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WILLINGNESS TO PAY TO IMPROVE DOMESTIC WATER SUPPLY IN RURAL AREAS OF CENTRAL TANZANIA: IMPLICATIONS FOR POLICY

ALOYCE R.M. KALIBA Postdoctoral Research Fellow, Aquaculture and Fisheries Center University of Arkansas at Pine Bluff 1200 North University Drive, Mail Slot 4912 Pine Bluff, Arkansas 71601 Tel: (870) 543 8791; e-mail: [email protected] DAVID W. NORMAN Professor, Department of Agricultural Economics Kansas State University 342 Waters Hall, Manhattan, KS 66502 Tel: 785 532 4484 E-mail: [email protected] and YANG-MING CHANG Associate Professor, Department of Economics Kansas State University 319 Waters Hall, Manhattan, KS 66502 Tel: 785 532 4573; E-mail: [email protected]

Submitted to: The International Journal of Sustainable Development and World Ecology

ABSTRACT This analysis estimates willingness to pay to improve community-based rural water utilities in the Dodoma and Singida Regions of Central Tanzania using Multinomial Logit functions. An estimate of willingness to pay provides an indication of the demand for improved services and potential for them being sustainable. Surveys were conducted in a total of 30 villages in the two regions. In the Dodoma Region about 14% of respondents indicated that they were satisfied with the status quo, 64% suggested increasing water discharge and watering points, and 22% proposed other improvements relating to water quality. In the Singida Region, 31% of the respondents were satisfied with the status quo, 59% wanted deeper boreholes and watering points, and 10% indicated other types of improvement relating to water quality. The Multinomial Logit functions indicated that the interaction between the water quality variable and proposed bids were important in making choices with reference to the type of improvement desired. Respondents who wanted to increase water supply in Dodoma Region were willing to pay 32 Tsh above the existing tariff of 20 Tsh/bucket. In the Singida Region, the analogous amount was 91 Tsh per household per year above the existing user fee of 508 Tsh per household per year. If the tariff or user fees have to be increased, the estimated average potential revenue for the surveyed villages was 252 million Tsh/year ($265,263) in the Dodoma Region, and 5.2 million Tsh/year ($5,474) in the Singida Region. In the future, strategic planning is needed to ensure that improvements proposed potentially improve cost recovery initiatives and increase the level of consumer satisfaction. Also, care will be needed to ensure that more disadvantaged community members do not suffer unduly from increases in tariff or user fees. Economic Literature Classification: Q25; C35 Keywords: Central Tanzania, Multinomial Logit, Probit, Willingness to pay

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INTRODUCTION Lack of access to safe water is at the heart of the poverty trap, especially for women and children, who suffer in terms of illness, drudgery in collection of water, and lost opportunities because of the time that water collection consumes. In rural Africa, according to the World Bank, 40 million hours are spent each year in collecting water for domestic use and half of Africa’s population is without access to safe water (Black, 1998). Recent renewed focus on poverty alleviation has resulted in increased attention to the benefits of improved water accessibility. Poverty assessment research has consistently shown that improvement in water services is a critical element in designing and implementing effective strategies for poverty alleviation. In Tanzania, experience with respect to rural water improvement has, in general, been an unmitigated disaster. Water provision facilities have invariably fallen into disuse or disrepair because the approach used to run them failed to ensure sustainability of services. As a result, a new water sector vision has emerged based on the demand responsive approach (DRA). The major principle of the vision regards water as an economic and social good to be managed at the lowest appropriate level. The DRA recognizes the inherent capacity of communities in taking greater responsibility for identifying and solving their water supply problems. Adoption of the DRA by most water and sanitation programs has changed the approach to evaluating their success and failure. Traditionally, water and sanitation programs have depended on detailed blue prints to provide the basis for control and predictability. Benefit-cost analysis has been a major economic tool in evaluating these projects. However, the DRA broadens the scope of evaluation and blueprints cannot be drawn up since decisions are made jointly with communities, and problem solving is based on those partnerships

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(Narayan, 1995). Participatory evaluation becomes an essential tool in assisting attaining stated objectives and is an integral part of overall program monitoring and evaluation activities. Participatory evaluation requires the utilization of different tools and approaches in evaluating the sustainability of such projects. This paper is part of a follow-up participatory evaluation exercise of two community water supply programs in Central Tanzania and involves presentation of the results on communities’ willingness to pay (WTP) towards improving the availability and quality of water services. WaterAid, a non-governmental organization (NGO) based in the United Kingdom and the Lutheran World Federation through the Tanganyika Christian Refugee Service (TCRS), provide substantial support and expertise towards such programs. The former operates in the Dodoma Region while the latter operates in the Singida Region. The two regions constitute the semi-arid plateau of Central Tanzania. It is anticipated that positive WTP indicates potential community ability to recover operation and management costs (Altaf, Jamal, and Wittington, 1992). Thus WTP can be used to help ascertain the potential for fulfilling sustainability, at least from a financial viewpoint. A major objective of the study was to find out if communities studied were willing to pay an increased user fee or tariff for improving and expanding existing water services. If willing, how much (i.e., what percentage) could be added to the current user fee or tariff to achieve the required improvement? Based on the random utility hypothesis, a system of Multinomial Logit functions were developed and used to estimate individuals’ willingness to pay and impact on revenue from increasing the water user fees or water tariff. The WTP was calculated by estimating the payments that would cause the respondent to be indifferent after a single unit increase in an independent variable. The estimated mean WTP

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and proportion of individuals who are able to pay were used to calculate the potential revenue that could be generated if the policy of increasing user fee or tariff was instituted. The paper is organized as follows. In the next section, a background of the study area and the programs studied, are presented.

This is followed by a discussion of the

Multinomial Logit model used in the analysis. Results are then presented and then the paper concludes with a summary and policy recommendations.

