetanus, three doses of [the] polio vaccine, three doses of the Hepatitis B (HepB) vaccine, three doses of the Haemophilus influenzae type b (Hib) vaccine, and a first dose of measles vaccination before a child’s first birthday’ (SORS & UNICEF, 2014, p.55). UNICEF and WHO set up as a development goal to reduce the under-five mortality rate, and they regard an expansion of immunization coverage as a way to achieve that. The aim is that over 90% of children under the age of 1 are vaccinated at national level and over 80% for all country administrative units (WHO, 2013). Given that Serbia belongs to the developing countries in which these objectives have been achieved, as a measure in this report, we used full immunization coverage of children aged 24-35 months, who had received all the recommended vaccines up to the moment of the survey.

The data indicate the existence of small differences in full immunization coverage among the general population in relation to the living area. Children in IPAs are covered best, and those in DPAs and IPAs somewhat less. Roma children are less often vaccinated than children in the general population. In addition, there is a much higher instance of Roma children not receiving any of the recommended vaccines (BCG, Polio, DPT, HepB, Hib, MMR1). Within the children from Roma settlements, there are significant differences depending on the type of living area. Most unvaccinated children live in TPA settlements, then in DPA settlements, and the least in IPA settlements.

The

Ana

lysis

of M

ICS

da

ta

24

2.4.2 Nutrition

Adequate nutrition after children’s birth is a key precondition for their survival, gaining immunity and optimal development. According to the recommendations of the WHO and UNICEF, natural breastfeeding is the most important source of food for child. Mothers are advised that a child should be breastfed during the first hour after birth and then exclusively breastfed up to 6 months, as the best recommended diet. Combining breastfeeding and other foods or use of artificial food in this period is not considered the best substitute for breast milk. Complementary feeding — breast milk and supplements — is recommended starting with the sixth to the 24th month of child’s life, while breastfeeding is recommended during this period if possible (Kramer & Kakuma, 2012). Although research indicates different relationships between the socio-economic characteristics of the household in which a child grows up and nutritional practices, mothers who are better educated and from better-off households are more often the bearers of new patterns (patterns in line with WHO recommendations) (Yeneabat, Belachew, & Haile, 2014; Barria, Santander, & Victoriano, 2008; and Heck, Braveman, Cubbin, Chávez, & Kiely, 2006). Differences in practices between urban and rural living areas usually depend on a country’s level of development. In very poor living areas, exclusively breastfeeding can be used as a consequence of material deprivation and not awareness of the importance of this practice. In more developed living areas, nutritional practices are in line with the WHO recommendations, and in urban living areas more often than in rural ones.

Figure 11

Full immunization coverage of children aged 24-35 months

Figure 12

No immunization coverage of children aged 24-35 months

78.7 88.4

77.3

43.7 43.1 45.7

DPA IPA TPA DPA IPA TPA

Serbia Roma settlements

Full [a]

0.9 0.7 0.0 5.1 1.9

10.2



None

The

Ana

lysis

of M

ICS

da

ta

25

Facts on the practice of breastfeeding indicate the dominance of different cultural models in different living areas in Serbia (Figure 13). Exclusive and predominant breastfeeding are less prevalent in TPAs, when compared to IPAs and DPAs, which indicates a greater awareness by people in more urban living areas13. The situation in Roma settlements is different. Exclusive breastfeeding dominates in DPAs, while predominantly breastfeeding is practiced in IPAs and TPAs more often.

Figure 13

Exclusively and predominantly breastfed children 0-5 months in three areas, Serbia

() Figures that are based on 25-49 unweighted cases

When we look at adequate nutrition for children up to 2 years, depending on the place of residence, there are only significant differences among infants 0-5 months. Infants 6-23 months are on the same feeding regime, regardless of the living area in which they live. These data indicate that informing and educating mothers/parents from TPAs would be important for ensuring the uniform practice of nutrition for children throughout the country. The data related to the children from Roma settlements indicate other differences. Although exclusively breastfeeding is a more dominant practice in DPAs and very rarely represented in IPAs and TPAs, children aged 0-23 months from Roma settlements in DPAs generally have slightly unfavourable nutrition in relation to other living areas. However, compared with the general population, breastfeeding practices for children from Roma settlements are to a larger extent aligned to medical standards. See Figure 14.

13 There is a disturbing fact proving that the gap between urban and rural areas in relation to this practice has increased in a period of only 4 years. Although there has been some increase in predominant breastfeeding practice both in rural and urban areas, the rate of exclusive breastfeeding in rural areas has dropped significantly.

17.2 (17.9)

2.4

21.2

(5.8) (6.7)

51.6 (55.2)

34.2

48.9

(73.6) (65.9)



Children age 0-5 months exclusively breastfed

Children age 0-5 months predominantly breastfed

The

Ana

lysis

of M

ICS

da

ta

26

Figure 14

Infant and young child feeding practices, in three areas

() Figures that are based on 25-49 unweighted cases

The key predictor (Table R.1) for exclusive breastfeeding practice among the general population is living area, i.e. urban living areas. Neither mothers’ education nor the wealth status of their household, but rather the place where they live, is a direct, significant factor determining whether mothers breastfeed their child exclusively. This finding points to the traditional cultural practices, which introduce other food into the diet earlier in comparison to current recommendations, while the diet pattern after the first 6 months equates with practices in other living areas. When it comes to predicting the exclusive breastfeeding practice for children from Roma settlements, only the wealth status of a child’s household determines whether a mother would breastfeed her children exclusively. Children that belong to the fourth and fifth quintile of the wealth index (the richest 40%) are five times more likely to be exclusively breastfed up to 5 months. Although, as stated above, this practice is more prevalent in DPAs than in other living areas, living areas represents a mediated variable, as there are generally more urban households with better standards.

The data indicate a gap in DPAs. Breastfeeding practices (for children aged 0-23 months) in accordance with medical standards are most prevalent in Belgrade (37.8%), which is followed by Šumadija and Western Serbia (28.2%), Vojvodina (11.3%), and finally Southern and Eastern Serbia (7.4%). These data indicate that the urban core of the capital is the most significant framework when it comes to adoption of new practices in the diet of children. Due to the small number of cases in other living areas, it is not possible to determine differences reliably.

17.2 (17.9)

2.4

21.2

(5.8) (6.7)

27.8 29.7 27.3

45.2

59.2 52.2

24.4 25.8 20.8

38.1

48.6 41.1



Children age 0-5months exclusivelybreastfed

Children age 6-23breastfeeding andreceiving solid,semi-solid or soft foods

Children age 0-23months appropriatelybreastfed

The

Ana

lysis

of M

ICS

da

ta

27

Table R.1

Logistic regression, causes of exclusive breastfeeding practices

Serbia

B Exp(B)Wealth index 40% (ref. poorest 60%) .591 1.807

Primary school or less 18.475 .000

Secondary school (ref. higher) .067 .936

TPA (ref. DPA/IPA) 1.616* .199

Constant 2.551 .078

Roma settlements B Exp(B)Wealth index 40% (ref. poorest 60%) 1.589* 4.898

Primary school or less(ref. secondary and higher) .654 1.923

TPA (ref. DPA/IPA) .318 .728

Constant 4.690 .009

Note. *p<.05; **p<.001

The MICS data on nutritional status point to certain differences between living areas of different population densities. However, trends are not linear. In the general population of children, underweight is somewhat more present in DPAs (2.3%) and TPAs (1.8%) than in IPAs (0.8%), while overweight is more prevalent in IPAs (16.0%) than in DPAs (12.6%) and TPAs (13.9%). The differences between children from Roma settlements are less pronounced; the only significant difference occurs in stunted, a phenomenon more frequently present in TPAs (26.2%) as opposed to DPAs (17.0%) and IPAs (14.9%) (Table 13 in Appendix).

The differences in the general population become more significant when children’s sex is added to the analysis. In DPAs, girls are underweight and boys overweight more often. In IPAs, boys are overweight and stunted more often; while in TPAs boys are underweight and stunted more often. In the Roma settlements in DPAs, wasted and overweight is more frequent among boys, while the situation in TPAs shows that problems related to underweight, stunted and wasted are more related to boys. See Figures 15 and 16.

Figure 15

Percentage of children under age 5 by nutritional status and sex of children, Serbia

1,6 3,3 ,7 ,9 2.8 ,9 6,5 6,7 6,3 3,6 7.5 4,6 5,0 3,9 3,8 4,5 3.5 2,7

15,1 9,3

18,8 13,3 14.3 13,6

Male Female Male Female Male Female

DPA IPA TPA

Underweight Stunted Wasted Overweight

The

Ana

lysis

of M

ICS

da

ta

28

Figure 16

Percentage of children under age 5 by nutritional status and sex of children, Roma settlements

Figure 4

Trends in exclusively and predominantly breastfed children 0-5 months in urban and rural areas

Alarmingly, the gap between urban and rural areas in relation to the practice of breastfeeding has increased in a period of only four years. Although there has been some increase in predominant breastfeeding practice both in rural and urban areas, the rate of exclusive breastfeeding in rural areas has dropped significantly. Roma settlements: In the period between the two surveys, the practices of exclusive breastfeeding have become more prevalent in urban areas and decreased in rural ones (Table a.3.1.1-a.3.1.3 in Appendix 2).

Underweight Stunted Wasted Overweight

11.2 12.8 6.4 4.6

17.7 6.1

17.3 16.6 20.3 8.7

28.2 24.3

6.2 2.8 4.2 2.4 9.3 5.3 6.1 2.8 3.0 8.4 4.9 5.3


DPA IPA TPA

18.3

2.2

50.1

41.4

16.1 11.1

40.8 37.6

Urban Rural Urban Rural

Exclusively breastfed Predominantly breastfed

2014 2010

The

Ana

lysis

of M

ICS

da

ta

29

2.4.3 Earlychilddevelopment

The MICS methodology recognizes early child development as developmental abilities of a child that it should develop by the age of 3 or 4 years in four domains: literacy-numeracy, physical, social-emotional, learning. Developmental abilities of a child are under the influence of macro and micro social environments. It is very important to allow proper development in the early development period (up to 3 or 4 years) because later in life it is far more difficult to compensate what was missed in this period. According to the WHO report, early child development ‘strongly influences the well-being, obesity/stunting, mental health, heart disease, competence in literacy and numeracy, criminality, and economic participation throughout life. What happens to the child in the early years is critical for the child’s developmental trajectory and life course’ (Irwin, Siddiqui, & Hertzman, 2007, p.7). The mother’s education and the material status of households appear as the most significant predictors of early child development (Schady, 2011; Engle & Black, 2008), but the family’s social resources (skills, cultural practices, family relations, etc.) also have influence.

The data indicate that a more urbanized environment is more stimulating for the overall development of children. When it comes both to the children of the general population and the children from Roma settlements, the lowest early child development index is recorded in TPAs. Likewise, there are more children who are not on track in any of the four domains (literacy-numeracy, physical, social-emotional and learning) in the TPAs, both in the general and in the Roma settlements (see Figures 17 and 18). The data indicate that settlements in IPAs represent the optimal framework for the development of children, as the children from these settlements show higher scores than other children.

Figure 17

Early child development index score, in three areas

Figure 18

Percentage of children not on track in any of the four domains

96.2 97.0 92.5 79.9

91.8

76.9



Early child development index score

1.1 0.2 2.7 1.8 0.8 2.2



Percentage of children not on track in anyof the four domains

The

Ana

lysis

of M

ICS

da

ta

30

Looking at specific developmental characteristics of children in the general population, literacy-numeracy and socio-emotional development show the largest differences. Children in IPAs are to a greater extent developmentally on track in literacy-numeracy (47.6%), in contrast to DPAs (38.5%) and TPAs (23.7%). Children in TPAs (91.9%) are less on track in social and emotional development than children in DPAs (95.7%) and IPAs (96.6%). The situation of children from Roma settlements is similar; when it comes to these two developmental characteristics, children in TPAs14 are behind children in IPAs15 and DPAs16 (Table 29 in Appendix).

In examining the cause of the variations in different aspects of child development, we have made several regression models in which we included the following predictors: wealth, mother’s education, attendance of a preschool program, and the living area of residence17 (Table R.2).

Table R.2

Logistic regression, early child development18

Physical Social-Emotional Learning ECD — on track B Exp(B) B Exp(B) B Exp(B) B Exp(B)Wealth index 40% (ref. poorest 60%) .370 1.448 .626 1.869 .188 1.207 .391 1.478

Primary school .563 1.755 .662 .516 .804 2.234 .593 .553

Secondary school(ref. higher) .262 1.299 .363 .696 .548 1.730 .265 .767

Early childhood education 3.068* 21.489 .773* 2.166 3.302* 27.168 1.121* 3.067

TPA (ref. DPA/TPA) .546 .579 .231 .794 .592 .553 .259 .772

Constant 3.049 21.090 2.116 8.298 3.023 20.551 2.387 10.885

Note. *p<.05; **p<.001

The results indicate that the geographic living area is not an important framework for explanation for early child development. Results also indicate that, when it comes to physical, social-emotional, and learning, as well as the overall index, attending a preschool programme is the key and the only predictor for development of these skills to a level that guarantees staying on track. Bearing in mind the significantly less developed network of preschool institutions in rural living areas (settlements with lower population density) than in urban ones, children in rural living areas are more exposed to developmental risks, while parents are under greater pressure to work with their children to keep them on track.

14 Literacy-numeracy developmentally on track 12%, socio-emotional developmentally on track 77.1%. 15 Literacy-numeracy developmentally on track 14.9%, socio-emotional developmentally on track 86.2%.16 Literacy-numeracy developmentally on track 18.7%, socio-emotional developmentally on track 83.3%.17 Mother’s education, material status of households, and home environment also appear to be related to early child development. For more information see study Lara

Lebedinski, Early childhood development, (2015), MICS report, Belgrade: UNICEF18 Literacy-numeracy is not included, although this model didn’t pass the Hosmer–Lemeshow test (p<.05).

The

Ana

lysis

of M

ICS

da

ta

31

Longitudinal data show that in the period of four years there has been some improvement in both urban and rural areas when it comes to children’s literacy-numeracy. On the other hand, there has been a decrease in the rate of rural children when it comes both to the social-emotional and the total early child development index. Meanwhile, in urban and rural areas (in the latter more significantly), there has been an increase in the number of children who are not on track in any of the four domains (in 2010, there were no such children in urban areas at all, while there were as little as 0.1% of them in rural areas) (Tables a.4.4.1-a.4.4.4 in Appendix 2).

Roma settlements: Longitudinal data indicate that in the period between the two surveys, there have been improvements in literacy-numeracy among children from Roma settlements, both in rural and urban areas (in 2010, there were 13.3% of those in urban areas, and 16.4% in 2014; in rural areas in 2010, there were 4.5%, and 13.5% in 2014). On the other hand, the data also indicate that, when it comes to all other developmental characteristics of children, there has been a certain reduction in the percentage of children who are on track (Tables a.4.4.1-a.4.4.4 in Appendix 2).

2.5 Upbringing 2.5.1 Supportforlearning

Children from more urban living areas have somewhat better conditions for development (Figure 19). Books and different types of toys are somewhat more available to them than children in less populated living areas. The availability of books at home develops a positive attitude towards learning and acquisition of knowledge among children, and influences performance during their later education. A variety of toys, especially those developmentally appropriate for their age, are very important for enabling children’s experimenting with objects (by assembling, disassembling, throwing, etc.), and for the development of their cognitive and motor skills. Among other things, the availability of non-manufactured/homemade toys that provide children with greater freedom and creativity is very important. Research indicates that in economically underdeveloped countries, use of homemade toys as a form of compensation for factory-made toys is more common (Iltus, 2006). However, domestic data may indicate the emergence of a new sensibility that recognizes the importance of homemade toys in urbanized living areas, and a slight tardiness when it comes to this trend in less populated living areas. See Figures 19 and 20.

Surprisingly, in a period of 10 years (from 2005 to 2014) the rate of the presence of children’s books dropped both in urban and rural areas (in 2005, 83.3% of urban households and 74.4% of rural households had three or more children’s books in their home). At the same time, there has been an increase in the use of all other objects that children use (homemade toys, store-bought/manufactured toys, household objects/objects found outside), as well as an increase in the use of two or more types of playthings (Tables a.4.3.1-a.4.3.5 in Appendix 2).

The

Ana

lysis

of M

ICS

da

ta

32

Figure 19

Percentage of children under age 5 by numbers of children’s books, and by type of playthings, Serbia

Figure 20

Percentage of children under age 5 by numbers of children’s books, and by type of playthings, Roma settlements

On average, households from Roma settlements have far fewer books and toys available compared to the general population. Only 11.9% of children from Roma settlements have three or more children’s books (compared to 71.9%

75.5

61.0

43.4

78.0 72.4

57.2

41.0

71.4 67.1

46.4

30.7

73.7

3+ children'sbooks

10 + children'sbooks

Homemadetoys

Two or moretypes of

playthings

3+ children'sbooks


Homemadetoys

Two or moretypes of

playthings

Serbia DPA Serbia IPA Serbia TPA

10.5 3.2

20.5

55.4

13.9

1.9

20.5

52.0

11.3

2.0

21.1

51.6

Roma settlements DPARoma settlements IPARoma settlements TPA

75.5

61.0

43.4

78.0 72.4

57.2

41.0

71.4 67.1

46.4

30.7

73.7

3+ children'sbooks


Homemadetoys

Two or moretypes of

playthings

3+ children'sbooks


Homemadetoys

Two or moretypes of

playthings


10.5 3.2

20.5

55.4

13.9

1.9

20.5

52.0

11.3

2.0

21.1

51.6

Roma settlements DPARoma settlements IPARoma settlements TPA

The

Ana

lysis

of M

ICS

da

ta

33

of children in the general population). Data show that, when it comes to the availability of learning materials, there are no significant differences between children living in settlements with different levels of population density. Bearing in mind the significant deprivation of Roma children in this domain, this only indicates that poverty and low opportunities/obstacles in early development are distributed equally in different areas.

When it comes to the availability of books in relation to regional differences, the data indicate that in the Belgrade region children’s books are more available in settlements of all levels of population density compared to the rest of Serbia (Table 27 in Appendix), indicating a somewhat more stimulating home environment that life in the capital offers.

Differences are not so large when it comes to adults’ support for learning and development19. However, differences are evident and constant in the general population. The percentage of children with whom mothers engaged in four or more activities is highest in DPAs and lowest in TPAs. Likewise, the average number of activities with mothers is highest in DPAs and IPAs, and lowest in TPAs. However, a good indicator of the difference is the extent of involvement of fathers in children’s play (Figures 21, 22).

19 In the MICS survey adult household members are asked about a number of activities that support early child learning. Activities included: ‘reading books or looking at picture books, telling stories, singing songs, taking children outside the home, compound or yard, playing with children, and spending time with children naming, counting, or drawing things’ (SORS & UNICEF, 2014, p. 150).

Figure 21

Parents’ involvement with children, in %

Figure 22

Parents’ involvement with children, mean number of activities

39.9 45.9

26.1 19.5 16.4 15.2

91.8 90.0 86.4

43.5 51.3 51.8



Percentage of children with whom biological fathers have engaged in four or more activities

Percentage of children with whom biological mothers have engaged in four or more activities

3.0 3.3

2.5

1.8 1.8 1.6

5.3 5.3 4.8

3.1 3.4 3.5



Mean number of activities with biological fathers

Mean number of activities with biological mothers

The

Ana

lysis

of M

ICS

da

ta

34

Fathers from IPA settlements are most engaged, somewhat less in DPAs, and the least in TPAs. The said differences may be part of the cultural models of parenting, but also the way of establishing of a work-family balance. We assume that in TPAs there is a greater influence of traditional cultural patterns of parenthood, which are manifested as a generally lower level of playing with the child and involvement in the child’s world, and significantly less frequent involvement of the father. In larger cities (DPAs), demands for enabling material security of the family (and often having more than one job) lead to difficulties in harmonizing work and parenthood, leaving the greater part of duties to mothers.

In relation to the sex of child, mothers do not show a different level of activity/involvement. However, the differences are evident when it comes to fathers. Fathers are more often involved in playing with male children and they are significantly more involved in playing with them (in four or more activities) than with female children. These differences exist in settlements of all types; however, surprisingly, differences in adequate extent of involvement of fathers is present in more urbanized living areas more often than in rural ones (TPAs) (Figures 23 and 24).

The relation of parents from Roma settlements to their children is somewhat different. In DPAs, both fathers and mothers are more often involved in their relationship with a female child. In IPAs, there is a pattern in which fathers are more likely to perform activities related to male children and mothers to female children. In TPAs, parents treat children equally, regardless of gender (Table 23 in Appendix).

Figure 23

Father’s involvement accordingto sex of child, in %

Figure 24

Father’s involvement according to sex of child, mean number of activities

Percentage of children with whom biological fathers have engaged in four or more activities

Mean number of activities with biological fathers

43.3 35.5

51.5

40.3

30.1 22.4


DPA IPA TPA

3.1 2.9

3.6

3.0

2.7 2.3


DPA IPA TPA

The

Ana

lysis

of M

ICS

da

ta

35

2.5.2 Childdiscipline

Among the children aged 1-4, there are no big differences in the practices of discipline depending on place of residence. In DPAs there is slightly less frequent use of physical punishment of children (any and severe). Children from Roma settlements are exposed to violent discipline methods more often than children from the general population in all types of living areas. The relationship of a settlement’s population density and styles of child disciplining among the Roma population is somewhat different. For almost all violent methods of discipline, the greater a region’s urbanization level, the greater degree of psychological and physical aggression. The lowest level of aggression is in TPA settlements (see Figure 25).

Figure 25

Percentage of children age 1-4 years by disciplining methods

However, when we try to explain and recognize the causes of using a certain discipline style, we realize that area of living becomes an important predictor. Namely, we made a regression model by which we tested the causes of any violent discipline method.

The regression models (Tables R.4.1 and R.4.2) that include wealth status, education of mother and living areas as covariates indicate that a child is more often subjected to any violent method of discipline when: 1) the household belongs to the poorest 60% (compared to children living in the richest 40% of households); 2) the mother has primary education or less (compared to higher educated mothers); and 3) he/she lives in a TPA (compared to a DPA/IPA). A similar explanation applies to the use of any violent methods in Roma settlements. Children whose mothers have primary education or less (compared to those children whose mothers have secondary or higher education) and those from a TPA (compared to a DPA/IPA) are more likely to be subjected to any violent form of discipline.

40.3 38.7 40.2

69.7 64.6

57.8

22.8 29.3 28.0

45.8 43.6 37.2

0.1 1.4 0.4 6.4

18.8

4.8

46.0 49.3 48.4

72.8 66.3

61.0



Psychological aggression Any physical punishment

Severe physical punishment Any violent discipline method

The

Ana

lysis

of M

ICS

da

ta

36

Table R.4.1

Logistic regression, practices of discipline, Serbia

Serbia Any violentB Exp(B)

Wealth Index 40%(ref. poorest 60%) .410* .664

Primary school .991** 2.694

Secondary school (ref. higher) .289* 1.335

TPA (ref. DPA/IPA) .301* .740

Constant .326 1.386

Note. *p<.05; **p<.001

Table R.4.1

Logistic regression, practices of discipline, Roma settlements

Serbia Any violentB Exp(B)

Wealth index 40%(ref. poorest 60%) .035 1.036

Primary school or less(ref. secondary and higher) .673** 1.960

TPA (ref. DPA/IPA) .360** .698

Constant .082 1.085

Note. *p<.05; **p<.001

Although, as we have seen, differences in terms of use of different types of discipline are not so large, when we control for material conditions and the mother’s education, the place of residence with its characteristics becomes a significant framework for explanation.

Looking at the trend in child-rearing practices (children 2-4 years old), we can see that between the two surveys (2010 and 2014), there was a significant decrease in the use of violent methods (the total share of violent methods was reduced from 68.2% to 50.9% in urban areas and from 71.5% to 48.8% in rural ones) and an increase in the use of non-violent methods (the total share of use of non-violent methods increased from 26.1% to 47% in urban areas and from 25.6% to 48.3% in rural ones). This trend indicates the influence of specific modernization effects on parental practices (Table a 2.1 in Appendix 2). Children (5-14). As children grow older, the use of physical and psychological punishment as a method of discipline decreases. The use of violent methods both in rural and urban areas also declined in the period between two surveys (2010 and 2014). However, the decline is more evident in rural areas (in 2010, violent methods were equally used by rural [66.5%] and urban [65.7%] populations) (Tables b.3 in Appendix 2).

