Education - Children living far from school

Education - Children living far from school Definition
This indicator reflects the distance from a child's household to the school s/he attends. Distance is measured through a proxy indicator: length of time travelled to reach the school attended, which is not necessarily the school nearest to the child’s household. The school the child attends is defined as “far” if a child has to travel more than 30 minutes to reach it, irrespective of mode of transport. Children aged 7 – 13 are defined as primary school age, and children aged 14 – 17 are defined as secondary school age. Data
What do the numbers tell us?
Access to schools and other educational facilities is a necessary condition for achieving the right to education. A school's location and distance from home can pose a barrier to education. Access to schools is also hampered by poor roads, transport that is unavailable or unaffordable, and danger along the way. Risks may be different for young children, for girls and boys, and are likely to be greater when children travel alone. For children who do not have schools near to their homes, the cost, risk and effort of getting to school can influence decisions about regular attendance, as well as participation in extramural activities and after-school events. Those who travel long distances to reach school may wake very early and risk arriving late or physically exhausted, which may affect their ability to learn. Walking long distances to school may also lead to learners being excluded from class or make it difficult to attend school regularly. Close to three-quarters (71%) of South Africa's learners walk to school, while 8% use public transport. Only 2% report using school buses or transport provided by the government. The vast majority (83%) of White children are driven to school in private cars, compared with only 12% of African children. Assuming that schools primarily serve the children living in communities around them, the ideal indicator to measure physical access to school would be the distance from the child's household to the nearest school. This analysis is no longer possible due to question changes in the Physical access to school remains a problem for many children in South Africa, particularly those living in more remote areas where public transport to schools is lacking or inadequate and where households are unable to afford private transport for children to get to school. ^{2 } A number of rural schools have closed since 2002, making the situation more diffiult for children in these areas.^{ }Nationally, the number of public schools has dropped by 9% (2,429 schools) between 2002 and 2014, with the largest decreases in the Free State, North West and Limpopo. Over the same period, the number of independent schools in the country has risen by 523 (45%).^{3} In the Eastern Cape province, the number of public schools decreased by 10% between 2002 and 2014, while the number of independent schools more than quadrupled over the same period.
Technical notes
The General Household Survey asks: “How long does it take ... to get to the educational institution he/she attends? Specify for one direction only, using the usual means of transport.” Responses categories are precoded: less than 15 minutes, 14-30 minutes, 31-60 minutes and so on.
For purposes of this indicator, where respondents indicate that children aged 7-17 who attend school have to travel more than 30 minutes to school, the distance to school is categorised as ‘far’. Where children spend 30 minutes or less travelling to school, the distance is categorised as “not far”. The indicator does not take into account those who are not attending school because schools are inaccessible - this is because the question is only asked in respect of household members who are reported to be attending an educational institution. Strengths and limitations of the data
The data are derived from the General Household Survey
^{4}, 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 stratificationThe 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 changesProvincial 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.
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 DisaggregationStatistics 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 errorError 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
Department of Basic Education (2015)
Branson N, Hofmeyer C & Lam D (2013) Gustafsson M (2011)
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Definition

This indicator reflects the distance from a child's household to the school s/he attends. Distance is measured through a proxy indicator: length of time travelled to reach the school attended, which is not necessarily the school nearest to the child’s household. The school the child attends is defined as “far” if a child has to travel more than 30 minutes to reach it, irrespective of mode of transport. Children aged 7 – 13 are defined as primary school age, and children aged 14 – 17 are defined as secondary school age.

Commentary

Access to schools and other educational facilities is a necessary condition for achieving the right to education. A school's location and distance from home can pose a barrier to education. Access to schools is also hampered by poor roads, transport that is unavailable or unaffordable, and danger along the way. Risks may be different for young children, for girls and boys, and are likely to be greater when children travel alone.

For children who do not have schools near to their homes, the cost, risk and effort of getting to school can influence decisions about regular attendance, as well as participation in extramural activities and after-school events. Those who travel long distances to reach school may wake very early and risk arriving late or physically exhausted, which may affect their ability to learn. Walking long distances to school may also lead to learners being excluded from class or make it difficult to attend school regularly.

Close to three-quarters (71%) of South Africa's learners walk to school, while 8% use public transport. Only 2% report using school buses or transport provided by the government. The vast majority (83%) of White children are driven to school in private cars, compared with only 12% of African children.^{1}** **These figures illustrate pronounced disparity in child mobility and means of access to school.

Assuming that schools primarily serve the children living in communities around them, the ideal indicator to measure physical access to school would be the distance from the child's household to the nearest school. This analysis is no longer possible due to question changes in the *General Household Survey*. Instead, the indicator shows the number and proportion of children who travel far (more than 30 minutes) to reach the actual school that they attend, even if it is not the closest school. School-age children not attending school are therefore excluded from the analysis.

Physical access to school remains a problem for many children in South Africa, particularly those living in more remote areas where public transport to schools is lacking or inadequate and where households are unable to afford private transport for children to get to school.

Strengths and limitations of the data

The data are derived from the General Household Survey^{4}, 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.

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 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

Analyses of the ten surveys from 2002 to 2011 suggest that some over- and under-estimation may have occurred in the weighting process:

§ When comparing the weighted 2002 data with the ASSA2008 AIDS and Demographic model estimates, it seems that the number of children was under-estimated by 5% overall. The most severe under-estimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield under-estimations of 15% and 16% respectively. The next age group (5 – 9 years) is also under-estimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14-year age group, although boys are still under-estimated by around 1% and girls by 3%. In contrast, the weighted data yield over-estimates of boys and girls in the upper age group (15 – 17 years), with the GHS over-counting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal male-to-female ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively.

