Private Financing of Water Supply in Developing Countries: The Potential in Sri Lanka

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1 Abstract Private Financing of Water Supply in Developing Countries: The Potential in Sri Lanka Lack of access to good quality water, unsustainable use and health impacts related to unsafe water are considerable challenges to water resource management and are a central public policy issue in many developing countries. Financial sustainability is important for the provision of safe and reliable water supply in developing countries. This case study employed a choice experiment approach to investigate community preferences for shifting from nonpiped to improve and reliable piped water supply. The results are used to examine whether a user payment scheme could be used to recover the costs of water provision. The empirical study was conducted in Sri Lanka, where 406 rural households currently without piped water supply were surveyed. The results suggest that rural households are willing to pay more for piped water supply, particularly for the improvements of quality, than the typical charges in existing piped water supply schemes. Therefore, the service provision could potentially be financed from user payment schemes. The results also indicate that lower connection fees and flexible payment schemes would help increase uptake of connections in rural communities. The preference heterogeneity is however substantial for piped water provision. Therefore, policy initiatives relying on household financing need to be evaluated for the cost/benefit effects across diverse classes of households. Water Supply, Private Financing, Choice Experiment, Willingness to Pay, Preference Heterogeneity

2 1. Introduction Nothing is more useful than water: but it will purchase scarce anything; scarce anything can be had in exchange for it. A diamond, on the contrary, has scarce any use-value; but a very great quantity of other goods may frequently be had in exchange for it. (Adams Smith, Diamond-Water Paradox, Wealth of the Nations, 1776) Despite the fact, that the Diamond-Water Paradox has been resolved many years ago with the realization of the difference between the concept of marginal utility and total utility of goods and services, the real paradox of lack of financing for water service improvements when it is so essential, still remains as a key challenge. Ensuring access to clean water supply and adequate sanitation is one of the Millennium Development Goals (MDGs). Yet, 800 million people are still without access to clean water and 2.5 billion lack improved sanitation (UNICEF/WHO, 2012). The provision of water supply and sanitation in developing world requires long-term sustainable investment (Panayotou, 1995; Baietti and Raymond, 2005). Financial sustainability is important for the provision of safe and reliable water supply in developing countries. However, developing country governments face difficult trade-offs when allocating public funds because of budgetary constraints and competing priorities. This often translates into insufficient financing of public water supply. There is a wide gap between available finance and the spending needed to achieve MDGs related to water supply and sanitation (Panayotou, 1997; Baietti and Raymond, 2005; Baietti and Curiel, 2005). Public policies related to water supply and quality can generate significant economic and environmental benefits for a range of stakeholders (Young, 2005; Hutton et al., 2007; WHO, 2012). There is a relationship between access to safe water supply and GDP: Investing an additional 0.3% of GDP for the provision of safe water will result in about 1% increase in GDP (World Bank, 1994; Haq et al., 2007). According to the UNHDR (2006, P.6) such investments do not only generate static gains, but also create dynamic impacts which in turn accelerate economic efficiency in the long run. However, despite international commitments and acknowledged benefits of water service improvements, there is insufficient finance available for water service improvements. It has been estimated that an additional billion USD is needed annually to meet the MDGs on water and sanitation. This investment

3 would generate seven-fold economic benefits (Baietti and Raymond, 2005). The shortage of investment in the water sector, despite a clear economic case, is due to a combination of factors including low income levels, capital-incentive investment requirements, lack of understanding of the range of benefits of water service improvements, non-existence of clear markets, and the lack of public information on the health benefits of safe water supply. Governments have been the conventional source of funding for investments in public utilities in developing countries. However, the combination of technical, financial and institutional issues related to public service monopolies has resulted in poor and unreliable service, poor cost recovery due to high subsidies, environmental externalities and water related health impacts (Panayotou, 1995, 1997; Baietti and Raymond, 2005). According to the WSUP (2012), achieving sustainable pro-poor financing of water supply and sanitation is not just about public financing: Water consumers and cost recovery through private financing will also have to play a role. Water management has often been considered an engineering problem rather than an economic one (Olmstead and Stavins, 2007). Therefore, water supply schemes have often been based on supply-oriented approaches and omitted the importance of public preferences and affordability for consumers (Whittington et al., 1993; Nam and Son, 2005). In the last three decades, researchers and policy makers have started focusing much more on demandoriented approaches for water supply and sanitation which need to be based on an understanding of the behaviour of water consumers and their ability and willingness to pay for improved services (Whittington et al., 1990; Nam and Son, 2005). According to Whittington et al. (1990) and Somanathan (2010), the lack of information available to the public is one of the key reasons hindering the improvement of environmental amenities like water supply and sanitation. As pointed out by Jalan et al. (2009), awareness of public goods such as environmental amenities is itself a public good: it is inadequately supplied in a market economy and one reason for the low demand and investment for environmental quality in developing countries.. Therefore, it is important to demonstrate and value such demand in order to document the evidence for the benefits of investments in water service improvements to increase awareness and inform policy decisions on water allocation and efficient management.yet, it is difficult to develop private financing schemes based on the User Pay Principle (UPP) without understanding how such schemes could benefit different consumer groups with potentially heterogeneous preferences. In particular, it is important to understand how such initiatives could impact the rural poor. Economic analysis can help to

