Education - School attendance
Education - School attendance
Author/s:  Arianne De Lannoy & Katharine Hall
Date: May 2012
Definition
This indicator reflects the number and proportion of children aged 7 – 17 years who are reported to be attending any school or educational facility.
Data
Data Source Statistics South Africa (2003-2011) General Household Survey 2002-2010. Pretoria, Cape Town: Statistics South Africa. Analysis by Katharine Hall, Children's Institute, University of Cape Town
Notes
  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.
What do the numbers tell us?
Education is a central socio-economic right that provides the foundation for life-long 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 (97%) attended some form of educational facility in 2010. Since 2002, the national attendance rate has seen a two percentage point increase. Of a total of 11.3 million children aged 7 – 17 years, just over 350,000 are reported as not attending school in 2010.

At a provincial level, the Eastern Cape, Northern Cape and KwaZulu-Natal have all seen significant increases in attendance rates. In the Northern Cape, attendance increased by five percentage points from 91% in 2002 to 96% in 2010, while attendance in KwaZulu-Natal increased by three percentage points and attendance in the Eastern Cape by two percentage points.
 
There has been a small but real increase in reported attendance rates for African and Coloured children over the nine-year period from 2002. Attendance rates for Coloured children remained slightly below the national average.  
 
Overall attendance rates tend to mask the problem of drop-out among older children. Analysis of attendance among discrete age groups shows a significant drop in attendance amongst children older than 14. Whereas 99% of 13-year-olds were reported to be attending an educational institution in 2010, the attendance rate dropped to 98% and 96% for 14- and 15-year-olds respectively. As schooling is compulsory only until the age of 15 or the end of grade 9, the attendance rate decreases more steeply from age 16 onwards, with 93% of 16-year-olds, 86% of 17-year-olds, and 71% of 18-year-olds reported to be attending school.1 There is no significant difference in drop-out rates between boys and girls overall. The cost of education is the main reason for non-attendance in the high school age group, followed by a perception that “education is useless”.2 Other reasons for drop-out are illness and exam failure. Pregnancy accounts for around 8% of drop-out amongst teenage girls not attending school.3
 
It is encouraging to note that 88% of children (just over 1.9 million) in the pre-school age group (5 – 6-year-olds) were attending some kind of educational institution in 2010, and 77% of children in the younger age group 3 - 4 years were attending an educational institution or ECD facility.

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 one or both parents – are indeed less likely to enrol in school and are more prone to dropping out or progressing more slowly than their more advantaged peers.4 Similarly, school attendance rates tell us nothing about the quality of teaching and learning that takes place in school. Systemic evaluations by the Department of Education have recorded very low pass rates in numeracy and literacy amongst both grade 3 and grade 6 learners,5 and continued inequities in the quality of education offered by schools serves to reinforce existing social inequalities, limiting the future work opportunities and life chances of poor children.6

Despite the inequities in the school system, there is little variation in school attendance rates across the income quintiles. Irrespective of whether they live in the poorest 20% or wealthiest 20% of households, children's school attendance rates remain high - between 96% and 98%,
Technical notes
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.

Younger children’s attendance at an educational facility (eg pre-school or early childhood development centre) was also analysed, specifically children younger than seven years of age.

Strengths and limitations of the data
The data are derived from the General Household Survey6, 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 longitudinal analysis, 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.
 
Weights
Person and household weights are provided by Statistics South Africa 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. For this project, GHS data were compared with estimates from the Statistics South Africa’s mid-year estimates, as well as the Actuarial Society of South Africa’s ASSA2003 AIDS and Demographic model.
 
