Estimating water demand for domestic use in rural ... - Water Policy

Water Policy 9 (2007) 513–528

Estimating water demand for domestic use in rural South Africa in the absence of price information B. M. Bandab, S. Farolfia and R. M. Hassanc a

Corresponding author. CIRAD, UMR G-Eau and Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa. Tel: þþ 27 420 4659, Fax: þ þ27 12 420 4958. E-mail: [email protected]

b

Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria and faculty member in the Department of Economics, University of Malawi, Zomba, Malawi c

Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa Received 18 August 2005; accepted in revised form 6 April 2006

Abstract The paper applies the travel cost method (TCM) to estimate the value that rural households in the Steelpoort subbasin of South Africa place on river and collective tap water. While the TCM calculations are based on the opportunity cost of the time household members spend on water collection, the resulting welfare values are close in magnitude to the estimates obtained using a contingent valuation method (CVM) on the same sample. The paper shows that in the absence of price data, the TCM provides satisfactory estimates of benefits where direct estimation of demand elasticity would otherwise be impossible. According to both methods, households consuming river water attribute higher value to the resource than collective tap users. The income elasticity of the trip generating function is much higher than that of the opportunity cost of time (price), implying that household’s water use behaviour would be more responsive to factors affecting household income than to price incentives. Comparing the estimated values with actual operating and maintenance cost of water provision in the study area suggests that policies promoting cost-covering water tariffs have a potential to succeed. Keywords: Contingent valuation; Decision support; Domestic uses; Source of water; Travel cost; Water demand; Water quality; Water value

1. Introduction The purpose of this paper is twofold. First, applying the travel cost method (TCM) it estimates water demand where there is no price data, or when there is insufficient variation in prices of water owing to the design of the supply scheme. Second, the reliability of the adopted empirical method is assessed by doi: 10.2166/wp.2007.023 q IWA Publishing 2007

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B. M. Banda et al. / Water Policy 9 (2007) 513–528

comparing the resulting welfare estimates with those from a contingent valuation model applied to the same sample. To achieve these two goals, household survey data on domestic water use were collected together with household socioeconomic characteristics that are assumed to influence water demand in rural Steelpoort, a sub-basin of the Olifants water management area in South Africa. The paper proceeds as follows. Section 2 is a general statement of the domestic water demand analysis problem in rural areas of developing countries. Section 3 provides a brief background of the water demand situation in the Steelpoort sub-basin (SPSB). Section 4 discusses the theoretical framework for estimating water demand with limited data. In Section 5 an empirical travel cost model for the SPSB is specified. Section 6 describes the survey and data. The empirical results are presented alongside welfare estimates in Section 7. The conclusions and some policy suggestions are provided in Section 8.

2. Statement of the problem Domestic water demand management is an important policy concern, especially when the supply of water is fairly limited. Demand management in resource constrained countries focuses on the effectiveness of alternative policy instruments in improving efficiency and equity for domestic water users (Renwick & Archibald, 1998). Analysing water demand behaviour generates relevant policy information on household water demand determinants including how household characteristics influence responsiveness of water demand to price and non-price factors (Strand & Walker, 2005). Such knowledge improves the policy maker’s ability to determine the most appropriate instruments to use in order to achieve the broad goals of public policy, including efficiency and equity (Billings & Agthe, 1980). Demand models are used in the estimation of benefits or values that households place on a resource that they consume. The most common welfare index is the consumer surplus measured as the household’s willingness to pay for successive units of a good. These welfare measures can be used to derive the appropriate tax or subsidy required when a pricing strategy is used to curtail demand (Binger & Hoffman, 1998). Although the demand-side management is conceptually appealing, direct estimation of water demand parameters is not always possible. Often information required to allow estimation of demand is lacking. This is usually the case in rural areas of developing countries, where water is not sold and quantities used or supplied are not metered. The non-existence of price and quantity data prevents direct estimation of demand behaviour for households fetching water from public sources including rivers and lakes. In such cases, other resource valuation approaches such as the contingent valuation method (CVM), travel cost (TCM) or hedonic pricing are adopted to characterise and measure demand parameters (Whittington, 1998). Water demand management in South Africa is required to balance supply and demand. Many regions of the country are either arid or receive low mean precipitation. As a result, areas where water is available are placed under high pressure to provide water for all other regions (DWAF, 2004). Water abundant areas also suffer from serious inequality in distribution of the resource. Before the National Water Act (NWA) 36 of 1998, water rights were based on land ownership. As a result, prior to 1994 commercial agricultural farms, mines and industries enjoyed priority in accessing water over rural households. With democratic dispensation, the number of households demanding better quality and quantity of water has increased tremendously. Annual domestic water use is expected to reach between 11.5 million and 22 million m3 by 2010 from about 5.6 million m3 per year (DWAF, 1999).


