Income and Social GrantsIncome and Social Grants

Children living in poverty

Author/s: Katharine Hall & Winnie Sambu
Date: August 2016


This indicator shows the number and proportion of children living in households that are income-poor. These households fall below a specific income threshold. The measure used is an upper-bound "ultra" poverty line, set at R779 per person per month in 2011 prices.1 The poverty line increases with inflation and was equivalent to R923 in 2014. Per capita income is calculated by adding all reported income for household members older than 15 years, including social grants, and dividing the total household income by the number of household members.


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Data Source Statistics South Africa (2004 - 2015) General Household Survey 2003 - 2014. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall & Winnie Sambu, Children’s Institute, University of Cape Town.
  1. Children are defined as persons aged 0 – 17 years.
  2. Population numbers have been rounded off to the nearest thousand.
  3. Poverty line is set at R322 per month in 2000 Rands, inflated using CPIX for July of each year. The real value of the per capita poverty line is R371 in 2002 and R515 in 2008.
  4. Income is calculated as total reported earnings for household members over 15 years, plus value of social grants received by household, and divided by household size.
  5. Sample surveys are always subject to error, and the proportions simply reflect the mid-point of a possible range. The confidence intervals (CIs) indicate the reliability of the estimate at the 95% level. This means that, if independent samples were repeatedly taken from the same population, we would expect the proportion to lie between upper and lower bounds of the CI 95% of the time. The wider the CI, the more uncertain the proportion. Where CIs overlap for different sub-populations or time periods we cannot be sure that there is a real difference in the proportion, even if the mid points differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.
Any definition of absolute poverty requires a poverty line. In the absence of “official” poverty lines, various lines have been used in South Africa. The definition of national poverty lines has been strongly contested as the poverty rate will depend on poverty line used. Until 2015 the Children Count project calculated child poverty rates using the Hoogeveen & Ozler poverty lines which were commonly used by economists.2 However recent poverty analyses have tended to use the national poverty lines proposed by Statistics South Africa, and in 2016 Children Count adopted these poverty lines.

In 2011 Statistics South Africa proposed three poverty lines for South Africa. These were calculated from the 2010/2011 Income and Expenditure Survey, using the internationally recognised “cost of basic needs” approach.3 Briefly, the poverty lines are calculated by 1) determining a reference food basket that would provide the minimum nutritional requirement of 2100 kilocalories per person per day; 2) calculating the cost of the food basket that would enable households to meet this nutritional standard; and 3) calculating an additional allowance for other basic necessities such as clothing, shelter, transport and education. Using these calculations, the three poverty lines are derived as follows:

·         The food poverty line is based on the cost of the minimum nutritional requirement of 2100 kilocalories per person per day, without any allowance for non-food basic necessities. The value of the food poverty line in 2011 prices was R335 per person per month. Anyone living below this line will be malnourished and their health and survival may be at risk.

·         The lower bound poverty line is calculated by adding to the food line the average expenditure on essential non-food items by households whose food expenditure is below but close to the food line. The value of the lower bound poverty line in 2011 prices was R501 per person per month. Those living below this line would not be able to pay for the minimum non-food expenses or would be sacrificing their basic nutrition in order to pay for non-food expenses.

·         The upper bound poverty line is calculated by adding to the food line the average expenditure on non-food items by households whose food expenditure is equivalent to the food line. The value of the upper bound poverty line in 2011 prices was R779 per person per month. This is lowest possible poverty line that allows for both minimum nutritional requirements and essential non-food expenses.

As money is needed to access a range of services, income poverty is often closely related to poor health, reduced access to education, and physical environments that compromise personal safety. A lack of sufficient income can therefore compromise children’s rights to nutrition, education, and health care services, for example.

International law and the Constitution recognise the link between income and the realisation of basic human rights, and acknowledge that children have the right to social assistance (social grants) when families cannot meet children’s basic needs. Income poverty measures are therefore important for determining how many people are in need of social assistance, and for evaluating the state’s progress in realising the right to social assistance.

No poverty line is perfect. Using a single income measure tells us nothing about how resources are distributed between family members, or how money is spent. But this measure does give some indication of how many children are living in households with severely constrained resources.

These households fall below a specific income threshold. The measure used is the Statistics South Africa “upper bound” poverty line, set at R779 per person per month in 2011 prices. The poverty line increases with inflation and was equivalent to R923 in 2014. Per capita income is calculated by adding all reported income for household members older than 15 years, including social grants, and dividing the total household income by the number of household members.

South Africa has very high rates of child poverty. In 2014, 63% of children lived below the upper bound poverty line. Income poverty rates have fallen substantially since 2003, when 79% (14.7 million) children were defined as “poor”. This poverty reduction is largely the result of a massive expansion in the reach of the Child Support Grant over the same period. Although there have been reductions in the child poverty rate, large numbers of children still live in extreme poverty: in 2014, 11.7 million children lived below the upper bound poverty line.