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BACKGROUND In Tanzania, only 40% of the population has access to clean and safe water (MoW, 1997). Between 1965 and 1985, the Government of Tanzania (GoT) spent a substantial amount of money in developing rural-based water projects to alleviate the water supply problem but without notable success. Most of the investments ended in failure. Government, in collaboration with NGOs, is currently trying to revive many of these projects. The government is providing technical assistance, while the NGOs supply initial capital requirements, overhead costs and supervision in their design and implementation. When the utility is operational, the beneficiaries (i.e., the villagers) either pay for the water from the utility or contribute an annual water fee, both of which are added to the village water fund. It is anticipated that the size of the village water fund will be sufficient to operate and expand the water utilities, based on market forces. To attain such sustainability related goals, existence of efficient water markets is essential. Sufficient funds have to be generated to meet both overhead costs and investment requirements. Information on WTP for required improvements provides an indicator of the communities’ potential capability of generating funds for such purposes.

Tanzania Water Policy and the Study Area Tanzania has sufficient surface and ground water to meet its present needs. Lack of capital and uneven availability of water across regions has limited Tanzania’s capacity of utilizing existing water resources to provide her population with clean and safe water. As population increases, conflicts in the allocation of water between energy, households, commercial livestock, and use in the agricultural sub-sector, increases. Weak water policies,

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inadequate community participation, and uncoordinated donor support has resulted in wastage or inefficiencies in the form of failed water development programs. After independence in 1960, donors supported large and small-scale water development schemes. However, communities participated little in their planning and management of, and also contributed little to both capital and operational/management costs. The policy of the government at independence was “free, clean and safe water for all,” the objective being to provide clean and safe water to all villages in rural Tanzania by the year 2000 (MoW, 1997). As indicated above this policy has now been abandoned in favor of a demand driven water development program. The new water policy enacted in 1997 (MoW, 1997), promotes the provision of efficient, affordable and sustainable water supplies and sanitation. The emphasis is on community planning and management, private provision of goods, works and services related to water supply, and public sector regulation, facilitation and environmental management. Water supply or water development programs have to be demand driven, and the community must be willing to participate in decentralized management and cost sharing. The Ministry of Water (MoW) has been restructured and its role has evolved from that of a water service provider to that of a regulator and a facilitator. The new water policy underscores the importance of community participation and management in ongoing and new projects. In order to improve water supply services at the village level, the roles of MoW and other stakeholders can be summarized as follows (MoW, 1997): •

Village level. Small-scale water supply projects are to be operated and managed at

the village level. Operation and management costs are to be met with funds raised within communities. The emphasis is on management by participation through formulation of village water committees that oversee and manage the utilities on behalf of community

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members. The communities agree upon the operational modes with specific emphasis being paid to women participation at all stages of water project development and management. •

District level. The water department at the district level is required to facilitate

training of water managers and attendants/mechanics at the village level. The department is expected to maintain a pool of experienced technicians who will collaborate with village level mechanics in servicing and repairing established utilities. Another role of the water department at the district level is to make sure that necessary spare parts are available when needed. •

Regional level. The water department at the regional level has jurisdiction in

providing guidance and making sure that government water policy and rules are adhered to. Apart from managing water supplies at the regional headquarter level, the department has to provide consulting services to districts and villages in terms of training, and facilitating availability and distribution of spare parts. •

National level. At the national level, the MoW has responsibility for financing and

managing large-scale water supply programs (i.e., especially in large cities), to train water professionals, and to finance maintenance units at the district and regional levels. Other responsibilities are to standardize capital equipment and tools used in water development to help facilitate availability of spare parts, and to coordinate donor and NGOs activities to make sure they follow Tanzanian water policies and local government rules and regulations. The general objective of the water policy is to propagate the new vision of community participation and management based on the DRA. Communities are expected to appreciate that water supply is no longer a free service provided by government, but rather government or other development agencies are available to facilitate and complement community efforts at meeting their own water supply needs. Thus this vision is currently

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guiding the development of community-based water supply projects at the village level in the Dodoma and Singida Regions of Central Tanzania. Detailed descriptions and the rules pertaining to the programs in each region can be found in Kaliba (2002) or the WaterAid website (http://www.wateraid.org.uk) (i.e., for the Dodoma Region program). In both regions, financing for the water projects is thus divided among the beneficiaries, the government and donors. Villagers make cash contributions towards capital costs. They contribute time and labor, local materials and contribute to hospitality costs of visiting government staff. The initial amount of money contributed to a project by a village has been standardized and agreed by the major donors working in the regions. The cash contribution is not currently expendable but acts as a reserve fund or safety valve for the project. The community based water fund has to be increased using profit margins generated from user fees (Dodoma Region) or tariffs (Singida Region) when the project is operational. The government provides professional staff, an annual cash contribution towards the program, and provides some transport and most of the construction equipment. Donor (i.e., WaterAid in the Dodoma Region and TCRS in the Singida Region) funding pays for the purchase of locally procured materials, government staff fieldwork allowances, training courses and running costs. The donors also procure all imported materials, equipment and vehicles, and employ technical and management back-up staff. In the Dodoma Region the village water utilities have emphasized deep boreholes and engine driven pumps, and charge a tariff (i.e., a specific amount per litre of water used). In contrast, in the Singida Region, much greater emphasis is placed on shallow wells (i.e., 8 - 10 metres deep) and a user fee (i.e., a specific amount per year per household), is charged.

