Source document: https://docs-lawep.s3.us-east-2.amazonaws.com/1698831221879.pdf (South Africa)
Child poverty in South Africa: A Multiple Overlapping Deprivation Analysis Overview of the multi-deprivation index on children statssa.gov.za twitter.com/StatsSA facebook.com/StatsSA IMPROVING LIVES THROUGH DATA ECOSYSTEMS About the report The report seeks to provide insight on child poverty in South Africa using the Multiple Overlapping Deprivation Analysis (MODA) methodology.
It provides information on: Multidimensional child poverty levels in SA Main contributors (dimensions) to the poverty situation of children in SA Socio-economic and geographic characteristics of poor children in South Africa.
The report is based on data collected through the Living Conditions Survey (LCS) conducted in 2014/15 The LCS is a household-based survey conducted by Stats SA to measure poverty in the country More than 25 000 households participated in the survey The LCS 2014/15 collected data on: Detailed income and expenditure Education Housing Social welfare Health and living circumstances IMPROVING LIVES THROUGH DATA ECOSYSTEMS Dimensions and indicators MODA for South Africa used 7 dimensions and 14 indicators Dimensions Indicators Age group WASH Housing Sanitation Waste disposal Drinking water source Shelter (materials used for roof, walls and floor) Energy (fuel for cooking and lighting) Nutrition Food security Protection Safety 0 to 4 5 to 12 13 to 17 0 to 4 5 to 12 13 to 17 0 to 4 5 to 12 13 to 17 0 to 4 5 to 12 13 to 17 IMPROVING LIVES THROUGH DATA ECOSYSTEMS Dimensions and indicators MODA for South Africa used 7 dimensions and 14 indicators Dimensions Indicators Age group Health Distance to healthcare centre Information Access to information devices Availability of RTHC Child development Education IMPROVING LIVES THROUGH DATA ECOSYSTEMS Child development (exposure to ECD for 0 to 2 years) ECE attendance (applicable to 3 to 4 years) School attendance Lateness in schooling School facilities 5 to 12 13 to 17 0 to 4 0 to 4 0 to 4 5 to 12 13 to 17 0 to 4 5 to 12 13 to 17 The South African population in 2021 is estimated at up by 604 281 (1,01% change) from 2020 estimates 60, 1 Million IMPROVING LIVES THROUGH DATA ECOSYSTEMS 5 Source MYPE 2021 Children and youth amount to nearly 38 million in SA South Africa is a demographically youthful population with a median age of 28 Population age structure 5 year groupings, 2021 s n o i l l i M 6 5 4 3 2 1 0 SA median age is 28 years Children 0-14 17,0M Youth 15-34 20,6M Adults 35-59 17,0M Elderly 60+ 5,5M 10-14 IMPROVING LIVES THROUGH DATA ECOSYSTEMS 0-4 5-9 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ MID YEAR ESTIMATES 2021 In 2015, more than a quarter of the population were living below the food poverty line Money-metric Poverty headcounts in 2015 Upper-Bound Poverty Line Lower-Bound Poverty Line Food Poverty Line 25,2% below this Line 40,0% below this line 55,5% below this line 45% Non Poor Threshold of absolute deprivation.
The amount of money required to purchase the minimum required daily energy intake IMPROVING LIVES THROUGH DATA ECOSYSTEMS Provides an austere threshold below which one has to choose between food and important non-food items Threshold of deprivation below which people cannot afford the minimum food and non-food requirements 7 Source: Living Conditions Survey Inflation-adjusted national poverty lines for 2021 (per person per month in Rands) R1 335,00 Upper-Bound Poverty Line Threshold of deprivation below which people cannot afford the minimum food and non-food requirements R890,00 Lower-Bound Poverty Line Austere threshold below which one has to choose between food and important non-food items R624,00 Food Poverty Line Threshold of absolute deprivation.
The amount of money required to purchase the minimum required daily energy intake The LBPL and UBPL lines contain both food and non-food components of household consumption expenditure.

Source: National Poverty Lines, 2021 IMPROVING LIVES THROUGH DATA ECOSYSTEMS 8 Grants remain a significant source of income for SA households, particularly in rural areas.
51% of household income is sourced from remittances and grants in EC Percentage distribution of sources of household income by province, 2019 Salaries 39% Remittances 13% Grants 38% Source: GHS 2019 Other Sources Pensions 6% 4,4% EC LP FS NW MP KZN NC RSA GP WC 55% 11% 20% 11% 3,0% 0 10 20 30 40 50 60 IMPROVING LIVES THROUGH DATA ECOSYSTEMS 9 70% 3% 70 10% 80 11% 90 5,7% 100 Vulnerability to hunger at an individual and household level has been declining whilst access to grants has been increasing.
