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CC-MODA – Cross Country Multiple Overlapping Deprivation Analysis:
Analysing Child Poverty and Deprivation in sub-Saharan Africa
Marlous de Milliano and Ilze Plavgo
Office of Research Working Paper
WP-2014-19 | November 2014
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de Milliano, M. and I. Plavgo (2014). Analysing Child poverty and deprivation in sub-Saharan Africa:
CC-MODA – Cross Country Multiple Overlapping Deprivation Analysis, Innocenti Working Paper
No.2014-19, UNICEF Office of Research, Florence.
© 2014 United Nations Children’s Fund (UNICEF)
ISSN: 1014-7837
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ANALYSING CHILD POVERTY AND DEPRIVATION IN SUB-SAHARAN AFRICA: CC-MODA – CROSS COUNTRY MULTIPLE OVERLAPPING DEPRIVATION ANALYSIS Marlous de Milliano and Ilze Plavgo UNICEF Office of Research, University of Florence
mdemilliano@unicef.org and iplavgo@unicef.org
Abstract. This paper analyses multidimensional child deprivation across thirty countries in sub-
Saharan Africa, applying the Multiple Overlapping Deprivation Analysis (MODA) methodology that
measures various aspects of child poverty. The methodology has been adapted to the particular
needs of this cross-country comparative study, standardising the indicators and thresholds to allow
comparability across countries. Child poverty is defined as non-fulfilment of children’s rights to
survival, development, protection and participation, anchored in the Convention on the Rights of
the Child. DHS and MICS household survey data is used, taking the child as unit of analysis and
applying a life-cycle approach when selecting dimensions and indicators to capture the different
deprivations children experience at different stages of their life. The main objective of the paper is
to present a direct method of child poverty measurement analysing deprivations experienced by
the child. The paper goes beyond mere deprivation rates and identifies the depth of child poverty
by analysing the extent to which the different deprivations are experienced simultaneously. The
analysis is done across thirty countries in sub-Saharan Africa that together represent 78% of the
region’s total population. The findings show that 67% of all the children in the thirty countries
suffer from two to five deprivations crucial to their survival and development, corresponding to
247 million out of a total of 368 million children below the age of 18 living in these thirty countries.
For the other 15 countries of sub-Saharan Africa where the CC-MODA analysis could not be carried
out, predictions of child deprivation rates have been made using GDP per capita, urban population
share, and population size. Based on the actual as well as the predicted multidimensional
deprivation rates, just under 300 million children in sub-Saharan Africa are multidimensionally
poor, being deprived in two to five dimensions crucial for their survival and development. The
findings are also compared with other existing poverty measures, showing that for the countries
included in the analysis, monetary poverty measures (both the international $1.25 a day and
national poverty measures) are weak predictors of multidimensional child poverty. The study finds
stronger correlation between multidimensional child deprivation and GDP per capita. The paper
underlines that monetary poverty and multidimensional deprivation are conceptually different,
complementary poverty measures and that there are advantages in measuring both
simultaneously, especially when measuring child poverty.
Keywords: child poverty; multidimensional poverty and deprivation; child rights
Acknowledgements: We are grateful for the valuable contribution of many UNICEF colleagues, as
well as the researchers working on multidimensional poverty measurement in OPHI, the University
of Bristol, the University of Maastricht, and the University of Sussex, for their advice and
inspiration. We are especially thankful to Chris de Neubourg, Jingqing Chai, Ziru Wei, Sudhanshu
Handa, and Goran Holmqvist for their substantive engagement throughout the project. We are also
very grateful to Keetie Roelen and Pierre Martel for their valuable comments and suggestions
when reviewing this paper.
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TABLE OF CONTENTS
1. Introduction 6
2. Background 7
3. Methodology 8
4. Findings 11
4.1 Single Deprivation Analysis 12
4.2 Deprivation Count for each Child: Deprivation distribution within and across countries 14
4.3 Deprivation Overlap Analysis 16
4.4 Multidimensional Deprivation Ratios 18
4.5 Decomposition of the Adjusted Multidimensional Deprivation Headcount 21
4.6 GDP per capita and Multidimensional Child Deprivation 22
4.7 Monetary Poverty and Multidimensional Child Deprivation 23
4.8 Multidimensional Deprivation among Children in sub-Saharan Africa 27
5. Conclusion 29
References 31
Annexes 33
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1. INTRODUCTION
This paper brings together the results of multidimensional deprivation analyses for thirty countries
in sub-Saharan Africa.1 As these thirty countries represent 78% of the total population in the
region, the paper also tries to shed light on the incidence and depth of child poverty across sub-
Saharan Africa as a whole. The analysis is child-centred taking into account various aspects of
children’s well-being, anchored in the Convention on the Rights of the Child. The paper presents
key results on single and multiple deprivations of children within and across the selected countries.
The results summarise part of larger-scale research using UNICEF’s Multiple Overlapping
Deprivation Analysis (MODA) methodology that extends to the existing approaches in the field of
child poverty. For this study, child poverty is defined as the non-fulfilment of children’s rights to
survival, development, protection and participation at different stages of children’s life.
More than a decade of research using multidimensional poverty measures has brought significant
progress in improving the measurement and understanding of poverty. Alongside monetary
poverty measurement, it has become more and more common to analyse the various direct
deprivations that people experience, and to analyse such deprivations simultaneously. Research
analysing the material well-being and monetary poverty simultaneously has found strong, but far
from complete, correlation between the two (e.g., Perry, 2002; Roelen and Notten, 2011; Roelen et
al, 2011; Bradshaw et al., 2008; Nolan and Whelan, 2011, among others), highlighting the
usefulness of regarding these as two distinct aspects of poverty. The identification of the monetary
poor individuals and those who are deprived of the basic goods and services necessary for their
survival and development can lead to better understanding of the situation people are faced with,
and therefore to better targeted and more effective policy responses. Similarly, it has been
recognised that it is necessary to make a distinction between household poverty and child poverty,
acknowledging that children may experience poverty differently to adults and that people’s needs
differ depending on their age.
Building on the existing methodologies of measuring poverty and as part of UNICEF's continued
efforts to generate quality evidence on child poverty and disparities, UNICEF Office of Research has
developed the Multiple Overlapping Deprivation Analysis (MODA) methodology to measure child
poverty (de Neubourg et al, 2012). This methodology has been created primarily for country-
specific child poverty analyses, where it is applied to specific national contexts with customised
dimensions, thresholds and indicators, utilising the best available household surveys and national
datasets. Although subject to data availability, national MODA analyses generally seek to comprise
both monetary child poverty and child deprivations. The analysis makes a conceptual distinction
between the two measures of poverty and includes a study of their overlap.
Cross-country multidimensional child deprivation analysis (CC-MODA) is a specific application of
the MODA methodology, analysing child deprivation for low- and lower-middle income countries
according to internationally accepted standards of child well-being, using internationally
1 Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Comoros, Republic of Congo, Democratic Republic of Congo, Côte d'Ivoire, Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Malawi, Mozambique, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Swaziland, Tanzania, Togo, Uganda, Zimbabwe.
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comparable datasets that contain child-specific information. The study builds on the idea of
UNICEF’s Global Study on Child Poverty and Disparities 2007-2008 (Gordon et al., 2003; UNICEF,
2007), enhancing knowledge on child poverty and thus strengthening the position of children in
global discussions on development.
This research paper summarises the CC-MODA methodology and compiles the main results of the
analyses of thirty countries in sub-Saharan Africa. The main purpose of the paper is to present a
methodology that directly measures children’s poverty, and to identify the depth of poverty among
children in sub-Saharan Africa by analysing the extent to which the different deprivations are
experienced simultaneously. The succeeding section gives a more detailed introduction to the
MODA methodology and the specifics of CC-MODA, followed by a presentation of the results of
thirty selected African countries comprising single deprivation analyses, dimensional deprivation
counts, deprivation overlap analyses, multidimensional deprivation ratios, and the comparison
between child deprivation and other existing poverty measures.
2. BACKGROUND
The Convention on the Rights of the Child, ratified by most countries in the world, determines that
children have the right to survival, development, protection and participation (United Nations,
1989). The MODA methodology defines child poverty as non-fulfilment of the rights listed in this
convention, moving from household-level to child-level poverty measurement.2 The approach
permits concentration on the access to various goods and services, as well as freedom from
violence and exploitation, which are all crucial for children’s survival and development. It allows
analysis of the multitude and interrelation of children's deprivations and helps identify the socio-
economically disadvantaged groups of children that experience multiple, overlapping deprivations,
recognising that multiple deprivations have significant adverse effects on individuals, especially
children (See de Neubourg et al., 2012a; 2014 for more background).
MODA methodology has built upon existing approaches of multidimensional poverty
measurement, such as UNICEF's Global Study on Child Poverty and Disparities (see Gordon et al.
2003; UNICEF, 2007), OPHI's Multidimensional Poverty Index (see Alkire and Santos, 2010; Alkire
and Foster, 2011), and other research carried out in the field of multidimensional poverty. The
methodology sets itself apart through combining the following key elements:
It selects the child, rather than the household, as unit of analysis, since poverty may affect
children and adults differently. Regarding children’s developmental needs and the
potential long-term effects, it is beneficial to calculate poverty levels separately for
children;
It emphasises the inclusion of individual-level indicators whenever possible as there may be
differences across children of the same age and children in the same household. Indicators
are only adopted if they are relevant to a child as opposed to indicators which have
2 While many of the indicators are collected at a household level, the analysis is child-centred as the unit of analysis is the child rather than the household. Some of the goods and services are measured at individual level (e.g., vaccinations and education), while others are measured at household level and applied to all children of the same household when relevant to all household members (e.g., access to improved water source or housing material).
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relevance to other people in the child’s household but not directly to the child observed.
Household level information is only applied if it has a direct relation with the child’s well-
being;
It adopts a life-cycle approach that reflects the changing needs between early childhood,
primary childhood, and adolescence. By separating the analysis in age-groups, the
methodology facilitates the selection of age-specific indicators, such as school attendance,
timely receipt of vaccinations, etc.;
It applies a whole-child oriented approach by measuring the number of deprivations each
child experiences simultaneously, revealing those most deprived;
It broadens the scope of compartmentalised, sector-based approaches through overlapping
deprivation analyses; and
It generates profiles based on geographical and socio-economic characteristics of the
multiply deprived, allowing for better targeted, more effective policy responses and
interventions.
The cross-country (CC-MODA) analysis has been developed to measure child poverty across
countries by applying international standards as guiding principles for the construction of a core
set of dimensions and indicators that are essential to any child's development irrespective of their
country of residence, socio-economic status, or culture. This particular study presents findings on
child poverty in sub-Saharan Africa using the results of CC-MODA.
3. METHODOLOGY
The findings in this paper are based on the cross-country application of the MODA methodology
(CC-MODA). The details of the general MODA methodology are set out in de Neubourg et al.,
2012a, while the specificities regarding this multi-country study (e.g. choice of datasets, indicators,
dimensions, and data treatment) are given in the CC-MODA Technical Note (de Neubourg et al.,
2012b). Results by country can be found in the web-portal: http://www.unicef-irc.org/MODA/. This
particular paper has required merging together data of thirty selected countries in sub-Saharan
Africa, using data from DHS and MICS surveys carried out between 2008 and 2012 (see Annex 1 for
the exact survey year per country). The results are weighted by the respective child population of
each country.3 The population size by country and age-group can be seen in Annex 2.
CC-MODA uses a rights-based approach to child poverty following the rights covered in the
Convention on the Rights of the Child (CRC) (United Nations, 1989). Table 1 lists the main
dimensions of child well-being retrieved from the CRC and used as the basis for selecting the
different dimensions for measuring child poverty.
3 Child population is calculated by multiplying the percentage of children as a share of the total population of each country (authors’ calculations based on MICS/DHS data) with the total population of each country in 2012 (World Bank, 2014).
