Housing and ServicesHousing and Services

Access to basic sanitation

Author/s: Katharine Hall & Lori Lake
Date: October 2016


This indicator shows the number and proportion of children living in households with basic sanitation. Adequate toilet facilities are used as proxy for basic sanitation. This includes flush toilets and ventilated pit latrines that dispose of waste safely and that are within or near a house. Inadequate toilet facilities include pit latrines that are not ventilated, chemical toilets, bucket toilets, or no toilet facility at all.


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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.
  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-point proportions differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.

A basic sanitation facility is defined in the government’s Strategic Framework for Water Services as the infrastructure necessary to provide a sanitation facility which is “safe, reliable, private, protected from the weather and ventilated, keeps smells to a minimum, is easy to keep clean, minimises the risk of the spread of sanitation-related diseases by facilitating the appropriate control of disease carrying flies and pests, and enables safe and appropriate treatment and/or removal of human waste and wastewater in an environmentally sound manner”.1

Adequate sanitation prevents the spread of disease and promotes health through safe and hygienic waste disposal. To do this, sanitation systems must break the cycle of disease. For example the toilet lid and fly screen in a ventilated pit latrine stop flies reaching human faeces and spreading disease. Good sanitation is not simply about access to a particular type of toilet. It is equally dependent on the safe use and maintenance of that technology; otherwise toilets break down, smell bad, attract insects and spread germs. 

Good sanitation is essential for safe and healthy childhoods. It is very difficult to maintain good hygiene without water and toilets. Poor sanitation is associated with diarrhoea, cholera, malaria, bilharzia, worm infestations, eye infections and skin disease. These illnesses compromise children’s health and nutritional status. Using public toilets and the open veld (fields) can also put children in physical danger. The use of the open veld and bucket toilets is also likely to compromise water quality in the area and to contribute to the spread of disease. Poor sanitation undermines children’s health, safety and dignity.

The data show a gradual and significant improvement in children’s access to sanitation over the 13-year period 2002 – 2014, although the proportion of children without adequate toilet facilities remains worryingly high. In 2002 less than half of all children (45%) had access to adequate sanitation. By 2014 the proportion of children with adequate toilets had risen by 29 percentage points to 74%. But 3.6 million children still use unventilated pit latrines or buckets, despite the state’s reiterated goals to provide adequate sanitation to all, and to eradicate the bucket system. Children (26%) are more likely than adults (20%) to live in households without adequate sanitation facilities.

As with other indicators of living environments, there are great provincial disparities. In provinces with large metropolitan populations, like Gauteng and the Western Cape, over 90% of children have access to adequate sanitation, while provinces with large rural populations have the poorest sanitation. The provinces with the greatest improvements in sanitation services are the Eastern Cape (where the number of children with access to adequate sanitation more than tripled from 0.6 million to 2 million over 13 years), KwaZulu-Natal (an increase of over 1.4 million children with adequate sanitation) and the Free State (where the proportion of children with sanitation improved from 51% in 2002 to 83% in 2014). 

Although there have also been significant improvements in sanitation provision in Limpopo, this province still lags behind, with only 51% of children living in households with adequate sanitation in 2014. It is unclear why the vast majority of children in Limpopo are reported to live in formal houses, yet access to basic sanitation is the poorest of all the provinces. Definitions of adequate housing such as those in the UN-HABITAT and South Africa’s National Housing Code include a minimum quality for basic services, including sanitation.

The statistics on basic sanitation provide yet another example of persistent racial inequality: Over 95% of Indian, White and Coloured children had access to adequate toilets in 2014, while only 70% of African children had access to basic sanitation. This is a marked improvement from 36% of African children in 2002.

Children in relatively well-off households have better levels of access to sanitation than poorer children. Amongst the richest 20% of households, 97% of children have adequate sanitation, while only 65% of children in the poorest 20% of households have this level of service.


Due to the different distributions of children and adults across the country, adults are more likely than children to have access to sanitation. However, there are no significant age differences in levels of access to adequate sanitation within the child population.
The Department of Water Affairs and Forestry 2 defines the minimum standard for basic sanitation as: 
  • appropriate hygiene (keeping toilets clean and washing hands after handling waste or using a toilet); 
  • a system for disposing of human faeces, waste water and rubbish which is affordable, easy to maintain, safe and environmentally acceptable; and 
  • an adequate toilet for each household.
Sanitation therefore includes infrastructure, service provisioning and behaviour. For the purposes of this indicator we use a narrow definition, based simply on the type of toilet available to each household. The General Household Survey asks about each household’s toilet facilities. The following facilities are included in the category of adequate sanitation: ‘flush on-site’, ‘flush off-site’ and ‘VIP’, standing for ventilated improved pit latrine. Inadequate sanitation includes the following: ‘chemical toilet’, ‘other pit’, ‘bucket’, ‘none’ and a small number of ‘unspecified’.
For purposes of measuring and monitoring persistent racial inequality, population groups are defined as 'African', 'Coloured', 'Indian', and 'White'.
The data are derived from the General Household Survey3, 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.

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.

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).
1 Department of Water Affairs and Forestry (2003) Strategic Framework for Water Services. Pretoria: DWAF
2 Department of Water Affairs and Forestry (2002) The policy on basic household sanitation made easy. Pretoria: DWAF
3 Statistics South Africa (2015). General Household Survey 2014 Metadata. Cape Town, Pretoria: Statistics South Africa.

Westwood A (2011) Diarrhoeal Disease in Stephen C, Bamford L, Patrick W & the MRC Unit for Maternal and Infant Health Care Strategies (eds) Saving Children 2009: Five years of data. A sixth survey of child healthcare in South Africa. Pretoria: Tshepesa Press, MRC, CDC; 2011.

World Health Organisation & Unicef (2010) Progress on sanitation and drinking water: 2010 update. Geneva: WHO Press.