School attendance

Author/s: Katharine Hall
Date: November 2019


This indicator shows the number and percentage of children aged 7 – 17 years who are reported to be attending a school or educational facility. This is different from “enrolment rate”, which reflects the number of children enrolled in educational institutions, as reported by schools to the national Department of Basic Education early in the school year.


Data Source Statistics South Africa (2003-2019) General Household Survey 2002-2018. 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. 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.
  4. Denominator is based on children of school-going age: 7-17 years.
Education is a central socio-economic right that provides the foundation for lifelong learning and economic opportunities. Children have a right to basic education and are admitted into grade 1 in the year they turn seven. Basic education is compulsory in grades 1 – 9, or for children aged 7 – 15. Children who have completed basic education also have a right to further education (grades 10 – 12), which the government must take reasonable measures to make available.

South Africa has high levels of school enrolment and attendance. Amongst children of school-going age (7 – 17 years), the vast majority (98%, or 11.3 million children) attended some form of educational facility in 2018. This is a small but significant increase from 2002, when the reported attendance rate was 95%. The overall increase is mainly due to a small but real growth in reported attendance rates for African and Coloured children over the 17-year period. In 2018, for the first time since this indicator was tracked, there are no significant differences in attendance rates across race groups. Of a total of 11.6 million children aged 7 – 17 years, 232,000 were reported as not attending school in 2018.

At a provincial level, the Northern Cape and KwaZulu-Natal have seen the most significant increases in attendance rates between 2002 and 2018. In the Northern Cape, attendance increased from 91% to 95% while in KwaZulu-Natal attendance increased from 93% to 98%.

Overall attendance rates tend to mask drop-out among older children. Analysis of attendance among discrete age groups shows a significant drop in attendance amongst children older than 15. This also coincides with the end of compulsory schooling. Whereas around 99% of children in each age year from seven to 14 are reported to be attending an educational institution, the attendance rate drops to 98% for 15-year-olds, 96% for 16-year-olds, 92% for 17-year-olds, and 83% of 18-year-olds are reported to be attending school (based on those who have not completed grade 12). Differences in reported school attendance rates between boys and girls are not statistically significant.

Amongst children of school-going age who are not attending school the main set of reasons for non-attendance relate to the quality of education or the learners ability to progress: “Education is useless or not interesting” is the reason given for 10% of those not attending school. Another 9% are “unable to perform at school” while 5% dropped out because they failed the exams. These signals of failures in the education system account for a quarter of all reported non-attendance. A further 7% of children not attending school are excluded because they were not accepted for enrolment.

The second main barrier to education is financial constraints. These include the cost of schooling (the reason given for 13% of children not attending schools) – which would also include related costs such as uniform and transport – and the opportunity costs of education where children have family commitments such as child minding (4%) or are needed to work in a family business or elsewhere to support household income (2%).

Disability is also an important reason, accounting for 15% of non-attendance, while illness accounts for an additional 5% of the non-attendance rate.
The main reasons for non-attendance can therefore be divided into three main categories: system failures (including exclusions and quality problems); financial barriers; and illness or disability. Together, these account for over 70% of non-attendance.

Pregnancy accounts for around 7% of drop-out amongst teenage girls not attending school, and only 3% of all non-attendance.1

Although the costs of education are cited as a barrier for those who are not attending (and who tend to be older), the overall attendance rate for children in the lower income quintiles is not significantly lower than those in the wealthier quintiles.

Attendance rates alone do not capture the regularity of children’s school attendance or their progress through school. Research has shown that children from more disadvantaged backgrounds – with limited economic resources, lower levels of parental education, or who have lost their mother – are less likely to enrol in school and are more prone to dropping out or progressing more slowly than their more advantaged peers. Racial inequalities in school advancement remain strong.2  Similarly, school attendance rates tell us nothing about the quality of teaching and learning.3 Inequalities in learning outcomes are explored through standardised tests such as those used in the international SAQMEC,4 TIMMS and PIRLS studies.5 The DBE’s Annual National Assessments have been discontinued.

1 Hall K analysis of General Household Survey 2018, Children’s Institute, UCT.
For more information on school drop-out, see also:
Branson N, Hofmeyer C & Lam D (2014) Progress through school and the determinants of school dropout in South Africa. Development Southern Africa, 31(1): 106-126;
Gustafsson M (2011) The When and How of Leaving School: The Policy Implications of New Evidence on Secondary School in South Africa. Stellenbosch Economic Working Papers 09/11. Stellenbosch: Stellenbosch University;

2 Crouch L (2005) Disappearing Schoolchildren or Data Misunderstanding? Dropout Phenomena in South Africa. North Carolina, USA: RTI International;
Lam D & Seekings J (2005) Transitions to Adulthood in Urban South Africa: Evidence from a Panel Survey. Prepared for the International Union for the Scientific Study of Population (IUSSP) general conference, 18 – 23 July 2005, Tours, France;
Lam D, Ardington A & Leibbrandt M (2011) Schooling as a lottery: Racial differences in school advancement in urban South Africa. Journal of Development Economics, 95: 121-136.

3 Spaull N & Taylor S (2015) Access to what? Creating a composite measure of educational quantity and educational quality for 11 African countries. Comparative Education Review, 59(1):133-165.

4 The Southern and Eastern Africa Consortium for Monitoring Education Quality. See

5 International Association for the Evaluation of Educational Achievement: Trends in International Mathematics and Science Study & Progress in International Reading Literacy Study. See

The General Household Survey asks: “Is (name) currently attending school or any other educational institution?” A simple “yes” or “no” reply is required.

‘Attendance’ thus reflects the proportion of children that were reported as “attending school” by one of the adults in their household interviewed for the GHS, which is conducted in July each year. This is different from “enrolment rates” that reflect the number of children enrolled in a basic or secondary educational institution, as reported by the schools to the national government early in the school year. Annual enrolment rates can be found in the Department of Education’s Education Statistics in South Africa, published each year.

The number of children aged 7 – 17 years (school-going age) who were attending an educational institution was extracted from the GHS data. This figure was divided by the number of children of school-going age to develop the proportion of children of school-going age attending an educational facility. The numbers of children in each province aged 7 – 17 years were also determined, and the same procedure was applied to develop the provincial attendance rates.

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