Grants and Vulnerability to hunger 2002 - 2019 50 45 40 35 30 25 20 15 10 5 30,8% 27,7% 22,8% 12,8% Grant: households 45,5% Grant: persons 30,9% Vulnerability to hunger: persons Vulnerability to hunger: HH 11,1% 10,3% 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Source GHS 2019 IMPROVING LIVES THROUGH DATA ECOSYSTEMS 10 IMPROVING LIVES THROUGH DATA ECOSYSTEMS Profiling child poverty in South Africa More than six out of ten (62,1%) children in SA experience multidimensional poverty where k=3.
All age groups show similar multidimensional poverty rates.

Multidimensional (k=3) child poverty by age group, 2015 All children 13 to 17 years 5 to 12 years 0 to 4 years IMPROVING LIVES THROUGH DATA ECOSYSTEMS The intensity of multidimensional child poverty (The average number of dimensions that are experienced by the poor children expressed in percentages) stood at 57,4% 62,1% 62,5% 63,4% 59,9% Intensity of poverty for all age groups is consistent around 57%.
On average, multidimensionally poor children in all age groups are deprived in about 4 out of 7 dimensions.

Intensity of Multidimensional (k=3) child poverty by age group, 2015 consistent around 57% 56,9% 57,6% 57,5% 57,4% y t i s n e t n i e g a r e v A 0 to 4 years 5 to 12 years 13 to 17 years All children IMPROVING LIVES THROUGH DATA ECOSYSTEMS Eight out of ten children experience multiple deprivation i.e.

number of deprivations greater than one.
Deprivation distribution by number of simultaneous deprivation for children at national level, 2015 d e v i r p e d n e r d l i h c f o e g a t n e c r e P 5,5% 22,4% 21,3% 19,1% 13,3% 13,8% Multiple deprivation analysis provide the number of dimensions that the children suffer from 4,3% 0,3% 0 IMPROVING LIVES THROUGH DATA ECOSYSTEMS Number of simultaneous deprivations Most children face deprivation in multiple dimensions at the same time while very few are only deprived in one specific dimension Nutrition Nutrition +6 other dimensions 0,3% +5 other dimensions 3,9% Nutrition +4 other dimensions 10,6% Percentage of children deprived in each dimension and additional 1-6 dimensions by age group 0 to 4 years Nutrition +3 other dimensions 11,7% Nutrition +2 other dimensions 9,5% Deprived in Nutrition Dimension only 1,6% Nutrition +1 other dimension 4,9% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% IMPROVING LIVES THROUGH DATA ECOSYSTEMS IMPROVING LIVES THROUGH DATA ECOSYSTEMS Major contributors to the child poverty situation Decomposition of the adjusted multidimensional poverty headcount (M0) by contribution of each dimension at national level, 2015 Nutrition Health Child development/education Protection WASH Housing Information 13 -17 years 13,9% 17,7% 22,7% 5,5% 18,1% 19,2% 2,9% 5 -12 years 14,0% 17,7% 22,1% 5,5% 18,3% 19,3% 3,1% 0 -4 years 15,1% 18,4% 18,2% 5,7% 18,9% 20,3% 3,6% IMPROVING LIVES THROUGH DATA ECOSYSTEMS Demographic characteristics of poor children in South Africa Overview of the multi-deprivation index on children IMPROVING LIVES THROUGH DATA ECOSYSTEMS There is no significant difference in the multidimensional child poverty when disaggregated by sex of a child.
Multidimensional (k=3) child poverty by sex of child, 2015 62,6% 61,7% IMPROVING LIVES THROUGH DATA ECOSYSTEMS Male Female Black African children (68,3%) are more likely to be multidimensionally poor when compared to other population groups.
Multidimensional poverty by population group of child, 2015 Black African Coloured 68,3% 37,9% Indian/Asian 16,8% White 11,5% IMPROVING LIVES THROUGH DATA ECOSYSTEMS Geographic location of poor children in South Africa Overview of the multi-deprivation index on children IMPROVING LIVES THROUGH DATA ECOSYSTEMS The majority of children in Limpopo (82,8%), Eastern Cape (78,7%) and KwaZulu-Natal (75,8%) are multidimensionally poor.
