The ill-provision of water and sanitation services poses the greatest risk to people living with HIV and AIDS in South Africa – a majority of whom reside in slum settlements. People living with HIV and AIDS (PLWHA) die after succumbing to opportunistic infections, especially water-borne diseases (e.g., diarrhoea, cholera). This study was based on 485 individuals with HIV and AIDs drawn from three types of settlements (rural, peri-urban and urban slums) and sampled from three selected provincial districts of Khayelitsha (Western Cape), Ukhahlamba (Eastern Cape) and Groblersdal (Limpopo). The results show PLWHA having higher willingness-to-pay (WTP) for sanitation at ZAR448.40/month compared to water (ZAR428.60). Those living in urban slum settlements show the highest WTP for sanitation (ZAR552.70), followed by the ones in rural areas (ZAR500.24). The results underscore important implications: PLWHA face greater sanitation challenges relative to water; those in slum settlements endure the worst sanitation insecurity compared to counterparts living in other settlement types; higher WTP for sanitation implies that PLWHA will derive greater benefits from improvements in sanitation services relative to water. To conclude, it is imperative for municipal authorities to prioritize the provision of sanitation facilities to PLWHA especially in urban slums as part of the ‘pro-poor service delivery’ campaigns.

INTRODUCTION

South Africa has made phenomenal progress towards achieving universal supply of water and sanitation to its citizens since the end of apartheid. At the beginning of the democratic transition (1994), about 15 and 21 million people were estimated to be without access to clean water and safe sanitation, respectively (National Sanitation Policy White Paper 1995). Provision of these amenities was severely limited in rural and slum settlements, where in some cases they were completely missing. However by 2012 (after nearly two decades of democracy), about 90% (an increase from 60% in 1994) had gained access to clean water while 80% (an increase from 55% in 1994) had gained access to safe sanitation (Stats SA 2010). A number of factors can be attributed to this great achievement, such as: an increasing share of the national budget committed and/or channelled towards developing and improving water infrastructure; establishment of effective water governance structures and institutions at both local and national level; implementation of sound water and sanitation policies (Makaudze & Gelles 2015).

However, despite this laudable achievement, South Africa still faces daunting challenges in achieving universal supply of water and sanitation to its citizens. Key problems among these include persistent backlogs in the provision of water and sanitation, increasing water shortages and scarcity in urban areas due to a rapid increase in population growth, and industrialization (Research Reports 2012). UN-Habitat (2008) predicts that a majority of people (>60%) in Africa will be living in major cities by 2020 in the search for employment and a better life. This trend is well observed in South Africa, where most of the major cities are currently experiencing rapid growth in the urban population.

Associated with an increase in urban population is the rise in slum settlements. Slum settlements are generally the hive and harbour of many socio-economic ills, such as high crime rates, drug-related gang wars, drug abuse, destitution, sexual violence and high levels of infection of sexually transmitted diseases, especially HIV and AIDs. A number of studies indicate South Africa as being one of the worst affected countries in the world with the highest HIV prevalence rate of 17.8% (UNAIDS 2007; AIDS Foundation 2010; Shisana et al. 2014). As observed in Table 1, the highest incidence of HIV infection among the most productive age group of 15–49 year olds is most prevalent in urban slums (25.8%), followed by rural slums (17.3%).

Table 1

Type of settlement and HIV/AIDs prevalence among productive age group (15–49 yrs)

Type of settlement HIV+ (whole sample) HIV+ (15–49) 
Urban formal 9.1% 13.9% 
Urban slum 17.6% 25.8% 
Rural slum 11.6% 17.3% 
Rural formal 9.9% 13.9% 
Type of settlement HIV+ (whole sample) HIV+ (15–49) 
Urban formal 9.1% 13.9% 
Urban slum 17.6% 25.8% 
Rural slum 11.6% 17.3% 
Rural formal 9.9% 13.9% 

STUDY OBJECTIVES

Currently, about 3.6 million people live in slum settlements across South Africa (AIDs Foundation 2010). With a high rural-to-urban migration rate estimated at 3.5% per year (Barry & Ruther 2005), the population of slum dwellers is expected to double by 2020. Further, it is estimated that a third of this population will be infected with HIV and AIDs (Shisana et al. 2014). However, one severe challenge currently experienced by many local municipalities is the burden of providing clean water and safe sanitation to the rapidly rising urban slum population (Kgalushi et al. 2004; Barry & Ruther 2005; Kamminga & Wegelin-Schuringa 2006).