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Table 1 - Child Well-being Dimensions according to the Convention on the Rights of the Child
Categories Dimensions
Survival and
Development
Food, nutrition; Water, sanitation; Health care; Environment/pollution (CRC Art. 24);
Shelter, housing (CRC Art. 27); Education (CRC Art. 28); Leisure; Cultural activities
(CRC Art. 31); Information (CRC Art.13, 17)
Protection
Exploitation, child labour (CRC Art. 32); other forms of exploitation (CRC Art. 33-36);
Cruelty, violence (CRC Art. 19, 37); Violence at school (CRC Art. 28); Social security
(CRC Art 16, 26, 27)
Participation
Birth registration, nationality (CRC Art. 7, 8); Information (CRC Art.13, 17); Freedom of
expression, views, opinions; Being heard; Freedom of association (CRC Art.12-15).
Source: www.unicef.org/crc (article numbers refer to the Convention on the Rights of the Child, 1989)
The selection of indicators and thresholds for CC-MODA is guided by internationally accepted
standards assuring the relevance to children's development irrespective of their country of
residence, socio-economic status, or culture (details on each of the indicators and thresholds are
given in the CC-MODA Technical Note, De Neubourg et al., 2012b). It uses Demographic and Health
Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) for their relatively rich information on
child deprivations as well as to ensure international comparability of the data.
Since CC-MODA aims to capture child poverty across low- and middle-income countries, the lack of
data availability in some key dimensions of child well-being as well as missing values for certain
groups of children in the DHS and MICS datasets present key challenges to the selection of
dimensions and indicators. The choice of variables for CC-MODA has been largely shaped by data
availability leading to the selection of only eight dimensions of well-being, which are used within
two age-groups. The dimensions of water, sanitation, housing, and protection from violence, refer
to all children irrespective of their age, while nutrition and health are measured for children below
age five,4 and education and information are measured for school-age children and adolescents.
Since the analysis is based on the DHS and MICS surveys, monetary poverty analysis is not included
in this specific application of the MODA methodology.5
Following a life-cycle approach CC-MODA is conducted distinguishing two age-groups: children
below the age of five (infancy and early childhood), and children of age 5 to 17 (school age and
adolescence). The dimensions selected for the CC-MODA analysis are grouped according to these
two life stages with specific indicators relevant to each age-group. Most findings are presented by
age-group, while key outcomes are also presented by combining the two age-groups.
Five or six dimensions are analysed for any one child, depending on the availability of the
'protection from violence' dimension. Although an essential aspect of child protection, this
dimension is not available in the datasets of some of the countries included in the study. To ensure
comparability, the main findings of the cross-country MODA in this paper are based on five
dimensions, thus excluding the violence dimension. Where possible, results based on six
4 Although nutrition and health are crucial for child well-being regardless of age, these dimensions are not included in the analysis for children aged 5-17 due to a lack of adequate information for this age group in the DHS or MICS datasets. 5 Monetary poverty has been included in the National (N-MODA) child poverty and deprivation studies of Senegal, Mali, and Madagascar (see UNICEF Senegal, forthcoming; De Milliano and Handa, forthcoming; Plavgo, forthcoming), due to household consumption/expenditure module availability in the data.
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dimensions are also presented (see Annex 1 for information on the countries, datasets and
availability of the protection from violence dimension).
Figure 1 – Life-cycle stages, dimensions and indicators used for the CC-MODA analysis
1 Infant and young child feeding:
breastfeeding and food frequency
1 Compulsory school attendance
2 Wasting 2 Primary school attainment
1 DPT immunization 1 Availability of information devices
1 Access to improved water source 1 Access to improved water source
2 Distance to water source 2 Distance to water source
1 Access to improved sanitation 1 Access to improved sanitation
1 Overcrowding 1 Overcrowding
2 Floor and roof material 2 Floor and roof material
1 Domestic violence 1 Domestic violence
The six deprivation dimensions comprise a total of thirteen indicators, one or two per dimension.
For the dimensions with two indicators, a child is considered deprived if he or she is deprived in at
least one of the two indicators (i.e., the union approach). The rationale for using this approach is
to capture all children showing any sign of deprivation in a specific dimension. If there is more than
one indicator per dimension, the selected indicators have been chosen to complement each other
in the explanation of the dimension they represent and to jointly identify the children’s status in
the respective dimension. This method does not account for the depth of deprivation within a
given dimension because the methodology is developed to focus on the dimensions rather than
separate indicators. The indicators and their thresholds are selected based on the international
standards set by the WHO, UN-HABITAT, UNESCO, and UN MDGs (see De Neubourg et al 2012b, for
the precise definition of each indicator).
CC-MODA comprises various steps to analyse multidimensional deprivation. The analysis starts
with a single deprivation analysis to inform about the deprivation levels in each of the indicators
and dimensions included in the multiple deprivation analysis. The multiple deprivation analysis
comprises the following components: (1) deprivation count and distribution analysis, (2)
deprivation overlap analysis, and (3) multidimensional deprivation ratios and their decomposition.
The single deprivation analysis shows deprivation rates per indicator and dimension and per age-
group. Results are presented as child population shares in which the nominator is the number of
children being deprived in the given variable, and the denominator is all children with information
in the respective indicator or dimension. For the multiple deprivation analysis, the numerator is the
cumulative number of deprivations among children deprived in the selected number of dimensions
(out of a total of five dimensions). From this aggregate the proportion of children deprived in 0, 1,
2, .., 5 dimensions is calculated using all children in the selected age-group as a denominator.
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To identify the multidimensionally poor children, MODA uses the multidimensional deprivation
headcount (H), representing the children whose total number of deprivations is equal to or above a
specified cut-off, as a percentage of the respective child population. Although a good indication of
deprivation incidence, the headcount ratio (H) is not sensitive to the breadth of multidimensional
poverty, as it remains unchanged regardless of whether children who are identified as
multidimensionally poor suffer from two to three or four to five deprivations simultaneously. For
this reason, two additional ratios are used in the analysis, applying the Alkire and Foster (2011)
methodology. The average deprivation intensity among the deprived (A) measures the breadth of
multidimensional deprivation. It is calculated using the number of deprivations that the
multidimensionally deprived children encounter, divided by the maximum number of dimensions
considered, showing the average number of deprivations the deprived children experience. The
adjusted multidimensional deprivation headcount (M0), adjusts the deprivation headcount rate by
the intensity of deprivation (i.e., the number of deprivations the multidimensionally deprived
children experience), and is calculated by the following formula:
𝑀0 =∑ 𝑞𝑘×𝑐𝑘
𝑛×𝑑 , with 𝑐𝑘 = 𝐷𝑖 × 𝑦𝑘
Where
k - cut-off point (no. of dimensions a child should be deprived in to be considered multidimensionally poor)
𝑞𝐾 - number of children affected by at least K deprivations;
cK - number of deprivations each multidimensionally deprived child i experiences
n - total number of children
d - total number of dimensions considered per child
Di - number of deprivations each child i experiences
yK - deprivation status of a child i depending on the cut-off point k, with yK =1 if Di ≥k; yK =0 if Di <k.
The number of multidimensionally deprived children is expressed as a share of the total child
population as well as in absolute numbers. The child population per country is calculated
multiplying the total population size in 2012 (retrieved from the World Bank Databank, Oct 2014)
with the percentage of children as a share of the total population of each country (derived by
authors’ calculations based on DHS/MICS data; see Annexes 1 and 2).
4. FINDINGS
The findings are based on the analysis of thirty countries in sub-Saharan Africa for which a recent
MICS or DHS dataset was available. In 2012 these countries represented 78% of the total
population of sub-Saharan Africa (and 10% of the world’s population). Children below age 18
represent 52% of the total population of these countries.6 The countries included in the analysis
have experienced a large population growth over the last decades, and the trend is expected to
continue. Population projections show a doubling of the African population between 2015 and
2050, predicting that 37% of all children under 18 will be found on the African continent by 2050
(UNICEF, 2014). These expected demographic and social changes strengthen the choice of focusing
6 Estimates based on child population shares per country (authors’ calculations using DHS/MICS data), and on the population estimates in 2012 (World Bank, 2014).
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on this specific region, in particular by concentrating on the well-being of children, central to the
projected transitions.
The findings presented in this paper serve a twofold purpose. On the one hand the results show
the total deprivation of all children in the selected sub-Saharan African countries, while on the
other hand the findings function as a comparison of children’s deprivation levels between
countries. The deprivation levels are firstly analysed for each dimension separately, followed by
counting the number of deprivations experienced by each child. The multiple deprivation analysis
shows the intensity of poverty and the distribution of deprivations among children, describes how
the different sectoral deprivations overlap, and analyses the multidimensional deprivation
incidence and severity. In particular, multidimensional deprivation ratios are used for comparing
deprivation levels across the selected countries. The paper also shows the correlation between
multidimensional child poverty and GDP per capita as well as monetary poverty, and predicts child
deprivation rates for the sub-Saharan region as a whole based on the findings of the thirty
countries analysed.
4.1 Single deprivation analysis
Single deprivation analysis is the basis for understanding the situation of children in each of the
sectors analysed. Knowledge of the deprivation levels by dimension creates an advantageous
starting point for the multidimensional deprivation analysis to follow.
The results show that the highest deprivation rates out of the eight dimensions studied in this
region are in sanitation (67% for the younger and 66% for the older age-group), protection from
violence (63% for both age-groups), health (56% for the younger age-group) and water (52% for
younger and 51% and older children), followed by housing (44% for both age-groups), nutrition
(40% for the younger age-group), education (35% for children above the compulsory school
starting age), and information (26% for the older age-group) (see Annex 3). As shown in Figure 2,
there is a considerable difference between the overall deprivation levels depending on where
children live: the deprivation rates are considerably higher in rural areas as compared to urban
areas in almost all dimensions, apart from nutrition and protection from violence. For children
below the age of five the main issues are sanitation (78%) and health (64%) in the rural areas, and
protection from violence (67%) and nutrition (41%) in urban areas. Children aged 5 to 17 mainly
experience deprivations in sanitation (77%), water (62%) and protection from violence (61%) in
rural areas, and protection from violence (66%) and sanitation (34%) in urban locations. Also, more
than one third (35%) of school age and adolescent children in sub-Saharan Africa are deprived in
schooling (41% in rural areas and 20% in urban areas). For the deprivation rates by indicator, see
Annex 3 which provides further details on the drivers of deprivation per dimension.
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Figure 2: Deprivation headcount rates by dimension and area
Children below age five Children between age 5 and 17
* Indicates statistically significant differences in deprivation rates by area ((p<0.05).
Unlike monetary poverty which is a household-level measure, deprivation analysis provides more
space to measure individual differences (e.g. gender or intra-household differences) when
individual level indicators are used. In the case of CC-MODA, four indicators are at an individual
level allowing for analysis by gender.7 When looking at the children of all thirty countries jointly,
some gender differences are observed (although there are some variations across countries).
Figure 3 shows the deprivation rates for boys and girls regarding wasting, immunisation, school
attendance, and primary school attainment.8 The general trend in sub-Saharan Africa is that for
children below the age of five, the deprivation rate in wasting is higher among boys (9.4% for boys
vs. 7.8% for girls), while no statistically significant gender differences can be observed in terms of
DPT immunisation. With regards to schooling indicators for older children, the percentage of
children not attending school at compulsory school age is significantly higher among girls, while the
percentage of adolescents without primary education is equally high for both boys and girls and
the difference is statistically insignificant (at a 95% level).
7 Analysis by gender using CC-MODA is only possible at indicator level; it is not done for multidimensional deprivation analysis because five out of eight dimensions are constructed using indicators that are applied to all household members. 8 All the other indicators used for this analysis (i.e., indicators on water, sanitation, housing, and violence) apply to all children from the same household; thus, gender differences cannot be measured.