Roma settlements (children 2-4 years old). Time trend analysis provides the following two facts: 1) the said rural-urban gap emerged in the period between the two surveys — 2010 MICS even shows

The

Ana

lysis

of M

ICS

da

ta

37

that violent practices were applied equally in rural areas (87.8%) and in urban ones (88.5%); and 2) that there has also been a significant decrease in use of these child-rearing methods among the Roma population (the same applies to older children, ages 5-14). (See Table a 2.1 for children age 2-4 years and Table b.3 for children 5-14 years in Appendix 2).

2.6 Education 2.6.1Earlychildhoodeducation

Institutional support is very important for the early development of children, although it usually cannot fully compensate for the shortcomings of very disadvantaged milieus (Burger, 2010). Another very important function of early childhood education (ECE) is absolving parents from a part of childcare and enabling them to engage in the labour market. Availability of infrastructure most often leads to absolving mothers from a part of their obligations, thus allowing a more equitable relationship between men and women in the public and private sphere (UNICEF, 2012a).

Children from less populated areas aged 36-59 months evidently do not have the same opportunities to attend ECE programmes as urban children. In 2014, the DPA-TPA difference in the coverage of children amounted as much as 41.3 percentage points. Children from Roma settlements attend ECE institutions far less. As little as 5.7% of Roma children attend ECE, as opposed to 50.2% of children from the general population. Although the differences are not very large, we can observe somewhat more frequent attendance of ECE in living areas in IPAs, as shown in Figures 26 and 27.

Figure 26

Percentage of children age 36-59 months attending early childhood education, Serbia

Figure 27

Percentage of children age 36-59 months attending early childhood education, Roma

settlements67.5

52.0

26.2

DPA IPA TPA

Serbia

5.0 8.1 3.2

DPA IPA TPA

Roma settlements

The

Ana

lysis

of M

ICS

da

ta

38

The best coverage of ECE institutions and level of attendance of children are in the Belgrade region. DPAs of Belgrade stands out significantly from all other regions by the level of coverage of children, while the coverage is otherwise generally uniform in settlements of this level of population density in the rest of Serbia. While the coverage in other parts of IPA settlements in Serbia is fairly uniform, differences occur in the level of outreach to children in TPAs. Within TPAs, the network of early childhood institutions is best developed in the Belgrade region, where more than half of children are enrolled in ECE. However, the region of central Serbia is significantly less developed infrastructurally, where only every fifth child attends ECE in TPA settlements. See Figure 28.

Figure 28

Percentage of children age 36-59 months attending early childhood education — regional differences, Serbia.

() Figures that are based on 25-49 unweighted cases.

In order to examine why children do (not) attend kindergartens, we created two logistic regression models (Tables R.3.1 and R.3.2) where we used the variable whether a child attends early childhood education as a dependent variable while the following categories are used as independent ones: wealth status (two levels), education of mother/caretaker (three levels as high as the reference category) and the living area. The results show that all predictors are relevant for the explanation of attending ECE. Children from poorer families (the poorest 60%) attend ECE 2.9 times less frequently than those from the richest 40%. Children whose mothers

80.8

(50.7) (44.0) 48.1

56.9

32.1

52.5

(44.1)

22.6

62.7

47.5

21.5

DPA IPA TPA DPA IPA TPA DPA IPA TPA DPA IPA TPA

Belgrade Vojvodina Šumadijaand Western Serbia

Southernand Eastern Serbia

The

Ana

lysis

of M

ICS

da

ta

39

have only primary school attend ECE 7.8 times less frequently than children whose mothers have university education. Also, children whose mothers have secondary school attend ECE 2.4 times less frequently than children whose mothers have faculty education. Regardless of these factors, children in TPAs tend to attend ECE less frequently (2.6 times less in comparison with children from DPAs/IPAs), simply because they live in rural areas (no matter the household wealth or parents’ level of education).

Table R.3.1

Logistic regression, whether a child attends early childhood education

Serbia ECEB Exp(B)

Wealth index 40%(ref. poorest 60%) 1.066** 2.904

Primary school or less 2.060** .127

Secondary school (ref. higher) .915** .400

TPA (ref. DPA/TPA) .947** .388

Constant .607 .545

Note. *p<.05; **p<.001

Table R.3.1

Logistic regression, whether a child attends early childhood education

Roma settlement ECEB Exp(B)

Wealth index 40%(ref. poorest 60%) .400 .670

Primary school or less(ref. secondary/higher) 2.371** .093

TPA (ref. DPA/TPA) .682 .506

Constant .186 .830

Note. *p<.05; **p<.001

The results of the logistic regression show that the only relevant explanatory framework for Roma children’s attendance of ECE is the mother’s education. Namely, those children whose mothers have primary school education are 10.8 times less likely to attend ECE than children whose mothers have secondary or higher education. Geographic area is not a relevant framework for explanation of the variations. Bearing in mind that in all areas there are far fewer Roma children included in the early education system, work on elimination of discrimination regardless of the place of residence should be an imperative.

The

Ana

lysis

of M

ICS

da

ta

40

Figure 5

Trends in attending early childhood education in urban and rural areas

Longitudinal data show that the coverage of rural children has increased since 2005 (although it stagnated in the period between 2010 and 2014), but that coverage of urban children has increased even more. This actually broadened the urban-rural gap in terms of the coverage of children attending this type of institution.

Roma settlements: Roma children attend early education institutions far less frequently. Data between surveys in 2010 and 2014 indicate a worrying fact proving decreased coverage of urban and rural children (in 2010, the coverage was 10% in urban and 4.1% in rural areas). (See Tables a.4.1.1-a.4.1.4 in Appendix 2.)

2.6.2 Schoolreadiness

School readiness is an indicator in the MICS that shows the participation of children currently attending the first grade of primary education who in the previous year attended the Preparatory Preschool Programme (PPP) (Figure 29). Attending PPP is a very important indicator of school readiness, because it indicates ‘equity in access to education and in learning outcomes, especially for marginalized children’ and represents an ‘important factor in education achievement; children’s development and learning; school completion including primary school; and ultimate success in adulthood’ (UNICEF, 2012b, p. 16).

The participation of children is at a very high level, due to the fact that this type of education in Serbia has been compulsory since 2006; it lasts at least 9 months in the school year before enrolment into primary school. Although coverage is not complete, differences between living areas in the general population are not statistically significant. The data related to children from Roma settlements, on the other hand, still show significantly lower participation. However, as in the case of the general population, there are no significant differences between living areas, as shown in Tables 47 and 48 in Appendix.

45.2

56.6 62.6

14.4

28.7 27.3

2005 2010 2014

Percentage of children age 36-59 months attending early childhood education

Urban Rural

The

Ana

lysis

of M

ICS

da

ta

41

Figure 29

Percentage of children attending first grade who attended preschool in previous year, three areas

2.6.3 Netintakerateinprimaryeducation

Net intake rate in primary education represents the participation of children ‘of school-entry age who enter the first grade of primary school’ (SORS & UNICEF, 2014, p. 18). This indicator aims at measuring how many children of appropriate age for enrolment to school actually started the process of education at this level (UNESCO, 2009, p. 6). The data indicate that the children of the general population and the Roma do not significantly differ between themselves in whether they start primary education on time in relation to the living area where they live. The said data indicate that public policy measures are equally effective in all living areas, as shown in Table 51 in Appendix.

Figure 30

Percentage of children of primary school entry age entering first grade, in three areas

98.6 99.6 96.8

81.9 77.9 80.8



99.5 97.0 94.2

63.6 68.2 79.4



The

Ana

lysis

of M

ICS

da

ta

42

2.6.4 Genderparityindex

According to UNESCO standards (UNESCO, 2003), gender parity20 is deemed reached if the index values range between 0.97 and 1.03. Values below this threshold indicate that girls are disadvantaged compared to men, while values above it indicate that significantly more girls are included in the process of education and that boys are disadvantaged. Figure 31 displays the GPI for Serbia in general, and Roma settlements.

Figure 31

GPI — Gender parity index, in three areas

The data for the general population indicate that gender equality has been achieved at the level of primary education. However, when it comes to secondary education, there are significantly more girls in all living areas. Surprisingly, the difference is largest in DPAs and smallest in TPAs. The Roma population’s situation is different and more extreme. At the level of primary education, there are no gender differences in DPAs and IPAs. However, they appear in TPAs, where more girls are involved in the process of education. At the level of secondary education, there are huge differences in all living areas, which prove more significant participation of boys in the school system. The greatest disparity is in IPAs.

2.6.5Out-of-schoolchildren

The out-of-school indicator presented here implies the children who, according to their age, should have been attending school (this includes children who have never attended school and those who drop out). According to UNESCO (n.d.), in 2012 there were about 5,900 boys and 4,600 girls out of primary school in Serbia. At this level of education of the general population, boys are out of school more often than girls in all types of settlements (Figure 32). The biggest gap is in TPAs.

20 The gender parity index (GPI) is a measure of the ratio of girls-to-boys in access to primary education.

0.98 0.99 0.99 1.02 0.98 1.05 1.10 1.08 1.07

0.77

0.28

0.79



GPI primary school GPI secondary school

The

Ana

lysis

of M

ICS

da

ta

43

Figure 32

Percentage out-of-school children, primary education

Figure 33

Percentage out-of-school children, secondary education

As for secondary education (Figure 33), in settlements in DPAs and IPAs there are almost no male or female children who are out of school. However, TPAs are different, with a certain percentage of both boys and girls. We can only assume that dropout is a cause of frequent withdrawals from secondary education in typical rural living areas. When it comes to children from Roma settlements, the situation is significantly different. When compared to the general population, Roma children are more often out of school at both levels of education. Furthermore, male children are more often out of primary school than female children in DPAs and TPAs. The situation with secondary school is somewhat different: in all areas, there are more girls out of secondary school (Tables 52-55 in Appendix).

0.4 0.1 1.8

14.5 15.8 16.2

0.1 0.8 2.3 12.4

17.7 12.2

0.2 0.4 2.1

13.4 16.8 14.1



male female total

12.0 7.2 12.5

50.0 53.2

69.4

5.7 3.2 3.8

67.0 76.2 76.6

9.2 5.3 8.8

58.0 64.8

72.9



male out of school female out of school total out of school

0.4 0.1 1.8

14.5 15.8 16.2

0.1 0.8 2.3 12.4

17.7 12.2

0.2 0.4 2.1

13.4 16.8 14.1



male female total

12.0 7.2 12.5

50.0 53.2

69.4

5.7 3.2 3.8

67.0 76.2 76.6

9.2 5.3 8.8

58.0 64.8

72.9



male out of school female out of school total out of school

The

Ana

lysis

of M

ICS

da

ta

44

2.7 Early labour engagementAccording to the International Labour Organisation, child labour is defined ‘as work that deprives children

of their childhood, their potential and their dignity, and that is harmful to physical and mental development’ (International Labour Organisation, n.d.). In the MICS framework, child labour is operationalized through the participation of children aged 5 to 17 years (divided into three cohorts: 5-11, 12-14 and 15-17) in: 1) economic activities; 2) household chores; 3) total child labour (which is the sum of the previous two); and 4) work under hazardous conditions. Regarding the first three measures, a specific age threshold has been determined so that if there are children who work over this threshold, it is considered to be a child labour issue. Regarding economic activities, the weekly threshold for ages 5-11 is 1 hour or more; ages 12-14, 14 hours or more; and for children aged 15-17, 43 hours or more. As for household chores, the weekly threshold for the age groups 5-11 and 12-14 is 28 hours or more, and for children ages 15-17, 43 hours or more (SORS & UNICEF, 2014, pp. 201-202).

The most prominent urban-rural difference is related to the level of involvement of children in the household’s economic activities. While 4.4% of DPA children aged 5-11 years are involved in an economic activity at least for 1 hour, one in five (20.5%) children of the same age from a TPA is engaged in economic activity. The same pattern goes for all ages of children. As for the DPA children aged 12-14, 4.8% of them work under 14 hours a week, and an additional of 1.2% of them work more. In TPAs, seven times more children (compared to DPAs) are involved in economic activities up to 14 hours per week (34.5%) and an additional 3.4% of them above this limit. In DPAs, 14.4% of children aged 15-17 years work up to 48 hours per week, and in TPAs 39.4% of children fall into this category. See Figure 34.

Figure 34

Children (aged 5-17) involvement in household’s economic activities, in three areas

4.4 9.0

20.5

2.5 1.2

11.3 4.8 12.9

34.5

1.2 3.8 7.4 1.2 0.0 3.4 0.0 1.1 0.6

14.4 18.7

39.4

16.5

5.2 10.8

0.0 0.0 0.0 0.0 0.3 1.8



% of children age 5-11 years involved in economic activity for at least one hour

% of children age 12-14 years involved in economic activity less than 14 hours

% of children age 12-14 years involved in economic activity for 14 hours or more

% of children age 15-17 years involved in economic activity less than 43 hours

% of children age 15-17 years involved in economic activity for 43 hours or more

The

Ana

lysis

of M

ICS

da

ta

45

An increasing level of urbanization brings a reduction of involvement of children in economic activities below, at or above the age-specific threshold. Almost every second child in TPAs is involved in economic activities below the age-specific threshold, and one in ten beyond this threshold. TPA children of the same age (5.7%) tend to work under hazardous conditions more often than children living in a DPA (1.4%) or IPA (2.2%). Total child labour shows the same logic, indicating that children in TPA settlements are more significantly burdened by economic activities.

Figure 35

Children (aged 5-17) involvement in economic activities, work under hazardous conditions, and child labour, in three areas

In general, TPA children aged 5-17 years are engaged in economic activities to a greater extent; they work under hazardous conditions more often; and the total number of child labour in TPAs is more than four times higher than in DPAs (Figure 35). When it comes to household chores among children under the age of 15, there are no major differences between living areas.

Regarding the children from Roma settlements, there is a problem related to the method of interpretation of this issue. Surprisingly, data indicate a significantly lower level of economic activities of children from Roma settlements. However, comparison of data on the population from Roma settlements indicates a similar trend as in the general population. Children from TPAs in all age cohorts are significantly more involved in economic activities and they often work under hazardous conditions compared to their peers in IPAs and DPAs. Furthermore, children from Roma settlements do not differ from children in the general population, either, when it comes to the fact that Roma children from TPAs in all age cohorts tend to work above the age-specific threshold more often.

4.7 8.8

18.6

3.1 2.8 3.8 2.7 4.8

11.1

1.5 0.9

7.2

1.4 2.2 5.7 3.5 1.4

7.3 3.9 5.9

16.2

4.3 2.6

8.9



Economic activities below the age specific threshold

Economic activities at or above the age specific threshold

Children working under hazardous conditions


The

Ana

lysis

of M

ICS

da

ta

46

In order to examine the causes of work engagement above a certain age-specific threshold (total child labour) and under hazardous conditions, we applied a logistic regression model (Table R.6) that included wealth, mother’s education and living area for children from the general and Roma population. The results for the general population show that the children living in households belonging to the poorest 60% and whose mothers have only primary education, work significantly more — as do the children from TPAs. Living in a TPA is the best predictor of child labour (children from TPAs are 3.9 times more likely to work above the age-specific threshold than children from IPAs, and 2.7 times more likely than children from DPAs). An almost identical situation applies to work under hazardous conditions. The results show that children coming from the poorest 60% of families work significantly more; the same applies, surprisingly, to those whose mothers have higher education (compared to those whose mothers have secondary education), as well as to the children from rural living areas. Again, living in a TPA is the best predictor of child labour (TPA children are 5 times more likely to work under hazardous conditions than children from DPAs, and 1.9 more likely than children from IPAs).

Table R.6

Logistic regression, child labour and work under hazardous conditions.

SerbiaTotal child labour Under hazardous conditions

B Exp(B) B Exp(B)Wealth index 40%(ref. poorest 60%) .306* .736 .478* .620

Primary school .525* 1.690 1.019** .361

Secondary school(ref. higher) .126 1.134 .884* .413

TPA (ref. DPA/IPA) 1.169** 3.220 .950** 2.587

Constant 2.660 .070 2.553 .078

Roma settlementsUnder hazardous conditions

B Exp(B)Wealth index 40%(ref. poorest 60%) 1.200** .301

Primary school or less(ref. secondary and higher) .257 .773

TPA (ref. DPA/IPA) .994** 2.701

Constant 1.905 .149

Note. *p<.05; **p<.001

When it comes to children from Roma settlements, factors that explain child labour and work under hazardous conditions are the same as for the general population. Household wealth, mother’s education, and place of residence are all causes of child labour among the Roma population, as well. Children from TPAs are two times more likely to work above the age-specific threshold than children from IPAs, and 3.6 times more likely than children from DPAs. The situation is similar with work under hazardous conditions.

The

Ana

lysis

of M

ICS

da

ta

47

Furthermore, on the level of Serbia as a whole, the results clearly indicate that children in TPAs in all regions are significantly more burdened with work (Figure 36). However, children’s level of involvement in work at or above the age-specific threshold is higher in central Serbia than in Belgrade and Vojvodina. As for the total workload, children in Vojvodina and Belgrade are the least burdened, while children from Southern and Eastern Serbia are the most, where every fifth child works above the age-specific threshold.

Figure 36

Children (aged 5-17) involvement in economic activities, work under hazardous conditions and child labour, in three areas — regional differences, Serbia

5.7

9.8

27.1

3.5

8.5

11.8

6.0 4.3

15.1

2.3

11.6

25.0

1.2

4.7

7.2 7.2 6.6 6.8

2.9

5.0

13.7

.5 .6

12.1

2.8

7.4

3.1 1.7

.8

6.0

0.0

4.0 4.1

.7 1.1

9.1

4.0

7.8

10.2

7.4 7.3

11.1

2.9

5.0

17.8

1.7 2.0

20.1


Belgrade Vojvodina Šumadija and WesternSerbia

Southern and EasternSerbia

Economic activities below the age specific threshold

Economic activities at or above the age specific threshold


Total child labour

The

Ana

lysis

of M

ICS

da

ta

48

2.8 Attitudes toward children with disabilities A particularly sensitive issue is the attitude of the community toward children with disabilities (the level of

acceptance of these children was measured by five attitudes that express an affirmative position towards them). In Serbia there is generally still a lot of prejudice toward these children, their capabilities and the impact on other children in institutions they attend. The MICS survey investigated to what extent it is possible for children with physical and sensory disabilities, as well as children with intellectual disabilities, to function in a family environment and within the school system21.

Figure 37

Percentage of respondents who express positive attitudes toward children with physical, sensory and intellectual disabilities, in three areas

Attitudes of the general population indicate a lower level of discrimination both against children with physical and sensory disabilities and against children with intellectual disabilities in DPAs compared to TPAs. There are no differences in accepting the attitude that these children can achieve a lot in life if they are adequately supported. However, other statements are significantly less affirmative; and percentages of acceptance decrease from DPAs to TPAs.

In the Roma settlements, there is a higher extent of acceptance of affirmative attitudes toward children with disabilities. However, there are no major differences depending on living area when it comes both to individual attitudes and the aggregate.

21 Attitudes on children with physical or sensory disabilities and children with intellectual disabilities: ‘Are better off to live in the family rather than in a specialized child care institution’; ‘Do not have a negative impact on the everyday life of other children in the family’; ‘Are better off to attend mainstream schools than special schools’; ‘Attending mainstream schools do not have a negative impact on the work of other students’.

40.6 33.5 29.8

51.2 58.1 54.0

22.4 18.1 18.2

38.2 36.7 37.7



% of respondents who express positive attitudes towardchildren with physical and sensory disabilities on all five statements

% of respondents who express positive attitudes towardchildren with intellectual disabilities on all five statements

The

Ana

lysis

of M

ICS

da

ta

49

In order to identify the causes of acceptance of positive attitudes toward children with disabilities, we made two models of logistic regression (Tables R.7.1 and R.7.2): in the first one, the dependent variable is positive attitudes toward children with physical and sensory disabilities on all five statements; in the second one, the dependent variable is positive attitudes toward children with intellectual disabilities on all five statements. We used the following predictors: wealth status (two levels), education of the respondents (three levels with high as the reference category) and living area.

Table R.7.1

Logistic regression, attitudes toward children with disabilities

SerbiaAttitudes toward children with physical and sensory

disabilities

Attitudes toward children with intellectual disabilities

B Exp(B) B Exp(B)Wealth index 40%(ref. poorest 60%) .033 1.034 .044 1.045

Primary school .304** .738 .084 .920

Secondary school(ref. higher) .042 .958 .073 1.075

TPA (ref. DPA/IPA) .269** .764 .105 .901

Constant .473 .623 1.433 .239

Note. *p<.05; **p<.001

Table R.7.2

Logistic regression, attitudes toward children with disabilities

Roma settlementAttitudes toward children with physical and sensory

disabilities

Attitudes toward children with intellectual disabilities

B Exp(B) B Exp(B)Windex 40%(ref. poorest 60%) .311* .733 .002 .998

Primary school or less(ref. secondary/higher) .821** 2.274 .560 1.751

TPA (ref. DPA/IPA) .127 .881 .019 1.020

Constant .003 .997 .966 .380

Note. *p<.05; **p<.001

The results indicate that educational level and living area are significant factors determining acceptance of attitudes towards children with physical and sensory disabilities. The respondents with university education tend to accept these attitudes more often (compared to those with primary school or less), as do those who live in a DPA/IPA, while children in a TPA with this form of disability are more likely to be discriminated (if we can predict based on attitudes of adults from these living areas). In the second group of attitudes, expressing

The

Ana

lysis

of M

ICS

da

ta

50

attitudes toward children with intellectual disabilities, no single variable proves to be an important factor in explaining differences. Two other models indicate significantly different attitudes among the Roma population. The results indicate that respondents belonging to the poorest 60% tend to have a more affirmative attitude towards children with physical and sensory disabilities, as does the population with primary school only. The place of residence is not significant for explanation of these differences. When it comes to affirmative attitudes toward children with intellectual disabilities, no single variable proves to be an important factor in explaining differences. Unlike the general population, where the level of acceptance of children with disabilities is a part of the modernization process and a matter of indirect experience, probable bases of such attitudes among the Roma population are direct experience and attempts at integration, especially of those who are the most disadvantaged and excluded.

The

Ana

lysis

of M

ICS

da

ta

51

T he life course of women in societies that are significantly influenced by tradition and patriarchal values differs significantly from their male peers. Choices of women are made less independently when it comes

to level of education, marriage and having children; their decisions can be directly influenced by parents, male relatives and spouses, or indirectly influenced by patriarchal values that they themselves adopt (White Stewart, 2014). Key steps typical for the period following childhood include education, employment, marriage/union, and having children. Whether and to what extent a woman can make a choice depends on the conditions in which she lives, including both material and value components. In the next section, we will focus on several dimensions of women’s lives in different areas of living. First, we will compare the material standard of women in relation to the areas of living, bearing in mind that a certain level of material well-being is necessary to open opportunities to independent living and decision-making (Ljubičić, 2012). The second dimension, very closely connected with resources, is activity status22. Employment and having an income indicate the level of independence of women in the private domain (Babović, 2009). The third dimension is marriage/union and family, by which we will try to recognize the differences between women in different areas of living when it comes to age of marriage, having children, kind of support available, and whether they feel that their wishes and plans are respected in marriage (related either to extending or limiting their family). Last but not least, the fourth dimension of interest is attitudes toward domestic violence. Studies show a significant level of vulnerability of women to domestic violence, women in rural areas being an especially vulnerable category as family violence is a part of the traditional patriarchal cultural practices (Babovic, Ginic, & Vukovic, 2010). In the analysis, women are divided provisionally into three categories: 15-24 years, where the cohort is still largely determined by education and gradual involvement into the labour sphere; 25-35 years, when education is over for most women, and this period is marked by intense reproductive activity; and 36-49 years, when the largest number of women are married/in union and have children. However, MICS’ bases do not indicate significant problems that middle-aged women face.

22 Data about activity status and income are not directly collected in MICS surveys. This information is obtained from a set of questions about life satisfaction (select aspects of life) and questions were asked only of young women aged 15-24 years.

3Women: Disparities and Gaps

The

Ana

lysis

of M

ICS

da

ta

52

3.1 Household environment3.1.1Regionaldifferences

As Figure 38 clearly shows, women (age 15-49 years) from TPA settlements live in households belonging to the poorest 60% and 20% in twice the proportion of women living in IPA settlements, and even more often than those from DPA settlements. One in five women from a TPA lives in a household belonging to the poorest 20% (the first quintile of the wealth index), while nearly four out of five women in these settlements live in the poorest 60% of households. The data also indicate (Tables 63-68 in Appendix) a somewhat less favourable relative position of young women (15-24) in all living areas than compared to the position of middle-aged women (25-35 and 36-49).

