Young children are particularly vulnerable to diseases associated with poor water quality. Gastro-intestinal infections with associated diarrhoea and dehydration are a significant contributor to the high child mortality rate in South Africa,² and intermittent outbreaks of cholera in some provinces pose a serious threat to children in those areas. Lack of access to adequate water is closely related to poor sanitation and hygiene. In addition, children may be responsible for fetching and carrying water to their homes from communal taps, or rivers and streams, which is a physical burden and can place them at risk.

 

It is of concern, then, that nearly seven million children live in households without access to clean drinking water on site. In 20 around three-quarters (74%) of adults lived in households with drinking water on site – a significantly higher proportion than children (64%). A year-on-year comparison from 2002 to 2010 suggests that there has been little improvement in children’s access to water over the seven-year period. A slight change in question formulation in the General Household Survey reduces the comparability of data before and after 2005 (see Technical Notes below). The effect of this change would, if anything, result in an exaggerated increase in reported access to water after 2005. This is not evident in the data.

 

 

Children living in formal areas are more likely than those living in informal or traditional dwellings to have services on site. While the majority of children in formal dwellings (75%) and informal dwellings (67%) had water in their home or on the property in 2008, only 17% of children living in ‘traditional’ housing had clean water available on the property.

 

The vast majority of children living in ‘traditional’ dwellings are African, and so we see pronounced racial inequality in access to water. Just 58% of African children had clean water on site in 2010, while over 95% of all other population groups had clean drinking water at home.

Inequality in access to safe water is also pronounced when the data are disaggregated by income category. Amongst children in the poorest 20% of households, less than half (46%) have access to water on site, while over 90% of those in the richest 20% of households have this level of service. In this way,inequalities are reinforced: the poorest children are most at risk of diseases associated with poor water quality, and the associated setbacks in their development.

 

This indicator only tells us how many children have access to the infrastructure to deliver clean drinking water to children’s homes. It does not give any indication of how many households have broken facilities, are unable to pay for water, have experienced interruptions in their water, or have been cut off for non-payment.

 

Policy guidelines on basic water supply indicate that water may be off-site, but must be within 200 metres of the house.³ This child-centred indicator has therefore used a slightly narrower definition and defines ‘adequate’ as being on site. Collecting water from a public source is physically burdensome and can be dangerous, especially for children.

 

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 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.

 

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.

 

Person and household weights are provided by Statistics South Africa and are applied in Children Count – Abantwana Babalulekile analyses to give estimates at the provincial and national levels. Survey data are prone to sampling and reporting error. Some of the errors are difficult to estimate, while others can be identified. One way of checking for errors is by comparing the survey results with trusted estimates from elsewhere. Such a comparison can give an estimate of the robustness of the survey estimates. For this project, GHS data were compared with estimates from the Statistics South Africa’s mid-year estimates, as well as the Actuarial Society of South Africa’s ASSA2003 AIDS and Demographic model.

 

Analyses of the seven surveys from 2002 to 2008 suggest that over- and under-estimation may have occurred in the weighting process:

The apparent discrepancies in the seven years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. Since 2005 the male and female patterns vary in respect of a particular characteristic, which means that the total estimate for this characteristic will be somewhat slanted toward the male pattern. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.

 

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).

¹ Ki-moon B (2007) Children and the Millennium Development Goals: Progress towards a World Fit for Children. UNICEF: New York

 

² 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

³ Department of Water Affairs and Forestry (1994) White Paper on Water Supply and Sanitation. Pretoria: DWAF

 

³ Statistics South Africa (2003-2011). General Household Survey 2002-2010 Metadata. Cape Town, Pretoria: Statistics South Africa

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