CONCEPTUAL AND METHODOLOGICAL
FRAMEWORK (CMF)
16 MARCH 2011
OOSC CMF March 2011
TABLE OF CONTENTS
LIST OF FIGURES AND TABLES ............................................................................................ 3
ABBREVIATIONS .................................................................................................................... 5
1. INTRODUCTION AND RATIONALE .................................................................................... 7
2. THE FIVE DIMENSIONS OF EXCLUSION (5DE) ................................................................. 9
2.1.
The Five Dimensions of Exclusion
2.2.
Flows between the Five Dimensions of Exclusion
2.3.
Categories of out-of-school children
2.4.
Disparity analysis across the Five Dimensions of Exclusion
2.5.
Benefits of the 5DE model and policy implications
3. PROFILES OF EXCLUDED CHILDREN IN RELATION TO THE 5DE ............................... 18
3.1.
Data sources and data quality
3.2.
Pre-primary age children out of school (Dimension 1)
3.3.
Children out of school (Dimensions 2 and 3)
3.3.1. The percentage and number of children out of school
3.3.2. Classification of children out of school by school exposure
3.3.3. Disaggregated data on children out of school
3.3.4. Children out of school and engagement in child labour
3.4.
Children at risk of exclusion (Dimensions 4 and 5)
3.4.1. Indicators of children at risk
3.4.2. Disaggregated data on children at risk
4. BARRIERS AND BOTTLENECKS IN RELATION TO THE 5DE ........................................ 34
4.1.
Research questions on the barriers and bottlenecks
4.2.
School supply side indicators linked to barriers and bottlenecks
4.3.
Education financing indicators linked to barriers and bottlenecks
5. POLICIES AND STRATEGIES IN RELATION TO THE 5DE .............................................. 39
5.1.
Research questions on education policies and strategies
5.2.
Social protection systems at the interface of education and development
6. STRUCTURE, METHODOLOGY, PROCESS AND TIMELINE FOR THE COUNTRY
STUDIES ................................................................................................................................ 44
6.1.
Structure of the country studies
6.2.
Methodology for writing the four chapters of the country studies
6.2.1. Profiles of excluded children
6.2.2. Barriers and bottlenecks
6.2.3. Education policies and strategies
6.2.4. Social protection systems
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OOSC CMF March 2011
6.3.
Country processes in writing the country studies and timeline
ANNEXES .............................................................................................................................. 50
Annex 1: Data inventory template
Annex 2: Software for classification of out-of-school children (Dimensions 2 and 3)
Annex 3: Example code to generate data for classification of out-of-school children
Annex 4: Data tabulation plan
Annex 5: Definitions of selected education indicators
Annex 6: Qualitative research with and about children: Methodological and ethical
considerations
Annex 7: Typology of social protection programmes
Annex 8: Grid for social protection programmes
REFERENCES ....................................................................................................................... 83
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OOSC CMF March 2011
LIST OF FIGURES AND TABLES
FIGURES
Figure 1: Five Dimensions of Exclusion (5DE)
Figure 2: Example data on school participation by age and education level
Figure 3: Example data on school participation, with the 5DE
Figure 4: Flows between the 5DE
Figure 5: Classification of the out-of-school population by school exposure
Figure 6: Example of disaggregation: Percentage of primary school age children in and out of
school by sex and residence
Figure 7: Example of disaggregation: Children ages 6-17 according to educational participation
and household wealth
Figure 8: Primary gross attendance ratio, by distance to nearest primary school and gender
Figure 9: Spreadsheet for calculation of data on out-of-school children
TABLES
Table 1: Percentage of children of pre-primary age in pre-primary or primary education, by sex
and other characteristics
Table 2: Percentage of children attending school, by age and level of education
Table 3: Adjusted net enrolment rate (ANER), by sex and level of education, with GPI
Table 4: Number of children out of school, by age group and sex
Table 5: Percentage of out-of-school children by school exposure, by age group and sex
Table 6: Adjusted primary school net attendance rate (ANAR), by age, sex and other
characteristics
Table 7a: Adjusted lower secondary school net attendance rate (ANAR), by age, sex and other
characteristics
Table 7b: Percentage of lower secondary age children attending primary school, by sex and
other characteristics
Table 8: Percentage of primary school age children out of school, by age, sex and other
characteristics
Table 9: Percentage of lower secondary school age children out of school, by age, sex and
other characteristics
Table 10: Percentage of out-of-school primary- and lower secondary-aged children who are
involved in child labour, by individual and household characteristics
Table 11: Number of out-of-school primary- and lower secondary-aged children who are
involved in child labour, by individual and household characteristics
Table 12: Percentage of primary- and lower secondary-aged child labourers who are out of
school, by individual and household characteristics
Table 13: Number of primary- and lower secondary-aged child labourers who are out of school,
by individual and household characteristics
Table 14: Percentage of primary- and lower secondary-aged out-of-school children at work in
employment, household chores, or both, by sector, time intensity and other
characteristics
Table 15: Percentage of out-of-school children suffering work-related illness or injury
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Table 16: Percentage of households with children out of school enjoying access to formal social
protection
Table 17: Percentage of households with children out of school enjoying access to credit
Table 18: Dropout rate by grade at the primary and lower secondary level of education, by sex
and other characteristics
Table 19: Survival rate to the last grade of primary education and to the last grade of lower
secondary education
Table 20: Dropout rate from primary education, by age, sex and other characteristics
Table 21: Dropout rate from lower secondary education, by age, sex and other characteristics
Table 22: Percentage of new entrants to primary education without ECCE education
Table 23: Repetition rate by grade at the primary and lower secondary level of education, by sex
and other characteristics
Table 24: Transition rate from primary to lower secondary education
Table 25: Percentage of primary- and lower secondary-aged students who are involved in child
labour, by individual and household characteristics
Table 26: Percentage of pupils in schools with basic resources
Table 27: Key education expenditure indicators
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ABBREVATIONS
5DE
ANAR
ANER
CMF
CREATE
DHS
ECCE
EFA
EGRA
GAR
GDP
GED
GER
GPI
ICLS
IHSN
ILO
ISCED
LCU
LSMS
MDG
MICS
NAR
NER
NGO
OECD
OOSC
OOSCI
OVC
PPP
PTA
REA
RO
RSE
SACMEQ
SIMPOC
SMC
SNA
SP
TOR
UCW
UIS
Five Dimensions of Exclusion
Adjusted net attendance rate
Adjusted net enrolment rate
Conceptual and Methodological Framework
Consortium for Research on Educational Access
Demographic and Health Survey
Early childhood care and education
Education for All
Early Grade Reading Assessment
Gross attendance rate
Gross domestic product
Global Education Digest
Gross enrolment rate
Gender parity index
International Conference of Labour Statisticians
International Household Survey Network
International Labour Organization
International Standard Classification of Education
Local currency unit
Living Standards Measurement Study
Millennium Development Goal
Multiple Indicator Cluster Survey
Net attendance rate
Net enrolment rate
Non-Governmental Organisation
Organisation for Economic Co-operation and Development
Out-of-school children
Out-of-School Children Initiative
Orphans and vulnerable children
Purchasing power parity
Parent-Teacher Association
Regional Education Adviser (for UNICEF)
Regional Office (for UNICEF)
Relative standard error
Southern and Eastern Africa Consortium for Monitoring Educational Quality
Statistical Information and Monitoring Programme on Child Labour
School Management Committee
System of national accounts
Social protection
Terms of reference
Understanding Children’s Work
UNESCO Institute for Statistics
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UNDP
UNESCO
UNGEI
UNICEF
UNPD
UPE
WEI
United Nations Development Programme
United Nations Educational, Scientific and Cultural Organization
United Nations Girls’ Education Initiative
United Nations Children’s Fund
United Nations Population Division
Universal primary education
World Education Indicators
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1. INTRODUCTION AND RATIONALE
68 million children of primary school age and 74 million of lower secondary school age were still
out of school in 2008, despite sizeable reductions in the out-of-school population over the past
10 years (UIS Data Centre, October 2010). Furthermore, based on current trends an estimated
56 million children of primary school age will not be in school in 2015 (UNESCO 2010).
Participation in pre-primary education, although on the increase, also remains very low
(UNESCO 2010). In addition, out-of-school children (OOSC) often face deep-rooted structural
inequalities and disparities linked to income-poverty, exposure to child labour, conflict and
natural disasters, location (urban or rural area, geographic sub-national regions), gender, HIV
and AIDS, disability, ethnicity, language, religion, and caste. These represent major barriers to
schooling and put even those countries able to improve access to and completion of education
at risk of not achieving universal primary education (UPE).
Underlying the problem of OOSC are key data, analysis and policy gaps. There is a general
lack of adequate tools and methodologies to identify OOSC, to measure the scope and describe
the complexity of exclusion and disparities, to assess the reasons for exclusion, and to inform
policy and planning. There is a need to acquire a better overview of existing data, utilize the
range of data collected through administrative records and household surveys and make more
effective use of such data sources.1 More information is needed on profiling children out of
school and on the multiple and overlapping forms of exclusion and disparities that affect them.
Often the data are not leveraged for policy purposes. Policies and programmes to address the
problem of OOSC and reduce inequalities remain inadequate and small-scale in many
countries, and there is no systematic analysis on the barriers and bottlenecks in reaching
underserved populations. The multi-dimensionality of disparities makes it extremely difficult for
countries to formulate and finance the needed multi-sectoral policies for addressing them. The
most disadvantaged OOSC need additional targeted measures and investments, some of which
are beyond the field of education and many that are also costly and difficult to manage.
Available resources need to be invested in a more cost-effective manner.
In order to realize the right of all children to education and to “reach the unreached”, complex
policy responses related to exclusion from education are needed which employ a holistic
approach and which are informed and monitored by robust statistical measures. In this regard
UNICEF and the UNESCO Institute for Statistics (UIS) launched a Global Initiative on Out-ofSchool Children at the beginning of 2010. The Initiative aims at working with 23 countries:
Bangladesh, Bolivia, Brazil, Cambodia, Colombia, Democratic Republic of the Congo, Djibouti,
Ethiopia, Ghana, India, Indonesia, Liberia, Mozambique, Nigeria, Pakistan, Philippines,
Romania, Sri Lanka, Sudan, Timor-Leste, Tajikistan, Yemen and Zambia. The objective is to
improve statistical information and analysis on OOSC and to scrutinize factors of exclusion from
schooling and existing policies related to enhanced participation (addressing the data, analysis
1
In many countries, national figures often mask high levels of disparities that exist at the sub-national
level or across different population groups.
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OOSC CMF March 2011
and policy gaps).2 The goal is to introduce a more systematic approach to address the problem
of out-of-school children and guide concrete education sector reforms in this regard. Activities
include national studies based on the work of national teams (consisting of government partners
and key decision-makers), as well as national capacity strengthening related to the collection
and management of education statistics and to policy analysis and strategy development. The
country studies will feed into regional overviews, a global study, and a global conference to
leverage resources for equity.
The present document provides a Conceptual and Methodological Framework (CMF) for the
Global Initiative on OOSC and for undertaking the national studies.3 It will be developed into a
Guidance Document based on the country studies and experiences. The CMF introduces a new
model for analyzing the problem of OOSC through “Five Dimensions of Exclusion (5DE)” that
capture excluded children from pre-primary to lower secondary school age and across a wide
range and multiple layers of disparities and various degrees of exposure to education. It also
supports a more systematic linkage and leveraging between three main components:
 PROFILES of excluded children capturing the complexity of the problem of OOSC in terms
of magnitude, inequalities and multiple disparities around the Five Dimensions of Exclusion.
 BARRIERS AND BOTTLENECKS to clarify the dynamic and causal processes related to
the Five Dimensions of Exclusion.
 POLICIES AND STRATEGIES to address the barriers and bottlenecks related to the Five
Dimensions of Exclusion within education and beyond (looking at social protection systems).
Section 2 of this document introduces the 5DE model. The three Sections that follow Section 2
provide a comprehensive approach to addressing the problem of out-of-school children that
leverages data for policy, through the analysis of bottlenecks. Section 3 presents PROFILES of
excluded children. Questions on BARRIERS AND BOTTLENECKS are presented in Section 4.
Section 5 proposes a set of questions for defining POLICIES AND STRATEGIES for more
equitable targeting of excluded groups of children, within education and through social
protection. Section 6 proposes a structure, methodology, process and timeline for undertaking
the country studies. The annexes support the methodological approach through the
presentation of software, tabulation plans, indicators, definitions/typologies and qualitative
research.
2
The Initiative builds on a 2005 joint report by UIS and UNICEF which introduced the typology of out-ofschool children and disaggregated data analysis.
3 The Framework was initiated at a Methodology Workshop in Istanbul from 21 to 25 June 2010, where
delegations from the participating countries engaged with global experts around diverse data and policy
issues. The Framework was subsequently further developed by UIS (for the data part) and UNICEF (for
the policy part) with contributions from UIS and UNICEF field staff and global experts (including Birger
Fredriksen, Keith Lewin from CREATE and Furio Rosati from UCW).
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2. THE FIVE DIMENSIONS OF EXCLUSION (5DE)
The Five Dimensions of Exclusion (5DE) model represents an innovative approach that
provides a broader, more complex and equity-oriented view of exclusion from education than is
addressed by the Millennium Development Goals, with key implications concerning barriers and
policy development. The present Section starts by introducing the 5DE together with the
different population groups, age ranges and school levels they cover. This is followed by a
presentation of the student flows between the 5DE, of the different categories of OOSC, and of
the multiple disparities that cut across the 5DE. The Section concludes with a summary of the
benefits and policy implications of the model.
2.1. The Five Dimensions of Exclusion
The model of the 5DE presents 5 target groups of children for the data and policy analysis
that span three levels of education: pre-primary, primary and lower secondary; and two different
population groups: children who are out of school, and those who are in school but at risk of
dropping out. Each group represents a distinct Dimension of Exclusion that requires specific
statistical and policy analysis. The term “exclusion” has a slightly different meaning depending
on the population concerned: children who are out of school are excluded from education, while
children who are at risk of dropping out may be excluded within education – because for
example they face discriminatory practices or attitudes within the school.
Children of primary school age are typically the focus of efforts to achieve universal primary
education given the importance of reaching UPE. However, it is also important to look beyond
this group as there are also large out-of-school populations among children of lower secondary
age (UIS 2010c).4 In general, children of primary or lower secondary school age are considered
as being in school if they participate in primary or secondary education (ISCED levels 1 and 2).
Children of primary or lower secondary age who do not participate in education programmes at
ISCED levels 1 and 2 are considered as being out of school, including those in pre-primary
and non-formal education (see box below).
Which children are counted as out of school?
In the framework, there are two groups of school-age children who are considered out
of school even though they may be participating in learning-related activities. In
contexts where these groups represent a large number or proportion of children, care
should be taken in analyzing these groups relative to others.
Although pre-primary education is key to a child’s development, children of primary
school age or older who are in pre-primary education are considered out of school.
This categorization is made for several reasons. First, the educational properties of
pre-primary education and the pedagogical qualifications of teaching staff in such
programmes may not meet the criteria that are applied to primary education. In
4
The respective age ranges vary according to national definitions. For international comparisons it is
recommended to refer to the International Standard Classification of Education (UNESCO 2006). ISCED
classifies education programmes according to their curriculum content by level and field of study.
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OOSC CMF March 2011
addition, pre-primary enrolment data by age are not available for all countries and it is
therefore difficult to arrive at a global estimate (UIS and UNICEF 2005). Nonetheless, it
is clear that primary age children participating in pre-primary education are different
from those not exposed to any schooling.
The educational properties of non-formal education programmes and limited data
availability are the main reasons to categorize children participating in non-formal
education as out of school. In addition, non-formal education programmes are more
often targeted at older age groups, including adults, than at younger age groups.
Children of primary or lower secondary age enrolled in non-formal education can only
be considered as being in school if the programmes they attend are recognized as
equivalent to formal primary or secondary education. In other words, children in nonformal education are not considered out of school if the programme they attend is
officially recognized and provides pathways into the formal education system.5
Nevertheless, participation in non-formal education that is not equivalent to formal
education is different from no exposure to school at all and should be considered
separately when analyzing data on OOSC.
Based on the definition of OOSC, the Five Dimensions of Exclusion include two dimensions
that capture the out-of-school population of primary school age (Dimension 2) and lower
secondary school age (Dimension 3). Pre-primary education is represented by Dimension 1,
which highlights children of pre-primary school age who are not in pre-primary or primary
education. The approach includes Dimensions 4 and 5 that focus on children who are in school
but at risk of dropping out. Understanding more about these groups of children is key to prevent
them from becoming the out-of-school children of tomorrow (Lewin 2007). Dimension 4 covers
children in primary school who are considered at risk of dropping out, and Dimension 5 covers
children in lower secondary school who are considered at risk. In summary, the 5DE, through
both the out-of-school and at-risk Dimensions set out specific groups of children who are not
participating in the intended level of education for the intended duration and at the intended age.
The Five Dimensions are listed below in the box and displayed in Figure 1.
The Five Dimensions of Exclusion (5DE)
Dimension 1: Children of pre-primary school age who are not in pre-primary or primary
school
Dimension 2: Children of primary school age who are not in primary or secondary
school
Dimension 3: Children of lower secondary school age who are not in primary or
secondary school
Dimension 4: Children who are in primary school but at risk of dropping out
Dimension 5: Children who are in lower secondary school but at risk of dropping out
As an example, pupils in Qur’anic schools that offer a curriculum covering mathematics, geography and
other areas in a manner equivalent to other schools in the national education system and officially
recognized as such, are considered as being in school.
5
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Figure 1: Five Dimensions of Exclusion (5DE)
Not in preprimary school
Pre-primary
age children
Dimension 3
Dimension 2
Dimension 1
Attended
but
dropped
out
Will never
enter
Will enter
late
Primary age children
Attended
but
dropped
out
Will never
enter
Will enter
late
Lower secondary age children
Dimension 4
Dimension 5
At risk of
dropping out of
primary school
At risk of dropping
out of lower
secondary school
Primary school students
Out of
school
In
school
Lower secondary school students
There are several important aspects to note regarding the 5DE. First, the distinct shape and
colour of Dimension 1 in Figure 1 reflects the notion that while pre-primary school is important
as preparation for primary education, it is also distinct from formal programmes at primary or
higher levels of education. Dimension 1 represents a group of children who do not benefit from
pre-primary education and who may therefore not be adequately prepared for primary
education, placing them at risk of not entering into primary education or, if they do enter, at risk
of dropping out. Although pre-primary education programmes may be longer than one year, the
5DE propose a standard approach for all countries by focusing on pre-primary participation of
children in the year preceding the official entrance age into primary school. This is done in the
interest of simplicity and to allow cross-national comparisons. As an example, if the official
primary entrance age in a country is 6 years, Dimension 1 includes children aged 5 years who
are not in pre-primary or primary education. Children who attend non-formal or non-recognized
pre-primary education programmes should be identified as a distinct group if the data are
available.
Second, each of the out-of-school Dimensions 2 and 3 is divided into three mutually exclusive
categories based on previous or future school exposure: children who attended in the past and
dropped out, children who will never enter school, and children who will enter school in the
future. This typology of children out of school is adopted from the 2005 report by UIS and
UNICEF and is discussed in greater detail in Section 2.3. Some OOSC of primary and lower
secondary age may be in pre-primary or non-formal education and these children should be
identified separately within the out-of-school Dimensions 2 and 3, if data are available.
Furthermore, OOSC of primary or lower secondary age who completed primary education are
different from children who did not complete the full primary cycle before leaving school. These
groups of children should also be identified separately within the out-of-school Dimensions 2
and 3.
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Third, children in Dimensions 4 and 5 – those in school but at risk of exclusion from education
– are grouped by the level of education they attend, regardless of their age: primary (Dimension
4) or lower secondary (Dimension 5). This is different from Dimensions 2 and 3, which group
out-of-school children by their age: primary age (Dimension 2) and lower secondary age
(Dimension 3). The framework thus covers two different types of populations: the population of
out-of-school children of school-going age, and the population of at-risk pupils of any age in
primary or lower secondary school.
In Figures 2 and 3, the 5DE are applied to data on school participation of children and young
adults between 5 and 17 years of age. Figure 2 shows the share of each age cohort that attend
pre-primary, primary, lower secondary, and upper secondary education in an example country.
In this example, the official primary school entry age is 6 years but children up to 9 years of age
are still in pre-primary education. At 11 years, children are meant to complete primary education
but the ages of primary school students range from 5 to 17 years due to late entry and
repetition. Finally, the official lower secondary school age is 12 to 14 years, but children and
young adults between 11 and 17 years attend lower secondary school. The area between the
top of the bars and the 100 percent line represents children who are out of school.
Figure 2: Example data on school participation by age and education level
100
Percent
80
60
40
20
0
5
6
Pre-primary
7
8
Primary
9
10
11
12
Lower Secondary
13
14
15
16
17
Upper Secondary
Figure 3 translates the participation data from Figure 2 into the 5DE. First, all children 5 years of
age who are not in pre-primary or primary school are in Dimension 1. Dimension 2 includes
children of official primary school age, for example 6 to 11 years, who are not attending primary
or secondary education. The out-of-school population in Dimension 2 also includes children
aged 6 to 11 who are either in pre-primary education (represented by the light blue bars located
in the lower left part of Figure 3) or in non-formal education (not shown as a separate group).
Dimension 3 is limited to children at official lower secondary age (12 to 14 years in the example)
who are not attending primary or secondary education, although they may be in pre-primary or
non-formal education.
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OOSC CMF March 2011
Next, consider children attending primary school, who are represented by the dark blue bars in
Figure 2. Children in Dimension 4, students in primary school who are at risk of dropping out,
are a subset of all children in primary school, who in this example range in age from 5 to 17
years. In Figure 3 this subset of the primary student population at risk of dropping out is shaded
yellow. Children in lower secondary school are represented by the green bars in Figure 2. A
subset of all lower secondary school students, who range in age from 11 to 17 years in the
example, are at risk of dropping out and this group makes up Dimension 5, also shaded yellow.
Figure 3: Example data on school participation, with the 5DE
In summary, the out-of-school Dimensions and the "in-school but at-risk" Dimensions cover
different populations and different age ranges. Because children of primary school age out
of school (Dimension 2) and children in primary school but at risk of dropping out (Dimension 4)
represent different populations, their numbers cannot be summed to represent the total
population that is excluded from primary education. To estimate the total number of excluded
children, as defined by the 5DE, the analysis must be limited to a certain age range. For
example, if the analysis is limited to children of primary school age, it is possible to add the
number of children in Dimension 2 to the number of primary-age children in Dimension 4 to
arrive at an estimate of the total number of children of primary school age who are excluded
from education (Dimension 2) or at risk of exclusion (Dimension 4).
2.2. Flows between the Five Dimensions of Exclusion
The model described in Section 2.1 provides a static snapshot at a particular point in time, but
there can be movement between the 5DE as children enter or leave the formal education
system, as they transfer from one level of education to another, or as they become older. Some
of these flows are depicted in Figure 4. Take the case of children in Dimensions 2 and 3 that
enter school at some point and are then no longer out of school. When OOSC enter the school
system, they may become part of Dimension 4, children in primary school at risk of dropping
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OOSC CMF March 2011
out.6 Late entry into school implies that the child is overage, a risk factor for non-completion of
primary or secondary education. If children at risk of dropping out (Dimensions 4 and 5) do in
fact leave school they become part of the out-of-school population (Dimension 2 or 3). Children
in primary school at risk (Dimension 4) who transfer to secondary education may continue to be
at risk of dropping out and would then be part of Dimension 5. Lastly, OOSC of primary age who
reach secondary age and remain out of school transfer from Dimension 2 of the framework to
Dimension 3.
Figure 4: Flows between the 5DE
Figure 4 does not show the population of primary and secondary school students who are not at
risk and movements between this group and the Dimensions of Exclusion are therefore not
depicted. For example, it is possible for primary-age children out of school to enter school
without being at risk of dropout. These children would move directly from Dimension 2 to the not
at risk population in school, bypassing Dimension 4. Similarly, children at risk may no longer be
at risk and would therefore move out of Dimensions 4 or 5 to the stable student population not
shown in Figure 4.
6
Children who enter school after the official primary entry age typically enter primary education. The
number of children who enter the formal education system at the secondary level, for example children
who were previously home schooled and thus counted as out of school, can be assumed to be negligible.
Children who are in a non-formal programme equivalent to primary education that enables them to enter
secondary school directly are not counted as out of school – meaning they are not part of Dimensions 2
or 3 – and would therefore not be considered new entrants when they start secondary education.
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OOSC CMF March 2011
Looking at how children interact with the school system over time adds an important dynamic
perspective to the development of profiles of children excluded from education. Several
indicators discussed in Section 3 examine progression through and exit from primary and lower
secondary school, including dropout rate, repetition rate and transition rate from primary to
lower secondary education.
2.3. Categories of out-of-school children
One important conceptual contribution of the 5DE model is the recognition that not all children
out of school are permanently excluded from education. In reality, out-of-school children have
various degrees of exposure to education that are visualized in Figure 1 as sub-groups of
Dimensions 2 and 3.
As shown in Figure 5, children out of school can be divided into two groups based on their
previous exposure to education: children who entered school in the past and dropped out, and
children who never entered school. Children who never entered school can be further divided
into two sub-groups: children who will enter school in the future (as late entrants), and children
who will never enter school.7 The relative size of these three mutually exclusive groups of outof-school children varies from country to country. For example, in some countries a high
proportion of children out of school is expected to enter in the future, whereas in other countries
most children out of school are expected to never attend school.
Figure 5: Classification of the out-of-school population by school exposure
Dropouts or early school leavers are children who attended school at some point in the past
but have since left. They may have done so before or after completion of primary or (lower)
secondary education. The consequences of dropping out depend on when dropout occurs.
Children who drop out in early grades are unlikely to have acquired even the most basic
7
This classification of out-of-school children is described in the 2005 report by UIS and UNICEF.
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OOSC CMF March 2011
mastery of reading and writing, numeracy and other skills.8 Some children may complete the
primary cycle but do not continue their education at the secondary level. Similarly, some
children may leave school before or after completion of lower secondary education. All school
leavers can in theory return to school in the future but research shows that many early school
leavers do not return to continue their formal education. Children who will enter in the future
are children that have not yet entered formal education but will do so in the future. Entry into
school may be delayed by one or more years. An increase in this delay is assumed to place
children at increased risk of dropout or low academic achievement. The third group of out-ofschool children is those never in school: they have never attended in the past and are not
expected to do so in the future. In contrast to the other two groups of out-of-school children,
dropouts and future entrants, the third group has no exposure to formal education at all.
2.4. Disparity analysis across the Five Dimensions of Exclusion
A key element of the framework is the analysis of disparities that cut across each of the 5DE.
The development of complex profiles of children within each of the Five Dimensions reflects an
effort to systematically disaggregate numbers and categories of out-of-school children according
to a wide range of individual, household and group characteristics that are linked to
marginalization and inequality, such as wealth, health, location, gender, and race/ethnicity.
Such disaggregation is crucial because it determines in many ways the positioning of children
across the 5DE as well as the movement of children within and between them.
In addition, disparities often interact with each other to create complex and mutually reinforcing
patterns of disadvantage and barriers to schooling; thus the multidimensional and
overlapping nature of disparities and the patterns of exclusion will be highlighted across the
5DE. The analysis of disparities is discussed in Section 3.
2.5. Benefits of the 5DE model and policy implications
The Five Dimensions of Exclusion framework combines four unique approaches that have key
policy implications with regards to the shifting equity-related challenges resulting from progress
on universal primary education.
First, by generating data on OOSC of both primary and lower secondary school age, as
well as pre-primary school age, the model underlines the importance of the life cycle
approach and of effectively linking the provision of education to children with different
developmental needs at different stages in life. Primary education alone is insufficient to ensure
that children are equipped with the skills and knowledge necessary for their own development
and to build societies and economies for the 21st century. Addressing the whole life cycle of
children’s education needs, including the transitions between the basic levels of education, is
necessary to successfully reach universal primary education. Evidence shows that pre-primary
8
Low acquisition of skills in early grades is documented in findings of Early Grade Reading Assessments
(EGRA) that have been carried out in about 40 countries since 2007. Results of EGRA studies are
available at www.eddataglobal.org.
16
OOSC CMF March 2011
education is key for entry into and success in primary education levels and that widening access
to lower secondary opportunities increases primary completion rates and improves school-tolabour market transitions.9 This feature of the model has implications in relation to improved
coherence and balance between policies throughout the basic education cycle and improved
attention to transitions between different education levels and grades.
Second, the model has a particular strength in drawing attention to the various patterns and
forms of exposure to schooling of OOSC (early school leavers, children who will enter in the
future, children who will never enter school, as well as exposure to community-based preprimary education and non-formal education services that are not recognized by the formal
system and not captured by statistics). This focus has key implications for an improved analysis
of the barriers to school participation, for improved targeting, and for accounting, strengthening
and developing policies and strategies that provide for multiple and alternative pathways to
education and learning.
Third, the disparity analysis within the 5DE is key for a better understanding of the multiple
and overlapping forms of exclusion and barriers to inclusion, for increasing the visibility of
marginalized groups (and especially those last 10% or 5%), for more effective tracking and
targeting of disadvantaged groups and areas (while working on universality of access), and for
improving the linkage between education policies and social protection systems. For example, a
high rate of child labour among children out of school could indicate that policies aimed at
getting children into school need to consider the opportunity costs of schooling borne by
households.
Finally, the 5DE framework covers children who are currently in school, but at risk of leaving
before completion, identifying at-risk groups who may become the OOSC of tomorrow. This is
a key feature in linking equity in access to quality education, demand-driven poverty-focused
policies to supply-side provision of quality (especially in relation to school level processes), and
policies for out-of-school children to policies for children in school. In fact, while the 5DE model
is focussed on issues of access and retention, it also opens channels for a more sophisticated
analysis of learning and completion and highlights the importance of education quality as a
factor related to parental decisions about sending children to school and school participation
more generally.
9
UNICEF is concerned with education issues affecting children from birth to 18 years of age. As such it
furthers the life cycle approach in education, introduced within the agency’s education equity strategy as
a “9+1” approach in education (1 year of pre-school together with a combination of 9 years of primary and
lower secondary education), or a continuous education cycle of 10 years (see forthcoming UNICEF 2011
Board Paper). UNESCO policy statements increasingly recognize and advocate for an understanding of
basic education which encompasses different levels of education (pre-primary, primary and lower
secondary) in a more holistic way.
17
OOSC CMF March 2011
3. PROFILES OF EXCLUDED CHILDREN IN RELATION TO THE 5DE
This Section addresses three main questions: How many children are out of school? Which
children are out of school? And which children are at risk of exclusion? To answer these
questions, the section is structured around the following:
 Description of key data sources: Section 3.1 presents two primary data sources for the
quantitative analysis of the 5DE: administrative data and household surveys. Each data
source has specific advantages and disadvantages and understanding them is an important
initial step in building a comprehensive and robust statistical profile of children excluded
from education. Some aspects of data quality are also discussed.
 Guidelines for the analysis of data on the Five Dimensions of Exclusion: Sections 3.2
to 3.4 provide guidelines for the analysis of data on pre-primary age children out of school
(Dimension 1), children of primary and lower secondary age out of school (Dimensions 2
and 3), and children in school at risk of exclusion (Dimensions 4 and 5). Example tables
show how the key findings of the quantitative analysis of OOSC and related topics can be
presented. These tables, taken together, constitute a proposed data tabulation plan for the
country studies. Countries are not limited to the example tables but may choose to
complement them with the results from additional analysis.
The emphasis of the analysis is to move beyond a definitive number of out-of-school children, or
of children at risk of dropping out, but – as noted in Section 2 – to develop complex profiles of
children in each dimension that can serve as the foundation for the analysis of barriers and
bottlenecks and the formulation of policies and strategies as discussed in Sections 4 and 5.
3.1. Data sources and data quality
No single data source can provide a complete profile of OOSC. Comprehensive analysis
requires the use of multiple sources of data because they provide information on different issues
measured at different points in time and because each data source has limitations that must be
considered during analysis. This Section describes two main sources of quantitative data that
can provide a generalisable picture of OOSC in a country: administrative data on pupils and
teachers, and household survey data on children’s schooling status. The data inventory
template in Annex 1 can be used to describe sources of data on children in and out of school in
a country. In addition, for analysis which looks more closely at individual-level decisions or
barriers, qualitative data from interviews or case studies can provide useful information on
patterns of participation.
National governments routinely collect data on their education systems for the purpose of
monitoring and managing schools, staff and programmes in the education sector.10 These
administrative data have a number of advantages (UIS 2008b). Typically the data are
Most education data in the UIS Data Centre at www.stats.uis.unesco.org – including data on enrolment,
teachers and finance – are provided by national authorities to the UIS in an annual education survey that
is described in the Global Education Digest 2008 (UIS 2008b). The data are collected and processed in a
manner consistent with international standards such as ISCED and they are therefore internationally
comparable.
10
18
OOSC CMF March 2011
collected on an annual basis and cover the whole country, thus allowing governments to monitor
and regularly assess the capacity and performance of the education system in relation to
educational goals and policy, both at the national and sub-national levels. Administrative data
also make it possible to link information about students to information about teachers, facilities
and expenditures with indicators such as pupil-teacher ratios or expenditures per pupil, which
require a clear correspondence between the data on pupils and on human and financial
resources.
Although administrative data have many advantages that make them indispensable to
quantitative research on national education systems, some limitations are important to note.
First, the data collection capacity of all countries is not equal and the coverage, accuracy and
timeliness of data may not be sufficient to draw a clear, up-to-date picture of the education
system. Furthermore, private educational institutions and non-formal programmes that are not
managed by the Ministry of Education may not be included in administrative statistics. In terms
of school participation, administrative data typically refer to enrolment at the beginning of the
school year and do not provide information on regular attendance. By definition, enrolment
records also provide no information on out-of-school children, the number of whom must be
estimated indirectly using data on enrolment and the relevant school-age population. In addition,
with the exception of some countries that collect more detailed data, administrative data provide
little insight into the individual or household characteristics of students.
In contrast with most administrative data, which typically can only be disaggregated by sex,
household survey or census data can be disaggregated according to numerous other
individual or household characteristics. Household surveys thus play an important role in
understanding the characteristics of children and their households. Analysis by sex, ethnicity,
area of residence, household wealth quintile, child labour status, the parents’ level of education
or other factors are possible with data collected by surveys or censuses. Another advantage of
survey data compared to administrative records of enrolment is that surveys cover children who
are not enrolled in school and direct analysis of the out-of-school population is therefore
possible. Surveys are also a source of data on child labour, a phenomenon related to school
attendance. Many OOSC work and, in addition, child labour serves as a risk factor for early
school leaving.
In addition to national survey programmes, there are several international survey programmes
that collect key data on education from a wide range of countries, including Demographic and
Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and Living Standards
Measurement Studies (LSMS). Child labour data are collected by the Statistical Information and
Monitoring Programme on Child Labour (SIMPOC). For availability of data from these or other
surveys, it is advised to consult the survey catalogue on the website of the International
Household Survey Network (IHSN), as well as the Understanding Children’s Work (UCW)
survey database.11
11
Information about these data sources can be found at the following addresses: DHS:
www.measuredhs.com; MICS: www.childinfo.org/mics.html; LSMS: www.worldbank.org/lsms/; SIMPOC:
19
OOSC CMF March 2011
There are some limitations to household surveys. It is often not possible to link results to
information about the education system. Large surveys like the DHS and MICS are generally
not carried out every year, unlike the collection of annual administrative data. The sample
population typically does not include the homeless (including street children) and nomadic or
mobile populations, which can constitute a significant number of out-of-school children in some
countries (UIS 2010b). Finally, the precision of estimates and the level of disaggregation are
limited by the design of a survey and the sample size.
Estimates of school participation and the number of out-of-school children can vary with the
underlying data source, whether administrative records or household surveys. Much of the
difference in estimates from administrative and household surveys can be explained from the
different measures of participation used, the misreporting of age data and the use of different
population data (UIS et al 2004; UIS and UNICEF 2005; UNESCO 2010b). Assessments of
data quality are an important initial step in the analysis. Data should be accompanied by a full
set of documentation, which includes for household surveys the questionnaire, sampling design
and implementation report, and codebook. These are essential for assessing the extent to which
the data were collected in a rigorous.12
Administrative data and household surveys measure education participation in different
ways. Administrative sources usually focus on reporting of enrolment at the beginning of the
school year. By contrast, household surveys estimate educational participation with data on
school attendance. The most commonly used measure in survey data is attendance at some
point during the school year, based on information provided by a parent or guardian. A child is
considered to have been in school even if he or she attended only for one day in the reference
school year. Furthermore, the collection of enrolment and attendance data does not always
occur at the same time. Household surveys are often not coordinated with the academic
calendar and the timing of a survey can affect estimates of participation rates and age reporting.
For example, the ages of children must be adjusted if a survey conducted in July collected
attendance data for an academic year that began in September because at the time of data
collection children were almost one year older than at the beginning of the school year.
Indicators such as the net enrolment rate (NER) and net attendance rate (NAR), depend on
accurate age data. Both administrative and household survey data are susceptible to problems
with the reliability of age information. One possible reason is lack of birth certificates (UIS
2010b). In household surveys this is also a factor, as well as the added problem that generally
one respondent provides age information for all household members, which can be inaccurate.
www.ilo.org/ipec//-en/; EGRA: www.eddataglobal.org; IHSN: www.ihsn.org; and UCW: www.ucwproject.org.
12 For further information on data quality standards for administrative data, see UIS (2008b), and UIS and
UNESCO Regional Bureau for Education in Africa (2010); for data quality standards for household survey
data, see UIS et al (2004) and UIS (2010b).
20
OOSC CMF March 2011
Improving the reliability of age data represents a challenging but significant step towards
improving data quality in many countries.
UIS calculations of enrolment rates combine national enrolment statistics with population
estimates by the UN Population Division (UNPD), which relies on projections from national
census data. If no recent census data are available, UNPD estimates of the national population
and the size of individual age cohorts (and the UIS indicators calculated with them) have to be
interpreted with caution. In some cases, UNPD population estimates may not be compatible
with national population estimates and enrolment statistics, which may lead to discrepancies in
the calculation of enrolment rates and other key education indicators.
Estimates of school participation from household survey data are subject to sampling and nonsampling errors. Sampling errors depend on the size of the population represented by a
survey and the number of respondents. For reliable indicator estimates for a particular group, at
least 30 observations are typically required. The estimate for an indicator calculated from a
sample may be different than the value for the entire population in a country and will vary with
the sample size. One frequently used measure of the quality and precision of an estimate is the
relative standard error (RSE).13 Non-sampling errors are caused by mistakes during data
collection and data processing.
In summary, both administrative records and household surveys provide important perspectives
regarding the profiles of OOSC. Data available from each source vary by country. The data
inventory template, found in Annex 1, provides a systematic way to document the available data
for a country. Sections 3.2 to 3.4 present suggested indicators and tables related to the 5DE
which can be adapted to the particular contexts of each country.
3.2. Pre-primary age children out of school (Dimension 1)
The analysis of data on Dimension 1 is relatively straightforward. Only data on the school
attendance status of the population one year younger than the official primary school entrance
age are required. These children may be in pre-primary or primary education, or they may be
out of school. The percentage of pre-primary age children out of school can be calculated as
follows.
13
The RSE is calculated as the standard error divided by the mean of an estimate, expressed as a
percentage. As an example, assume that the primary NAR is 50% and the standard error 1%. The
relative standard error is then 1% / 50% = 2%. Estimates with an RSE above 30% are commonly
considered unreliable.
21
OOSC CMF March 2011
(1) Percentage of children of pre-primary age who are in pre-primary or primary
education
Number of children of pre-primary school age
enrolled in pre-primary or primary education