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In many parts of South Africa, especially in rural areas, households fetch water directly from public sources at no cost other than their time or receive water from collective village taps where a fixed rate is charged. In both cases price data necessary for measuring and studying water demand behaviour are either missing or do not contain sufficient information to support the analysis. 3. Water demand situation in Steelpoort sub-basin (SPSB) SPSB is one of the five sub-basins of the Olifants river, covering an area of 7,139 km2 (13% of the Olifants) (Stimie et al., 2001). It is situated in the northeast of South Africa, extending over the border of Mpumalanga and Northern Province (Figure 1). Using the South Africa Explorer software and the 2001 Stats SA Census data, the total population in the sub-basin was estimated at 293,225 people (59,780 households). Water users in Steelpoort include irrigation farms (65 million m3 per year), mines and quarries (12 million m3 per year), industries and domestic users (13 million m3 per year)1. Competition and potential for conflict exists in the SPSB area between irrigation farms and mines (Farolfi & Perret, 2002). Rural communities generally face poor service delivery and there is a large backlog of households with no water infrastructure. Rural households are also generally unaware of the establishment of a catchment management agency (CMA), through which their water quality and quantity concerns would be addressed (Stimie et al., 2001). The 2001 Census indicates that 39% of the rural households in the sub-basin use a collective tap, while 27% of the households get their water from rivers and streams (Statistics South Africa, 2004). For 64.4% of rural collective tap users, the water facility is located over 200 m from their dwellings. Households in rural areas of the three municipalities of Greater Tubatse, Greater Groblersdal, and Makhuduthamaga are located in former Bantustans2. These areas have the lowest rates of employment, literacy and per capita income in the SPSB. According to the 2001 Census, about 82% of the rural households in SPSB earn no income from formal employment, while only 5.5% of the households have monthly incomes above R8003 (Statistics South Africa, 2004). Both access to and quality of water are problems facing rural households in the basin. The sub-basin has a runoff of between 369 million m3and 397.9 million m3 per year, which, added to a storage capacity of about 5% of the streamflow, is apparently enough to satisfy the water demand of various users (DWAF, 1991; 1999). Nevertheless, DWAF (1999) found that people in rural areas, especially those in the Northern Province have difficulties accessing water. DWAF (2003) also observed that increased levels of contaminants from industrial, agricultural, mining and residential effluents threaten surface and ground water. 4. Theoretical framework This study adopts the travel cost method (TCM) to estimate water demand for domestic uses in SPSB. Beginning with Hotelling (1947) and later Trice & Wood (1958) and Clawson (1959), the travel cost 1

These water use figures are for years between 1991 and 1997 (Hassan & Farolfi, 2005). Formerly areas for “Africans-only” established under the Group Areas Act of 1950 and consolidated by the Bantu Authorities Act of 1951. 3 1 South African Rand ¼ 0.163 US Dollars (10 April 2006). 2

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GREATER TUBATSE Burgersfort Steelpoort

MA

KH

UD

UT HA MA GA

SOUTH AFRICA WATER MANAGEMENT AREAS

Lydemburg GREATER GROBLERSDAL

THABA CHWEU

HIGHLANDS

Belfast

Fig. 1. The Steelpoort sub-basin: Municipalities and main urban centres.

approach has been applied to the problem of estimating demand for recreation services (ecotourism). The essence of TCM stems from the need to travel to a site to enjoy its service. The inference from TCM is based on the assumption that demand for recreation depends on the cost incurred to travel to the tourism site to enjoy the environmental amenities anticipated from recreation at the visited area (Haab & McConnell, 2002). Travel cost models are synonymous with empirical models of demand for environmental services using observations of the cost of accessing an environmental site. TCM as applied to a recreation demand model assumes that the number of trips a person makes for recreation purposes in a season depends on the price of the trip, the quality of the recreation site, their income and other socioeconomic characteristics of that person. Recreation demand models are categorised into two types: (1) single site models and (2) multiple site models. Single site models use aggregate data from zones where visits per capita from each zone are regressed to travel cost from the zone to the site. Other studies use individual observations as a unit of measurement rather than the zonal aggregates. Multiple site models generalise single site models by estimating demand for a set of sites over a period of time (Haab & McConnell, 2002). There are several advantages to using the travel cost approach in valuing natural resources. First, the TCM provides estimates of use value of the natural resources upon which recreation services are derived. Second, the TCM provides a framework for estimating the value of the unique characteristics of a site. For instance, multiple site models capture the differences in use and non-use values of various sites in terms of the differences in environmental quality. When one works with heterogeneous sites, the