40%64%
62%
78%
52%
62%41%
30%
21%
34%
20%
67%
0%
25%
50%
75%
100%Nutrition
Health*
Water*
Sanitation*
Housing*
Protectionfrom
violence*
Rural Urban
41%
33%
62%
77%
51%
61% 20%
8%
21%
34%
23%
66%
0%
25%
50%
75%
100%
Education*
Information*
Water*
Sanitation*
Housing*
Protectionfrom
violence*
Rural Urban
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Figure 3: Deprivation headcount rates by indicator and gender
* Indicates statistically significant differences in deprivation rates by gender ((p<0.05).
4.2 Deprivation count for each child: deprivation distribution within and across countries
The multiple deprivation analysis moves from sector-analysis to child deprivation analysis,
examining how many and what combinations of deprivations each child experiences
simultaneously. It shows: (1) the distribution of the number of deprivations, (2) the deprivation
overlap between dimensions, (3) multidimensional deprivation ratios, (4) the profile of the
multidimensionally deprived and the most vulnerable, and (5) the contribution of each country and
dimension to the total adjusted multidimensional deprivation ratio. The results create an
understanding about the intensity of deprivation and the overlap of certain dimensions. At a
country-level these types of results can be used as a basis to further identify the most vulnerable in
the society. In a cross-country context, these findings are telling about the overall level of
deprivation among children in the region, and the differences between multiple deprivation levels
across countries.
The figures below show that among children below the age of five in the thirty countries analysed,
8.5% (10.2 million out of a total of 119.7 million) are not deprived in any of the five selected
dimensions, while a similar share of children under five years (8% or 9.4 million) are experiencing
all five deprivations simultaneously. More than half of the children under five in these countries
(54% or 64.3 million) are deprived in three to five dimensions. Among children of the older age-
group the breadth of deprivation is relatively lower, with 36% of children between the age 5 and
17 deprived in only one or none of the five dimensions studied. 41% of the older children lack basic
needs in three to five dimensions, representing 102.2 million children out of the 248.2 million
children in this age-group across the thirty countries analysed.
The difference between the two age-groups is driven by their dimension choice: apart from the
three dimensions that are common and relevant to all children, for the younger children critical
dimensions on health and nutrition are included to capture their survival and developmental
rights. These dimensions focusing on issues of survival and development have higher deprivation
rates than the dimensions on development (education) and participation (information) which are
selected for the children of primary school-age and adolescents.
Overall, 67% of all the children in the thirty countries of sub-Saharan Africa experience two to five
deprivations that are seen as non-fulfilment of their rights to survival, development and
participation. This represents 247 million of a total of 368 million children below the age of 18.
50.7%
24.2%
37.3%
7.8%
51.3%
20.9%
37.4%
9.4%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Primary school attainment
Compulsory school attendance*
DPT immunisation
Height for weight (wasting)*
Deprivation rate in %
Boy
Girl
15
Figure 4: Number of deprivations children suffer from, by age-group
Children below age five Children between age 5 and 17
A large variation within the region can be observed in terms of the distribution of deprivations
among children at the country level. For instance, in Rwanda (Figure 5) nearly one fifth of all
children below the age of five (19.3%) in rural areas are not deprived in any of the five dimensions
analysed while this is so only for 1.3% of the rural children in Tanzania. In rural areas in Rwanda
more than two thirds (65%) of the children under the age of five experience only one or two
deprivations at the same time, while more than a half (61%) of the young children in rural Tanzania
suffer from three or four deprivations simultaneously.9
Figure 5: Deprivation distribution in rural areas by country - children under five years old
Rwanda (DHS 2010-11) Tanzania (DHS 2010)
9 In Rwanda, the highest deprivation rates are in the water dimension, while in Tanzania, the main contributors to multiple deprivation are sanitation, followed by water and health issues. See Figure 12 for decomposition by dimension to see which dimensions contribute the most to multiple deprivation by country.
0
10
20
30
40
50
60
0%
5%
10%
15%
20%
25%
0 1 2 3 4 5
Mill
ion
s o
f ch
ildre
n
% o
f ch
ildre
n 0
-4 y
ears
In % In numbers
0
10
20
30
40
50
60
0%
5%
10%
15%
20%
25%
0 1 2 3 4 5
Mill
ion
s o
f ch
ildre
n
% o
f ch
ildre
n 5
-17
yea
rs
In % In numbers
0%
10%
20%
30%
40%
0 1 2 3 4 5% o
f ch
ildre
n 0
-4 y
ears
in
rura
l are
as
Number of deprivations experienced
Rwanda
0%
10%
20%
30%
40%
0 1 2 3 4 5
% o
f ch
ildre
n 0
-4 y
rs in
ru
ral
area
s
Number of deprivations experienced
Tanzania
16
Large disparities in the deprivation distributions can also be found when breaking the total child
population into sub-groups based on children’s geographic location or children’s and their parents’
socio-economic characteristics. When comparing deprivation distribution for children below five by
households’ exposure to child mortality, the results show that children living in households where
at least one child has died experience a higher number of deprivations. 62% of children from
households where under-five child mortality has been experienced suffer from three to five
deprivations at the same time, versus 52% of children living in households where no child mortality
has been observed.
4.3 Deprivation overlap analysis
A deprivation overlap analysis aims at improving the understanding of the nature and depth of
child deprivation by analysing the combinations of deprivations that children experience
simultaneously. The knowledge derived from studying deprivation overlap can be used to direct
policy mechanisms and help address children’s needs more adequately.
For example, Figure 6 shows whether dimensional deprivations are unique issues or whether the
deprivation in a given dimension is experienced simultaneously with other deprivations. Knowing
whether particular deprivations are stand-alone issues may be useful when identifying possible
entry points for policy interventions. Figure 6 demonstrates the deprivation incidence for three
dimensions, subdivided by the extent of overlap with other dimensions by urban and rural areas
separately. The first part of the bar (on the left) shows the proportion of children deprived in only
the specified dimension and no other dimensions, while the other parts of each bar show how
many children are deprived in the specified dimension and also other dimensions simultaneously.
The graph shows that although the percentage of children deprived in nutrition is similar among
children below the age of five in both, rural and urban areas (the entire bar; 39% for both), the
extent to which children experiencing malnutrition are exposed to also other deprivations differs
depending on where children live. Out of all the children under five in urban areas who are
deprived in nutrition, more than one-third (37%) experience malnutrition as a unique problem,
while this is so only for 6% of the malnourished children in rural areas. More than a half (58%) of
the malnourished children living in rural areas are deprived in three to five other dimensions, while
this is so only for 10% of the malnourished children living in urban areas.
Similarly, health and education cannot be regarded in isolation from other deprivations as they are
not stand-alone problems in the region. Most of the children who experience these deprivations
suffer also from other deprivations (Figure 6). In rural areas, for example, roughly one half of all the
children deprived of access to health care or deprived of education, experience three to five other
deprivations.
17
Figure 6: Deprivation overlap by dimension
The results from the overlap analysis reconfirm the need for integrated approaches to address the
multiple facets of children’s poverty. For instance, Figure 7 shows that in rural areas, while 39% of
all children below the age of five are deprived in nutrition (the pink circle), 90% of these
malnourished children also suffer from lack of access to health facilities or services and/or are
using an unimproved toilet or latrine. Thus, addressing the nutritional problems of these children
would only solve one of the many problems they are faced with; even if their nutritional status was
improved, they would still suffer from other deprivations crucial to their survival. A holistic
approach to resolve children’s problems in an integrated manner would in this case be more
efficient and effective in safeguarding children’s rights to survival and development. Identifying the
children suffering from single and multiple deprivations can help to target the interventions. For
instance, Figure 7 also shows that the overlap of nutrition, health, and sanitation deprivations is
much smaller for children living in urban areas, which may need different intervention strategies
than for children in rural areas where more than one fifth of all children experience all three
specified deprivations at the same time.
Figure 7: Overlap of nutrition, health and sanitation by area of residence
Rural areas Urban areas
0% 10% 20% 30% 40% 50% 60% 70%
Rural
Urban
Rural
Urban
Rural
Urban
Edu
cati
on
(5-1
7)
Hea
lth
(0
-4)
Nu
trit
ion
(0
-4)
Deprived only in the specified dimension Deprived in 1 other dimension
Deprived in 2 other dimensions Deprived in 3-5 other dimensions
18
4.4 Multidimensional deprivation ratios
To compare the incidence and depth of children’s deprivation across countries and to help
determine the multidimensional poverty threshold, we first identify all children deprived in any
one dimension, and then look at the average number of deprivations these children experience. In
the thirty countries included in the analysis, 86.4% of all the children below the age of 18
experience at least one out of a total of five deprivations analysed (representing 317.7 million
children). These 86.4% of all children on average suffer from 2.6 deprivations simultaneously, as
shown in Figure 8 by the dots representing average deprivation intensity among children deprived
in one to five dimensions. The average number of deprivations among children with at least one
deprivation ranges from 1.7 in Gabon and Swaziland to 3.4 in Chad and Ethiopia. The average
deprivation intensity is calculated using a cut-off of one dimension to avoid censoring the
deprivations that may be experienced in isolation from other deprivations. See Annex 5 for the
percentage of children deprived in one to five dimensions for which the deprivation intensity was
calculated.
For the purposes of this study, children are identified as multidimensionally deprived when
experiencing two or more deprivations out of a total of five dimensions studied (results for all
indices using all possible cut-off points are shown in Annex 5). The results are presented in Figure 8
by the bars showing that the multidimensional deprivation incidence in the selected countries of
sub-Saharan Africa is 67%. In other words, two thirds of all children in this region experience two to
five deprivations, which in absolute numbers is 247 million children. The prevalence of
multidimensional deprivation ranges from 30% in Gabon to 90% in Ethiopia.
This measure shows slightly different results in terms of country ranking when compared to the
measure of deprivation intensity. For example, while the average number of deprivations that
children deprived in any one dimension experience is lower in Malawi compared to Mozambique
(2.6 vs. 2.9 deprivations respectively), Malawi has a higher percentage of children deprived in two
to five dimensions than Mozambique (79% vs. 75%). The combination of these findings means that
the depth of the deprivation is larger in Mozambique, while proportionally Malawi has a slightly
higher share of children deprived in a multitude of dimensions (this holds as well when using a cut-
off of one deprivation; See Annex 5).
19
Figure 8: Multidimensional deprivation incidence and average deprivation intensity by country for children below 18 years of age
Since the dimensions analysed differ depending on the age of the child, we also look at the
multidimensional deprivation rates for the two age-groups separately. As can be seen in Figure 9,
children below the age of five have a considerably higher multidimensional deprivation incidence
compared to the older children across all countries apart from Malawi. Among children of the
thirty countries analysed, 75% of the children below the age of five experience two to five
deprivations, compared to 64% among school age children and adolescents. The difference
between the deprivation levels of the two age-groups differs by country, which also means that the
ranking of countries by deprivation rate changes based on the age-group chosen. Countries such as
Malawi, Burkina Faso, Benin, and Zimbabwe rank higher when looking at multidimensional
deprivation rates of children aged 5 to17 compared to the children below the age of five. Gambia,
Guinea and Gabon, on the contrary, are performing relatively better with regards to the
deprivation rates for the children in the older age-group. The discrepancy between the deprivation
rates for younger and older children depends on the deprivation levels of the age-specific
dimensions, i.e. nutrition and health for the first age-group and information and education for the
second.
29.6%
67.1%
90.1%
1.7
2.6
3.4
0.00.51.01.52.02.53.03.54.04.55.0
0%10%20%30%40%50%60%70%80%90%
100%G
abo
n
Gam
bia
Swaz
ilan
d
Rw
and
a
Gh
ana
Sen
ega
l
Co
mo
ros
Equ
ato
rial
Gu
inea
Zim
bab
we
Co
ngo
Cam
ero
on
Co
te d
'Ivo
ire
Nig
eria
Ben
in
Leso
tho
Gu
ine
a
Togo
Sier
ra L
eon
e
Ken
ya
Bu
run
di
Bu
rkin
a Fa
so
TOTA
L
Cen
tral
Afr
ican
Rep
.
