Climbing the water ladder in poor urban areas: Preferences for ‘limited’ and ‘basic’ water services in Accra, Ghana

Science of the Total Environment 673 (2019) 605–612

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Climbing the water ladder in poor urban areas: Preferences for ‘limited’ and ‘basic’ water services in Accra, Ghana William F. Vásquez ⁎, Ellis A. Adams a

b

Department of Economics, Fairfield University, 1073 North Benson Rd, Fairfield, CT 06824, United States of America Global Studies Institute and Department of Geosciences, Georgia State University, 25 Park Place, 18th Floor (Rm 1819), Atlanta, GA 30303, United States of America

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Alternative services are needed where barriers impede providing water on premises. • Using a choice experiment we estimate willingness to pay for alternative services. • We also investigate preferences for management of the proposed standpipe system. • Willingness to pay varies with accessibility, availability, and quality of water. • Households prefer basic services over limited ones under the new JMP water ladder.

a r t i c l e

i n f o

Article history: Received 23 January 2019 Received in revised form 21 March 2019 Accepted 5 April 2019 Available online 8 April 2019 Editor: Jóse Virgílio Cruz Keywords: Drinking water Willingness to pay Choice experiment Ghana

a b s t r a c t While providing drinking water on premises to all citizens in urban areas may be desirable, economic and institutional challenges coupled with poverty, insecure tenure, and other barriers prevent many water utilities from providing private taps to all households. To meet growing water demand and fill gaps in service delivery, alternative forms of public water service provision are critical. We implemented a choice experiment in NimaMaamobi, a poor, underserved urban settlement in Accra, Ghana, to investigate household preferences for public standpipes based on the basic and limited water service categories under the WHO/UNICEF Joint Monitoring Programme's new water ladder. We also elicited local preferences for potential service administrators of the standpipes. Choice responses provided by 344 respondents were analyzed using a generalized multinomial logit model. Households were willing to pay up to US$1.25 for a 20-liter bucket of safe drinkable water, which is consistent with the average household water expenditure in the study site. Households spend at least 22% of their monthly income on water. Households' willingness to pay varied according to alternative levels of accessibility, availability, and quality of water services. Households showed strong preferences for community-based committees and nongovernmental organizations over the current water utility and the municipal assembly. The policy implications of the findings are discussed. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: wvasquez@fairfield.edu (W.F. Vásquez), [email protected] (E.A. Adams).

https://doi.org/10.1016/j.scitotenv.2019.04.073 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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W.F. Vásquez, E.A. Adams / Science of the Total Environment 673 (2019) 605–612

1. Introduction By ratifying the Sustainable Development Goals (SDGs) of the United Nations, 193 countries committed to alleviating poverty and other deprivations worldwide, while taking care of the environment for future generations. One of these goals is to provide universal access to safe drinking water by 2030 (SDG 6). Up to date, water utilities in many developing countries struggle to meet growing water demand due to a pervasive mix of economic constraints, institutional weaknesses, and geographical limitations (Dos Santos et al., 2017; Adams et al., 2019). Even with abundant freshwater resources, lack of financial resources and old infrastructure undermine efforts by public water utilities to tap water from distant sources, sufficiently treat it, and deliver to both old and emerging urban settlements. Institutional barriers also hinder the extension of water services to poor urban areas. Public utilities are reluctant to extend water connections to informal settlements because inhabitants may lack formal property titles (Amoako and Cobbinah, 2011). Consequently, water services in many urban informal settlements are unreliable or simply inexistent. Where economic and institutional barriers impede the provision of water on premises, providing alternative improved water services may be the best short-term solution or immediate feasible step to reach underserved urban areas (Adams, 2018). This is why the new ladder proposed by the WHO/UNICEF Joint Monitoring Programme (JMP) to monitor progress towards SDG 6 includes three levels of improved water services: safely managed, basic, and limited (World Health Organization, 2017).1 When water is not available on premises (i.e. safely managed), improved water services are classified as basic if collection time (i.e. roundtrip including queuing) is not more than 30 min, and as limited if collection time exceeds 30 min. Basic and limited water services may be alternatives to satisfy the increasing demand for drinking water in urban informal settlements. Yet, little is known about the household preferences for alternative improved water services in urban informal settlements where universal ownership of household taps is not attainable in the short term. An improved understanding of the demand for basic and limited water services may help policymakers and water utilities to make informed investment decisions on water infrastructure as they pursue the SDG target of universally providing improved water services. This study investigates local preferences (i.e. willingness to pay) for improved standpipe water services in Nima-Maamobi, one of the most impoverished urban slums in Accra, the capital city of Ghana. Our overarching research question was: what are the preferences and willingness to pay for public standpipes in Nima, Accra, and what service attributes do residents consider most important? To address this question, we implemented a discrete choice experiment (DCE) depicting a variety of service attributes including days of service, service hours, water quality, distance to the nearest standpipe, and queue time. By analyzing a more comprehensive list of service attributes than prior DCE studies (e.g. Abramson et al., 2011; Cook et al., 2016), this article provides estimates of the benefits derived from supplying drinking water at different levels of accessibility, availability, and quality of standpipe water services consistent with the new JMP drinking water ladder. In addition, it elicited household preferences for different potential administrators for the proposed standpipe system including the current public water company, the municipal assembly, non-

