Author/s: Katharine Hall
Date: October 2016


 “NEETs” is a term used to describe young people who are Not in Employment, Education or Training. The definition used here includes youth aged 15 – 24 who are not attending any educational institution and who are not employed or self-employed. 


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Data Source  Statistics South Africa (2003 – 2014) General Household Survey 2002 – 2013. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall & Winnie Sambu, Children’s Institute, University of Cape Town.
Notes          1. Young people are defined as persons aged 15 - 24 years.
         2. Population numbers have been rounded off to the nearest thousand.

Widespread concerns about the large numbers of youth in this situation centre on two main issues: the perpetuation of poverty and inequality, including intergenerational poverty; and the possible implications of a large “idle” youth population for risk behaviour, social cohesion and the safety of communities.

Little is known about what NEETs actually do with their time. Young people who are neither learning nor engaged in income-generating activities may neverthless be “productive” within their households, for example by helping maintain the home or look after children and others in need of care. However, in the absence of income, NEETs remain dependent on the earnings of other household members, and on grants that are directed to children and the elderly. The Old Age Pension in particular has been found to support job-seeking activities for young people1 and it has been argued that this unenvisaged expenditure of the grant could be addressed by extending social security to unemployed youth.2

The large number of NEETs in South Africa is linked to underlying problems in the education system and the labour market. Young people in South Africa have very high participation rates in education, including at secondary level. But less than half successfully complete grade 12, and this reduces prospects for further study or employment.3 Low quality and incomplete education represent what are termed the “supply-side” drivers of youth unemployment, where young people do not have the appropriate skills or work-related capabilities to be employable or to set up successful enterprises of their own, and so struggle to make the transition from education to work.4 The “demand-side” driver relates to a shortage of jobs or self-employment opportunities for those who are available to work.  

In 2014 there were over 10 million young people aged 15 – 24 in South Africa. Of these, 33% (3.4 million) were are neither working nor enrolled in any education institution such as a school, university or FET college. The number of young people nationally who are not in education, training or employment has remained consistent over the last decade, but has increased over the two decades since 1996 when only two million NEETs were recorded.5

The NEET rates are fairly even across the provinces. This is hard to interpret without further analysis. Limpopo, for example, is a very poor and largely rural province. It is possible that the slightly lower-than-average proportion of NEETs results partly from the fact that many young people migrate to cities in search of work and are therefore counted among the NEETs in more urban provinces. It is possible that young people who are not employed in the labour market may nevertheless be employed in small-scale agriculture if their household has access to land, and this could also help to smooth the provincial inequalities that are characteristic of many other indicators.

The number and proportion of NEETs in KwaZulu-Natal has declined between 2002 and 2014. Again, this could be related to changing levels of productive activity, or to youth migration. While the proportion of NEETs has not changed substantially in Gauteng, the number of NEETs in that province has increased by 25% (from 555,000 to 696,000) as the young population grows. 

There is enormous variation within the broad youth group of 15 – 24 years. Only 5% of children aged 15 – 17 are classified as NEET because the vast majority are attending school. Within the 18 – 20 age band, educational attendance decreases to 58% and less than 10% are working. The remaining 34% are NEETs. In the 21 – 24 age band educational attendance decreases further to less than 19%, and the employment rate increases to 29%. Over half the young people in this age group are neither working nor in education.

While education attendance rates are fairly even for boys and girls, the gender disparity among NEETs becomes more pronounced with age. In the upper age band (21 – 24 years), 58% of young women are not in employment, education or training – compared with 49% of young men.
This indicator refers to the number and proportion of young people in South africa aged 15 - 24 years who are not in employment, education or training. The proportions are calculated by dividing the number of NEETs per category by the total number of young people in the population.

The population numbers are drawn from the General Household Survey after person weights are applied. The person weights are calculated to yield the mid-year population figures for each year, as estimated by Statistics South Africa.
 The data are derived from the General Household Survey2, 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 analysis over time, 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).
1Ardington C, Bärninghausen A, Case A & Menendez A (2013) Social Protection and Labour Market Outcomes of Youth in South Africa. Working Paper Number 96. Cape Town: Southern Africa Labour & Development Research Unit, UCT.

2Altman M, Mokomane Z & Wright, G 2014. Social security for young people amidst high poverty and unemployment: Some policy options for South Africa. Development Southern Africa, 31(2): 347-362.

3Timaeus & Moultrie (2015) Teenage childbearing and educational attainment in South Africa. Studies in Family Planning, 46(2):143-160.

4Smith J (2011) Connecting young South Africans to Opportunity: Literature Review and Strategy. Cape Town: DG Murray Trust;

   Lam D, Leibbrandt M & Mlatsheni C (2008). Education and Youth Unemployment in South Africa. Working Paper Number 22. Cape Town: South    Africa Labour and Development Research Unit, UCT.

5Department of Higher Eduation and Training (2013) Fact sheet on NEETs: An analysis of the 2011 South African census. Pretoria: DHET.