4 measure how beneficial improvements would be for different water users (Altaf et al., 1992; Nam and Son, 2005). To assess the potential of private financing in water sector improvements, it is important to establish the economic value of water and to identify public preferences and their heterogeneity for water service improvements. Water is formally recognized as an economic asset in the Dublin statement in Water and Sustainable Development (1992) and according to its principle 4 Water has an economic value in all its competing uses and should be recognized as an economic good. The key European Union water policy, the EU Water Framework Directive (2000/60/EC) highlights the importance of pricing water based on UPP, which is now used across the world. The government of Sri Lanka has established the National Water Resources Policy (2000) in line with the global acceptance of water as an economic good to manage the water resources of the country. The main objective of the policy is to ensure the use of water resources in an effective, efficient, equitable manner, consistent with the social, economic, and environmental needs of present and future generations. Management of water resources through a water pricing system is one of the key aspects of this policy. In Sri Lanka, only about 35% of households and businesses have access to pipe-borne water supply (Central Bank of Sri Lanka, 2009). In rural areas, the vast majority of households depend on non-piped sources such as open wells, tube wells or rainwater harvesting to meet their drinking water needs. These sources are often contaminated and water-related health problems are common. The National Water Supply and Drainage Board (NWSDB), the main authority for piped water supply in Sri Lanka, has estimated that Rs. 136 billion (approx. USD 1.2 billion) will be needed for current water projects and planned new schemes between : Additional funds would be needed to meet the MDGs (Central Bank of Sri Lanka, 2006). Yet, the government funding for water supply has been only around Rs. 7 billion (approx. USD 60 million) annually (Central Bank of Sri Lanka, 2004). As the supply of piped water is highly subsidised in Sri Lanka, the cost of supplying water far exceeds the revenue it generates from the users. In this paper, we employ Choice Experiment (CE) approach to measure the social benefits of water service improvements and explore the potential for private financing of rural piped water supply schemes. The service improvement assessed is the provision of pipe-borne water to communities currently depending on non-piped sources of water. A number of studies have measured the demand for water service improvements using various valuation methods (see Young (2005) and Birol et al. (2006b) for discussions on the theoretical basis

5 and approaches to economic valuation on water resources). However, only a few studies have valued the benefits of pipe-borne water supply using a CE approach in a developing country context (Nam and Song, 2005; Kanayoka et al., 2008). The studies that do exist have not paid adequate attention to preference heterogeneity. However, this is important for designing policies that affect the provision of essential goods, such as water, when many households are poor and have limited access to credit. Existing literature has primarily focused on valuing the service improvements of existing water supply projects. Therefore, there is a knowledge gap regarding the welfare effects of introducing pipe-borne water supply to the rural poor, who often depend on unsafe and unreliable water sources. Blamey et al. (1999) have used choice modeling to examine public preferences and values relating to various water supply options to meet the demands of the growing population in the Australian Capital Territory, while taking in to account the associated environmental costs. Their results highlight that choice modeling provides a flexible alternative to Contingent Valuation Method (CVM) and that it is well suited to evaluation of multiple policy options, both in terms of monetary valuation and community ranking. have estimated the benefits of improved water services in Ho Chi Minh City, Vietnam. The study considered three characteristics of a public water supply system in the design: water quality, pressure and price. The result of the study is that the households without piped borne water supply are willing to give up around % of their monthly income to achieve the water service improvements, which is much more than the cost of water supply. Kanyoka et al. (2008) evaluate the benefits of multiple use water services in rural South Africa finding that people are willing to pay substantial amounts for improvements in water services. Hensher et al., (2005a) estimate the WTP for attributes related to the drinking water and waste water services in Canberra, Australia and find that waste water services are important for the public, and that WTP for improvements in service levels are substantial. A study conducted by Yacob et al. (2011) explore the public WTP for improvements to current water services in Selangor, Malaysia. They find that consumers are willing to pay for improved quality, fewer supply interruptions in piped water service. Yang et al. (2006) estimate the demand for the quality of water service using conjoint analysis in Greater Negombo and the coastal strip from Kalutara to Galle in Southwest Sri Lanka. This study finds that the majority of the respondents preferred their current situation compared to three proposed alternatives. This may be because the majority of the households are already using water from existing