1 As part of efforts to monitor progress towards the SDGs, the WHO/UNICEF Joint Monitoring Programme proposes using a ladder to assess and monitor household drinking water at five levels: 1) No Service, 2) Unimproved, 3) Limited, 4) Basic, and 5) Safely Managed services (World Health Organization, 2017). The three highest levels represent improved water services with varying levels of accessibility and availability. A water service is safely managed (i.e. the highest service quality level) if drinking water is provided from an improved water source located on premises, available when needed, and free of fecal and priority chemical contamination.

governmental organizations, and a community-based committee. Until now, household preferences for service management have received little attention in the literature. Choice responses were analyzed using conditional logit, mixed logit, and generalized multinomial logit models. Based on our findings, households' willingness to pay for improved water services is responsive to days and hours when water is supplied, water quality, and queuing time, but not to the distance to the nearest standpipe. Results also suggest that Nima-Maamobi's residents would prefer a community-based committee or a nongovernmental organization to manage standpipes over the current water utility and the municipal government.

2. Willingness to pay for alternative improved water services An extensive number of recent studies have estimated urban households' willingness to pay (WTP) for improved water services on premises (e.g. Casey et al., 2006; Dauda et al., 2015; Haider and Rasid, 2002; Hensher et al., 2005; Latinopoulos, 2014; Orgill-Meyer et al., 2018; Snowball et al., 2008; Tarfasa and Brouwer, 2013; Willis et al., 2005). Conversely, few studies have elicited households' willingness to pay for basic or limited water services (see Van Houtven et al., 2017), and even fewer studies have taken place in urban centers (see Venkatachalam, 2015 for a recent exception). This limited attention to basic and limited water services in urban centers is unsurprising because those areas likely have water infrastructure and the logical step in many of those sites is to improve the reliability and quality of water services. However, as the pursuit of universal provision of safe drinking water continues worldwide under the new SDG framework, alternative levels of improved water services will be needed particularly in growing urban informal settlements where institutional, natural, and financial constraints impede the expansion of water infrastructure and in-house water connections. In addition to estimating households' WTP for accessibility, availability, and quality of drinking water, investigating household preferences for different forms of service management is equally important for designing alternative water supply systems. As an example, Hope (2015) found that a community-based approach was the least preferred management relative to governmental and private management despite being the most common approach in rural Kenya. Vásquez (2014) also found in a small town of Guatemala that households with municipal water services were willing to pay for service improvements while households with community-managed services were not. In León, Nicaragua, Vásquez and Franceschi (2013) estimated that households were willing to pay a premium if an improved service was managed by the current national water company rather than decentralizing management at the municipal level. Combined, these studies suggest that household preferences for service management approaches are specific to the context and therefore service management would be a relevant attribute to be included in willingness-to-pay studies. Among preference elicitation methods, discrete choice experiments (DCE) are appropriate to investigate household preferences for specific attributes of basic and limited water services from which households may derive benefits. For instance, Abramson et al. (2011) implemented a DCE in rural Zambia to investigate household preferences for different levels of water quality, quantity, distance, water-drawing method, and financing approach of public taps. They found strong household preferences for safe drinking water, increased quantity, and reduced distance to public taps, particularly when offered microfinance or given the option of paying labor time instead of cash. Cook et al. (2016) also implemented a DCE to investigate household preferences for water pumps in rural Kenya, with particular attention to the time it would take for the household to gather water from the nearest water pump. Their findings indicate that the value assigned to water fetching time is about 50% of the unskilled wage rate in Kenya (18 Kenyan Shillings or 0.21 US dollars per hour). Similar stated-preference studies are needed in urban

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Fig. 1. Map of the study area.

informal settlements to provide insights into the potential benefits that households can accrue from different service levels defined by the new JMP ladder. 3. Study site: Nima-Maamobi, Accra Accra, the capital of Ghana, represents an example where the imbalance between the demand and supply for piped water has grown over time due to unprecedented urban growth. According to Ghana Statistical Service (2014), the population in the Greater Accra region increased from 2.9 million to more than four million between 2000 and 2010. In the same period, the piped water coverage rate in Accra decreased by 22 percentage points (World Bank, 2015). Water access conditions are precarious in Accra, particularly in the urban informal settlements. While the exact statistics are hard to come by because census data does not delineate informal settlements from other urban areas, and available statistics are debatable, unofficial estimates suggest that at least half of Accra's residents live in urban informal settlements where poor access to safe water threatens health and wellbeing (Ghana Web, 2012; The Rockefeller Foundation, 2013). The study was conducted in Nima-Maamobi, a twin urban slum located in the center of Accra (see Fig. 1). Nima used to be part of the Accra Metropolitan Assembly until March 152,018 when Nima was declared the capital of the recently created district known as Ayawaso East Municipal Assembly.2 According to the 2010 population and housing census (the most current census data available), Nima had a population of 80,843 inhabitants living in 19,196 households, and Maamobi had a population of 61,724 people in a total of 14,477 households (Ghana Statistical Service, 2014). Most of those residents are poor, and trading is their main economic activity (Machdar et al., 2013).