Many municipalities are currently struggling to provide efficient water and sanitation delivery services to their citizens, especially in urban slums. Frequent protests by urban residents against poor service delivery (water and sanitation in particular) bear evidence of the severe challenges that many local municipalities across the country are facing. The recent violent protests in Cape Town by slum dwellers in Khayelitsha – in what are often dubbed the ‘toilet wars’ – provides a case in point.

The objective of this study is to assess the willingness-to-pay (WTP) for improvements in water and sanitation by people living with HIV and AIDs (PLWHA) in South Africa. Since the government declared access to clean water and safe sanitation a basic human right, upholding this fundamental right to PLWHA deserves more priority (South African Human Right Commission 2014). Ensuring adequate access to clean water and safe sanitation is vital, as this is likely to prolong the lives of PLWHA – a majority die after succumbing to one or two opportunistic diseases, especially diarrhoea, cholera, TB or skin diseases (Barnett & Whiteside 2002; Makaudze et al. 2011).

It is important to stress that the problems of water and sanitation are not necessarily confined to HIV and AIDs households but are common problems affecting all residents especially those living in slum settlements. However, water and sanitation problems are more pronounced for PLWHA for certain reasons: as they become increasingly frail and bed-ridden, the need for water and sanitation becomes a critical factor for the survival of PLWHA; the ‘burden of caring’ is significantly reduced when water and sanitation are readily available and/or accessible; ensuring that there are adequate quality water and sanitation services is vital for minimizing opportunistic infection.

The rest of the paper is based on the empirical results involving a sample of 485 households of PLWHA drawn from the three selected districts of Khayelitsha, Ukhahlamba and Groblersdal, which are geographically located in the provinces of Western Cape, Eastern Cape and Limpopo, respectively. Hence, the remainder of the paper is structured as follows: the next section reviews the current access to water and sanitation by PLWHA, followed by a section presenting the methodology and data necessary for the estimation of WTP measures. Then, a section discussing the results follows and, finally, conclusions are drawn.

CURRENT WATER AND SANITATION AVAILABILITY

Water sources available to PLWHA differ considerably among the three study areas – Khayelitsha, Ukhahlamba and Groblersdal. As shown in Table 2, households in Groblersdal have the greatest access to ‘on-yard water’ (52%) compared to Ukhahlamba (41%) and Khayelitsha (21%). A significant proportion of PLWHA in Ukhahlamba (18%) are accessing typically unsafe water from sources such as rivers, streams or dams. Outside-yard ‘communal tap’ is the predominant source of water accessible to households living in slum settlements (>60%) in Khayelitsha.

Table 2

Household water sources

  All areas Khayelitsha Ukhahlamba Groblersdal 
  (N = 485) (N = 198) (N = 175) (N = 112) 
 N 
In-house piped 55 11.3 14.6 8.0 10.7 
On-yard piped 172 35.5 21.2 41.1% 51.8% 
On-yard borehole 20 4.1 1.0 1.7 13.5 
Communal tap 182 37.5 62.1 27.4 9.8 
Outside-yard communal borehole 13 2.7 0.5 2.3 7.1 
River/stream/dam 39 7.6 0.0 17.9 3.6 
  All areas Khayelitsha Ukhahlamba Groblersdal 
  (N = 485) (N = 198) (N = 175) (N = 112) 
 N 
In-house piped 55 11.3 14.6 8.0 10.7 
On-yard piped 172 35.5 21.2 41.1% 51.8% 
On-yard borehole 20 4.1 1.0 1.7 13.5 
Communal tap 182 37.5 62.1 27.4 9.8 
Outside-yard communal borehole 13 2.7 0.5 2.3 7.1 
River/stream/dam 39 7.6 0.0 17.9 3.6 

It is important to stress that ‘in-house piped’ water is the safest and most convenient water source ideal for PLWHA households. However, across the three study regions, the proportion of households with access to ‘in-house piped’ water is very low (11%) and varies from about 8% in Ukhahlamba to about 15% in Khayelitsha.

The current system of sanitation available and/or accessible to PLWHA across the three study areas is shown in Table 3. What is notable to observe is that a significant proportion (20%) of PLWHA are without access to any form of toilet and rather resort to using ‘bush’ or simply open spaces for defecation. This unsafe practice is most common in slum settlement in Khayelitsha (25.8%), followed by Ukhahlamba (17.1%) and least in Groblersdal (6.3%). The ‘bucket system’ is an abhorred practice that often sparks social protests especially in urban slums. This is an old practice in existence since the days of apartheid, that involves the use of buckets as a faeces disposal system and ought to have been discontinued by now. The results indicate that the bucket remains a common practice in Khayelitsha (14%) and Ukhahlamba (10%) but rare in Groblersdal (2.7%).