Uga
nd
a
Mo
zam
biq
ue
Tan
zan
ia
Mal
awi
Co
ngo
DR
Nig
er
Ch
ad
Eth
iop
ia
Ave
rage
dep
riva
tio
n in
ten
sity
(1
-5 d
epri
vati
on
s)
Dep
riva
tio
n r
ate
(2-5
dep
riva
tio
ns)
, as
% o
f al
l ch
ildre
n
% of children deprived in 2-5 dimensions
Average number of deprivations among children with 1-5 deprivations
20
Figure 9: Multidimensional deprivation rates of children with 2-5 deprivations by age group
The adjusted deprivation headcount (M0) combines the two aforementioned deprivation measures
to show an overall multidimensional deprivation measure that captures both the incidence of the
deprived children and the depth of their deprivation. This ratio ranges between 0 and 1, with zero
showing no deprivation (according to the cut-off chosen) and one showing that everyone included
in the analysis is deprived in all the dimensions analysed. In the thirty countries of sub-Saharan
Africa, the adjusted multidimensional deprivation ratio is 0.42 when using a threshold of two
deprivations (i.e., children are multidimensionally deprived if they suffer from two to five
deprivations). For children under the age of five, the ratio is 0.48, ranging from 0.19 in Swaziland to
0.70 in Ethiopia. For the children of age 5 to 17 years, the total is 0.39 when using the same
threshold, varying between 0.11 in Gabon to 0.62 in Ethiopia (see Annex 5).
Figure 10 presents the findings for all children below age 18 in a map, indicating a few clear
groupings of countries with different deprivation levels. The highest multidimensional deprivation
levels are found at the centre of the continent (Chad, the Democratic Republic of Congo, Niger, and
Central African Republic, ranging between 0.64 and 0.48), followed by a stretch of countries with
high levels of deprivation in the East (Mozambique, Malawi, Tanzania, Uganda and Kenya, ranging
from 0.49 to 0.37), and Burkina Faso, Sierra Leone, Guinea and Togo in West Africa (0.40 – 0.35).
Figure 11 shows the contribution of each country to the total adjusted multidimensional
deprivation ratio of the selected sub-Saharan African countries (total M0 = 0.42). 10. The largest
contributions come from Ethiopia (20%), Nigeria (17%) and the Democratic Republic of the Congo
(13%). The extent to which each country contributes to the total adjusted deprivation ratio
depends not only on the percentage of multidimensionally deprived children per country or their
deprivation intensity (average number of deprivations experienced simultaneously), but also on
10 Annex 4 presents the contribution of each country to the total multidimensional deprivation headcount ratio (H) and the total adjusted deprivation headcount ratio (Mo) by age-group. The patterns are very similar, with the exception of a few cases. For Ethiopia and the Democratic Republic of Congo, the contribution to the total deprivation level in the region is smaller when using the deprivation ratio, H (17% and 12%) than when using the adjusted deprivation ratio M0 (20% and 13%, respectively). This is because the adjusted deprivation ratio takes into account that the average number of deprivations children in these countries experience is relatively higher compared to other countries with a similar deprivation headcount ratio. For Nigeria, on the other hand, the contribution to the total deprivation level of the selected countries is slightly smaller when using the adjusted deprivation ratio M0 than when using the deprivation headcount ratio H (17% vs. 18%) due to the differences in the average intensity of deprivation across countries.
37%
75%
94%
32%
64%
88%
0%
20%
40%
60%
80%
100%
Swaz
ilan
d
Gab
on
Rw
and
a
Gh
ana
Gam
bia
Sen
ega
l
Zim
bab
we
Co
mo
ros
Co
ngo
Equ
ato
rial
Gu
inea
Cam
ero
on
Co
te d
'Ivo
ire
Ben
in
Nig
eria
Leso
tho
Togo
Ken
ya
Bu
rkin
a Fa
so
Bu
run
di
Sier
ra L
eon
e
Gu
ine
a
TOTA
L
Cen
t. A
fric
an R
ep.
Uga
nd
a
Mal
awi
Mo
zam
biq
ue
Tan
zan
ia
Co
ngo
DR
Nig
er
Ch
ad
Eth
iop
ia
Per
cen
tage
of
child
ren
dep
rive
d
in 2
-5 d
imen
sio
ns
Age 0-4 Age 5-17
21
the size of the child population per country (see Annex 2). For this reason, countries such as Chad
with a high deprivation incidence and intensity contribute relatively little since their child
population is small compared to the other countries analysed. The composition of the pie chart
helps to understand where the largest shares of the total amount of all deprivations experienced
across the thirty countries are found.
Figure 10: Adjusted multidimensional deprivation ratio: all children, 2-5 deprivations
Figure 11: Contribution of each country to the total adjusted multidimensional deprivation ratio: all children, 2-5 deprivations
Note: See Annex 1 for country names and respective abbreviations
4.5 Decomposition of the adjusted multidimensional deprivation headcount
In addition to producing a comparable deprivation ratio, one of the special features of the adjusted
multidimensional deprivation ratio is that it can be decomposed. This means that the total
adjusted deprivation ratio can be broken down to show the percentage contribution of each single
dimension to the multidimensional measure. When comparing the composition of the adjusted
deprivation headcount of, for instance, Equatorial Guinea and Malawi, the deprivation in
Equatorial Guinea is mainly driven by health and water deprivations, whereas in Malawi sanitation
and housing play a far larger role compared to the other dimensions. Overall, sanitation and health
are the main contributors to the total adjusted deprivation ratio of all children below the age of
five, apart from Benin, Burkina Faso, Comoros, Congo, Congo DR, the Gambia, and Malawi where
either nutrition or housing issues also dominate, and Rwanda where water deprivation has the
highest contribution. The sanitation dimension contributes considerably to the adjusted headcount
of Benin, Burkina Faso, Comoros, Congo, Lesotho, Malawi and Togo (more than 30% for the
younger age-group). For the school-age children and adolescents, the highest contributor to the
total adjusted deprivation ratio is sanitation, followed by water deprivation, apart from Burkina
BEN1.1%
BFA2.3%
BDI1.3%
CMR2.2%
CAF0.8%
TCD3.0%
COM0.1%
COG0.4%
COD13.3%
CIV1.7%
GNQ0.1%
ETH19.9%
GAB0.1%GMB
0.1%GHA1.7%
GIN1.4%
KEN5.2%
LSO0.2%
MWI2.7%
MOZ4.3%
NER3.8%
NGA16.7%
RWA0.7%
SEN1.1%
SLE0.7%
SWZ0.1%
TZA7.6%
TGO0.8%
UGA5.8%
ZWE1.2%
22
Faso where the second highest contributor is education, and Lesotho, Malawi, and Senegal where
the second highest contributor after sanitation is housing. Education, information, and housing
have the highest variation across the thirty countries of the sub-Saharan region. The contribution
of education deprivation is relatively high (ranging between 17% and 26% of the total
multidimensional poverty ratio) in the following countries: Burkina Faso, Côte d’Ivoire, Gabon,
Gambia, Ghana, Guinea, Senegal, and Swaziland.
Figure 12: Contribution of each dimension to the total adjusted multidimensional deprivation ratio: children experiencing 2-5 deprivations
4.6 GDP per capita and multidimensional child deprivation
This section looks at the correlation between GDP per capita of each country and the
multidimensional child deprivation rates to assess whether in sub-Saharan Africa a higher average
economic activity is correlated with lower child deprivation levels.
As can be seen from Figure 13, the correlation between per capita GDP and the multidimensional
deprivation measure is moderate. For instance, Chad and Senegal in 2012 had a very similar per
capita GDP, while the multidimensional child deprivation rate in Chad was more than double the
rate of Senegal (88% and 44%, respectively). Similarly, while the per capita GDP in the Republic of
0%
20%
40%
60%
80%
100%
TOTA
L
Ben
in
Bu
rkin
a Fa
so
Bu
run
di
Cam
ero
on
Cen
tral
Afr
ican
Rep
.
Ch
ad
Co
mo
ros
Co
ngo
Co
ngo
DR
Co
te d
'Ivo
ire
Equ
ato
rial
Gu
inea
Eth
iop
ia
Gab
on
Gam
bia
Gh
ana
Gu
ine
a
Ken
ya
Leso
tho
Mal
awi
Mo
zam
biq
ue
Nig
er
Nig
eria
Rw
and
a
Sen
ega
l
Sier
ra L
eon
e
Swaz
ilan
d
Tan
zan
ia
Togo
Uga
nd
a
Zim
bab
we
Co
ntr
ibu
tio
n t
o t
he
adju
setd
dep
riva
tio
n h
ead
cou
nt,
in % Housing
Sanitation
Water
Health
Nutrition
Children below age five
0%
20%
40%
60%
80%
100%
TOTA
LB
enin
Bu
rkin
a Fa
soB
uru
nd
iC
amer
oo
nC
entr
al A
fric
an R
ep.
Ch
adC
om
oro
sC
on
goC
on
go D
RC
ote
d'Iv
oir
eEq
uat
ori
al G
uin
eaEt
hio
pia
Gab
on
Gam
bia
Gh
ana
Gu
ine
aK
enya
Leso
tho
Mal
awi
Mo
zam
biq
ue
Nig
erN
iger
iaR
wan
da
Sen
ega
lSi
erra
Leo
ne
Swaz
ilan
dTa
nza
nia
Togo
Uga
nd
aZi
mb
abw
e
Co
ntr
ibu
tio
n t
o t
he
adju
setd
dep
riva
tio
n h
ead
cou
nt,
in % Housing
Sanitation
Water
Information
Education
Children between age 5-17
23
Congo was more than three times higher than in Zimbabwe, the child deprivation rates in these
two countries were almost identical (50% and 49%, respectively).
The modest correlation between the two measures may be explained by the fact that GDP per
capita measures country’s average economic activity in terms of monetary transactions, but does
not capture distribution of wealth and societal behaviour. Although the two measures are
correlated to some extent (R2=0.29), 11 the level of the average economic activity of the country is
not a perfect predictor of the level of multidimensional child deprivation. For a more accurate
prediction, other factors should also be taken into account, such as resource use and distribution in
the society, availability and affordability of public and private goods and services, legislation and
legislative accountability, as well as societal behaviours, beliefs and traditions, among others.
Figure 13: GDP per capita and multidimensional child deprivation
4.7 Monetary poverty and multidimensional child deprivation
Following the conceptual framework set out in the MODA methodology, MODA distinguishes two
main concepts of poverty: monetary poverty and multidimensional deprivation (see de Neubourg
et al., 2014, for more details), and uses both to analyse child poverty whenever the data allows.12
Monetary poverty measures the lack of financial means of households to provide the household
members with basic goods and services deemed to be necessary for their survival and
development. Deprivations measure the individual deprivation status in each of the various sectors
considered as crucial for individuals’ survival and development. Deprivations can stem from the
lack of financial means (i.e., monetary poverty), but they can also be the result of unavailability of
basic goods in the market, the lack of service provision, or societal beliefs, customs and behaviour,
among other reasons. Especially regarding children some differences between deprivation and
monetary poverty are expected, as children need goods and services that are more likely to be
subject to missing or incomplete markets (e.g. health care, school or nutritional needs). It is also
11 Gabon and Equatorial Guinea have been omitted as outliers. The regression has also been performed omitting Nigeria, Congo and Swaziland, which gives a considerably stronger correlation (R2=0.51). 12 See footnote 5 for MODA analyses where monetary poverty is measured alongside multidimensional deprivation.