Figure 38

Percentage of poorest 60% and 20% of women aged 15-49, in three areas, Serbia and Roma settlements

On a regional basis there are no major differences in the wealth of women. However, the Belgrade region stands out with its somewhat better conditions for life (Figure 39). As for the age cohorts and settlements in DPAs (Tables 63-68 in Appendix), in all three generations of women, Belgrade has the least number of women belonging to the category up to the third wealth quintiles. The situation in Vojvodina is significantly different, while women from Southern and Eastern Serbia live in the most unfavourable material life conditions. In Southern and Eastern Serbia, about half (50.2%) of young women (15-24) live in the poorest 60% of households.

33.3

49.8

78.5

51.9 53.3

73.9

4.5 9.6

22.7 16.3 13.8

31.8



Poorest 60% Poorest 20%

The

Ana

lysis

of M

ICS

da

ta

53

Figure 39

Percentage of poorest 60% and 20% of women aged 15-49, in three areas — regional differences, Serbia

Differences between TPA settlements are smaller than between IPA and DPA settlements, and indicate a clear separation of the north and the south of the country. Women in Vojvodina and Belgrade are in a somewhat more favourable position than those in the regions of central Serbia. In Southern and Eastern Serbia, almost nine out of ten (89%) young women (15-24) live in the poorest 60% of households.

Roma settlements: The differences between Roma settlements in DPAs and IPAs are smaller than those in the general population. The wealth status of women from Roma settlements is generally lower, but the level of poverty is more evenly distributed between DPAs and IPAs. However, there are visible differences between these living areas and TPAs (Figure 38). In TPAs, there are twice as many women who live in the poorest 20% of households and significantly more women who live in the poorest 60% of households. Older middle-aged women (36-49) are in a somewhat better position in settlements in DPAs and IPAs, while the most vulnerable ones are currently those belonging to the middle generation of middle-aged women (26-35), although the latter is in a somewhat better position in TPA settlements (Tables 63-68 in Appendix).

21.3

48.2

68.9

45.3 47.7

73.6

39.8

51.1

79.5

42.7

53.7

84.8

2.8 4.9 11.8 11.7 9.9

16.8

3.4 3.5

22.8

2.7

15.3

31.9


Belgrade Vojvodina Šumadija and West Serbia Southern and East Serbia

Poorest 60% Poorest 20%

The

Ana

lysis

of M

ICS

da

ta

54

Trendsinlevelsofhouseholdwealth

Trend analysis indicates that secondary effects of the economic crisis on women manifest in the same way as on the general population: the relative position of the rural population improves, while it worsens in urban living areas. The urban-rural gap is not reduced through economic growth and redistribution of wealth, but rather through redistribution of the risks associated with economic crisis. However, such a gap indicates the permanence of enormous urban-rural differences.

Figure w.1

Trends in wealth status of woman aged 15-49 in urban and rural areas

Roma settlements: In the period of 10 years, the only change in urban and rural living areas was a certain deterioration of the position of women belonging to the middle generation of middle-aged women (26-35) (Tables w.1.1.1.1-w.1.1.1.6, w.1.1.2.1-w.1.1.2.6 and w.1.1.3.1-w.1.1.3.6 in Appendix 2).

Poorest 60% Urban Poorest 60% Rural

35.2 31.9 39.1

84.7 84.7 79.0

2005 2010 2014

The

Ana

lysis

of M

ICS

da

ta

55

3.2 Education and activity status3.2.1Regionaldifferences

The data on the educational structure of women indicate clear differences between the living areas (Figure 40). As for the general population, with the level of urbanization, the proportion of women with only primary school education declines along with the increase of the proportion of women who attended tertiary education (Figure 40). Differences in educational attainment are visible, and represent a clear indicator of women’s opportunities in the labour market in relation to place of residence.

Figure 40

Education of women age 15-49, in three areas, Serbia

We can determine differences in educational transitions if we look at the participation of women aged 15-24 years currently in the process of education (Figure 41). In terms of education until the age of 18, i.e. completion of secondary school, the differences related to living area are not large among the general population. The differences become more significant after the completion of secondary school, i.e. the beginning of tertiary education, where significantly fewer rural women (in TPAs) continue education. In the Roma settlements, far fewer women are in the process of education, but the differences in transition are similar, with the following specificity: there are even fewer chances in less urbanized areas to remain in the process of education until the age of 18, and it is also unlikely that a woman would remain in the process of education in a TPA after the age of 18.

6.1 8.0 16.2

47.8

33.5 21.8

DPA IPA TPA

Serbia

None or primary Tertiary education

The

Ana

lysis

of M

ICS

da

ta

56

Figure 41

Percentage of participation of women aged 15-24 years (age groups 15-18 and 19-24) currently in the process of education in three areas, Serbia and Roma settlements

Regional differences indicate that the best educational structure of women is in the Belgrade region (Figure 42) in all types of settlements. Outside of the Belgrade region, in all TPA settlements, every fourth woman has primary education only, or no education. In the Roma population, there are no large, significant differences in the educational status of women by living area, implying that they are not able to utilize infrastructural capacities of small and large cities.

Figure 42

Education of women aged 15-49 years, in three areas — regional differences, Serbia.

98.7

82.6

30.4 21.8

99.7

86.5

33.1

11.6

98.5

77.3

22.0 16.8

15-18 19-24 15-18 19-24

Serbia Roma settlementsDPA IPA TPA

4.3 2.5 3.9

12.8 7.3

19.4

6.9 11.2

16.7

2.5

9.4

18.7

60.5

45.2

22.7

42.8

30.5

22.6

38.4 36.6

23.5

32.6 31.5

18.2



None or primary Tertiary education

The

Ana

lysis

of M

ICS

da

ta

57

Figure 43 shows a high proportion of women (age 15-24) currently in the educational process. Settlements in IPAs represent a somewhat favourable framework for education but also for the income of young women, while in DPAs and TPAs there are somewhat more employed women. In TPAs, there are fewer women currently attending school and somewhat fewer women with some source of income. On the other hand, patterns related to women from Roma settlements are different than for women from the general population. The urban-rural differences in school attendance are not large. However, only one in ten young Roma women goes to school. Less than 5% of Roma women in all living areas have a job, and a somewhat larger share have some income. However, DPAs are living areas with the least opportunities for Roma women to earn revenue, although in these living areas they have more employment opportunities.

Figure 43

Activity statuses of women aged 15-24 years, in three areas

The highest percentage of young women attending school in a DPA is in Belgrade, while other regions show more or less equal trends. The highest share of employed women in a DPA is also in Belgrade. The income share is somewhat different in settlements in DPAs and IPAs. The highest rate of young women with some source of income is in Vojvodina, and the lowest in central Serbia (in Šumadija and Western Serbia, and Southern and Eastern Serbia). Differences between TPA regions are almost non-existent when it comes to level of involvement in the education system. However, they appear when comparing the level of employment and sources of income of young women. Again, the regions of Belgrade and Vojvodina provide women with a more favourable framework for employment and earnings.

74.5 77.5 66.0

14.0 13.6 9.7 12.7 8.5 11.6 3.1 .7 2.5

25.4 30.2 23.0

11.9 21.5 17.5



Are attending school Have a job Have an income

The

Ana

lysis

of M

ICS

da

ta

58

Figure 44

Activity statuses of women aged 15-24 years, in three areas — regional differences, Serbia

() Figures are based on 25-49 unweighted cases

(*) Figures are based on less than 25 unweighted cases

Trendsineducationalattainmentofwomen

Trend analysis of data indicates that the level of education of both rural and urban women is improving (Table W.2.1 in Appendix 2). A decrease in the share of those with only primary education is more significant in rural living areas. In turn, this has influenced a significant decrease in the gap at this level of education over the last 10 years (the youngest cohort aged 15-24 has an equal number of women who have only primary education — 5.5% in urban and 5.7% in rural living areas). The situation with higher education is somewhat different. At this level, there are significant discrepancies between urban and rural women. These discrepancies remain constant over the entire period of time.

The situation of women from Roma settlements is different (Figure w.3). The educational level of the population is improving very slowly. Moreover, it happens only in the form of a very slow decrease in the share of those with primary education and a gradual increase in the number of women with secondary or higher education.

81.6

(*)

([VALUE]) 68.2

77.7

67.9 69.1 76.8

64.1 ([VALUE])

68.1 67,6

17.2

(*)

([VALUE])

8.2 8.3 12.9 11.4 11.3 10.5 ([VALUE]) 6.8 7.0

24.3

(*)

([VALUE]) 37.8

47.9

37.1

26.3

16.7 17.2 ([VALUE]) 12.9 15.9



Are attending school Have a job Have an income

The

Ana

lysis

of M

ICS

da

ta

59

In the period between the two surveys (2010-2014), the number of rural and urban women’s schooling increased, but significantly more in rural living areas. At the same time, the number of both rural and urban women who have a job decreased. What is surprising, however, is the increase of the share of young urban women with some sort of income (temporary and occasional jobs, scholarships and loans). On the other hand, patterns related to women from Roma settlements are completely different than for women from the general population. Economic and political changes in the last 4 years have had significant negative effects on young Roma women. Both in rural and urban living areas, there has been a significant reduction in employment of women and a decrease in the share of women with some income (in most cases, probably in the informal economy). There has been a significant reduction in the number of young urban women with a permanent or temporary job, along with a significant decrease in the number of rural women that state to have some source of income. The general population of young women has certain mechanisms to mitigate the effects of the crisis: education and additional income through work or public support. The Roma population faces the effects of the crisis without institutional support, which makes them even more deprived and multiply vulnerable (Tables w.1.2 in Appendix 2).

Figure w.2

Trends in higher education of woman (25-49) in urban and rural areas in Serbia

Figure w.3

Trends in primary education of woman (25-49) in urban and rural areas in Roma settlements

28.5

37.4

41.6

9.9 15.2

21.0

2005 2010 2014

Higher education women 25-49

Urban Rural

10.7

8.4

7.6

33.8

24.8 18.6

2005 2010 2014

Primary education women 25-49

Urban Rural

The

Ana

lysis

of M

ICS

da

ta

60

3.3 Marriage/union and family3.3.1Partnershipandearlymarriages

3.3.1.1 Present situationIn this chapter, we examine marriage and family life, especially practices that can pose a risk to the development

and chances in life of children, i.e. early marriage. The manner of organization of partnership in a society is one of the standard indicators of the level of openness of that society (Beck & Beck-Gernsheim, 2002). According to the theory of the second demographic transition, a modern family domain is characterized by later marriage and at the same time decreasing importance of marriage, leading to an increase in alternative forms of partnership (cohabitation, living apart together [LAT] union, homosexual partnership, etc.), postponement of parenthood, a larger number of divorced marriages and greater symmetry of inter-family life. These changes are accompanied with new value orientations that support said practices (Lesthaeghe & Neels, 2002; Van de Kaa, 2002; Petrović, 2009). However, cohabitations, depending on the cultural heritage, can be indicators of traditional practices. Therefore, when we talk about their presence, we should take into account that in the domestic context they represent indicators of both the modernization processes — whose protagonists in the general population are young, urban, more educated and better positioned in the labour market (Tomanović, 2012; Bobić, 2010) — and also partly cultural patterns, which applies to one part of the Roma population.

In the MICS, partnership status is presented through the following categories: 1) married, 2) in union, and 3) single. Looking at the differences in the partnership practices in relation to the living area of the general population (Figure 45), two trends are indicated: 1) among women over 25 in less urbanized areas, less importance is placed on single life and cohabitation, while marriages are more widespread; and 2) with age, the importance of cohabitation decreases along with the growing importance of marriage. These data confirm to a certain extent that new generations living in urban areas are the protagonists of the new forms of partnership.

Figure 45

Partnership status of women 15-49, Serbia

8,1

4,6

10,4

52,1

61,6

66,8

67,6

74,0

79,0

3,0

4,1

2,6

8,2

10,3

6,4

7,1

6,8

5,9

88,8

91,3

87,0

39,6

28,1

27,2

25,3

19,2

15,2

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

15-2

425

-35

36-4

9

Married In union Not married, not in union


24,5

12,9

17,9

39,8

28,1

38,7

68,1

66,3

53,3

31,8

42,8

36,3

43,2

52,8

43,5

18,7

19,5

31,3

43,7

44,3

45,8

17,0

19,1

17,7

13,2

14,2

15,4

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

15-2

425

-35

36-4

9

The

Ana

lysis

of M

ICS

da

ta

61

Figure 46

Partnership status of women 15-49, Roma settlements

What is the most obvious in the population of women from Roma settlements (Figure 46) is that in all areas, single life is equally (un)acceptable for certain age cohorts. Secondly, in the Roma population in general, an increase in age means a growing importance of marriage and decline in the relative share of cohabitations. There are no clear tendencies when it comes to the relation of marriage/cohabitation and areas of living.

In many cultures, early marriage is a part of traditional practices implying that a female child marries before adulthood in order for her family to receive economic and social benefits, by which — although still a child — she assumes the responsibilities of an adult (she is often put under great pressure related to household chores and parenthood care) (UNICEF, 2005). In the MICS, early marriage is presented through three thresholds: 1) percentage of women (age 15-24) who have entered into marriage before 15; 2) percentage of women (age 20-49) who have entered into marriage before 15; and 3) percentage of women (age 20-49) who have entered into marriage before 18 years of age.

Early marriage, before the age of 15, is not very common among the general population and in this respect there are almost no significant differences by living areas (Figure 47). On the other hand, there is a slight tendency for girls in TPAs to marry before the age of 18. In settlements in TPAs, about one tenth (10.0%) of women aged 20-49 years are married before the age of 18, while such is the case with one in twenty (47%) urban women (in a DPA or IPA). Although Roma women enter into marriage far earlier compared to the general population, there are no major differences in relation to the living areas.

8,1

4,6

10,4

52,1

61,6

66,8

67,6

74,0

79,0

3,0

4,1

2,6

8,2

10,3

6,4

7,1

6,8

5,9

88,8

91,3

87,0

39,6

28,1

27,2

25,3

19,2

15,2

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

15-2

425

-35

36-4

9Married In union Not married, not in union


24,5

12,9

17,9

39,8

28,1

38,7

68,1

66,3

53,3

31,8

42,8

36,3

43,2

52,8

43,5

18,7

19,5

31,3

43,7

44,3

45,8

17,0

19,1

17,7

13,2

14,2

15,4

DPA

IPA

TPA

DPA

IPA

TPA

DPA

IPA

TPA

15-2

425

-35

36-4

9

The

Ana

lysis

of M

ICS

da

ta

62

Figure 47

Early marriage: Women age 20-49 married before age 15, women age 20-49 married before age 18

Trends in early marriage

The trend related to entry into marriage before the age of 15 has shown stability over the last 10 years (Table w.0 in Appendix 2), in urban and rural living areas alike. Entering into a marriage before the age of 18 has gradually reduced in rural living areas (13.3% in 2005, 12.3% in 2010 and 10.1% in 2014) (Figure w.4). In urban areas, however, the latter trend is still steady (4.7% in 2005, 4.5% in 2010 and 4.7% in 2014). The urban-rural gap has been drastically reduced when it comes to the share of currently married or in-union individuals aged 15-19. In the general urban population, entering into a marriage has remained at a similar level for 10 years. However, there has been a significant decline in early marriages among rural youth, which in 2014 almost equalled the practice of marrying in rural and urban living areas. From these data we can ascertain that the new generations are converging and harmonizing urban-rural lifestyles faster.

.7 .5 1.2

18.6 14.4

20.1

4.9 4.8 10.0

59.9 54.3 57.3

4.7 1.6 3.7

40.5 41.1 48.8



Women age 20-49 years married before age 15Women age 20-49 years married before age 18Women age 15-19 years currently married/in union

The

Ana

lysis

of M

ICS

da

ta

63

Roma settlements: If we look at the share of the population of young women aged 15-19 who are currently married or in union (Figure w.5), we see that the urban-rural gap has reduced and almost disappeared. The gap reduction has been induced by a decrease in the number of early marriages in rural living areas, on the one hand, but, surprisingly, also by an increase in the same among the urban population, on the other. Data from the three surveys indicate a general trend of increasing the number of early marriages (both before the age of 15 and before the age of 18) in urban living areas (before 15: 11.4% in 2005, 13% in 2010 and 16.5% in 2014 for women aged 15-49; before 18: 43.8% in 2005, 48.3% in 2010 and 57.6% in 2014 for women aged 20-49) (Tables w.0.1-w.0.6 in Appendix 2).

3.3.2Familyplanning—Contraception

3.3.2.1 Present situationThe data for the general population of women indicate clear differences in use of modern contraceptive

methods (Figure 48). In all age categories of women, the greater increase in the degree of urbanization, the greater proportion of use of modern methods of contraception. When looking at the relationship of the two older cohorts (25-35 and 36-49), there is no difference in the use of any contraceptive method, indicating that the use of contraception is at the same level, but that in less populated areas, modern methods are compensated with traditional ones.

Figure w.4

Currently married women aged 15-19, Serbia

Figure w.5

Currently married women aged 15-19, Roma settlements


3.1 3.7 3.2

9.0 7.5

4.0

2005 2010 2014

35.8 40.3 42.2

55.3 51.9

44.1

2005 2010 2014

The

Ana

lysis

of M

ICS

da

ta

64

Figure 48

Percentage of women age 15-49 currently married or in union who are using (or whose partner is using) a contraceptive method, Serbia

There are differences in use of (modern and any) contraceptive method among all age cohorts, but they are most visible among the population of women aged 25-35. Modern methods are somewhat more frequently used in DPAs (30.0%) than in IPAs (19.6%) or TPAs (14.9%), while traditional methods are more often used in TPAs (47.1%) than in IPAs (37.5%) or DPAs (25.7%). Surprisingly, among girls aged 15-24 in settlements in DPAs, the usage of traditional contraceptive methods (35.6%) is equally represented as in TPAs (33.9%), while IPAs stand out with half as frequent usage of these methods (18.7%).

Figure 49

Use of modern contraceptive methods, women 15-49 years old, in three areas — regional differences, Serbia

19.3

54.9

30.0

55.8

20.4

61.0

14.5

33.2

19.6

57.1

18.4

57.5

14.1

47.7

14.9

62.0

12.7

58.7

Any modern method Any method Any modern method Any method Any modern method Any method

15-24 25-35 36-49


28.8 24.6

18.0

27.1 21.1 18.1

22.6

12.2 10.6 13.2 14.6 12.7


Belgrade Vojvodina Šumadija and WestSerbia

Southern and EastSerbia

Any modern method

The

Ana

lysis

of M

ICS

da

ta

65

Modern methods in settlements in DPAs are used the most in Belgrade, followed by Vojvodina and central Serbia (Šumadija and Western Serbia, and Southern and Eastern Serbia). The reverse order applies to traditional methods. When it comes to the differences between regions and settlements in TPAs, Belgrade and Vojvodina have somewhat more frequent use of modern methods than in central Serbia.

In order to determine the reasons of use of modern contraceptive methods, we conducted a regression analysis (Table R.8) in which we used the following predictors: wealth status (two levels), education (three levels with high the reference category), age (three cohorts, with the 25-35 year cohort as the reference category) and living areas. Data show that wealth represents the most relevant factor for explanation of the use of modern methods in Serbia: those women who belong to the richer 40% of society are more likely to use such methods. Another factor is education. Women with university education use modern contraception 2.2 times more than those with primary and 1.6 times more than those with secondary education. Women aged 25-35 belong to the age group that use this method most often. Also, women from DPAs/IPAs use modern contraception 1.3 times more than girls from TPAs.

Table R.8

Logistic regression, use of modern contraceptive methods

Serbia SerbiaModern contraceptive prevalence B Exp(B)Wealth index 40%(ref. poorest 60%) .561** 1.753

Primary school .777** .460

Secondary school (ref. higher) .466** .628

Age 15 25 .148 .862

Age 36 49 (ref. age 25 35) .264* .768

TPA (ref. DPA/IPA) .221* .802

Constant 1.744 .175

Note. *p<.05; **p<.001

3.3.3Earlychildbearingandabortion

Early childbearing indicates multiple risks to young women. In the MICS, in the general population, the share of women aged 20-24 who have had a live birth before age 18 is low, with a somewhat higher share of those in DPAs (1.8%) than in TPAs (1.4%) and IPAs (0.6%). With regard to women from Roma settlements, there are more women who gave birth before the age of 18. Data show that this is a slightly more urban phenomenon: 41.4% in DPAs, 37.9% in IPAs and 34.3% in TPAs (Table 107 in Appendix).

Abortion is an indicator of two problems: firstly, it carries more health risks, and secondly, it indicates problems related to planning the family and number of children. Research indicates that abortion may be an indicator of silent resistance of women in the private sphere, given that most of the household work and parental care falls to women (Blagojević, 1997). In the cohort of women aged 15-25, abortion is almost non-existent. For that reason, we analysed older age groups: of women aged 26-35, 8.4% from DPAs, 10.8% from IPAs

The

Ana

lysis

of M

ICS

da

ta

66

and 11.4% from TPAs had at least one induced abortion; the proportion of those who had abortion is even larger among women aged 36-49 — 21.1% from DPAs, 26.2% from IPAs and 27.2% from TPAs. With older age, the proportion of women who had induced abortion increases, as does the proportion of women who had multiple abortions (which was expected, given that older women had more years of exposure — a longer period of time was observed).

In order to determine the causes of using abortion as a method of birth control, we conducted a regression analysis (Tables R.9.1 and R.9.2), where we used the following predictors: wealth status (two levels), education (three levels with high as the reference category), age (three cohorts, aged 25-35 as the reference) and living area. Data indicates that the important predictors of abortion practice are age and level of education. Women with secondary and primary education are more likely to perform an abortion when compared to highly educated women. The urban-rural difference is not very important in explaining these practices. In the Roma settlements, the situation is somewhat different. While Roma women with a lower level of education have an abortion experience more often, so do women who live in the richest 40% of households. Also, living in settlements in TPAs significantly increases the likelihood of abortions compared to DPAs/IPAs.

Table R.9.1

Logistic regression, induced abortion

Serbia SerbiaInduced abortion B Exp(B)Wealth index 40%(ref. poorest 60%) .101 .904

Primary school or less 1.224** 3.401

Secondary school (ref. higher) .644** 1.904

Age 15 25 2.619** .073

Age 36 49 (ref. age 25 35) .983** 2.672

TPA (ref. DPA/IPA) .004 .996

Constant 2.532 .080

Note. *p<.05; **p<.001

Table R.9.2

Logistic regression, induced abortion

Serbia Roma settlementInduced abortion B Exp(B)Wealth index 40%(ref. poorest 60%) .386** 1.471

Primary school or less(ref. secondary/higher) .560* 1.751

Age 15 25 1.698** .183

Age 36 49 (ref. age 25 35) .696** 2.006

TPA (ref. DPA/IPA) .398* 1.489

Constant 1.806 .164

Note. *p<.05; **p<.001

The

Ana

lysis

of M

ICS

da

ta

67

3.3.4Unmetneed

In the MICS methodology unmet need ‘refers to fecund women who are married or in union and are not using any method of contraception, but who wish to postpone the next birth (spacing) or who wish to stop childbearing altogether (limiting)’ (SORS & UNICEF, 2014, p.114). This indicator is related to Millennium Development Goal 5, which is aimed at improving maternal health. As for the general population of women, differences in living areas are related to a somewhat greater need for spacing in DPAs and for limiting in TPAs, resulting in total higher unmet need among women from TPAs and IPAs. Analyses of cohorts indicate that the older a woman, the less unmet need for spacing she has, and the greater need for limiting. When planning a family, younger women require time and postponement more often, while with ageing and having children, requirements for limiting the number of children grow.

Exploring the causes of unmet needs of women, we have made two models of logistic regression (Tables R.10.1 and R.10.2); in the first one, unmet need for spacing is the dependant characteristic, and in the second one, it is unmet need for limiting, with the following predictors: wealth status (two levels), education (three levels with high as the reference category), age (three cohorts, with the 25-35 cohort as the reference) and living area. The results indicate that the relevant predictors for unmet need for spacing are education and women’s age. Specifically, the higher the level of education, the greater the unmet need for spacing. Those with secondary and primary education have significantly less unmet need for spacing than those with university education. Also, the older a woman, the less unmet need for spacing.

Age is the only relevant explanatory framework for trends among the Roma population: the older a woman, the less unmet need for spacing. Area of residence is not a significant factor in explaining unmet needs. As for the explanation of the need for limiting, level of education and age are again useful. However, they follow a different pattern. Highly educated women have fewer unmet needs for limiting compared to those with secondary school. Also, with age, women tend to have this kind of unmet need more often. Women living in IPAs have less unmet need for limiting than women in TPAs. The situation of the Roma population is somewhat different. Only younger women (age 15-24) have significantly less unmet need for limiting, as well as women living in DPAs, as compared to women from TPAs.