Table 3

Currently available sanitation facilities

  All areas
 
Khayelitsha (N = 198) Ukhahlamba (N = 175) Groblersdal (N = 112) 
 N 
Toilet system 
 Flush system 205 42.3 55.1 53.1 2.7 
 Pit latrine 135 27.8 1.5 18.9 88.4 
 Bucket system 46 9.5 13.6 9.7 1.8 
 Chemical chlorination* 1.9 3.5 0.6 
 No toilet/Bush 88 18.1 25.8 17.1 6.3 
  All areas
 
Khayelitsha (N = 198) Ukhahlamba (N = 175) Groblersdal (N = 112) 
 N 
Toilet system 
 Flush system 205 42.3 55.1 53.1 2.7 
 Pit latrine 135 27.8 1.5 18.9 88.4 
 Bucket system 46 9.5 13.6 9.7 1.8 
 Chemical chlorination* 1.9 3.5 0.6 
 No toilet/Bush 88 18.1 25.8 17.1 6.3 

*This is a process that involves the addition of chlorine to portable toilet systems for disinfection and prevention of water-borne diseases.

Of practical significance is to determine whether households are currently paying for water and sanitation services. This is important as this will guide us in eliciting the WTP as discussed later (see the section ‘The CV scenario’). The results show that less than 10% of all sampled households of PLWHA are currently paying for water (Table 4). The highest percentage (17%) is observed in Khayelitsha, where households are paying on average R62.50/month (GBP2.86). Typically, households in rural settlements (Ukhahlamba) are paying the lowest amount (<ZAR10.00, GBP0.45). However, more than half of the sampled households agree they should pay for water. In Khayelitsha, more than 70% of the households believe they should pay compared to 55.3% in Groblersdal and 33.7% in Ukhahlamba. A significant proportion (24.5%) argued that it is the government's responsibility to pay for water.

Table 4

Proportion of households paying for water

  All areas (N = 485) Khayelitsha (N = 198) Ukhahlamba (N = 175) Groblersdal (N = 112) 
Proportion paying for water (%) 9.7 17.2 1.4 7.1 
Amount paid (ZAR/month) 58.90 62.50 9.50 43.60 
Should you pay for water? (Yes) (%) 55.1 73.2 33.7 56.3 
Government's responsibility to pay (%) 24.5 15.7 35.4 23.2 
  All areas (N = 485) Khayelitsha (N = 198) Ukhahlamba (N = 175) Groblersdal (N = 112) 
Proportion paying for water (%) 9.7 17.2 1.4 7.1 
Amount paid (ZAR/month) 58.90 62.50 9.50 43.60 
Should you pay for water? (Yes) (%) 55.1 73.2 33.7 56.3 
Government's responsibility to pay (%) 24.5 15.7 35.4 23.2 

METHODOLOGY

In order to understand how PLWHA would value improvements in water and sanitation provision, a contingent valuation method (CVM) was used to elicit the WTP for these public amenities. (WTP is the maximum amount of money an individual will pay in exchange for an improvement in circumstances.) CVM has become widely applied in water-related studies as an instrument to evaluate policy alternatives, set affordable tariffs, assess financial sustainability and design socially equitable subsidies (Mitchell & Carson 1989; Hutton 2001). In this study, empirical models based on single-bound probit and double-bound bivariate probit models are constructed as suggested by Haab & McConnell (2002), and shown in detail in Chapter 5 of their book. The constructed models are used to estimate the WTP measures/values underlying the benefit associated with improvements in water and sanitation provision as perceived by PLWHA.

A single-bound and/or double-bound dichotomous choice model can be constructed as follows: each respondent is presented with an initial bid price (t); based on the initial response (Yes/No), the respondent is presented with a follow-up bid which can be lower(higher) depending on whether the initial response is a ‘no’(‘yes’). A single-bound model is constructed on the basis of the ‘initial response’ only, while a double-bound takes into consideration both the ‘initial and follow-up’ responses. Following the approach of Haab & McConnell (2002), a random utility function for respondent j can be specified as: 
formula
1
where i=1 denotes the final improved state of water or sanitation that will prevail after the CV programme is implemented and i=0 denotes the current state. The determinants of the random utility function (uij) are: mj, which represents the jth respondent's disposable income; hj, which represents the k-dimensional vector of household characteristics and choice attributes (e.g., age, education, gender, region, etc.), and ɛij, which is a random unobserved component of the utility function unknown to the researcher.