BEN
BFABDI
CMR
CAF
TCD
COMCOG
COD
CIV
ETH
GMB
GHA
GINKEN
LSO
MWI
MOZ
NEG
NGA
RWA
SEN
SLE
SWZ
TZA
TGO
UGA
ZWE
20
40
60
80
10
0
% o
f ch
ildre
n b
elo
w 1
8 d
epri
ved
in 2
-5 d
imen
sio
ns
0 2000 4000 6000GDP per capita PPP in current international USD in 2012
R-squared=0.2882
24
important to remark that while monetary poverty measurement concentrates on the average
financial means available to the households where children live, deprivation measurement
attempts to determine whether children’s basic needs are satisfied. Measuring child outcomes,
child-related practices, the fulfilment of children’s basic rights gives the possibility to account for
intra-household differences in the distribution of resources; or to reflect decisions (either explicit
or implicitly made) on issues such as schooling, labour and marriage which may be driven by socio-
cultural norms, traditions or lack of awareness rather than lack of resources (see also de Neubourg
et al., 2014, Gordon et al., 2003; Minujín et al., 2006; Minujín and Nandy, 2012). Despite the
expected differences, the MODA methodology encourages analysing both concepts of poverty and
studying the overlap between children experiencing deprivations and children living in monetary
poor families when possible.
Even though the specificities of the CC-MODA analysis do not allow the measurement of monetary
poverty and child deprivation at the same time, we still try to identify the extent to which the two
measures correlate by using aggregate data on international and national poverty rates coming from
other data sources. We look at the correlation between monetary poverty and child deprivation by
using the following two monetary poverty measures: an internationally comparable monetary
poverty rate based on $1.25 PPP a day poverty line, and a nationally determined poverty rate based
on national poverty lines. For a sub-set of countries, we also compare multidimensional child
deprivation rates with child poverty rates based on national poverty lines calculated specifically for
children, so that the same unit of analysis for both measures can be used in the comparison.
Figure 14 compares multidimensional deprivation rates for children with absolute monetary
poverty rates based on the $1.25 PPP per day poverty line for the total population in each country.
We can see a fairly large spread among the countries included in the figure indicating a moderate
correlation between multidimensional deprivation for children and absolute monetary poverty for
the total population. For most of the countries analysed, monetary poverty rates for the total
population are considerably lower than multidimensional deprivation rates for children. There are,
however, a few countries, such as Rwanda and Burundi, with higher monetary poverty levels
compared to deprivation. A sizable group of countries clusters around the trend line suggesting
moderate correlation between the two measures. Nevertheless, the relatively high margin of
unexplained variance between the two measures (R2=0.19) suggests that the absolute monetary
poverty measure is not a good predictor of child deprivation rates in this region.
25
Figure 14: Monetary poverty based on $1.25 PPP poverty line and multidimensional child deprivation (2-5 dimensions) for all children
Note: Poverty rates retrieved from the World Bank (Oct 2014); see Annex 1 for the poverty rate estimation year per country.
Figure 15 shows national poverty rates that are calculated based on the national poverty line which
is the estimated budget needed to pay for a basic basket of goods and services within a given
national context. These rates are not internationally comparable as different methodologies and
poverty lines have been used to estimate each of the national poverty rates. However, the national
poverty rates are normally used by the governments when identifying the poor and designing
policy responses, making it an interesting measure to be compared with the child deprivation rates
of the respective countries.
As shown in Figure 15, in nine out of thirty countries analysed, monetary poverty rates using
national poverty line and child multidimensional deprivation levels are similar: the two measures
give almost identical poverty and deprivation rates in Burundi (67% and 66%, respectively) and
Lesotho (57% and 58%), and very similar monetary poverty and deprivation rates in Comoros,
Gabon, Republic of Congo, Rwanda, Senegal and Togo. For the remaining countries in sub-Saharan
Africa, however, the poverty and deprivation rates differ considerably. The low R-squared and the
horizontal trend line indicate that there is no correlation between the two measures of poverty
across the thirty countries in sub-Saharan Africa. This finding underlines the usefulness of using
these two measures of poverty in a complementary manner to identify monetary poor and
deprived households and children.
BEN
BFA BDI
CMR
CAF
TCD
COMCOG
COD
CIV
ETH
GABGMB
GHA
GINKEN
LSO
MWI
MOZ
NEG
NGA
RWA
SEN
SLE
SWZ
TZA
TGO
UGA
20
40
60
80
10
0
Perc
enta
ge o
f ch
ildre
n d
ep
rive
d in
2-5
dim
ensio
ns
0 20 40 60 80Poverty rate at 1.25 USD (PPP) a day (% of population)
R-squared=0.1910
26
Figure 15: Monetary poverty based on national poverty line and multidimensional child deprivation (2-5 dimensions) for all children
Note: Poverty rates retrieved from the World Bank (Oct, 2014); see Annex 1 for the poverty rate estimation year per country.
For a sub-set of the countries included in the sample a monetary child poverty rate is available
providing an opportunity to make a comparison between deprivation and monetary poverty rates
representing the same population. In Ghana and Togo, the difference between the child monetary
poverty and child deprivation rates is the smallest (2 percentage point (p.p.) difference), followed
by Comoros (3 p.p.), and Cameroon, Nigeria and Senegal (5 to 6 p.p.). For Swaziland and
Zimbabwe, the child monetary poverty rates are considerably higher than child deprivation levels.
In the remaining countries for which child monetary poverty rates were available (Benin, Chad,
Côte d’Ivoire, Malawi, Niger and Uganda), the percentage of children deprived in two to five
dimensions is higher than the percentage of children living below the national poverty line. In
general, even though the results on both poverty measures are closer than when using national
poverty rates for the total population, national child poverty rates are not good predictors of the
level of child deprivation and the correlation between the two measures remains weak.
BEN
BFA BDI
CMR
CAF
TCD
COMCOG
COD
CIV
GNQ
ETH
GABGMB
GHA
GINKEN
LSO
MWI
MOZ
NEG
NGA
RWA
SEN
SLE
SWZ
TZA
TGO
UGA
ZWE
20
40
60
80
10
0
Perc
enta
ge o
f ch
ildre
n d
ep
rive
d in
2-5
dim
ensio
ns
20 40 60 80Poverty rate using national poverty line (% of population)
R-squared=0.0006
27
Figure 16: Child monetary poverty based on national poverty line and multidimensional child deprivation (2-5 dimensions) for all children
Note: Poverty rates retrieved from UNICEF DPR (Sept. 2014); see Annex 1 for the poverty rate estimation year per country.
Each of the above figures shows that the two approaches for measuring child poverty are
complementary to each other as the two measures cannot substitute (or predict) the other. As
argued in de Neubourg et al. (2014) having enough financial resources does not always mean that
the fulfilment of children’s rights is guaranteed. The lack of basic goods and services, as well as the
violation of various children’s rights, can stem from lack of services or infrastructure, lack of
information, administrative restrictions, discrimination, and other reasons. At the same time, it
may well be that the access to certain goods and services is guaranteed without the need of the
financial resources at the household level because, for instance, the goods or services are available
for free or are partially subsidised. The figures above confirm that monetary poverty and
multidimensional deprivation are two different concepts that complement each other when
analysing child poverty.
4.8 Multidimensional deprivation among children in sub-Saharan Africa
Based on the results of the thirty selected countries in sub-Saharan Africa, we have predicted the
multidimensional child deprivation rates for the remaining 15 countries to be able to estimate the
total number of multidimensionally poor children in the whole region.13 In order to predict the
deprivation levels for sub-Saharan countries which are not among the thirty selected countries, we
use an OLS regression model estimating the relationship between multidimensional deprivation
rates and the GDP per capita of the countries included in the analysis. Since the correlation
between multidimensional deprivation and GDP per capita has been found to be only moderately
strong (see Figure 13) the regression has been made more robust by including various control
variables. The regression is based on the multidimensional deprivation levels for 2 to 5 dimensions
13 Calculations are made for 45 developing countries in Sub-Saharan Africa as classified by the World Bank, excluding Mauritius, Seychelles, and Somalia, while adding Equatorial Guinea.
BEN
CMR
TCD
COM
CIV
GHA
MWI
NEG
NGA
SEN
SWZ
TGO
UGA
ZWE
20
40
60
80
10
0
Perc
enta
ge o
f ch
ildre
n d
ep
rive
d in
2-5
dim
ensio
ns
30 40 50 60 70 80Child poverty rate using national poverty line (% of children)
R-squared=0.0632
28
of 28 countries (excluding Equatorial Guinea and Gabon due to outlier values), and the countries’
GDP per capita, the share of urban population and the population size in 201214 (see Table 2). The
regression is weighted by the countries’ population size. The regression provides coefficients which
are used in the following formula to predict the deprivation rates for the missing countries:
Deprived (2-5 dim)=β0 + β1 * GDP per capita+ β2* Share of urban population + β3 * Population + β4*
Population2
The predicted multidimensional deprivation rates are multiplied by the total number of children15
to estimate the total number of children being multidimensionally deprived in the remaining sub-
Saharan African countries.
Table 2: OLS regression on the relationship between multidimensional deprivation (2-5 dimensions) and GDP per capita
VARIABLES Deprived in 2-5
dimensions
GDP per capita -7.54e-05***
0.000022
Share of urban population -0.290*
0.152287
Population 3.84e-09**
0.000000001
Squared population -1.24e-17
9.23e-18
Constant 0.790***
0.068064
Observations 28
R-squared 0.811
The estimates for the region as a whole show that 298 million out of a total of 468 million children
in the 45 countries in sub-Saharan Africa are multidimensionally poor. In other words, 63.6%, or
just below 300 million children in the 45 countries of sub-Saharan Africa are multidimensionally
poor, being deprived in two to five dimensions of basic child rights out of a total of five dimensions
analysed per child.
To compare these figures with the number of children in the region living in monetary poverty, we
use the extreme poverty rates of the total population applied to the child population per country.
Among the children in the 28 selected countries (361 million children in total), 181 million children
(50%) are living below the extreme poverty line of $1.25 a day, and 244 million children (67.5%) are
multidimensionally deprived. It therefore follows that the proportion of children in poverty based
on CC-MODA (i.e., multidimensionally deprived in 2-5 dimensions) is 17 percentage points higher
than the estimated proportion of children in extreme monetary poverty based on $1.25 a day
poverty line. This is similar also when using the predicted deprivation rates for children in the other
countries of sub-Saharan Africa. Out of a total of 41 countries that are home to 453 million
14 The GDP per capita, share of urban population and population in 2012 are retrieved from the World Bank (2014). 15 Based on our calculations using DHS/MICS data for 30 countries in sub-Saharan Africa, children on average represent 52% of the total population across the region. This proportion is used to calculate the absolute number of children for the 15 remaining countries.
29
children, 215 million children (47%) live in extreme poverty below $1.25 a day, while 288 million
children (64%) are multidimensionally deprived (see Annex 6 for estimates and a list of countries
included).
It should be noted that the number of children in extreme poverty is likely to be an
underestimation as the calculations are based on poverty rates of the total population, while child
poverty rates are generally higher. The World Bank’s report on ‘the State of the Poor’ reveals that
in developing countries children aged 0 to 18 represent 47% of the population living below the
extreme poverty line of $1.25 PPP, while among the non-poor only 33% are children (Olinto et al,
2013).
Given the data restrictions of this study, we are unable to conclude whether the children living
below $1.25 PPP a day are also multidimensionally deprived, or whether the two measures identify
two different groups of children. To analyse whether the same children lacking the goods and
services crucial to their survival and development are also among the extremely poor in monetary
terms, an overlap analysis is necessary using data that comprises information on both deprivations
and household income/consumption. Based on MODA-related research done so far, monetary
poverty and deprivations overlap to some extent, but a large proportion of children are
multidimensionally deprived, but are not considered as monetary poor, and vice versa.16 This
underlines the need to use the two measures of poverty as complementary measures in order to
identify the poor children.
5. CONCLUSION
This paper is based on the cross-country application of the Multiple Overlapping Deprivation
Analysis (MODA) methodology to analyse the poverty status of children in sub-Saharan Africa. The
methodology has been developed by UNICEF to analyse the number and the combinations of
deprivations children experience, moving from sector-by-sector analyses to a child level analysis by
looking at each child’s outcomes or access to various goods and services and exposure to harmful
practices, to determine children’s status of well-being in the various dimensions simultaneously.
