Housing and ServicesHousing and Services

Access to adequate water

Author/s: Katharine Hall
Date: October 2016

Definition

This indicator shows the number and proportion of children who have access to a safe and reliable supply of drinking water at home – either inside the dwelling or on site. This is used as a proxy for access to adequate water. All other water sources, including public taps, water tankers, dams and rivers, are considered inadequate because of their distance from the dwelling or the possibility that water is of poor quality. The indicator does not show whether the water supply is reliable or if households have broken facilities or are unable to pay for services.

Data


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

Clean water is essential for human survival. The World Health Organisation has defined “reasonable access” to water as being a minimum of 20 litres per person per day.1 The 20-litre minimum is linked to the estimated average consumption when people rely on communal facilities and need to carry their own water for drinking, cooking and the most basic personal hygiene. It does not allow for bathing, showering, washing clothes or any domestic cleaning.2 The water needs to be supplied close to the home, as households that travel long distances to collect water often struggle to meet their basic daily quota. This can compromise children’s health and hygiene.

 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,3 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. Carrying water is a physical burden which can lead to back problems or injury from falls. It can also reduce time spent on education and other activities, and can place children at personal risk.4 For purposes of the child-centred indicator, therefore, adequacy is limited to a safe water source on site.

Close to six million children live in households that do not have access to clean drinking water on site. In 2014, over three-quarters (77%) of adults lived in households with drinking water on site – a significantly higher proportion than children (69%). A year-on-year comparison from 2002 – 2014 suggests that there has been little improvement in children’s access to water over this period.  

Provincial differences are striking. Over 90% of children in the Free State, Gauteng and the Western Cape provinces have an adequate supply of drinking water. However, access to water remains poor in KwaZulu-Natal (59%), Limpopo (53%) and the Eastern Cape (36%). The Eastern Cape appears to have experienced the striking improvement in water provisioning since 2002 (when only 23% of children had water on site). Kwazulu-Natal and the Free State have also recorded significant improvements: the proportion of children who had water on site increased from 45% (2002) to 59% (2014) in KwaZulu-Natal, and from 81% to 93% in the Free State over the same period.The significant decline in access to water in the Northern Cape may represent a deterioration in water access, or may be the result of weighting a very small child population. 

Children living in formal areas are more likely to have services on site than those living in informal settlements or in the rural former homelands. While the majority (77%) of children in formal dwellings have access, it decreases to 65% for children living in informal dwellings. Only 18% of children living in traditional housing have clean water available on the property.

The vast majority of children living in traditional dwellings are African, so there is a pronounced racial inequality in access to water. Sixty-three percentof African children had clean water on site in 2014, while over 95% of all other population groups had clean drinking water at home.There are no significant differnces in access to water across younger age groups.

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, only 51% have access to water on site, while 97% 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.

The General Household Survey asks questions about the household’s main source of water. From 2002 to 2004 there was a single question that asked about the household’s main water source (for all purposes). Since 2005, the question was split into two parts so that respondents report the main water source for drinking water and for water that is used for other purposes. Since then, Children Count – Abantwana Babalulekile presents the main source of drinking water because of the importance of having clean water for children and babies. The slight change in question formulation means that the data before and after 2005 are not directly comparable.
 
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.5 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 Survey5, 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).

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

2Howard G & Bartram  J (2003) Domestic Water Quantity, Service Level and Health. Geneva: World Health Organisation.

3Westwood 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, Medical Research Council & Centre for Disease Control and Prevention.

COHRE, AAAS, SDC & UN-Habitat (2007) Manual on the Right to Water and Sanitation. Geneva: Centre on Housing Rights and Evictions.

 Statistics South Africa (2003-2014). General Household Survey 2002-2013 Metadata. Cape Town, Pretoria: Statistics South Africa.