Each respondent must answer a CV question asking whether or not he/she would be willing to pay a prescribed amount of money needed to improved the supply of municipal water and/or the sanitation service (see discussion below). The response is usually ‘yes’ or ‘no’, depending on the individual's perceptions about the proposed programme and his/her willingness and ability to pay the proposed initial price bid (tj). It is assumed that the respondent knows which choice maximizes his/her utility. For respondent j, who answers ‘yes’ to the CV choice question, this means that he/she perceives higher benefits under the proposed ‘improved’ programme compared to the currently existing programme.

The utility function can be cast in a linear functional form as follows: 
formula
2
The first component on the right-hand side (of Equation (2)) denotes the deterministic part, while the second component represents the random or stochastic part. The deterministic part can be re-expressed in a more specific linear functional form as: 
formula
3
where mj is discretionary income and hj is an m-dimensional vector of covariates related to individual j and αi is an m-dimensional vector of parameters. A CV question posed to respondent j, requires that he/she chooses between the proposed (improved) programme at the required cost/payment, t or opts for the continuation of the current (unimproved) status quo. For the improved CV scenario, the deterministic component (Equation (3)) incorporating the payment t becomes 
formula
4
where tj is the price bid offered to the jth respondent.
For the unimproved scenario (the status quo), the deterministic component remains as: 
formula
5
Considering Equations (4) and (5), the change in utility between the two states becomes: 
formula
6
Assuming the marginal utility of income is constant between the two states it must be and the corresponding utility difference becomes: 
formula
7
where .
The probit and bivariate probit models are constructed based on the ‘yes’/‘no’ responses to the CV choice questions. Using the linear specification defined in Equations (4) and (5), WTP can be determined as follows: 
formula
8
Using Equation (8), the WTP can solve and be estimated as: 
formula
9

Sampled households

A survey based on CVM was administered to a sample of PLWHA households drawn from three types of settlements – urban slum, peri-urban and rural settlements. With the help of the provincial Departments of Health, NGOs (e.g., Red Cross) and home-based caregivers a total of 485 households of PLWHA were purposively sampled. The sample was drawn from the three municipal districts of Khayelitsha, Ukhahlamba and Groblersdal located in the provinces of Western Cape, Eastern Cape and Limpopo, respectively. As shown in Table 5, a majority of people interviewed were women (73%) and more than half were also heads of households. For the entire sample, more than a third of the sampled households were drawn from slum settlements in Khayelitsha (37.1%), 33.1% from rural areas in Ukhahlamba and the rest (29.1%) from the peri-urban areas in Groblersdal.

Table 5

Household demographic profiles

  Sample Gender (%)
 
Household head (%)
 
Settlement 
Study area size (Ntype 
Khayelitsha 198 79.3 20.7 59.6 39.9 Urban slums 
Ukhahlamba 175 67.6 32.4 52.6 47.4 Rural 
Groblersdal 112 69.6 30.4 53.6 45.5 Peri-urban 
Total 485 72.6 27.0 55.5 43.7  
  Sample Gender (%)
 
Household head (%)
 
Settlement 
Study area size (Ntype 
Khayelitsha 198 79.3 20.7 59.6 39.9 Urban slums 
Ukhahlamba 175 67.6 32.4 52.6 47.4 Rural 
Groblersdal 112 69.6 30.4 53.6 45.5 Peri-urban 
Total 485 72.6 27.0 55.5 43.7  

F = female; M = male.

CVM

For the respondents to participate effectively in the proposed CV programme, we started by holding focus group meetings where key notions central to the CVM were introduced and discussed at length: e.g., the notion of ‘improvements’ in water and sanitation provision, ‘vote referendum’ and ‘payment vehicle’. Upon ensuring that the respondents have understood these key notions, we proceeded to elicit the ‘willingness to pay’ by PLWHA for improved provision of water and sanitation services (see discussion below).