The definition of deprivation is rooted in the child-rights framework using the Convention on the
Rights of the Child as its main source to select dimensions relevant to children’s well-being.
The analysis covers 30 out of 48 countries in sub-Saharan Africa, representing 78% of the total
population in this region. Children below the age of eighteen represent more than a half (52%) of
the total population in the region. The findings show that 67% of all the children across the thirty
countries experience at least two out of five deprivations critical to children’s survival and
development. This percentage represents 247 out of a total of 368 million children in the 30
countries. In an effort to predict the number of multidimensionally poor children in the entire sub-
Saharan Africa, a function was created using regression outcomes including GDP per capita,
population size and share of urban population. This function was then used as a predictor of child
16 For example, research in Senegal (UNICEF Senegal, forthcoming) shows that 20% of children are multidimensionally poor (i.e., lacking several goods or services crucial to their survival and development) but living in families above the national monetary poverty line, which means that a monetary poverty measure alone underestimates children’s poverty; at the same time, 15% of the monetary poor children are not deprived in any of the dimensions analysed, indicating that children’s basic rights may have been fulfilled through public service provision and through other channels. Analyses of child monetary and multidimensional poverty in Mali (de Milliano and Handa, forthcoming) and Madagascar (Plavgo, forthcoming) show similar findings and will be available soon.
30
deprivation rates for the remaining 15 countries that were not included in the analysis. The results
show that 298 out of a total of 468 million children in the 45 countries in sub-Saharan Africa are
multidimensionally poor, being deprived in two to five dimensions of basic child rights.
In order to place deprivation analysis in the wider context of other measures regarding the wealth
of a country and people’s monetary well-being, the findings of the multidimensional deprivation
analysis were compared to the GDP per capita and national and international poverty. The
comparison between GDP per capita and multidimensional child deprivation headcount ratio
shows a negatively sloped correlation indicating that higher GDP per capita is associated with
lower multidimensional deprivation rates. The monetary poverty measures indicate, somewhat
surprisingly, relatively weak correlation with the multidimensional deprivation measure for
children. The poverty rates based on the internationally comparable $1.25 PPP poverty line
highlight a reasonably strong and positive correlation between the level of monetary poverty and
multidimensional child deprivation. There is, however, a large proportion of unexplained variance
between the two measures of poverty. When looking at the correlation between the poverty rates
based on the national poverty lines and multidimensional deprivation, a weak correlation is
observed, regardless of whether the monetary poverty rates are calculated only for children or for
the total population. These findings could be taken forward by further investigating the correlation
between national poverty rates and country-specific multidimensional deprivation analyses
(resulting from an N-MODA application).
Overall, the combined results of this multidimensional deprivation analysis for children using the
MODA methodology has given an indication of the level of multidimensional deprivation across the
sub-Saharan region. It has provided further details on the depth of deprivation and the
simultaneous experience of deprivations. The comparison between multidimensional deprivation
for children and countries’ GDP per capita has shown a moderate correlation indicating some
ability to predict child deprivation on the basis of a country’s economic activity. In addition, there
has been a modest to absent correlation between deprivation and the various monetary poverty
measures emphasising that the two concepts of poverty identify (partly) different groups of people
and that the two measures of poverty should therefore be used complementary to each other,
especially regarding child poverty.
31
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33
ANNEX 1 - List of countries and data sources
Country
ISO code
Survey year
Data Violence indicator
Poverty ratio at $1.25 a
day (WB): year of
estimation
Poverty ratio at national
poverty line (WB):
year of estimation
Child poverty ratio
at national poverty line:
year of estimation
Benin BEN 2011-12 DHS No 2012 2011 2010 Burkina Faso BFA 2010-11 DHS Yes 2009 2009 N/A Burundi BDI 2010-11 DHS No 2006 2006 N/A Cameroon CMR 2011 DHS No 2007 2007 2007 Cent. African Rep. CAF 2010 MICS Yes 2008 2008 N/A Chad TCD 2010 MICS Yes 2011 2011 2002 Comoros COM 2012 DHS-MICS Yes 2004 2004 - Congo COG 2011-12 DHS No 2011 2011 N/A Congo DR COD 2010 MICS Yes 2006 2005 N/A Côte d'Ivoire CIV 2011-12 DHS Yes 2008 2008 2008 Equatorial Guinea GNQ 2011 DHS No N/A 2006 N/A Ethiopia ETH 2011 DHS No 2011 2011 N/A Gabon GAB 2012 DHS Yes 2005 2005 N/A Gambia GMB 2010-11 MICS Yes 2003 2010 N/A Ghana GHA 2011 MICS Yes 2006 2006 2005 Guinea GIN 2012 DHS-MICS No 2012 2012 N/A Kenya KEN 2008-09 DHS Yes 2005 2005 N/A Lesotho LSO 2009-10 DHS No 2010 2003 N/A Malawi MWI 2010 DHS No 2010 2010 - Mozambique MOZ 2011 DHS No 2009 2009 N/A Niger NEG 2012 DHS Yes 2011 2007 2011 Nigeria NGA 2011 MICS Yes 2011 2012 2012 Rwanda RWA 2010-11 DHS No 2011 2011 N/A Senegal SEN 2010-11 DHS No 2011 2011 2011 Sierra Leone SLE 2010 MICS Yes 2011 2011 N/A Swaziland SWZ 2010 MICS Yes 2010 2009 2009/10 Tanzania TZA 2010 DHS Yes 2012 2012 N/A Togo TGO 2010 MICS Yes 2011 2011 2011 Uganda UGA 2011 DHS No 2009 2009 2009 Zimbabwe ZWE 2011-12 DHS Yes N/A 2011 -
Note: Poverty estimates retrieved from the World Bank Databank (WB Databank, October 2014).
34
ANNEX 2 – Share and size of child population by country and age-group
Children below age five Children between age 5-17
Total population, 2012 (WB)
As a share of total population per country
In numbers
As % of children <5 in 30 selected countries
As a share of total population per country
In numbers
As % of children 5-17 in 30 selected countries
Benin 10,050,702 16.4% 1,652,855 1.4% 38.0% 3,821,287 1.5% Burkina Faso 16,460,141 18.0% 2,967,184 2.5% 36.3% 5,973,739 2.4% Burundi 9,849,569 18.4% 1,811,019 1.5% 35.3% 3,474,913 1.4% Cameroon 21,699,631 16.7% 3,621,510 3.0% 34.1% 7,391,185 3.0% Cent. African Rep. 4,525,209 19.7% 890,284 0.7% 33.4% 1,509,219 0.6% Chad 12,448,175 20.3% 2,529,921 2.1% 37.4% 4,657,983 1.9% Comoros 717,503 14.1% 101,032 0.1% 33.3% 239,216 0.1% Congo, Republic of 4,337,051 17.4% 753,622 0.6% 31.4% 1,361,984 0.5% Congo DR 65,705,093 18.5% 12,167,197 10.2% 35.6% 23,377,307 9.4% Cote d'Ivoire 19,839,750 16.0% 3,166,527 2.6% 32.5% 6,453,504 2.6% Equatorial Guinea 736,296 15.4% 113,337 0.1% 28.2% 207,708 0.1% Ethiopia 91,728,849 15.5% 14,225,062 11.9% 36.4% 33,348,005 13.4% Gabon 1,632,572 15.1% 246,008 0.2% 30.2% 492,829 0.2% Gambia 1,791,225 16.8% 300,419 0.3% 34.5% 617,771 0.2% Ghana 25,366,462 13.5% 3,434,583 2.9% 34.2% 8,679,224 3.5% Guinea 11,451,273 16.1% 1,841,084 1.5% 36.9% 4,225,239 1.7% Kenya 43,178,141 15.6% 6,720,436 5.6% 35.0% 15,107,354 6.1% Lesotho 2,051,545 11.0% 225,763 0.2% 31.2% 640,452 0.3% Malawi 15,906,483 16.9% 2,683,137 2.2% 38.0% 6,047,379 2.4% Mozambique 25,203,395 18.0% 4,540,898 3.8% 36.4% 9,162,223 3.7% Niger 17,157,042 21.3% 3,657,243 3.1% 39.2% 6,720,659 2.7% Nigeria 168,833,776 17.2% 28,966,558 24.2% 33.0% 55,630,915 22.4% Rwanda 11,457,801 16.0% 1,838,104 1.5% 35.1% 4,016,848 1.6% Senegal 13,726,021 16.8% 2,307,411 1.9% 33.4% 4,580,114 1.8% Sierra Leone 5,978,727 13.2% 790,351 0.7% 34.5% 2,062,677 0.8% Swaziland 1,230,985 13.9% 170,942 0.1% 35.8% 440,796 0.2% Tanzania 47,783,107 16.9% 8,061,564 6.7% 35.6% 17,026,239 6.9% Togo 6,642,928 15.1% 1,005,752 0.8% 34.7% 2,304,245 0.9% Uganda 36,345,860 18.9% 6,879,464 5.7% 38.6% 14,038,817 5.7% Zimbabwe 13,724,317 14.7% 2,018,254 1.7% 33.7% 4,631,843 1.9% Total 707,559,629 16.9% 119,687,523 100% 35.1% 248,241,673 100% Note: Total population per country in 2012 retrieved from World Bank Databank (Oct 2014). Child population as a share of total population per country based on authors’ calculations using most recent DHS and MICS surveys.
35
ANNEX 3 – Deprivation headcount rate by indicator and age-group
Dimension Indicator Deprivation headcount in %
0 to 4 years 5 to 17 years
Nutrition 40.3% - Infant and young child feeding 56.6% - Wasting (weight for height) 8.6% -
Health 55.8% - DPT immunisation (1-4 years) 37.4% - Skilled birth attendance 49.1% -
Education17 35.2% Compulsory school attendance - 22.6% Primary school attainment - 51.0%
Information 26.3% Information devices - 26.3%
Water 51.8% 50.8% Drinking water source 40.4% 38.9% Distance to water source 24.6% 25.2%
Sanitation 67.1% 66.0% Toilet type 67.1% 66.0%
Housing 44.2% 43.7% Floor and roof material 35.3% 32.2% Overcrowding 16.3% 20.2%
Protection from Violence 63.2% 62.7% Domestic violence 63.2% 62.7%
17 Compulsory school attendance is calculated for children at official compulsory school age, which varies from country to country. Primary school attainment calculated for children who have reached the age of entering lower secondary school, up until the age of 17. The official starting and ending age of compulsory school, as well as the official duration of primary school and starting age of lower secondary school retrieved from: http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx?ReportId=163. One year of delay in schooling is allowed when calculating deprivation rates, allowing for delayed entry in schooling or one year of repetition. See background material in the MODA web-portal (http://www.unicef-irc.org/MODA/) for information on compulsory and primary school duration per country.
