Income poverty & grants

Unemployment in the household

Author/s: Katharine Hall
Date: August 2024

Definition

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

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.

Notes

Children are defined as persons aged 0 – 17 years.
Population numbers have been rounded off to the nearest thousand.
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.

What do the numbers tell us?

Unemployment in South Africa continues to be a serious problem and the situation worsened during lockdown. In the 2nd quarter of 2020, the expanded unemployment rate breached the 40% mark for the first time since the Quarterly Labour Force Survey was introduced in 2008, and it remained above 40% for the rest of the year.¹

Although there was some clawback of jobs, in the last quarter of 2020 Stats SA still recorded a net decrease of 1.4 million (8.5%) in total employment numbers, compared with the same period the previous year. By the end of 2020, 39% of men and 46% of women in the labour force were unemployed.²

The official national unemployment rate was 29.1% in the fourth quarter of 2019 and 32.5% in the fourth quarter of 2020.³ In the fourth quarter of 2021 it had risen to 35.3, and then dropped slightly to 32.7% in the fourth quarter of 2022. This official 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 had 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, gives a higher, and more accurate, indication of unemployment. The expanded unemployment rate (which includes those who are not actively looking for work) was 38.7% in the fourth quarter of 2019 and 42.6% a year later at the end of 2020. It rose further, reaching 46.2% in 2021 and then settled back to 42.6% in the last quarter of 2022.

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 are consistently higher for women than for men. At the end of 2019, 42.4% of women were unemployed by the expanded definition (compared with 35.5% of men) and this increased to 44.5% for women at the end of 2022 (compared with 41.1% of men).⁴ Of the 12.5 million women who were available and willing to work, 5.6 million could not find work or had given up trying to do so.

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 2019, before lockdown, 70% of children in South Africa lived in households with at least one working adult. The other 30% lived in households where no adults were working. The number of children living in workless households had decreased by 1.4 million since 2003, when 41% of children lived in households where there was no employment. But by late 2020, the share of children in workless households had increased again to 36% (7.3 million) – effectively back to the 2007 rate in percentage terms, though substantially higher in terms of numbers due to a growing population. By 2022, despite slight declines in adult unemployment, the 2019 levels had still not been regained. Nearly 6.7 million children (32%) lived in households where no adults were earning income from 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. In 2022, nearly 50% of children in the Eastern Cape lived in households without any employed adults, and nearly 40% of those in North West, KwaZulu-Natal and the Free State were in workless households. These provinces are home to large numbers of children and also have relatively high rates of child poverty. In contrast, Gauteng and the Western Cape have the lowest poverty rates, and the lowest unemployment rates, although the effects of job loss were also evident in these provinces in 2020. In the Western Cape, 22% of children lived in households where nobody was working in 2020 (up from 12% in 2019), and in Gauteng the rate was 23% in 2020 (up from 14% in 2019). By 2022 the Western Cape rates had dropped again to 13% but the Gauteng rate remained persistently high at 21%).

Racial inequalities are striking: 35% of African children had no working adult at home in 2022, while 19% of Coloured children and less than 4% of Indian and White children lived in these circumstances. There are no significant differences in child-centred unemployment measures when comparing girls and boys or different age groups. In the rural former homelands, 48% of children lived in workless households in 2022, while the rate was 22% among children in urban areas.

Income inequality is clearly associated with unemployment. Over 70% of children in the poorest income quintile live in households where no adults are employed.

1 Statistics South Africa. QLFS Trends 2008-2020 Q4 (Historical tables). Pretoria: Stats SA.
2 Statistics South Africa (2021). Quarterly Labour Force Survey, 4th quarter 2020. Pretoria: Stats SA.
3 Statistics South Africa (2020-2024) Quarterly Labour Force Survey, 4th quarter 2019-2023. Pretoria: Stats SA.
4 K Hall calculations from Statistics South Africa. Quarterly Labour Force Survey: Quarter 4, 2022. Statistical Release No. P0211. Pretoria. 2023

Technical notes

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.

Strengths and limitations of the data

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 GHS uses a Master Sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household-based surveys that have design requirements that are reasonably compatible with the GHS. The sample is drawn from Census enumeration areas using a stratified two-stage design with probability proportional to size sampling of PSUs in teh first stage, and sampling of dwelling units with systematic sampling in the second stage. The resulting sample consists of just over 20,000 households with around 70,000 individuals, and should be representative of all households in South Africa. It is also designed to be representative at provincial level and within provinces at metro/non-metro levels and three geography types (urban areas, rural areas under traditional authority, and farms).

The 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 probably do not have a noticeable impact on the findings in respect of children.

Changes in sample frame and stratification
Since 2014 the GHS has been based on the 2013 master sample that that is, in turn, 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 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.

Weights
Person and household weights are provided by Stats SA and are applied in Children Count analyses to give population estimates on the indicators. The GHS weights are derived from Stats SA’s mid-year population estimates for the relevant year. 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 Census data and larger population surveys such as the Community Survey.

In 2017, Stats SA revised its demographic model to produce a new series of mid-year population estimates and the GHS data were re-released with the revised population weights. 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 all years. The revised weights particularly affected estimates for the years 2002 – 2007.

The 2017 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 distrusted because it seemed to over-count children in the 0 – 4 age group and under-count children in the 4 – 14-year group. It is now thought that the fertility rates recorded in the 2011 population census may have been an accurate reflection of demopraphic trends, with an unexplained upswing in fertility around 2009 after which fertility rates declined again 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.

Stats SA has subsequently developed a new population model - the 2022 series, which provides revised mid-year population estimates back to 2002 and projected to 2032. However, the GHS series has not yet been reweighted.The population estimates in Children Count are therefore based on weights derived from outdated population model (2017). It is not yet clear when and how the population model will be revised again following the 2022 Census, as there are concerns around census under-count and plausibility of its findings.

Disaggregation
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