Notions of ‘improvements’ in water and sanitation provision

According to UN-HABITAT (2008), ‘improved’ water provision means ‘reasonable access to a water supply from a household connection, a public standpipe, a borehole, a protected well, or protected spring or rain water connection. At least 20 litres per person per day must be available from a source within one kilometre of the user's dwelling’. For sanitation on the other hand, ‘improved’ provision means ‘access to a private or shared toilet connected to a public sewer or septic tank, or access to a private or shared pour-flush latrine, simple pit latrine or ventilated improved pit latrine’ (UN-HABITAT 2008).

To assist the respondents understanding of the notions of ‘improved and unimproved’ water and sanitation systems, these concepts were demonstrated using pictures and flipcharts showcasing improved/unimproved water sources and/or sanitation practices. For instance, an unprotected well in rural areas and water tanker-truck in slum settlements were illustrated as examples of unimproved water source. We further showed open pit latrines and the bucket system as examples of unimproved sanitation facilities.

The CV scenario

After ensuring that respondents understood the concepts of ‘improved versus unimproved’ water and sanitation provision, we cast the CV scenario in the form of a ‘vote referendum’ as follows:

Consider the following hypothetical but plausible scenario: residents in this community have raised serious concern against poor water provision by the local municipality. To improve the situation, the municipality intends to introduce mandatory water charges to all local residents – you included. Before introducing this legislation, the municipality will hold a ‘vote referendum’ where you are required to vote either for or against the legislation. Voting for the legislation is likely to see an improvement in water provision while voting against will maintain the status quo. How would you vote in this referendum?

The CV scenario is repeated, though with slight adjustments, for sanitation. Table 6 shows the voting pattern in this referendum. The results show that for all sampled sites, more PLWHA will ‘vote’ in favour of sanitation (80%) than for water (75%). In Groblersdal, for instance, a majority of PLWHA will overwhelmingly vote for sanitation (80%) relative to water (60%); while in other areas the voting pattern looks approximately the same.

Table 6

Results of water and sanitation ‘referendum bill’ votes

  Vote for water (Yes)
 
Vote for sanitation (Yes)
 
 N N 
All areas 362 74.5 385 79.4 
Khayelitsha 159 80.3 161 81.3 
Ukhahlamba 136 77.7 134 76.6 
Groblersdal 112 59.8 90 80.4 
  Vote for water (Yes)
 
Vote for sanitation (Yes)
 
 N N 
All areas 362 74.5 385 79.4 
Khayelitsha 159 80.3 161 81.3 
Ukhahlamba 136 77.7 134 76.6 
Groblersdal 112 59.8 90 80.4 

The next task is to determine the initial price bid (tj) necessary for eliciting responses underlying WTP for water and sanitation by PLWHA. In order to get credible responses, the starting price bid must be reasonably sound and as realistic as possible in order to minimize the ‘starting bid’ bias. Following meetings with local municipalities, we were able to determine (from the revenue and tariff departments) the monthly water charges that households in low income suburbs are paying on average – which was ZAR116.22/month (GBP5.33) including refuse collection. This figure was used as the starting bid for both water and sanitation across the three types of settlements – assuming that these amenities are separately charged. We were able to authenticate this figure using actual monthly water bills obtained from a few households living in similar settlements who are paying for water and sanitation. Armed with this information, we framed the CV payment question as follows: ‘You voted ‘yes’ in favour of the water bill, and suppose the local council charges you ZAR116 per month for water, would you be willing and able to pay this amount as your monthly water bill?’

This CV question is repeated for sanitation with the same starting bid (R116/month) price. Although in reality the monthly water bill is inclusive of sanitation charges, our approach was to treat payment of these amenities separately in line with the objectives of study. The elicited price bids for the perceived improvements in water and sanitation provision are shown in Table 7. The results show that on average the elicited bids for sanitation were higher (R138) than for water (R135). In particular, price bids were highest in Khayelitsha for both water (R160) and sanitation (R165) compared to other areas.

Table 7

Water and sanitation price bids (/month)

  Water
 
Sanitation
 
 Initial Yes
 
Initial No
 
Initial Yes
 
Initial No
 
 N Upper price bid (ZAR) N Lower price bid (ZAR) N Upper price bid (ZAR) N Lower price bid (ZAR) 
 253 136 206 68 290 138 218 78 
Khayelitsha 94 160 59 81 104 165 55 81 
Ukhahlamba 92 126 80 66 96 129 73 70 
Groblersdal 67 114 67 60 90 117 90 83 
  Water
 