6 piped water supply schemes. Such households may experience fewer service quality problems compared to the rural dry zone of Sri Lanka where the current study is conducted. This study analyses the attributes associated with the provision of piped water and preference variability in rural communities to fill a gap in the existing literature. Affordability needs to be considered in developing countries where households often need to pay a lump sum connection fee, which is significantly higher than the monthly payment for water consumption. Panayotou (2002) indicates that low-income groups are often willing and able to pay the monthly water tariff, but that they often face a capital constraint regarding the connection fee. He suggests that introducing a flexible payment scheme could increase the uptake of piped water provision. (Jimenez-Redal et al., 2014) further claimed that despite the high connection fee is a constraint, households are willing to pay if the connection fee spread across the extended period. In Sri Lankan context, the study done by (Pattanayak et al., 2006b) also suggested that the demand for existing piped water may increase through the subsidization for the connection fee. This is relevant where people depend on agriculture, have seasonal incomes and limited access to credit. Therefore, it is proposed that in order to assess the true preferences for the water service improvements among the rural poor households, affordability aspect of the households should be taken into account. This is the first study which empirically tests the effects of affordability through the inclusion of connection fee and the repayment scheme as separate attributes allowing the respondents to trade off with other attributes in the choice experiment. Increasing the uptake of piped water service is particularly important where non-piped water sources are not safe, nor reliable. The SP approach assumes that households have correct and adequate information about the good and services they are asked to value. If communities are well-informed about the quality of their drinking water, WTP for water quality improvements is expected to be higher in areas of poor water quality. Valuation studies do not often include water quality indicators, although these would be important for testing whether the responses to hypothetical improvements to current water quality are correlated with actual water quality indicators. To test this relationship, this study collects data both on the respondents awareness of water quality issues as well as measured indicators of water quality. This paper contributes to the existing literature by analyzing the relative importance of different attributes of piped water supply schemes that may affect their uptake. Further, this case study also contributes to understanding how water quality influences the preferences in

7 communities for improved water provision by using water quality indicators from water samples in the analysis of household preferences for piped water provision. As highlighted by the Van den Berg et al. (2006), consumer preferences are heterogeneous hence exploring the insight of the various segments of the water market may useful in order to provide the more efficient service to the consumers. In order to elicit public preferences for water service improvements and to identify their welfare effects, first estimate a standard choice model (Conditional Logit (CL) model and a CL model with interaction effects to investigate whether observed socio-economic characteristics of the households and water quality indicators are correlated with choice behavior. Then compare these results to those obtained using a Latent Class Model (LCM) and a Random Parameter Logit (RPL) model. Finally, derive WTP estimates and compare these to the cost estimates of water supply schemes and with the payments for the existing water supply schemes. 2. Materials and Method Study area Sri Lanka is an island with an area of km 2 and 2.1 million inhabitants. This study is conducted in the North Central Province (NCP), where the majority of poor households depend on non-piped sources of water. Water sources in the area are polluted by both natural and man-made contaminants. Ground water contains high concentration of fluoride (F) and has high electrical conductivity (EC) and hardness (H) (Dissanayake, 1991,1996; Padmasiri and Dissanayake, 1995; Padmasiri, 2004; Tennakoon, 2004; Padmasiri and Jayawardana, 2010). The high fluoride content causes dental fluorosis, particularly common in children, and skeletal fluorosis cases have also been recorded (Dissanayake, 1996; Padmasiri, 2004). Surface and ground water are also polluted with heavy metals due to excessive use of agrochemicals, and they cause a variety of health problems. Chronic kidney disease (CKD) is one of the ilnesses particularly prominent in the province. It has been suggested that arsenic, cadmium and other agrochemical residues in water are the main factors responsible for CKD, and hardness and electrical conductivity are associated with an increased risk of renal failure (WHO/Sri Lanka study report, 2012). Households are boiling and filtering water, buying water from vendors, using bottled water, and collecting water from more distant but better sources to avoid the adverse health effects of contaminated water.

8 Choice experiment method Valuing non-market benefits is important for determining the costs and benefits of public projects (Alpizar et al., 2003) and integration of such benefits into the decision making processes has become an integral part of policy formulation in developing countries (Bennett and Birol, 2010). Stated Preference (SP) and Revealed Preference (RP) approaches are two key approaches for determining the costs and benefits of public projects (Bateman et al. 2002). While SP focuses on individual responses to hypothetical questions, RP focuses on the actual choice behavior of individuals (Freeman, 2003). Choice experiments (CE) are a survey based SP technique to measure the welfare effects of changes in environmental quality (i.e. non-market goods and services) by presenting a series of choices between alternative policy options to respondents, and asking them to select their most preferred option (Hanley et al., 1998). Each alternative is characterised by a series of monetary and non-monetary attributes, which are offered at different levels. The monetary attribute is used to measure implicit prices related to changes in the levels of other attributes. CE has been developed to address weaknesses in conventional SP approaches. It helps to derive monetary value for each attribute and the marginal rate of substitution between the non-monitory attributes this generates valuable information for public policy making (Alpizar et al. 2003; Hanley et al. 1998). Because of this advantage, CE is used widely in marketing and in the valuation of environmental quality and ecosystem services. In this research, CE is used to determine the benefits of providing piped water supply to people who currently depend on non-piped water supply, and to explore the extent and implications of preference heterogeneity in the population. Theoretical framework and the econometric specification The theoretical foundation of CE is the Lancaster s consumer theory of value (Lancaster, 1966) and it s econometric basis is the random utility model (RUM) developed by McFadden (McFadden, 1974; Ben-Akiva and Lerman, 1985). According to Lancaster, the utility of goods and services is derived from their attributes rather than directly from the goods or services themselves (Birol and Cox, 2007). The RUM assumes that individuals make tradeoffs between attributes, which makes it possible to assign implicit prices for changes in attributes. The utility function for each household, U hj, can be expressed as a sum of two components:

9 U hj = V(A hj ) + ε hj (1) Where V(A hj ) is the deterministic component of utility, described as a function of the attributes (A) of the policy option, j, and any interactions terms with socio-economic variables of the household, h. The random component of utility, ε hj, represents the unobservable factors affecting households choices. The random part of the utility function allows a probabilistic specification of the respondents choice behavior (Adamowicz et al., 1998). Study assumes that households (h) face a choice between alternatives (j) from a given set of policy options C. The probability of a household,(h) selecting an option (j) over the other options (k) can then be expressed as follows: P hj = prob V hj V ; j C = prob V hj hk hk hj V hk This reflects that the probability, P hj, that household h selects policy option j over option k is equal to the probability, that the utility associated with alternative j is greater than the utility of any of the other alternatives in choice set C. Assuming that the relationship between utility and the attributes is linear in parameters and that the error terms are identically and independently distributed with a type 1 extreme value (Gumbel) distribution; then the above equation [2] can be estimated with a conditional logit model specification (McFadden, 1974; Greene, 2003pp ). Furthermore, the probability of a household h selecting option j can be expressed as follows, exp( V ( Ahj)) P hj (3) exp( V ( A )) hk Where the conditional indirect utilities function, V hj, is estimated using the functional form below: V hj = ASC SQ + β 1 A 1hj + β 2 A 2hj + β 3 A 3hj + +β n A nhj + ε hj (4) The alternative specific constant (ASC) is only included in the SQ option capturing the effects on utility of signing up to a policy, other than the utility captured by the water service attributes included in the model. The number of attributes, n, relates to a particular water hk hj (2)

10 service improvement option j, and the parameters, β 1 -β n, are the coefficients of the attributes, A 1 -A n. Advanced specifications in CE CL model provides foundation for the analysis of CE (Greene and Hensher, 2003). However, its validity has been questioned due to its two key assumptions about the independence of irrelevant alternatives (IIA) and respondents homogenous preferences. The IIA suggests that the relative probabilities of two options are not affected by adding or removing options to/from a choice set. This is not true in most real world settings. Preferences in a population are also often heterogeneous. Therefore, more advanced econometric specifications relaxing the assumptions of IIA and homogeneous preferences are increasingly being used. LC and RPL models relax the IIA assumption and capture unobserved heterogeneity while still allowing capturing the observed heterogeneity through inclusion of socio-economic variables as interaction terms in the model (Hynes and Hanley, 2005). Capturing the heterogeneity is important for modelling of choice behaviour, and increases the value of the results for policy making. This is because it sheds light not only on aggregate welfare changes but also on equity (Boxall and Adamowicz, 2002; Birol et al., 2006a). LC Model The LC model implies that the behaviour of individuals depends on both observable attributes and latent heterogeneity, which is unobserved by the researcher (Greene and Hensher, 2003; Swait 1994). However, LC models assume preferences to be homogeneous within each latent segment and thus the utility functions vary only between the segments (Greene, 2003; Train, 2009;Hynes and Hanley, 2005;Swait, 1994; Birol et al., 2006a; Kosenius, 2010) The LC model is specified as a RUM where the probability of water supply option j selected by household h in segments s can be expressed as follows, P hjs = exp (μ sβ s A hj ) k exp(μ s β s A hk ) Where μ s and β s are the scale parameters and segment specific utility parameters respectively. The probability of an individual s membership of segment s can be expressed as: (5) P hjs = exp (αλ s) s s=1 exp (αλ s ) (6)

11 Where λ s denotes a vector of segment-specific parameters and α is a scale factor, which is assumed to be equal to one: hence each individual has a probability of belonging to a particular segment (Boxall and Adamowicz; 2002). By substituting the equations for the choice probability (5) and membership (6) the probability equation can be expressed as follows: P hjs = [ S exp (μ s β s A hj ) ] [ exp (αλ s ) s=1 ] h k exp(μ s β s A hk ) s (7) s=1 exp (αλ s ) This integrated model allows to explain choice behaviour in terms of both choice attribute data and individual characteristics. There are several statistical criteria for deciding the number of segments, S, to be included in the LC model such as the minimum Akaike Information Criteria (AIC) and the minimum Bayesian Information Criteria (BIC) along with ρ 2 and Log Likelihood Ratio (LLR) (Swait, 1994). However, it is also important to select a model which provides evidence of preference variability which offers interpretative simplicity and yields policy relevant insights (Swait, 1994; Boxall and Adamowicz (2002). RPL model In real world choice settings preferences of individuals (as distinct from classes of them) are also heterogeneous. Therefore, it may be important to accommodate preference variation among individuals for accurate and unbiased welfare estimation (Greene and Hensher, 2003; Hensher and Greene, 2003; Birol et al., 2006a). The RPL model accounts for preference heterogeneity by estimating the distribution of model parameters across individuals. RPL probabilities are the integrals of standard logit probabilities over a density of parameters and can be specified in the following form (Train, 2009), P hj = L hj (β)f(β)dβ (8) Where L hj (β) is the logit probability evaluated at parameters β; L hj (β) = ev hj (β) J j=1 e V hk (β) (9)