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In the Dodoma Region, between 1991 and 1999, 357 projects were implemented under the WAMMA1 project funded by WaterAid. During this study period, 29% of the projects were not functioning (Table 1). For those not working, 75% were waiting to be repaired, and 25% had permanent problems (i.e., missing engines or damaged pumps or dried up water holes). Useful indicators of progress are the growth in the populations served and village water funds voluntarily contributed by communities. The total population served increased from 0.27 million 1994 to 1.23 million in 1999. This is an increase of about 320% over a period of six years. During the same period, the contributions to the village water funds increased, in real Tsh terms, from about 0.24 million in 1994 to about 14.87 million in 1999. Since there is no evidence of a regional increase in purchasing power, and given the low income per capita in the region (BoT, 2000), this is a good indicator of the success of community-based water development projects in the region. With respect to the Singida Region, Table 2 outlines project achievement in terms of water utilities constructed at the village level between 1986 and 1995 by TCRS. During that period, TCRS constructed 49 boreholes, 465 shallow wells, and three windmills. The number of villages with water committees increased from three in 1986 to 244 in 1995. In the same period, three villages did set up water committees without the help of TCRS. The number of villages with water funds increased from zero in 1986 to 229 in 1995. Although these numbers indicate the magnitude of time and money invested in water utility projects, they of course do not provide by themselves any explicit evidence of the potential sustainability of the projects and/or progress towards achievement of sustainability goals.

1

Acronym for: WaterAid, Afya (health), Maji (water), and Maendeleo ya Jamii (community

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THE EMPIRICAL MODEL To elicit WTP information from individuals, the contingent valuation (CV) method is used. There are three major procedures used in CV surveys, namely conjoint analysis, dichotomous choice and the payment scale approaches. The conjoint analysis procedure asks respondents to rate rather than to price alternatives. It allows for measurement of consumer preference between items with multiple attributes (Baidu-Forson, Ntare and Waliyar, 1997). In the dichotomous choice approach, respondents are asked whether they would vote to change the provision of some public good at a cost of $X to themselves. The respondents answer yes or no. For the respondent who answers yes, the experiment is repeated by varying X, until the respondent says no. After that, the respondent is asked to state the value he/she is willing to pay for the good or service if provided. For the respondent the amount mentioned is considered the maximum WTP for that good or service. The payment scale procedure allows respondents to choose a value or price (cost) on a given scale measure. A range of values are presented to the respondents and they are asked to identify one value or price (cost) they are willing to pay or incur to purchase the good or service being evaluated. The respondent’s choice from the given scale indicates the WTP for the stated good or service. Positive values indicate positive demand, and zero values indicate zero demand for the good or service services. In all cases, the surplus benefits are calculated using the parameters estimated from a model specified based on the type of distribution attached to WTP. In most cases, the models and type of distribution to use are dictated by the type of questions imposed and responses obtained. For examples see: Alberni, Kanninem and Carson (1997) for random effects models; Rollins (1997); Lindberg, Johnson and Barrens (1997) for models with

development).

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logistic distributions; and Cooper (1997) for two-step or one-way up models. Other good references are those of Rosenberger and Walsh (1997) for ordinary least squares models; Kenkel and Norris (1995) for models with log-normal distribution; Steven, Barrett and Willis (1997) for Tobit models; and a classical work by Haneman (1984) for Probit and Logit models. CV surveys entail three operations: designing a questionnaire, conducting a survey, and analyzing the results. The validity of the estimates depends on the how skillfully these operations have been done (Christe and Schwab, 1995). Donaldson, Thomas and Torgerson (1997) suggest that the use of a payment scale is more valid than the open-ended or closed ended approach when presenting hypothetical bids. The limitation of the payment scale is constructing scaling benchmarks. This limitation can be reduced through using available market and nonmarket information to construct the scales as suggested by Donaldson, Thomas and Torgerson (1997). From the above review, it can be seen that there are different approaches to modeling WTP. Nevertheless, all procedures involve the respondent choosing one option from a range of other alternative services or goods based on their expectations. In order to develop models that are consistent with economic theory, the McFadden (1981) random utility hypothesis has become a standard approach to modeling WTP. As far as our study was concerned, the objective was to find out if communities are willing to pay an increased fee or tariff in order to get additional money for improvement or expansion of existing water utilities. If willing, how much (i.e., what percentage) should be added to the current user fee or tariff to achieve the required improvement? Based on the McFadden random utility, U, the condition on choice j can be specified as an additive separable, linear function of water and any other goods’ consumption. Each

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individual is willing to pay for the desired water services improvement designed as j, to maximize the conditional utility. The resulting indirect utility function is: V

j

= β

0j

+ β

1j H

j

+ β

M

( 2j Y

i

- P j) +

∑β

3i

D

i

+ ε

(1)

i=1

Where: Vj is the utility obtained from improving the desired water service j, and j=1,2,..K; Hj is the expected improvement in water quality or quantity; Yi is individual income; Pj is the amount an individual is willing to pay to get improved water services j; Di is a vector of variables describing the demographic characteristics of the respondent; âij are parameters of the model; and å is the normally distributed random error term. The stable utility maximization condition requires imposing the following restrictions, â1j=â1k , â2j=â2k , â3j=â3k . The restriction allows utility to be rational and transitive. However, in most studies, the restrictions are tested for statistical validity rather than being imposed. Furthermore, the McFadden randomly utility model relies on additive separability. This condition is violated by Equation (1) because it is linear in parameters. The additive separability condition can be relaxed by allowing interaction between Hj and (YiPj) variables (Russell, Rushby and Arhin, 1995). The interpretation of the interaction is that the marginal utility of payment depends on an expected improvement in water quality or quantity. When deciding to pay for quality, an individual compares the change in utility, ÄVj) between seeking improvement and maintaining the status quo. The utility difference of available alternatives and interaction between Hj and (Yi-Pj) variables is given by: ∆V j = ( β 0 j + β 0 ) + β1 j ( H j − H 0 ) + β2 j (Yi − Yi − Pj ) M