Sanitation
 
 Initial Yes
 
Initial No
 
Initial Yes
 
Initial No
 
 N Upper price bid (ZAR) N Lower price bid (ZAR) N Upper price bid (ZAR) N Lower price bid (ZAR) 
 253 136 206 68 290 138 218 78 
Khayelitsha 94 160 59 81 104 165 55 81 
Ukhahlamba 92 126 80 66 96 129 73 70 
Groblersdal 67 114 67 60 90 117 90 83 

ESTIMATED REGRESSION RESULTS

Based on the theoretical models developed earlier, linear random utility models are estimated using probit (single-bound) and bivariate probit (double-bound) model specifications. Responses to CV question are coded 1/0 for yes/no and specified as dependent variable. The covariates include such variables as gender, age, education level, household size, household income and regional-dummies. Table 8 shows the definition of the variables and their respective means.

Table 8

Variable description

Variable Description Mean/proportion 
Gender Sex of the household – 1 if female; 0 otherwise 0.73 
Mstatus Marital status – 1 if married or staying with partner; 0 otherwise 0.75 
Educ Level of education – 1 if household attained high school; 0 otherwise 0.41 
Hhead Household head – 1 if respondent is female; 0 otherwise 0.55 
Age Age of household 38 
Hsize Size of the household 
Income* Average disposable monthly income 1,123 (ZAR) 
WaterBid Average elicited price bid for water 72 (ZAR) 
SanitBid Average elicited price bid for sanitation 89 (ZAR) 
Region1 Regional dummy variable if district is 1 (=Khayelitsha) – 
Region3 Regional dummy variable if district is 2 (=Groblersdal) – 
Variable Description Mean/proportion 
Gender Sex of the household – 1 if female; 0 otherwise 0.73 
Mstatus Marital status – 1 if married or staying with partner; 0 otherwise 0.75 
Educ Level of education – 1 if household attained high school; 0 otherwise 0.41 
Hhead Household head – 1 if respondent is female; 0 otherwise 0.55 
Age Age of household 38 
Hsize Size of the household 
Income* Average disposable monthly income 1,123 (ZAR) 
WaterBid Average elicited price bid for water 72 (ZAR) 
SanitBid Average elicited price bid for sanitation 89 (ZAR) 
Region1 Regional dummy variable if district is 1 (=Khayelitsha) – 
Region3 Regional dummy variable if district is 2 (=Groblersdal) – 

*Government provides financial assistance to PLWHA under temporary ‘social disability grant’.

The regression results based on the probit and bivariate probit models are shown in Table 9. The results show that a number of variables are statistically significant at 5% level, and signs are generally robust across the two models. For instance, hsize variable shows a negative coefficient (for both models) underlying that the probability that a household will answer ‘yes’ to a CV question asking the respondent's WTP for water decreases as size of household increases. In contrast, for sanitation models on the other hand, the hsize variable is positive, perhaps underlying the fact that a household is more willing to pay for sanitation as the size of the household increases, with other factors constant. This makes intuitive sense, as larger sanitation facilities become necessary and more convenient as the household size increases.

Table 9

Estimated regression results for probit and bivariate probit models

  Probit model (SB)
 
Bivariate probit model (DB)
 
Variable Water coefficient Sanitation coefficient Water coefficient Sanitation coefficient 
Constant −0.1663 −0.0646 −0.0896 0.1095 
(0.73) (0.72) (0.46) (0.47) 
Gender 0.3094 0.2728 0.2400 0.2002 
(0.29) (0.28) (0.18) (0.18)* 
Mstatus −0.4759 −0.4048 −0.4269 −0.495 
(0.29)* (0.29)* (0.17)* (0.18)* 
Age 0.0022 −0.0034 0.0028 −0.0019 
(0.01) (0.01) (0.00) (0.01) 
Hhead −0.0008 −0.1144 0.0488 −0.1085 
(0.25) (0.28) (0.17) (0.18) 
Hsize −0.0016 0.0275 −0.0005 0.0302 
(0.05) (0.06) (0.03) (0.03) 
Educ 0.1755 0.2373 0.1356 0.2930 
(0.27) (0.25) (0.16) (0.16) 
Income_t 0.0003 0.0004 0.0002 0.0004 
(0.00)* (0.00)* (0.00)* (0.00)* 
Region1 0.1065 −0.1192 0.1345 −0.0257 
(0.27) (0.28) (0.17) (0.18) 
Region3 −0.7591 −0.0646 −0.7263 −0.0288 
(0.36) (0.32) (0.21) (0.22) 
  Probit model (SB)
 
Bivariate probit model (DB)
 