2

See http://www.ghanadistricts.com/Home/District/243 (last accessed on May 9, 2018).

607

Three reasons justify our choice of Nima as a study site. First, similar to many other slums in Ghana and developing countries at large, Nima-Maamobi has minimal access to improved water and sanitation services. Most residents are without safely managed water. Although most residents will be considered having access to improved sources, the service level available is mostly basic and limited. Residents obtain water from a wide array of sources, most notably water from commercial, small scale vendors and sachet water distributors (Oppong et al., 2015; Rachmadyanto et al., 2016). Other common sources include private water resellers, hand dug wells, and rainwater harvested during the wet season (Fiasorgbor, 2013). Hence, the lack of improved water services imposes considerable costs on Nima's residents and puts their health at risk. Second, Nima-Maamobi is one of the largest, oldest, and most dense slum settlements in Accra. Finally, it is a vulnerable slum noted for its overcrowded layout, poor sanitation and drainage, and insecure housing tenure despite close proximity to some affluent urban areas in Accra. Nima-Maamobi also exemplifies urban poverty in Accra and Ghana and presents a unique context to understand preferences for limited and basic water services. In poor urban neighborhoods in Accra, it is common for households with private in-house connections to sell water to other households. Some of these private resellers have contracts with GWCL to sell water from their private taps and pay the utility a commercial rate. Others sell water illegally despite paying domestic rates to the utility. In Accra, sachet water and water from these vendors may be five to seven times more expensive than piped water (World Bank, 2015). Pathogenic contamination of water is prevalent in the area and is exacerbated by inappropriate in-home water storage practices (Machdar et al., 2013). The public utility, Ghana Water Company Limited (GWCL), is legally mandated to supply drinking water in the city of Accra. However, GWCL's investments in water infrastructure have been insufficient to meet the growing water demand in Accra (World Bank, 2015). Sometimes, GWCL avoids extending piped water services to slums because several households lack property titles or building permits (Amoako and Cobbinah, 2011). Previous work shows that GWCL is able to meet just about 60% of the current demand for drinking water in Accra and routinely rations water to different urban areas to manage the rapidly growing demand (Peloso and Morinville, 2014). Given GWCL's inability to increase household water connections, public standpipes remain an immediate, feasible means for addressing growing water demand in underserved urban areas. This study of household preferences for standpipe water services can provide relevant information to help design public standpipe services.

4. Survey and choice experiment design The survey questionnaire was designed using information previously gathered through conversations with community leaders and residents regarding existing water supplies and current water practices at the household level. The research team also consulted two local researchers with extensive experience on current conditions of the Nima-Maamobi community. Their feedback was incorporated into the questionnaire in several iterations. Preliminary drafts of the survey questionnaire were tested in the community by two senior undergraduate students from the University of Ghana with prior training and experience in survey data collection. The final survey questionnaire had a total of 23 questions (some of them with multiple parts) organized in three sections. First, respondents were asked about water sources and household practices regarding in-home water storage and treatment. The second section elicited household preferences for improved water services using a DCE. Finally, the survey gathered respondents' sociodemographic information.

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Table 1 Choice experiment design. Attributes

Levels

Days of service Service hours

Every day; Weekdays; Three alternate days; Weekends 8 AM–4 PM; 5 AM–1 PM; 12 PM–8 PM; 5 AM–9 AM & 3 PM–7 PM Treat to drink; safe to drink 5 min; 15 min; 30 min No waiting; 10 min; 20 min; 30 min; 60 min Neighborhood committee; Ghana Water Company Limited (GWCL); Accra Metropolitan Assembly (AMA); Nongovernmental organization (NGO) 5; 10; 15; 20; 25 Pesewas per bucket of 20 l