§ Similarly in 2003, there was considerable under-estimation of the youngest age groups (0 – 4 years and 5 – 9 years) and over-estimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are under-estimated by as much as 16%, with under-estimates for babies below two years in the range 19 – 30%. The results also show that the over-estimation of males in the 15 – 17-year age group (9%) is much more severe than the over-estimation for females in this age range (1.4%), resulting in a male-to-female ratio of 1.09 in this age group, compared with ratios around 1.02 in the younger age groups.

§ In the 2004 results, all child age groups seem to have been under-estimated, with the under-estimate being more severe in the upper age group (15 – 17 years). This is the result of severe under-estimation in the number of girls, which outweighs the slight over-estimation of boys in all age groups. Girls are under-estimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while over-estimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in male-to-female ratios of 1.10, 1.11, 1.12 and 1.14 for the four age groups.

§ In 2005, the GHS weights seem to have produced an over-estimate of the number of males and an under-estimate of the number of females within each five-year age group. The extent of under-estimation for girls (by 7% overall) exceeds that of the over-estimation for boys (at 2% overall). These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.13 respectively for the four age groups covering children.

§ The 2006 weighting process yields different patterns from other years when compared to population estimates for the same year derived from ASSA2008, in that it yielded an under-estimation of both females and males. The under-estimation of females is greatest in the 0 – 4 and 5 – 9-year age groups, while the under-estimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14-year age group. This results in male-to-female ratios of 1.09, 0.99, 0.96 and 1.00 respectively for the four age groups covering children.

§ The 2007 weighting process produced an over-estimation for boys and an under-estimation for girls. The under-estimation of females is in the range of 4 – 8% while the over-estimation for boys is in the range of 1 – 5%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.06 respectively for the four age groups covering children.

§ In 2008, the GHS weighted population numbers when compared with ASSA2008 over-estimated the number of boys aged 10 and over, in the range of 3% for the 10 – 14 age group, and 8% for the 15 – 17 age group. The total weighted number of girls is similar to the ASSA population estimate for girls, but this belies an under-estimate of female babies below two years (by 7 – 8%), and an over-estimate of young teenage girls. The GHS 2008 suggests a male-to-female ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA2008 model.

§ A comparison of the GHS and ASSA for 2009 suggests a continuation of the general pattern from previous years, which is that GHS weights result in an under-estimation of children in the 0 – 4 age group (especially infants), and an over-estimate of older children. In 2009 the under-estimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHS-weighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The male-to-female ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHS-derived ratio is higher, at 1.08, compared to 1.00 in ASSA.

§ In 2010, the GHS weights again produce an underestimation of children in the 0 – 4 age group and an over-estimate of children aged 15 – 17 years. For the middle age groups, and for the child age group as a whole, there is less than 1% difference in the estimates from the two sources. For the 0 – 4 age group the under-estimate is lower than previously, at 2%, but for the oldest age group there is an over-estimate of 5%. The male-to-female ratios are similar across the two sources, although the ratio is 1.00 for all but the 0 – 4 age group in ASSA as against 1.01 for the youngest age group in ASSA and for all age groups in the GHS.

§ A comparison of the GHS2011 to ASSA2008 (projected to 2011) suggests an under-estimation of children below two years and an over-estimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The under-estimation is particularly pronounced for babies under a year, at 8%. The male-to-female ratio for all children under 17 is 1.00 in ASSA, and 1.01 in the GHS.

The apparent discrepancies in the ten years of data may slightly affect the accuracy of the *Children Count – Abantwana Babalulekile* estimates. From 2005 to 2008, consistently distorted male- to-female ratios means that the total estimates for certain characteristics would be somewhat slanted toward the male pattern. This effect is reduced from 2009, where more even ratios are produced, in line with the modelled estimates. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.

Statistics South Africa suggests caution when attempting to interpret data generated at low level disaggregation. The population estimates are benchmarked at the national level in terms of age, sex and population group while at provincial level, benchmarking is by population group only. This could mean that estimates derived from any further disaggregation of the provincial data below the population group may not be robust enough.

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: “How long does it take ... to get to the educational institution he/she attends? Specify for one direction only, using the usual means of transport.” Responses categories are precoded: less than 15 minutes, 14-30 minutes, 31-60 minutes and so on.

For purposes of this indicator, where respondents indicate that children aged 7-17 who attend school have to travel more than 30 minutes to school, the distance to school is categorised as ‘far’. Where children spend 30 minutes or less travelling to school, the distance is categorised as “not far”. The indicator does not take into account those who are not attending school because schools are inaccessible - this is because the question is only asked in respect of household members who are reported to be attending an educational institution.

References

^{1}K Hall & W Sambu Analysis of General Household Survey 2014, Children’s Institute, UCT.

^{2}See no. 1 (Hall & Sambu) above.

^{3}Department of Education (2004) *Education Statistics in South Africa at a Glance in 2002*. Pretoria: DOE*; * and

Department of Basic Education (2015) *Education Statistics in South Africa 2012.* Pretoria: DBE. [Calculations by K Hall, Children’s Institute, UCT]

*, see also: *

Branson N, Hofmeyer C & Lam D (2013) *Progress through School and the Determinants of School Dropout in South Africa.* Southern Africa Labour and Development Research Unit working paper no. 100. Cape Town: SALDRU, UCT.

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.

^{4}Statistics South Africa (2003-2015). *General Household Survey 2002-2014 Metadata*. Cape Town, Pretoria: Statistics South Africa.

**RELATED LINKS**

**> Department of Basic Education **

**> Education Management and Information Systems (EMIS) **