Author/s: Katharine Hall, Arianne De Lannoy, & Shirley Pendlebury
Date: October 2016
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 Source  Statistics South Africa (2003  2015) General Household Survey 2002  2014. Pretoria, Cape Town: Statistics South Africa. Analysis by Katharine Hall & Winnie Sambu, Children's Institute, University of Cape Town. 
Notes 

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 afterschool 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 threequarters (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. Schoolage children not attending school are therefore excluded from the analysis.
For purposes of this indicator, where respondents indicate that children aged 717 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.
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 midyear 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 underestimation 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 underestimated by 5% overall. The most severe underestimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield underestimations of 15% and 16% respectively. The next age group (5 – 9 years) is also underestimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14year age group, although boys are still underestimated by around 1% and girls by 3%. In contrast, the weighted data yield overestimates of boys and girls in the upper age group (15 – 17 years), with the GHS overcounting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal maletofemale ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively.
§ Similarly in 2003, there was considerable underestimation of the youngest age groups (0 – 4 years and 5 – 9 years) and overestimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are underestimated by as much as 16%, with underestimates for babies below two years in the range 19 – 30%. The results also show that the overestimation of males in the 15 – 17year age group (9%) is much more severe than the overestimation for females in this age range (1.4%), resulting in a maletofemale 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 underestimated, with the underestimate being more severe in the upper age group (15 – 17 years). This is the result of severe underestimation in the number of girls, which outweighs the slight overestimation of boys in all age groups. Girls are underestimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while overestimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in maletofemale 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 overestimate of the number of males and an underestimate of the number of females within each fiveyear age group. The extent of underestimation for girls (by 7% overall) exceeds that of the overestimation for boys (at 2% overall). These patterns result in maletofemale 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 underestimation of both females and males. The underestimation of females is greatest in the 0 – 4 and 5 – 9year age groups, while the underestimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14year age group. This results in maletofemale 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 overestimation for boys and an underestimation for girls. The underestimation of females is in the range of 4 – 8% while the overestimation for boys is in the range of 1 – 5%. This results in maletofemale 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 overestimated 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 underestimate of female babies below two years (by 7 – 8%), and an overestimate of young teenage girls. The GHS 2008 suggests a maletofemale 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 underestimation of children in the 0 – 4 age group (especially infants), and an overestimate of older children. In 2009 the underestimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHSweighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The maletofemale ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHSderived 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 overestimate 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 underestimate is lower than previously, at 2%, but for the oldest age group there is an overestimate of 5%. The maletofemale 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 underestimation of children below two years and an overestimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The underestimation is particularly pronounced for babies under a year, at 8%. The maletofemale 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 tofemale 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 – 14yearolds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.
^{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]
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 (20032015). General Household Survey 20022014 Metadata. Cape Town, Pretoria: Statistics South Africa.
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> Department of Basic Education
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