Number of children of pre-primary school age
(2) Pre-primary age children out of school = 100 – percentage of children of preprimary age who are enrolled in pre-primary or primary education
Table 1 presents a suggested layout for data on attendance of pre-primary age children. As
explained in section 2.1, only children in formal pre-primary or primary education programmes
should be identified as being in school. If data for other forms of early childhood care and
education are available, they can be reported separately, for example in a note attached to
Table 1.
Table 1: Percentage of children of pre-primary age in pre-primary or primary education,
by sex and other characteristics (see Annex 4)
Concerning child labour, household survey data from most countries indicate that child labour is
limited among pre-primary aged children. Nonetheless, it may be of interest to add child labour
status as an additional disaggregation for the share of pre-primary aged children in pre-primary
and primary school. It is also worth noting in this context that even if few pre-primary aged
children are in child labour, pre-primary education can be relevant to the discussion of child
labour because participation in pre-primary education can limit children’s susceptibility to child
labour later in childhood.
3.3. Children out of school (Dimensions 2 and 3)
3.3.1. The percentage and number of children out of school
To address the problem of OOSC it is necessary to establish first how many children are out of
school and second which children are out of school. An examination of school attendance by
age can reveal trends in school participation. Table 2 provides a breakdown of school
attendance by age and level of education. As with Dimension 1, participation in non-formal
education programmes can be reported separately, for example in a note attached to Table 2,
but such non-formal programmes should be clearly distinguished from the formal programmes
listed in Table 2.
Table 2: Percentage of children attending school, by age and level of education (see
Annex 4)
The most common indicators for the measurement of participation are the net enrolment rate
(NER) and the net attendance rate (NAR). The net enrolment rate (NER) is derived from
22
OOSC CMF March 2011
enrolment records and indicates the share of children of primary (or lower secondary) age who
are enrolled in primary (or lower secondary) education.14
(3) Primary NER 
Number of children of primary school age enrolled in primary education
Number of children of primary school age
(4) Lower secondary NER 
Number of children of lower secondary school age
enrolled in lower secondary education
Number of children of lower secondary school age
An additional indicator, the adjusted net enrolment rate (ANER), takes into consideration that
some children of primary or lower secondary school age may be enrolled in other levels of
education. With the NER defined above, these children would be wrongly counted as out of
school.
Number of children of primary school age
enrolled
in primary or secondary education
(5) Primary AN ER 
Number of children of primary school age
(6) Lower secondary ANER 
Number of children of lower secondary school age
enrolled in lower or upper secondary education
Number of children of lower secondary school age
The net attendance rate (NAR) is derived from household survey data. The NER formulas
above only have to be slightly modified to yield the net attendance rate (NAR) and adjusted net
attendance rate (ANAR).
Number of children of primary school age
attending primary education
(7) Primary NAR 
Number of children of primary school age
Number of children of lower secondary school age
attending lower secondary education
(8) Lower secondary NAR 
Number of children of lower secondary school age
Number of children of primary school age
(9) Primary AN AR  attending primary or secondary education
Number of children of primary school age
(10) Lower secondary ANAR 
Number of children of lower secondary school age
attending lower or upper secondary education
Number of children of lower secondary school age
The gender parity index (GPI) provides information on disparity in educational participation
between boys and girls (see also Section 3.3.3). When applied to the primary ANER, the GPI is
calculated by dividing the female by the male ANER. Values of the GPI between 0.97 and 1.03
are usually considered gender parity. If the GPI for the ANER is less than 0.97, girls are at a
disadvantage. If the GPI for the ANER is greater than 1.03, boys are at a disadvantage.
14
Formulas and additional information for most indicators described in this section can be found in the
Education Glossary of the UIS at www.uis.unesco.org/glossary.
23
OOSC CMF March 2011
(11) GPI for primary ANER  Female primary ANER
Male primary ANER
The GPI can be applied to any sex-disaggregated indicator, including the dropout rate or the
survival rate, but the interpretation may change. For example, if the GPI for the dropout rate is
less than 1, dropout rates for girls are lower than those for boys and boys are thus at a
disadvantage. The GPI for primary ANAR is calculated in the same way as the GPI for
indicators derived from enrolment data. The following example shows the calculation of the GPI
for the primary ANAR.
(12) GPI for primary AN AR  Female primary AN AR
Male primary AN AR
Table 3 presents enrolment rates for primary and lower secondary by sex and education level.
Table 3: Adjusted net enrolment rate (ANER), by sex and level of education, with GPI (see
Annex 4)
The percentage of children out of school is calculated by the difference between 100%
(universal enrolment or attendance) and the ANER or ANAR.15 The following formulas describe
the calculations of primary age children out of school.
(13) Percentage of children of primary school age out of school = 100 - primary ANER
(14) Percentage of children of primary school age out of school = 100 - primary ANAR
The calculation for children of lower secondary age out of school is slightly different than the
calculation for primary school age out of school. The lower secondary ANER and ANAR only
consider children who are in secondary school. However, some children of lower secondary age
attend primary school and thus should not be considered out of school. The following formulas
take this into consideration.
(15) Percentage of children of lower secondary school age out of school = 100 - lower
secondary ANER - percentage of children of lower secondary school age enrolled in
primary education
(16) Percentage of children of lower secondary school age out of school = 100 - lower
secondary ANAR - percentage of children of lower secondary school age attending
primary education
By applying the percentage of children out of school to the number of children of official primary
or lower secondary school age, one can calculate the absolute number of children out of
15
For further information regarding the comparability of school enrolment and attendance data, see UIS
et al (2004), UIS and UNICEF (2005) and UNESCO (2010).
24
OOSC CMF March 2011
school, shown in Table 4. The UN Population Division is the official source of population data in
the UN system and UNPD population estimates will be shared with participating countries.
Table 4: Number of children out of school, by age group and sex (see Annex 4)
3.3.2. Classification of children out of school by school exposure
The next step in the development of OOSC profiles is to classify OOSC in the three groups
described in Section 2.3: dropouts or early school leavers, children who will enter school in the
future, and children who will never enter school.
Dropouts or early school leavers can be identified directly with either administrative or
household survey data: they have had some contact with schooling but are not currently in
school. With the same data it is usually also possible to identify school leavers with different
levels of educational attainment within the dropout category in Dimensions 2 and 3. In contrast
to dropouts, children who will enter school in the future cannot be directly identified in
administrative or survey data while they are still out of school. It is not possible to state for an
individual child whether he or she will attend school in the future, it is only possible to estimate
the proportion of children who will enter school among the total out-of-school population by
estimating the probability using school entry rates at each age. Like children who will enter in
the future, children who will never enter school cannot be individually identified in
administrative or survey data, only their share of the total number of children out of school can
be estimated based on an assessment of the probability of future school attendance.
For the purpose of estimating the size of each group in Dimensions 2 and 3, the UIS has
prepared a spreadsheet for the country studies that is described in Annex 2. With the
spreadsheet, Table 5 can be prepared.
Table 5: Percentage of out-of-school children by school exposure, by age group and sex
(see Annex 4)
3.3.3. Disaggregated data on children out of school
As mentioned in Section 2.4 the analysis of disaggregated household survey data is an
important component of the Global Initiative on Out-of-School Children. Disaggregation of
administrative data is typically limited to children in school by sex and location. For further
disaggregation it is necessary to turn to data from household surveys or censuses. While the
Dimensions of Exclusion framework groups children by varying levels of school exposure, it is
also possible to disaggregate the data by other characteristics, such as sex, area of residence,
or household wealth, and to identify disparities between different groups of children.
Disaggregated data are used to compare subgroups within a population. Tables 6 to 9
exemplify useful ways of disaggregating education data. These tables examine educational
participation or educational exclusion by sex; area of residence; household wealth quintile;
25
OOSC CMF March 2011
ethnicity, language or religion; and child labour status. Tables 6, 7a and 7b examine attendance
rates whereas Tables 8 and 9 examine the out-of-school population. The official primary school
age range in these examples is designated as 6 to 11 years, whereas the official lower
secondary age range is designated as 12 to 14 years. The column “Number of children”
indicates the size of each sub-sample. As a rule of thumb, only data from groups with at least 30
observations can be considered reliable. Other groups of disaggregation can be added to the
analysis, including disability, orphan-hood, early marriage, and mother’s level of education.
Table 6: Adjusted primary school net attendance rate (ANAR), by age, sex and other
characteristics (see Annex 4)
Table 7a: Adjusted lower secondary school net attendance rate (ANAR), by age, sex and
other characteristics (see Annex 4)
Table 7b: Percentage of lower secondary age children attending primary school, by age,
sex and other characteristics
Table 8: Percentage of primary school age children out of school, by age, sex and other
characteristics (see Annex 4)
Table 9: Percentage of lower secondary school age children out of school, by age, sex
and other characteristics (see Annex 4)
Disaggregated data are often best expressed through visual or graphic presentation. Figure 6
shows the share of children in and out of school disaggregated by sex and area of residence
(urban or rural). In this example, the light blue bars represent children of primary school age out
of school (Dimension 2). The data highlight the urban-rural divide as a particularly salient
disparity, and the rural population as a key target group for further analysis.
Figure 6: Example of disaggregation: Percentage of
primary school age children in and out of school by sex and residence
Total
76
Male
24
77
Female
23
75
Urban
25
86
Rural
72
Urban male
28
86
Urban female
85
Rural male
73
Rural female
70
0
14
14
15
27
30
20
40
60
80
100
Percentage of children of primary age in and out of school
In school
Out of School (Dimension 2)
26
OOSC CMF March 2011
Figure 7 compares the school participation rates of children in the richest and poorest
household quintiles. As the graph clearly shows, the proportion of OOSC and adolescents,
indicated by the red bars, is far greater in the poorest households across all age groups, from 6
to 17 years. In addition, overage attendance is a significant problem for children and
adolescents in the poorest quintile. As being overage is deemed a risk factor for dropping out,
this kind of disaggregation also provides information for the analysis of Dimensions 4 and 5 (see
Section 3.4).
10 11 12 13 14 15 16 17
Richest quintile
Poorest quintile
6
7
8
9
Age (years)
Figure 7: Example of disaggregation: Children ages 6-17
according
to educational
wealth
India 2005-06:
Schoolparticipation
attendanceand
by household
age and HH
wealth
100
80
60
40
Primary
Out of School
20
0
20
Attendance rate (%)
40
Secondary
Missing
60
80
100
Tertiary
Finally, disaggregated survey data can be used to compare trends in school participation of
various groups over time, or studied with multivariate regression models. In a multivariate
regression, demand-side characteristics like those described in Section 3.3.3 can be linked to
supply-side characteristics like those described in Section 4.2 to identify the strongest
determinants of school participation.
3.3.4. Children out of school and engagement in child labour
Classifying children out of school by whether or not they are performing child labour is also
important for policy purposes. Survey data on school participation can be linked with data on
child labour to study the relationship between child labour and school participation. The global
guidelines for child labour statistics set out in Resolution II (2008) of the Eighteenth International
Conference of Labour Statisticians (ICLS) provide a basis for the child labour measure.16
16
The resolution states that child labour may be measured in terms of the engagement of children in
productive activities either on the basis of the general production boundary or on the basis of the SNA
27
OOSC CMF March 2011
Examples of such analyses and country reports on child labour can be found in the work of
Understanding Children’s Work (UCW), an inter-agency research project of the International
Labour Organization (ILO), UNICEF and the World Bank.17
The indicators in Tables 10-1318 provide a general picture of the degree to which the child
labour and OOSC populations overlap. They answer the following two questions:
 What proportion of children out of school are child labourers?
 What proportion of child labourers are out of school?
While they fall short of establishing a robust causal link between child labour and children out of
school, they nonetheless serve to illustrate the degree of incompatibility between child labour,
on the one hand, and school participation, on the other.19
Table 10: Percentage of out-of-school primary- and lower secondary-aged children who
are involved in child labour, by individual and household characteristics (see
Annex 4)
Table 11: Number of out-of-school primary- and lower secondary-aged children who are
involved in child labour, by individual and household characteristics (see Annex
4)
Table 12: Percentage of primary- and lower secondary-aged child labourers who are out
of school, by individual and household characteristics (see Annex 4)
Table 13: Number of primary- and lower secondary-aged child labourers who are out of
school, by individual and household characteristics (see Annex 4)
Effective policy responses also require more detailed information on the nature and extent of
the work that OOSC perform instead of attending school. An additional set of indicators can
be constructed in this context to assess the involvement of children out of school in employment
and household chores, and in combinations of the two. For children out of school in
employment, the nature and time intensity of their employment are examined, as well as the
extent to which employment presents health-related risks. For children out of school performing
production boundary. The former includes unpaid household services (i.e., household chores) while the
latter excludes them. When the general production boundary is used as the basis for measuring child
labour, the resolution recommends classifying those performing hazardous unpaid household services as
part of the group of child labourers for measurement purposes. Hazardous unpaid household services, in
turn, are defined as those requiring long hours; involving unsafe equipment or heavy loads; in dangerous
locations; etc. For further details, see: Resolution II, Resolution Concerning Statistics of Child Labour, as
cited in ILO (2009).
17 Statistics and research reports on child labour and school attendance are available at the UCW
website, www.ucw-project.org.
18 Tables 10 to 17 and Table 25 will be provided by Understanding Children’s Work Project (upon request
by country teams), see Section 6.2.1.
19 While suggestive, a causal relationship between child labour and school cannot be asserted from
descriptive data on these indicators. Establishing causality is complicated by the fact that child labour and
school attendance are usually the result of a joint decision on the part of the household, and by the fact
that this decision may be influenced by possibly unobserved factors such as innate talent, family
behaviour and or family preferences.
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OOSC CMF March 2011
household chores, the total time burden of these chores can be assessed from some household
survey datasets. Tables 14 and 15 address work activities of out-of-school children. The
following tables answer the questions:
 What work activities do OOSC perform?
 How much time do they spend performing these work activities?
Table 14: Percentage of primary- and lower secondary-aged out-of-school children at
work in employment, household chores, or both, by sector, time intensity and
other characteristics (see Annex 4)
Table 15: Percentage of out-of-school children suffering work-related illness or injury
(see Annex 4)
Detailed disaggregation for the indicators above will be important to pinpointing particular subgroups of children that may be at special risk of being exposed to child labour and denied
schooling. These may include individual characteristics (girls or boys, orphans, ethnic minorities,
refugee children, children living in particular regions) or may include aspects reflecting
household vulnerability (female head of household, wealth quintile, household access to basic
services like electricity and water, etc.).
Depending on data availability, it may be possible to assess the degree to which households
with OOSC have access to programmes seeking to reduce household vulnerability. Access
to social protection programmes and credit in particular enable households to offset social risk
without having to resort to removing the child from school for child labour, and therefore can
constitute important components of a broader policy response to child labour and children out of
school. Tables 16 and 17 explore the following question:
 What forms of social protection do households with OOSC have access to?
This analysis will link to the research on social protection proposed in Section 5.2.
Table 16: Percentage of households with children out of school enjoying access to
formal social protection (see Annex 4)
Table 17: Percentage of households with children out of school enjoying access to credit
(see Annex 4)
3.4. Children at risk of exclusion (Dimensions 4 and 5)
3.4.1. Indicators of children at risk
Dimensions 4 and 5 cover children in school who are at risk of dropping out, in other words, the
potential OOSC of tomorrow. All children face some risk of dropping out, but not all do in the
same way. The analysis of Dimensions 4 and 5 focuses on those children who are at the
greatest risk of dropping out of school.
A simple way to analyze the population of children at risk is to look at the at-risk children of
yesterday, that is, children who recently dropped out of school. Understanding the profiles of
29
OOSC CMF March 2011
children whose risk of dropping out was realized provides insight into the profiles of children
currently at risk. For example, analyzing dropout rates provides information about which children
leave school early (see Table 18).
Table 18: Dropout rate by grade at the primary and lower secondary level of education,
by sex and other characteristics (see Annex 4)
Some countries have sophisticated education management information systems that follow
children throughout their education life cycle and can thus produce actual survival rates. In the
absence of such detailed data collection, survival rates can be created with the use of data on
grade promotion, grade repetition and grade drop out.20
(17) Survival rate to last grade of primary education 
Number of children who entered grade 1 of primary education
and reached the last grade of primary education
Number of children who entered grade 1 of primary education
(18) Survival rate to last grade of lower secondary education =
Number of children who entered grade 1 of lower secondary education
and reached the last grade of lower secondary education
Number of children who entered grade 1 of lower secondary education
Table 19 presents survival rates, which emphasize achievement rather than failure or dropout.
Table 19: Survival rate to the last grade of primary education and to the last grade of
lower secondary education (see Annex 4)
Dropout rates by level are calculated by using the survival rate to the last grade of primary or
lower secondary education.21 For the calculation of the survival rate it does not matter if children
repeated a grade or not.
(19) Dropout rate from primary education = 100 - survival rate to last grade of primary
education
(20) Dropout rate from lower secondary education = 100 - survival rate to last grade of
lower secondary education
The dropout rates can be reported as shown in Tables 20 and 21.
Table 20: Dropout rate from primary education, by age, sex and other characteristics (see
Annex 4)
20
For
more
information
on
survival
rates,
see
the
UIS
glossary:
http://www.uis.unesco.org//.aspx?=SURVIVAL%20RATE%20BY%20GRADE%20%28SR%29&lang=en.
21 Only dropout before the last grade of primary or lower secondary school is considered due to the
general lack of data on graduation from the last grade of primary and lower secondary.
30
OOSC CMF March 2011
Table 21: Dropout rate from lower secondary education, by age, sex and other
characteristics (see Annex 4)
A second way to analyze the at-risk population is to examine various indicators linked to
children in school. The following indicators on participation in pre-primary education, under and
overage participation, and repetition, all address children who are in school but may be at risk of
dropping out.
Participation in pre-primary has been shown to reduce the likelihood of early school leaving
(Hammond et al 2007). The percentage of new entrants to primary without ECCE experience is
an indicator of the risk of exclusion from education.
(21) Percentage of new entrants to primary education without ECCE experience =
New entrants to grade 1 of primary education who have not attended an organized ECCE programme
Total number of new entrants to primary grade 1 in a given school year
Table 22 shows an example of examining one group of children who have greater risk of
dropping out – those who did not participate in pre-primary education.
Table 22: Percentage of new entrants to primary education without ECCE education (see
Annex 4)
As noted in Section 2.2, as children grow older, their interactions with the school evolve,
potentially moving them between the Five Dimensions of Exclusion, as well as into or out of
them. Additional indicators derived from administrative and household survey data can help to
measure progression through the system, including dropout and repetition. In addition, these
indicators may be especially relevant to understand dropout risk factors such as being overage
(Hammond et al 2007; Hunt 2008).
The gross enrolment rate (GER) or gross attendance rate (GAR) are indicators of underage
and overage enrolment or attendance, when compared to the NER or NAR. For example, the
primary GER (GAR) is greater than the primary NER (NAR) if some children in primary school
are younger or older than the official primary school age.
(22) Primary GER  Number of children enrolled in primary education
Number of children of primary school age
(23) Primary GAR  Number of children attending primary education
Number of children of primary school age
(24) Lower Secondary GER  Number of children enrolled in lower secondary education
Number of children of lower secondary school age
(25) Lower Secondary GAR  Number of children attending lower secondary education
Number of children of lower secondary school age
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OOSC CMF March 2011
Two main factors explain overage enrolment: late enrolment and grade repetition. Grade
repetition can be calculated as follows.
(26)
Repetition rate, by grade 
Number of repeaters in a given grade in a school year
Number of pupils from the same cohort enrolled
in the same grade in the previous school year
Table 23 presents repetition rates by grade to highlight the points at which children are not
progressing in school.
Table 23: Repetition rate by grade at the primary and lower secondary level of education,
by sex and other characteristics (see Annex 4)
One last way to analyze dropout is to look at transition rates from primary to lower secondary
(see Table 24).
(27) Transition rate from primary to lower secondary education 
Number of new entrants in the first grade of lower secondary education
Number of pupils who were enrolled in the final grade
of primary education in the previous school year
Table 24: Transition rate from primary to lower secondary education (see Annex 4)
3.4.2. Disaggregated data on children at risk
Research on characteristics linked to increased risk of dropping out has found that no single
factor, nor any specific combination of factors, can predict dropout with perfect accuracy
(Hammond et al 2007). Studies have also shown that there are several types of dropouts, with
differing factors linked to their leaving school (Elliot and Voss 1974; Goldschmidt and Wang
1999). Furthermore, the profile of children who left primary school before completion may differ
from the profile of those who left lower secondary school early. Thus, disaggregating data on
children at risk of early school leaving is essential. Depending on the data source and sample
size, individual and household factors can be analyzed. The profile of at-risk students may vary
by region and residence.
Individual factors for early school leaving may include background characteristics such as age,
sex, race or ethnicity, first language, and disability. Poor school performance, absenteeism,
misbehaviour, low personal expectations of return from education and frequent change of
schools as well as high-risk attitudes, values and behaviours are additional factors of student
drop-out. Special circumstances, such as being a teen parent, caring for siblings and child
labour also contribute significantly to early school leaving.
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OOSC CMF March 2011
Children’s involvement in labour is a particularly important individual risk factor. Children in
school who are simultaneously involved in child labour often lag behind their non-working peers,
and may ultimately be at greater risk of premature drop-out, placing them in Dimensions 4 and
5. Table 25 looks at children attending school who are involved in child labour.
Table 25: Percentage of primary- and lower secondary-aged students who are involved in
child labour, by individual and household characteristics (see Annex 4)
An additional disaggregation of drop-out rates by child labour status offers descriptive evidence
of the extent to which child labourers are more likely to drop out. Other education indicators on
repetition rate and being overage for grade also provide insight into how child labour can affect
children’s schooling outcomes (see Table 23).
Household factors include socioeconomic status, parental education, family structure
(household headed by a single parent or by a child), household stress levels caused by financial
or health problems, and parental attitudes and beliefs about education.
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OOSC CMF March 2011
4. BARRIERS AND BOTTLENECKS IN RELATION TO THE 5DE
The analysis of the barriers, the dynamic and causal processes of exclusion, is intrinsically
linked to the disaggregation of data on children out of school and to risk factors for children in
school. This Section takes the identification and analysis of “profiles” of children and proposes a
set of questions that would guide the identification of the causes of exclusion that are linked
to these profiles.
There are methodological issues that will need to be addressed in terms of overlaps between
the profiles of excluded children and the causes for exclusion. Caution will need to be taken in
terms of using the profile as a cause or a barrier. The information generated in Section 3 will
provide information on profiles (girls, poor children, children living in rural areas, etc.); the
questions below should help to clarify the causes of exclusion and barriers to access. For
example, a certain profile in Dimensions 2 and 4 could suggest that the causes of exclusion are
related to socio-cultural practices and/or school fees.
The first part of this Section deals with the set of questions guiding the barriers and bottlenecks
analysis. The second and third parts outline school supply and financing indicators that could be
used to complement such analysis. More information on methodology is provided in Section 6.
4.1. Research questions on the barriers and bottlenecks
The following four sets of research questions are meant to provide a broad and standardized
structure for the analysis in the country studies. A few notes:
 Examples are by no means exhaustive.
 Where possible, it is important to identify barriers and bottlenecks in relation to each of the
5DE and the disparities within them. Barriers and bottlenecks will obviously differ for preprimary, primary and lower secondary education and for children with different profiles.
 Where possible, it is important to highlight the relative weight/impact of one type of barrier
and bottleneck in relation to other barriers and bottlenecks.
 Countries may choose to dig deeper into specific questions and into a specific Dimension.
The sets of questions are structured around the following22: demand-side socio-cultural and
economic barriers, supply-side barriers, and bottlenecks that impede the successful
implementation of policies and strategies (related to political conditions, conflicts, issues of
governance, capacity, and financing).
(i) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on demand side socio-cultural barriers? What is the relative weight/impact of such
barriers in relation to other barriers?
Some examples to consider:
 Individual emotional experiences of children.
 Socio-cultural practices (in the household and in the community).
22
These questions are inspired by UNICEF et al (2009) and UNGEI EFA FTI (2010).
34
OOSC CMF March 2011