There are substantial differences in poverty rates across the provinces. Using the upper bound poverty line, over three quarters of children in Limpopo, KwaZulu-Natal and the Eastern Cape are poor. Gauteng and the Western Cape have the lowest child poverty rates – both at 39%. Child poverty remains most prominent in the rural areas of the former homelands, where 84% of children are below the poverty line. Urban child poverty rates are 44% in formal areas, and 68% in informal areas.

There are glaring racial disparities in income poverty: while 70% of African children lived in poor households in 2014, and 41% of Coloured children were defined as poor, only 3% of White and 5% of Indian children lived below this poverty line.

There are no significant differences in child poverty levels across gender or between different age groups in the child population.  

The international ultra-poverty line used to track progress towards the Millennium Development Goals (MDG) is $1.25 per person per day. This translates to R220 per person per month in 2014, using
the 8 million) lived below the MDG poverty line. By 2014 this had reduced to 13% (2.5 million).

The General Household Survey asks a set of questions to establish whether household members over 15 years are economically active. For those who are economically active and report their earnings, these amounts are standardised to monthly values. 

For those who report earnings in income bands rather than discrete amounts, each income bracket is split into deciles for those who indicated an income in that bracket, and a uniform distribution of income is assigned within each income bracket decile, for those who indicated an income in that bracket.

For those who are economically active but did not provide a discrete income amount or indicate an income bracket (unspecified/refused), the median income for men and women in each population group is allocated. The medians are calculated separately for each year.

The method for assigning income is derived from that used by Daniele Bieber and adapted by Debbie Budlender.

Total household income from earnings iscalculated as the total earnings for all household members over 15 years. Total household income from social grants is calculated by allocating the grant amounts for that year for each type of grant reported to be received by household members. Total household income is derived by adding total income from earnings and grants.

Three poverty lines are set in 2000 Rand values. This are inflated using CPIX reported by Statistics South Africa at July each year. Per capita income is calculated by dividing total household income equally by the number of household members.

There are many limitations to working with poverty lines, and this method almost certainly results in an over-estimation of the poverty rate because both income and social grants are under-reported in the General Household Survey.

There are numerous poverty lines to choose from.4 In addition to the three poverty lines (upper, lower and food poverty), the $1-a-day poverty line is also published on the Children Count website. This poverty line is used by the World Bank, the OECD and other international groups.

The data are derived from the General Household Survey5, a multi-purpose annual survey conducted by the national statistical agency, Statistics South Africa, to collect information on a range of topics from households in the country’s nine provinces. The survey uses a sample of 30,000 households. These are drawn from Census enumeration areas using multi-stage stratified sampling and probability proportional to size principles. The resulting estimates should be representative of all households in South Africa.
The GHS sample consists of households and does not cover other collective institutionalised living-quarters such as boarding schools, orphanages, students’ hostels, old age homes, hospitals, prisons, military barracks and workers’ hostels. These exclusions should not have a noticeable impact on the findings in respect of children.
Changes in sample frame and stratification
The current master sample was used for the first time in 2004, meaning that, for longitudinal analysis, 2002 and 2003 may not be easily comparable with later years as they are based on a different sampling frame. From 2006, the sample was stratified first by province and then by district council. Prior to 2006, the sample was stratified by province and then by urban and rural area. The change in stratification could affect the interpretation of results generated by these surveys when they are compared over time.
Provincial boundary changes
Provincial boundary changes occurred between 2002 and 2007, and slightly affect the provincial populations. Comparisons on provincial level should therefore be treated with some caution. The sample and reporting are based on the old provincial boundaries as defined in 2001 and do not represent the new boundaries as defined in December 2005.

Person and household weights are provided by Stats SA and are applied in Children Count – Abantwana Babalulekile analyses to give estimates at the provincial and national levels. Survey data are prone to sampling and reporting error. Some of the errors are difficult to estimate, while others can be identified. One way of checking for errors is by comparing the survey results with trusted estimates from elsewhere. Such a comparison can give an estimate of the robustness of the survey estimates. The GHS weights are derived from Stats SA’s mid-year population estimates. For this project, weighted GHS population numbers were compared with population projections from the Actuarial Society of South Africa’s ASSA2008 AIDS and Demographic model. 

Analyses of the ten surveys from 2002 to 2011 suggest that some over- and under-estimation may have occurred in the weighting process: 

§  When comparing the weighted 2002 data with the ASSA2008 AIDS and Demographic model estimates, it seems that the number of children was under-estimated by 5% overall. The most severe under-estimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield under-estimations of 15% and 16% respectively. The next age group (5 – 9 years) is also under-estimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14-year age group, although boys are still under-estimated by around 1% and girls by 3%. In contrast, the weighted data yield over-estimates of boys and girls in the upper age group (15 – 17 years), with the GHS over-counting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal male-to-female ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively. 

§  Similarly in 2003, there was considerable under-estimation of the youngest age groups (0 – 4 years and 5 – 9 years) and over-estimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are under-estimated by as much as 16%, with under-estimates for babies below two years in the range 19 – 30%. The results also show that the over-estimation of males in the 15 – 17-year age group (9%) is much more severe than the over-estimation for females in this age range (1.4%), resulting in a male-to-female ratio of 1.09 in this age group, compared with ratios around 1.02 in the younger age groups. 