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price of each site can be decomposed into a set of implicit prices for each environmental attribute it possesses (Font, 2000). Third, the absence of information, especially prices, does not prevent demand estimation. Usually, information sets can be imputed from data collected during the survey and from secondary sources. For example, when the use of time is measured together with the occupation of the respondents, time can be valued in monetary terms using labour market prices. Various studies assume an opportunity cost of time equal to or below the market wage rate (Smith et al., 1983; Haab & McConnell, 2002). Although TCM has traditionally been used in the estimation of recreation demand, the method can also be used in other behavioural models analysing household allocation of time. The versatility of the TCM could be exploited to analyse situations where the opportunity cost of time spent accessing resources is used to estimate welfare measures associated with the resource in question. In an ideal world, welfare measures from a CVM should be comparable to welfare estimates obtained from an indirect method such as the TCM. CVM welfare measures are sensitive to the design of survey and questions, formulation of scenarios and the proportion of the sample who are not willing to pay. In addition the CVM welfare measure assumes that the willingness to pay (WTP) bids are chosen independently of the other exogenous variables (Anand, 2000; Whittington et al., 1990b; Ryan et al., 2000). On the other hand, the TCM welfare measure is sensitive to the specification of the travel cost variable as a proxy for price in the demand equation (Smith et al., 1983). In the next section, the TCM model is specified to estimate the demand for rural domestic water where there are no nominal prices for water. The behavioural aspects of water demand in rural SPSB are inferred from the statistical realisation of household water use indicators including water consumption per capita, the number and length of trips taken when collecting water, the role of household members in fetching water, the type of containers used and the source of water. The model will identify those social aspects that are important in assessing the benefits of investing in infrastructure aimed at improving access to water and also for to categorise the stakeholders by the type of benefits they stand to gain from improving water infrastructure.

5. Empirical travel cost model for the study area Households in rural SPSB use river water and communal standpipes for drinking, cooking, bathing, washing and other domestic water uses. While metering is a common pricing mechanism in piped water systems in urban areas, the rural SPSB setting is different because water demand has no corresponding price data but varies with the time taken for a trip to a standpipe or river, the household income and the family members involved in collecting water4. The time spent fetching water and the environmental hazards from the quality of water sources are assumed to be the main determinants of household water demand. In addition water demand depends on the contribution of different family members to the effort of collecting water. The role of women and 4

Some of the rural households in Steelpoort pay to get water from collective taps. However the amount they pay is based on a fixed flat rate and not charged according to the amount used. As a result, there is no significant statistical correlation between the price paid and the amount of water collected.

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children is of particular significance in most African communities. Women are traditionally responsible for providing the rural family’s water needs in many African countries. Women endure the ill effects of water scarcity and degradation as they try to provide for the needs of their family members (Simpson-Hebert, 1992; Schneiderman & Reddock, 2004). Children are also unduly affected by the nature of social responsibilities in cultural settings where they are involved in collecting water. Water demand in rural Steelpoort is specified for a rural household i, choosing the number of trips tij to take per month to collect water from the preferred source5. The number of trips is related directly to the amount of water a household uses in each period and is influenced by a number of factors such as the roundtrip time taken to travel to the source of water, the number of family members involved in collecting water, the income of the household, availability of alternative sources of water and quality of water. The time spent on collecting water has an opportunity cost equal to the wage that an individual (with a particular skill) could get on the local labour market. Since the household has income from other sources including work-related remuneration, the budget constraint for the household is written as follows6: H X Z i # Mi þ tij hij wij ð1Þ j¼1

where H is household size, tij is the number of trips taken by member j, hij is the time taken per trip to fetch water, Z is a bundle of market goods, the consumption of which can be supported by household income M and wij is the wage rate for individual j’s time. Equation (1) addresses the issue of variability of wages for individuals depending on the quality of labour. It also says that a household could increase its consumption of market goods by working the extra hours committed to fetching water. Following Becker (1965), the allocation of time to household tasks and labour market activities is constrained by the maximum available time7: X ð2Þ tij hij þ Li ¼ T i j

where Li is household i’s participation time in the labour market and the other variables are as defined above. Recreation demand offers a classic application of trip generating functions, the related travel cost and the opportunity cost of time. Unlike the classic application in which a person seeking recreation selects the sites to visit, most households in rural Steelpoort have one major source of water8. The classic trip generating function is modified to specify demand in terms of number of trips to the water source as influenced by changes in one or more independent variables. It is assumed that demand for water varies 5

If j is the index of household members involved in collecting water, the total number of trips per household is a sum of trips of all members. 6 This generalised model is adapted from Haab & McConnell (2002). 7 Leisure is not reflected in this formulation because the maximum available time T only considers an 8-hour working day. When leisure is filtered in, the opportunity cost of leisure would have to be valued by calculating the marginal rate of substitution between labour income and the consumption of Z in a utility maximising framework. 8 Note that some households in the sub-basin resort to other sources of water for other non-essential domestic water uses. The 2001 Census data identify boreholes, spring, and stagnant water as other sources of water for rural Steelpoort households (Statistics South Africa, 2004). Strand & Walker (2005) refer to all non-metered facilities as “coping” water sources. The random utility framework could capture the substitution among alternative sources of water induced by the declining marginal utility of visits to a single source of water. However, we do not use the random utility framework because most households reported that they had only one source of water.