Water quality Distance Waiting Governance

Price

A generic (unlabeled) DCE was implemented to investigate local preferences for accessibility, availability, and quality of standpipe water services provided under different governance approaches. Table 1 shows seven attributes of the proposed water project including business days and hours, water quality, distance to and waiting time at the delivery point, service management, and the price of a 20-liter bucket. The business model used as base of comparison consists of standpipes installed in the community to provide water for 8 h every day, from 8 AM to 4 PM. The period of 8 h per day is consistent with a regular workday in the study site under the assumption that one employee would take care of the standpipe. The basic unit of water was a 20-l bucket, which is commonly used by water vendors and customers in the urban slums of Accra. Cook et al. (2016) used the same unit of water in their choice experiment implemented in rural Kenya. Variations of the business model were allowed by different attribute levels in the choice task. Two attributes were included to depict household preferences for water availability: 1) days in a week with water services, which could vary from every day to weekends only, and 2) service hours in a day (i.e. how the eight service hours would be distributed along a business day). Preferences for water quality were also investigated using a binary attribute that would provide safe drinking water or water that is unsafe and would require in-home treatment before drinking. The DCE included two attributes related to the time it would take to fetch water: 1) the average time that individuals would have to walk to the nearest standpipe varying from 5 to 30 min, and 2) the expected time they would have to wait at the standpipe due to customer crowding which could vary from no waiting to 60 min. In their choice experiment in rural Kenya, Cook et al. (2016) also used the time spent on fetching water as a measure of water availability, although they aggregated walking and waiting time in a single attribute. However, we separate those attributes to gain further insights about time preferences of Nima-Maamobi's residents. The standpipe water service would be considered a basic service when the sum of the attributes Distance and Waiting is not more than 30 min, and a limited one when walking and waiting time exceeds 30 min. Following Vásquez and Franceschi (2013) and Hope (2015), our choice experiment also included a service management attribute. We varied the service management across four potential providers. The proposed providers were: 1) the public utility known as Ghana Water Company Limited (GWCL), which currently provides water to the city of Accra and other urban centers across the country; 2) the Accra Metropolitan Assembly (AMA), which was the political and administrative authority for the city of Accra at the time the survey was implemented; 3) a nongovernmental organization; or 4) a neighborhood committee. With the exception of AMA, these service management approaches already exist in the country. Finally, the price of a 20-liter bucket of water could vary from 5 to 25 Pesewas (cents of Ghanaian Cedis). In DCE studies, respondents are asked to perform choice tasks to reveal their preferences. A task consists of choosing among two or more

options that vary from each other in attribute levels. The choice task can also include the status quo as an alternative in case the respondent is not satisfied with any of the options provided. Since it was not viable to compare the 9600 water service specifications that can be derived in our DCE (43 × 2 × 3 × 52), a total of 20 choice tasks were selected following an orthogonal experimental design. Each of those tasks had two alternative water service specifications with varied levels for each of the seven attributes of the DCE (see Table 1), and a status quo option. See Appendix A for an example of the choice tasks that respondents performed.3 After comparing those service specifications against each other and against the status quo, respondents were asked to choose their preferred option. The choice tasks were grouped into five sets so each respondent was asked to perform four choices to avoid respondent fatigue. A random sample of 344 households completed a questionnaire via an in-person interview process implemented in June 2017. Households were selected using a stratified sampling strategy. In the first stage, the study site was divided into 16 geographical strata using principal streets that were easily accessible. Then, a central point was selected in each stratum and the next home to be interviewed was selected by casting a dice. Surveyors were instructed to only interview households without private in-house water connections. When they encountered a sampled household with a private tap, surveyors moved on to the next house without a tap. Surveyors were also instructed to ask for a household head above 18 years to respond the survey. In their absence, a spouse or any adult older than 18 years was eligible. Respondents performed 1368 choice tasks, which provided the data analyzed in this study.4 5. Analytical framework and econometric modeling Results from DCEs can be analyzed using the Random Utility Model (RUM). In this model, the utility derived from an alternative depends on observed and unobserved attributes of the alternative and individual. While observed attributes are depicted by explanatory variables, random variables are used to represent unobserved characteristics. Given that individuals are assumed to choose the alternative that gives them the highest utility, there is a monotonic relationship between the conditional indirect utility (V) that the individual n derives from alternative i at time t (or choice task t) and the probability of choosing alternative i over any other alternative j [i.e. Pr(i) = Pr(Vnit N Vnjt)]. Hence, choice responses can be empirically analyzed using a multinomial modeling approach. The conditional logit model was traditionally used to analyze choice responses. However, that model does not consider that the association between utility and attributes can vary across individuals due to their heterogeneity. Different modeling strategies have been proposed to account for the unobserved heterogeneity of respondents. For instance, under the assumption that most of the heterogeneity across respondents is due to taste differentials, McFadden and Train (2000) propose adding random coefficients to the average level of utility derived from a given attribute (i.e. the mixed logit model). On the other hand, Louviere et al. (1999, 2008) argue that respondents are heterogeneous because choice behavior is more random for some individuals than others. This implies that the scale of their errors is greater. Hence, Louviere et al. (1999) proposed allowing the scale parameter to vary

3 Interviewers were instructed to explain to respondents the choice tasks they were asked to perform. First, respondents were reminded about current water conditions in the community. Then, interviewers carefully explained the attributes included in the DCE. Respondents were informed that the project would entail payments that would reduce their disposable income for other needs to imprint realism to the choice task. Finally, respondents were provided with an example of the choice tasks so they could become familiar with the process of choosing among the three alternatives in each task. This example was not included in the analysis. 4 Respondents were asked to perform a total of 1376 choice tasks (344 × 4). However, a small number of respondents declined to perform all of the four choice tasks included in the survey. Thus, the econometric analysis was conducted on 1368 choices.