36
ANNEX 4 – Contribution of multidimensional deprivation ratio (H) and adjusted multidimensional
deprivation ratio (M) by country to the total deprivation ratio (children deprived in 2-5
dimensions)
Contribution to the total
multidimensional deprivation ratio (H, K=2)
Contribution to the total adjusted multidimensional deprivation ratio
(M0 , K=2)
Age 0-4 Age 5-17 All children Age 0-4 Age 5-17 All children
Benin 1.2% 1.3% 1.3% 1.0% 1.1% 1.1%
Burkina Faso 2.3% 2.5% 2.4% 2.1% 2.4% 2.3%
Burundi 1.4% 1.4% 1.4% 1.2% 1.3% 1.3%
Cameroon 2.5% 2.2% 2.3% 2.4% 2.0% 2.2%
Central African Republic 0.8% 0.7% 0.7% 0.8% 0.7% 0.8%
Chad 2.6% 2.5% 2.6% 3.1% 3.0% 3.0%
Comoros 0.1% 0.1% 0.1% 0.0% 0.1% 0.1%
Congo, Republic of 0.5% 0.4% 0.4% 0.5% 0.3% 0.4%
Congo DR 11.9% 12.0% 12.0% 12.5% 13.8% 13.3%
Cote d'Ivoire 2.3% 1.9% 2.0% 2.0% 1.6% 1.7%
Equatorial Guinea 0.1% 0.1% 0.1% 0.1% 0.0% 0.1%
Ethiopia 15.0% 18.7% 17.3% 17.2% 21.5% 19.9%
Gabon 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
Gambia 0.2% 0.1% 0.1% 0.1% 0.1% 0.1%
Ghana 1.7% 2.2% 2.0% 1.4% 1.8% 1.7%
Guinea 1.5% 1.5% 1.5% 1.4% 1.4% 1.4%
Kenya 5.2% 5.7% 5.5% 5.0% 5.3% 5.2%
Lesotho 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%
Malawi 2.4% 3.0% 2.8% 2.1% 3.0% 2.7%
Mozambique 4.0% 4.2% 4.2% 4.2% 4.4% 4.3%
Niger 3.6% 3.5% 3.6% 4.0% 3.7% 3.8%
Nigeria 21.9% 16.3% 18.3% 20.5% 14.5% 16.7%
Rwanda 0.9% 1.0% 1.0% 0.6% 0.8% 0.7%
Senegal 1.3% 1.2% 1.2% 1.2% 1.0% 1.1%
Sierra Leone 0.6% 0.8% 0.7% 0.6% 0.7% 0.7%
Swaziland 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
Tanzania 7.4% 7.9% 7.7% 7.7% 7.5% 7.6%
Togo 0.8% 0.9% 0.8% 0.7% 0.8% 0.8%
Uganda 6.1% 6.3% 6.2% 5.9% 5.7% 5.8%
Zimbabwe 1.3% 1.3% 1.3% 1.1% 1.2% 1.2%
TOTAL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
37
ANNEX 5 – Multidimensional deprivation ratios for all cut-off points, by country and age-group
Multidimensional deprivation ratios: all children below the age of 18
Deprivation headcount rate (H) Average deprivation
intensity of deprived (A) Adjusted deprivation headcount rate (M)
No. of deprivations 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5
TOTAL 86.4% 67.1% 45.3% 23.5% 2.6 3.1 3.7 4.3 0.46 0.42 0.33 0.20
Benin 86.0% 57.6% 28.1% 9.0% 2.1 2.7 3.4 4.2 0.36 0.31 0.19 0.08
Burkina Faso 87.6% 67.0% 42.2% 18.3% 2.5 3.0 3.5 4.2 0.44 0.40 0.30 0.15
Burundi 90.1% 66.0% 35.5% 12.5% 2.3 2.8 3.4 4.2 0.41 0.36 0.24 0.10
Cameroon 74.1% 51.5% 31.0% 14.5% 2.4 3.0 3.6 4.3 0.35 0.30 0.22 0.12
Central African Republic 87.5% 72.2% 55.7% 32.5% 2.9 3.4 3.8 4.3 0.52 0.49 0.42 0.28
Chad 95.4% 88.3% 75.8% 50.7% 3.4 3.6 3.9 4.3 0.66 0.64 0.59 0.44
Comoros 83.6% 47.1% 17.0% 4.0% 1.8 2.5 3.3 4.1 0.30 0.23 0.11 0.03
Congo 77.1% 49.6% 24.3% 8.5% 2.1 2.7 3.4 4.2 0.32 0.27 0.17 0.07
Congo DR 93.8% 83.1% 66.9% 41.5% 3.2 3.5 3.8 4.3 0.60 0.58 0.51 0.36
Cote d'Ivoire 80.1% 52.0% 25.8% 8.4% 2.1 2.7 3.4 4.2 0.34 0.28 0.18 0.07
Equatorial Guinea 79.1% 47.7% 20.2% 3.8% 1.9 2.5 3.2 4.1 0.30 0.24 0.13 0.03
Ethiopia 97.0% 90.1% 76.3% 50.2% 3.4 3.6 3.9 4.3 0.66 0.64 0.59 0.43
Gabon 63.0% 29.6% 10.2% 2.3% 1.7 2.4 3.2 4.1 0.21 0.14 0.07 0.02
Gambia 65.3% 32.7% 13.1% 3.5% 1.8 2.5 3.3 4.1 0.23 0.17 0.09 0.03
Ghana 72.3% 41.3% 18.5% 5.2% 1.9 2.6 3.3 4.1 0.28 0.21 0.12 0.04
Guinea 83.2% 61.2% 37.7% 15.0% 2.4 2.9 3.5 4.2 0.40 0.36 0.26 0.13
Kenya 85.5% 62.8% 37.6% 16.5% 2.4 2.9 3.5 4.2 0.41 0.37 0.27 0.14
Lesotho 84.7% 58.3% 32.8% 12.8% 2.3 2.8 3.5 4.2 0.38 0.33 0.23 0.11
Malawi 94.9% 79.2% 51.3% 21.4% 2.6 3.0 3.5 4.2 0.50 0.47 0.36 0.18
Mozambique 87.7% 74.8% 55.4% 29.8% 2.9 3.2 3.7 4.3 0.51 0.49 0.41 0.25
Niger 94.0% 85.0% 67.1% 37.0% 3.1 3.3 3.7 4.2 0.58 0.57 0.50 0.31
Nigeria 79.6% 53.5% 29.6% 12.4% 2.2 2.8 3.5 4.2 0.36 0.30 0.21 0.11
Rwanda 77.0% 40.2% 14.3% 3.1% 1.8 2.4 3.2 4.1 0.27 0.20 0.09 0.03
Senegal 73.0% 44.2% 23.1% 8.7% 2.1 2.8 3.4 4.2 0.30 0.24 0.16 0.07
Sierra Leone 86.5% 62.7% 36.9% 15.9% 2.4 2.9 3.5 4.2 0.41 0.36 0.26 0.13
Swaziland 68.2% 33.6% 11.6% 3.3% 1.7 2.5 3.3 4.1 0.23 0.17 0.08 0.03
Tanzania 92.0% 75.9% 50.7% 25.0% 2.7 3.1 3.6 4.2 0.50 0.47 0.37 0.21
Togo 84.0% 62.3% 35.3% 13.8% 2.4 2.8 3.5 4.2 0.40 0.35 0.24 0.12
Uganda 92.0% 73.6% 44.5% 18.1% 2.5 2.9 3.5 4.2 0.46 0.43 0.31 0.15
Zimbabwe 70.8% 48.6% 28.7% 11.5% 2.3 2.9 3.5 4.1 0.32 0.28 0.20 0.10
38
Multidimensional deprivation ratios: all children below the age of 5
Deprivation headcount rate (H)
Average deprivation intensity of deprived (A)
Adjusted deprivation headcount rate (M)
No. of deprivations 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5
TOTAL 91.5% 74.6% 53.7% 29.4% 2.8 3.2 3.7 4.3 0.51 0.48 0.40 0.25
Benin 89.7% 64.5% 34.0% 12.0% 2.3 2.7 3.4 4.2 0.41 0.36 0.23 0.10
Burkina Faso 90.1% 69.5% 44.7% 19.6% 2.5 3.0 3.5 4.2 0.46 0.42 0.32 0.17
Burundi 92.8% 70.1% 39.5% 13.1% 2.3 2.8 3.4 4.2 0.44 0.39 0.27 0.11
Cameroon 84.1% 62.6% 41.5% 20.4% 2.5 3.1 3.6 4.3 0.43 0.39 0.30 0.17
Central African Republic 92.4% 78.9% 61.8% 37.8% 3.1 3.4 3.8 4.3 0.57 0.54 0.47 0.33
Chad 97.6% 92.2% 82.2% 59.4% 3.6 3.8 4.0 4.4 0.71 0.69 0.65 0.52
Comoros 89.5% 56.7% 23.7% 5.9% 2.0 2.5 3.3 4.1 0.35 0.29 0.16 0.05
Congo 87.1% 62.1% 34.4% 13.3% 2.3 2.8 3.5 4.2 0.40 0.35 0.24 0.11
Congo DR 97.0% 87.0% 69.1% 39.6% 3.1 3.4 3.7 4.3 0.61 0.59 0.52 0.34
Cote d'Ivoire 88.0% 63.8% 37.5% 14.2% 2.4 2.9 3.5 4.2 0.41 0.37 0.26 0.12
Equatorial Guinea 90.1% 62.1% 35.4% 7.9% 2.2 2.7 3.2 4.1 0.39 0.34 0.23 0.06
Ethiopia 98.3% 94.3% 84.7% 58.8% 3.6 3.7 3.9 4.3 0.70 0.70 0.66 0.50
Gabon 76.3% 41.2% 17.4% 4.7% 1.8 2.5 3.3 4.1 0.28 0.21 0.11 0.04
Gambia 80.2% 48.8% 23.2% 7.4% 2.0 2.7 3.4 4.2 0.32 0.26 0.16 0.06
Ghana 75.7% 44.3% 21.8% 6.9% 2.0 2.7 3.4 4.2 0.30 0.24 0.15 0.06
Guinea 92.1% 72.9% 50.1% 23.8% 2.7 3.1 3.6 4.3 0.49 0.45 0.36 0.20
Kenya 89.0% 69.3% 45.8% 23.4% 2.6 3.1 3.6 4.2 0.47 0.43 0.33 0.20
Lesotho 87.8% 67.7% 43.9% 18.5% 2.5 3.0 3.5 4.2 0.44 0.40 0.31 0.15
Malawi 94.9% 79.4% 48.9% 18.7% 2.6 2.9 3.5 4.2 0.49 0.46 0.34 0.16
Mozambique 92.1% 79.4% 61.1% 35.1% 3.0 3.3 3.7 4.2 0.55 0.53 0.45 0.30
Niger 96.0% 88.7% 75.7% 48.6% 3.4 3.6 3.8 4.3 0.65 0.63 0.58 0.42
Nigeria 90.1% 67.6% 42.6% 20.1% 2.5 3.0 3.6 4.3 0.45 0.41 0.31 0.17
Rwanda 77.8% 41.7% 14.6% 2.9% 1.8 2.4 3.2 4.1 0.28 0.20 0.09 0.02
Senegal 80.1% 50.5% 30.5% 14.9% 2.2 3.0 3.6 4.2 0.36 0.30 0.22 0.13
Sierra Leone 92.9% 72.7% 45.7% 19.4% 2.5 3.0 3.5 4.2 0.47 0.43 0.32 0.16
Swaziland 71.4% 37.2% 13.9% 4.9% 1.8 2.5 3.4 4.1 0.26 0.19 0.09 0.04
Tanzania 94.5% 82.4% 63.2% 37.0% 3.0 3.3 3.8 4.3 0.58 0.55 0.48 0.32
Togo 88.0% 68.1% 42.5% 18.4% 2.5 2.9 3.5 4.2 0.44 0.40 0.30 0.16
Uganda 94.2% 79.1% 56.2% 26.7% 2.8 3.1 3.6 4.3 0.53 0.50 0.40 0.23
Zimbabwe 79.1% 55.3% 34.9% 15.3% 2.4 3.0 3.5 4.2 0.37 0.33 0.25 0.13
39
Multidimensional deprivation ratios: all children of age 5-17
Deprivation headcount rate (H) Average deprivation
intensity of deprived (A) Adjusted deprivation headcount rate (M)
No. of deprivations 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5 1-5 2-5 3-5 4-5
TOTAL 83.9% 63.5% 41.2% 20.7% 2.6 3.1 3.6 4.3 0.43 0.39 0.30 0.18
Benin 84.3% 54.7% 25.5% 7.7% 2.1 2.6 3.3 4.2 0.35 0.29 0.17 0.06
Burkina Faso 86.4% 65.7% 41.0% 17.6% 2.5 3.0 3.5 4.2 0.43 0.39 0.29 0.15
Burundi 88.7% 63.9% 33.5% 12.2% 2.3 2.7 3.4 4.2 0.40 0.35 0.23 0.10
Cameroon 69.3% 46.0% 25.9% 11.6% 2.2 2.9 3.6 4.2 0.31 0.27 0.18 0.10
Central African Republic 84.6% 68.3% 52.1% 29.3% 2.9 3.3 3.7 4.3 0.49 0.45 0.39 0.25
Chad 94.3% 86.2% 72.4% 45.9% 3.3 3.5 3.8 4.3 0.63 0.61 0.56 0.40
Comoros 81.2% 43.0% 14.2% 3.2% 1.7 2.4 3.2 4.1 0.28 0.21 0.09 0.03
