Author/s: Katharine Hall & Winnie Sambu
Date: October 2016
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 get income from labour or incomegenerating activities.
Data Source  Statistics South Africa (2003  2015) General Household Survey 2002  2014. Pretoria, Cape Town: Statistics South Africa. Analysis by Katharine Hall, DoubleHugh Marera & Winnie Sambu, Children’s Institute, University of Cape Town. 
Notes 

Unemployment in South Africa continues to be a serious problem. The official national unemployment rate was 25.4 in the third quarter of 2014.^{1} 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) who actively looked but failed to find work in the four weeks preceding the survey. An expanded definition of unemployment, which includes “discouraged workseekers” 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 35.8%. 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 (28%) than for men (23%).^{2}
Apart from providing regular income, an employed adult may bring other benefits to the household, including health insurance, unemployment insurance and maternity 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 selfemployment, including unpaid work in a family business.
In 2014, 70% of children in South Africa lived in households with at least one working adult. The other 30% (5.5 million children) lived in households where no adults were working. The number of children living in workless households has decreased by 2.2 million since 2003, when 42% 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 only around 10% of children in these provinces live in unemployed households.
Racial inequalities are striking: 34% of African children have no working adult at home, while 13% of Coloured children and 3% of Indian and white children live in these circumstances. There are no significant differences in childcentred unemployment measures when comparing girls and boys. However older children are slightly more likely than younger children to live in workless households. This may be because babies and very young children tend to live with their parents, while older children are more likely to be cared for by extended family members, especially grandparents. In the rural former homelands, 48% of children live in households where nobody works.
Income inequality is clearly associated with unemployment. Nearly 70% of children in the poorest income quintile (4.5 million) live in households where no adults are employed.Weights
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 midyear 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 underestimation 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 underestimated by 5% overall. The most severe underestimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield underestimations of 15% and 16% respectively. The next age group (5 – 9 years) is also underestimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14year age group, although boys are still underestimated by around 1% and girls by 3%. In contrast, the weighted data yield overestimates of boys and girls in the upper age group (15 – 17 years), with the GHS overcounting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal maletofemale ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively.
§ Similarly in 2003, there was considerable underestimation of the youngest age groups (0 – 4 years and 5 – 9 years) and overestimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are underestimated by as much as 16%, with underestimates for babies below two years in the range 19 – 30%. The results also show that the overestimation of males in the 15 – 17year age group (9%) is much more severe than the overestimation for females in this age range (1.4%), resulting in a maletofemale 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 underestimated, with the underestimate being more severe in the upper age group (15 – 17 years). This is the result of severe underestimation in the number of girls, which outweighs the slight overestimation of boys in all age groups. Girls are underestimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while overestimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in maletofemale 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 overestimate of the number of males and an underestimate of the number of females within each fiveyear age group. The extent of underestimation for girls (by 7% overall) exceeds that of the overestimation for boys (at 2% overall). These patterns result in maletofemale 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 underestimation of both females and males. The underestimation of females is greatest in the 0 – 4 and 5 – 9year age groups, while the underestimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14year age group. This results in maletofemale 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 overestimation for boys and an underestimation for girls. The underestimation of females is in the range of 4 – 8% while the overestimation for boys is in the range of 1 – 5%. This results in maletofemale 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 overestimated 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 underestimate of female babies below two years (by 7 – 8%), and an overestimate of young teenage girls. The GHS 2008 suggests a maletofemale 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 underestimation of children in the 0 – 4 age group (especially infants), and an overestimate of older children. In 2009 the underestimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHSweighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The maletofemale ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHSderived 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 overestimate 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 underestimate is lower than previously, at 2%, but for the oldest age group there is an overestimate of 5%. The maletofemale 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 underestimation of children below two years and an overestimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The underestimation is particularly pronounced for babies under a year, at 8%. The maletofemale 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 tofemale 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 – 14yearolds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.
^{1}Statistics South Africa (2014) Quarterly Labour Force Survey: Quarter 3, 2014. Statistical release P0211. Pretoria: StatsSA.
^{2}Statistics South Africa (2014) General Household Survey Metadata. Pretoria: StatsSA.