∑β i =1

3j

( Di − Di ) + β 4 j ( H j − H 0 ) (Yi − Yi − Pj ) + (ε 1 − ε 0 )

= β j + β ijQ j − β2 j Pj − β4 j Q j Pj + µ j

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(2)

In Equation (2), Qj=(Hj - H0 ) is the marginal improvement in water quantity or quality after improving alternative j, and H0 is the status quo (i.e, no improvement). For each respondent, income and demographic characteristics do not change and, hence, they drop out of the model. If ÄVj > 0, an individual will seek improvement in water services and if ÄVj = 0, an individual will seek no improvement. Because only a change in utility matters, and it is a change in utility that is observable by the researcher, Equation (2) can be used to estimate willingness to pay for improved water services. The solution to Equation (2) yields a system of demand functions, whose forms are probabilities that improvement in category j is chosen given that an individual is willing to pay $ P for water services improvement. Furthermore, estimates of Equation (2) can also be used to calculate the optimal price level or the project’s impact on revenue from increasing the user fees or water tariff. Following Johnston and Swallow (1999), and Persson, Norinder and Svensson (1995), WTP is obtained by estimating the payments that would cause the respondent to be indifferent after a single unit increase in an independent variable. This is achieved through setting Equation (2) to zero and then solving for Pj. Accordingly, the WTP for improvement in category j is given by, Pj* = (âj+â1jQj)/( â2j + â4jQj), that is, WTP depends on the utility from the expected quality or quantity improvement. Actually, Pj* is the lower bound of the true WTP. Based on the theoretical background presented above, the Probit model was therefore used to determine the factors influencing positive demand for improved water services. The model was specified as follows: P( S = 1) =θ 0 + θ1 D1 + θ2 X2 + θ3 X3 + θ4 X4 + θ 5 X5 + θ 6 X6 + +θ 7 X7 +θ8 i D8 i +νi P( S = 0) = −ν j

(3)

Where: P(.) is the probability, S = 1 for individuals who indicated positive demand for water improved water services, S = 0 for individuals who wanted to maintain the status quo (no improvement); D1 was the dummy variable for sex of the respondent; X2 was the 14

age of respondent in years; X3 was the education level (schooling) of the respondent in years; X4 was family size; and X5 was the respondent’s score on the wealth variable on a 0 (i.e., very poor) to 5 (i.e., very rich) scale; X6 was the respondent’s ranking on participation in the project activities on a 0 to 1 scale; and X7 was the individual’s cash contribution during the project initiation and development. Finally D8i was the dummy for the type of clusters (D8i =1 for i =1; D8i = 0 otherwise). For the Dodoma Region, i = 1,2,3,4 representing Gogo, Rangi, Sandawi, Kaguru and Bena/Hehe tribal clusters respectively). For the Singida Region, clustering was based on agroecological conditions (i.e., suitability for agricultural production), Therefore, in D8i dummy variables, i=1,2,and 3. In the Singida Region, D81 is the dummy variables for high potential area, D82 is the dummy variable for medium potential, and D83 is the dummy variable for low potential areas. The expected signs and rationale for the variables in the Probit model are given in Table 3. Further information on their measurement is available in Kaliba (2002). For factors affecting choice of improvement required, a Multinomial Logit function, based on Equation (2), was specified as follows: ∆ V j* = β j + β ij Q *j − β 2 j P j − β 4 j Q j*P j + µ

j

Q *j = S *j .

(4)

In Equation (4), Vj* is the latent variable representing change in indirect utility, with j = 0, 1, 2, or 3 (i.e., corresponding to the desired type of improvement). The latent variables were formulated as follows: Vj* = 0 if the respondent desired the status quo; Vj* = 1 if the respondent wanted an increase in quantity of water supplied or pumping capacity; Vj* = 2 if the respondent wished to have an increase in the number of water distribution points or any other improvement aimed at decreasing congestion at watering points; and Vj* = 3 if the respondent desired other types of improvement such as construction of new water tanks, improved cleanness of the water, and fencing and construction of watering points for

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animals. In the equation, S* is the estimated utility index from Equation (3). The WTP was calculated by setting Equation (4) to zero and solving for Pj* . SOURCE OF DATA Field data were collected from 30 villages that participated in community water utility projects for a period of three years in Dodoma and Singida Regions (i.e., 15 villages in each region) prior to the field surveys. Dodoma Region was divided into five clusters based on tribal distribution. From each cluster, three villages were randomly selected differentiated according to accessibility. The results of past participatory rural appraisals (PRAs) conducted by WAMMA (i.e., some as early as June 1999) were reviewed and used in making adjustments to the questionnaire and in helping to develop pre-coded responses and socioeconomic indicators. In the Singida Region, due to transportation problems (i.e., part of the El -Nino legacy) only Singida Rural District was included. Based on information gathered at the District and Regional Water Departments and the Regional Development Office, the district was divided into three agroecological zones. From each zone, five villages were identified to form a sample cluster. The Singida Regional Development Officer also had some PRA results that were used to fine tune the questionnaire used in the study. Two survey instruments were developed to gather the necessary information (i.e., the village checklist and the structured questionnaire). The village checklist solicited general information at the village level. This was done through meetings and group discussions attended by community members, government leaders, project managers, pump operators, extension agents, and water committee members. The major objective of the checklist was to get a general overview of the village projects based on group discussions. The discussion also helped to pretest the structured questionnaire and in further adjusting the pre-coded responses. The questionnaire was administered to 225 individual respondents in each region.

