Education - School attendance
Education - School attendance
Author/s:  Katharine Hall & Arianne De Lannoy
Date: August 2014
Definition

This indicator reflects the number and proportion 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
Data Source Statistics South Africa (2003-2013) General Household Survey 2002-2012. 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.
  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?

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 2012. Since 2002, the national attendance rate has seen a 2.5 percentage point increase. Of a total of 11.2 million children aged 7 – 17 years, 290,000 are reported as not attending school in 2012.

At a provincial level, the Northern Cape, North West 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 2012. In KwaZulu-Natal, the attendance increased from 93% in 2002 to 98% in 2012, while in the North West, it increased by three percentage points in the same period. There has been a small but real increase in reported attendance rates for African and Coloured children over the 11-year period since 2002. Attendance rates for Coloured children remained slightly below the national average in 2012, at 95%.  


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 children in each age year from seven to 13 are reported to be attending an educational institution, the attendance rate drops to 98% and 97% for 14- and 15-year-olds respectively. Schooling is compulsory only until the age of 15 or the end of grade 9, and the attendance rate decreases more steeply from age 16 onwards, with 94% of 16-year-olds, 89% of 17-year-olds, and 81% of 18-year-olds reported to be attending school (based on those who have not successfully completed grade 12).1 

Although there are differences in school attendance rates between boys and girls in the upper teens, with boys more likely to be attending school, the difference is not significant if one excludes those who have successfully completed grade 12.

Amongst children of school-going age who are not attending school the main set of reasons for non-attendance relate to financial constraints. These include the cost of schooling (18%), or the opportunity costs of education, where children have family commitments such as child minding (9%) or are needed to work in a family business or elsewhere to support household income (3%). The second most common set of reasons is related to perceived learner or education system failures, such as a perception that “education is useless” (12%), feeling unable to perform at school (8%), or exam failure (4%).Other reasons for drop-out are illness (8%) and disability (9%). Pregnancy accounts for around 10% of drop-out amongst teenage girls not attending school (or 5% of all non-attendance).2

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. Racial inequalities in school advancement remain strong.3 Similarly, school attendance rates tell us nothing about the quality of teaching and learning. 

There is little variation in school attendance rates across the income quintiles. Irrespective of whether children live in the poorest or wealthiest 20% of households, school attendance rates remain high – between 96% and 99%.

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 Survey4, 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 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. 

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
 1 A 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: Department of Basic Education (2011) Macro Indicator Trends in Schooling: Summary Report 2011. Pretoria: DBE.

2K Hall analysis of General Household Survey 2011, Children’s Institute, UCT.

For more information on school drop-outsee 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.

3Crouch 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.

4Statistics 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: Katharine Hall & Arianne De Lannoy

Definition

This indicator reflects the number and proportion 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.

Commentary

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 2012. Since 2002, the national attendance rate has seen a 2.5 percentage point increase. Of a total of 11.2 million children aged 7 – 17 years, 290,000 are reported as not attending school in 2012.

At a provincial level, the Northern Cape, North West 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 2012. In KwaZulu-Natal, the attendance increased from 93% in 2002 to 98% in 2012, while in the North West, it increased by three percentage points in the same period. There has been a small but real increase in reported attendance rates for African and Coloured children over the 11-year period since 2002. Attendance rates for Coloured children remained slightly below the national average in 2012, at 95%.  


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 children in each age year from seven to 13 are reported to be attending an educational institution, the attendance rate drops to 98% and 97% for 14- and 15-year-olds respectively. Schooling is compulsory only until the age of 15 or the end of grade 9, and the attendance rate decreases more steeply from age 16 onwards, with 94% of 16-year-olds, 89% of 17-year-olds, and 81% of 18-year-olds reported to be attending school (based on those who have not successfully completed grade 12).1 

Although there are differences in school attendance rates between boys and girls in the upper teens, with boys more likely to be attending school, the difference is not significant if one excludes those who have successfully completed grade 12.

Amongst children of school-going age who are not attending school the main set of reasons for non-attendance relate to financial constraints. These include the cost of schooling (18%), or the opportunity costs of education, where children have family commitments such as child minding (9%) or are needed to work in a family business or elsewhere to support household income (3%). The second most common set of reasons is related to perceived learner or education system failures, such as a perception that “education is useless” (12%), feeling unable to perform at school (8%), or exam failure (4%).Other reasons for drop-out are illness (8%) and disability (9%). Pregnancy accounts for around 10% of drop-out amongst teenage girls not attending school (or 5% of all non-attendance).2

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. Racial inequalities in school advancement remain strong.3 Similarly, school attendance rates tell us nothing about the quality of teaching and learning. 

There is little variation in school attendance rates across the income quintiles. Irrespective of whether children live in the poorest or wealthiest 20% of households, school attendance rates remain high – between 96% and 99%.

Strengths and limitations of the data
The data are derived from the General Household Survey4, 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 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. 

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
 1 A 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: Department of Basic Education (2011) Macro Indicator Trends in Schooling: Summary Report 2011. Pretoria: DBE.

2K Hall analysis of General Household Survey 2011, Children’s Institute, UCT.

For more information on school drop-outsee 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.

3Crouch 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.

4Statistics 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)