12 f(β) is a density function of β and V hj (β) depicts the observed portion of the utility, which depends on the parameters β. If utility is linear, then V hj (β)=β x hj. In this case, RPL probability takes the following form, P hj = ( eβ xhj e j β x hk )f(β)dβ (10) The RPL probability is thus, a weighted average of the logit formula evaluated at different values of β, with the weights given by the density f(β). Design of choice experiment and data collection In this study, the water service options are explained in terms of the characteristics water quality, reliability (available service hours per day), connection fee, repayment scheme and monthly payment for residential water provision (Table 1). The final selection of the attributes and their levels were based on focus group discussions, expert interviews and key informant discussions. Experimental design techniques (Louviere et al., 2000; Hensher et al., 2005b) can be used to create a set of choices by combining levels of the attributes into choice sets (Alpizar et al., 2003). The full factorial of the choice design of this study generated 64 (2 4 *4 1 ) possible combinations of choices. However, it is not feasible or appropriate for a single respondent to handle all these combinations. Therefore, a fractional factorial orthogonal design (Louviere et al., 2000; Hensher et al., 2005b) was used to make the design manageable for participants. For a detailed discussion on the steps to be followed in the design of CE see (Alpizar et al., 2003). Table 1: Attributes and the levels of the choice design Attributes No of Levels Description Water Quality Moderately Improved: only 50% improvement 2 to current level. Purification in the household still needed. Highly Improved: meets WHO standards. No need to follow further household level averting actions and directly drinkable from the tap. Reliability(hrs/day) Effects Coding

13 Connection Fee (Rs) Repayment Scheme 2 Immediate payment 3 year repayment scheme Monthly 4 300, 500, 700, 900 Payment(Rs) The selected choice design has 8 choice cards. Each choice set comprises two unlabelled water supply alternatives namely A and B, and the Status-Quo (SQ) option: the current nonpiped supply. The Study employs effects coding; for example highly improved quality level was coded as 1 while moderately improved coding -1 (see Table 1) and attributes are coded as zero for the no policy option, SQ. For the monetary attribute linear coding is applied. It is well documented that the higher connection fee is one of the major barriers to sign up with the piped water supply network among the poor communities. Therefore, the connection fee is included as an attribute allowing the respondents to trade off with other attributes to examine how this changes the preferences. With regard to the repayment scheme attribute, households have their own time preference discount rate and this is taken into account when they make choices. Therefore, the study values the overall effect of delaying payments in the scheme. Figure 1 Example of choice card Data collection has been carried out between September 2010 and January 2011 and included 3 focus group discussions, numerous key informant interviews and randomly selected 406 household surveys in the selected 15 Divisional Secretariats areas (DS) in the North Central

14 Province Sri Lanka. Water samples have also been collected from households for testing for F, EC and H. In addition to the CE questions, a series of follow up questions have been asked as part of the household survey to capture the respondent s attitudes and preferences towards their current water supply and to identify motivational factors behind their choices. 3. Results The sample of 406 households has 45% male and 55% female respondents of an average age of 45 years, and an average household size of 4 members (Table 2). It is evident that most participants have secondary education. About 52% of the households practice some form of in house averting action (boiling, filtering, etc) and 33% of households have suffered from a water related illness. Average household income is Rs per month, less than the official government statistics of Rs per month (Department of Census and Statistics, Sri Lanka 2009/10). Table 2: Socio-economic and demographic characteristics of households Variable Minimum Maximum Mean Std. Deviation Gender (Male = 1) Members Age Education Income (Rs/Month) Averting Action (Yes=1) Fluoride(mg/l) Health Impact(Yes=1) Conductivity(µS/Cm) TDS (mg/l) Hardness (Mg/l CaCO3) CL and CL with Interactions The standard CL model and the interaction model with ASC are specified (Table 3). Heterogeneity in the sample is included by introducing socio-economic variables as interaction terms in the alternative policy options A and B and the results helps to identify the socio-economic determinants influencing the preference signing up to the piped borne water supply (Table 3).