Congo 71.6% 42.7% 18.7% 5.8% 2.0 2.6 3.4 4.1 0.28 0.22 0.13 0.05
Congo DR 92.2% 81.1% 65.8% 42.5% 3.2 3.5 3.9 4.3 0.59 0.57 0.51 0.37
Cote d'Ivoire 76.2% 46.2% 20.1% 5.5% 2.0 2.6 3.3 4.1 0.30 0.24 0.13 0.05
Equatorial Guinea 73.1% 39.8% 11.9% 1.6% 1.7 2.3 3.1 4.0 0.25 0.19 0.08 0.01
Ethiopia 96.5% 88.2% 72.7% 46.5% 3.3 3.5 3.8 4.3 0.64 0.62 0.56 0.40
Gabon 56.4% 23.8% 6.6% 1.0% 1.6 2.3 3.2 4.1 0.18 0.11 0.04 0.01
Gambia 58.1% 24.9% 8.1% 1.7% 1.6 2.4 3.2 4.1 0.19 0.12 0.05 0.01
Ghana 70.9% 40.2% 17.2% 4.6% 1.9 2.6 3.3 4.1 0.27 0.21 0.11 0.04
Guinea 79.3% 56.0% 32.3% 11.2% 2.3 2.8 3.4 4.2 0.36 0.32 0.22 0.09
Kenya 83.9% 59.8% 33.9% 13.4% 2.3 2.8 3.5 4.2 0.39 0.34 0.24 0.11
Lesotho 83.5% 54.9% 28.8% 10.8% 2.2 2.8 3.4 4.2 0.36 0.30 0.20 0.09
Malawi 95.0% 79.1% 52.4% 22.5% 2.7 3.0 3.5 4.2 0.51 0.48 0.37 0.19
Mozambique 85.6% 72.5% 52.6% 27.2% 2.9 3.2 3.7 4.3 0.49 0.46 0.38 0.23
Niger 92.9% 83.0% 62.4% 30.7% 3.0 3.2 3.6 4.2 0.55 0.53 0.45 0.26
Nigeria 74.2% 46.2% 22.9% 8.4% 2.1 2.7 3.4 4.2 0.31 0.25 0.16 0.07
Rwanda 76.6% 39.5% 14.2% 3.2% 1.7 2.5 3.3 4.1 0.27 0.19 0.09 0.03
Senegal 69.5% 41.1% 19.3% 5.6% 2.0 2.6 3.3 4.1 0.27 0.22 0.13 0.05
Sierra Leone 84.0% 58.9% 33.6% 14.5% 2.3 2.9 3.5 4.2 0.39 0.34 0.24 0.12
Swaziland 66.9% 32.2% 10.8% 2.7% 1.7 2.4 3.3 4.1 0.23 0.16 0.07 0.02
Tanzania 90.8% 72.8% 44.8% 19.3% 2.5 2.9 3.5 4.2 0.46 0.43 0.31 0.16
Togo 82.3% 59.8% 32.1% 11.8% 2.3 2.8 3.4 4.2 0.38 0.33 0.22 0.10
Uganda 91.0% 70.9% 38.7% 13.9% 2.4 2.8 3.4 4.2 0.43 0.39 0.27 0.12
Zimbabwe 67.2% 45.7% 26.0% 9.8% 2.2 2.8 3.4 4.1 0.30 0.26 0.18 0.08
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ANNEX 6 - Estimates and predictions of multidimensional child poverty and monetary poverty
Country
GDP per capita, $
PPP, 2012 (WB)
Total population
Share of
urban pop.
Share of children as % of
the total pop.2
No. of children below 18
Multi-dimensional
child deprivation
ratio
No. of children
deprived in 2-5
dimensions
% of total pop. living
on less than $1.25PPP a
day
No. of poor children (less
than $1.25PPP a
day)3
Countries included in the CC-MODA analysis 1 Benin $1,716 10,050,702 42.7% 54.5% 5,474,142 57.6% 3,153,106 51.6% 2,825,205 2 Burkina Faso $1,555 16,460,141 27.3% 54.3% 8,940,923 67.0% 5,990,418 44.5% 3,975,134 3 Burundi $750 9,849,569 11.2% 53.7% 5,285,932 66.0% 3,488,715 81.3% 4,298,520 4 Cameroon $2,596 21,699,631 52.7% 50.8% 11,012,695 51.5% 5,671,538 27.6% 3,040,605 5 CAR $948 4,525,209 39.3% 53.0% 2,399,503 72.2% 1,732,441 62.8% 1,507,608 6 Chad $2,038 12,448,175 22.1% 57.7% 7,187,904 88.3% 6,346,919 36.5% 2,625,022 7 Comoros $1,519 717,503 28.0% 47.4% 340,248 47.1% 160,257 46.1% 156,888 8 Congo $5,730 4,337,051 64.1% 48.8% 2,115,607 49.6% 1,049,341 32.8% 694,342 9 Congo DR $697 65,705,093 41.0% 54.1% 35,544,504 83.1% 29,537,483 87.7% 31,179,639 10 Côte d'Ivoire $2,795 19,839,750 52.0% 48.5% 9,620,031 52.0% 5,002,416 35.0% 3,370,859 11 Eq. Guinea $35,908 736,296 39.5% 43.6% 321,045 47.7% 153,138 N/A N/A 12 Ethiopia $1,240 91,728,849 18.2% 51.9% 47,573,067 90.1% 42,863,333 36.8% 17,502,131 13 Gabon $18,347 1,632,572 86.4% 45.3% 738,836 29.6% 218,696 6.1% 44,995 14 The Gambia $1,604 1,791,225 57.7% 51.3% 918,191 32.7% 300,248 33.6% 308,787 15 Ghana $3,732 25,366,462 52.1% 47.8% 12,113,807 41.3% 5,003,002 28.6% 3,463,337 16 Guinea $1,237 11,451,273 35.7% 53.0% 6,066,323 61.2% 3,712,590 40.9% 2,479,306 17 Kenya $2,189 43,178,141 24.4% 50.6% 21,827,789 62.8% 13,707,852 43.4% 9,466,712 18 Lesotho $2,432 2,051,545 25.8% 42.2% 866,215 58.3% 505,003 56.2% 486,986 19 Malawi $753 15,906,483 15.8% 54.9% 8,730,517 79.2% 6,914,569 72.2% 6,299,941 20 Mozambique $985 25,203,395 31.4% 54.4% 13,703,121 74.8% 10,249,935 60.7% 8,319,165 21 Niger $899 17,157,042 18.0% 60.5% 10,377,902 85.0% 8,821,217 40.8% 4,235,222 22 Nigeria $5,535 168,833,776 45.2% 50.1% 84,597,473 53.5% 45,259,648 54.4% 45,995,646 23 Rwanda $1,406 11,457,801 25.9% 51.1% 5,854,952 40.2% 2,353,691 63.0% 3,689,791 24 Senegal $2,212 13,726,021 42.8% 50.2% 6,887,525 44.2% 3,044,286 34.1% 2,345,891 25 Sierra Leone $1,610 5,978,727 38.9% 47.7% 2,853,028 62.7% 1,788,849 56.6% 1,615,670 26 Swaziland $6,502 1,230,985 21.4% 49.7% 611,738 33.6% 205,544 39.3% 240,413 27 Tanzania $1,685 47,783,107 29.5% 52.5% 25,087,804 75.9% 19,041,643 43.5% 10,908,177 28 Togo $1,337 6,642,928 38.5% 49.8% 3,309,997 62.3% 2,062,128 52.5% 1,736,424 29 Uganda $1,357 36,345,860 15.1% 57.6% 20,918,282 73.6% 15,395,855 37.9% 7,930,121 30 Zimbabwe $1,696 13,724,317 32.8% 48.5% 6,650,097 48.6% 3,231,947 N/A N/A Countries for which deprivation rates are predicted1
31 Angola $5,539 20,820,525 41.7% 52.0% 10,826,620 32.6% 3,529,562 43.4% 4,695,505 32 Botswana $7,255 2,003,910 56.7% 52.0% 1,042,028 8.6% 89,814 13.4% 139,736 33 Cape Verde $3,554 494,401 63.4% 52.0% 257,087 34.0% 87,434 13.7% 35,272 34 Eritrea $504 6,130,922 21.4% 52.0% 3,188,064 71.3% 2,273,581 N/A N/A 35 Guinea-Bissau $494 1,663,558 46.9% 52.0% 865,046 62.3% 538,947 48.9% 423,007 36 Liberia $414 4,190,435 48.5% 52.0% 2,179,015 63.4% 1,381,254 83.8% 1,825,143 37 Madagascar $443 22,293,914 33.2% 52.0% 11,592,778 74.0% 8,576,047 87.7% 10,163,389 38 Mali $696 14,853,572 37.6% 52.0% 7,723,819 68.3% 5,274,167 50.6% 3,909,025 39 Mauritania $1,043 3,796,141 58.0% 52.0% 1,973,984 55.8% 1,100,607 23.4% 462,504 40 Namibia $5,931 2,259,393 43.7% 52.0% 1,174,879 22.5% 264,094 23.5% 276,566 41 São Tomé & Prin. $1,400 188,098 63.3% 52.0% 97,810 50.2% 49,071 43.5% 42,577 42 South Africa $7,314 52,274,945 63.3% 52.0% 27,182,838 22.2% 6,031,499 9.4% 2,560,623 43 South Sudan $974 10,837,527 18.2% 52.0% 5,635,486 70.4% 3,967,045 N/A N/A 44 Sudan $1,695 37,195,349 33.3% 52.0% 19,341,486 69.1% 13,369,963 19.8% 3,829,614 45 Zambia $1,463 14,075,099 39.6% 52.0% 7,319,015 61.6% 4,512,022 74.3% 5,439,492
Total 30 countries 367,929,196 246,965,808 As % of total 100.00% 67.1%
Total 28 countries (excl. ZWE and GNQ) 360,958,054 243,580,722 180,742,538 As % of total 100.00% 67.5% 50.1%
Total sub-Saharan Africa (SSA - 45 countries)4 468,329,152 298,010,915 As % of total 100.00% 63.6%
Total SSA (41 countries - excl. ZWE, GNQ, ERI, SSD) 452,534,460 288,385,203 214,544,993 As % of total 100.00% 63.7% 47.4% 1 Multidimensional child deprivation rates have been predicted for the remaining 15 countries of sub-Saharan Africa using coefficients retrieved from an OLS regression model estimating the relationship between multidimensional deprivation rates and the GDP per capita of 28 countries included in the analysis, controlled for the share of the urban population and the size of the population of each country. See Table 2 for regression results and the formula used for calculations. The countries that have been excluded from the regression as outliers: Equatorial Guinea and Gabon. 2 Children as a share of the total population calculated using DHS/MICS data for 30 countries. For the remaining 15 countries, child population assumed to be equal to 52% of the total population of each country, which is the average of child population of the 30 countries analysed. 3 This estimate is not adjusted for demographics; generally, child poverty rates in developing countries are higher than those of the total population. 4 Although part of sub-Saharan Africa according to the World Bank classification, the following three countries are excluded from this analysis: Somalia (no data on per capita GDP), Mauritius and Seychelles.
41