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directly with the number of trips, but indirectly with factors such as the number of containers, household members involved in fetching water and the use of wheelbarrows, all of which affect the volume of water collected on a trip. Following Englin & Shonkwiler (1995) the integer values for the number of trips could be modelled as a latent variable of the form: tij ¼ f ðpi* ; S*i ; q * ; bÞ þ ni

ð3Þ

where pi* is the average opportunity cost of time for each household per trip as defined below, S*i is a vector of household characteristics, q * is a vector of perceived quality attributes of the water source, b is a vector of unknown parameters and ni is an error term: PH j¼1 t ij hij wij * ð4Þ pi ¼ PH j¼1 tij hij Since the number of trips taken by members of a household to collect water is a random, non-negative integer value with a non-negative mean, the number of trips can be modelled using a Poisson distribution. The probability of observing a non-negative number of trips tijis given by the Poisson probability density function: e 2li lni ; n ¼ 0; 1; 2; . . . ð5Þ n! where li is the mean and variance of the distribution assumed to be positive for a given vector of explanatory variables Vi, and a set of parameters b. The sample likelihood function in terms of observed number of trips per household is given by the probability of observing that number of trips: Pðtij ¼ nÞ ¼

N Y exp ð2exp ðV i bÞÞexp ððV i bÞti Þ LðbjV; ti Þ ¼ ti ! i¼1

ð6Þ

The expected number of trips is an equivalent representation of the demand for water since each household reports the amount of water that it collects on each trip. In other words, observing the number of trips a household takes to fetch water is the same as observing the household’s demand for water. From the foregoing, the demand for water is an exponential function of the variables determining the expected number of trips, li: 0

li ¼ e ðb VÞ

ð7Þ

Alternatively, the demand curve can be expressed as a semi-logarithmic function: 0 0 ln li ¼ bw pi* þ bs S * þ bq q * ( V 0 b

ð8Þ

Linearisation of the demand function allows estimation of the total consumer surplus by integrating the demand function over the relevant price range. This gives the value of water for collective tap or river water on the assumption that water has a cost in terms of the money value of time spent collecting water. Since demand is an exponential function of the opportunity cost of time, the integral of the function over the relevant price range gives the consumer surplus. Ignoring all variables except the opportunity cost of

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time from equation (8) and integrating directly gives: ðx li CS ¼ li dW * ¼ 0 bw

ð9Þ

v

The application of the TCM to valuation of rural water resources is appropriate because the model does not suffer from the same theoretical limitations as ecotourism models. In particular, when the proportion of non-participants increases with the distance to the recreation site, the ecotourism model would have the problem of a truncated sample with possible exaggerated consumer surplus estimates (Smith et al., 1983; Shaw, 1988). In this particular application, there is only one purpose for a trip to the source of water and therefore the spatial limitation of multiple objectives of a trip does not arise. Further, the problem of truncated samples does not arise because in a given group of households there will be no household which does not participate in the activity of collecting water. The concern about overdispersion of the dependent variable is left to be established empirically. 6. Data collection and estimation procedures The data for this study come from a household survey conducted in 2003 for the valuation of water in the SPSB. The target population for the study was defined as the households in the SPSB using water for domestic purposes. This population is distributed in 43 wards of five municipalities, namely Greater Groblersdal, Greater Tubatse, Highlands, Makuduthamaga and Thaba Chweu. The original sample had 375 households (315 rural and 60 urban) obtained through a stratified random sampling (Banda et al., 2004). In terms of water use, four different sources of domestic water were identified: private tap, collective tap, river water and vending water. The sub-sample for this study was defined as all households from the SPSB sample except those with private taps. This gave a sample of 239 households (230 rural and 9 urban) made up from 163 households using collective taps, 71 using river water and five households buying water from vendors. Each household provided data on income, family size, occupation and gender of family head. Information was also collected on various water use indicators including the average amount of water consumed each month, the source of water, the number of times water is accessed in a month, the payment for water supply per month (if any), the member(s) of the household involved in collecting water, the perception of quality of water and whether or not the family would be willing to pay for increased water availability and for improved water quality. The information on water use was mapped to the socioeconomic characteristics of the household including income, household size, education level of the household head and location in order to explore behavioural patterns of water use. Table 1 provides a description of the variables used in the analysis. Whittington et al. (1990a) used discrete choice random utility models to estimate upper and lower bounds of the value of time spent collecting water. Households in their sample were choosing between three sources of water: a vendor, a kiosk and an open well. In estimating the value of time, two approaches were adopted. The first considered the actual or contingent prices of water and the distance from the various sources, as the only additive components of utility. The second also considered the price and distance from the source and incorporated household characteristics. In both cases, the value of time was given by the marginal rate of substitution (of utility) between the time spent collecting water and the money paid for water.