Table R.10.1

Logistic regression, unmet need for limiting and for spacing

SerbiaFor limiting For spacing

B Exp(B) B Exp(B)Wealth index 40%(ref. poorest 60%) .061 1.063 .246 .782

Primary school .407 1.502 .797* .451Secondary school (ref. higher) .421* 1.523 .595* .552Age 15 25 1.699* .183 1.162** 3.197Age 36 49 (ref. age 25 35) .817** 2.264 1.405** .245TPA (ref. DPA/IPA) .151 1.163 .061 .941Constant 3.144 .043 1.824 .161

Note. *p<.05; **p<.001

The

Ana

lysis

of M

ICS

da

ta

68

Table R.10.2

Logistic regression, unmet need for limiting and for spacing

Roma settlements

For limiting For spacing

B Exp(B) B Exp(B)

Wealth index 40%(ref. poorest 60%) .163 1.177 .065 1.067

Primary school or less(ref. secondary/higher) .415 1.514 .403 .668

Age 15 25 .986** .373 1.175** 3.237

Age 36 49 (ref. age 25 35) .383* .682 2.685* .068

TPA (ref. DPA/IPA) .241 1.273 .021 .979

Constant 2.519 .081 3.101 .045

Note. *p<.05; **p<.001

The pattern of unmet need among the Roma women is different from the one among the general population. As they are married at an earlier age, they tend to have need for limiting at an earlier age, while a part of these needs remains unfilled. The youngest cohort of women (15-24) has even less unmet need than the general population, but the middle one (25-35) tends to have more. In the middle cohort, however, there is somewhat more unmet need for limiting in the DPA (19.7%) group than in the TPA (11.6%) and IPA ones (8.3%) (Table 79 in Appendix). In the oldest cohort, unmet need for limiting prevails in the TPA (15.1%) group compared to the IPA (8.0%) and DPA (8.3%) ones (Table 90 in Appendix). These data indicate that rural women aged 36-49 years have the least favourable family situation because their need of family planning is not met.

Figure w.6

Trends in unmet need among woman (15-24) in urban and rural

areas

Figure w.7


areas

Figure w.8


areas

16.8 18.3 19.9 16.2

8.5 6.4 1.5 1.0

25.2 24.6 21.4 17.1


2005 2014

For spacingFor limitingTotal



4.6 4.1 6.7 7.0 17.0

25.5

6.1 7.3

21.6 29.6

12.8 14.4

Urban Rural RuralUrban

2005 2014

.4 0.0 1.5 2.0

29.6 33.1

14.2 13.4

30.0 33.1

15.7 15.4


2005 2014

The

Ana

lysis

of M

ICS

da

ta

69

The longitudinal analysis (for the period 2005-2010, Figures w.6-8) shows that in all age cohorts, need for limiting decreased, which also caused a decrease in the total unmet need. At the same time, there has been a certain increase in the extent of unmet need for spacing both in rural and in rural living areas in all age cohorts (except for the rural youngest).

Roma settlements: Longitudinal data show that unmet need reduced in all age cohorts, and that the situation is improving substantially in this respect; however, these changes come slower for the oldest rural women in comparison to other age cohorts and women from urban living areas (Tables w.3.1.3, w.3.2.2 and w.3.3.3 in Appendix 2).

3.3.5Antenatalcare

The fifth Millennium Development Goal relates to the importance of improving the health of mothers, and comprehensive and adequate antenatal care is deemed one of the fundamental strategies by which it should be achieved (UNICEF, n.d). These practices are very important for the maintenance of pregnancy as well as mother and child health. Consultations with specialists provide pregnant women/mothers with knowledge about the proper maintenance of pregnancy and child rearing, as well as information about the risks they may face in different stages of development of the fetus/child (WHO & UNICEF, 2003). Out of the MICS indicators, we have selected two indicators, the first relating to the participation of mothers who have attended a childbirth preparation programme, and the second relating to the frequency of visits to a specialist during pregnancy. The share of women of the general population (age 15-49) who had given birth in the previous two years and attended a childbirth preparation programme is highest in DPAs, where every fourth woman was a participant, while the lowest share is in IPAs; the disparity indicates the infrastructure potentials but also an awareness of the importance of these programmes in urban centres. As for the women living in Roma settlements, participation in these programmes is lower, and in TPAs there were no such cases (Figure 50).

Figure 50

Percentage of women who attended a childbirth preparation programme, in three areas

24.0

5.0 9.1

2.5 4. 0.0



The

Ana

lysis

of M

ICS

da

ta

70

The coverage of professional services indicates certain problems faced by DPAs. UNICEF and WHO recommend pregnant women a minimum of four visits to specialists (93.9% of mothers in Serbia and 74.4% of mothers from Roma settlements received antenatal care at least four times — see Figure 51). The general population in DPAs has the highest percentage of mothers who have never visited a specialist, indicating that some mothers remain out of the reach of protection services. As for TPAs, a more common problem is that the number of visits is below the recommended number, although there are almost no pregnant women who have not been visited at least once.

For women living in Roma settlements this practice is far less widespread than women in the general population. The best coverage is in DPAs, whereas a very alarming fact is that in TPAs almost every tenth pregnant woman never visited a specialist, and more than one third of women have less than the recommended visits to a specialist.

Figure 51

Antenatal care coverage — number of visits, three areas23

23 The indicator showing four or more visits to specialists is omitted from the graph in order to present more clearly which areas have significantly fewer visits than the minimum recommended by UNICEF and WHO.

3.7

.4 .1

5.4

1.4

8.4

.2 2.2

.9

4.8

1.7 3.0

1.7 0.0

2.4

7.2

4.0

10.9

1.0 .6 2.7

9.8 7.5

13.1



No antenetal care visits One visit Two visits Three visits

The

Ana

lysis

of M

ICS

da

ta

71

3.4 Risks of domestic violenceThe MICS indicator measuring the level of risk of domestic violence is the belief of women that a husband

may be justified in beating his wife for several reasons24. By using this indicator, it is possible to determine the level of justification of violent behaviour between partners and, indirectly, a representation of violent practices

Figure 52

Percentage of women age 15-49 years who believe a husband is justified in beating his wife in various circumstances, three areas

Compared with IPAs and DPAs, there are more women from TPAs in all age cohorts who feel that a husband may be justified in beating his wife for some reason (Figure 52). The most common justification is related to neglecting the children. Attitudes justifying violence against women are far more widespread among Roma women than the general population of women. In DPA Roma settlements, almost half of the women in all age cohorts (Figure 52) justify some form of violence. DPAs prove to be the riskiest framework for partnership relations in the Roma community. The least accepting positions on violence are found in IPAs, though they are still very high.

Longitudinal data observed between the two surveys (2005 and 2014) indicate that in this period there was a decline in the extent of justification of violence in all age cohorts of women (Tables w.5.1, w.5.2 and w.5.3 in Appendix 2).

24 Five statements are used as indicators: 1) ‘If she goes out without telling him’; 2) ‘If she neglects children’; 3) ‘If she argues with him’; 4) ‘If she refuses sex with him’; and 5) ‘If she burns the food’.

2.8 1.7 5.8

45.4

31.7 39.2

2.3 3.7 5.3

42.3

25.7 32.0

2.3 3.0 6.2

48.6

27.4

44.8



15-24 25-35 36-49

The

Ana

lysis

of M

ICS

da

ta

72

For the purpose of examining the causes of attitudes towards violence, we conducted a logistic regression (Table R.10) in order to identify the profile of women who provided at least one reason that justifies beating a woman. We used the following predictors: wealth status (two levels), education (three levels with high as the reference category), and living area. The results indicate that the predictors of these attitudes for the general population are wealth status and education. Specifically, women who come from households belonging to the poorest 60% tend to justify violence more often that those better-off. Women with primary school are 14 times more likely and women with secondary school three times to justify violence when compared to women with university education. However, when we control for these covariates, living area is not a significant framework for explaining these attitudes. The results indicate that poverty and lower education explain this phenomenon, although we have to bear in mind that both of them are more common in less urban areas.

Table R.10

Logistic regression, attitudes toward domestic violence

Serbia Serbia Roma settlementAttitudes toward domestic violence B Exp(B) B Exp(B)

Wealth index 40%(ref. poorest 60%) 1.269** .281 .112 .894

Primary school 2.649** 14.134 .641** 1.899

Secondary school(ref. higher) 1.100** 3.005

TPA (ref. DPA/IPA) .171 1.187 .072 1.074

Constant 2.971 .051 .958 .384

In the case of Roma settlements, too, we have investigated the causes of justification of domestic violence against women by conducting a logistic regression with the same predictors as in the previous case. The results indicate a similar character of justification of violence among Roma women. Here, education emerges as the only important predictor: women with primary education tend to justify violence twice as often as those with secondary education.

The

Ana

lysis

of M

ICS

da

ta

73

T he analysis in this study was divided into two large parts: women and children. The analysis had three goals: 1) descriptive — to recognize all differences in the lives of women and children depending on the area of

living; 2) longitudinal — to recognize the trends related to these differences/inequalities in the last 10 years; and 3) analytical — to offer an explanation for these differences wherever possible. Given the already recognized differences between the general population and the population from Roma settlements, in the study we also strived to understand differences arising from ethnicity. All analysed dimensions of life of children and women indicate differences in relation to areas of living. DPAs and IPAs, when compared to TPAs, represent in many ways safer and more stimulating areas for development.

4.1 Children 4.1.1Lifeconditionsforchildren0-17yearsold

The data related to material conditions of children’s life (we observed only households with children) indicate prevalent urban-rural differences in terms of access to basic infrastructure: drinking water and sanitation. Also, there is the issue of the quality of drinking water from the water supply system. Especially critical in this respect is the region of Vojvodina, where people use the water supply drinking water the least. Poor water quality is usually compensated for by buying bottled water, which exposes people to additional costs. Bearing in mind that the rural (TPA) population is in a worse material position compared to the urban one, lack of quality water puts an additional pressure on household budgets. The data for TPAs of central Serbia also indicate ambivalence related to water use. On the one hand, these TPAs have the highest level of use of drinking water from the water supply, and on the other hand, people from these regions use unimproved sources the most. The Roma urban population is almost entirely dependent on the quality of water from the water supply. The facts on frequent ‘borrowing’ of water from neighbours prove their low material position.

The sewerage system is another important aspect of infrastructure. There are clear differences in the use of unimproved sanitation facilities in TPAs compared to DPAs and IPAs. Additionally, the TPA population tends to use an improved sanitation facility that carry health risks (such as a septic tank and pit latrine with slab) more often compared with other improved sanitation facilities. Bearing in mind that septic tanks and pit latrines often involve negative effects on the environment (especially on groundwater that can be used for drinking), the risks of using these sanitation facilities are greater in rural living areas. The sewage system in the Belgrade region is underdeveloped for the needs of a capital and largest city area, as proved by the fact that every sixth

4Conclusions

The

Ana

lysis

of M

ICS

da

ta

74

inhabitant from the households with children does not have access to the sewage network. The situation in urban Vojvodina is even more alarming, because over a quarter of the observed population lives in households that are not connected to the sewage network. Still more worrying are the facts regarding infrastructure in the Roma settlements: every sixth member of the urban population and over a quarter of the rural population live in a household that has no access to improved sanitation facilities.

The findings on the living conditions of children indicate that despite very large urban-rural (DPA/IPA-TPA) differences in wealth, the gap in their material position has been reduced over time. However, a worsening of the material living conditions of the entire population has been reflected in the urban population to a somewhat greater extent. Essentially, this does not mean that the living conditions in rural living areas have improved, but rather that the urban living conditions have deteriorated. While the gap in wealth has reduced, the gap related to family cultural capital (in the form of education) has increased over time (the latter gap is the result of a more comprehensive, and primarily higher education of the urban population). Education (of the mother) is also a resource on the labour market, and thereby an indirect indicator of a household’s economic opportunities and an indicator of a specific family ambient that often carries certain values, child-rearing practices, tolerance and so on. The data indicate a concentration of educated people in both urban and rural Belgrade, which, on the one hand, provides the Belgrade population with greater chances of finding better-paying jobs and thus financial security; on the other hand, it makes this region a privileged one in terms of the educational achievement of children. Educational frameworks related to the population from Roma settlements are worse in comparison with the general population. A continuous deterioration in the educational structure in urban living areas is particularly worrying.

4.1.2Children0-4yearsold

Nutrition.TPA children are exclusively breastfed for a shorter period than children from IPAs and DPAs. Moreover, this practice continuously decreases in rural living areas. Predominant breastfeeding practice shows similar urban-rural differences. Together, these indicate different levels of risk faced by children depending on the living area. Urban mothers and those who are better off have a greater awareness of the importance of these practices. Therefore, it appears that these practices of breastfeeding are to a large extent part of the lifestyle of urban middle-class women. In order for this not to remain just a matter of a certain lifestyle, but to become the medical standard, it is necessary to spread awareness of its importance among lower-income mothers and particularly women living in rural areas (especially in the region of Southern and Eastern Serbia).Earlychilddevelopment. According to all developmental characteristics, urban children show better scores

than rural (TPA) ones. Also, urban children are on track more often. In the period between the two last surveys (2010-2014), there were some improvements in the early child development index scores in urban living areas, along with some deterioration in rural ones. Examination of these differences reveals that most aspects of children’s development are primarily explained by attending preschool programmes. The fact that TPA children are less involved in such programmes may explain the higher frequency of their lagging behind DPA/IPA children. Providing conditions for children’s attendance of kindergarten in rural living areas — as well as in all urban living areas — will prevent the creation of a gap between children in developing skills that may affect their chances of achievement in life.

The

Ana

lysis

of M

ICS

da

ta

75

Children from TPAs are less likely to be enrolled in preschools and kindergartens than children from urban living areas. Although the coverage of children in both rural and urban living areas is slowly increasing, the data indicate that this increase is far more intense in urban living areas, which actually broadens the urban-rural gap. The causes of not enrolling in preschool education are manifold. However, as the urban-rural gap implies, we have identified living area as a significant factor influencing whether a child will attend kindergarten or not (in addition to material standard and education of parents). The practice of not enrolling in early education programmes has multiple negative effects on early child development.Support for learning. Rural children have a less stimulating environment for learning and development

than urban ones. They have fewer children’s books available, playing with fewer toys and somewhat less diverse types of playthings. The relation of adults toward them also differs. Although adults engage with children in urban and rural living areas alike, urban fathers tend to be engaged more than rural ones. In TPAs, other adults (grandparents, aunts, etc.) play with children more often. Research indicates an increasing importance of the involvement of fathers in child development. The quality of the relationship between father and child has significance for the balanced psychological development of a child, but also for the father, who has entered into a new life phase (Marsiglio, Amato, Day, Lamb 2000). The other benefit related to fathers’ involvement in duties regarding children is the relieving of mothers (as well as other members of the extended family) from duties and responsibilities of caregiving, which enables women to pursue (further) education, work and have leisure time.

4.1.3Children5-17yearsold25

Childdiscipline. Violent methods of discipline are almost equally represented in urban and rural living areas. The Belgrade region stands out with the least use of any violent practices (in both urban and rural living areas). Over time, this style is gradually losing its importance in both living areas. Although equally represented in both living areas, when put in the context of the importance of education and wealth status, we see that children in urban households are more likely to be exposed to violent methods of discipline, regardless of the household’s material status and the mother’s education. On the other hand the violent practices in rural living areas significantly decrease along with the increase of education level of mother and wealth status of household.ChildLabour. The most considerable urban-rural difference applies to the issue of child labour. TPA children of

all age groups tend to spend more time working (either above or below the age-specific threshold, i.e. regardless of whether it is classified as harmful or not), tend to work under hazardous conditions, and far more often fall into the category of child labour. Children from areas of central Serbia are especially burdened with work. In addition to the education of parents (mothers), living area (TPA) represents a significant predictor of whether a child will work above the age-specific threshold that is not harmful to his/her development. A similar situation exists with the Roma population, whose children from poorer families and those from TPAs work more often, as do, surprisingly, those whose mothers have secondary education. Given the importance of the production function of rural households, children become a part of the workforce at an early age and are more involved in work as they grow up, which leaves them less time for education, involvement in social networks and leisure.

25 For the main differences in children’s educational transitions, see Dejan Stankovic (2015), Education in Serbia in the light of MICS data, Belgrade: UNICEF.

The

Ana

lysis

of M

ICS

da

ta

76

4.2 Women Thematerialpositionofwomen. Rural (TPA) women are in a far less enviable material position than urban

women, especially in comparison to women from DPAs. The effects of the economic crisis have influenced a decrease in the relative relations of inequality between these two populations; however, this was mostly due to a greater decline in standards in urban areas than in rural living areas. A positive trend is that more young women aged up to 25 remain in the educational process, both in urban and rural living areas. Belgrade TPAs and DPAs stand out with the best chances for continuous education and employment of young women. As for TPAs, Vojvodina and Belgrade again provide women with more opportunities for work and earning than central Serbia does. The Roma population of young women is in a far less favourable position than the general population as far as education is concerned. What is worrying is that in a period of only four years, there has been a decrease in the percentage of both rural and urban Roma women who are in the process of education, on the labour market, or earning any income.Earlymarriage. The number of rural and urban women entering into marriage has gradually evened out, so

that the youngest categories in urban and rural living areas tend to show similar patterns of marriages. Rural and urban Belgrade stands out with its lowest percentage of women who entered into marriage before the age of 18, whereas the region of central Serbia (especially its rural parts) stands out with the highest percentage of early marriage.Familyplanning. Young urban and rural women show no significant differences in sexual practices. Data

clearly indicate that over time they are increasingly adopting modern methods of contraception, but they still use traditional ones to a greater extent. Rural and urban women older than 25, however, show different patterns in the use of sexual protection: rural women use modern methods significantly less. There is a worrying trend showing that over time, in spite of a greater general awareness about contraception proved by a higher total percentage of usage of protection, there has been no increase in the use of modern methods, but rather exclusively in traditional ones. This trend is almost equal when we compare urban and rural living areas. Roma women tend to use modern methods of contraception far less. Longitudinal data, however, indicate that an increase in the use of contraception among this population is connected to the increase in use of traditional methods, as was the case with the general population.

Data related to unmet need of women in marriage for either spacing or limiting do not point to great urban-rural differences. The youngest category of urban women (15-24) tends to have somewhat more unmet need, although the explanation of this phenomenon among the general population of women is primarily related to the level of education and age of the woman. Over the period of 10 years, the Roma population decreased their unmet need in all age cohorts, which points out an improvement of family relations. However, older rural Roma women do not follow the same trend and fall into the category with the highest level of unmet need (for limiting).Risksofdomesticviolence. Relations between spouses or partners is reflected in the extent of justification

of domestic violence against women. The percentage of women justifying such behaviour in the general population is not high; it is, however, to a somewhat greater extent more widespread in TPAs, and very present among the Roma population. Longitudinal data indicate a reducing trend of the justification of violence among women in all cohorts in both urban and rural living areas.

The

Ana

lysis

of M

ICS

da

ta

77

5RecommendationsT he conducted analysis provides us with sufficient material to propose two types of measures: 1) those

related to the methodology and possibility to improve monitoring of the situation of women and children in settlements with different levels of population density; and 2) those related to practical policy, namely the possibility of improving the lives of children and women in settlements with different levels of population density, as well as reducing the urban-rural gap.

5.1 Recommendations regarding research methodology

Using dual operationalization for analysing the same phenomenon has provided us with sufficient evidence that the division of settlements in accordance to the level of population density (TPA, IPA and DPA) is justified and methodologically more accurate. Data and analyses indicate that there are some differences in the various domains in the lives of children and women living in settlements with different population densities, confirming that the hitherto used division of ‘urban’ and ‘other’ could have explained a smaller part of the variability than the one predominantly used in this study. We suggest the tripartite classification become part of the standard MICS and that, if technically possible, earlier databases now include the variable with the said division of settlements. In this way, in addition to the current situation, we would also be able to keep track of trends more accurately. Moreover, this study has shown that the use of this classification would be justified in other survey research conducted by the Statistical Office of the Republic of Serbia for the additional reason that the above classification is part of international standards and therefore allows international comparability.

Another type of methodological recommendation relates to the indicators that would enable better and deeper insight into the lives of children and women. Although the MICS set of data provides insight into the significant features of the life course of children and women, several interventions would significantly improve the explanatory power of statistical models. The MICS data set does not keep track of some of the important characteristics of individuals (through life course) in a systematic way (or it does so only occasionally); therefore, the following set of indicators may be a viable solution:

´ activity status of household members (employed, unemployed, type of work, etc.); ´ sources and level of income of individuals within a household; ´ informal exchange and support networks (social capital); and ´ division of domestic work (household work and childcare).

The

Ana

lysis

of M

ICS

da

ta

78

5.2 Policy recommendationsAs for the life course of children, the study data indicate several important topics that practical policy measures

should focus on. The first one is the unfavourable financial situation of children living in TPAs and the more frequent lack of basic infrastructure in households, which can affect health and quality of life. At this level there are salient differences between the population living in Roma settlements (especially TPAs) and the general population. Another issue that practical policies should focus on is the creation of equal chances for adequate physical and psychosocial development of children in all types of settlements. Children who live in TPAs are less likely to be developmentally on track, due to lower wealth status and mother’s education, but also the lack of PPP. A set of measures that deal with reducing the gap in developmental opportunities of children must address all the conditions that contribute to the existence of these differences. Monitoring of the situation of children in Roma settlements is especially needed, as the trends point out an increase in the number of children who are not developmentally on track. The third topic, which is very important when it comes to risks to child development, is child labour. The data clearly indicate that children in rural areas (both in the general population and the population living in Roma settlements) are significantly more burdened with work regardless of whether it is classified as harmful or not. Being burdened with too much work is accompanied with less time for learning and extracurricular activities, and it creates conditions for social isolation. Therefore, efforts should focus on finding a way to postpone the involvement of children in economic activity as long as possible, and, when they are involved, to keep it at a level not harmful to either physical or psychosocial development. The fourth issue that should be the focus of practical measures is related to attitudes towards children with disabilities.

When it comes to recommendations about the situation of women with regard to differences between the settlements with different population density, we can present them as measures to be aimed at:

´ Reducing the material and the educational gap between TPAs and DPAs/IPAs, both in general and in the population living in Roma settlements. The measures should aim at enabling women in TPAs to stay in education as long as possible, and developing strategies to support women in these settlements to find (self-) employment.

´ Reducing the health risks arising from the use of traditional methods of contraception and abortion as a form of birth control. Lower-educated populations with a low material standard in TPAs should be particularly targeted.

´ Developing multiple supports to parenting in TPAs and especially breaking gender stereotypes related to parenting, primarily the level and the manner of the father’s involvement in childcare.

´ Reducing the risk of domestic violence in general, and particularly in TPAs. As for the risk of violence, women living in Roma settlements are particularly vulnerable.

The

Ana

lysis

of M

ICS

da

ta

79

ReferencesBabovic, M., Ginic, K., & Vukovic, O. (2010) Mapiranje porodicnog nasilja prema zenama u Centralnoj Sribji,

Belgrade: Uprava za rodnu ravnopravnost; Mapping domestic violence against women in central Serbia, Belgrade: Gender Equality Directorate.

Babović, M., & Vuković, O. (2008) Žene na selu kao pomažući članovi poljoprivrednog domaćinstva: Položaj, uloge i socijalna prava, Belgrade: UNDP.

Babović, M. (2009): ‘Radne strategije i odnosi u domaćinstvu: Srbija 2003-2007’, in: A. Milić & S. Tomanović (Eds.), Porodice u Srbiji danas u komaparativnoj perspektivi, Belgrade: Institut za sociološka istraživanja Filozofskog fakulteta (ISIFF).

Babović, M. (Ed.) (2011), Social Inclusion: Concepts, Conditions, Policies, Belgrade: SeConS, ISIFF.Barria R. M., Santander, G., Victoriano, T. (2008), ‘Factors associated with exclusive breastfeeding at 3 months

postpartum in Valdivia, Chile’, Nursing 24(4) 439-445.Beck, U., & Beck–Gernsheim, E. (2002), Individualization, London: Sage.Blagojević, M. (1997), Roditeljstvo i/ili fertilitet, Belgrade: ISIFF.Bobić, M. (2010), ‘Partnerstvo kao porodični sistem’ in: A. Milić (Ed.) Vreme porodica: Sociološka studija o

porodičnoj transformaciji u savremenoj Srbiji, Belgrade: ISIFF: 115-147.Bogdanov, N. (2007), Small Rural Households in Serbia and Rural Non-farm Economy, Belgrade: UNDP. Bourdieu, P. (1986) ‘The forms of capital’, in: A. H. Halsey, H. Lauder, P. Brown & A. S. Wells (Eds.), Education:

Culture, Economy, Society, Oxford: Oxford University Press.Brooks-Gunn, J., & Duncan, G. (1997), ‘The effects of poverty on children’, Children and Poverty, 7(2), 55-71Burger, K. (2010), ‘How does early childhood care and education affect cognitive development? An international

review of the effects of early interventions for children from different social backgrounds’, Early Childhood Research Quarterly 25(2), 140-165.