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across respondents so it proportionally modifies the average effect of all attributes (i.e. the scale-heterogeneity multinomial logit model).5 Building upon those approaches, Fiebig et al. (2010) proposes a generalized multinomial logit (GMNL) model that accounts for heterogeneity in both taste and choice behavior. Under the assumption that taste and choice behavior heterogeneity are independent, random components can be introduced into the conditional indirect utility (V) that the individual n derives from alternative i in choice task t as follows:   V nit ¼ σ n β þ ηn X it þ ε nit

609

Table 2 Profile of respondents and their household. Characteristics

Mean

S.D.

Proportion of female respondents Age of the respondent Monthly household income Household size Years of residence in current home Number of rooms

0.66 37.20 586.61 6.43 20.00 1.86

0.23 14.10 334.30 4.81 15.60 1.22

ð1Þ

In Eq. (1), Xit is a vector that represents the attributes of alternative i in choice task t (chosen from attribute levels presented in Table 1), and β is the conformable vector of coefficients to be estimated in order to depict the average utility derived from each attribute. ηn is a vector of normally distributed random variables that depict unobserved taste heterogeneity of individual n, andσn is the scale parameter that varies across individuals to account for heterogeneity in choice behavior. To ensure that the scale parameter is positive, it is modeled as an exponential function of a normally distributed random variable (wn) and an estimable parameter τ [i.e. σn = exp.(τ wn)]. Fiebig et al.'s (2010) GMNL model nests other approaches commonly used to analyze DCEs. The mixed logit model can be derived from Eq. (1) if the scale parameter σn is normalized to one for all respondents. The scale-heterogeneity multinomial logit model is also a special case of the GMNL model in which the variance of random parameters ηn equals zero. The GMNL model also nests the conditional logit model [i.e. σn = 1 and Var(ηn) = 0]. Therefore, Fiebig et al.'s estimation approach allows for empirically selecting the model specification that best fits the results of a DCE. We estimated the GMNL model using the userwritten Stata command gmnl, which implements a maximum simulated likelihood approach (Gu et al., 2013). 6. Survey and estimation results Table 2 shows a profile of the average respondent. A majority of respondents were female household heads, which is not surprising given that interviews were held at business hours when males are more likely to be at work than females. The average respondent was 37 years old. On average, sampled households earned GHC 587 (US$ 146.75) in a month and had more than six members. The average respondent had lived 20 years in their current home, which had approximately two rooms. Survey results indicate that Nima-Maamobi's residents procure water primarily from two sources: sachet water (94%) and water sold by private tap owners (98%). For cooking and hygiene practices, approximately 98% of the respondents use the water they fetch at neighboring households (see Fig. 2). For drinking, however, sachet water is the primary source for most respondents (68%), followed by the tap water fetched at neighboring households (32%). This suggests that a majority of Nima-Maamobi's residents considers sachet water to be of better quality or have better taste than tap water. Findings indicate that both tap water from neighbors and sachet water are accessible in the settlement (see Table 3). On average, households spend 3.7 min walking to the nearest neighbor's tap, and 2.28 min to purchase sachet water. Based on these accessibility estimates, neighbor's taps and sachet water could be considered as basic water sources for Nima-Maamobi's residents, according to definitions of the new JMP water ladder. Yet, questions about service availability remain given that water supply is often interrupted for long periods of time

(Peloso and Morinville, 2014). Equally important is the issue of affordability. Our findings indicate that households spend an average of GHC 78 (US$ 19.50) per month on water procured at neighboring households with private water connections (see Table 3). In addition, households that consume sachet water spend an average of GHC 51 (US$ 12.75) in a month. As almost all sampled households consume water from those two sources, households in Nima-Maamobi may spend more than GHC 129 (US$ 32.25) on water in a month. This expenditure is equivalent to approximately 22% of the average household income, which raises concerns about water affordability in Nima-Maamobi. Table 4 shows three choice models estimated under different assumptions regarding the heterogeneity of respondents: 1) conditional logit (CL) model, 2) mixed logit (MIXL) model, and 3) generalized multinomial (GMNL) model. The CL model assumes that respondents are homogenous. The MIXL model relaxes that assumption by controlling for taste heterogeneity. The GMNL model controls for both taste and choice behavior heterogeneity. Estimation results are consistent across all model specifications. Yet, the Akaike and Bayesian information criteria indicate that the GMNL model fits the data better than the CL and MIXL specifications. In addition, the estimated parameter τ and the standard deviation of some random variables (i.e. random effects) are statistically significant suggesting the presence of both taste and choice behavior heterogeneity. Hence, we discussed the results based on the GMNL model. Estimated coefficients on the days of service are negative and statistically significant. These results indicate that, compared to the utility derived from having water every day (i.e. the base of comparison), households lose utility when water is provided for fewer days. Households would experience the lowest level of utility if water were provided in the weekends only. On the other hand, households seemed indifferent about service hours in a day. The exception was the service period of 5 AM to 1 PM, which decreases the utility level relative to the base period of 8 AM to 4 PM. It can be presumed that it would be difficult to fetch water between 5 AM and 1 PM because most household members are preparing for work or school or at those places. In contrast, other proposed periods are more convenient as they include hours in the morning and afternoon. Water quality was the most important attribute for respondents, as indicated by corresponding coefficients that were the largest across all