Violence in the home and in the community.
Lack of consensus on the purpose or process of education (in the household and in the
community).
(ii) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on demand side economic barriers? What is the relative weight/impact of such
barriers in relation to other barriers?
Some examples to consider:
 Household poverty and subsequent pressures on resources and time.
 School fees and other out-of-pocket expenditures for education.
 Opportunity costs and exposure to child labour.
 Economic repercussions of seasonal factors such as rains and flooding, including migration.
(iii) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on supply side barriers? What is the relative weight/impact of such barriers in
relation to other barriers?
Some examples to consider (see also section 4.2):
 School infrastructure: remoteness of schools; lack of transportation; inadequate
infrastructure and space; poor condition of school facilities; lack of school water and
sanitation; lack of appropriate infrastructure for children with disabilities.
 Teacher supply: inadequate number of teachers per class; teacher attendance
(regular/irregular); lack of female teachers; weak system of recruitment and deployment;
inadequacy of teacher training and low quality of teachers.
 Textbooks: inadequate provision of school instruction books and materials.
 School and classroom management, organizational and pedagogical characteristics:
inadequate pedagogical approaches (including for multi-grade teaching); inadequate
development of cognitive and learning capacities; inadequate competitive strategies;
teaching in non-mother tongue language; lack of integration of local values and cultures in
teaching; inadequate achievement expectations and ineffective evaluation approaches;
inadequate role of teachers in identifying children at risk; inadequate assistance to children
with special learning needs and to children from the poorest and marginalized groups;
dysfunctional relations between teachers and students.
 School safety: violence in school; sexual harassment; corporal punishment.
(iv) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on the political, governance, capacity and financial bottlenecks? What is the
relative weight/impact of such barriers in relation to other barriers?
Some examples to consider:
 Political bottlenecks: lack of political commitment to inclusion; non-conducive legal and
administrative frameworks; lack of mandatory, clearly defined and enabling policies to
35
OOSC CMF March 2011