15 Willingness to pay estimates As the CE approach is compatible with utility maximization and demand theory (Hanemann 1984; Batemen et al. 2003), the Marginal Willingness to Pay (MWTP) estimated by calculating the marginal rate of substitution between the attribute concerned and the monetary attribute (monthly payment for water supply in this study). For effects coded water service attributes with two levels, the households MWTP for a change in the water service attribute, the implicit price function can be expressed as follows, MWTP j = -2 ( β j β m ) (11) β j denotes the coefficients of non-monetary attributes and β m is the coefficient of the monetary attribute (assumed equal to the marginal utility of income) and the multiplication factor (2) accounts for the difference between the effcts coded levels of the attributes (-1 and 1). Table 3: Parameter and WTP estimates for CL and CL interaction with ASC Attributes and Variables Standard CL Model CL with interaction effects Coefficients (S.E) WTP Coefficients(S.E) WTP ASC (0.106)*** (0.285)** Quality 0.957(0.031)*** (0.032)*** 803 Reliability 0.102(0.029)*** (0.029)*** 83 Connection Fee (0.029)*** (0.029)*** Repayment Sch: 0.218(0.029)*** (0.029)*** 188 Price (0.000)*** (0.000)*** Gender (0.094)*** Age (0.003)*** Education (0.076)** Averting Actions (0.095)*** Income (0.000)*** Fluoride (0.093)*** Health (0.106)** (LLR) Pseudo R 2 (ρ 2 ) *,**,*** refers to 10%, 5% and 1% significance level in two-tailed tests 1$= approx. 112 Rupees as at January 2011

16 Both CL standard and the interaction models (Table 3) provide good estimations in terms of McFadden s Pseudo R 2 (ρ2), which are 0.28 and 0.33 respectively 1, but the interaction model, which captures some observed heterogeneity, is better fit to the data than the standard CL model. All parameters of the attributes have the expected signs and are highly significant (Table 3). The negative and significant ASC in both models indicates strong preference for signing up with piped water supply alternatives rather than continuing with non-piped water supply. Furthermore, the interaction model suggests that socio-economic and water quality parameters influence preferences for sign up with community piped water supply. Women, younger and richer people with a higher educational status have a higher propensity to sign up to piped water supply. Furthermore, households having experienced adverse health outcomes, with high levels of fluoride and currently undertaking averting behaviours are also more inclined to choose a piped policy option rather than the SQ. Latent Class Model results There are several statistical criteria for deciding the number of segments to be included in a LC model. In order to find the LC model best representing the data, 1, 2 and 3 segment models were estimated. Models with more than 3 segments could not be specified. The minimum Akaike Information Criteria (AIC) and the minimum Bayesian Information Criteria (BIC) (Swait, 1994) along with ρ 2 and LLR have been used to select the best LC model. The LC model with 3 segments meet the criteria of lowest AIC, BIC, LLR and highest ρ 2, and also represents the heterogeneity of preferences in a way which points to clear economic interpretation and policy implications. 1 Well-fitted models have ρ2 between (Hensher, Rose and Greene 2005, page 338)

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18 Table 4: Parameter and WTP estimates for LC model Attributes/Variables Segment 1 Segment 2 Segment 3 Coefficients (S.E) WTP Coefficients (S.E) WTP Coefficients (S.E) WTP Weighted ASC (0.180)*** (0.279)*** 0.508(0.374) Quality 1.014(0.035)*** (0.092)*** (0.199)*** Reliability 0.130(0.036)*** (0.087) (0.159) - Connection Fee (0.037)*** (0.070)** (0.150) - Repayment Scheme 0.157(0.036)*** (0.078)*** (0.200)* Price (0.000)*** (0.0005)*** (0.000)*** Membership determinants Gender -.172(.339) (0.383) - Age (.013) (0.015) - Education 0.024(.274) (0.309) - Averting Actions 0.979(.351)***.982(0.390)** - Income 0.000(.000) 0.000(0.000) - Fluoride 0.712(.358)** 0.478(0.392) - Class Probabilities LLR Pseudo R *,**,*** refers to 10%, 5% and 1% significance level with two-tailed tests 1$= approx. 112 Rupees as at January 2011.Weighted WTP was calculated based on the class probabilities WTP

19 The LC model (Table 4) yields both utility coefficients of the attributes for the three segments as well as the association between socio-economic status and segment membership. About 65% of the respondents belong to segment 1 attributing the highest value to water service improvements in general and quality improvements in particular. Segments 2 and 3 include the remaining 23% and 12% of respondents, respectively. Negative and significant ASC parameter for segment 1 and 2 suggest that 88% of all respondents tend to prefer to join piped water supply. The remaining 12% of respondents belonging to segment 3 would prefer to continue with their current non-piped water supply, other things being equal. All parameters in the LC model are significant and have expected signs for segment 1, while only quality and repayment schemes are significant for all three segments. Reliability is not significant for segments 2 and 3 and connection fee is not significant for segment 3. The LC model assumes that socio-economic and attitudinal characteristics of respondents influence their choices via segment membership. In order to find the sources of heterogeneity and the distributional effects of the welfare estimations, probabilities of each respondents belonging to each segments were estimated. Then based on the highest probability achieved, the respondents were assigned to one of the segments. The preference variability is characterised regressing the segment membership against the socio-economic and water quality parameters. The effects of socio-economic variables on preferences for belonging to segment 1 and 2 is evaluated relative to segment 3, which is specified as a reference category. The results show that the averting action and the Fluorides level parameters are significant at 1% and 10% significant level for segment 1 while only averting action is significant at 5% level for segment 2 (Table 4). Profile of the respondents in LC model In a policy context, it is important to identify the socio-economic background of the respondents belonging to each segment (Table 5). The results show that respondents who belong to the high quality preference group (Segment 1) on average have relatively fewer members in the household, practice averting actions and have higher concentration of the indicators of poor water quality in their current water sources. However, F and Pearson Chi Square test results show that the Fluoride level and averting actions are the only variables significantly differentiate the segments.