Hygiene

98.2%

0.0%

Cooking

97.6%

0.6%

Drinking

31.2%

0%

20%

68.2%

40%

60%

80%

5

The latent-class logit model is another approach commonly used to control for heterogeneity among respondents. While appealing for generating discrete types of consumers, it has been suggested that latent-class models understate respondents' heterogeneity in choice data (Allenby and Rosi, 1998; Elrod and Keane, 1995).

Neighbor's Tap

Sachet Water

Others

Fig. 2. Primary water source by household activities.

100%

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Table 3 Time and money spent on water from main sources.

Walking time to nearest water source (in minutes) Monthly household expenditure on water (in GHC)

Neighbor's tap

Sachet water

Mean

Mean

S.D.

S.D.

3.74

3.72

2.28

2.96

78.24

287.35

51.15

132.79

models. This result indicates that households prefer safe drinking water over water that still requires in-home treatment to be drinkable. Households are indifferent about the time they would have to walk to reach the nearest standpipe, which could be explained by the fact that the timeframe used for this attribute (5 to 30 min) is longer than the time households currently spend procuring water. In contrast, waiting at the standpipe to be served was relevant, with the household's utility decreasing with waiting time. This suggests that respondents would prefer a basic water service over a limited one. In terms of service management type for the proposed public water service, respondents were indifferent between a community-based approach and a system managed by a nongovernmental organization. Conversely, relative to the community-based management approach, respondents seem to dislike the public water company (GWCL) and the municipal authority (AMA), as indicated by corresponding coefficients that were negative and statistically significant. As expected,

estimated coefficients on the price attribute were negative and statistically significant. This indicates that the utility derived from having access to water decreases as the water price increases. It was also expected that the alternative-specific constant (ASC) was negatively related to utility levels because the proposed project would be an improvement over the current water access condition. By including a payment variable among the attributes, one can estimate the marginal willingness to pay (also known as part-worth) for moving from one attribute level to another. This can be achieved by multiplying estimated utility coefficients by the negative reciprocal of the coefficient corresponding to the payment variable (i.e. –β / βPAYMENT). Table 5 presents marginal willingness-to-pay estimates for each attribute. Relative to daily provision (i.e. every day in a week), the respondents' willingness to pay for a 20-l bucket of water is about 40 Pesewas (US$ 0.10) lower if water services are provided only during weekdays, 45 Pesewas (US$ 0.11) lower if water is provided three days a week, and 72 Pesewas (US$ 0.18) lower if standpipes function only during weekends. The willingness to pay for 20 l of water is about 33 Pesewas (US$ 0.08) lower if standpipes are open from 5 AM to 1 PM rather than at 8 AM to 4 PM. As stated above, households value having access to safe drinking water. Estimation results indicate that respondents would pay about 76 Pesewas (US$ 0.19) more for 20 l of safe drinking water than for the same amount of water that would have to be treated at home for drinking purposes. The willingness to pay for a bucket of water would decrease by about one Pesewa for each minute that respondents

Table 4 Choice models in utility space. Conditional logit model

Days of service Weekdays Three days Weekends Daily service hours 5 AM–1 PM 12 PM–8 PM 5–9 AM & 3–7 PM Quality Distance Waiting Management GWCL AMA NGO Price ASC τ No. of choice tasks Pseudo R2 AIC BIC

Mixed logit model

Generalized multinomial model

Coefficient

Random effects

Coefficient

Random effects

Base = every day −0.462 (0.150)⁎⁎⁎ −0.715 (0.118)⁎⁎⁎

−0.686 (0.290)⁎⁎ −1.298 (0.264)⁎⁎⁎

−2.536 (1.223)⁎⁎ −2.842 (0.968)⁎⁎⁎

−0.851 (0.144)⁎⁎⁎

−1.633 (0.306)⁎⁎⁎

0.751 (0.451)⁎ −0.249 (0.430) −1.270 (0.447)⁎⁎⁎

0.558 (0.423) −0.348 (0.443) −1.280 (0.449)⁎⁎⁎

Base = 8 AM–4 PM −0.424 (0.143)⁎⁎⁎ 0.144 (0.163) 0.038 (0.145) 1.472 (0.097)⁎⁎⁎ 0.005 (0.005) −0.012 (0.003)⁎⁎⁎

−1.033 (0.307)⁎⁎⁎ −0.054 (0.292) −0.254 (0.273) 3.026 (0.408)⁎⁎⁎ 0.001 (0.008) −0.024 (0.005)⁎⁎⁎