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In each village, 15 respondents (i.e., at least five men and five women) were interviewed. The respondents were selected randomly from the village register and grouped based on age and social status of the individuals. Sample selection was stopped when there were enough sample respondents in each group. The two survey instruments and further information about the approach used in the study can be found in Kaliba (2002). Regarding information on participation and management, several questions were asked. The questions related to the source of the project initiative, personal involvement in decision making, labor and financial contributions, and the involvement of women in project design and implementation, emphasizing participation in decision making and informed choice. Questions were also asked regarding consumer satisfaction with the services and management of the water utility. The questions focused on: getting information on personal involvement in management; rating of the water managers’ performance; knowledge of how the water management structure functioned; and the possibility of influencing change. To quantify respondent participation in project activities, and satisfaction in project’s performance, individual’s responses were aggregated and used to calculate participation and satisfaction indexes as suggested by Sara and Katz (1998) and by the World Bank/UNDP (2000). Once again further details on the approach used as far as this paper is concerned can be found in Kaliba (2002). On the WTP section, a respondent was asked if there was a need to improve the current water service. The answer was either “yes” or “no”. If yes, what kind of improvement was needed? The respondent was encouraged to identify only one improvement. After the respondent identified the type or choice of improvement required, she/he was asked to suggest an increase in tariff (i.e., for the Dodoma Region) or user fees (for the Singida Region) in order to cover the costs of the proposed improvement.

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RESULTS AND DISCUSSION General Results As explained above, the analysis was directed at identifying types of water services that need improvement and ascertaining the WTP for the improvement. In the Dodoma Region, the results concerning the water utility service can be summarized as follows: about 14% of respondents indicated that they were satisfied with the status quo (no improvement); and 31% suggested increasing pump capacity or buying a more powerful engine (i.e., thereby increasing the rate of water discharge). Thirty-three percent of respondents proposed increasing the number of water distribution points (i.e., water reticulation), and 22% proposed other improvements such as fencing, construction of new water tanks, construction of security houses, and construction of livestock watering points. The suggested tariff increase ranged from 5 to 100 Tsh/20 liters. The existing tariffs averaged 20 Tsh with a standard deviation of 9 Tsh (i.e., per 20L bucket of water) and on average amounted to a total of 895 Tsh/household/year. In the Singida Region, 31% of the respondents were satisfied with the status quo, 30% wanted deeper wells that can produce more water, 29% wanted to increase the number of hand pumps in their village for water distribution purposes, and 10% indicated other types of improvement enumerated above. The suggested increase in a user fee per household per year ranged from 20 to 200 Tsh. The average user fee was 503 Tsh per year with a standard deviation of 170 Tsh. Table 4 presents the Probit model results concerning factors affecting demand for improved water services. During the analysis, variables for Clusters 4 and 5 in the Dodoma Region and Cluster 3 in the Singida Region were not included in the models due

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to correlation problems. The likelihood ratio statistics that test for no effects was statistically significant at the 5% level in both the Dodoma and Singida Regions models. The positive and statistically significant variables in the Dodoma Region were family size and satisfaction in the performance of project activities. These results imply a respondent residing in a relatively large family or who was satisfied in the project’s performance were more likely to answer “yes” to the question regarding the need for water service improvement. A large family implies the need for more frequent water collection trips. Improvements relating to reducing congestion at watering points (i.e., increased pump capacity or number of watering points) are likely to reduce time and effort expended in water collection. Satisfaction with reference to project performance implied both an increased demand and willingness to commit resources for improvement. Negative and statistically significant variables were age, wealth and cash contributions. Older people and richer respondents were thus more likely to choose to maintain the status quo. In the study area, older people are less likely to be directly involved in water collection activities while rich respondents could conceivably have access to other water sources (e.g., have their own wells) or delegate others to collect water for them. The negative sign on the cash contribution variable can possibly be attributed to “contribution fatigue.” Respondents who contributed more during project initiation or development were more likely to say “no” to the improvement question. Finally, respondents in Clusters 4 and 5 were more likely to be predisposed to commit resources to improvements, because the general performance of the projects in these clusters was relatively poor (Kaliba, 2002). The underlying implications of the above results for water utility projects in the Dodoma Region are that projects whose performance creates a sense of satisfaction among consumers and a strategic water fund that is rising (i.e., so as

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not to create contribution fatigue) are critically important ingredients in generating community related resources for water related improvements. In the Singida Region, females were more willing to pay for improvement than male respondents, not surprising since they are primarily responsible for water fetching activities. (The fact that this variable was not statistically significant in the Dodoma Region was unexpected. However, in the Dodoma Region water utility projects produced a more reliable water supply and there were generally fewer complaints about their performance (Kaliba, 2002). Another important and positively significant variable was the level of satisfaction concerning the performance of the project. In the table, for both models, ÄP/ÄX represents the change in marginal probability of wanting improvement (for positive signs) and voting for status quo (negative signs) respectively. It can be seen that, in both models, satisfaction with reference to current project performance is very important in influencing desire for improvement. The Multinomial Logit model in Equation (4) was used to model factors affecting choice of services, that is, maintain the status quo, increase rates of water discharge (i.e., pump capacity or depth of water well or more powerful engines), increase the number of water distribution points/water reticulation, and other improvements as explained above. The results are presented in Table 5. In constructing the Multinomial Logit models, the parameters of status quo variables were set to zero. Again, the signs on the estimated parameters show the direction of the marginal effects on choice of improvement desired. The comparison is related to the deleted choice.