Variable Water coefficient Sanitation coefficient Water coefficient Sanitation coefficient 
Constant −0.1663 −0.0646 −0.0896 0.1095 
(0.73) (0.72) (0.46) (0.47) 
Gender 0.3094 0.2728 0.2400 0.2002 
(0.29) (0.28) (0.18) (0.18)* 
Mstatus −0.4759 −0.4048 −0.4269 −0.495 
(0.29)* (0.29)* (0.17)* (0.18)* 
Age 0.0022 −0.0034 0.0028 −0.0019 
(0.01) (0.01) (0.00) (0.01) 
Hhead −0.0008 −0.1144 0.0488 −0.1085 
(0.25) (0.28) (0.17) (0.18) 
Hsize −0.0016 0.0275 −0.0005 0.0302 
(0.05) (0.06) (0.03) (0.03) 
Educ 0.1755 0.2373 0.1356 0.2930 
(0.27) (0.25) (0.16) (0.16) 
Income_t 0.0003 0.0004 0.0002 0.0004 
(0.00)* (0.00)* (0.00)* (0.00)* 
Region1 0.1065 −0.1192 0.1345 −0.0257 
(0.27) (0.28) (0.17) (0.18) 
Region3 −0.7591 −0.0646 −0.7263 −0.0288 
(0.36) (0.32) (0.21) (0.22) 

*Statistically significant at 5% level; Standard error in parenthesis.

SB = single bound; DB = double bound.

The main task of the study is to determine the WTP for improvements in water and sanitation provision by PLWHA. The estimated WTP values based on the probit (SB) and bivariate probit (DB) models are shown in Table 10. Overall, the results demonstrate higher WTP for sanitation (ZAR450/month, GBP20.67) by PLWHA compared to water (ZAR410/month, GBP18.83) except the SB model for Groblersdal. In particular, the DB model shows PLWHA in slum settlements having the highest WTP for sanitation (R553/month, GBP25.38), compared to other types of settlements – rural (R500/month, GBP22.95) and peri-urban (R455/month, GBP20.88) areas. The results help to underscore some important implications: (a) by demonstrating higher WTP for sanitation than water, this implies PLWHA are facing greater sanitation challenges relative to water; (b) higher WTP for sanitation implies that PLWHA perceive greater benefits from improvements in sanitation; (c) in particular, PLWHA in slum settlements face the greatest sanitation insecurity compared with counterparts living in rural and peri-urban areas. The often violent protests against the poor system of sanitation that are predominant in slum settlements in South Africa (e.g., Makaza, Khayelitsha) bear evidence to the severe challenges these households face.

Table 10

Estimated WTP (in ZAR) for water and sanitation by PLWHA

  WTP (single bound)
 
WTP (double bound)
 
Region Type of settlement Water Sanitation Water Sanitation 
Khayelitsha Slum 428.60 448.35 496.20 552.69 
Ukhahlamba Rural 398.62 410.21 457.99 500.24 
Groblersdal Peri-urban 359.88 351.31 425.56 455.82 
Average  395.70 403.29 459.90 502.92 
  WTP (single bound)
 
WTP (double bound)
 
Region Type of settlement Water Sanitation Water Sanitation 
Khayelitsha Slum 428.60 448.35 496.20 552.69 
Ukhahlamba Rural 398.62 410.21 457.99 500.24 
Groblersdal Peri-urban 359.88 351.31 425.56 455.82 
Average  395.70 403.29 459.90 502.92 

CONCLUSION

HIV and AIDS is not a water-borne disease and neither is it caused by unhygienic or poor sanitation conditions. Yet, ostensibly, the disease is linked to issues of water, sanitation and hygiene (WASH). PLWHA are likely to die after succumbing to one or two opportunistic diseases transmitted through contaminated water, and unhygienic and unsanitary practices (e.g., diarrhoea, cholera and typhoid). In South Africa, policymakers are largely preoccupied with treatment and/or prevention of HIV and AIDs, with little attention paid to other important socio-economic programmes that can positively impact the lives of PLWHA. WASH is one sector that can play a vital role in the fight against HIV and AIDs. This study has demonstrated the perceptions by PLWHA regarding their WTP for the improvements in water and sanitation that are so central in their battle for survival. The results demonstrated that PLWHA are likely to derive greater benefits from improvements in sanitation services relative to water. This is particularly true for households living in slum settlements, where a vast majority faces limited access and/or severe constraints to safe sanitation. It is therefore imperative for municipalities to prioritize sanitation provision in slum settlements, as this is likely to make a significant contribution to the livelihoods of PLWHA.