20 Table 5: Profile of the respondents in LC model Characteristics Segment 1 N=267 Segment 2 N=94 Segment 3 N=46 Members of household Gender (Male) 46% 38% 54% Age Education Averting Actions*** 53% 54% 30% Health 30% 40% 30% Income (Rs) Fluoride(mg/l)* Conductivity (µs/cm) TDS(mg/l) Hardness(Mg/l CaCO3) *.**,***, refers to F test and Pearson Chi Square tests depict significance differences at the 10%, 5% and 1% significance level with two-tailed tests RPL Model and RPL with Interactions The RPL model captures the individual random heterogeneity while the RPL interaction model captures both random and observed heterogeneity (Revelt and Train, 1998c; Birol et al., 2006a) in preferences for signing up to piped water supply schemes. Both types of heterogeneity are important when the results are interpreted in a policy context. The price attribute is specified as a fixed parameter while the other non-price attributes including ASC are specified as normally distributed (Revelt and Train, 1998b; Birol et al., 2006a) Table 4: Parameter and WTP estimates for RPL model RPL Model RPL with Interactions Attributes Coefficients (S.E) SD of RP WTP Coefficients SD of RP WTP (S.E) ASC (0.406)*** 4.52(0.39)*** (1.371) 4.03(0.33)*** Quality 2.120(0.169)*** 1.73(0.18)*** (0.156)*** 1.64(0.15)*** 727 Reliability 0.228(0.056)*** 0.43(0.08)*** (0.053)*** 0.41(0.08)*** 75 Connec: Fee (0.049)*** 0.24(0.99)*** (0.048)*** 0.21(0.11)** - Repayment 0.552(0.055)*** 0.25(0.11)* (0.051)*** 0.194(0.12) 188 Price (0.000)*** (0.00)***

21 Socio-Economic Variables Gender (0.511)* Age (0.019)* Education 0.737(0.369)** Averting Ac 2.231(0.518)*** Income 0.000(0.000)*** Fluoride 1.223(0.491)** Health 0.844(0.551) LLR Pseudo R *,**, *** refers to 10%, 5% and 1% significance level with two-tailed tests 1$= approx. 112 Rupees as at January 2011 All parameters for attributes are as expected and significant in both models (Table 6). Estimated standard deviations for randomly distributed parameters were also significant, except the repayment scheme of the interaction model, suggesting the presence of random taste variation among respondents. Model selection criteria Table 7 presents the standard criteria for selecting the most appropriate model. However, selecting an appropriate model is not straightforward. RPL models are considered to be state of art and yields better estimation in terms of goodness of fit. However, LC models provide more information on the distributional effects of welfare changes in relation to policy change across the different classes of households. Table 5: Model selection criteria Model Number of parameters (P) Pseudo R 2 Log likelihood at convergence (LLC) CL CL with interact: LCM RPL RPL with interact: Pseudo R 2 is calculated as 1-(LLC/LL0) AIC is calculated as -2*(LL-P)} and BIC is calculated as {-LLC+[P/2)*ln(N)] No of households (N) = 406, Log likelihood at zero (LL0) is 3568 AIC BIC

22 Model fit indicators (Table 7) suggest that the variables characterise well the choices made by the respondents over water supply alternatives. It is clear that the introduction of random heterogeneity into the model provides better estimation than the CL models. However, RPL model provide slightly better estimation compared to LC model yielding highest Pseudo R 2 and lowest LLR, AIC and BIC. WTP estimates for MNL, LC and RPL models Table 8 provides the mean WTP and confidence intervals of the mean WTP estimations for the MNL, LC and RPL models. Table 6: Summary of the WTP estimates for MNL, LC and RPL models Variables CL LC Seg: 1 LC Seg: 2 LC Seg: 3 RPL Quality 840 ( ) 1373( ) 216 ( ) 243 (59-427) 734 ( ) Reliability 89 (35-144) 176 (61-291) (40-118) Repayment 191 ( ) 213 ( ) 139 ( ) 152 (41-345) 191 ( ) Confidence intervals for the WTP estimates shown in brackets are calculated using the DELTA method The results show that the respondents value the improvements in quality and as well as the repayment scheme in all tested models. The respondents belonging to segment 1 have the highest valuation of quality improvements ranging from Rs to Rs with a mean value of Rs The valuation of quality improvements in segment 2 and 3 are considerably lower with the mean of Rs. 216 and Rs Given the better fit of the RPL model and the more conservative estimates, it is suggested that referring to the WTP estimations based on RPL model, is preferred in a policy context.