521

Table 1. Descriptive statistics on variables included in the Steelpoort water demand analysis. Variable

Definition

Obs

Mean

Sd s

Trips H Y Swr

Number of trips per month Household size (number of members) Monthly household head income (R/month) Source of water: 1 ¼ private tap, 2 ¼ collective tap, 3 ¼ river, 4 ¼ vending water Person(s) who fetch water: 1 ¼ husband, 2 ¼ wife, 3 ¼ husband & wife, 4 ¼ husband, wife & children, 5 ¼ wife &children, 6 ¼ children, 7 ¼ other Household water consumption (m3/month) Round trip distance to source of water (min) Number of containers per trip Volume of container (litres) Opportunity cost of time per trip (R/trip) 1 ¼ Female-headed household where women and children collect water, 0 ¼ other Household’s perception of water quality (1 ¼ poor quality, 0 ¼ other) Means of carrying water: 1 ¼ wheelbarrow, 0 ¼ other

236 233 239 239

67.2 7.2 1026.6

43.1 3.0 1054.1

Wurpf

Wurau Wurd2 Wurc Vol p*i Femchld Poor Wheelbar

Min

Max

4.0 1.0 0.0 2.0

280.0 23.0 6000.0 4.0

1.0

7.0

0.0 2.0 1.0 10.0 0.0 0.0

45.0 240.0 16.0 30.0 6.6 1.0

234

0.0

1.0

239

0.0

1.0

236

239 204 239 239 204 239

5.1 45.3 3.1 22.8 0.7

5.3 46.2 2.1 4.8 1.1

The multiplicity of water sources in the SPSB would at first glance suggest that a random utility framework consistent with the double-hurdle travel cost model would be an appropriate empirical model. However, the model specified below would not trade-off the quality of water between the different water sources since our data was only collected information on a household’s primary source of water. The design of the survey also did not take into account the possibility that households might want to choose between the different sources of water given changes in contingent circumstances. However, we shall assume that a household chooses the number of trips it takes to the primary source of water in a utility maximising consistent framework. Following Haab & McConnell (2002), the following empirical model was specified to estimate equation (9). The coefficients of the log-linear model are elasticities and the error term is contemporaneously independent (Strand & Walker, 2005): ln trips ¼ b0 þ b1 ln p*i þ b2 ln Y þ b3 ln H þ b4 Wheelbar þ b5 Poor þ m

ð10Þ

Welfare measures are estimated using equation (9). These values are compared with values obtained using a CVM on the same sample found in Banda et al. (2004). The results are reported and discussed in the following sections. Empirical studies assume an opportunity cost of time lower than the market wage rate, while others take the local wage rate of unskilled labour as a proxy (van Zyl et al., 2000). In this study, the opportunity cost of time is defined as the minimum daily wage for agriculture (R20/day) in the study area. This wage rate also took into consideration that among the sampled households about 41.84% are unemployed while 27.62% are receiving state pensions. Given this distribution of employment, the study assumed that the opportunity cost of time for an adult male is 50% of the market agricultural wage, while an adult woman’s

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time was compensated at the rate of 25% of the market wage9. Children were also assumed to contribute half of the women’s effort and so were compensated at 12.5% of the market wage rate.