As an example, for Dodoma Region,

respondents who perceived that water service quality was high were more likely to say “no” for an increase in water supply or the number of water distribution points but were more likely to say “yes” for other improvements (Table 5). The interaction between quality and

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proposed bids were important in making the choices or indicating the type of improvement desired. The opposite was true for the Singida Region where the interaction between quality and bids influenced the choices negatively. There, the important factor that significantly influenced the choice of improvements was individual bids. This may be related to the technology and poorer level of services in the Singida Region. Respondents may not be willing to spend more money on failing projects. Singida Region projects will have to increase performance in terms of quantity and quality of water supplied, in order to motivate community members to contribute more resources toward project development and expansion. This was particularly important in some villages where community participation in project initiation development was low (Kaliba, 2002). The results in Table 6 reinforce the results of Table 5. The table shows the changes in marginal probability given a unit change in the independent variable. Again, the reference is the status quo bid. The value of -0.145 in the quality variable in Cluster 1 of the Dodoma Region implies an increase in the quality of water by one unit decreases the percentage of respondents who want increase in water supply by 14.5%. Combining the results of Tables 5 and 6 it is apparent that based on the choice of improvement required, respondents were eager to reduce congestion at watering points. About 64% of respondents in Dodoma and 59% of respondents in Singida voted for an increase in water supply through increased pump capacity or number of water distribution points (i.e., water reticulation). Most villages have only one watering point and it is clear that a lot of time and effort is required for water-collection activities. Estimated Mean Willingness to Pay Based on the Multinomial Logit model results, Table 7 presents estimated mean WTP for identified and desired improvements. Respondents who wanted to increase water

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supply in Dodoma Region were willing to pay 32 Tsh per 20L of water above the existing tariff. Respondents whose choice was improving water reticulation possibilities and other types of improvement were willing to pay 29 and 5 Tsh above the existing tariffs. In the Singida Region, the analogous amounts were 91, 68 and 80 Tsh per household per year respectively. A joint t-test failed to reject a null hypothesis that there is no gender difference on willingness to pay in both regions at the 5% level of significance. In the study area, water fetching is not a men’s responsibility. However, they care. Time wasted by women and children in search for water, has a compounding effects on all members of the household. Potential Revenue from Communities Potential revenue that can be generated for project improvements was estimated through using the estimated mean willingness to pay, proportion of respondents who chose the services, and reported average daily consumption of water per household. From Table 8, it can be seen that there is the potential for generating much more in the way of community-derived funds in the Dodoma Region compared with the Singida Region. For the Dodoma Region, the amount that could be generated is substantial by Tanzanian standards. In fact, the amount may be more than the budget allocated by the central government to regional development programs in all sectors of the economy. However, a note of caution is in order. Care will be needed to implement policies aimed at increasing revenue generation for water improvement purposes. Higher tariff or user fees may exacerbate inequitability of access to water services because poorer households will undoubtedly be more price-sensitive than richer households. Although there are potentially mechanisms to subsidize the poor and the elderly, such arrangements tend to alienate those affected and limit their willingness to participate and be involved in decisionmaking processes.

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6. POLICY IMPLICATIONS Results for the WTP analysis and on proposed choices for improvement, imply that respondents were eager to improve the ease and efficiency of the water collection process. For most respondents in both regions this was explicitly articulated in the form of a desire to increase water supply and the number of water distribution points. Most villages have only one or two watering points, and it was clear that a great deal of time and effort is expended in water-collection activities. There is some potential of increasing project revenues to address such needs through increases in water tariff or water user fees. However, due to the differences in the technologies to access water, it appears that substantially more funds can potentially be generated for such purposes in Dodoma than in Singida. The water technology being used in the Singida Region is apparently not solving the problem of the dry season water scarcity, let alone the issue of water quality. A new approach and vision is needed to provide a long and lasting solution to the problem in that region. Dependency on shallow wells seems to have less potential of contributing to financial sustainability of water utility projects in the Singida Region than the deep boreholes in the Dodoma Region. Whatever happens, in both regions, if increases in water utility levies are planned in order to improve water services two issues will need to be carefully considered. One is to take into account the views of women especially with respect to the location of new watering points or wells. The other is to ensure that any adjustment in water levies do not create undue hardships for the community or unduly inhibit access by the more disadvantaged families/individuals in the communities (i.e. the poor or elderly).

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In conclusion, project sustainability at least from a financial viewpoint will be largely determined by the degree to which it continues to deliver its intended benefits over a long period of time. As far as Central Tanzania is concerned, there is some potential for developing sustainable water supply programs based to a large extent on a community cost recovery mechanism. In villages where there has been strong satisfaction on projects’ performance, this paper has demonstrated that individuals are willing to contribute more resources for improvement. It is important to note that in these villages, community members are highly motivated and participate strongly in the daily management of the projects. An increase in community participation and changing of the water provision technology for the Singida Region is important for future project expansion and new project development.

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Acknowledgements The first author is an Agricultural Economist with the Livestock Production Research Institute, Ministry of Water and Livestock Development, Mpwapwa, Tanzania. The founding for this study was provided by International Development Research Center (IDRC) and Rockefeller Foundation, Nairobi Offices. We appreciate the support rendered by these institutions and by allowing us to publish the results of the study. However, the options expressed and any shortcoming remains to be the authors.

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REFERENCES Alberini, A., Kanninem, B. and Carson, R. T. (1994). Modeling Response Incentive Effects in Dichotomous Choice Contingent Valuation Data.

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Economics, 73, 309-24. Altaf, M. A., Jamal H.and Whittington, D. ( 1992). Willingness to Pay for Water in Rural Punjab, Pakistan. Water and Sanitation Report # 4. Washington DC: UNDP/World Bank Water and Sanitation Program. Baidu-Forson, J., Ntare, B.R., and Waliyar, F. (1997). Utilizing Conjoint Analysis to Design Modern Crop Varieties: Empirical Example for Groundnuts in Niger. Agricultural Economics, 16, 219-226. Bank of Tanzania (BoT). (2000). 4th Quarter Report. Dar-es-Salaam, Tanzania: Government Printers, 2000. Black, M. (998). Learning What Works: A 20-Year Retrospective View on International Water and Sanitation Cooperation (1978 -98). Washington DC: UNDP/World Bank Water and Sanitation for Health Program. Christe, N. and Schwab, G. (1995). The Valuation of Human Costs by the Contingent Method: The Swiss Experience. In Christe, N., Schwab, G. and Soguel. N.D. (eds.), Contingent Valuation, Transport Safety and the Value of Life. (The Hague Netherlands: Kluwer Academic Publisher). Cooper, J. C. (1997). Combining Actual and Contingent Behavior Data to Model Farmer Adoption of Water Quality Protection Practices. Journal of Agriculture and Resource Economics, 22, 30-43.