23 5. Discussion Private payment schemes based on the UPP for the provision of water supply are widely used in developed countries. However, lack of information to evaluate the benefits of water service improvements in developing countries remains a challenge for justifying investment in them. Therefore, it is important to inform policy makers and investors about public preferences and their heterogeneity regarding improvement of the water supply to ensure its costs can be recovered through user payments. As evidenced by the negative and significant sign of the ASC in all tested models (except in the segment 3 of the LC model), a large majority of respondents prefer to sign up to piped water supply rather than to continue with the non-piped supply. This is likely to reflect poor quality drinking water and associated health consequences in the study area. The analysis also finds, in line with the existing literature, that water quality improvements would yield welfare improvements (Nam and Son, 2005; Hensher et al., 2005a; Yacob et al., 2011). Households whose current water supply has high fluoride and conductivity levels would stand to benefit the most. Poor water quality is a serious issue in the area and this suggests that people may be aware of the poor quality of the current water supply: the benefits from hypothetical improvements to perceived current water quality are negatively correlated with the measured water quality. In contrast to the study of Yang et al. (2006), the results from this analysis show that water quality is valued higher than the reliability of water supply. This provides further evidence of prevailing lower quality level in the study area compared to other parts of the country. The analysis also highlights that socio-economic characteristics are important determinants of preferences for piped water supply, providing useful information for understanding why some respondents value improvements in water supply higher than others. The interaction coefficients of gender and age in the CL and RPL models indicate that women and younger people are more likely to prefer piped water supply than men and older people in general. This may be because women devote more time and effort to accessing water and improving its quality, and because young people may be more aware about the consequences of poor water quality. Education and household income are also associated with preference for piped water supply as depicted in the CL and RPL model results. People who engage in averting behaviours are also more likely to prefer piped water supply, despite the fact that may have already invested in averting measures and may have transaction costs when moving away

24 from the current non-piped water sources. However, averting behaviour is not a perfect substitute for improved water quality; but rather reflects preferences for better water quality. According to the results obtained from using the LC model, the respondents break down to three groups in terms of their preferences for water service and quality improvements. These differences are reflected in the WTP for quality improvements; Rs.1373, Rs.216 and Rs.243 per month for quality improvements in the three segments respectively. The weighted average in terms of class probabilities is Rs. 934 per month. It is clear that the welfare estimates are sensitive to the choice of econometric specification. Using the standard CL model, we find that people are willing to pay Rs. 840 per month for water quality improvements. The RPL model yields a WTP of Rs. 734 per month for water quality improvements, which is lower than the weighted average under the LC model. The study also finds that a lower connection fee and some form of repayment scheme rather than a conventional lump sum connection fee is important for the respondents. It is interesting to note that water quality and the repayment scheme are the only attributes, which are significant in all tested models. The results show that the respondents are willing to pay for a delay in the payment of the connection fee, rather than having to pay the entire amount immediately. This finding corroborates the argument of Panayotou (2002); Jimenez-Redal et al., (2014) that a flexible payment scheme could increase the uptake of piped water supply in low-income countries. Calculated WTP demonstrate that households are willing to pay Rs. 734 per month for water quality improvements (using the most conservative estimate), which is substantially more than the current average payment of about Rs. 300 per month. The estimated WTP would be sufficient to cover the short run MC which is Rs. 16 and the substantial part of the long run MC of water supply which is around Rs. 47 per cubic metre nationally (Dharmarathna et al., 2010), assuming an average household water consumption of 17 units per month So, there is a clear scope and high potential for expending the piped water service coverage in order to provides improved water service for the households currently depending on the non-piped sources. Furthermore, utility providers may be able to capture economies of scale of their investment in the long run by expanding the coverage of water supply. Welfare estimations further suggest that the water service improvements would be feasible in general as the estimates are within the World Bank affordability benchmark of 4 % of household average income of the study area. As investment in water infrastructure is more

25 capital incentive in the short run, it may not be easy to implement through household level financing alone (UNHDR p.7). Therefore, it is stressed that government involvement is still important to take risk-mitigating measures and to take the necessary regulatory measures for sustainable economic management of future water supply projects. 5. Conclusion Valuing water is important for public policy making in developing countries where poor water quality and its health impacts are common, and the financing of water sector improvements is insufficient to meet the societal demand. This case study contributes to the limited literature on public demand for water service improvement with the application of array of modelling techniques under the umbrella of discrete choice modelling. The analysis provides clear evidence that people prefer to sign up for piped water supply in general and to have improved water quality in particular. Social benefits of water service improvements would outweigh its costs, and investment could be financed using private payment schemes. The study also finds that there is substantial preference heterogeneity for piped water provision, suggesting that policy initiatives relying on household financing need to be evaluated for the cost/benefit effects across diverse classes of households. The results imply that policy decisions about water service improvements should consider their affordability to users. This could be achieved e.g. by introducing a relatively low connection fee and a payment scheme for the people who are unable to pay the conventional lump sum connection fee.