0.137 (0.362) 1.150 (0.378)⁎⁎⁎ −0.753 (0.462) 2.243 (0.369)⁎⁎⁎ 0.055 (0.014)⁎⁎⁎ 0.020 (0.009)⁎⁎

−2.122 (1.011)⁎⁎ 0.464 (0.897) −0.359 (0.717) 4.882 (1.389)⁎⁎⁎ 0.023 (0.028) −0.062 (0.025)⁎⁎

−0.491 (0.619) 0.795 (0.374)⁎⁎

−1.154 (0.599)⁎ −1.268 (0.640)⁎⁎

0.116 (0.124) −0.019 (0.008)⁎⁎ −4.898 (0.699)⁎⁎⁎ –

−0.224 (0.205) −0.257 (0.221) 0.042 (0.225) −0.045 (0.015)⁎⁎⁎ −7.056 (0.940)⁎⁎⁎ –

−0.035 (0.361) 0.020 (0.022) –

0.900 (0.642) −0.064 (0.033)⁎ −27.714 (14.376)⁎ 1.103 (0.253)⁎⁎⁎

1368 0.544 1397.93 1486.40

1368 – 1327.62 1498.25

– – – –

Base = community-based committee −0.096 (0.106) −0.213 (0.118)⁎

–

−4.621 (1.769)⁎⁎⁎

1368 – 1305.83 1482.78

Notes: Standard errors, reported in parenthesis, are clustered by respondent to account for potential correlation among choices made by the same individual. ⁎ Implies statistical significance at 10% level. ⁎⁎ Imply statistical significance at 5% level. ⁎⁎⁎ Imply statistical significance at 1% level.

0.222 (0.414) 0.470 (0.366) 0.848 (0.454)⁎ 1.787 (0.410)⁎⁎⁎ 0.028 (0.016)⁎ −0.0003 (0.011) 1.412 (0.396)⁎⁎⁎ 0.010 (0.448) −0.476 (0.356) 0.007 (0.024) – – – – – –

W.F. Vásquez, E.A. Adams / Science of the Total Environment 673 (2019) 605–612 Table 5 Part worth estimates derived using the generalized multinomial model. Pesewas Days of service Weekdays Three days Weekends Daily service hours 5 AM–1 PM 12 PM–8 PM 5–9 AM & 3–7 PM Quality Distance Waiting Management GWCL AMA NGO

−39.62 (20.51)⁎ −44.39 (21.14)⁎⁎ −72.18 (37.79)⁎ −33.15 (17.45)⁎ 7.24 (16.17) −5.61 (11.02) 76.25 (36.87)⁎⁎ 0.36 (0.55) −0.97 (0.50)⁎ −18.02 (9.91)⁎ −19.80 (15.55) 14.06 (13.25)

Notes: Standard errors, reported in parenthesis, are clustered by respondent to account for potential correlation among choices made by the same individual. A Pesewa is a cent of a Ghanaian Cedi. ⁎ Implies statistical significance at 10% level. ⁎⁎ Implies statistical significance at 5% level. ⁎⁎⁎ Implies statistical significance at 1% level.

would have to wait at the standpipe due to consumer crowding. This implies that the marginal benefit of moving from a limited service to a basic service would be at least one Pesewa per bucket of water supplied (i.e. reducing collection time from 31 to 30 min). The willingness-to-pay estimates are statistically insignificant for management attributes except for the GWCL coefficient which is statistically significant at 10% level. This coefficient indicates that respondents would be willing to pay 18 Pesewas (US$ 0.04) less for the 20-liter bucket of water if the standpipe service is managed by GWCL instead of a community-based committee. 7. Discussion and conclusions Many urban settlements in developing countries continue to lack access to improved water services as population growth and rapid urbanization cause demand for water to outpace available supply. For poor urban areas and informal settlements, poverty, insecure tenure, and economic and institutional restraints impede the necessary investments in infrastructure needed to provide safely managed water services on premises. Under those circumstances, alternative forms of water provision with different levels of water availability, accessibility, and quality, may be considered as an immediate step towards helping households in poor urban areas to climb the water ladder (Adams, 2018). Using a DCE, this study examined household preferences for intermediate forms of improved water services (i.e. limited and basic services) and management approaches in the urban settlement of Nima-Maamobi, Ghana. Consistent with the JMP ladder of safe drinking water (see World Health Organization, 2017), our experimental design included two water availability attributes (i.e. days and hours of service), an attribute on water quality, two accessibility attributes (i.e. walking and queuing time), and an attribute on service governance. After controlling for the heterogeneity of respondents, this study found strong local preferences for basic services over limited ones, and provided WTP estimates for different levels of availability, quality, and accessibility of water services. Our estimates can be used to compute households' willingness to pay for a 20-l bucket provided under different service configurations. For instance, households would be willing to pay approximately GHC 5 (about US$ 1.25) for 20 l of safe drinking water provided by a community-based committee every day of the week from 8:00 AM to 4:00 PM, and about GHC 4.30 (about US$ 1.07) if the water still needs to be treated at home for drinking. These estimates indicate that the average household would pay up to 0.85% of its monthly income for a 20-l bucket of drinking water, which may raise questions about the