7. Results 7.1. Socioeconomic characteristics of water demand The majority of the surveyed households (68.2%) get water from collective taps while about a third of the households obtain water from rivers. For most households (65.2%), the chore of collecting water lies in the hands of women and children, while about 30% of the households leave the task entirely to children. Wheelbarrows are the main means of carrying water for most households (75%) while about a quarter of the respondents walk, carrying buckets. Only three households (1% of the sample) use a car for carrying water. The average time taken on a round trip by female-headed households (31% of the sampled households) does not significantly differ from the sample mean of 45 min. For the entire sample, the average amount of water consumed each month per household is 5.1 m3, while river water users consume slightly more, on average (5.7 m3/month). However, there are clear differences in the volume of water collected depending on the person(s) who fetch water. For all households and for collective tap users, the category “wife and children” collect the highest amount of water (6.2 m3/month), while the whole family – “husband, wife & children” – collects the highest amount of water (8.5 m3/month) for river water users. On average, children alone collect more water (4.7 m3/month) than the wife (2.9 m3/month) or husband alone (2.7 m3/month). About 42% of the household heads are unemployed, while 28% depend on state pensions. Mining and industrial workers head about 10% of the households, while self-employed individuals head 9% of the households. Farm workers and public workers head 5% and 4% of the households, respectively. Formal employment in the public sector yields the highest average income (R2570/month) followed by industries (R2023/month), mining (R1794/month) and self-employment (R1452/month). As expected, the unemployed are on the lower tier of income distribution (R563/month). Most of the variables have skewed distributions. The median household income is R700/month compared with the mean income of R1026.6/month. About 25% of the sampled households have an income of either less than R320/month or above R1400/month. The median number of trips to the source of water is two per day and the median round trip lasts 30 min (Table 2). The lower 10% of the sampled households take on average one trip a day while the corresponding upper 10% take over four trips per day. In terms of trip duration, the lower quintile takes on average 12-minute trips while the upper quintile take one-hour trips, the upper 10% taking two-hour trips. The age distribution of household heads is fairly normally distributed with an average of a 7-year difference between categories. Water consumption is however highly skewed. About 25% of the surveyed households consume less than 2 m3 of water per month, while another 25% use more than 7 m3 of water per month. The upper 10% of the sampled households consume over 10 m3 of water per month. 9

Although unemployment rates were 43% and 41% for female-headed and male-headed households respectively, the major difference comes from the number of households on state pensions: 43% of female-headed households compared with 20% of male-headed households.


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Table 2. Distribution of selected variables.

Percentile

No. of trips per day

Distance (minutes)

Age of household head

Household size

Y (income)

Person fetching water*

Water consumption (m3/month)

Per capita water consumption (m3/month/person)

p10 p25 p50 p75 p90

1 1 2 3 4

6 12 30 60 120

37 44 52 63 71

4 5 7 9 11

0 320 700 1,400 2,160

2 4 5 6 6

1 2 5 7 10

0.1 0.3 0.6 1.0 1.5

*

Refer to Table 1 for description and scale.

In terms of person(s) responsible for fetching water, most of the households involve the entire family, while more than 25% rely on children. The latter confirms that children contribute a significant amount of effort in collecting water. 7.2. Results of the analyses of water demand functions Separate regressions were estimated for collective tap and river water using equation (10). The reason for this separation is that the average number of trips by households is statistically different between the two sources of water10. The separation is also justified since households perceive quality of water from the two sources differently11. The single parameter Poisson assumption for the dependent variable is reasonable because the coefficient of variation is low (Table 3). It would have been unacceptable if the coefficient of variation was above one, in which case, the appropriate model would be the negative binomial regression. The regression results are reported in Table 4. Because of the assumed variation in the expected number of trips between female-headed households using children to fetch water and all other households, the standard errors in the regression coefficients are adjusted to correct for clustering effects arising from the underlying factors that distinguish female-headed households from male-headed households12. The coefficients of the Poisson regressions have no other direct interpretation except being elasticities of the expected number of trips. The signs of the coefficients show the direction of the impact, while the p-values have the usual interpretation. 7.2.1. Collective tap water. The opportunity cost of time has the expected negative sign and is significant. Rural households are rational because they value the alternative use of time and would therefore be inclined to make fewer trips when the collective tap is far (costs more time to reach). In 10

A t-test for the null hypothesis of equality of mean number of trips between collective tap and river water users yielded a t-statistic of 2 3.28 (n ¼ 234, df ¼ 232), which led us to reject the null hypothesis at a 1% significance level. 11 This was checked using the Pearson chi-squared statistic, a test of independence for a two-way table equivalent to a test of homogeneity of row or column proportions. 12 The variance of the expected number of trips is not the same between female-headed households and male-headed households. Grouping the observations by femchld allows adjustment of the variance-covariance matrix in the estimation process. As a result, the standard errors are robust – adjusted for effects arising from the heterogeneity between female-headed households and male-headed households.

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Table 3. Dispersion of the number of trips for collective tap and river water users. Source of water