REFERENCES

REFERENCES
AIDS Foundation
2010
HIV/AIDS in South Africa. www.aids.org.za
(
accessed April 2015
).
Barnett
T.
Whiteside
A.
2002
AIDS in the 21st Century: Disease and Globalization
.
Palgrave McMillan
,
London
,
UK
.
Barry
M.
Ruther
H.
2005
Data collection techniques for informal settlement upgrade in Cape Town, South Africa
.
URISA Journal
17
(
1
),
43
57
.
Department of Water Affairs and Forestry
1995
National Sanitation Policy. https://www.dwa.gov.za/Documents/Policies/NationalSanitationPolicy.pdf (accessed 15 November 2014)
.
Haab
T. C.
McConnell
K. E.
2002
Valuing Environmental and Natural Resources, The Econometrics of Non Market Valuation
.
Edward Elgar
,
Cheltenham
,
UK
.
Hutton
G.
2001
Economic evaluation and priority setting in water and sanitation interventions
. In:
World Health Organization (WHO). Water Quality: Guidelines, Standards and Health
(
Fewtrell
L.
Bartram
J.
, eds).
IWA Publishing
,
London
,
UK
.
Kamminga
E.
Wegelin-Schuringa
M.
2006
HIV and AIDS and Water, Sanitation and Hygiene
.
Thematic overview paper. IRC International Water and Sanitation Centre
,
Delft
,
the Netherlands
.
Kgalushi
R.
Smits
S.
Eales
K.
2004
People Living with HIV and AIDS in a Context of Rural Poverty: Importance of Water and Sanitation Services and Hygiene Education. Mvula Trust, Johannesburg
.
South Africa and IRC
,
Delft
,
the Netherlands. www.irc.nl/page/10382 (accessed 30 December 2014)
.
Makaudze
E.
Gelles
G.
2015
The challenges of providing water and sanitation to urban slum settlements in South Africa
. In:
Understanding and Managing Urban Water in Transition
(
Grafton
Q.
Daniell
A.
Nauges
C.
Rinaudo
J.
Chan
W.
(eds).
Global Issues in Water Policy, Vol 15. Springer
,
Dordrecht
,
the Netherlands
,
Chapter 4,
pp.
123
155
.
Makaudze
E.
du Preez
M.
Potgieter
N.
2011
How does the HIV and AIDS Epidemic in South Africa Impact on the Water, Sanitation and Hygiene Sectors
?
Water Research Commission
,
Pretoria
,
South Africa
.
Mitchell
R. C.
Carson
R. T.
1989
Using Surveys to Value Public Goods: The Contingent Valuation Method
.
Resources for the Future
,
Washington DC
,
USA
.
Research Reports
2012
South Africa Informal Settlement Status
.
Housing Development Agency
,
Johannesburg
,
South Africa
.
Shisana
O.
Rehle
T.
Simbayi
L. C.
Zuma
K.
Jooste
S.
Zungu
N.
Labadarios
D.
Onoya
D.
2014
South African National HIV Prevalence, Incidence and Behaviour Survey, 2012
.
Human Science Research Council Press
,
Cape Town
,
South Africa
.
South African Human Right Commission
2014
Report on right to sufficient water and decent sanitation in South Africa. http://www.sahrc.org.za/home/FINAL/21/files/Final4thProof4March-WaterSanitationlowres.pdf (accessed 20 January 2015)
.
StatsSA
2010
Mid-year population estimates. Statistical release P0302. Statistics South Africa. http://www.statssa.gov.za/publications/P0302/P03022008.pdf
(
accessed 11 December 2015
).
Tomlinson
R.
2006
Impacts of HIV and AIDS at the local level in South Africa. Paper prepared for Urban Management Program, UN-Habitat. https://www.wits.ac.za/media/migration/files/40582181Impacts20of20HIV20AIDS20Local20Level2020SA.pdf (accessed 10 October 2014)
.
UNAIDS
2007
AIDS epidemic update. Sub-Saharan Africa. Regional update. http://data.unaids.org/pub/Report/2008/jc1526_epibriefs_ssafrica_en.pdf
(
accessed 9 February 2009
).
UN-HABITAT
2008
HIV and AIDS Checklist for Water and Sanitation Projects
.
United Nations Human Settlements Programme
,
Nairobi
,
Kenya
.