support strategies; lack of effective awareness and communication strategies; conflict,
(nationally and in sub-national “pockets of emergency”23) and natural disasters.
Governance and capacity bottlenecks: ineffectiveness of institutional arrangements at
central and decentralized levels; lack of clearly defined roles and responsibilities for different
education actors; lack of effective delegation and devolution of responsibility; lack of
transparency and accountability mechanisms; lack of specialized departments; lack of
technical capacities to address needs of excluded groups; weak monitoring and monitoring
mechanisms (including inspectorate); lack of mechanisms for inter-sectoral coordination;
lack of effective participatory mechanisms at the community and school levels (PTAs,
SMCs, etc.); lack of effective space for community-based organizations and NGOs; lack of
system for birth registration and provision of identification documents.
Financial bottlenecks: lack of equity-based budgeting; inequitable budget allocations and
resource distribution; lack of costed strategies to reach the poor; wastage of resources;
funding gaps (see section 4.3).
4.2. School supply side indicators linked to barriers and bottlenecks
Indicators about supply side factors related to the questions in 4.1 may be found in national
statistics based on administrative records. These could be also comprehended with information
from qualitative studies. Examples of data on a wide range of issues in primary schools are
found in a facility survey by UIS (2008a). Studies such as these provide important data on topics
like the availability of school resources, the school administration and teaching staff, parental
involvement in school, and teacher attitudes, perceptions and satisfaction with the primary
school system, etc. Examples of indicators include the following:



Physical: average distance from school to home; school space per pupil in square meters;
pupil/textbook ratio; percent of schools with electricity; percent of schools with functional
toilets (separate for boys and girls); percent of schools with running water.
Human: average class size, by level (UIS has data for WEI and OECD countries);
pupil/teacher ratio (UIS has data); percent of trained teachers (UIS has data); percent of
single teacher schools; percent of pupils taught in non-mother tongue.
School conditions: incidence of violence in school; incidence of armed attacks on school.
Figure 8 shows an example of how distance to primary school is related to children’s school
attendance in a country. In this case, analysis of the barriers to participation could explain why
attendance rates fall when the distance between home and school increases. For example,
analysis of barriers could explore whether this pattern of school attendance is caused by lack of
roads, lack of transportation, or security concerns.
A “pocket of emergency” is a sub-national area where there is an ongoing crisis while the rest of the
country is in “normal” development or post-crisis transition (example East of DRC, North of CAR, etc.).
23
36
OOSC CMF March 2011
Figure 8: Primary gross attendance ratio, by distance
to nearest primary school and gender
120
100
80
60
40
20
0
<1 km
1 km
2 km
3-4 km
Distance to primary school (km)
Total
Male
5+ km
Female
Table 26 presents the percentage of pupils in schools with basic resources.
Table 26: Percentage of pupils in schools with basic resources (see Annex 4)
4.3. Education financing indicators linked to barriers and bottlenecks
Education finance indicators can be obtained from administrative sources. The UIS compiles
education finance indicators (see Table 27), which, taken together, provide a financial overview
of a national education system. Many of these indicators can be disaggregated by education
level and are available across multiple years. In some cases, the proportion spent by level of
government can also be derived; that is, the proportion of resources which come from the
federal, state (or regional) and local levels of government. See the 2010 edition of the annual
Global education digest (GED) by UIS for the most recent data and the 2007 GED for further
discussion of the role education finance plays in meeting EFA goals (UIS 2007; UIS 2010a). 24
The definitions for the indicators listed below can be found in Annex 5.


Total public expenditure on education as a percentage of GDP.
Total public expenditure on education as a percentage of total government expenditure.
24
For the purposes of international comparison, the UIS reports data in terms of percentage of GDP, or in
US dollars, purchasing power parity (PPP). Similarly, for the purpose of comparing data across countries
in the Global Initiative on OOSC, it is advised to convert education finance data to these units, although
data expressed in local currency units (LCUs) may also be helpful for national use.
37
OOSC CMF March 2011




Public education expenditure as a percentage of total government education expenditure, by
level.
Public expenditure per student by level as a percentage of GDP per capita.
Public expenditure per student, by level (PPP US$).
Educational expenditure by nature of spending as a percentage of total educational
expenditure on public institutions, by level.
Table 27: Key education expenditure indicators (see Annex 4)
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OOSC CMF March 2011
5. POLICIES AND STRATEGIES IN RELATION TO THE 5DE
This Section builds on the preceding two and aims at providing a more focussed and sharpened
analysis of policies and strategies regarding the problem of OOSC that articulates the profiles
of OOSC and the reasons of exclusion towards more effective interventions.
Examining policies which remove barriers and bottlenecks related to OOSC and the 5DE
requires work both within the education sector and beyond, specifically within poverty reduction
frameworks. Two categories of questions related to policies and strategies to address the
problem of OOSC are proposed in this Section. The first category addresses education sector
policies and strategies; these are at the forefront of a programmatic response for OOSC. The
second category addresses social protection programmes with a specific focus on social
protection “systems” and education-related policies within such systems. Overlaps with the first
category will be highlighted with a view of proposing more rationalized and effective social
protection interventions concerning OOSC.
5.1. Research questions on education policies and strategies
The following five sets of research questions are meant to provide a broad and standardized
structure for the analysis in the country studies. A few notes:
 Examples are by no means exhaustive.
 Where possible, it is important to identify policies and strategies in relation to each of the
5DE and the disparities within them. Policies and strategies will obviously differ for preprimary, primary and lower secondary education and for children with different profiles.
 Where possible, it is important to highlight the relative weight/impact of one type of policy
and strategy in relation to other policies and strategies.
 Countries may choose to dig deeper into specific policies and strategies and into a specific
Dimension.
The sets of questions are structured around the following 25: demand side socio-cultural and
economic policies and strategies, supply side policies and strategies, and strategies related to
management and governance and budgeting and finance.
(i) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on results and good practice related to demand side socio-cultural policies and
strategies? What is the relative impact of such policies and strategies in relation to other
policies and strategies?
Some examples to consider:
 Community mobilization and strategies aimed at empowerment and participation.
 Community awareness raising on gender issues.
 Addressing stigmatizing attitudes towards marginalized children in the community and in the
school.
 Partnerships with religious and civil society organizations.
25
These questions are inspired by UNICEF et al (2009) and UNGEI EFA FTI (2010).
39
OOSC CMF March 2011
(ii) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on results and good practice related to demand side economic policies and
strategies? What is the relative impact of such policies and strategies in relation to other
policies and strategies?
Some examples to consider:
 Abolition (or phased reduction) of school fees and reducing indirect costs such as uniforms
and books.
 Targeted interventions such as scholarships for girls, take home food rations.
 Addressing opportunity costs especially for working children.
(iii) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on results and good practice related to supply side policies and strategies? What is
the relative impact of such policies and strategies in relation to other policies and strategies?
Some examples to consider:
 School infrastructure: improving school facilities; water and sanitation; adaptations for
children with disabilities.
 Teacher supply: increasing teacher supply; reducing class size; increasing female
participation in teaching; pre- and in-service teacher training in knowledge and skills aimed
at assisting at risk students; developing support structures to teachers for addressing the
needs of at risk students.
 Textbooks and learning materials: review of curriculum for inclusive teaching and learning;
providing didactic material that stimulates learning; including local content in curriculum;
providing textbooks in minority languages.
 Quality schooling and school organization: effective pedagogical administrative methods
with regards to student’s performance; use of promotion criteria based on processes;
assistance to the students at risk of dropping out; teaching methods based on the
development of capabilities.
 Multiple pathways to learning: remedial education and second chance learning programmes;
bridging programmes for returning child workers to school; effective multi-grade teaching;
expansion of lower secondary education to rural areas; development of programmes
supporting education transitions and addressing school failure.
 Intersectoral interventions: school health; feeding/nutrition programmes.
(iv) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on results and good practice related to management and governance related
policies and strategies? What is the relative impact of such policies and strategies in relation
to other policies and strategies?
Some examples to consider:
 Accelerating and scaling successful pilots.
 Provision of mixed and inter-sectoral packages.
 Development of institutional arrangements and technical capacity within Ministries of
Education to address the needs of excluded; development of effective regulations and
monitoring mechanisms affecting the children’s timely access and transitions.
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OOSC CMF March 2011



Development of capacities in policy analysis and building effective data and monitoring
systems
Development of accountability structures.
Local support to schools (school grants); participatory and management mechanisms in and
around schools.
(v) In relation to the profiles of children in the 5DE (and key disparities within them), what is the
evidence on results and good practice related to budgeting and finance policies and
strategies? What is the relative impact of such policies and strategies in relation to other
policies and strategies?
Some examples to consider:
 Budgetary allocations to education (in terms of GDP and in relation to other development
sectors) and within education (education levels, teacher salaries, capital expenditures, other
non-salary expenditures), including in times of economic crisis.
 Effective use of resources for reaching the poor, including within resource constrained
environments.
 Redistributive policies to the benefit of local communities and the poor.
 Costed and budgeted targeted strategies.
 Effective and equitable regulatory framework for non-public provision of education.
5.2. Social protection systems at the interface of education and development
There is a justification why a two-pronged approach is adopted here (an analysis of education
policies and strategies and a review of social protection systems) and why it is proposed to
include a specific social protection component in the OOSC country studies. Social protection
programmes have demonstrated a variety of impacts directly related to OOSC: higher school
enrolment rates, less school dropouts and child labour by removing demand-side barriers to
education, reduced need for families to rely on harmful coping strategies, reduction of
vulnerabilities, and impact on barriers to gender equality and empowerment of women. Social
protection policies can also support inclusive education by introducing changes in the supply
side to address the specific needs of children who are marginalized or excluded (such as
children with disabilities and learning difficulties or girls who may not go to school if families
consider it unsafe for them) to ensure they can access and benefit from education. Most
important, by reaching out to those who are economically and socially disadvantaged, social
protection represents an “umbrella” for the synchronization of cross-sector interventions in
health and nutrition, education, child protection, and HIV/AIDS to increase equity outcomes.
However, ensuring that the cross-sectoral benefits of social protection are maximized presents
a number of challenges. Much of the synergies across the different parts of the (potential or
actual) social protection system tend to be unexplored and remain un-evaluated. These
synergies are thus likely to be under-utilized, and in some cases, unintended policy
consequences or contradictions arise. A possible example is the incoherence of taking financing
from the Education Ministry to pay the Health Ministry, or increasing funding for school feeding
programmes with very little support for local governments and local schools and health centres
41
OOSC CMF March 2011
to adequately finance the “surge” in services provision required by such a policy. While
substantial work has been done in documenting the impacts of specific types of social protection
programmes (e.g. cash transfers), limited documentation of lessons has occurred on how
countries have successfully created and sustained overall social protection systems. Little also
has been done to look at concrete challenges, incentives and successes from the perspective of
specific sectors. This makes it further difficult to engage sector ministries who may see little
practical benefits or political incentives for coordination, or may not see how social protection is
relevant to them. Even in countries which attempt to maximize a systems approach, challenges
in coordination across ministries, sequencing, etc. remain.
The purpose here is to look at the coherence and effectiveness of policies and strategies
of different line ministries which address needs related to social protection and to locate
OOSC education specific policies and strategies related to social protection within this
exercise. This analysis can help to inform which types of new or expanded policies might be
most effective and which of the line ministries should be leading and implementing them. Within
the policy analysis,
 the overall research question is to what extent does a social protection “system” exist in the
country, and what actual or potential benefits does it provide to getting children to school
and retaining them there?
 A sub-question is how social protection related education policies are effectively integrated
into such a system?
Four key sets of questions can be explored here:
(i) What are the key social protection programmes which exist in country?26
 What are the diversity of programmes and policies which exist, and which are the key ones
(see Annex 7)?
 What is the coverage (and distribution of coverage), target groups and targeting methods,
and levels of benefits of these programmes? Who implements them? How long have they
been in place and have they changed over time?
(ii) What are the demonstrated and perceived impacts of social protection programmes on
OOSC, in relation to each of the 5DE (and disparities within them)?
 What information exists on education impacts, via impact evaluations, analysis of household
surveys, qualitative research? Similarly, what analysis exists on impacts on factors affecting
children being out of school, particularly those emerging from the analysis on barriers and
causal processes (e.g. poverty, child labour and time use, nutrition, etc.)?
 To what extent does the education sector view social protection as relevant to achieving
education outcomes, and why?
 Is there information on how children, particularly OOSC (or those at risk), and their families,
view the benefits or issues with these programmes?
 What do key informants, policy makers, and families see as social protection interventions
which would reduce the number of children out of school – either through changes to
26
See Annex 7 for a typology of social protection programmes.
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OOSC CMF March 2011
existing programmes or potential new programmes? Again, how do these link to the analysis
of barriers and bottlenecks?
(iii) To what extent does social protection policy and implementation adopt a cross-sectoral
approach, and what role does the education sector play?
 How strong are synergies across different parts of the system? What are/were the obstacles
and how were they overcome (or if not overcome, for what reasons)?
 What are the different types of institutional arrangements and mechanisms used to increase
coordination and its effectiveness?
 How involved is the education sector in social protection strategy development,
implementation and financing? To what extent does the Ministry of Education collaborate
with others on social protection? To what extent are education interests reflected in the
overall framework/approach? What do they see as the challenges/obstacles to a crosssectoral approach?
 Are cross-sectoral social protection policies/frameworks in place, how/when were they
developed, and to what extent do they effectively support an integrated approach?
(iv) How are the social protection systems financed, and how has this evolved over time?
 How much does the public sector allocate to the various components every year and how is
it financed? Are these financed through loans? Special taxes or fees? Or the general
budget? What is the role of local governments in over-all financing? What, if any, is the
private sector contribution? How sustainable is the financing?
 How is the financing managed through the different line ministries?
 Are some financing mechanisms more pro-poor/pro-equity than others, and what are the
political economy implications?
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OOSC CMF March 2011
6. STRUCTURE, METHODOLOGY, PROCESS AND TIMELINE FOR THE COUNTRY
STUDIES
6.1. Structure of the country studies
It is suggested that the length of the document should not exceed 100-pages, including
annexes. Below is a proposed structure.
STRUCTURE OF THE COUNTRY STUDY
Content
Executive Summary
Introduction
 Country context (geographic, political, socioeconomic
 development, education sector, main players and stakeholders)
 General introduction to the 5DE in the country
 Methodology
Chapter 1: Profiles of excluded children
 Overview and analysis of data sources
 Profiles of children in Dimension 1
 Profiles of OOSC in Dimensions 2 and 3
 OOSC and involvement in child labour
 Profiles of children at risk in Dimensions 4 and 5
 Analytical summary
Chapter 2: Barriers and bottlenecks
 Socio-cultural demand side
 Economic demand side
 Supply side
 Political, governance, capacity, financing
 Analytical summary
Chapter 3: Education policies and strategies
 Socio-cultural demand side
 Economic demand side
 Supply side
 Management and governance
 Budgeting and finance
 Analytical summary
Chapter 4: Social protection systems
 Mapping
 Impact
 Cross-sectorality
 Financing
 Analytical summary
Conclusion (recommendations and way forward for the country)
Annexes (tables, figures, references, etc.)
Number of pages
5 pages
5 pages
15-20 pages
10-15 pages
10-15 pages
10-15 pages
10-15 pages
10-15 pages
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OOSC CMF March 2011
6.2. Methodology for writing the four chapters of the country studies
Two points need to be taken into consideration regarding the methodology:
 The parts titled as “all country studies” are designed for all countries and to help
standardize the approach, compare between countries and produce the global study. The
parts titled as “optional and complementary” are for countries who would like to further
explore an issue that is context-specific or use more in-depth quantitative and qualitative
methodologies. These efforts will be also highlighted in the global study.
 To the extent possible, efforts should be made to ensure a logical flow between the parts
on PROFILES, BARRIERS AND BOTTLENECKS, and POLICIES AND STRATEGIES and
to leverage the work on the profiles for policy and reform purposes.27
Below is the proposed methodology for each of the 4 chapters of the country study.
6.2.1. Profiles of excluded children
All country studies:
 As a first step, the country team will undertake an inventory of existing data on education
participation and OOSC from the past 5 years, with national or sub-national coverage. If no
data from the past 5 years are available, older sources of data can be used. No new data
collection is planned.
 A template for the data inventory is provided in Annex 1. The country can use this template
to describe, among other things, the name of the data source, the agencies responsible for
data collection and dissemination, the date and frequency of data collection, the definition of
out-of-school children, the sampling and coverage of data collection, the smallest
administrative area for which statistics on the out-of-school population are statistically
accurate, characteristics available for disaggregated analysis, availability of the data, and
known limitations. The questionnaire used for data collection, if applicable, should be
attached.
 Approximately one month is allocated to the preparation of the inventory in step 1.
 As a second step the country team will provide a best estimate for the number and
percentage of children in each of the Five Dimensions of Exclusion. The country team will
also provide the completed spreadsheet for the calculation of data on out-of-school children
that is described in Annex 2, at minimum for the total, male and female population and if
possible for further groups of disaggregation. The estimates and the spreadsheet can be
generated with data from one or more of the sources identified during the inventory in step
1.
 One additional month is allocated to the completion of step 2.
27
This can be done by highlighting for example how what we know on barriers and bottlenecks does not
match what we have found out about the scope and complexity of each of the 5DE profiles; or by
highlighting how what we know about policies and strategies is not well related to barriers and bottlenecks
and does not address what we have found out about the scope and complexity of each of the 5DE.
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OOSC CMF March 2011