Mean

Standard deviation

Coefficient of variation

Collective tap River water Total

61.6 81.2 67.6

40.5 45.7 43.0

0.4 0.3 0.4

particular, the farther away the collective tap is, the greater the sacrifice a household faces in terms of forgone productive activities when fetching water. The negative sign of the variable for perception of quality implies that households that perceive poor quality of water would make fewer trips to the collective tap. This seems to indicate that households in rural areas may be sacrificing productive time to fetch better quality water for drinking and cooking from collective taps that may be far, but are using other subsistence strategies for non-essential domestic water use. The negative sign on the quality variable is consistent with the fact that some collective tap users pay for water and would generally demand less water when poor quality is perceived. The price effect cannot be demonstrated directly, but it is reasonable to assume that quality is factored into the pricing of collective tap water. Rural households are therefore sensitive to the quality of water when essential uses of water such as drinking and cooking are considered, but would react marginally when there is no infrastructure for water provision or the domestic water uses are non-essential13. Household income and larger families have a positive and significant influence on the expected number of trips. Since water is both an essential good and a normal good, household demand for water is marginally higher for households with higher incomes in both female- and male-headed households. A larger family size could work either way: (a) increase the number of trips because (i) larger families need more water and (ii) large size means more people available for taking trips to fetch water, e.g. make more trips, (b) but also can lead to reduced trips as more members mean there is an ability to bring home more water per trip if more people go on the trip. However, wheelbarrows enable households to collect larger volumes of water per trip. The significance of wheelbarrows is that it reduces the strain involved in carrying water. Although the dummy variable for users of wheelbarrows is not significant, the negative sign implies that households that own wheelbarrows are able to reduce the number of trips that they take to collect water.

7.2.2. River water. For river water users, the negative sign of the opportunity cost of time has the same inference as that for collective tap users (Table 4). The greater is the cost of alternative uses of time that are forgone, the more that households reduce the number of trips they make to the river. The trips become infrequent the farther away the dwelling is from the river. Since households cannot do without water, there are other strategies that households use to substitute for the necessity of making trips. These include carrying more containers by using wheelbarrows. The negative coefficients on the dummy variables for households using wheelbarrows to haul water confirm these behavioural adjustments. In particular, wheelbarrows enable households to collect larger volumes of water per trip, thereby allowing households to reduce the frequency of trips. 13

Since collective taps provide treated water, its quality is much better than the quality of water from any other sources.


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Table 4. Estimated trip generating functions and welfare estimates for collective tap and river water users (Poisson regression with robust standard errors). Dependent variable: no. of trips Log Pi (opportunity cost of time) Log Y (income) Log H (household size) Poor (1 ¼ water is of poor quality, 0 ¼ other) Wheelbar (1 ¼ uses wheelbarrow to carry water, 0 ¼ other) Number of cases Chi-squared goodness of fit P-value Welfare estimates Predicted number of trips Consumer surplus per household per month (R/month) Consumer surplus per household per m3 (R/m3)

Collective tap water

River water

Coefficient (z-statistics in parenthesis) 2 0.04 (2 1.87) 0.52 (11.02) 0.35 (2.68) 2 0.17 (2 0.83)

Coefficient (z statistics in parenthesis) 20.10 (2 7.45) 0.31 (29.27) 1.00 (10.08) 0.24 (2.14)

2 0.10 (2 0.68)

20.24 (2 3455.49)

113 3,697.37 0.00*

46 1,057.23 0.00**

57.51 8.21

78.52 25.55

2.91***

4.34

*

Prob . x 2 (111), ** Prob . x 2 (44). R1.66/m3 from the regression þ R1.25/m3 (the average price per cubic metre of water that some of the collective tap users are paying for water). ***

Household income and larger families have a positive and significant influence on the expected number of trips. Similar to collective tap users, household demand for river water is marginally higher for households with higher incomes in both female- and male-headed households. Large family sizes increase the number of trips for river water users either because large families need more water or the large size allows the family to take more trips. Similarly, households are expected to make more trips when they perceive poor quality of water, although statistically the significance of the quality perception is not as important as all the other variables. Lack of alternative sources of water may explain the rigidity in river water use behaviour even when households are faced with poor quality of water. That however, should not lead to a positive relationship with poor water quality, implying higher demand for poorer quality. In particular, river water must be satisfying both essential and non-essential domestic water uses for this sample.

7.3. Welfare measurements The area under the trips generating function approximates the value of collective tap and river water. The coefficients in the double log-linear regression are elasticities of the trips with respect to the independent variables and hence cannot be interpreted in the same way as marginal coefficients. Equation (9) provides the basic formula for consumer surplus. The integral has a unique solution, which can be evaluated at the sample mean (Haab & McConnell, 2002). The mean price per trip for collective tap users is R 0.25 while that of river water users is R0.49. The results show that the consumer surplus per household for collective tap users is R2.91/m3 while the