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Donaldson, C., Thomas, R. T. and Torgerson, D. J. (1997). Validity of Open-Ended and Payment Scale Approaches to Eliciting Willingness to Pay. Applied Economics, 29,79-84. Haneman, W.M. (1984). Welfare Evaluation in Contingent Valuation Experiment with Discrete Responses. American Journal of Agricultural Economics, 66, 332-41. Johnston, R.J. and Swallow, S.K. (1999). Asymmetries in Ordered Strength of Preference Models: Implication for Focus Shift for Discrete-Choice Preference Estimation. Land Economics, 75, 295-310. Kaliba, A.R.M. (2002).

Participatory Evaluation of Community Based Water and

Sanitation Programs: The Case of Central Tanzania. Ph.D. Dissertation. (Manhattan: Department of Agricultural Economics, Kansas State University). Kenkel, P. L., and Norris, P.E. (1995). Agricultural Producers’ Willingness to Pay for Real-Time Mesoscale Weather Information. Journal of Agriculture and Resource Economics, 20, 356-372. Lindberg, K., Johnson R. L. and Berrens , R. P. (1997). Contingent Valuation of Rural Tourism Development with Tests of Scope and Mode Stability. Journal of Agriculture and Resource Economics, 22, 44-60. McFadden, D. (1981). Economic Models of Probabilistic Choice. In Manski, C. and McFadden, D. (eds). Structural Analysis of Discrete Data with Econometric Application. (Cambridge USA: The MIT Press). Ministry of Water (MoW). (1997). Tanzania Water Policy. (Dar-es-Salaam Tanzania: Government Printer) Narayan, D. ( 1995). Participatory Evaluation: Tools for Managing Change in Water and Sanitation. Paper No. 207. (Washington, D.C: The World Bank).

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Persson, U., Norinder A. L. and Svensson, M. ( 1995). Valuing the Benefits of Reducing the Risk of Non-Fatal Road Injuries: The Swedish Experience. In N. Christe, N., Schwab, G. and Soguel N.C.(eds.). Contingent Valuation, Transport Safety and the Value of Life. (The Hague Netherlands: Kluwer Academic Publisher). Rollins, K. (1997). Wilderness Canoeing in Ontario: Using Cumulative Results to Update Dichotomous Choice Contingent Valuation Offer Amounts. Canadian Journal of Agricultural Economics, 45,1-16. Rosenberger, R. S., and Walsh, R. G. (1997). Nonmarket Value of Western Valley Ranchland Using Contigent Valuation. Journal of Agriculture and Resource Economics, 22, 296-309. Russell, S., Fox- Rushby J. and Arhin, D. ( 1995). Willingness and Ability to Pay for Health Care: A Selection of Methods and Issues. Health Policies and Planning, 10, 94-101. Sara, J. and. Katz, T. (1998). Making Rural Water Supply Sustainable: Reports on the Impact of Project Rules. (Washington DC: UNDP/World Bank Sanitary Program). Steven, H. T., Barrett, C. and Willis, C. E. (1997 ). Conjoint Analysis of Groundwater Protection Programs. Journal of Agriculture and Resource Economics, 26, 229-236. World Bank/UNDP (2000). Sustainability Monitoring the VIP Way: A Ground-Level Exercise. Washington DC: Water and Sanitation Program-South Asia, UNDP/World Bank.

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Table 1: Water Utilities under the WAMMA Project in Dodoma, 1999. District Total projects Projects not working (%) Villages not covered (%) Population covered, 1994 Population covered, 1999 Village water fund, 1994a Village water fund, 1996a Village water fund, 1999a

Mpwapwa Kongwa 103 17 31 62074 347919 128.06 323.59 3897.32

Kondoa

Dodoma Urban

105 37 38 88314 415098 66.59 437.70 3305.61

Dodoma Rural

32 34 14 70248 116486 6.90 74.45 2948.92

117 35 9 47896 346518 33.56 319.78 4713.88

Total 357 29 20 268532 1226021 235.11 1155.52 14865.73

Source: Modified from WAMMA records, 1999; a. Thousands Tanzania shillings (Tsh) in real terms.

Table 2: Water Utilities Constructed by TCRS and Others in Singida (1986/95) Variable Villages with boreholes Villages with gravity schemes Villages with shallow wells Villages with charco dams Villages with windmills Number of boreholes Number of gravity water schemes Number of shallow wells Number of charco dams Number of windmills Total number of villages in the region Number of villages with a water committee Number of villages with water funds Number of villages with no water services

1986 139 4 34 37 61 354 2 235 37 65 341 3 156

1990 162 6 109 37 63 417 2 475 37 67 341 78 50 121

1995 188 6 156 37 64 540 2 700 37 68 341 247 229 69

Source: SIRDP, 1999. a. Under Tanganyika Christian Refugees Services (TCRS). “na” means not applicable. b. Charco dams are large ponds constructed on flat surfaces to collect water runoff

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TCRSa 49 2 113 0 3 16 0 465 0 3 na 244 229 na

Table 3: Expected Signs and Rationale for Variables Included in the Probit Model Variable

Ho Sign

Sex (male = 1, female = 0)

-

Age in years

-

Education in years

+

Family size in numbers

+

Wealth variable

+

Participation in project initiation and development Cash contribution

+

Satisfaction concerning project performance

+

+

Explanation Women are more likely to demand improved water services because they are primarily responsible for water fetching Older people are more likely to be less supportive of improved water utility services Education increases the probability of desiring improved water utility services Large families have to spend a lot of time in search of water -therefore they are more likely to demand improved water related services Richer individuals are likely to demand improved services as resources are not a major constraint Participation is likely to engender support for the project and stimulate desire for improvements Cash contributions are also likely to reflect support for the project and stimulate desire for improvements An individual who is happy with the project performance is more likely to be willing to commit more resources for improvement

a. Details on how the variables were derived and measured are given in Kaliba (2002).