As a third step, country teams will produce Tables 1 to 9, 18 to 24, and 26 to 27 from the
tabulation plan in Annex 4 of the Conceptual and Methodological Framework. The data
should be disaggregated by age, sex, urban/rural, administrative region, socio-economic
status, and, if possible, by other characteristics.
 One additional month is allocated to the creation of the profiles in step 3.
 If country teams do not have the capacity to produce the spreadsheet on out-of-school
children from Annex 2 or the tables from the tabulation plan in Annex 4, the UNESCO
Institute for Statistics (UIS) can assist in producing some or all of the tables upon the
submission of complete datasets with unit-level data (preferably in SPSS or Stata format),
along with relevant documentation (questionnaires, data dictionaries, codebooks, etc.).
 For the global report and for quality assurance, all countries teams are encouraged to share
datasets, survey reports, questionnaires, explanations of survey methodology, and other
documents with the UIS. The procedure of sharing such information will be coordinated by
the UNICEF Regional Office. The UIS will provide support to facilitate data sharing if
necessary.
Optional and complementary:
 Further quantitative and qualitative research can be undertaken by the country team to
provide more in depth insights into the 5DE profiles (interviews with children and other
stakeholders, local district-level surveys and data collection, school surveys, etc.). See
Annex 6 for a summary of methodological issues related to qualitative research with and
about children.
 Issues to be addressed can relate to community-based pre-primary education and nonformal education provision and to exploring the at-risk population (Dimensions 4 and 5) in
more depth.
 Note that any optional items may lengthen the number of pages of the country study.
6.2.2. Barriers and bottlenecks
All country studies:
 The proposed methodology for this component is NOT to undertake new research, but
rather to analyze systematically existing evidence around the set of proposed questions in
Section 4.1 and to connect this analysis to the profiles of excluded children.
 This analytical desk review will draw on available literature, research, situation analysis,
surveys, reports, evaluations, analysis of sector plans and budgets, as well as ongoing
specific work undertaken at the country level by various partners.
 The analytical desk review will be produced by the country team.
 The analytical desk review will follow the structure of the proposed questions in Section 4.1.
Optional and complementary:
 Some country teams, under the guidance of the UNICEF Regional Offices, may choose to
undertake further (innovative) qualitative research on the barriers and bottlenecks (or on one
specific barrier or bottleneck, like school fees). Data collected on school supply and
financing can be leveraged here (as noted in Sections 4.2 and 4.3).
 Here too, the data analysis on the profiles of excluded children should be used and
leveraged.
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OOSC CMF March 2011


Such research can be done through interviews, focus groups and other qualitative research
techniques and methods (see Annex 6).
Note that this part may lengthen the number of pages of the country study.
6.2.3. Education policies and strategies
All country studies:
 The proposed methodology for this component is NOT to undertake new research, but
rather to systematically analyze existing evidence around the set of proposed questions in
Section 5.1 and to connect this analysis to the profiles of excluded children and to the
findings on the barriers and bottlenecks.
 This analytical desk review will draw on available literature, research, situation analysis,
surveys, reports, evaluations, analysis of sector plans and budgets, as well as ongoing
specific work undertaken at the country level by diverse partners.
 The analytical desk review will be produced by the country team.
 The analytical desk review will follow the structure of the proposed questions in Section 5.1.
Optional and complementary:
 Some country teams, under the guidance of the UNICEF Regional Offices, may chose to
undertake further (innovative) qualitative research on education policies and strategies (or
on one specific education policy or strategy, like the abolition of school fees).
 Here too, the data analysis on the profiles of excluded children should be used and
leveraged
 Such research can be done through interviews, focus groups and other qualitative research
techniques and methods (see Annex 6).
 Note that this part may lengthen the number of pages of the country study.
6.2.4. Social protection systems
All country studies:
 This part of the research will be guided by the Social Policy and Economic Analysis (SPEA)
Unit of UNICEF’s Division of Policy and Practice DPP) in New York and the Innocenti
Research Centre (IRC). It will be undertaken in close collaboration with the UNICEF
Education and Social Policy colleagues at the country level.
 IRC will hire consultants who will engage with the UNICEF Education and Social Policy
colleagues at the country level.
 Concerning parts (i) and (iv) on the mapping of social protection programmes and on the
financing of social protection systems, the IRC consultants will undertake the work and will
engage with the country teams on this work.
 Concerning parts (ii) and (iii) on the impact of social protection programmes and on crosssectoral approaches with regards to social protection systems, the IRC consultants will
provide country teams with generic interview questions (to be adapted to country contexts)
that the country teams will conduct with key informants to gather information on these parts.
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OOSC CMF March 2011


The IRC consultants will engage with the country teams on the way to undertake these
interviews, as well as provide support.
The IRC consultants will fill the grid as outlined in Annex 8 (for comparative purposes
between countries) and will engage with country teams on this grid.
The IRC consultants will provide support to the country team in writing this part of the
country study.
6.3. Country processes in writing the country studies and timeline




Constitution of country teams to lead and guide the research: These teams will need to
have the full approval of government officials and be linked to Local Education Groups or
the equivalent. The project work should build on what countries are doing and planning in
order to respond to country needs and strategies as well as ensure ownership and follow up.
It is proposed that these teams include experts (especially statisticians and economists) and
it is important to engage research institutions where possible. It is up to the countries to
determine the roles of the country team, frequency of meetings, etc.
Role of UNICEF Country Office: The UNICEF Education Chiefs in the Country Offices will
be coordinating the work on the country study and on the OOSCI. It is proposed that they
engage other UNICEF colleagues, especially Social Policy, Child Protection and
Planning/M&E. In some cases, UIS colleagues are also present in the country. In this case,
the project will be coordinated with them.
Development of country activity plans: It is proposed that countries develop an activity
plan according to the work to be done and the timeline below. Different milestones will be
needed for each of the country study chapters. The activity plan would be sent to the
UNICEF Regional Education Advisers (REA) for coordination purposes. The activities will
include, but not be confined to, the following:
 Development of methodological approach in the country.
 Development of potential consultant TOR based on the CMF and on country needs, and
potential hiring of consultants. It is up to the countries to decide how many consultants
they need and what for.
 Research work.
 Meetings of the country team to discuss, share, provide technical input and support,
monitor, etc.
Development of country support needs: Countries will formulate the technical support
needed for development of the country studies. This support will be communicated to the
UNICEF REA who will devise a capacity development and activity plan that may include the
following:
 Support from UIS Regional Advisers.
 Support from regional consultants: Some UNICEF Regional Offices (RO) have already
hired consultants to that effect.
 Support through regional meetings/workshops.
 Support from global experts to be coordinated with UNICEF’s Education Section in New
York.
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OOSC CMF March 2011

