Income poverty & grantsIncome poverty & grants

Unemployment in the household

Author/s: Katharine Hall
Date: March 2024


This indicator measures unemployment from a children’s perspective and gives the number and proportion of children who live in households where no adults are employed in either the formal or informal sector. It therefore shows the proportion of children living in “unemployed” households where it is unlikely that any household members derive income from labour or income-generating activities.


Data Source Statistics South Africa (2003 - 2023) General Household Survey 2002 - 2022. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall, 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. 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 the vertical lines at the top of each bar.
Unemployment in South Africa continues to be a serious problem. The official national unemployment rate was 27.5% in the third quarter of 2018. This rate is based on a narrow definition of unemployment that includes only those adults who are defined as economically active (i.e. they are not studying or retired or voluntarily staying at home) and who actively looked but failed to find work in the four weeks preceding the survey. An expanded definition of unemployment, which includes “discouraged work-seekers” who were unemployed but not actively looking for work in the month preceding the survey, would give a higher, more accurate, indication of unemployment. The expanded unemployment rate (which includes those who are not actively looking for work) was 37.3%. Gender differences in employment rates are relevant for children, as it is mainly women who provide for children’s care and material needs. Unemployment rates remain higher for women (41.2%) than for men (33.9%), using the expanded definition.

Apart from providing regular income, an employed adult may bring other benefits to the household, including health insurance, unemployment insurance and parental leave that can contribute to children’s health, development and education. The definition of “employment” is derived from the Quarterly Labour Force Survey and includes regular or irregular work for wages or salary, as well as various forms of self-employment, including unpaid work in a family business.

In 2018, 70% of children in South Africa lived in households with at least one working adult. The other 30% (5.9 million children) lived in households where no adults were working. The number of children living in workless households has decreased by 1.4 million since 2003, when 41% of children lived in households where there was no employment.

This indicator is very closely related to the income poverty indicator in that provinces with relatively high proportions of children living in unemployed households also have high rates of child poverty. Over 40% of children in the Eastern Cape and Limpopo live in households without any employed adults. These two provinces are home to large numbers of children and have the highest rates of child poverty. In contrast, Gauteng and the Western Cape have the lowest poverty rates, and the lowest unemployment rates. In the Western Cape, only 8% of children live in households where nobody is working.

Racial inequalities are striking: 33% of African children have no working adult at home, while 13% of Coloured children, 10% of Indian children and 2% of White children live in these circumstances. There are no significant differences in child-centred unemployment measures when comparing girls and boys or between age groups. In the rural former homelands, 48% of children live in households where nobody works.

Income inequality is clearly associated with unemployment. Over two-thirds of children in the poorest income quintile (5.2 million) live in households where no adults are employed.

1 Statistics South Africa (2018) Quarterly Labour Force Survey: Quarter 3, 2018. Statistical Release P0211. Pretoria: StatsSA.
This indicator is calculated by identifying adults in the General Household Survey (GHS) who are economically active according to StatsSA's definition, and then generating a binomial household-level variable to distinguish between households with at least one working adult, and those with no working adults. The child-centered percentages are then calculated by dividing the number of children living in households with no employed adults, by the total number of children. 
This indicator gives the number and share of children who live in households where there are no employed adults. Adults are defined as people aged 18 years and older; so economically active children are excluded from the analysis, even though children over 15 years may work legally. 
The standard derived ‘employed’ category in the GHS encompasses regular or irregular work for wages or salary, as well as various forms of self-employment, including unpaid work in a family business, subsistence agriculture, construction and home maintenance, and even begging. This category may therefore slightly exaggerate employment as a proxy for earned income to the household.
The numbers are derived from the General Household Survey, 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 sample design for the 2015 GHS was based on a master sample that was designed in 2013 as a general purpose sampling frame to be used for all Stats SA household-based surveys. The same master sample is shared by the GHS, the Quarterly Labour Force Survey, the Living Conditions Survey and the Income and Expenditure Survey. The 2013 master sample is based on information collected during the 2011 population census. The previous master sample for the GHS was used for the first time in 2008, and the one before that in 2004. These again differed from the master sample used in the first two years of the GHS: 2002 and 2003. Thus there have been four different sampling frames during the 14-year history of the annual GHS, with the changes occurring in 2004, 2008 and 2013. In addition, there have been changes in the method of stratification over the years. These changes could compromise comparability across iterations of the survey to some extent, although it is common practice to use the GHS for longitudinal monitoring and many of the official trend analyses are drawn from this survey.

Person and household weights are provided by Stats SA and are applied in Children Count analyses to give estimates at the provincial and national levels. The GHS weights are derived from Stats SA’s mid-year population estimates. The population estimates are based on a model that is revised from time to time when it is possible to calibrate the population model to larger population surveys (such as the Community Survey) or to census data.

In 2013, Stats SA revised the demographic model to produce a new series of mid-year population estimates. The 2013 model drew on the 2011 census (along with vital registration, antenatal and other administrative data) but was a “smoothed” model that did not mimic the unusual shape of the age distribution found in the census. The results of the 2011 census were initially questioned because it seemed to over-count children in the 0 – 4 age group and under-count children in the 4 – 14-year group.

The 2013 model was used to adjust the benchmarking for all previous GHS data sets, which were re-released with the revised population weights by Stats SA, and was still used to calculate weights for the GHS up to and including 2015, even though it is now known that the mid-year population estimates on which the weights are based are incorrect. All the Children Count indicators were re-analysed retrospectively, using the revised weights provided by Stats SA, based on the 2013 model. The estimates are therefore comparable over the period 2002 to 2015. The revised weights particularly affected estimates for the years 2002 – 2007.

It is now thought that the fertility rates recorded in the 2011 population census may have been an accurate reflection of recent trends, with an unexplained upswing in fertility around 2009 after which fertility rates declined gradually. Similar patterns were found in the vital registration data as more births were reported retrospectively to the Department of Home Affairs, and in administrative data from schools, compiled by the Department of Basic Education. In effect, this means that there may be more children in South Africa than appear from the analyses presented in these analyses, where we have applied weights based on a model that it is now known to be inaccurate.

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, i.e. 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).

For more information on the methods of the General Household Survey, see the metadata for the respective survey years, available on Nesstar or DataFirst