Children in Gauteng and Western Cape provinces have the lowest multidimensional poverty rates at 33,6% and 37,1% respectively Multidimensional (k=3) child poverty by province, 2015 Limpopo Eastern Cape KwaZulu-Natal Mpumalanga North West Northern Cape Free State Western Cape Gauteng IMPROVING LIVES THROUGH DATA ECOSYSTEMS 82,8% 78,7% 75,8% 69,2% 64,1% 53,8% 53,8% 37,1% 33,6% Children in Gauteng and Western Cape provinces have the lowest multidimensional poverty rates Multidimensional child poverty rate is higher (88,4%) for children living in rural areas than those from urban areas (41,3%).
Multidimensional (k=3) child poverty by settlement type, 2015 88,4% 62,1% 41,3% Rural Urban RSA IMPROVING LIVES THROUGH DATA ECOSYSTEMS Children living in metropolitan municipalities have low (39,6%) rates of multidimensional poverty when compared to non-metropolitan municipalities (73,7%).
Multidimensional (k=3) child poverty by metropolitan municipality category, 2015 73,7% 22,5% Point Difference 62,1% 39,6% Multidimensional child poverty rate for metros is lower than the national rate by 22,5 percentage points Non-metropolitan Metropolitan National IMPROVING LIVES THROUGH DATA ECOSYSTEMS Living circumstances and household characteristics of poor children in South Africa Overview of the multi-deprivation index on children IMPROVING LIVES THROUGH DATA ECOSYSTEMS Multidimensional child poverty rate is higher where households are headed by females at 71,3% as compared to those that are headed by males at 53,6% Multidimensional (k=3) child poverty by sex of household head, 2015 53,6% 71,3% IMPROVING LIVES THROUGH DATA ECOSYSTEMS Male Female Multidimensional child poverty rate is the highest (86,1%) where the household head’s highest level of education is “no schooling” Multidimensional (k=3) child poverty by education level of household head, 2015 No schooling Incomplete primary Complete primary Incomplete secondary Completed matric 86,1% 78,1% 69,5% 62,8% 42,3% Higher education 21,6% IMPROVING LIVES THROUGH DATA ECOSYSTEMS Education level of household head Multidimensional poverty is the highest (79,9%) where a household has no adult employed Multidimensional (k=3) child poverty by adult employment status, 2015 45,5% 43,6% 23,1% Point Difference 79,9% 56,8% 3 or more adults employed 2 adults employed 1 adult employed No adult employed IMPROVING LIVES THROUGH DATA ECOSYSTEMS Adult employment status Multidimensional child poverty rate is higher where households have a large number of children (71,2%) Multidimensional child poverty is the highest (73,8%) where a household has 7 or more members; while it is the lowest at 52,6% where household has between 1 and 3 members Multidimensional (k=3) child poverty by median number of children, 2015 71,2% Median number of children is 2 per household 48,7% Above median number Below median number IMPROVING LIVES THROUGH DATA ECOSYSTEMS Children that are
 double-orphans and single orphans where only a mother is alive have higher multidimensional poverty rates (77,3% and 71,2% respectively) Multidimensional (k=3) child poverty by orphan-hood status, 2015 Only mother alive Only father alive Double orphan Non-orphan IMPROVING LIVES THROUGH DATA ECOSYSTEMS 71,2% 67,7% 77,3% 59,5% Relationship between money-metric and multidimensional poverty Overview of the multi-deprivation index on children IMPROVING LIVES THROUGH DATA ECOSYSTEMS Multidimensional poverty amongst children is higher than the lower bound child poverty rate by 11,1 percentage Multidimensional (k=3) child poverty and Money metric child poverty (LBPL) 66,8% 62,1% 51,0% 20,5% Food poverty rate Lower bound poverty rate Upper bound poverty rate Multidimensional poverty rate IMPROVING LIVES THROUGH DATA ECOSYSTEMS About 4 out of every 10 children in South Africa are both money-metric and multidimensionally poor Money-metric Poverty headcounts in 2015 Money Metric Poverty Only 9% Overlap between Money metric poverty and Multidimensional poverty 42% Multidimensional Poverty Only 20,2% 28,8 % Non Poor based on Money metric and Multidimensional Poverty IMPROVING LIVES THROUGH DATA ECOSYSTEMS 33 Source: Living Conditions Survey Summary and conclusion Higher rates of poverty are found among: Households with many members; Households with many children; Households with no adults employed; Households where the household head has no or low levels of education; Black African children are more likely to be in poverty than other population groups; Children that are single orphans where only a mother is alive have higher poverty rates compared to single orphans where only a father is alive; Double orphans as compared to other children; and An interesting phenomenon is revealed for age group 0 to 4 years where poverty rates are higher for children whose births are not registered Children are not only multidimensionally poor but also have overlapping deprivations There is a
 higher overlap rate between money-metric and multidimensional poverty IMPROVING LIVES THROUGH DATA ECOSYSTEMS