Review process: The first draft of the country studies will be submitted on 1 May 2011 or
earlier to the UNICEF REA and HQ Education Section. A review process will be devised and
communicated to countries and include the OOSCI regional and global UNICEF/UIS teams
as well as regional and global experts. The review teams will work closely with the countries
to improve the draft for final submission on 15 June 2011 (a detailed Working Document will
be sent to all participants). Prior to 1 May 2011, the country teams can engage with the
UNICEF REAs on initial drafts and reviews.
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OOSC CMF March 2011
ANNEXES
Annex 1: Data inventory template
INVENTORY OF NATIONAL DATA ON OUT-OF-SCHOOL CHILDREN (OOSC)
Country
Please complete this document for all sources of data on out-of-school children collected
during the last five years. Use a separate form for each data source. Examples for household
survey data and administrative data are attached.
Include information on data collection systems and sources that are not national in coverage
but provide information on out-of-school children for a specific geographic region of the
country (for example, a province or state) or for a specific sub-population group.
If applicable, please provide questionnaires, codebooks and other documents that provide a
better understanding of the data.
Name
Position
Department
Agency
Address
Telephone
Email
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OOSC CMF March 2011
Sources of data on out-of-school children
Complete one form for each data source. Please attach the questionnaire, codebook and
other information, if applicable.
Data source
Agencies responsible for collection and dissemination of data
Data collection date (not publication date)
Frequency of data collection (for example, annual, every two years)
Definition of an out-of-school child (for example, is not enrolled, did not attend in the last
three months)
Definitions of other education terms
School entrance age
Enrolment
Attendance
Drop-out
Educational
attainment
Other relevant terms
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OOSC CMF March 2011
Sample design and coverage of data collection (for example, national, specific geographic
region, specific sub-population group)
Smallest administrative area for which statistics on the out-of-school population are
statistically accurate
Types of disaggregation possible with data (for example, by age, sex, area, wealth quintile,
socio-economic group, ethnicity, religion, type of school)
Data availability and access (include information on type of data available and procedure to
acquire the data)
Data limitations (coverage, accuracy)
Other information
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OOSC CMF March 2011
Sources of data on out-of-school children: Example for household survey data
Complete one form for each data source. Please attach the questionnaire, codebook and
other information, if applicable.
Data source
National Household Expenditure Survey
Agencies responsible for collection and dissemination of data
National Statistical Office
Data collection date (not publication date)
January - March 2010
Frequency of data collection (for example, annual, every two years)
Every two years since 2000
Definition of an out-of-school child (for example, is not enrolled, did not attend in the last
three months)
The child did not attend school during the three weeks preceding the survey (reference:
survey manual)
Definitions of other education terms
School entrance age
Not used in the data collection (reference: survey manual)
Enrolment
Not applicable
Attendance
A child who attended school at any time during the current
school year (reference: survey manual)
Drop-out
A child who attended school during the previous school year but
did not attend during the current school year (reference: survey
manual)
The highest educational level attended by a person (primary,
secondary, tertiary)
Educational
attainment
Other relevant terms
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OOSC CMF March 2011
Sample design and coverage of data collection (for example, national, specific geographic
region, specific sub-population group)
Nationally representative survey. Excludes two remote islands that account for 2% of the
national population.
Smallest administrative area for which statistics on the out-of-school population are
statistically accurate
Province level
Types of disaggregation possible with data (for example, by age, sex, area, wealth quintile,
socio-economic group, ethnicity, religion, type of school)
Age group, sex, wealth quintile, urban/rural, education level
Data availability and access (include information on type of data available and procedure to
acquire the data)
Individual-level data (without personal information) available from National Statistical Office
upon request.
Data available in SPSS and Stata format.
Survey report available at www.xxx.gov.xx/surveys/hhsurvey2010.
Data limitations (coverage, accuracy)
Survey excluded two remote islands with 2% of the national population.
High number of missing values in responses to questions on household income.
Other information
Questionnaire and tables with sampling errors are attached.
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OOSC CMF March 2011
Sources of data on out-of-school children: Example for administrative data
Complete one form for each data source. Please attach the questionnaire, codebook and
other information, if applicable.
Data source
National Education Management Information System
Agencies responsible for collection and dissemination of data
Division of Planning, Ministry of Education
Data collection date (not publication date)
March 2010
Frequency of data collection (for example, annual, every two years)
Annual
Definition of an out-of-school child (for example, is not enrolled, did not attend in the last
three months)
A child who is not registered in school
Definitions of other education terms
School entrance age
A child who reached the age of 5 before 1 September.
Enrolment
All children registered in school (available from the school
census)
Attendance
All children attending school (available from the school
attendance sheet)
Drop-out
A child who stopped attending school during the school year
Educational
attainment
The highest grade a person completed
Other relevant terms
Repeater: A student who enrolled in the same grade in the
previous and current school year.
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Sample design and coverage of data collection (for example, national, specific geographic
region, specific sub-population group)
National, but some conflict areas did not submit data (about 5% of national enrolment).
Smallest administrative area for which statistics on the out-of-school population are
statistically accurate
School district
Types of disaggregation possible with data (for example, by age, sex, area, wealth quintile,
socio-economic group, ethnicity, religion, type of school)
Age, sex, geographic region, type of school (public, private, NGO-run), grade, education level,
with or without school grant, disability
Data availability and access (include information on type of data available and procedure to
acquire the data)
The Planning Department maintains the database since 2005. Annual statistical reports are
available at www.moe.gov.xx/schcensus/reports.
Data limitations (coverage, accuracy)
In some cases, enrolment is likely to be inflated.
Data on age-specific enrolment should be interpreted with caution.
Due to the flood in 2009, data for provinces A and B are not available for the school year
2009-2010.
Other information
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OOSC CMF March 2011
Annex 2: Software for classification of out-of-school children (Dimensions 2 and 3)
The UIS has designed a spreadsheet to facilitate the calculation of the number of out-of-school
children in Dimensions 2 and 3 as defined by the Five Dimensions of Exclusion (see Figure 9).
This guide explains the main components of the spreadsheet and provides instructions for its
use.
Software requirements: The spreadsheet can be used with MS Excel and OpenOffice.org
Calc.28
Abbreviations used in spreadsheet:
DHS
Demographic and Health Survey
ISCED
International Standard Classification of Education
MICS
Multiple Indicator Cluster Survey
OOSC
Out-of-school children
UIS
UNESCO Institute for Statistics
UNESCO
United Nations Educational, Scientific and Cultural Organization
UNPD
United Nations Population Division
Layout of spreadsheet: The spreadsheet is divided into two parts: in Tables 1, 2 and 3 the
user inputs data and in Tables 4, 5 and 6, the spreadsheet automatically calculates the share
and number of children in Dimensions 2 and 3. Each table is organized by age and level of
education.29
In table 1 of the spreadsheet, “Education system”, the entry ages and duration of primary
(ISCED 1) and lower secondary education (ISCED 2) are entered by the user. These ages are
needed for the calculation of the values in tables 4 and 5. Table 1 also lists the sources of the
population data in table 2 and the data for the basic calculations in table 3.
The user also enters data in table 2 “Population by age” and table 3 “School attendance status
(%)”. In table 2, the user enters population data by age, which may be from UN population
estimates30 or a national population census. Table 3 is for basic calculations on the population
from 4 to 17 years of age (depending on available data). For instructions on how to obtain the
data required for each variable in table 3 from household surveys, see Annex 3. Calculation for
sub-groups of the population – for example disaggregated by sex, area of residence, or wealth
28
OpenOffice.org Calc is a free, open-source alternative to Excel that can be obtained at
www.openoffice.org for various operating systems, including Windows, Mac OS and Linux.
29 The UIS has prepared customized spreadsheets for countries that are part of the Global Initiative on
Out-of-School Children. In these spreadsheets, the levels of education will be specified according to the
International Standard Classification of Education (ISCED), but the spreadsheets can be modified to
reflect the education system as defined in a country.
30 United Nations Population Division (UNPD). 2009. World population prospects: The 2008 revision. New
York: UNPD.
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OOSC CMF March 2011
quintile – is possible by creating copies of the spreadsheet and entering data for the target
groups in tables 2 and 3.
After entering the data for these four key variables, the spreadsheet will calculate the
percentage and absolute number of children in each category of out-of-school children by age.
Table 5 “Categories of OOSC (%)” lists the share of each age cohort that is expected to enter
by age 17, has dropped out and is expected never to enter. The second part of table 5,
“Categories of OOSC (population)”, uses the population data from table 2 to estimate the
absolute number of children in each category by age.
In table 6, “Categories of OOSC (%)” shows the share of the population of primary age and
lower secondary age in the three out-of-school categories, as well as the total percentage of
children in Dimensions 2 and 3. Similarly, “Categories of OOSC (population)” presents an
estimate of the absolute size of the out-of-school population and the three out-of-school
categories.31
Figure 9: Spreadsheet for calculation of data on out-of-school children
Explanation of calculation and functions in spreadsheet: The formulas to calculate the data
in tables 4, 5 and 6 of the spreadsheet can be reviewed in Excel and Calc by clicking on the
31
The results are weighted by each age cohort’s size in the school age population.
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OOSC CMF March 2011
respective cells. To follow each calculation step by step in the spreadsheet, the Evaluate
function in Excel can be helpful.32
In table 4 “New entrants as a % of OOSC never in school”, the percentage of children at each
single age who had never been in school and who entered school for the first time is calculated.
The probability to enter in the future refers to the likelihood of entering school for the first time by
age 17. Entry into the education system after age 17, as an adult, is not considered. Out-ofschool children who are expected to enter school after they have reached age 18 are grouped
together with persons who never enter school.33
In the second row of table 5, “Categories of OOSC (%)”, the above values are used to calculate
the probability that a child will enter at each consecutive age. The cumulative percentage of
children who enter school for the first time at any given age yields the total percentage of
current children who have never been to school who are expected to enter by the age 17 for
each age cohort. The formula for this calculation is relatively complex because the percentage
of children who are expected to enter by age 17 is the product of probabilities to enter in the
future.
By contrast, the group of children who have “dropped out” (row 1 of Table 5) can be measured
directly as the percentage of children at each age who attended school previously but are no
longer in school. The final category of children out of school, those who are “expected to never
enter” (row 3), is calculated as a residual, given by the proportion of children who are neither
expected to enter, nor have dropped out.
In the second half of table 5, “Categories of OOSC (population)”, the calculations above are
converted to absolute numbers by referring to the population data in table 2.
In table 6, the calculations by single age are grouped into primary and lower secondary age.
This table shows the percentage of children in Dimension 2 and Dimension 3, and the
breakdown of this population by categories based on school attendance. In “Categories of
OOSC (population)”, the calculations above are converted to absolute numbers by referring to
the population data in table 2.
To the right of table 6, the total population of children in primary and lower secondary age is
presented. In addition, the table shows the percentage and number of children in school
(primary or higher education) for both age groups. These numbers are used to generate the
graph in the lower right corner of the spreadsheet.
32
To access the Evaluate function in Excel 2003, first click on the cell to be evaluated. Next, click on
Tools in the menu and select Formula Auditing - Evaluate Formula. Each click on the Evaluate button
performs one step of the calculation in the cell formula. OpenOffice.org Calc does not have a similar
feature.
33 Analysis of survey data shows that only a negligible percentage of out-of-school children of primary and
lower secondary school age enter school as adults. To keep the framework simple these children are not
reported as a separate group.
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OOSC CMF March 2011
Annex 3: Example code to generate data for classification of out-of-school children
Annex 2 describes the spreadsheet that can be used to calculate and classify the number of
children in Dimensions 2 and 3. For the calculations it is necessary to provide a minimum set of
data that can be obtained from household surveys, for example DHS or MICS (see Section 3.1).
The example below shows how the data can be extracted from DHS datasets with Stata. It is
possible to adapt the code for use with other statistical packages, for example SPSS or SAS.34
For disaggregated analysis, the code below can be easily changed to create the basic variables
for subgroups. To do this in Stata, use the “keep if” command to select subgroups of the entire
sample (for example, only boys or only girls) before the schooling variables are calculated. The
user can input these variables in table 3 of the calculation spreadsheet. It is important to note
that the population estimates in table 2 must also be updated to reflect the population of the
subgroup analyzed.
Stata code: Variable coding using DHS data
*
*
*
*
*
Stata do-file to create out-of-school typology data, Cambodia 2005-06 DHS.
Calculation with school attendance data for two consecutive years.
Missing values are excluded from calculations.
UNESCO Institute for Statistics, 25 February 2011.
Required files: "Cambodia 2005-06 DHS HH members.dta".
#delimit cr
version 11.1
clear
set memory 50m
set more off
capture log close
* Load data
use "Cambodia 2005-06 DHS HH members.dta"
* Country information
local country = "Cambodia"
local year = "2005-06"
local survey = "DHS"
* ========================================
* Age variable
* Age
gen age = hv105
* Option 1: Keep children aged 4 to 17 years, or children with school data
* keep if age>=4 & age<=17
* Option 2: If ages are adjusted in next step, keep children up to 18 years of age
keep if age>=5 & age<=18
* Adjust ages if survey was conducted more than 6 months after beginning of school year
replace age = age-1
34
Stata is described at www.stata.com, SPSS at www.spss.com, and SAS at www.sas.com.
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* ========================================
* Weight variable
* Household weight
gen hhweight = hv005/1000000
lab var hhweight "HH weight"
* ========================================
* Schooling variables
* Ever attended school
gen schlever = hv106
replace schlever = 1 if (schlever==2 | schlever==3)
replace schlever = 1 if schlever!=1 & (hv121==1 | hv121==2 | hv125==1)
replace schlever = . if schlever>1
lab var schlever "Ever attended school"
lab def schlever 0 "Never school" 1 "Attended school"
lab val schlever schlever
* Highest level attended
gen highlevl = hv106
replace highlevl = 0 if schlever==0 & highlevl>=8
recode highlevl 1=2 2=3 3=4
replace highlevl = 1 if highlevl==0 & schlever==1
replace highlevl = . if highlevl>=8
lab var highlevl "Highest level attended"
lab def highlevl 0 "None" 1 "Preschool" 2 "Primary" 3 "Secondary" 4 "Higher" 5 "Non-formal"
lab val highlevl highlevl
* School attendance in current school year
gen school = (hv121==1 | hv121==2) if hv121<9
replace school = 0 if hv106==0 & school>=.
lab var school "School attendance"
lab def school 0 "Not in school" 1 "In school"
lab val school school
* Level of education attended in current school year
gen edlevel = hv122
replace edlevel = 0 if school==0 & edlevel>=8
recode edlevel 1=2 2=3 3=4
replace edlevel = 1 if edlevel==0 & school==1
replace edlevel = . if edlevel>=8
lab var edlevel "Current level attended"
lab def edlevel 0 "None" 1 "Preschool" 2 "Primary" 3 "Secondary" 4 "Tertiary" 5 "Non-formal"
lab val edlevel edlevel
* Grade attended in current school year
gen edgrade = hv123
replace edgrade = . if edgrade>=98 & school==1
lab var edgrade "Current grade attended"
* School attendance in previous school year
gen schlly = hv125
replace schlly = 0 if hv106==0 & school>=.
lab var schlly "School attendance last year"
lab def schlly 0 "Not in school" 1 "In school"
lab val schlly schlly
* Level of education attended in previous school year
gen edlevlly = hv126
replace edlevlly = 0 if schlly==0 & edlevlly>=8
recode edlevlly 1=2 2=3 3=4
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OOSC CMF March 2011
replace
replace
lab var
lab def
lab val
edlevlly
edlevlly
edlevlly
edlevlly
edlevlly
= 1 if edlevlly==0 & schlly==1
= . if edlevlly>=8
"Level attended last year"
0 "None" 1 "Preschool" 2 "Primary" 3 "Secondary" 4 "Higher" 5 "Non-formal"
edlevlly
* Set school attendance to 0 for preschool and non-formal education
replace school = 0 if edlevel==1 | edlevel==5
replace schlly = 0 if edlevlly==1 | edlevlly==5
* Drop cases with missing data
drop if school>=. | schlever>=. | highlevl>=. | schlly>=. | edlevel>=.
drop if edlevel>=2 & edlevel<=4 & edgrade>=.
* Drop cases with data error
drop if edlevlly > highlevl
* ========================================
* Variables for typology of out-of-school children
* Variable to identify children out of school
gen oos = school==0
* Set preschool or non-formal education to out of school
replace oos = 1 if edlevel==1 | edlevel==5
lab var oos "Out of school"
lab def oos 0 "In school" 1 "Out of school"
lab val oos oos
* Set schlever to 1 if in primary or higher in previous or current school year
replace schlever=1 if inlist(edlevlly,2,3,4)
replace schlever=1 if inlist(edlevel,2,3,4)
* Variable to identify children never in school
gen neverschl = schlever==0
* Set preschool or non-formal education to never in school
replace neverschl = 1 if (inlist(highlevl,0,1,5,.,.a) & inlist(edlevlly,0,1,5,.,.a) ///
& inlist(edlevel,0,1,5,.,.a))
lab var neverschl "Never in school"
lab def neverschl 0 "Attended school" 1 "Never in school"
lab val neverschl neverschl
* Dropped out with or without primary completed, after having attended primary or higher
gen dropped = (oos==1 & highlevl>=2 & highlevl<=4)
lab var dropped "Dropped out"
lab def dropped 0 "Didn't drop out" 1 "Dropped out"
lab val dropped dropped
* Entered school (not in school last year and in first grade of primary this year)
* Identify children who entered grade 1 of primary school
gen entered = schlly==0 & edlevel==2 & edgrade==1
lab var entered "Entered school"
lab def entered 0 "Did not enter" 1 "Entered"
lab val entered entered
* Log file with data check
log using "`country' `year' `survey' OOS typology.txt", text replace
* Sum of values must be 1
egen check1 = rowtotal(school oos)
egen check2 = rowtotal(school neverschl dropped)
forval i = 1/2 {
tab check`i', m
}
tabstat check1 check2, by(age)
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log off
* ========================================
* Create variables for single year of age
* Number of observations in each age group
levelsof age, local(ages)
foreach a of local ages {
sum age if age==`a' [aw=hhweight]
local obs`a' = round(r(sum_w))
}
* Mean values per age
collapse (mean) school oos neverschl dropped entered [aw=hhweight], by(age)
* Store number of observations
gen obs = .
levelsof age, local(ages)
foreach a of local ages {
replace obs = `obs`a'' if age==`a'
}
order obs
* Data check log continued
log on
* Sum of values must be 1
gen check1 = school + oos
gen check2 = school + neverschl + dropped
format check1 check2 %9.3f
forval i = 1/2 {
tab check`i', m
}
tabstat check1 check2, by(age) format
log close
* Drop data check variables
drop check1 - check2
* ========================================
* Save data
* Drop in school variable (= 100 - oos)
drop school
* Convert variables to percent
foreach var of varlist oos - entered {
replace `var' = `var' * 100
}
* Format variables
format oos - entered %5.1f
* Add country identifiers
gen country = "`country'"
gen year = "`year'"
gen survey = "`survey'"
* Label
lab var
lab var
lab var
lab var
variables
country "Country"
year "Year"
survey "Survey"
age "Age"
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lab
lab
lab
lab
lab
var
var
var
var
var
obs "Observations"
oos "Out of school (%)"
neverschl "Never in school (%)"
entered "In school, not in school in previous year (%)"
dropped "Left school with or without primary completed (%)"
* Save data
order country year survey age obs oos dropped neverschl entered
sort age
compress
save "`country' `year' `survey' OOS typology.dta", replace
* Transpose data for typology calculation matrix
drop country year survey obs
xpose, clear varname
ren _varname group
order group
* Save as comma-separated text file, for import into Excel
outsheet using "`country' `year' `survey' OOS typology.csv", nonames replace comma
* End of do-file
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Annex 4: Data tabulation plan
The data tabulation plan provides a format for the presentation of quantitative results to ensure
consistency across all country studies. Descriptions of the indicators in the following tables are
provided in Section 3 and definitions are listed in Annex 5. The present Annex lists information
regarding all proposed tables. The tables can be customized depending on data availability;
country teams may also choose to present additional data. Furthermore, it is valuable to
produce tables from different data sources, for example to compare values calculated from
different household surveys. For some indicators or groups of disaggregation, it is important to
note that surveys may not have large enough sample sizes to produce reliable estimates.
Table 1: Percentage of children of pre-primary age in pre-primary or primary education,
by sex and other characteristics
Not attending
school
Attending preprimary school
Atrending
primary school
Male
Attending either
pre-primary or
primary
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
Female
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
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Table 2: Percentage of children attending school, by age and level of education
Pre-Primary
Primary
Lower
Secondary
Upper
Secondary
PostSecondary
Total
Male
5
6
7
8
9
10
11
12
13
14
15
16
17
Female
5
6
7
8
9
10
11
12
13
14
15
16
17
Total
5
6
7
8
9
10
11
12
13
14
15
16
17
Table 3: Adjusted net enrolment rate (ANER), by sex and level of education, with GPI
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Table 4: Number of children out of school, by age group and sex
Table 5: Percentage of out-of-school children by school exposure, by age group and sex
Table 6: Adjusted primary school net attendance rate (ANAR), by age, sex and other
characteristics
Male
Net attendance
Number of
rate
children
Female
Net attendance
Number of
rate
children
Total
Net attendance
Number of
rate
children
Age
6
7
8
9
10
11
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
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Table 7a: Adjusted lower secondary school net attendance rate (ANAR), by age, sex and
other characteristics
Male
Net attendance
Number of
rate
children
Female
Net attendance
Number of
rate
children
Total
Net attendance
Number of
rate
children
Age
12
13
14
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
Table 7b: Percentage of lower secondary age attending primary school, by age, sex and
other characteristics
Male
Attendance rate Number of
children
Female
Attendance Number of
rate
children
Total
Attendance Number of
rate
children
Age
12
13
14
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
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Table 8: Percentage of primary school age children out of school, by age, sex and other
characteristics
Total
Female
Male
%
Number of
children
%
%
Number of
children
Number of
children
Age
6
7
8
9
10
11
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
Table 9: Percentage of lower secondary school age children out of school, by age, sex
and other characteristics
Male
%
Number of
children
%
Female
Number of
children
Total
%
Number of
children
Age
12
13
14
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
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Table 10: Percentage of out-of-school primary- and lower secondary-aged children who
are involved in child labour, by individual and household characteristics (to be provided
by UCW)
Table 11: Number of out-of-school primary- and lower secondary-aged children who are
involved in child labour, by individual and household characteristics (to be provided by
UCW)
Table 12: Percentage of primary- and lower secondary-aged child labourers who are out
of school, by individual and household characteristics (to be provided by UCW)
Table 13: Number of primary- and lower secondary-aged child labourers who are out of
school, by individual and household characteristics (to be provided by UCW)
Table 14: Percentage of primary- and lower secondary-aged out-of-school children at
work in employment, household chores, or both, by sector, time intensity and other
characteristics (to be provided by UCW)
Table 15: Percentage of out-of-school children suffering work-related illness or injury (to
be provided by UCW)
Table 16: Percentage of households with children out of school enjoying access to
formal social protection (to be provided by UCW)
Table 17: Percentage of households with children out of school enjoying access to credit
(to be provided by UCW)
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Table 18: Dropout rate by grade at the primary and lower secondary level of education,
by sex and other characteristics
Primary education
1
2
3
4
Lower Secondary education
5
6
7
8
9
Residence
Urban
Rural
Wealth index
quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/
Language/ Religion
Group 1
Group 2
Group 3
Total
Table 19: Survival rate to the last grade of primary education and to the last grade of
lower secondary education
Male
Female
Total
GPI
Survival rate to the last grade of
primary education
Survival rate to the last grade of
lower secondary education
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Table 20: Dropout rate from primary education, by age, sex and other characteristics
Cumulative dropout rate from primary education
Male
Female
Total
Age
6
7
8
9
10
11
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
Table 21: Dropout rate from lower secondary education, by age, sex and other
characteristics
Cumulative dropout rate from lower secondary education
Male
Female
Total
Age
12
13
14
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Child labour status
Child labourer
Not child labourer
Total
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Table 22: Percentage of new entrants to primary education with no ECCE
experience
Male
Female
Total
Residence
Urban
Rural
Wealth index quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/Language/Religion
Group 1
Group 2
Group 3
Total
Table 23: Repetition rate by grade at the primary and lower secondary level of education,
by sex and other characteristics
Primary education
1
2
3
4
Lower Secondary education
5
6
7
8
9
Residence
Urban
Rural
Wealth index
quintiles
Poorest
Second
Middle
Fourth
Richest
Ethnicity/
Language/ Religion
Group 1
Group 2
Group 3
Total
Table 24: Transition rate from primary to lower secondary education
Male
Female
Total
GPI
Transition rate to lower
secondary education
Table 25: Percentage of primary- and lower secondary-aged students who are involved in
child labour, by individual and household characteristics (to be provided by UCW)
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Table 26: Percentage of pupils in schools with basic resources
Table 27: Key education expenditure indicators
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Annex 5: Definitions of selected education indicators
The following list provides the definitions of key education indicators for the quantitative
analysis. The basic calculation method for most of these indicators can be found in Section 3.
For detailed information regarding the calculation methods for these indicators, consult the UIS
online education glossary.
Education indicators calculated from administrative or household survey data:
 Net intake rate. The number of new entrants in the first grade of primary education who are
of the theoretical primary school entrance age, expressed as a percentage of the population
of the same age.
 Survival rate, by level. The percentage of a cohort of pupils (or students) enrolled in the first
grade of a given level or cycle of education in a given school year who are expected to
reach a given grade, regardless of repetition. Survival rates are calculated on the basis of
the reconstructed cohort method, which uses data on enrolment and repeaters for two
consecutive years.
 Transition rate from primary to secondary general education. The number of new entrants to
the first grade of secondary education in a given year, expressed as a percentage of the
number of pupils enrolled in the final grade of primary education in the previous year.
 Percentage of repeaters. The number of pupils or students who are enrolled in the same
grade (or level) as the previous year, expressed as a percentage of the total enrolment in
the given grade (or level) of education.
 Percentage of new entrants to primary education with ECCE experience. The number of
new entrants to primary education who have attended some form of organized early
childhood care and education (ECCE) programmes, expressed as percentage of total
number of new entrants to primary education.
 Drop-out rate by grade. Proportion of pupils from a cohort enrolled in a given grade in a
given school year who are no longer enrolled in the following school year.
Education indicators calculated from administrative data:
 Primary adjusted net enrolment rate (ANER). Total number of pupils of the official school
age group who are enrolled at primary or secondary education levels, expressed as a
percentage of the primary population.
 Lower secondary adjusted net enrolment rate (ANER). Total number of pupils of the official
lower secondary school age group who are enrolled at lower or upper secondary education
levels, expressed as a percentage of the lower secondary age population.
 Gross enrolment rate (GER), by education level. Total enrolment in a specific level of
education, regardless of age, expressed as a percentage of the eligible official school-age
population corresponding to the same level of education in a given school year.
 Gender parity index of ANER or GER, by education level. The ratio of female-to-male values
of a given indicator. A GPI between 0.97 and 1.03 indicates gender parity.
 Pupil/teacher ratio. The average number of pupils per teacher at the level of education
specified in a given school year, based on headcounts for both pupils and teachers.
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OOSC CMF March 2011


