EducationEducation

Early learning

Author/s: Katharine Hall
Date: November 2018

Definition

This indicator shows the number and percentage of children aged 5 – 6 years who are reported to be attending an early childhood development (ECD) programme or educational institution – in other words, those attending out-of-home care and learning centres including ECD centres, pre-grade R, grade R or grade 1 in ordinary schools. While all these facilities provide care and stimulation for early learning for young children, the emphasis on providing learning opportunities through structured learning programmes differs by facility type.

Data


Data Source Statistics South Africa (2003 - 2016) General Household Survey 2002 - 2015. Pretoria, Cape Town: Statistics South Africa. Analysis by Katharine Hall & Winnie Sambu, Children's Institute, University of Cape Town.
Notes
  1. Children are defined as persons aged 0 – 17 years. The proportions are based on children in the age group appropriate for school level: Primary 7 – 13 years; Secondary 14 – 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. 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-point proportions differ. CIs are represented in the bar graph by vertical lines at the top of each bar. 
Educational inequalities are strongly associated with structural socio-economic (and therefore also racial) inequalities in South Africa.1 These inequalities are evident from the early years, even before entry into primary school. They are exacerbated by an unequal schooling system,2  and are difficult to reverse. But early inequalities can be reduced through pre-school exposure to developmentally appropriate activities and programmes that stimulate cognitive development.3  Provided that they are of good quality, early learning programmes are an important mechanism to interrupt the cycle of inequality by reducing socio-economic differences in learning potential between children before they enter the foundation phase of schooling.

The Five-year Strategic Plan4  of the Department of Basic Education (DBE) includes a broad goal to improve the quality of ECD provisioning and specifically to improve access to grade R through the supply of learning materials and improving the quality of grade R educators. Evidence suggests that quality group learning programmes are beneficial for cognitive development from about three years of age5  and the National Development Plan (NDP) priorities, cited in the DBE’s strategic plan, include universal access to two years of early childhood development programmes.The DBE funds and monitors thousands of community-based grade R centres in addition to the school-based grade R classes. The NDP proposes the introduction of a second year of pre-school education, and that both years be made universally accessible to children.6  It therefore makes sense to monitor enrolment in early learning programmes of children in the 5 – 6-year pre-school age group.

In 2015, there were 288,212 learners attending 4,058 ECD centres in South Africa, according to the DBE’s administrative data.7  The number of learners in the ECD centres rose by 7% between 2013 and 2014 and then declined slightly again. Preliminary results from DBE, based on data from the Learner Unit Record Information and Tracking System (LURITS) and other provincial data sources show that approximately 862,200 learners were attending grade R or pre-grade R at primary schools in 2017, of whom 95% were at public (government schools) while 5%, or approximately 40,240 learners, were at independent schools.8 

In 2017, 92% of children (2 million) in the pre-school age group (5 – 6-year-olds) were reported to be attending some kind of educational institution, mostly in grade 0 or grade 1. This was an increase of 37 percentage points since 2002, when 1.1 million in the same age group were reported to be attending an educational institution.

Attendance rates are high across all provinces. The highest attendance rates in 2017 were in Limpopo (99%), Eastern Cape and Free State (both at 96%) and Gauteng (95%) while the lowest rates were in the Western Cape (84%) and North West province (87%).This pattern differs from many other indicators, where the Western Cape usually out-performs poorer and more rural provinces like the Eastern Cape and Limpopo. Similar patterns were found in analyses of the 2007 Community Survey and the 2008 National Income Dynamics Study data.9 

Given the inequities in South Africa, it is pleasing to see that there are no substantial racial differences in access to educational institutions by African and White children of pre-school age, although levels of attendance among Coloured children remain below the national average, at 83%. It is also encouraging that, as with formal school attendance, there are no strong differences in pre-school enrolment across the income quintiles. There are also no significant gender differences in access to pre-school.

As with the indicator that monitors school attendance, it should be remembered that this indicator tells us nothing about the quality of care and education that young children receive. High rates of attendance provide a unique opportunity because almost all children in an age cohort can be reached at a particularly important developmental stage; but this is a lost opportunity if the service is of poor quality.
 


1 See for example: Van der Berg S, Burger C, Burger R, de Vos M, Gustafsson M, Moses E, Shepherd D, Spaull N, Taylor S, van Broekhuizen H & von Fintel D (2011) Low Quality Education as a Poverty Trap. Stellenbosch: Stellenbosch University.

2 Spaull N (2012) Poverty & Privilege: Primary School Inequality in South Africa. Paper presented at the Towards Carnegie3: Strategies to Overcome Poverty & Inequality conference, 3 – 7 September 2013, UCT.

3 Heckman J (2006) Skill formation and the economics of investing in disadvantaged children. Science, 312: 1900-1902;

Southern and Eastern Africa Consortium for Monitoring Education Quality (2011) Learner Preschool Exposure and Achievement in South Africa. SACMEQ Policy Brief No. 4, April 2011. Pretoria: Ministry of Education.


4 Department of Basic Education (2016) Five-year strategic plan 2015/16 – 2019/20). Pretoria: DBE.

5 Engel P, Black M, Behrman JR, de Mello MC, Gertler PJ, Kapiriri L, Martorell R, Young ME & International Child Development Steering Group l (2007) Strategies to avoid the loss of developmental potential in more than 200 million children in the developing world. The Lancet, 369(9557): 229-242.

6 National Planning Commission (2012) National Development Plan – Vision for 2030. Pretoria: The Presidency.

7 Administrative data supplied on special request by the Department of Basic Education from their Education Management Information System (EMIS).

8 Department of Basic Education (2018) School Realities 2017. Pretoria: DBE.

9 Gustafsson M (2010) Policy Note on Pre-primary Schooling: An Empirical Contribution to the 2009 Medium Term Strategic Framework. Stellenbosch Economic Working Papers 05/10. Stellenbosch: Stellenbosch University.

The General Household Surveys asks, for all children aged 5 years and above, "Is ... currently attending any educational institution?" It then asks which type of institution, with response cateories including pre-school (incl day care, creche, pre-primary, ECD centre, nursery school), school (incl Grade R/Grade 0).

A change to the question formuation in 2009 is likely to have increased the response rate, as the initial question about education attendance was then followed by a clarification that an educational institution includes "school, technical university, home school, pre-school, creche, day care, etc). Prior to 2009, and without the clarification, respondents may have understood an educational institution to refer specifically to a formal school rather than a pre-school.

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.

Weights
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.

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