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affordability of improved water services and the validity of our findings. Yet, the survey results indicate that, on average, sampled households currently spend more than 22% of their monthly income on water, which is consistent with what other studies report about the cost of water from vendors in the slums of Accra (Van Rooijen et al., 2008; Ainuson, 2010). Moreover, our WTP estimates are also consistent with current prices of alternative water supplies in Ghana. As a point of comparison, 20 l of sachet water, which 94% of sampled households consume, cost approximately GHC 6.25 (US$ 1.56).6 The willingness to pay gradually decreases if water services worsen in terms of availability, particularly if services are provided fewer days in a week. Service hours between 5:00 AM to 1:00 PM also seem to be inconvenient for households. Contrary to our expectations, respondents were not sensitive to the distance of standpipes relative to their home (i.e. the value of traveling time is minimal), which could be explained by the proximity of current water sources. In contrast, respondents showed strong preferences against waiting in line for water services once they are at the standpipe. This suggests that households in poor urban areas would benefit more from functional standpipes dispersed across the community than from intermittent sources that would require longer waiting times. Our estimates indicate that respondents value that waiting time at a rate of GHC 0.60 per hour (US$ 0.15), which is equivalent to 55% of the official minimum wage.7 It can be argued that the value of time spent on procuring water is lower than wage rates because households reallocate housework time to fetch water rather than leisure time, and the value that households assign to housework time is lower than wages paid in labor markets (Abramson et al., 2011; Vásquez, 2014). WTP estimates above clearly indicate that Nima-Maamobi's residents have strong preferences for a basic service that would provide them with safe drinking water every day of the week in regular business hours. Those WTP estimates can be compared with water supply costs under different business arrangements to investigate whether extending improved water services to Nima-Maamobi's residents is economically viable. WTP estimates can also be used to implement price structures that may help with cost recovery. Moreover, with careful design, the findings of this study can be extrapolated to communities with characteristics similar to the study site. This study also provided insights on local preferences regarding potential service administrators. The GWCL is legally mandated to provide water services in the city of Accra. Despite being at the center of the city, Nima-Maamobi's residents have minimal access to improved water services presumably due to economic and institutional constraints. Over time, this may have diminished the public trust in GWCL as a provider. A similar trust issue may exist between the community and the Accra Metropolitan Assembly, whose mandate to improve sanitation in the Accra Metropolitan area inclusive of Nima-Maamobi leaves much to be desired. This may partially explain why respondents would prefer a community-based committee or a non-governmental organization to manage the proposed public standpipes rather than GWCL or AMA. Some recent work suggests that community-based governance of water may hold promise for urban informal settlements (Adams and Boateng, 2018). However, other studies found strong local preferences against community-based management approaches (e.g. Hope, 2015; Vásquez, 2014). These contradictory findings indicate that household preferences for management approaches depend on context and trust in existing institutions. Therefore, future studies can pay more attention to local preferences regarding service administrators to identify service governance arrangements that would garner support from potential beneficiaries.

6 See https://www.modernghana.com/news/670807/sachet-water-price-goes-up.html (last accessed on October 23, 2018). 7 In 2017, the official minimum wage in Ghana was GCH 8.80 per day.

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A potential limitation of our study is that women were slightly overrepresented in our sample of respondents. Gender unbalanced samples could introduce some biases in willingness-to-pay estimates; although that is not always the case (see Vásquez and Franceschi, 2013). Given that women often carry the burden of fetching water, they could perceive greater benefits from a project intended to improve water services than men, and thus would be willing to pay more for those improvements (Genius et al., 2008). On the other hand, because males tend to be the income-earning members of the household, women may have limited control on household's financial decisions and therefore could be willing to pay less than males (Null et al., 2012). In this study, women and men were equally likely to choose an improved service specification over the status quo, suggesting that gender dynamics had little influence on our willingness-to-pay estimates. Yet, since our study was conducted at the household level, we could not investigate intra-household differentials in willingness to pay for improved water services. This could be a logical extension to our study. Acknowledgments Funding for the project was provided by the Global Studies Institute, 38 Georgia State University. The authors are grateful to all research assistants: John Essel, Lord 39 Bobbie, Ebeneezer Arthur, Peter Bamfo, and Philip Adjuah. Appendix A. Example of choice task

Option 1

Option 2

Opt out

Days in a week with water

Hours in a day with water (8 h)

Water quality Maximum walking time to the standpipe Average waiting at the standpipe Provider Price per bucket of 20 l (Pesewas) Your choice

5:00 AM–9:00 AM and 3:00 PM–7:00 PM TREAT TO DRINK 5 min

8:00 AM–4:00 PM

SAFE TO DRINK 30 min

60 min

10 min

GWCL 20 Pesewas □

5 Pesewas

Would rather fetch water from current sources

NGO

□

□

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