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Table 4: Probit Model Results on Factors Affecting Demand for Improved Water Related Services ---------- Dodoma Region --------- --------- Singida Region ----------Variable

Constant Cluster 1 Cluster 2 Cluster 3 Sex (male = 1, female = 0) Age in years Education in years Family size in numbers Wealth variable Participation in project Cash contribution in Tsh Satisfaction in project performance scale (0 to 1)

Estimated Asymptotic Parameter Errors 1.909 0.755** -1.471 0.491** -0.750 0.476* -1.078 0.465** 0.383 0.297 -0.026 0.012** 0.018 0.056 0.120 0.057** -0.045 0.270** -0.527 0.753 -0.003 - 0.001** 1.934 0.719**

ÄP/ÄXa Estimated Asymptotic Parameter Errors 0.332 -0.981 0.863 -0.256 0.324 0.349 -0.120 0.377 0.265 -0.187 0.067 -0.423 0.248* -0.004 -0.015 0.015 0.003 0.018 0.063 0.021 0.063 0.067 0.008 -0.006 0.068 -0.092 0.520 0.529 0.001 -0.009 0.000 0.336 1.567 0.693**

ÄP/ÄXa -0.296 0.098 0.114 -0.128 -0.005 0.005 0.019 -0.002 0.157 0.001 0.474

Percent of correct prediction 0.86 0.71 0.16 Pseudo R2 0.14 Likelihood ratio statistics 35.00** 25.70** a. ÄP/ÄX = Change in marginal probability. One or two asterisks (*) means significance at the 5% or 1% level respectively.

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Table 5: Multinomial Logit Model Results on Choice of Improvement Variables

Dodoma Region Estimated Parameters

Asymptotic Errors a

Singida Region Estimated Parameters

Asymptotic Errors a

Increase in water supply: Constant Quality variable (Qj ) Respondent bid (Pj ) Interaction terms (Qj* Pj )

-0.189 -3.149 -0.360 0.177

3.475 2.280 0.099 0.074**

-5.954 3.163 0.062 -0.014

1.047 2.055 0.011** 0.024

Water distribution: Constant Quality variable (Qj ) Respondent bid (Pj ) Interaction terms (Qj* Pj )

-5.537 -1.413 0.421 0.143

2.977* 1.961 0.094** 0.070**

-3.929 0.969 0.057 -0.012

0.858 1.840 0.011** 0.024

Other improvements:b Constant -1.718 1.730 -4.342 0.909 Quality variable (Qj ) 0.706 1.336 1.704 1.965 Respondent bid (Pj ) 0.044 0.068 0.054 0.011** Interaction terms (Qj *Pj ) 0.039 0.016** -0.020 0.024** a. One or two asterisks (*) means significant at more than 10% or 5% level. b. Other improvements included fencing to improve cleanness, building larger water tanks, covering open water tanks and troughs for animals.

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Table 6: Independent Variables Marginal Effects on Choice of Improvementa Variable

------------- Dodoma -------------0

Constant Quality variable Respondent bid Interaction variable

0.178 -0.052 -0.004 -0.004

1 -0.068 -0.145 -0.001 0.004

2 -0.211 -0.044 -0.001 0.004

---------- Singida Region --------

3

0

0.102 0.241 0.004 0.004

0.129 -0.046 -0.002 0.001

1

2

-0.259 0.258 0.001

3

0.155 -0.231 0.001 0.000

-0.026 0.020 0.000 -0.001

a. In the column headings: 0 = Status quo, 1 = improve water supply, 2 = improve water distribution, 3 = other improvements

Table 7: Mean Willingness to Pay for Proposed Water Service Improvement a b Choices

Sample

Male

Female

Tsh per 20L of Water Dodoma: Increase water supply Water reticulation Others

31.74 28.58 5.13

42.85 42.24 14.75 Tsh/Household/Year

42.99 42.40 14.80

Singida: Increase water supply 90.79 96.32 91.28 Water reticulation 68.39 68.88 68.43 Others 79.90 80.37 79.95 a. Calculated using Equation 4. b. The sample means include all respondents (i.e., those willing to pay for improvements and those not willing to pay for improvements). However, female and male means only include those willing to pay for improvements.

Table 8: Potential Extra Revenue for Water Services Improvement in ‘000’ Tsh/Year Type of Improvement Dodoma: Increase water supply Water reticulation Other improvement Improvement total Singida: Increase water supply Water reticulation Others improvement Improvement total

Cluster 1 23,086.06 22,128.77 2,648.12

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Total

18,644.28 37,944.97 17,871.18 36,371.54 2,138.53 4,352.37

20,657.65 19,801.06 2,369.48

21,155.12 20,277.90 2,426.54

121,148.81 116,450.45 13,934.94

47,862.85

38,564.00

78,668.90

42,828.20

43,859.53

251,873.46

538.81 358.76 1,293.23 2,190.80

378.91 252.30 909.44 1,540.65

354.55 236.10 850.98 1,441.63

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1,272.27 847.16 3,053.65 5,173.08