Burton, A., Monasch, R., Lautenbach, B., Gacic-Dobo, M., Neill, M., Karimov, R., … Birmingham, M. (2009), ‘WHO and UNICEF estimates of national infant immunization coverage: Methods and processes’, Bulletin of the World Health Organization, 87, 535-541.

Davis-Kean, P. (2005, June), ‘The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment’, Journal of Family Psychology, 19(2), 294-304.

Elder, Jr., G. H., Kirkpatrick Johnson M., & Crosnoe R. (2002), ‘The emergence and development of Life Course Theory’, in J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the Life Course, New York, NY: Kluwer Academic Publishers, 3-23.

Engle, P. L. & Black, M. M. (2008), ‘The effect of poverty on child development and educational outcomes’, Annals of the New York Academy of Sciences, 1136: 243-256.

Gollin, D., Lagakos, D., & Waugh, M. E. (2014), ‘The agricultural productivity gap’, The Quarterly Journal of Economics, 129 (2), 939-993.

Hanson, T., McLanahan, S., & Thomson, E. (1997), ‘Economic resources, parental practices, and child well-being’, in: G. Duncan and J. Brooks-Gunn (Eds.), Consequences of growing up poor, New York: Russell Sage.

The

Ana

lysis

of M

ICS

da

ta

80

Heck, K. E., Braveman, P., Cubbin, C., Chávez, G. F., & Kiely, J. L. (2006), ‘Socioeconomic status and breastfeeding initiation among California mothers’, Public Health Reports, 121(1), 51-59.

Iltus, S., (2006), ‘Significance of home environments as proxy indicators for early childhood care and education’, Paper commissioned for EFA Global Monitoring Report 2007, Strong foundations: early childhood care and education, Paris: UNESCO.

International Labour Organisation (n.d.), What is child labour?, Retrieved from http://www.ilo.org/ipec/facts/lang--en/index.htm, accessed 6 June, 2016.

Irwin, L. G., Siddiqui, A., & Hertzman, C. (2007), Early child development: A powerful equalizer, Geneva: WHO.

Kraaykamp, G. & van Eijck, K. (2010), ‘The intergenerational reproduction of cultural capital: A threefold perspective’, Social Forces, 89(1), 209-231.

Kramer, M. S., & Kakuma, R. (2012), ‘Optimal duration of exclusive breastfeeding (Review)’, Cochrane Database of Systematic Reviews, 8(3).

Lesthaeghe, R., & Neels, K. (2002), ‘From the first to the second demographic transition: An interpretation of the spatial continuity of demographic innovation in France, Belgium and Switzerland’, European Journal of Population, 18, 325 -360.

Ljubičić, M. (2012), ‘Psychological separation of young people: Towards construction of an integrative model of growing up’, in: S. Tomanović, D. Stanojević, I. Jarić, D. Mojić, S. Dragišić Labaš, & M. Ljubičić (Eds.), Young people are present. The study on social biographies of young people in Serbia, Belgrade: Čigoja and the Institute for Sociological Research, 245-273.

Lazić, M., & Cvejić, S. (2004), ‘Promene društvene strukture u Srbiji: Slučaj blokirane postsocijalističke transformacije’, in: A. Milić (Ed.), Društvena transformacija i strategije društvenih grupa, Belgrade, ISIFF.

Lebedinski, L. (2015), Early childhood development, MICS report, Belgrade: UNICEFMarsiglio, W., Amato, P., Day, R. D., Lamb, M. E. (2000), „Scholarship on fatherhood in the 1990s and beyond“,

Journal of Marriage and the Family, 62, 1173-1191.Mayer, U. K. (2002), ‘The sociology of the life course and life span psychology: Diverging or converging

pathways?’ in: U. M. Staudinger and U. Lindenberger (Eds.), Understanding human development: Lifespan psychology in exchange with other disciplines, Dordrecht: Kluwer Academic Publishers.

McLanahan, S., & Sandefur, G. (1999), Growing up with a single parent: What hurts, what helps. Cambridge, MA: Harvard University Press.

McLoyd, V. C. (1998), ‘Socioeconomic disadvantage and child development’, American Psychologist, 53(2), 185-204.

Milić, A. (Ed.) (2010), Vreme porodica, Belgrade: ISIFF.Paciorek, C. J., Stevens, G. A., Finucane, M. M., & Ezzati, M. (2013), Children’s height and weight in rural

and urban populations in low-income and middle-income countries: a systematic analysis of population-representative data, Lancet Global Health, 1(5): e300-09.

Petrović, M. (2009), ‘Domaćinstva u Srbiji prema porodičnom sastavu: Između (pre)modernosti i (post)modernosti’, in: A. Milić & S. Tomanović (Eds.), Porodice u Srbiji danas u komparativnoj perspektivi, Belgrade: ISIFF, 115-135.

The

Ana

lysis

of M

ICS

da

ta

81

Pickett, K. (2007), ‘Child wellbeing and income inequality in rich societies: Ecological cross sectional study’, BMJ 335(1080).

Sastry, N. (1997), ‘What explains rural-urban differentials in child mortality in Brazil?’ Social Science and Medicine, 44(7), 989-1002.

Schady, N. (2011), ‘Parents’ education, mothers’ vocabulary, and cognitive development in early childhood: Longitudinal evidence from Ecuador’, American Journal of Public Health, 101(12), 2299-2307.

Stankovic, D. (2015), Education in Serbia in the light of MICS data, Belgrade: UNICEF. Stanojević, D. (2012), ‘Obeležja društvenog položaja mladih’, in: S. Tomanovic S. (Ed.), Mladi — Naša

sadašnjost, Belgrade: ISIFF. Stanojevic, D. (2013), ‘Intergenerational educational mobility in Serbia in the 20th century’, in M. Lazic M,

and S. Cvejic (Eds.), Changes of basic structures of society during the period of accelerated transformation in Serbia, Belgrade: Institute for Sociological Research.

Thomson E., Hanson, T. L., & McLanahan, S. S. (1994), ‘Family structure and child well-being: Economic resources vs. parental behaviors’, Social Forces, 73(1), 221-242.

Tomanović S. (2012): ‘Changes in transition to adulthood of young people in Serbia between 2003 and 2011’, Sociologija, LIV, 2, 227-243.

Tomanović, S., & Ignjatovic S., (2006): Transition of Young People in a Transitional Society: The Case of Serbia, Journal of Youth Studies, Vol. 9, No. 3: 269-285.

Tomanovic, S., Stanojevic, D., Jaric, I., Mojic, D., Dragisic Labas, S., Ljubicic, M., & Zivadinovic, I. (2012), Mladi — nasa sadasnjost: Istrazivanje socijalnih biografija mladih u Sribji. Belgrade: ISIFF.

Tomanovic, S., Stanojevic, D., & Ljubicic, M. (2014), Jednoroditeljske porodice u Sribji: Socioloska studija. Belgrade: ISIFF.

Statistical Office of the Republic of Serbia & UNICEF (2014), Serbia Multiple Indicator Cluster Survey and Serbia Roma settlements Multiple Indicator Cluster Survey, 2014, Final reports. Belgrade, Serbia: Statistical Office of the Republic of Serbia and UNICEF.

UNESCO (n.d.), eAtlas of out-of-school children, retrieved from http://tellmaps.com/uis/oosc/index.jsp?subject=-528275754&geoitem=CEE&regions=true&lang=en, accessed 6 June 2016.

UNESCO (2003), Gender and Education for All: The leap to equality (2003), Paris: UNESCO.UNESCO (2009), Education indicators, technical guidelines, Montreal: UNESCO Institute for Statistics. UNICEF (n.d.), Millennium Development Goal 5: Improve Maternal Health, retrieved from http://www.unicef.

org/mdg/maternal.html, accessed 6 June 2016.UNICEF (2002, November), Poverty and exclusion among urban children, Innocenti digest (10).UNICEF (2005), Early marriage a harmful traditional practice, New York: UNICEF.UNICEF (2012a), Investing in early childhood education in Serbia, Belgrade: UNICEF.UNICEF (2012b), School Readiness: A Conceptual Framework, New York: UNICEF.UNICEF (2013), Happiness and families with children in Serbia: How to design public policies for the well-being

of families with children, Belgrade: UNICEF. UNICEF (2015), The world has missed the MDG sanitation target by almost 700 million, retrieved from http://

data.unicef.org/water-sanitation/sanitation, accessed 6 June 2016.

The

Ana

lysis

of M

ICS

da

ta

82

United Nations (2013, September), Challenges, 16, New York, NY: United Nations.Van de Kaa, D. J. (2002), The idea of a second demographic transition in industrialized countries, Paper

presented at Sixth Welfare Policy Seminar of the National Institute of Population and Social Security, Tokyo, Japan, 29 January 2002.

White Stewart, M. (2014), Ordinary violence: Everyday assaults against women worldwide (2nd edition), Westport, CT: Praeger.

World Health Organization (WHO) (2013), Global vaccine action plan 2011-2020, Geneva: WHO.WHO & UNICEF, (2003), Antenatal Care in developing Countries: Promises, Achievements and Missed

Opportunities: An Analysis of Trends, Levels, and Differentials: 1990-2001, Geneva, Switzerland: WHO.WHO & UNICEF (2006), Meeting the MDG drinking water and sanitation target: The urban and rural challenge

of the decade, Geneva, Switzerland: WHO and UNICEF. WHO & UNICEF (2014), Progress on sanitation and drinking-water: 2014 update, Geneva: WHO.Yeneabat T., Belachew, T., & Haile, M. (2014), ‘Determinants of cessation of exclusive breastfeeding in Ankesha

Guagusa Woreda, Awi Zone, Northwest Ethiopia: a cross-sectional study’, BMC Pregnancy and Childbirth, 14, 262.

The

Ana

lysis

of M

ICS

da

ta

83

Appendix

Impr

oved

sour

ces

Unim

prov

ed so

urce

s

Tota

l

Wat

er

Perc

ent-

age

usin

g im

prov

ed

sour

ces

of d

rink-

ing

wat

er

[1]

Num

ber

of h

ouse

-ho

ld

mem

bers

in

hou

se-

hold

s with

ch

ildre

n

11 P

iped

in

to

dwel

ling

Pip

ed

into

co

m-

poun

d.

yard

or

plot

Pipe

d to

ne

igh-

bour

Publ

ic

tap

/ st

and-

pipe

Tube

w

ell.

Bore

-ho

le

Prot

ect-

ed w

ell

Pro-

tect

ed

sprin

g

Bottl

ed

wat

er

[a]

Unpr

o-te

cted

w

ell

Unpr

o-te

cted

sp

ring

Tank

-er

-truc

kBo

ttled

w

ater

[a

]O

ther

2014

Serb

ia

Den

sely

pop

-ul

ated

are

a85

.70.

20.

30.

50.

60.

00.

811

.80.

00.

00.

00.

00.

010

0.0

100.

033

36

Inte

rmed

iate

ar

ea78

.10.

10.

02.

20.

60.

71.

715

.80.

00.

00.

00.

00.

710

0.0

99.3

2265

Thin

ly- p

opu-

late

d ar

ea79

.90.

90.

41.

43.

14.

91.

76.

80.

60.

00.

00.

00.

210

0.0

99.2

4070

Bel-

grad

e

Den

sely

pop

-ul

ated

are

a87

.40.

20.

10.

00.

00.

00.

212

.10.

00.

00.

00.

00.

010

0.0

100.

012

29

Inte

rmed

iate

ar

ea78

.80.

00.

00.

62.

73.

70.

613

.60.

00.

00.

00.

00.

010

0.0

100.

029

4

Thin

ly- p

opu-

late

d ar

ea78

.80.

00.

00.

05.

75.

62.

87.

00.

00.

00.

00.

00.

010

0.0

100.

049

0

Vo-

jvod

i-na

Den

sely

pop

-ul

ated

are

a66

.50.

01.

52.

63.

50.

10.

425

.40.

00.

00.

00.

00.

010

0.0

100.

061

8

Inte

rmed

iate

ar

ea67

.70.

10.

14.

10.

20.

02.

824

.30.

00.

00.

00.

00.

710

0.0

99.3

1107

Thin

ly- p

opu-

late

d ar

ea70

.11.

30.

22.

55.

40.

02.

817

.70.

00.

00.

10.

00.

010

0.0

99.9

757

Šu-

mad

ija

and

W

est-

ern

Serb

ia

Den

sely

pop

-ul

ated

are

a88

.80.

30.

00.

20.

00.

01.

98.

70.

10.

00.

00.

00.

010

0.0

99.9

847

Inte

rmed

iate

ar

ea92

.00.

00.

00.

30.

01.

60.

43.

00.

00.

00.

00.

02.

610

0.0

97.4

331

Thin

ly- p

opu-

late

d ar

ea84

.90.

00.

30.

51.

15.

91.

14.

61.

40.

00.

00.

10.

010

0.0

98.5

1628

Sout

h-er

n an

d

East

-er

n Se

rbia

Den

sely

pop

-ul

ated

are

a96

.80.

00.

00.

00.

00.

00.

82.

40.

00.

00.

00.

00.

010

0.0

100.

064

1

Inte

rmed

iate

ar

ea90

.90.

10.

00.

30.

60.

10.

87.

20.

00.

00.

00.

00.

010

0.0

100.

053

2

Thin

ly- p

opu-

late

d ar

ea79

.92.

10.

82.

53.

26.

41.

32.

90.

10.

10.

00.

00.

610

0.0

99.2

1194

Tab

le 1

Perc

ent d

istrib

utio

n of

hou

seho

ld p

opul

atio

n fro

m h

ouse

hold

s w

ith c

hild

ren

— M

ain

sour

ce o

f drin

king

wat

er in

hou

seho

ld, S

erbi

a 20

14

The

Ana

lysis

of M

ICS

da

ta

84

Impr

oved

sani

tatio

n fa

cilit

yUn

impr

oved

sani

tatio

n fa

cilit

yO

pen

defe

catio

n (n

o fa

cilit

y,

bush

fiel

d)To

tal

Num

ber o

f ho

useh

old

mem

bers

in

hou

se-

hold

s with

ch

ildre

n

Flu

sh to

pi

ped

sew

er

syst

em

Flush

to

sept

ic ta

nkFlu

sh to

pit

(latri

ne)

Flush

to

unkn

own

plac

e /

Not

sure

/

DK w

here

Ven

tilat

ed

Impr

oved

Pi

t lat

rine

(VIP

)

Pit

latri

ne

with

slab

Flu

sh to

so

me-

whe

re

else

Pit

latri

ne

with

out

slab

/ O

pen

pit

Oth

erM

issin

g/DK

2014

Serb

ia

Den

sely

pop

ulat

-ed

are

a87

.110

.40.

10.

00.

02.

10.

10.

00.

00.

20.

010

0.0

3336

Inte

rmed

iate

are

a70

.826

.80.

10.

30.

01.

20.

00.

80.

00.

00.

110

0.0

2265

Thin

ly- p

opul

ated

ar

ea24

.463

.60.

10.

40.

06.

62.

32.

00.

30.

00.

210

0.0

4070

Bel-

grad

e

Den

sely

pop

ulat

-ed

are

a83

.914

.90.

30.

00.

00.

30.

20.

00.

00.

40.

110

0.0

1229

Inte

rmed

iate

are

a82

.916

.10.

60.

00.

00.

30.

00.

00.

00.

00.

010

0.0

294

Thin

ly- p

opul

ated

ar

ea21

.678

.20.

00.

00.

00.

00.

20.

00.

00.

00.

010

0.0

490

Vo-

jvod

ina

Den

sely

pop

ulat

-ed

are

a70

.421

.20.

00.

00.

08.

20.

00.

20.

00.

00.

010

0.0

618

Inte

rmed

iate

are

a53

.444

.80.

00.

60.

01.

10.

00.

00.

00.

00.

110

0.0

1107

Thin

ly- p

opul

ated

ar

ea10

.082

.00.

00.

00.

05.

90.

02.

10.

00.

00.

010

0.0

757

Šu-

mad

ija

and

W

este

rn

Serb

ia

Den

sely

pop

ulat

-ed

are

a99

.20.

50.

00.

00.

00.

00.

00.

00.

00.

30.

010

0.0

847

Inte

rmed

iate

are

a89

.08.

40.

00.

00.

02.

60.

00.

00.

00.

00.

010

0.0

331

Thin

ly- p

opul

ated

ar

ea32

.259

.00.

30.

50.

11.

94.

31.

10.

60.

00.

010

0.0

1628

Sout

h-er

n an

d

East

ern

Serb

ia

Den

sely

pop

ulat

-ed

are

a93

.34.

40.

00.

00.

02.

30.

00.

00.

00.

00.

010

0.0

641

Inte

rmed

iate

are

a88

.86.

50.

00.

00.

01.

10.

03.

40.

00.

00.

210

0.0

532

Thin

ly- p

opul

ated

ar

ea23

.852

.30.

00.

80.

016

.41.

94.

00.

00.

00.

710

0.0

1194

Impr

oved

sour

ces

Unim

prov

ed so

urce

s

Tota

l

Wat

er

Perc

ent-

age

usin

g im

-pr

oved

so

urce

s of

dr

inki

ng

wat

er

[1]

Num

-be

r of

hous

e-ho

ld

mem

-be

rs in

ho

use-

hold

s w

ith

child

ren

11 P

iped

in

to

dwel

ling

Pip

ed

into

co

m-

poun

d,

yard

or

plot

Pipe

d to

ne

igh-

bour

Pub

lic

tap

/ st

and-

pipe

Tube

w

ell,

Bore

-ho

le

Prot

ect-

ed w

ell

Pro-

tect

ed

sprin

g

Bottl

ed

wat

er

[a]

Unpr

o-te

cted

w

ell

Unpr

o-te

cted

sp

ring

Tank

-er

-truc

kBo

ttled

w

ater

[a

]O

ther

Rom

a se

ttle-

men

ts

Den

sely

pop

u-la

ted

area

78.8

12.2

3.0

1.6

1.7

0.7

0.0

1.8

0.0

0.0

0.0

0.0

0.3

100.

099

.727

95

Inte

rmed

iate

ar

ea86

.87.

33.

80.

30.

40.

80.

00.

20.

00.

00.

00.

00.

410

0.0

99.6

2904

Thin

ly- p

opul

at-

ed a

rea

44.6

15.4

8.9

7.2

3.9

7.9

0.8

1.8

1.3

0.1

5.9

0.5

1.8

100.

090

.517

17

Tab

le 2

Perc

ent d

istrib

utio

n of

hou

seho

ld p

opul

atio

n fro

m h

ouse

hold

s w

ith c

hild

ren

— M

ain

sour

ce o

f drin

king

wat

er in

hou

seho

ld,

Serb

ia, R

oma

settl

emen

ts 2

014

Tab

le 3

Perc

ent d

istrib

utio

n of

hou

seho

ld p

opul

atio

n fro

m h

ouse

hold

s w

ith c

hild

ren

— T

ype

of to

ilet f

acili

ty, S

erbi

a, 2

014

The

Ana

lysis

of M

ICS

da

ta

85

Improved sanitation facility Unimproved sanitation facility

Total

Number of house-

hold members in house-holds with children

Flush to piped sewer system

Flush to septic tank

Flush to pit (la-trine)

Flush to unknown place /

Not sure / DK where

Venti-lated

Improved Pit latrine

(VIP)

Pit latrine with slab

Flush to some-where else

Pit latrine without slab / Open

pit

Bucket Other Miss-ing/DK

Open defeca-tion (no facility, bush field)

Roma settle-ments


49.6 13.6 0.2 0.0 0.0 15.0 0.2 19.8 0.2 0.0 0.2 1.1 100.0 2795

Intermediate area 44.8 28.1 0.3 0.8 0.0 17.6 0.2 6.6 0.0 0.1 0.0 1.7 100.0 2904

Thinly- popu-lated area 19.6 13.4 0.0 1.2 0.0 32.6 3.1 23.6 0.0 0.0 0.0 6.6 100.0 1717

Table 4

Percent distribution of household population from households with children — Type of toilet facility, Serbia, Roma settlements, 2014

Table 5

Percent distribution of household population from households with children under five — Welath index, and education of househodl head, Serbia, 2014

Children under five in urban and other areas: Family frameworks and material conditions of life of children

Wealth index Education of household headMissing/DK

Number of children under 5

Poorest 60 percent

Rich 40 percent

No school or primary Secondary Higher

2014

Serbia

Densely populated area 32.6 67.4 11.0 43.5 45.4 0.1 384

Intermediate area 44.6 55.4 11.8 65.2 22.9 0.1 199Thinly- populated area 74.3 25.7 39.2 50.5 10.2 0.1 315

Belgrade



Vojvodina



Šumadija and Western Serbia



Southern and Eastern Serbia



The

Ana

lysis

of M

ICS

da

ta

86

Wealth index Education of household headMissing/DK

Number of children under 5

Poorest 60 percent

Rich 40 percent

No school or primary

Secondary or higher

Roma settle-ments

Densely populated area 63.5 36.5 84.6 15.3 .2 411

Intermediate area 64.2 35.8 88.6 11.4 0.0 402Thinly- populated area 84.2 15.8 90.3 9.7 0.0 262

Table 6

Percent distribution of household population from households with children under five — Welath index, and education of househodl head, Serbia, 2014, Roma

settlements

Mother’s education Number of children under 5None Primary Secondary Higher

degurba Degree of urbanization

1 Densely populated area 1.9 7.8 33.9 56.4 3842 Intermediate area 0.8 7.4 59.1 32.8 1993 Thinly- populated area 0.5 18.5 65.8 15.2 315

HH7 Region

1 Bel-grade

1 Densely pop-ulated area 0.6 4.0 26.0 69.5 174

2 Intermediate area 1.4 5.0 48.3 45.3 32

3 Thinly- popu-lated area 0.0 6.5 75.9 17.6 34

2 Vo-jvodina




3 Su-madija and West Serbia




4 South and East Serbia




Table 7

Education of mother — children under 5, by area and region, Serbia, 2014

melevel Mother’s education 1.00 Number

of children under 51 None 2 Primary 3 Secondary

or higher


1 Densely populated area 28.2 64.2 7.6 4112 Intermediate area 18.8 69.8 11.4 4023 Thinly- populated area 22.6 71.6 5.7 262

Table 8

Education of mother — children under 5, by area and region, Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

87

Children under five: Family frame-works and material conditions of life of children

Wealth index Wealth index quintile 1.00 Number of children

under 5Poorest 60 percent

rich 40 percent Poorest Second Middle Fourth Richest

2014 Serbia

Densely populated area 32.6 67.4 9.3 7.9 15.4 24.5 42.9 384

Intermediate area 44.6 55.4 8.7 13.2 22.7 26.2 29.2 199Thinly- populated area 74.3 25.7 25.7 27.3 21.2 16.8 9.0 315

Table 9

Percent distribution of household population from households with children under five — Welath index Serbia 2014

Children under five in urban and other areas: Family frameworks and material conditions of life of children

Wealth index Wealth index quintile 1.00 Number of children

under 5Poorest 60 percent

Rich 40 percent Poorest Second Middle Fourth Richest

2014

Roma settle-ments

Densely populat-ed area 63.5 36.5 23.7 14.0 25.9 19.9 16.6 411

Intermediate area 64.2 35.8 23.7 22.9 17.6 19.5 16.3 402

Thinly- populated area 84.2 15.8 42.2 28.4 13.6 9.4 6.4 262

Table 10

Percent distribution of household population from households with children under five — Welath index Serbia, Roma settlements, 2014

Children under five: Care and protection Only non-vio-lent discipline

Psychological aggression

Physical punishment t Any violent discipline

method [1]

Numebr of children age

2-4yearsAny Severe

2014

Serbia

Densely populated area 49.4 40.3 22.8 0.1 46.0 496


Belgrade



Vojvodina









Table 11

Child discipline Percentage of children age 2-4 years by child disciplining methods experienced

during the last one month, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

88

Children under five in urban and other areas: Care and protection

Only non-violent discipline

Psychologi-cal aggres-

sion

Physical punishment t Any violent discipline

method [1]