Analyses of the seven surveys from 2002 to 2008 suggest that over- and under-estimation may have occurred in the weighting process:
  • When comparing the weighted 2002 data with the ASSA2003 AIDS and Demographic model estimates, it seems that the number of children aged 0 – 9 years was under-estimated in the GHS, while the number of children aged 10 – 19 was over-estimated. The pattern is consistent for both sexes. The number of very young males aged 0 – 4 years appears to be under-estimated by 15%. Girls in this age group have been under-estimated by 15.8%. Males in the 10 – 14-year age group appear to be over-estimated by 5.7%.
  • Similarly in 2003, there was considerable under-estimation of the youngest age group (0 – 9 years) and over-estimation of the older age group (10 – 19 years). The pattern is consistent for both sexes. The results also show that the over-estimation of males (9%) in the 10 – 19-year age group is more than double the over-estimation for females in this age range (3.8%).
  • In the 2004 results, it seems that the number of children aged 7 – 12 years was over-estimated by 6%, as well as the number of persons aged 13 – 22 years. The number of very young children appeared to have been under-estimated. The patterns of over- and under-estimation appear to differ across population groups. For example, the number of White children appears to be over-estimated by 14%, while the number of Coloured persons within the 13 – 22-year age group appears to be 9% too low.
  • In 2005, the GHS weights seem to have produced an over-estimate of the number of males within each five-year age group. The extent of the overestimation is particularly severe for the 10 – 14-year age group. In contrast, the weights produce an under-estimate of the number of girls – the error seems greatest in respect of the younger age groups. These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.09 respectively for the four age groups covering children (ie 0 – 4, 5 – 9, 10 – 14 and 15 – 19 years).
  • The 2006 weighting process yielded the same results as in 2005. The one exception is that the under-estimation of females is greatest in the 5 – 9 and 15 – 19-year age groups. This results in male-to-female ratios of 1.03, 1.10, 1.11 and 1.12 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 3 – 5% while the over-estimation is in the range of 1 – 7%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.08 respectively for the four age groups covering children.
  • Overall, assuming the ASSA2003 Aids and Demographic model to be the ‘gold standard’, it appears that the GHS2008 over-estimates both male and female populations under the age of 19 years, except for 0 – 4- year-old females. The extent of over-estimation for boys is in the range 0 – 7%. It is particularly severe for boys aged 10 – 14 years. Over-estimation is in the range of 2 – 5% for girls aged five years and above. For girls aged 0 – 4 years, the ASSA2003 model suggests that these may have been under-estimated by about 1%. The GHS2008 suggests a sex ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA model and Statistics South Africa's mid-year estimates.
The apparent discrepancies in the seven years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. Since 2005 the male and female patterns vary in respect of a particular characteristic, which means that the total estimate for this characteristic will be somewhat slanted toward the male pattern. 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.
 
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, 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).
References and Related Links
1A similar trend of lower numbers among higher grades is found in the enrolment data presented by the Department of Education over the years. See,for example, Dept of Education (2009) Trends in Education Macro-Indicators: South Africa. Pretoria: Department of Education.

2 Statistics South Africa (2011) General Household Survey 2010. Pretoria: StatsSA.

3 Ibid

4 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.

5 Department of Education (2008) 2007 Grade 3 Systemic Evaluation. Pretoria: DOE. (leaflet); Department of Education (2005) Grade 6 Intermediate Phase Systemic Evaluation Report. Pretoria: DOE.

6 van der Berg S, Burger C, Burger R, de Vos M, Gistafsson M, Moses E, Shepherd D, Spaull N, Taylor S, van Broekhuizen H & von Fintel D (2011) Low quality education as a poverty trap. Stellenbosch: University of Stellenbosch.

7 Statistics South Africa (2003-2011). General Household Survey 2002-2010 Metadata. Cape Town, Pretoria: Statistics South Africa.


RELATED LINKS

> Deparment of Basic Education 

> Education Management and Information Systems (EMIS)

Author: Arianne De Lannoy & Katharine Hall

Definition
This indicator reflects the number and proportion of children aged 7 – 17 years who are reported to be attending any school or educational facility.
Commentary
Education is a central socio-economic right that provides the foundation for life-long 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 (97%) attended some form of educational facility in 2010. Since 2002, the national attendance rate has seen a two percentage point increase. Of a total of 11.3 million children aged 7 – 17 years, just over 350,000 are reported as not attending school in 2010.

At a provincial level, the Eastern Cape, Northern Cape and KwaZulu-Natal have all seen significant increases in attendance rates. In the Northern Cape, attendance increased by five percentage points from 91% in 2002 to 96% in 2010, while attendance in KwaZulu-Natal increased by three percentage points and attendance in the Eastern Cape by two percentage points.
 
There has been a small but real increase in reported attendance rates for African and Coloured children over the nine-year period from 2002. Attendance rates for Coloured children remained slightly below the national average.  
 
Overall attendance rates tend to mask the problem of drop-out among older children. Analysis of attendance among discrete age groups shows a significant drop in attendance amongst children older than 14. Whereas 99% of 13-year-olds were reported to be attending an educational institution in 2010, the attendance rate dropped to 98% and 96% for 14- and 15-year-olds respectively. As schooling is compulsory only until the age of 15 or the end of grade 9, the attendance rate decreases more steeply from age 16 onwards, with 93% of 16-year-olds, 86% of 17-year-olds, and 71% of 18-year-olds reported to be attending school.1 There is no significant difference in drop-out rates between boys and girls overall. The cost of education is the main reason for non-attendance in the high school age group, followed by a perception that “education is useless”.2 Other reasons for drop-out are illness and exam failure. Pregnancy accounts for around 8% of drop-out amongst teenage girls not attending school.3
 
It is encouraging to note that 88% of children (just over 1.9 million) in the pre-school age group (5 – 6-year-olds) were attending some kind of educational institution in 2010, and 77% of children in the younger age group 3 - 4 years were attending an educational institution or ECD facility.