Percentage of trained teachers. The number of teachers who have received the minimum
organized teacher-training (pre-service or in service) required for teaching at the relevant
level of education in the given country, expressed as a percentage of the total number of
teachers at the given level of education.
Percentage of private enrolment. The number of pupils at a given level of education enrolled
in institutions that are not operated by a public authority but controlled and managed,
whether for profit or not, by a private body such as a non-governmental organization,
religious body, special interest group, foundation or business enterprise, expressed as a
percentage of the total number of pupils or students enrolled at the given level of education.
Education indicators calculated from household survey data:
 Adjusted primary net attendance rate (ANAR). The percentage of the official primary school
age population that attends either primary or secondary school.
 Adjusted lower secondary net attendance rate (ANAR). The percentage of the official lower
secondary age population that attends lower or upper secondary school.
 Primary gross attendance rate (GAR). The total number of students attending primary
school, regardless of age, expressed as a percentage of the official school level population.
 Gender parity index for primary and secondary NAR and ANAR. The ratio of female-to-male
values of a given indicator. A GPI between 0.97 and 1.03 indicates gender parity.
Education indicators calculated from education finance data:
 Educational expenditure by nature of spending as a percentage of total educational
expenditure on public institutions, by level. Spending by nature (salaries, other current, total
current or capital) expressed as a percentage of the expenditure for public educational
institutions of the specified level. Salaries and other current add up to the total current
expenditure. Public subsidies to the private sector and administrative costs should be
excluded.
 Public expenditure per student, by level, as a percentage of GDP per capita. Total public
expenditure per student in the specified level expressed as a percentage of GDP per capita.
 Total public expenditure per student, by level (PPP US$). Total public expenditure per pupil
or student in the specified level expressed in US$ and adjusted in terms of purchasing
power parity.
 Total public expenditure on education as a percentage of GDP. Current and capital
expenditures on education by local, regional and national governments, including
municipalities (household contributions are excluded), expressed as a percentage of GDP.
 Public expenditure on a specific ISCED level as a percentage of total public expenditure on
education. Public expenditure for a given education level (primary, lower secondary)
expressed as a percentage of total public expenditure on education.
 Total public expenditure on education as a percentage of total government expenditure.
Current and capital expenditures on education by local, regional and national governments,
including municipalities (household contributions are excluded), expressed as percentage of
total government expenditure on all sectors (health, education, and social services).
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Annex 6: Qualitative research with and about children: Methodological and ethical
considerations
There are a variety of techniques that are used to elicit the views of children. The techniques
chosen by researchers have implications for the data produced. The following account on
qualitative research methods with and about children is mainly based on Fargas-Malet et al
(2010). For additional references see Chawla (2001) and Graham and Fitzgerald (2010). There
are also a number of useful computer applications for analyzing qualitative data (CAQDAS),
such as Nudist, Atlasti (www.atlasti.com) and NVivo (www.qsrinternational.com).
Stages of the research process
 Gaining access and seeking consent: Researchers must gain consent from a range of
different ‘gatekeepers’: school staff and parents, all of whom may wish to contribute to
children’s responses. Informed consent should be freely given. Also, the notion of consent
might exclude some children, such as disabled or refugee children, since it might not always
be possible to obtain consent in those particular contexts. Gaining access has another
dimension: One must also be aware of questions of inside/outside power dimensions and
consider how to deal with them. Who is the researcher in relation to the informants? How will
the informants respond to the researcher? The researcher may lack a context for what
children are trying to convey. The researcher must understand the everyday language of the
interviewees, ask understandable questions and interpret what the interviewees say in
response. Adults often believe that they know more than children do and it can be hard for
researchers to really listen to what children say.
 Context/location: The research context might affect what children will talk about. At school,
for example, once school staff members have given consent, children might find it difficult to
decline to take part or may become passive as a way of gaining control. Children may
perceive the researcher in a ‘teacher’ role and feel pressured to give the ‘right’ answers to
the research questions. Children might say what they think adults want them to say. Using
the child’s own home as a location can also entail some difficulties. Finding a private and
quiet space in the home can be problematic due to child protection issues.
 Data collection: questions and activities: To establish a rapport, one should start asking
about things the child already knows or sees as relatively unthreatening such as specific
daily events, routines or feelings (happiest, saddest, most embarrassing event). When the
research involves sensitive issues, it is advisable to present less difficult questions first. Use
non-verbal behaviours (e.g. keeping eye contact, sounds like ‘mm’ or ‘really’, and head
nods) and verbal prompts (such as ‘tell me more about that’), which indicate that the
interviewer is listening and wants to hear the child’s story. Use open or why-questions. Use
follow-up questions to make sure that the interviewee is not just guessing an answer. Ask
questions about issues that are pertinent and related to children’s own experiences.
 Confidentiality and child protection issues: What would the researcher do if the child
revealed serious harm or ill-treatment, or if you as the researcher identify a medical
condition or learning difficulty about which the parents could take action? What should the
researcher do when abuse is disclosed by the child and she or he does not agree to talk to
somebody that can help?
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OOSC CMF March 2011
Techniques, methods and tools when researching children
The methodology chosen needs to match the research questions of the project, respect
limitations of time and resources, be sensitive and ethical, and take into account the
characteristics and needs of participants, as well as the cultural and physical setting where it
takes place.
 Interviews can be individual interviews or group discussions. Group discussions with
children can be difficult because children may judge others’ responses, be open to peer
rejection if they put forward an alternative viewpoint or just tell the researcher what they
think the researcher may want to hear. They can also refuse to cooperate.
 Focus Groups present a potential challenge because they are not natural discussion
groups and often group together people who wouldn’t normally discuss or disclose
information.
 Triangulation (checking the facts in other ways) is very useful when interviewing children,
as they sometimes link fact and fiction or deliberately tell lies. Backing up interviews and
focus group discussions with observations, interviews with adults and other children and
home (or school) visits can act as triangulation opportunities.
 Using photography: With our renewed vision of children as valid interpreters of their own
experiences, researchers are now asking children to take their own photographs to be used
later as interview stimuli, rather than using other people’s pictures, as children’s own
photographs are probably more likely to reflect what matters to them. Some children may be
confident and experienced with cameras and enjoy the activity, while others may lose their
camera, struggle to find inspiration or may be embarrassed about their photography skills,
and just take very few pictures. Children’s own pictures can also act as prompts to a child’s
personal story. Photographs can give structure to an interview, provide a focus, and act as a
clear prompt.
 Drawings: Until recently, researchers focused on what they understood the child’s drawing
meant rather than on the child’s explanation of what the drawing was about. However, there
appears to have been a shift of focus ‘from what the children draw to what the children say
about what they draw’. Drawing maps or plans is also a popular method in research with
children; and it has been used in many studies to gather information about significant
spaces for children. However, not all children consider drawing to be fun and some children
may be inhibited about their drawing capabilities.
 Participatory research techniques are now frequently used in interviews and focus groups
with young children to serve different aims. They enable participants to create ‘inclusive
accounts using their own words and frameworks of understanding. Charts and diagrams can
enable children to express themselves at depth. Grouping and ranking exercises, ‘can be
used to stimulate discussion on relevant topics.
 Stimulus material’ or prompts: Written prompts – such as sentence completion, wishes,
word choice prompts, or unfinished stories to complete – have been widely used in
interviews with children. Feelings faces or feelings cards have also been used especially to
facilitate communication when asking about sensitive issues.
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OOSC CMF March 2011




Diaries: Young children have been encouraged to tell their stories in memory books, diaries
or life maps. Diaries are especially useful to explore children’s use and perception of time.
For some children, diaries can be too much like school work.
‘Storygames’ have been used when children’s own stories may be deeply traumatic. This
involves children creating a story, where each child is invited to give a line of the story and
the story goes from one child to another until it is finished. Life story books have been
frequently used in research people with learning disabilities and as a therapeutic tool in
family therapy with traumatised children.
Observation is especially suited for researching very young children, but not as useful for
older children, who can be interviewed instead, since as children grow up, they become
more aware of the presence of observers.
Questionnaires may have some advantages, including being relatively quick to administer;
their potential capacity to collect large amounts of standard data and reach large samples;
and the fact that some children might find it easier to answer questions in this way rather
than face-to-face with a stranger. However not all children find it easy to communicate well
in writing. Young children may respond to a question, even if they do not know the answer.
Analysis of qualitative data can be complicated. Use is made of transcripts of kinds of data
collection. Data are often taken at face value without considering the influence of the researcher
and the act of doing the research. For example, there are power relations between young
children when there are differences in age, sex, race and language. The interpersonal dynamics
of data collection is largely taken as a given. Few researchers actually explain how they decide
what is the focus of their findings and how they arrived at such a decision. There are at least
four basic steps to analyzing qualitative data:
 Organize the data: Get the data into a format that is easy to work with. After organizing the
data, you should have an overall picture of the complete set of data.
 Shape the data into information: After looking at the data, assess what type of themes are
coming through. This analysis is done by sorting. Note down the different categories or
types of responses found. You can use separate cards or sheets of paper to do this step.
 Interpret and summarize the information: Don’t try to quantify the responses (for
example, you cannot say "half the people said ..."). Instead look for the range of views
expressed. It is possible to say "some ..." or "others ...", but you cannot say "most ..." or "few
...". It is important to make sure all opinions or views are represented in the summary and
give examples from the raw data.
 Explain the information: When trying to explain what the information means, it is advisable
to discuss it at length with the other researchers in the team. It is always better to be
cautious about leaping to conclusions or making assumptions.
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Annex 7: Typology of social protection programmes
In order to proceed coherently with regards to the mapping and analysis of social protection
programmes, below is a definition and typology that can be used. Please note that this is a
draft based on a social protection guidance that is being developed. So do not circulate
this info beyond the present OOSC work.
UNICEF’s working definition of social protection: Social protection is the set of public and
private policies and programmes aimed at reducing the economic and social vulnerability of
children, women and families, in order to ensure their access to a decent standard of living and
essential services. At the core of social protection measures, UNICEF focuses on four
components:
 Social Transfers;
 Programmes to ensure economic and social access to services;
 Social support and care services; and
 Legislation and policies to ensure equity and non-discrimination in children’s and families’
access to services and employment/livelihoods.
It is important however to remember that social protection has many definitions used by different
actors. There is common ground in these definitions and it simply important when working to
others to be clear where there is common understanding and where there may be differences.
While acknowledging that the appropriate measures need to be identified and owned within
each national context, UNICEF focuses on four core social protection components:
 Social Transfers: Social transfers encompass both cash and in-kind transfers. While
UNICEF has and will continue to play a strong role in supporting and building the evidence
on predictable, state-provided cash transfers, other types of transfers can be appropriate
and require assessment (political and social as well as technical) in any given context.
Social transfers also include more short-term safety net programmes which can play an
important in responding to aggregate shocks – for example economic, natural (drought,
floods, etc), conflict and displacement.
 Programmes to ensure economic and social access to services: For children and
adults, access to services is crucial – yet even where quality supply exists, a number of
economic and social barriers stand in the way. Programmes which address barriers to
accessing services – particularly financial and social – reduce children’s and adults’
vulnerability to factors such as heightened nutritional vulnerability of young children, and the
economic or social vulnerabilities which compound this. Core to this are various types of
economic support (again, cash or in-kind), including removal of user fees, subsidies and
vouchers; and programmes which support overcoming social barriers to access at the
community or household/individual level for those who are vulnerable and/or marginalized.
While obviously coordination with services is crucial, social protection itself would not
include the core supply side of education and health services – which are part of broader
social policy but covered by other sectors.
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OOSC CMF March 2011


Social support and care services: Recognizing that social sources of vulnerability may
require specific types of social support, this component captures a range of human resource
intensive support that helps to identify and respond to vulnerability and deprivation
particularly at the child and household level. These services help to reduce social
vulnerability and exclusion, to strengthen resilience and capacity to cope and overcome
shocks and strains, and to link children, women and families to existing programmes and
services. Examples include family based care, family support services, home based care.
This component is often overlooked by others, and is an important part of UNICEF’s
contribution to the policy debate.
Legislation and policies to ensure equity and non-discrimination in children’s and
families’ access to services and employment/livelihoods: Considering social protection
from a child rights perspective requires removing legal and policy barriers and proactively
ensuring equity through protection against exclusion and discrimination. This is part of the
“transformative”35 dimension of social protection and the need to more fundamentally
transform societies in order to reduce vulnerability. This component is not meant to be so
broad as to encompass all anti-discrimination policy, but to focus on the link to accessing to
services and income security. Examples include equal pay legislation, inheritance rights,
childcare policy, or maternity and paternity leave.
TYPOLOGY OF SOCIAL PROTECTION PROGRAMMES
Types of instruments
Social transfers - long-term
predictable transfers and
safety net/ humanitarian
response
Programmes to ensure
economic and social access
to services
Social support and care
services
Legislation and policies to
ensure equity and nondiscrimination in children’s
and families’ access to
services and
employment/livelihoods
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Specific instruments
Cash transfers (including pensions, child benefits, poverty-targeted,
seasonal)
Food transfers
Food and fuel subsidies
Nutritional supplementation
Public works
User fee abolition
Social health insurance
Exemptions, vouchers, subsidies
Provision of ARVs
Family support services
Home-based care
Birth registration
Minimum and equal pay legislation
Employment guarantee schemes
Childcare policy
Maternity and paternity leave
Removal of discriminatory legislation or policies affecting service
provision or employment
35
Transformative social protection addresses the structural aspects of vulnerability, with a strong focus
on social exclusion. For more information, see Devereux and Sabates-Wheeler (2004).
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OOSC CMF March 2011
Annex 8: Grid for social protection programmes
COUNTRY
TYPE OF
PROGRAMME
COVERAGE
Percent of
total
population
Universal
or
targeted
(and
targeting
method)
BENEFIT
(Description)
FINANCING
Total
cost
(%GDP)
Financing
source
IMPLEMENTATION DETAILS
Method of
delivery (e.g.
Periodicity banking
(e.g.
system,
weekly,
postal
monthly,
service,
etc.)
NGOs, local
government
unit, etc.)
YEAR PROGRAMME
INITITIATED
Lead
Agency/
Ministry
Child benefit
Old age
benefit
Others
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OOSC CMF March 2011
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