Numebr of children age

2-4yearsAny Severe

Roma settlements



Table 12

Percentage of children age 2-4 years by child disciplining methods experienced during the last one month, Roma settlements, 2104

Children under five: Child health

1.00 Weight for age

waCount Num-ber of

children under age 5

1.00 Height for age

haCount Num-ber of


1.00 Weight for height

whCount Num-ber of


1.00 Under-weight

waMean Mean

Z-Score (SD)

1.00 Stunted

haMean Mean

Z-Score (SD)

1.00 Wasted

1.00 Over-weight

whMean Mean

Z-Score (SD)

1.00 Percent below

1.00 Percent below

1.00 Percent below

1.00 Percent above

wa2sd - 2 SD [1]

ha2sd - 2 SD [3]

wh2sd - 2 SD [5]

wh2sd Above

+ 2 SD [7]

2014

Serbia

Densely populated area 2.3 0.6 945 6.6 0.6 937 4.5 12.6 0.4 891

Intermediate area 0.8 0.6 531 4.9 0.4 528 4.1 16.0 0.6 523Thinly- populated area 1.8 0.6 877 6.0 0.3 872 3.1 13.9 0.6 857

Belgrade



Vojvodina


Intermediate area 0.9 0.5 273 4.6 0.2 271 2.8 11.4 0.5 271Thinly- populated area 6.1 0.3 193 10.7 -0.1 193 4.6 7.0 0.4 190







Table 13

Nutritional status of children Percentage of children under age 5 by nutritional status according to three anthropometric indices:

weight for age, height for age, and weight for height, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

89

1.00 Weight for age

waCount Number of


1.00 Height for age

haCount Number of



whCount Number of


1.00 Un-derweight

waMean Mean

Z-Score (SD)

1.00 Stunted

haMean Mean

Z-Score (SD)

1.00 Wasted

1.00 Over-weight

whMean Mean

Z-Score (SD)

1.00 Percent below

1.00 Percent below

1.00 Percent below

1.00 Percent above

wa2sd - 2 SD [1]

ha2sd - 2 SD [3]

wh2sd - 2 SD [5]

wh2sd Above

+ 2 SD [7]

Roma set-tlements

Densely popu-lated area 11.9 -0.6 504 17.0 -0.9 501 4.7 4.6 -0.2 499

Intermediate area 5.6 -0.5 515 14.9 -0.9 514 3.3 5.5 0.0 513

Thinly- populat-ed area 11.7 -0.8 344 26.2 -1.2 342 7.2 5.1 -0.1 345

Table 14


weight for age, height for age, and weight for height,Serbia, Roma settlements, 2014

1.00 Weight for age

waCount Num-ber of


1.00 Height for age

haCount Num-ber of



whCount Num-ber of


1.00 Under-weight

waMean Mean

Z-Score (SD)

1.00 Stunted

haMean Mean

Z-Score (SD)

1.00 Wasted

1.00 Over-weight

whMean Mean

Z-Score (SD)

1.00 Percent below

1.00 Percent below

1.00 Percent below

1.00 Percent above

wa2sd - 2 SD [1]

ha2sd - 2 SD [3]

wh2sd - 2 SD [5]

wh2sd Above

+ 2 SD [7]

degurba Degree of urbaniza-tion

1 Densely pop-ulated area

1 Male 1.6 0.7 554 6.5 0.8 550 5.0 15.1 0.5 5092 Female 3.3 0.3 391 6.7 0.4 386 3.9 9.3 0.2 382

2 Intermediate area

1 Male 0.7 0.7 256 6.3 0.3 256 3.8 18.8 0.8 2552 Female 0.9 0.6 275 3.6 0.5 272 4.5 13.3 0.4 268

3 Thinly- popu-lated area

1 Male 2.8 0.6 429 7.5 0.4 426 3.5 14.3 0.6 4222 Female 0.9 0.6 447 4.6 0.3 446 2.7 13.6 0.6 435

Table 15


weight for age, height for age, and weight for height, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

90

wa2 Weight for

age:

wamean Weight for

age:

totwa Weight for

age:ha2 Height

for age:hamean Height for

age:

totha Height for

age:

wh2 Weight for

height:

wh3a Weight for

height:

whmean Weight for

height:

totwh Weight for

height:

% below -2 sd [1]

Mean Z-Score

(SD)Number of

children% below -2 sd [3]

Mean Z-Score

(SD)Number of

children% below -2 sd [5]

% above +2 sd

Mean Z-Score

(SD)Number of

children


1 Densely populated area

1 Male 11.2 -0.5 281 17.3 -0.8 279 6.2 6.1 -0.1 275

2 Female 12.8 -0.7 224 16.6 -0.9 222 2.8 2.8 -0.2 224

2 Interme-diate area

1 Male 6.4 -0.7 273 20.3 -1.1 273 4.2 3.0 -0.1 2712 Female 4.6 -0.3 242 8.7 -0.7 241 2.4 8.4 0.2 242

3 Thinly- populated area

1 Male 17.7 -0.8 166 28.2 -1.3 165 9.3 4.9 -0.1 166

2 Female 6.1 -0.7 178 24.3 -1.1 178 5.3 5.3 -0.1 179

Table 16

Nutritional status of children (based on NCHS/CDC/WHO International Reference Population) Percentage of children under age 5 by nutritional status according to three anthropometric indices: weight

for age, height for age, and weight for height, Roma settlements 2014

Children under five : Child health

100.00 Children age 0-5 months breastfed12_15 Children age 12-15 months

breastfed20_23 Children age 20-23 months

Percent exclusively

breastfed [1]

Percent pre-dominantly

breastfed [2]Number of

children

Percent breastfed

(Continued breastfeeding at 1 year) [3]

Number of children

Percent breastfed

(Continued breastfeeding at 2 years) [4]

Number of children

2014 Serbia


17.2 51.6 143 27.8 57 3.1 67

Intermedi-ate area (17.9) (55.2) 80 (11.8) 22 (15.9) 29

Thinly- populated area

2.4 34.2 99 26.4 49 12.0 58

Table 17

Breastfeeding Percentage of living children according to breastfeeding status at selected age groups,

Serbia, 2014

100.00 Children age 0-5 months breastfed12_15 Children age 12-15 months

breastfed20_23 Children age 20-23 months

Percent exclu-sively breast-

fed [1]

Percent pre-dominantly


children

Percent breast-fed (Continued breastfeeding at 1 year) [3]

Number of children

Percent breast-fed (Continued breastfeeding at 2 years) [4]

Number of children

Roma settle-ments

Densely populated area 21.2 48.9 66 (46.8) 44 (21.7) 37

Intermediate area (5.8) (73.6) 46 (73.1) 52 (44.4) 47Thinly- populated area (6.7) (65.9) 34 (65.2) 24 (30.0) 29

Table NU.4

Breastfeeding Percentage of living children according to breastfeeding status at selected age groups,

Serbia, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

91

approp0_5 Children age 0-5 months


appropAll Children age 0-23 months

Percent exclusively breastfed

[1]

Number of children

Percent currently

breastfeed-ing and

receiving solid,

semi-solid or soft foods

Number of children

Percent ap-propriately breastfed

[2]

Number of children


1 Densely populated area 17.2 143 27.8 297 24.4 4402 Intermediate area (17.9) 80 29.7 161 25.8 2413 Thinly- populated area 2.4 99 27.3 276 20.8 375

HH7 Region

1 Bel-grade


1 Densely pop-ulated area (36.5) 54 38.4 115 37.8 169

2 Intermediate area (*) 2 (*) 24 (*) 27

3 Thinly- popu-lated area (*) 10 (6.3) 30 (5.6) 40

2 Vo-jvodina


1 Densely pop-ulated area (*) 35 16.8 70 11.3 105

2 Intermediate area (*) 57 25.6 74 23.8 131

3 Thinly- popu-lated area (*) 31 21.5 65 16.0 96

3 Su-madija and West Serbia



2 Intermediate area (*) 5 (34.9) 25 (28.9) 31





2 Intermediate area (*) 14 27.5 38 20.6 52


Table 19

Age-appropriate breastfeeding Percentage of children age 0-23 months who were appropriately breastfed

during the previous day, Serbia, 2014



appropAll Children age 0-23 months

Percent exclusively


children

Percent current-ly breastfeeding and receiving

solid, semi-solid or soft foods

Number of children

Percent appro-priately breast-

fed [2]Number of

children


1 Densely pop-ulated area 21.2 66 45.2 157 38.1 223

2 Intermediate area (5.8) 46 59.2 188 48.6 234

3 Thinly- popu-lated area (6.7) 34 52.2 104 41.1 137

Table 20

Age-appropriate breastfeeding Percentage of children age 0-23 months who were appropriately breastfed

during the previous day, Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

92

Percentage of children age 36-59 months attending early childhood

education

1.00 Number of children age 36-59

months

2014

SerbiaDensely populated area 67.5 541Intermediate area 52.0 252Thinly- populated area 26.2 407

BelgradeDensely populated area 80.8 285Intermediate area (50.7) 58Thinly- populated area (44.0) 43

VojvodinaDensely populated area 48.1 97Intermediate area 56.9 112Thinly- populated area 32.1 74


Densely populated area 52.5 116Intermediate area (44.1) 30Thinly- populated area 22.6 163


Densely populated area 62.7 44Intermediate area 47.5 53Thinly- populated area 21.5 127

Table 21

Attendance to early childhood education 36-59 months, Serbia, 2014

Percentage of children age 36-59 months attending early childhood

education


months

Roma settlementsDensely populated area 5.0 260Intermediate area 8.1 223Thinly- populated area 3.2 157

Table 22

Attendance to early childhood education 36-59 months, Serbia, Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

93

ind62 Per-centage of

children with whom adult household members have en-gaged in

four or more activities [1]

ind62sum Mean

number of activities with adult household members

1.00 Num-ber of

children age 36-59

months

ind63 Per-centage

of children with whom biological

fathers have engaged in four or more activities [2]

ind63sum Mean

number of activities

with biolog-ical fathers

1.00 Num-ber of

children age 36-59

months living

with their biological

fathers

ind64 Per-centage

of children with whom biological mothers have en-gaged in


ind64sum Mean

number of activ-ities with

biological mothers

1.00 Num-ber of

children age 36-59

months living

with their biological mothers

2014

Serbia

Densely populated area 96.6 5.6 541 39.9 3.0 481 91.8 5.3 532

Intermediate area 95.3 5.6 252 45.9 3.3 233 90.0 5.3 246Thinly- populated area 94.1 5.3 407 26.1 2.5 371 86.4 4.8 389

Belgrade


Intermediate area (100.0) (5.9) 58 (59.7) (3.9) 55 (94.7) (5.5) 56Thinly- populated area 96.2 5.5 43 (37.9) (2.6) 38 (90.5) (5.3) 43

Vojvodi-na





Intermediate area (97.8) (5.5) 30 (34.8) (3.2) 28 (85.2) (5.1) 29Thinly- populated area 94.7 5.3 163 23.4 2.4 146 85.1 4.8 152




Table 23

Support for learning Percentage of children age 36-59 months with whom adult household members engaged in activities

that promote learning and school readiness during the last three days, and engagement in such activities by biological fathers and mothers, Serbia, 2014

ind62 Per-centage of

children with whom adult household members have en-gaged in


ind62sum Mean



months

ind63 Per-centage


fathers have engaged in four or more activities [2]

ind63sum Mean


with biologi-cal fathers

1.00 Number of children age 36-59 months liv-

ing with their biological

fathers

ind64 Per-centage

of children with whom biological mothers have en-gaged in


ind64sum Mean


with biologi-cal mothers

1.00 Number of children age 36-59 months liv-

ing with their biological mothers

Roma settle-ments

Densely popu-lated area 64.5 3.8 260 19.5 1.8 230 43.5 3.1 248

Intermediate area 71.0 4.1 223 16.4 1.8 202 51.3 3.4 215

Thinly- populat-ed area 69.7 4.1 157 15.2 1.6 129 51.8 3.5 151

Table 24


that promote learning and school readiness during the last three days, and engagement in such activities by biological fathers and mothers, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

94

ind62 Per-centage

of children with whom

adult household members

have engaged in four or more ac-tivities [1]

ind62sum Mean


1.00 Number of

children age 36-59

months

ind63 Per-centage


fathers have

engaged in four or more ac-tivities [2]

ind63sum Mean


biological fathers

1.00 Number of

children age 36-59

months living


fathers

ind64 Per-centage

of children with whom biological mothers


ind64sum Mean


biological mothers

1.00 Number of

children age 36-59

months living


degurba Degree of urbani-zation


HL4 Sex

1 Male 96.6 5.6 306 43.3 3.1 267 92.2 5.3 300

2 Female 96.6 5.6 235 35.5 2.9 214 91.2 5.4 233

2 Intermedi-ate area

HL4 Sex

1 Male 93.9 5.6 127 51.5 3.6 116 88.3 5.2 1242 Female 96.8 5.6 125 40.3 3.0 117 91.6 5.3 122

3 Thinly- pop-ulated area

HL4 Sex

1 Male 93.3 5.3 197 30.1 2.7 180 87.6 4.8 1912 Female 94.9 5.4 210 22.4 2.3 191 85.3 4.8 198

Table 25


that promote learning and school readiness during the last three days, and engagement in such activities by biological fathers and mothers, Serbia, 2014

ind62 Per-centage

of children with whom

adult household members


ind62sum Mean


with adult household members

1.00 Number

of children age 36-59

months

ind63 Per-centage


fathers have

engaged in four or more ac-tivities [2]

ind63sum Mean


biological fathers

1.00 Number


months living


fathers

ind64 Per-centage

of children with whom biological mothers


ind64sum Mean


biological mothers

1.00 Number


months living


degurba Degree of urbani-zation


HL4 Sex

1 Male 56.3 3.6 152 17.7 1.8 134 38.8 2.9 143

2 Female 76.2 4.2 108 22.0 1.9 96 50.2 3.4 105

2 Intermedi-ate area

HL4 Sex

1 Male 66.5 3.9 113 20.4 2.0 103 46.2 3.3 1082 Female 75.6 4.4 110 12.2 1.7 100 56.6 3.6 107

3 Thinly- pop-ulated area

HL4 Sex

1 Male 66.8 4.1 72 15.1 1.6 65 46.5 3.3 682 Female 72.2 4.2 85 15.2 1.6 64 56.2 3.6 82

Table 26


that promote learning and school readiness during the last three days, and engagement in such activities by biological fathers and mothers, Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

95

1.00 Percentage of children living in households that have

for the child:1.00 Percentage of children who play with:

1.00 Number of children

under age 5ind65 3 or more chil-

dren’s books [1]

ten 10 or more chil-

dren’s booksEC2A Home-made toys

EC2B Toys from a shop/

manufac-tured toys

EC2C House-hold objects/objects found

outside

ind66 Two or more types of playthings [2]

2014

Serbia



Belgrade



Vojvodina









Table 27

Learning materials Percentage of children under age 5 by numbers of children’s books present in the household,

and by playthings that child plays with, Serbia, 2014

1.00 Percentage of children living in households that have

for the child:1.00 Percentage of children who play with:

1.00 Number of children

under age 5ind65 3 or more chil-

dren’s books [1]

ten 10 or more chil-

dren’s booksEC2A Home-made toys

EC2B Toys from a shop/manufactured

toys

EC2C Household objects/objects found outside

ind66 Two or more types of playthings [2]

Roma settle-ments



Table 28

Learning materials Percentage of children under age 5 by numbers of children’s books present in the household,

and by playthings that child plays with, Serbia, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

96

Percentage of children age 36-59 months who are developmen-tally on track for indicated domains Early child

development index score

Percentage of children not on track in

any of the four domains

Number of children age 36-59 monthsLiteracy-nu-

meracy Physical Social-Emo-tional Learning

2014

Serbia



Belgrade


Intermediate area (69.0) (100.0) (97.2) (100.0) (97.2) (0.0) 58Thinly- populated area (35.3) (98.1) (86.7) (98.1) (88.6) (1.9) 43

Vojvodina





Intermediate area (50.6) (100.0) (100.0) (100.0) (100.0) (0.0) 30Thinly- populated area 20.8 96.5 92.7 96.2 93.0 3.5 163




Table 29

Early child development index Percentage of children age 36-59 months who are developmentally on track in literacy-numeracy,

physical, social-emotional, and learning domains, and the early child development index score, Serbia 2014

Percentage of children age 36-59 months who are developmen-tally on track for indicated domains Early child

development index score

Percentage of children not on track in

any of the four domains

Number of children age 36-59 months

Literacy-nu-meracy Physical Social-Emo-

tional Learning

Roma settle-ments



Table 30

Early child development index Percentage of children age 36-59 months who are developmentally on track in literacy-numeracy,

physical, social-emotional, and learning domains, and the early child development index score, Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

97

Full [a] NonePercentage with vacci-nation card

seen



1 Densely pop-ulated area 78.7 0.9 84.4 188

2 Intermediate area 88.4 0.7 91.4 110

3 Thinly- popu-lated area 77.5 0.0 86.1 167

Table 31

Vaccinations by background characteristics Percentage of children age 12-23 months currently vaccinated against vaccine preventable

childhood diseases (children age 24-35 months for measles), Serbia, 2014

Full [a] NonePercentage with vacci-nation card

seen






Table 32

Vaccinations by background characteristics Percentage of children age 12-23 months currently vaccinated against vaccine preventable childhood

diseases (children age 24-35 months for measles), Serbia Roma Settlements, 2014


of children 5-171 None 2 Primary 3 Secondary 4 Higher 5 Cannot be

determineddegurba Degree of urbaniza-tion

1 Densely populated area 1.7 7.9 48.3 40.4 1.6 67372 Intermediate area .3 9.9 60.7 25.4 3.6 4728

3 Thinly- populated area .9 20.8 63.9 12.3 1.9 7748

HH7 Region

1 Belgrade

1 Densely populated area .8 7.2 34.8 54.7 2.5 2939

2 Intermediate area .4 4.7 52.8 33.6 8.4 5863 Thinly- populated area 0.0 7.1 77.2 13.5 2.3 820

2 Vojvodina

1 Densely populated area 5.7 15.7 42.3 36.2 .1 1264

2 Intermediate area .2 10.7 62.6 23.0 3.5 22733 Thinly- populated area 4.1 27.0 57.3 9.4 2.3 1576

3 Sumadija and West Serbia

1 Densely populated area .8 6.9 63.2 26.5 2.6 1428

2 Intermediate area 0.0 15.3 62.6 18.3 3.7 7773 Thinly- populated area 0.0 20.1 62.1 15.5 2.2 3078


1 Densely populated area 0.0 2.0 63.9 34.0 .2 1105

2 Intermediate area .8 8.1 60.0 30.2 .9 10923 Thinly- populated area .4 23.4 65.6 9.4 1.1 2274

Table 33

Mother’s education Percentage of children 5-17 Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

98


of children 5-171 None 2 Primary 3 Secondary

or higher5 Cannot be determined





Table 34

Mother’s education Percentage of children 5-17 Serbia Roma Settlements, 2014

Wealth index Education of household head 1.00 Number


yearsFamily frameworks and material conditions of life of children (children age 5-17)

Poorest 60 percent

Rich 40 percent

No school or Primary Secondary Higher 9.00 Missing/

DK

2014

SerbiaDensely populated area 34.9 65.1 14.2 52.8 32.1 0.9 850Intermediate area 52.4 47.6 17.8 64.0 18.3 0.0 655Thinly- populated area 78.5 21.5 36.0 54.7 9.0 0.2 1068

BelgradeDensely populated area 22.6 77.4 11.6 44.6 43.8 0.0 293Intermediate area 57.6 42.4 8.0 66.4 25.6 0.0 84Thinly- populated area 69.4 30.6 17.5 76.2 6.2 0.1 129

VojvodinaDensely populated area 50.9 49.1 21.9 42.3 35.8 0.0 167Intermediate area 48.6 51.4 15.0 69.9 15.1 0.0 332Thinly- populated area 78.9 21.1 43.5 49.9 6.6 0.0 209


Densely populated area 33.7 66.3 13.9 64.9 17.5 3.6 222Intermediate area 53.0 47.0 19.7 68.2 12.1 0.0 94Thinly- populated area 77.2 22.8 31.3 56.1 12.0 0.6 433



Table 35

Percent distribution of household population from households with children age 5-17 - Welath index, and education of househodl head, Serbia, 2014

Family frameworks and material conditions of life of children (children age 5-17)

Wealth index Education of household head 1.00 Number of children age

5-17 yearsPoorest 60 percent Rich 40 percent No school or

PrimarySecondary or

Higher 9.00 Missing/DK

Roma settlements



Table 36

Percent distribution of household population from households with children age 5-17 — Welath index, and education of househodl head, Serbia, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

99

windex2 Wealth index windex5 Wealth index quintile 1.00 Number of children age 5-17

years1.00 Poorest 60 percent

2.00 Richest 40 percent 1 Poorest 2 Second 3 Middle 4 Fourth 5 Richest


1 Densely pop-ulated area 34.9 65.1 8.4 7.6 18.8 27.3 37.9 850

2 Intermediate area 52.4 47.6 11.2 18.9 22.2 22.7 24.9 655

3 Thinly- popu-lated area 78.5 21.5 27.3 27.3 23.9 14.1 7.4 1068

Table 37

Percent distribution of household population from households with children age 5-17 — Welath index Serbia 2014

windex2 Wealth index windex5 Initial (common) wealth index quintile 1.00 Number





1 Densely pop-ulated area 60.9 39.1 17.6 17.4 25.9 20.0 19.1 947

2 Intermediate area 60.5 39.5 19.8 25.0 15.6 22.3 17.2 904

3 Thinly- popu-lated area 75.4 24.6 38.7 21.3 15.3 12.6 12.0 533

Table 38

Percent distribution of household population from households with children age 5-17 — Welath index Serbia, Roma Settlements, 2014

1.00 Percentage of children age 5-11 years

involved in: 1.00 Num-ber of

children age 5-11

years



children age 12-14

years



children age 15-17

years

hhc28less Household chores less

than 28 hours

hhc28more Household chores for 28 hours or

more


than 28 hours


more


than 43 hours


more

2014 Serbia



Table 39

Children’s involvement in household chores Percentage of children by involvement in household chores during the last week,

according to age groups, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

100

1.00 Percentage of children age 5-11 years involved in:


years

1.00 Percentage of children age 12-14 years involved

in: 1.00 Number of children age 12-14

years



years


than 28 hours


more


than 28 hours


more


than 43 hours


more

Roma settle-ments



Table 40

Children’s involvement in household chores Percentage of children by involvement in household chores during the last week,

according to age groups, Serbia, Roma settlements 2014

Percentage of children age 5-11

years involved in economic activity for

at least one hour

Number of children age

5-11 years



years



years Economic activity less

than 14 hours

Economic activity for 14 hours or

more

ea43less Economic

activity less than 43 hours

ea43more Economic activity for 43 hours or

more

2014 Serbia

Densely populated area 4.4 752 4.8 1.2 305 14.4 0.0 324

Intermediate area 9.0 569 12.9 0.0 224 18.7 0.0 265Thinly- populated area 20.5 862 34.5 3.4 436 39.4 0.0 430

Table 41

Children’s involvement in economic activities Percentage of children by involvement in economic activities during the last week,

according to age groups, Serbia, 2014

Percentage of children age 5-11

years involved in economic activity for

at least one hour

Number of children age

5-11 years



years



years Economic activity less

than 14 hours

Economic activity for 14 hours or

more

ea43less Economic

activity less than 43 hours

ea43more Economic activity for 43 hours or

more

Roma settle-ments

Densely populated area 2.5 676 1.2 0.0 233 16.5 0.0 187

Intermediate area 1.2 475 3.8 1.1 237 5.2 0.3 241Thinly- populated area 11.3 345 7.4 0.6 117 10.8 1.8 124

Table 42

Children’s involvement in economic activities Percentage of children by involvement in economic activities during the last week,

according to age groups, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

101

1.00 Children involved in economic activities for a total number of hours

during last week:

1.00 Children involved in household chores for a total number of hours during last

week:

hazardCon-ditions Chil-dren work-ing under hazardous conditions

childLabor Total child labour [1]


yearseaLess

Below the age specific

threshold

eaMore At or above the age specific

threshold

hhcLess Below the

age specific threshold

hhcMore At or above the age specific

threshold

2014

Serbia



Belgrade



Vojvodina









Table 43

Child labour Percentage of children age 5-17 years by involvement in economic activities or household chores

during the last week, percentage working under hazardous conditions during the last week, and percentage engaged in child labour during the last week, Serbia, 2014

1.00 Children involved in economic activities for a total

number of hours during last week:

1.00 Children involved in household chores for a total number of hours during last

week:hazardConditions Children working under hazardous

conditions

childLabor Total child labour [1]


yearseaLess Below the age spe-cific threshold

eaMore At or above the age specific

threshold

hhcLess Below the

age specific threshold

hhcMore At or above the age specific

threshold

Roma settlements



Table 44

Child labour Percentage of children age 5-17 years by involvement in economic activities or household chores

during the last week, percentage working under hazardous conditions during the last week, and percentage engaged in child labour during the last week, Serbia, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

102

Child Discipline

1.00 Percentage of children age 5-14 years who experienced:1.00 Number of children age 5-14

years

nonViolent Only non-vi-olent disci-

pline

anyPsycho-logical Psy-chological aggression

1.00 Physical punishment anyViolent Any violent discipline

method [1]anyPhysical

AnyseverePhysi-cal Severe

2014

Serbia


Intermediate area 56.7 34.4 11.8 .4 36.1 793Thinly- populated area 48.1 38.1 11.2 .8 40.6 1299

Belgrade

Densely populated area 47.7 40.9 19.3 .5 49.2 336


Vojvodina


Intermediate area 49.0 43.1 11.0 .6 44.3 414Thinly- populated area 52.3 36.9 15.9 2.6 40.3 273



Intermediate area 63.9 23.2 12.3 .4 26.4 107Thinly- populated area 49.0 33.5 6.6 .7 34.3 528


Densely populated area 52.6 34.4 11.6 .1 38.6 221


Table 45


during the last one month, Serbia, 2014

1.00 Percentage of children age 5-14 years who experienced:1.00 Number of children age 5-14

years

nonViolent Only non-vi-olent disci-

pline

anyPsycho-logical Psy-chological aggression

1.00 Physical punishment anyViolent Any violent discipline

method [1]anyPhysical

AnyseverePhysi-cal Severe

Roma settlements



Table 46


during the last one month, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

103

firstGrade Percentage of

children attend-ing first grade who attended

preschool in pre-vious year [1]

1.00 Number of children attend-ing first grade of primary school


1 Densely pop-ulated area 98.6 91

2 Intermediate area 99.6 45

3 Thinly- popu-lated area 96.8 81

Table 47

School readiness Percentage of children attending first grade of primary school who attended pre-school the

previous year, Serbia, 2014

firstGrade Percentage of

children attend-ing first grade who attended

preschool in pre-vious year [1]

1.00 Number of children attend-ing first grade of primary school





Table 48

School readiness Percentage of children attending first grade of primary school who attended pre-school the previous year, Serbia Roma Settlements, 2014

firstGrade Per-centage of chil-dren of primary

school entry age entering grade

1 [1]

1.00 Number of children of

primary school entry age





Table 49

Primary school entry Percentage of children of primary school

entry age entering grade 1 (net intake rate), Serbia, 2014

firstGrade Percentage of children of primary

school entry age entering grade 1 [1]

1.00 Number of children of

primary school entry age





Table 50

Primary school entry Percentage of children of primary school

entry age entering grade 1 (net intake rate), Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

104

1 M

ale

2 Fe

mal

eTo

tal

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

degu

rba

Deg

ree

of

urba

niza

-tio

n

1 D

ense

ly p

op-

ulat

ed a

rea

99.6

0.3

0.1

0.4

235

98.0

0.1

0.0

0.1

281

98.7

0.2

0.0

0.2

516

2 In

term

edia

te

area

99.9

0.1

0.0

0.1

211

99.2

0.1

0.7

0.8

194

99.6

0.1

0.3

0.4

404

3 Th

inly

- pop

u-la

ted

area

98.2

1.8

0.0

1.8

316

97.1

1.7

0.6

2.3

334

97.6

1.7

0.3

2.0

650

[1] S

urve

y-sp

ecifi

c in

dica

tor 7

.S4

— P

rimar

y sc

hool

net

atte

ndan

ce ra

tio (a

djus

ted)

[a

] The

per

cent

age

of c

hild

ren

of p

rimar

y sc

hool

age

out

of s

choo

l are

thos

e no

t atte

ndin

g sc

hool

and

thos

e at

tend

ing

pres

choo

l

Tab

le 5

1

Prim

ary

scho

ol a

ttend

ance

and

out

of s

choo

l chi

ldre

n Pe

rcen

tage

of c

hild

ren

of p

rimar

y sc

hool

age

atte

ndin

g pr

imar

y or

sec

onda

ry s

choo

l (ad

just

ed n

et a

ttend

ance

ratio

), pe

rcen

tage

atte

ndin

g pr

esch

ool,

and

perc

enta

ge o

ut o

f sch

ool,

Serb

ia, 2

014

1 M

ale

2 Fe

mal

eTo

tal

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Net

at

tend

-an

ce

ratio

(a

djus

t-ed

) [1]

1.00

Per

cent

age

of c

hild

ren:

Num

ber

of c

hil-

dren

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

Not

at

tend

ing

scho

ol

or p

re-

scho

ol

Atte

nd-

ing

pre-

scho

ol

Out

of

scho

ol

[a]

degu

rba

Deg

ree

of

urba

niza

-tio

n

1 D

ense

ly p

op-

ulat

ed a

rea

85.2

12.5

2.0

14.5

277

87.0

10.6

1.7

12.4

321

86.2

11.5

1.9

13.4

599

2 In

term

edia

te

area

84.2

15.8

0.0

15.8

272

82.1

17.0

0.7

17.7

334

83.0

16.5

0.4

16.8

606

3 Th

inly

- pop

u-la

ted

area

83.8

15.8

0.4

16.2

155

87.8

10.8

1.4

12.2

164

85.9

13.2

0.9

14.1

320

[1] S

urve

y-sp

ecifi

c in

dica

tor 7

.S4

— P

rimar

y sc

hool

net

atte

ndan

ce ra

tio (a

djus

ted)

[a

] The

per

cent

age

of c

hild

ren

of p

rimar

y sc

hool

age

out

of s

choo

l are

thos

e no

t atte

ndin

g sc

hool

and

thos

e at

tend

ing

pres

choo

l

Tab

le 5

2

Prim

ary

scho

ol a

ttend

ance

and

out

of s

choo

l chi

ldre

n Pe

rcen

tage

of c

hild

ren

of p

rimar

y sc

hool

age

atte

ndin

g pr

imar

y or

sec

onda

ry s

choo

l (ad

just

ed n

et a

ttend

ance

ratio

), pe

rcen

tage

at

tend

ing

pres

choo

l, an

d pe

rcen

tage

out

of s

choo

l, Se

rbia

Rom

a Se

ttlem

ents

, 201

4

The

Ana

lysis

of M

ICS

da

ta

105

1 Male 2 Female Total

Net attend-ance ratio (adjusted)

[1]

Percentage of children: Attending primary school

Percentage of children:

Out of school [a]

Number of children


[1]



Out of school [a]

Number of children


[1]


1 Densely pop-ulated area 85.5 1.2 12.0 152 94.0 0.2 5.7 129 89.4

2 Intermediate area 86.3 6.5 7.2 122 93.2 3.7 3.2 108 89.5

3 Thinly- popu-lated area 86.1 1.4 12.5 224 92.1 3.2 3.8 165 88.6

[1] Survey-specific indicator 7.S5 — Secondary school net attendance ratio (adjusted) [a] The percentage of children of secondary school age out of school are those who are not attending primary, secondary, or higher education [b] Children age 15 or higher at the time of the interview whose mothers were not living in the household

Table 53

Secondary school attendance and out of school children Percentage of children of secondary school age attending secondary school or higher (adjusted net attendance ratio), percentage attending primary school, and percentage out of school, Serbia, 2014

1 Male 2 Female Total


[1]



Out of school [a]

Number of children


[1]



Out of school [a]

Number of children


[1]


1 Densely pop-ulated area 27.3 21.9 50.0 128 21.0 10.0 67.0 114 24.3

2 Intermediate area 34.3 12.5 53.2 129 9.5 14.3 76.2 131 21.8

3 Thinly- popu-lated area 18.8 11.0 69.4 78 14.9 8.4 76.6 74 16.9

[1] Survey-specific indicator 7.S5 — Secondary school net attendance ratio (adjusted) [a] The percentage of children of secondary school age out of school are those who are not attending primary, secondary, or higher education [b] Children age 15 or higher at the time of the interview whose mothers were not living in the household

Table 54

Secondary school attendance and out of school children Percentage of children of secondary school age attending secondary school or higher (adjusted net attendance ratio), percentage attending primary school, and percentage out of school, Serbia Roma

Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

106

1.00 Primary school 1.00 Secondary school

outOfPrimar-ySchool Per-centage of

out of school children

numPrimar-ySchoolAge Number of

children of primary school age

girlsInOut-OfPrima-rySchool

Percentage of girls in

the total out of school

population of primary school age

numOut-OfPrima-rySchool

Number of children

of primary school age

out of school

outOfSec-ondary-

School Per-centage of


numSec-ondary-

SchoolAge Number of children of secondary school age

girlsInOut-OfSecond-arySchool



population of second-ary school

age

numOut-OfSecond-arySchool Number of children of secondary school age

out of school


1 Densely pop-ulated area 0.2 516 (*) 1 9.2 281 (*) 26

2 Intermediate area 0.4 404 (*) 2 5.3 230 (*) 12

3 Thinly- popu-lated area 2.0 650 (*) 13 8.8 390 (18.3) 34

[a] Children age 15 or higher at the time of the interview whose mothers were not living in the household

Table 55

Out of school gender parity Percentage of girls in the total out of school population, in primary and secondary school, Serbia, 2014

1.00 Primary school 1.00 Secondary school

outOfPrimar-ySchool Per-centage of


numPrimar-ySchoolAge Number of

children of primary school age

girlsInOut-OfPrima-rySchool



population of primary school age

numOut-OfPrima-rySchool

Number of children

of primary school age

out of school

outOfSec-ondary-

School Per-centage of


numSec-ondary-

SchoolAge Number of children of secondary school age

girlsInOut-OfSecond-arySchool



population of second-ary school

age

numOut-OfSecond-arySchool Number of children of secondary school age

out of school


1 Densely pop-ulated area 13.4 599 49.6 80 58.0 243 54.4 141

2 Intermediate area 16.8 606 57.7 102 64.8 260 59.2 168

3 Thinly- popu-lated area 14.1 320 44.3 45 72.9 153 51.2 111

Table 56

Out of school gender parity Percentage of girls in the total out of school population, in primary and secondary school,

Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

107

primarySchool-CompletionRate Primary school

completion rate [1]

denominator Number of chil-dren of primary school comple-

tion age

transitionRate-ToSecondary Transition rate to secondary

school [2]

inLastPrima-ryGrade Number of children who were in the last

grade of primary school the previ-

ous year

effectiveTransi-tionRateToSec-

ondary Effective transition rate to

secondary school

adjInLastPrima-ryGrade Number of children who were in the last

grade of primary school the pre-vious year and

are not repeating that grade in the current school

year



(92.7) 42 (92.9) 75 (92.9) 75

2 Interme-diate area (85.8) 46 (100.0) 51 (100.0) 51

3 Thinly- populated area

99.4 63 97.1 90 97.1 90

[1] Survey indicator 7.14 — Primary completion rate [2] Survey indicator 7.15 — Transition rate to secondary school

Table 57

Primary school completion and transition to secondary school Primary school completion rates and transition and effective transition rates to secondary

school, Serbia, 2014

primarySchool-CompletionRate Primary school

completion rate [1]

denominator Number of chil-dren of primary school comple-

tion age

transitionRate-ToSecondary Transition rate to secondary

school [2]

inLastPrima-ryGrade Number of children who were in the last

grade of primary school the previ-

ous year

effectiveTransi-tionRateToSec-

ondary Effective transition rate to

secondary school

adjInLastPrima-ryGrade Number of children who were in the last

grade of primary school the pre-vious year and

are not repeating that grade in the current school

year


1 Densely pop-ulated area 61.0 59 (53.9) 45 (59.3) 40

2 Intermediate area 56.0 69 (63.7) 27 (63.7) 27

3 Thinly- popu-lated area (90.5) 27 (*) 20 (*) 19

[1] Survey indicator 7.14 — Primary completion rate [2] Survey indicator 7.15 — Transition rate to secondary school

Table 58

Primary school completion and transition to secondary school Primary school completion rates and transition and effective transition rates to secondary

school, Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

108

pgirls Primary school adjusted net attendance ratio (NAR), girls

pboys Primary school adjusted net attendance

ratio (NAR), boys

primaryGPI Gen-der parity index (GPI) for primary school adjusted

NAR [1]

sgirls Secondary school adjusted net attendance ratio (NAR), girls

sboys Secondary school adjusted net attendance

ratio (NAR), boys

secondaryGPI Gender parity index (GPI) for

secondary school adjusted NAR [2]


1 Densely pop-ulated area 98.0 99.6 0.98 94.0 85.5 1.10

2 Intermediate area 99.2 99.9 0.99 93.2 86.3 1.08

3 Thinly- popu-lated area 97.1 98.2 0.99 91.9 86.1 1.07

[1] Survey-specific indicator 7.S9 — Gender parity index (primary school) [2] Survey-specific indicator 7.S10 — Gender parity index (secondary school) [a] Children age 15 or higher at the time of the interview whose mothers were not living in the household

Table 59

Education gender parity Ratio of adjusted net attendance ratios of girls to boys, in primary and secondary school, Serbia, 2014

pgirls Primary school adjusted net attendance ratio (NAR), girls

pboys Primary school adjusted net attendance

ratio (NAR), boys

primaryGPI Gen-der parity index (GPI) for primary school adjusted

NAR [1]

sgirls Secondary school adjusted net attendance ratio (NAR), girls

sboys Secondary school adjusted net attendance

ratio (NAR), boys

secondaryGPI Gender parity index (GPI) for

secondary school adjusted NAR [2]


1 Densely pop-ulated area 87.0 85.2 1.02 21.0 27.3 0.77

2 Intermediate area 82.1 84.2 0.98 9.5 34.3 0.28

3 Thinly- popu-lated area 87.8 83.8 1.05 14.9 18.8 0.79

[1] Survey-specific indicator 7.S9 — Gender parity index (primary school) [2] Survey-specific indicator 7.S10 — Gender parity index (secondary school) [a] Children age 15 or higher at the time of the interview whose mothers were not living in the household

Table 60

Education gender parity Ratio of adjusted net attendance ratios of girls to boys, in primary and secondary school,

Serbia Roma Settlements, 2014

1.00 Women age 15-49 years 1.00 Women age 20-49 years 1.00 Women age 15-19

years

before15_1 Percentage

married before age

15 [1]

1.00 Number of women age 15-49

years

before15 Percentage married be-fore age 15

before18 Percentage

married before age

18 [2]


years

current-lyMarried

Percentage currently

married/in union [3]


years

2014

general

Densely populated area 0.6 1746 0.7 4.9 1572 4.7 174

Intermediate area 0.4 1158 0.5 4.8 1031 1.6 128Thinly- populated area 1.1 1809 1.2 10.0 1595 3.7 213

Table 61

Early marriage Percentage of women age 15-49 years who first married or entered a marital union before their 15th

birthday, percentages of women age 20-49 years who first married or entered a marital union before their 15th and 18th birthdays, and the percentage of women age 15-19 years currently married or in union,

Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

109

1.00 Women age 15-49 years 1.00 Women age 20-49 years 1.00 Women age 15-19 yearsbefore15_1 Percentage

married before age

15 [1]


years

before15 Percentage married be-fore age 15

before18 Percentage

married before age

18 [2]


years

currentlyMarried Percentage cur-rently married/in

union [3]


years

Roma Set-tlements

Densely populated area 18.4 736 18.6 59.9 606 40.5 130

Intermediate area 14.4 834 14.4 54.3 671 41.1 163Thinly- populated area 18.9 511 20.1 57.3 422 48.8 89

Table 62

Early marriage Percentage of women age 15-49 years who first married or entered a marital union before their 15th

birthday, percentages of women age 20-49 years who first married or entered a marital union before their 15th and 18th birthdays, and the percentage of women age 15-19 years currently married or in union, Serbia,

Roma Settlements 2014

WOMEN: DISPARITIES AND GAPS Family frameworks and material conditions of life 15-24

windex2 Wealth index windex5 Wealth index quintile 1.00 Number of women age 15-24



2014

Serbia



Belgrade

Densely populated area 21.1 78.9 3.0 .3 17.9 32.9 45.9 157

Intermediate area (*) (*) (*) (*) (*) (*) (*) 29Thinly- populated area (64.5) (35.5) (9.5) (16.3) (38.6) (31.6) (3.9) 46

Vojvodina







Densely populated area (48.4) (51.6) (0.6) (25.4) (22.4) (30.6) (21.1) 66


Table 63

Woman 15-24, Welath index Serbia 2014

The

Ana

lysis

of M

ICS

da

ta

110

windex2 Wealth index windex5 Wealth index quintile 1.00 Number

of women age 15-24



Roma settle-ments

Densely popu-lated area 50.3 49.7 19.3 13.8 17.2 25.4 24.3 267


Thinly- popu-lated area 75.6 24.4 35.7 25.0 14.9 10.4 14.0 181

Table 64

Woman 15-24, Welath index Serbia, Roma settlements, 2014





2014

Serbia



Belgrade



Vojvodina



Šumadija and West-ern Serbia






Table 65





Roma settlements



Table 66


The

Ana

lysis

of M

ICS

da

ta

111



yearsPoorest 60 percent

Richest 40 percent 1 Poorest 2 Second 3 Middle 4 Fourth 5 Richest

2014

Serbia



Belgrade


Intermediate area (49.9) (50.1) (8.1) (28.1) (13.6) (40.9) (9.3) 64Thinly- populated area 72.0 28.0 7.8 21.2 43.0 19.2 8.8 110

Vojvodina









Table 67



yearsPoorest 60 percent

Richest 40 percent 1 Poorest 2 Second 3 Middle 4 Fourth 5 Richest

Roma settle-ments



Table 68


The

Ana

lysis

of M

ICS

da

ta

112


goSchool Are attending

schoolhaveJob Have

a jobhaveIncome

Have an income

numWomen Number of

women age 15-24 years

2014

SerbiaDensely populated area 74.5 12.7 25.4 393Intermediate area 77.5 8.5 30.2 261Thinly- populated area 66.0 11.6 23.0 423

BelgradeDensely populated area 81.6 17.2 24.3 157Intermediate area (*) (*) (*) 29Thinly- populated area (66.1) (25.5) (37.2) 46

VojvodinaDensely populated area 68.2 8.2 37.8 73Intermediate area 77.7 8.3 47.9 117Thinly- populated area 67.9 12.9 37.1 84


Densely populated area 69.1 11.4 26.3 97Intermediate area 76.8 11.3 16.7 55Thinly- populated area 64.1 10.5 17.2 178


Densely populated area (72.3) (9.2) (13.0) 66Intermediate area 68.1 6.8 12.9 60Thinly- populated area 67.6 7.0 15.9 116

Table 69

Activity status women 15-24, Serbia, 2014

goSchool Are attending

schoolhaveJob Have

a jobhaveIncome

Have an income

numWomen Number of


Roma settlements

Densely populated area 14.0 3.1 11.9 267

Intermediate area 13.6 0.7 21.5 310Thinly- populated area 9.7 2.5 17.5 181

Table 70

Activity status women 15-24, Serbia, Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

113

WO

MEN

: DIS

PARI

TIES

AN

D G

APS

: Edu

-ca

tion:

15-

49

Wom

ens a

ge1.

00 1

5-24

2.00

25-

353.

00 3

6-49

New

Wel

evel

Wom

ens e

duca

tion

1.00

Num

-be

r of

wom

en

New

Wel

evel

Wom

ens e

duca

tion

1.00

Num

-be

r of

wom

en

New

Wel

evel

Wom

ens e

duca

tion

1.00

Num

-be

r of

wom

en1.

00 N

one

or p

rimar

y2.

00 S

ec-

onda

ry3.

00

High

er1.

00 N

one

or p

rimar

y2.

00 S

ec-

onda

ry3.

00

High

er9.

00

Miss

ing

1.00

Non

e or

prim

ary

2.00

Sec

-on

dary

3.00

Hi

gher

2014

Serb

ia

Den

sely

pop

ulat

ed

area

6.5

46.5

47.0

393

5.2

34.8

60.0

0.0

630

6.8

55.6

37.6

723

Inte

rmed

iate

are

a4.

056

.839

.226

16.

953

.739

.40.

035

810

.662

.726

.754

0Th

inly

- pop

ulat

ed

area

5.7

68.7

25.6

423

18.3

53.2

28.4

0.0

536

20.2

64.2

15.6

850

Belg

rade

Den

sely

pop

ulat

ed

area

4.5

33.8

61.6

157

4.3

24.6

71.1

0.1

286

4.2

45.6

50.2

311

Inte

rmed

iate

are

a(*

)(*

)(*

)29

3.2

50.9

45.9

0.0

38(2

.7)

(60.

5)(3

6.8)

64Th

inly

- pop

ulat

ed

area

(1.0

)(8

3.0)

(16.

1)46

6.4

53.1

40.5

0.0

643.

881

.215

.011

0

Voj

vodi

na

Den

sely

pop

ulat

ed

area

16.6

39.5

43.9

7310

.334

.954

.80.

011

613

.055

.431

.613

1

Inte

rmed

iate

are

a4.

555

.140

.411

79.

356

.833

.90.

018

17.

169

.523

.324

5Th

inly

- pop

ulat

ed

area

7.3

67.8

25.0

8427

.247

.325

.50.

011

120

.260

.119

.718

1

Šum

adija

an

d W

est-

ern

Serb

ia

Den

sely

pop

ulat

ed

area

4.7

48.2

47.1

975.

751

.542

.80.

012

99.

461

.329

.315

5

Inte

rmed

iate

are

a4.

149

.945

.955

3.6

43.9

52.5

0.0

4718

.857

.523

.798

Thin

ly- p

opul

ated

ar

ea4.

564

.630

.917

815

.357

.127

.60.

021

224

.358

.916

.832

2

Sout

hern

an

d Ea

ster

n Se

rbia

Den

sely

pop

ulat

ed

area

(2.6

)(8

2.1)

(15.

3)66

1.2

42.3

56.5

0.0

983.

473

.623

.012

5

Inte

rmed

iate

are

a4.