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 one or both parents – are indeed less likely to enrol in school and are more prone to dropping out or progressing more slowly than their more advantaged peers.4 Similarly, school attendance rates tell us nothing about the quality of teaching and learning that takes place in school. Systemic evaluations by the Department of Education have recorded very low pass rates in numeracy and literacy amongst both grade 3 and grade 6 learners,5 and continued inequities in the quality of education offered by schools serves to reinforce existing social inequalities, limiting the future work opportunities and life chances of poor children.6

Despite the inequities in the school system, there is little variation in school attendance rates across the income quintiles. Irrespective of whether they live in the poorest 20% or wealthiest 20% of households, children's school attendance rates remain high - between 96% and 98%,
Strengths and limitations of the data
The data are derived from the General Household Survey6, 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 longitudinal analysis, 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.
 
Weights
Person and household weights are provided by Statistics South Africa 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. For this project, GHS data were compared with estimates from the Statistics South Africa’s mid-year estimates, as well as the Actuarial Society of South Africa’s ASSA2003 AIDS and Demographic model.
 
Analyses of the seven surveys from 2002 to 2008 suggest that over- and under-estimation may have occurred in the weighting process:
  • When comparing the weighted 2002 data with the ASSA2003 AIDS and Demographic model estimates, it seems that the number of children aged 0 – 9 years was under-estimated in the GHS, while the number of children aged 10 – 19 was over-estimated. The pattern is consistent for both sexes. The number of very young males aged 0 – 4 years appears to be under-estimated by 15%. Girls in this age group have been under-estimated by 15.8%. Males in the 10 – 14-year age group appear to be over-estimated by 5.7%.
  • Similarly in 2003, there was considerable under-estimation of the youngest age group (0 – 9 years) and over-estimation of the older age group (10 – 19 years). The pattern is consistent for both sexes. The results also show that the over-estimation of males (9%) in the 10 – 19-year age group is more than double the over-estimation for females in this age range (3.8%).
  • In the 2004 results, it seems that the number of children aged 7 – 12 years was over-estimated by 6%, as well as the number of persons aged 13 – 22 years. The number of very young children appeared to have been under-estimated. The patterns of over- and under-estimation appear to differ across population groups. For example, the number of White children appears to be over-estimated by 14%, while the number of Coloured persons within the 13 – 22-year age group appears to be 9% too low.
  • In 2005, the GHS weights seem to have produced an over-estimate of the number of males within each five-year age group. The extent of the overestimation is particularly severe for the 10 – 14-year age group. In contrast, the weights produce an under-estimate of the number of girls – the error seems greatest in respect of the younger age groups. These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.09 respectively for the four age groups covering children (ie 0 – 4, 5 – 9, 10 – 14 and 15 – 19 years).
  • The 2006 weighting process yielded the same results as in 2005. The one exception is that the under-estimation of females is greatest in the 5 – 9 and 15 – 19-year age groups. This results in male-to-female ratios of 1.03, 1.10, 1.11 and 1.12 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 3 – 5% while the over-estimation is in the range of 1 – 7%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.08 respectively for the four age groups covering children.
  • Overall, assuming the ASSA2003 Aids and Demographic model to be the ‘gold standard’, it appears that the GHS2008 over-estimates both male and female populations under the age of 19 years, except for 0 – 4- year-old females. The extent of over-estimation for boys is in the range 0 – 7%. It is particularly severe for boys aged 10 – 14 years. Over-estimation is in the range of 2 – 5% for girls aged five years and above. For girls aged 0 – 4 years, the ASSA2003 model suggests that these may have been under-estimated by about 1%. The GHS2008 suggests a sex ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA model and Statistics South Africa's mid-year estimates.
The apparent discrepancies in the seven years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. Since 2005 the male and female patterns vary in respect of a particular characteristic, which means that the total estimate for this characteristic will be somewhat slanted toward the male pattern. 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.
 
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, 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).
Technical notes
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.

Younger children’s attendance at an educational facility (eg pre-school or early childhood development centre) was also analysed, specifically children younger than seven years of age.

References
1A similar trend of lower numbers among higher grades is found in the enrolment data presented by the Department of Education over the years. See,for example, Dept of Education (2009) Trends in Education Macro-Indicators: South Africa. Pretoria: Department of Education.

2 Statistics South Africa (2011) General Household Survey 2010. Pretoria: StatsSA.

3 Ibid

4 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.

5 Department of Education (2008) 2007 Grade 3 Systemic Evaluation. Pretoria: DOE. (leaflet); Department of Education (2005) Grade 6 Intermediate Phase Systemic Evaluation Report. Pretoria: DOE.

6 van der Berg S, Burger C, Burger R, de Vos M, Gistafsson M, Moses E, Shepherd D, Spaull N, Taylor S, van Broekhuizen H & von Fintel D (2011) Low quality education as a poverty trap. Stellenbosch: University of Stellenbosch.

7 Statistics South Africa (2003-2011). General Household Survey 2002-2010 Metadata. Cape Town, Pretoria: Statistics South Africa.


RELATED LINKS

> Deparment of Basic Education 

> Education Management and Information Systems (EMIS)

Department of International Development UK Children's Institute