§  In the 2004 results, all child age groups seem to have been under-estimated, with the under-estimate being more severe in the upper age group (15 – 17 years). This is the result of severe under-estimation in the number of girls, which outweighs the slight over-estimation of boys in all age groups. Girls are under-estimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while over-estimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in male-to-female ratios of 1.10, 1.11, 1.12 and 1.14 for the four age groups. 

§  In 2005, the GHS weights seem to have produced an over-estimate of the number of males and an under-estimate of the number of females within each five-year age group. The extent of under-estimation for girls (by 7% overall) exceeds that of the over-estimation for boys (at 2% overall). These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.13 respectively for the four age groups covering children. 

§  The 2006 weighting process yields different patterns from other years when compared to population estimates for the same year derived from ASSA2008, in that it yielded an under-estimation of both females and males. The under-estimation of females is greatest in the 0 – 4 and 5 – 9-year age groups, while the under-estimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14-year age group. This results in male-to-female ratios of 1.09, 0.99, 0.96 and 1.00 respectively for the four age groups covering children. 

§  The 2007 weighting process produced an over-estimation for boys and an under-estimation for girls. The under-estimation of females is in the range of 4 – 8% while the over-estimation for boys is in the range of 1 – 5%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.06 respectively for the four age groups covering children. 

§  In 2008, the GHS weighted population numbers when compared with ASSA2008 over-estimated the number of boys aged 10 and over, in the range of 3% for the 10 – 14 age group, and 8% for the 15 – 17 age group. The total weighted number of girls is similar to the ASSA population estimate for girls, but this belies an under-estimate of female babies below two years (by 7 – 8%), and an over-estimate of young teenage girls. The GHS 2008 suggests a male-to-female ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA2008 model. 

§  A comparison of the GHS and ASSA for 2009 suggests a continuation of the general pattern from previous years, which is that GHS weights result in an under-estimation of children in the 0 – 4 age group (especially infants), and an over-estimate of older children. In 2009 the under-estimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHS-weighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The male-to-female ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHS-derived ratio is higher, at 1.08, compared to 1.00 in ASSA. 

§  In 2010, the GHS weights again produce an underestimation of children in the 0 – 4 age group and an over-estimate of children aged 15 – 17 years. For the middle age groups, and for the child age group as a whole, there is less than 1% difference in the estimates from the two sources. For the 0 – 4 age group the under-estimate is lower than previously, at 2%, but for the oldest age group there is an over-estimate of 5%. The male-to-female ratios are similar across the two sources, although the ratio is 1.00 for all but the 0 – 4 age group in ASSA as against 1.01 for the youngest age group in ASSA and for all age groups in the GHS. 

§  A comparison of the GHS2011 to ASSA2008 (projected to 2011) suggests an under-estimation of children below two years and an over-estimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The under-estimation is particularly pronounced for babies under a year, at 8%. The male-to-female ratio for all children under 17 is 1.00 in ASSA, and 1.01 in the GHS.   

The apparent discrepancies in the ten years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. From 2005 to 2008, consistently distorted male- to-female ratios means that the total estimates for certain characteristics would be somewhat slanted toward the male pattern. This effect is reduced from 2009, where more even ratios are produced, in line with the modelled estimates. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.

Statistics South Africa suggests caution when attempting to interpret data generated at low level disaggregation. The population estimates are benchmarked at the national level in terms of age, sex and population group while at provincial level, benchmarking is by population group only. This could mean that estimates derived from any further disaggregation of the provincial data below the population group may not be robust enough.
Reporting error
Error may be present due to the methodology used, ie the questionnaire is administered to only one respondent in the household who is expected to provide information about all other members of the household. Not all respondents will have accurate information about all children in the household. In instances where the respondent did not or could not provide an answer, this was recorded as “unspecified” (no response) or “don’t know” (the respondent stated that they didn’t know the answer).

1Statistics South Africa (2014) Poverty Trends in South Africa: An examination of absolute poverty between 2006 and 2011. Pretoria: Statistics South Africa.

2Hoogeveen J &
Özler B (2006) Poverty & inequalitiy in post-apartheid South Africa: 1995 - 2000. In: Bhorat H & Kanbur R (eds) Poverty and Policy in Post-Apartheid South Africa, Cape Town: HSRC Press.

3Statistics South Africa (2011) Methodological report on rebasing of national poverty lines and development on pilot provincial poverty lines: Technical report, No.03-10-11. Pretoria: Statistics South Africa.

4Woolard I & Leibbrandt M (2006) Towards a poverty line for South Africa: Background note. Cape Town, Southern Africa Labour and Development Research Unit, UCT.

5Statistics South Africa (2003 -2014). General Hosuehold Survey 2002 -2014 Metadata. Cape Town, Pretoria: Statistics South Africa.