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consumer surplus per household for river water users is R4.34/m3 (Table 4). Although both sources of water are inelastic to the price per trip, river water users have a much higher predicted number of trips compared with collective tap users, indicating that collective tap users are more sensitive to the opportunity cost of time spent on water collection trips than river water users. The consumer surplus values were compared with household willingness to pay for more quantity and quality of water from the same sample. In the contingent valuation study (Banda et al., 2004), households were asked how much they were willing to pay for improved availability and improved quality of water. Collective tap users were willing to pay R19.92/month for more quantity and improved quality of water, or equivalently R4.03/m3. The value obtained from the TCM is about 72% of the value from the CVM. Likewise for river water users, the willingness to pay for more quantity and improved quality of water was R34.79/month, or equivalently R6.15/m3. This value is also about 40% more than the value from the TCM. Using both the TCM and the CVM, the consumer surplus per household for river water is about 1.5 times that for collective tap water. The resemblance between welfare values from completely different methodologies gives more confidence in our empirical estimates of water demand parameters. In the CVM study, respondents might have wished to influence the decision to supply the service by overstating their willingness to pay since the provision of safe potable water is one of the priorities on the agenda of the incumbent government. On the other hand, the TCM required some assumptions about the economic valuation of opportunity costs. In this study, child labour was compensated at 12.5% of the agricultural wage, which could be an underestimation since the productivity of children in collecting water may be the same if not higher than that of adult males14. Nevertheless, the similarity of welfare estimates implies that the CVM survey and the assumptions of the empirical TCM were sound. In the absence of nominal prices of water, the valuation of water is made possible by imputing wages forgone to the time spent fetching water. Future studies may wish to include the age distribution of children and apply labour market valuations of their efforts in collecting water. There is also a need to harmonise the method of arriving at marginal wage rates for individuals who do not actually enter the labour market. Currently there is no empirical basis for imputing wages, although studies of travel mode choices in developing countries indicate that people typically value travel time saving less than their market wage rate15. If a better water service was to be provided, river water users would benefit more than collective tap users because they would receive a triple dividend: (a) an improvement in quality of water, (b) a reduction in the distance to the source of water, which would also imply that (c) more water could be collected with the same effort available to these households at present. For collective tap users the highest possible level of service that they would rather have is a private connection. Their perception of quality is more on service delivery than on the physical characteristics of the water. 14

The shadow opportunity cost of time used for child labour did not take into account the loss of other opportunities that children would have. The value imputed considers what a child would be compensated if she/he was to participate in the labour market but does not consider other opportunities that might be available to the child. In particular, children who spend time collecting water forgo the opportunity to develop their intellectual capabilities and other social skills, which represent much higher long-term opportunity costs. 15 Other studies assume that time savings should be valued at half the market wage rate for unskilled labour in the local economy (e.g. van Zyl et al., 2000).


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8. Conclusions and policy implications This study revealed significant differences between collective tap and river water users demand behaviour and the value they assign to access to water for domestic use. Policy makers must pay particular attention to such differences because failure to recognise behavioural dynamics in public infrastructure projects may nullify the estimates of benefits of even the most articulate of public projects. In particular, this study provides evidence that river water users have unfavourable options when trying to adjust their water use behaviour to water availability. While the poor quality of water does not seem to deter river water users from making trips to fetch water because there are no alternative sources, collective tap users are able to reduce the number of trips when they perceive poor quality of water. In terms of welfare, households where women and children collect water would be those benefiting the most if water was provided in a potable form. Any intervention in rural water development must be based on an analysis of the impact of any suggested policy on women and children in terms of the burdens associated with time spent fetching water, the environmental hazards they are exposed to from the quality of water sources, the mode of carrying water and child labour. In fact, where a child contributes to collecting water for the household, his/her labour is not rewarded at the market wage rate and the workload may not consider the impact on the child’s health, educational opportunities forgone, mental and moral development of the child. In terms of policy implications, resource managers must balance between equity, sustainability and efficiency (DWAF, 2004). In particular, the South African government inherited massive inequality, poverty and deprivation from water and public infrastructure from the previous regime. Investment in collective taps may be useful for clearing the backlog of the masses without potable water created by decades of unequal distribution of public infrastructure. In addition, there is a prospect of a cost recovery scenario in rural areas with negligible subsidies in water bills. DWAF (2003) indicates that the average operating and maintenance (O&M) cost for a communal water tap network in SPSB is about R18.5/ household/month, or R4.16/capita/month. Comparing these data on actual costs with households’ willingness to pay and travel costs allows the conclusion that an objective of O&M cost recovery might be feasible through the adoption of a system of water tariffs based on local willingness and ability to pay for a basic service such as the provision of potable water. Our results also indicate higher income elasticity than price elasticity, implying that income is more effective than price in influencing households’ water use behaviour. Acknowledgements The National Research Foundation (NRF), the South Africa/France Science and Technology Cooperation Programme and the French Embassy in South Africa provided partial funding for this study. The authors gratefully acknowledge their support. References Anand, P. (2000). Decisions vs. willingness to pay in social choice. Environmental Values, 9, 419– 430. Banda, B. M., Farolfi, S. & Hassan, R. M. (2004). Determinants of quality and quantity values of water for domestic uses in the Steelpoort sub-basin: a contingent valuation approach. Proceedings of the International Workshop on Water Resource Management for Local Development: Governance, Institutions and Policies, November 8 – 11, Loskop Dam, South Africa.

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