476

.219

.460

5.3

53.7

41.0

0.0

9214

.655

.130

.313

4Th

inly

- pop

ulat

ed

area

8.1

70.2

21.7

116

21.3

52.1

26.6

0.0

149

22.2

66.6

11.2

237

Tab

le 7

1

Leve

l of e

duca

tion

(you

ng w

omen

) Pe

rcen

tage

of w

omen

age

15-

49 y

ears

by

area

of r

esid

ence

, age

gro

ups

and

educ

atio

n le

vel,

Serb

ia, 2

014

The

Ana

lysis

of M

ICS

da

ta

114

Womens age1.00 15-24 2.00 25-35 3.00 36-49

NewWelevel Womens education 1.00

Number of women

NewWelevel Womens education1.00

Number of women

NewWelevel Womens education 1.00

Number of women1.00 None

or primary2.00 Sec-ondary or

higher1.00 None or primary

2.00 Sec-ondary or

higher9.00

Missing1.00 None or primary

2.00 Sec-ondary or

higher

Roma settle-ments

Densely popu-lated area 82.6 17.4 267 91.7 8.3 0.0 247 91.9 8.1 222

Intermediate area 82.0 18.0 310 89.5 10.2 0.3 221 85.7 14.3 302

Thinly- popu-lated area 84.5 15.5 181 92.3 7.7 0.0 156 90.5 9.1 175

Table 72

Level of education (young women) Percentage of women age 15-49 years by area of residence, age groups and education level,

Roma settlements 2014

NewWelevel Womens education

1.00 Number of women1.00 None or

primary2.00 Second-

ary 3.00 Higher 9.00 Missing


1 Densely populated area 6.1 46.0 47.8 .0 17462 Intermediate area 8.0 58.6 33.5 0.0 11583 Thinly- populated area 16.2 62.0 21.8 0.0 1809

HH7 Region

1 Belgrade

1 Densely pop-ulated area 4.3 35.2 60.5 .0 755



2 Vojvodina












Table 73

Education (young women) Percentage of women age 15-49 years by area of residence, and education level, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

115

WOMEN: DISPARITIES AND GAPSanyModern Any modern

method

anyTraditional Any traditional

method

anyMethod Any method

[1]

Number of women age 15-24 years

currently married or in

union

2014 Serbia



Table 74

Use of contraception Percentage of women age 15-24 years currently married or in union who are using

(or whose partner is using) a contraceptive method, Serbia, 2014

anyModern Any modern

method


methodanyMethod

Any method [1]

Number of women age 15-24 years

currently married or in

union

Roma settlementsDensely populated area 7.0 33.9 40.9 150Intermediate area 7.1 42.2 49.3 173Thinly- populated area 5.7 38.0 43.7 98

Table 75

Use of contraception Percentage of women age 15-24 years currently married or in union who are

using (or whose partner is using) a contraceptive method, Roma, 2014

pctAbortedWomen Percentage of wom-en with at least one induced abortion [1]

numWomen Number of

women age 15-24

2014 Serbia

Densely populated area 0.9 393

Intermediate area 0.7 261Thinly- populated area 0.8 423

Table 76

Experience with abortions Percentage of women age 15-24 years who have

ever had an induced abortion by area Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

116


numWomen Number of

women age 15-24

Roma settlements



Table 77

Experience with abortions Percentage of women age 15-24 years who have ever had an induced abortion

by area Roma settlements, 2014

WOMEN: DISPARITIES AND GAPS 15-24

.00 Met need for contraception .00 Unmet need for contraception 1.00 Number of women currently

married or in union

metspc For spacing

metlmt For limiting met Total space For

spacinglimit For limiting

unmetnd Total [1]

2014 Serbia



Table 78

Unmet need for contraception Percentage of women age 15-24 years currently married or in union with an unmet need

for family planning by area, Serbia, 2014

.00 Met need for contraception .00 Unmet need for contraception 1.00 Number of women cur-rently married

or in unionmetspc For

spacingmetlmt For

limiting met Total space For spacing

limit For limiting

unmetnd Total [1]

Roma settlements



Table 79


for family planning by area, Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

117

WOMEN: DISPARITIES AND GAPS 25-35anyModern Any modern

method


method


[1]

numWomen Number of

women cur-rently married

or in union

2014


BelgradeDensely populated area 37.1 12.8 49.9 142Intermediate area 30.7 13.9 44.6 31Thinly- populated area 19.6 21.4 41.0 45






Table 80




method


method


[1]

numWomen Number of


or in union

Roma settlements



Table 81


(or whose partner is using) a contraceptive method,Roma settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

118

WOMEN: DISPARITIES AND GAPS: Marriage/union and family planning: 25-35


Women Number of women age 25-35

years

2014 Serbia



Table 82


by area Serbia, 2014


numWomen Number of


Roma settlements



Table 83


by area Roma settlements, 2014

WOMEN: DISPARITIES AND GAPS 25-35.00 Met need for contraception .00 Unmet need for contraception 1.00 Number

of women cur-rently married

or in unionmetspc For

spacingmetlmt For

limiting met Total space For spacing

limit For limiting

unmetnd Total [1]

2014

SerbiaDensely populated area 35.8 20.0 55.8 7.5 4.7 12.2 380Intermediate area 30.3 26.9 57.1 6.3 6.6 12.9 257Thinly- populated area 26.2 35.8 62.0 6.5 8.4 14.9 390

BelgradeDensely populated area 42.4 7.6 49.9 7.7 8.3 16.0 142Intermediate area 21.7 22.9 44.6 11.5 4.3 15.7 31Thinly- populated area 22.4 18.6 41.0 11.7 17.5 29.1 45

VojvodinaDensely populated area 36.3 19.1 55.4 5.4 1.6 6.9 83Intermediate area 30.4 27.0 57.5 6.5 5.8 12.3 130Thinly- populated area 19.9 35.1 55.0 7.5 10.9 18.5 71

Šumadi-ja and Western Serbia

Densely populated area 25.3 30.5 55.8 11.7 4.6 16.3 94Intermediate area 42.4 20.5 62.9 4.9 7.5 12.3 27

Thinly- populated area 27.8 31.4 59.2 6.4 6.5 12.9 157

Southern and East-ern Serbia


Table 84



The

Ana

lysis

of M

ICS

da

ta

119


married or in union

metspc For spacing



unmetnd Total [1]

Roma settlements



Table 85


for family planning by area, Roma settlements, 2014


method


method


[1]

numWomen Number of


or in union

2014


BelgradeDensely populated area 24.2 24.7 48.9 215Intermediate area (21.5) (22.7) (44.2) 59Thinly- populated area 17.6 14.5 32.2 89






Table 86



The

Ana

lysis

of M

ICS

da

ta

120


method


method


[1]

numWomen Number of


or in union

Roma settlements



Table 87


(or whose partner is using) a contraceptive method, Roma settlements, 2014

WOMEN: DISPARITIES AND GAPS: Mar-riage/union and family planning: 36-49

pctAbortedWom-en Percentage

of women with at least one induced

abortion [1]

numWomen Num-ber of women age

36-49

2014 Serbia



Table 88


by area Serbia, 2014


numWomen Number of women

age 36-49

Roma settlements



Table 89


by area Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

121

WOMEN: DISPARITIES AND GAPS 36-49


married or in union

metspc For spacing



unmetnd Total [1]

2014

Serbia



Belgrade


Intermediate area (3.1) (41.1) (44.2) (3.0) (31.3) (34.3) 59Thinly- populated area 1.0 31.1 32.2 0.0 32.5 32.5 89

Vojvodina



Šumadi-ja and Western Serbia



Southern and East-ern Serbia



Table 90




married or in union

metspc For spacing



unmetnd Total [1]

Roma settlements



Table 91


for family planning by area, Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

122


method

anyTradi-tional Any traditional method


[1]

numWomen Number

of women currently

married or in union


1 Densely populated area 24.2 34.5 58.7 9652 Intermediate area 18.7 37.9 56.6 7163 Thinly- populated area 13.5 45.8 59.3 1166

HH7 Region

1 Belgradedegurba Degree of urbanization




2 Vojvodinadegurba Degree of urbanization














Table 92



WOMEN: DISPARITIES AND GAPS: Motherhood 15-49

Percent distribution of women who had:

total100 Total

numWomen Number of

women with a live birth in the last two

years

No anten-etal care

visitsOne visit Two visits Three visits 4 or more

visits9.00 Missing/

DK

2014 Serbia



Table 93

Number of antenatal care visits Percent distribution of women age 15-49 years with a live birth in the last two years by number of antenatal

care visits by any provider and by the timing of first antenatal care visits, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

123

Percent distribution of women who had:

total100 Total

numWomen Number of

women with a live birth in the last two

years

No anten-etal care

visitsOne visit Two visits Three visits 4 or more

visits9.00 Missing/

DK

Roma settle-ments

Densely popu-lated area 5.4 4.8 7.2 9.8 70.4 2.5 100.0 143


Thinly- popu-lated area 8.4 3.0 10.9 13.1 63.7 0.9 100.0 95

Table 94

Number of antenatal care visits Percent distribution of women age 15-49 years with a live birth in the last two years by number of antenatal

care visits by any provider and by the timing of first antenatal care visits, Roma settlements, 2014

WOMEN: DISPARITIES AND GAPS: Motherhood 15-49

programe Percentage of women

who attended a childbirth preparation programme

[1]

1.00 Number of women age

15-49 years with live birth in the last 2

years

2014 Serbia

Densely populated area 24.0 152Intermediate area 5.0 91Thinly- populated area 9.1 141

Table 95

Counselling during childbirth preparation programme Percentage of women age 15-49 years with a live birth in the last two years who attended

a childbirth preparation programme, Serbia, 2014

programe Percentage of women

who attended a childbirth preparation programme

[1]

1.00 Number of women age 15-49 years with live birth in the last 2

years

Roma settlements



Table 96

Counselling during childbirth preparation programme Percentage of women age 15-49 years with a live birth in the last two years who attended

a childbirth preparation programme, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

124

WOMEN: DISPARITIES AND GAPS: Attitudes towards domestic violence 15-24

1.00 Percentage of women age 15-24 years who believe a husband is justified in beating his wife: 1.00 Number

of women age 15-24

years

goesOut If she goes

out without telling him

neglectsKids If she ne-glects the children

sheArgues If she argues

with him

refusesSex If she refuses

sex with him

burnsFood If she burns

the food

anyReason For any of these five

reasons [1]

2014 Serbia



Table 97

Attitudes toward domestic violence (women) Percentage of women age 15-24 years who believe a husband is justified

in beating his wife in various circumstances, Serbia, 2014



of women age 15-24

years

goesOut If she goes




with him


sex with him


the food


reasons [1]

2014 Roma settlements



Table 98

Attitudes toward domestic violence (women) Percentage of women age 15-24 years who believe a husband is justified in beating his wife

in various circumstances,Roma settlements, 2014



of women age 25-35

years

goesOut If she goes




with him


sex with him


the food


reasons [1]

2014 Serbia

Densely populated area .6 2.2 .5 .4 .4 2.3 630

Intermediate area .7 3.1 1.0 .1 .1 3.7 358Thinly- populated area 2.1 4.5 1.9 .6 .6 5.3 536

Table 99


in various circumstances, Serbia, 2014

The

Ana

lysis

of M

ICS

da

ta

125


of women age 25-35

years

goesOut If she goes




with him


sex with him


the food


reasons [1]

2014 Roma settle-

ments


Table 100





of women age 36-49

years

goesOut If she goes




with him


sex with him


the food


reasons [1]

2014 Serbia

Densely populated area .6 1.7 1.3 .8 .0 2.3 723Intermediate area .4 2.9 .3 .2 .2 3.0 540Thinly- populated area 1.9 4.9 2.3 2.2 1.7 6.2 850

Table 101





of women age 36-49

years

goesOut If she goes




with him


sex with him


the food


reasons [1]

2014 Roma settle-

ments


Table 102


in various circumstances, Roma settlements 2014

The

Ana

lysis

of M

ICS

da

ta

126

.00

Perc

enta

ge o

f res

pond

ents

who

bel

ieve

that

chi

ldre

n w

ith p

hysic

al o

r sen

sory

dia

bilit

ies:

Perc

ent-

age

of

resp

ond-

ents

who

ex

pres

s po

sitiv

e at

titud

es

tow

ard

child

ren

with

phy

s-ic

al a

nd

sens

ory

disa

bilit

ies

on a

ll fiv

e st

ate-

men

ts

.00

Perc

enta

ge o

f res

pond

ents

who

bel

ieve

that

chi

ldre

n w

ith in

telle

ctua

l dia

bilit

ies:

Perc

ent-

age

of

resp

ond-

ents

who

ex

pres

s po

sitiv

e at

titud

es

tow

ard

child

ren

with

phy

s-ic

al a

nd

sens

ory

disa

bilit

ies

on a

ll fiv

e st

ate-

men

ts

Num

ber o

f re

spon

d-en

ts to

the

hous

ehol

d qu

estio

n-na

ire

Are

bet

ter

off t

o liv

e in

the

fam

ily

rath

er

than

in

a sp

e-ci

alise

d ch

ild c

are

inst

itutio

n

Do n

ot

have

a

nega

tive

impa

ct o

n th

e ev

ery-

day

life

of o

ther

ch

ildre

n in

th

e fa

mily

Are

be

tter o

ff to

atte

nd

mai

n-st

ream

sc

hool

s th

an

spec

ial

scho

ols

Atte

ndin

g m

ain-

stre

am

scho

ols

do n

ot

have

a

nega

tive

impa

ct o

n th

e w

ork

of o

ther

st

uden

ts

Can

ac

hiev

e a

lot i

n lif

e if

they

are

ad

e-qu

atel

y su

ppor

ted

Are

bet

ter

off t

o liv

e in

the

fam

ily

rath

er

than

in

a sp

e-ci

alise

d ch

ild c

are

inst

itutio

n

Do

not

have

a

nega

tive

impa

ct o

n th

e ev

ery-

day

life

of o

ther

ch

ildre

n in

th

e fa

mily

Are

bet

ter

off t

o at

tend

m

ain-

stre

am

scho

ols

than

sp

ecia

l sc

hool

s

Atte

ndin

g m

ain-

stre

am

scho

ols

do n

ot

have

a

nega

tive

impa

ct o

n th

e w

ork

of o

ther

st

uden

ts

Can

ac

hiev

e a

lot i

n lif

e if

they

are

ad

e-qu

atel

y su

ppor

ted

2014

Serb

ia

Den

sely

pop

ulat

ed

area

91.2

81.3

51.6

66.4

95.4

40.6

82.3

72.5

32.9

49.0

89.8

22.4

2260

Inte

rmed

iate

are

a87

.675

.547

.862

.194

.433

.578

.766

.031

.946

.289

.818

.115

93Th

inly

- pop

ulat

ed

area

82.7

73.2

43.6

57.9

95.6

29.9

75.7

64.1

30.3

42.1

91.6

18.2

2338

Belg

rade

Den

sely

pop

ulat

ed

area

92.4

82.8

54.3

67.9

92.4

42.2

85.2

74.0

30.5

52.2

86.1

22.1

1045

Inte

rmed

iate

are

a78

.274

.538

.455

.682

.625

.076

.666

.029

.943

.375

.214

.417

3Th

inly

- pop

ulat

ed

area

86.9

77.5

49.0

62.0

93.5

33.7

87.1

74.8

32.0

42.6

88.6

18.4

240

Voj

vodi

na

Den

sely

pop

ulat

ed

area

91.8

82.9

44.5

65.7

98.5

38.6

82.7

75.1

30.4

46.7

92.0

21.7

458

Inte

rmed

iate

are

a91

.579

.048

.866

.997

.138

.681

.372

.031

.847

.793

.619

.578

8Th

inly

- pop

ulat

ed

area

86.6

73.0

40.2

62.4

96.7

30.2

78.5

65.6

26.1

45.0

93.8

17.5

539

Šum

adija

an

d W

est-

ern

Serb

ia

Den

sely

pop

ulat

ed

area

86.7

76.8

49.4

64.6

96.3

37.0

77.1

71.3

38.5

47.8

93.7

22.9

449

Inte

rmed

iate

are

a86

.776

.553

.165

.094

.934

.681

.064

.338

.653

.892

.222

.827

6Th

inly

- pop

ulat

ed

area

81.6

74.6

44.3

59.2

95.9

31.3

77.1

65.4

33.3

45.5

92.2

21.1

921

Sout

hern

an

d Ea

ster

n Se

rbia

Den

sely

pop

ulat

ed

area

93.0

80.7

56.5

65.4

99.3

43.3

79.8

65.6

36.5

43.8

93.7

23.5

308

Inte

rmed

iate

are

a84

.267

.346

.252

.494

.025

.772

.054

.227

.738

.186

.613

.235

7Th

inly

- pop

ulat

ed

area

79.4

69.5

43.5

50.8

95.1

26.4

67.0

56.8

28.8

34.7

90.0

14.5

639

Tab

le 1

03

Atti

tude

s to

war

d ch

ildre

n w

ith d

isabi

litie

s Pe

rcen

tage

of r

espo

nden

ts to

the

mod

ule

on a

ttitu

des

tow

ard

child

ren

with

disa

bilit

ies

by s

peci

fic a

ttitu

des

expr

esse

d to

war

d ch

ildre

n w

ith d

isabi

litie

s, S

erbi

a, 2

014

The

Ana

lysis

of M

ICS

da

ta

127

.00

Perc

enta

ge o

f res

pond

ents

who

bel

ieve

that

chi

ldre

n w

ith

phys

ical

or s

enso

ry d

iabi

litie

s:Pe

rcen

tage

of

resp

ond-

ents

who

ex

pres

s po

sitiv

e at

titud

es

tow

ard

child

ren

with

phy

s-ic

al a

nd

sens

ory

disa

bilit

ies

on a

ll fiv

e st

atem

ents

.00

Perc

enta

ge o

f res

pond

ents

who

bel

ieve

that

chi

ldre

n w

ith

inte

llect

ual d

iabi

litie

s:Pe

rcen

tage

of

resp

ond-

ents

who

ex

pres

s po

sitiv

e at

titud

es

tow

ard

child

ren

with

phy

s-ic

al a

nd

sens

ory

disa

bilit

ies

on a

ll fiv

e st

atem

ents

Num

ber o

f re

spon

d-en

ts to

the

hous

ehol

d qu

estio

n-na

ire

Are

bet

ter

off t

o liv

e in

th

e fa

mily

ra

ther

than

in

a sp

e-ci

alise

d ch

ild c

are

inst

itutio

n

Do

not

have

a

nega

tive

impa

ct

on th

e ev

eryd

ay

life

of o

ther

ch

ildre

n in

th

e fa

mily

Are

bet

ter

off t

o at

-te

nd m

ain-

stre

am

scho

ols

than

spe-

cial

scho

ols

Atte

ndin

g m

ain-

stre

am

scho

ols d

o no

t hav

e a

nega

tive

impa

ct o

n th

e w

ork

of o

ther

st

uden

ts

Can

ac

hiev

e a

lot i

n lif

e if

they

are

ad

equa

tely

su

ppor

ted

Are

bet

ter

off t

o liv

e in

th

e fa

mily

ra

ther

than

in

a sp

e-ci

alise

d ch

ild c

are

inst

itutio

n

Do

not

have

a

nega

tive

impa

ct

on th

e ev

eryd

ay

life

of o

ther

ch

ildre

n in

th

e fa

mily

Are

bet

ter

off t

o at

-te

nd m

ain-

stre

am

scho

ols

than

spe-

cial

scho

ols

Atte

ndin

g m

ain-

stre

am

scho

ols d

o no

t hav

e a

nega

tive

impa

ct o

n th

e w

ork

of o

ther

st

uden

ts

Can

ac

hiev

e a

lot i

n lif

e if

they

are

ad

equa

tely

su

ppor

ted

Rom

a se

ttle-

men

ts

Den

sely

pop

u-la

ted

area

93.1

82.9

69.9

66.5

95.2

51.2

83.0

75.4

57.6

56.5

90.2

38.2

565

Inte

rmed

iate

ar

ea91

.182

.477

.569

.694

.458

.179

.068

.951

.155

.590

.036

.768

2

Thin

ly- p

opul

at-

ed a

rea

90.6

77.7

71.8

67.8

95.7

54.0

80.5

69.4

56.2

53.1

89.1

37.7

496

Tab

le 1

04

Atti

tude

s to

war

d ch

ildre

n w

ith d

isabi

litie

s Pe

rcen

tage

of r

espo

nden

ts to

the

mod

ule

on a

ttitu

des

tow

ard

child

ren

with

disa

bilit

ies

by s

peci

fic a

ttitu

des

expr

esse

d to

war

d ch

ildre

n w

ith d

isabi

litie

s, R

oma

settl

emen

ts, 2

014

Serb

ia

MA

1 C

urre

ntly

mar

ried

or li

ving

with

a m

anC

M1

Ever

giv

en b

irth

1.00

Num

ber

of w

omen

ag

e 15

-49

year

sm

arrie

din

uni

onN

ot m

arrie

d,

not i

n un

ion

1 Ye

s2

No

15-2

4D

PA8.

13.

088

.88.

991

.139

3IP

A4.

64.

191

.36.

293

.826

1TP

A10

.42.

687

.011

.089

.042

3

25-3

5D

PA52

.18.

239

.653

.546

.563

0IP

A61

.610

.328

.164

.535

.535

8TP

A66

.46.

427

.273

.027

.053

6

36-4

9D

PA67

.67.

125

.381

.718

.372

3IP

A74

.06.

819

.286

.813

.254

0TP

A79

.05.

915

.290

.79.

385

0

Tab

le 1

05

Partn

ersh

ip s

tatu

s of

wom

en a

ge 1

5-49

, Ser

bia

2014

The

Ana

lysis

of M

ICS

da

ta

128

Roma settlementsMA1 Currently married or living with a man CM1 Ever given birth 1.00 Number

of women age 15-49

yearsmarried in union Not married,

not in union 1 Yes 2 No

15-24DPA 24.5 31.8 43.7 52.4 47.6 267IPA 12.9 42.8 44.3 48.8 51.2 310TPA 17.9 36.3 45.8 52.1 47.9 181

25-35DPA 39.8 43.2 17.0 94.3 5.7 247IPA 28.1 52.8 19.1 92.4 7.6 221TPA 38.7 43.5 17.7 90.6 9.4 156

36-49DPA 68.1 18.7 13.2 97.9 2.1 222IPA 66.3 19.5 14.2 95.8 4.2 302TPA 53.3 31.3 15.4 96.4 3.6 175

Table 106

Partnership status of women age 15-49, Roma settlements, 2014

Percentage of women age 15-19 who:

Number of women age

15-19

Percentage of women age 20-24 who have had a live

birth before age 18 [1]

Number of women age

20-24Have had a

live birthAre preg-

nant with first child

Have begun childbearing

Have had a live birth before age

15

Total 2.7 0.0 2.8 0.1 515 1.4 562

Degree of urbanization

Densely populated area 3.1 0.0 3.1 0.0 174 1.4 218

Intermediate area 1.8 0.2 2.0 0.2 128 0.6 133Thinly- populated area 2.9 0.0 2.9 0.2 213 1.8 210

1 MICS indicator 5.2 — Early childbearing

Table 107

Early childbearing Percentage of women age 15-19 years who have had a live birth, are pregnant with the first child,

have begun childbearing, and who have had a live birth before age 15, and percentage of women age 20-24 years who have had a live birth before age 18, Serbia, 2014

Percentage of women age 15-19 who:

Number of women age

15-19

Percentage of women age 20-24 who have had a live

birth before age 18 [1]

Number of women age

20-24Have had a

live birthAre preg-

nant with first child

Have begun childbearing

Have had a live birth before age

15

Total 23.8 9.0 32.8 3.7 382 38.3 377

Degree of urbanization

Densely populated area 24.7 9.2 33.9 4.8 130 41.4 137Intermediate area 22.1 8.2 30.3 2.2 163 37.9 147Thinly- populated area 25.6 10.2 35.8 5.0 89 34.3 92

1 MICS indicator 5.2 — Early childbearing

Table 108

Early childbearing Percentage of women age 15-19 years who have had a live birth, are pregnant with the first child,

have begun childbearing, and who have had a live birth before age 15, and percentage of women age 20-24 years who have had a live birth before age 18, Serbia Roma Settlements, 2014

The

Ana

lysis

of M

ICS

da

ta

129

godistezene Womens age1.00 zene 15-24 2.00 zene 25-35 3.00 zene 36-49

MA1 Currently married or living with a man

1.00 Number of wom-en age 15-49 years





1 Yes, currently married

2 Yes, living with a man

3 No, not in union



3 No, not in union



3 No, not in union


1 Densely pop-ulated area 8.1 3.0 88.8 393 52.1 8.2 39.6 630 67.6 7.1 25.3 723

2 Intermediate area 4.6 4.1 91.3 261 61.6 10.3 28.1 358 74.0 6.8 19.2 540

3 Thinly- popu-lated area 10.4 2.6 87.0 423 66.4 6.4 27.2 536 79.0 5.9 15.2 850

Table 109

Partnership status of women 15-49, Serbia 2014

godistezene Womens age1.00 zene 15-24 2.00 zene 25-35 3.00 zene 36-49









3 No, not in union



3 No, not in union



3 No, not in union



24.5 31.8 43.7 267 39.8 43.2 17.0 247 68.1 18.7 13.2 222

2 Intermedi-ate area 12.9 42.8 44.3 310 28.1 52.8 19.1 221 66.3 19.5 14.2 302

3 Thinly- popu-lated area 17.9 36.3 45.8 181 38.7 43.5 17.7 156 53.3 31.3 15.4 175

Table 110

Partnership status of women 15-49 — Roma settlements 2014

wage2 Age1.00 15-18 2.00 19-24

goSchool Are attend-ing school

haveJob Have a job

haveIncome Have an income

numWomen Number of



haveJob Have a job


numWomen Number of



1 Densely populated area 98.7 .2 6.0 91 82.6 2.3 8.5 83

2 Intermediate area 99.7 2.7 28.4 81 (86.49) (3.19) (32.28) 463 Thinly- populated area 98.5 0.0 8.6 119 (77.27) (8.26) (15.67) 94

Table 111

Activity status women 15-24 Serbia 2014

The

Ana

lysis

of M

ICS

da

ta

130

wage2 Age1.00 15-18 2.00 19-24


haveJob Have a job


numWomen Number of



haveJob Have a job


numWomen Number of



1 Densely populated area 30.4 2.7 7.6 76 21.8 2.1 16.2 54

2 Intermediate area 33.1 0.0 17.5 85 11.6 0.0 40.2 783 Thinly- populated area 22.0 3.0 18.0 49 (16.76) (3.53) (14.48) 39

Table 112

Activity status women 15-24 Roma settlements 2014

Documents

The analysis of Multiple Indicator Cluster Survey data · PDF fileThe analysis of Multiple Indicator Cluster Survey data ... The analysis of Multiple Indicator Cluster Survey data