Sanitation Sector
D. Bid Distribution
4.7 Using CV Study Results
4.7.2 Demand and Policy Analysis
Given a good CV study, establishing the effective demand for improved services is relatively easy. A properly designed and administered CV study is a good source of information for demand analysis of proposed WSS services. The results of a CV study, together with the estimated WTP functions, can be used to gauge effective demand and to predict the rate of acceptance of a proposed improvement in water supply. In order to understand the impact of income on effective demand, the estimated WTP function can be simulated as shown in Chapter 4. Simulation can be done using different income levels. Sometimes, a proxy for income is incorporated in the regression models when accurate income data are difficult to obtain. An index of assets the household currently possesses and the household monthly expenditure, for example, can be used as proxies. When reasonable data about future income or any change in related variables are available, these data can be used to predict future demand for improved services.
To predict acceptance of the improved services among respondents, household characteristics of the subpopulation of interest and attributes of a WSS service (reliability, charges, quality) can be used in the simulation exercise. This prediction exercise could be repeated for alternative scenarios to generate a series of probability maps of coverage under
Box 4.1 Estimating Mean WTP: An Illustration
T
he approach involves estimating a probit regression model where the dependent variable is a yes or no answer as to whether the respondent is willing to pay the specified bid price for the water service on offer. The probit model will be of the form Y = α + β1X + β2B + εwhere Y is the yes/no response, X is a vector of variables reflecting household, area or other characteristics, B is the bid price and ε is an error term. Mean WTP is derived from the expression
(∑(β1*Xa)/ β 2)*-1
where Xa is the mean value of X variables.
Mean WTP is thus derived by first summing the product of the regression coefficients for the variables and their mean from the probit analysis (∑(β1*Xa) and
then dividing this by the coefficient on the bid price (β2). This expression is then multiplied by minus unity to give a positive number. Where, as illustrated above, there is a constant in the probit model (α) this must be added to the sum of the products to give (α + ∑(β1*Xa) so that mean WTP becomes
(α + ∑(β1*Xa)/ β2)*-1.
This approach is illustrated below using realistic data from a CV study on improved water provision. Monetary units are Renminbi (RMB).
Mean Willingness to Pay Calculation
Variable Coefficient Mean Coefficient*Mean
Bid -0.19779 Income 0.00002 24,501.0 0.48468 Education -0.00826 10.60700 -0.08765 Gender 0.04213 0.49380 0.02080 Age -0.01020 43.27100 -0.44149 Dwelling 0.11087 0.58058 0.06437 Yard 0.00146 121.68000 0.17805 Impact -0.07108 4.38220 -0.31146 Squality -0.12587 3.04340 -0.38307 Constant 1.89640 1.89640 Total 1.42062 Mean WTP =1.42062 / -0.19779 * -1 7.18249
The same approach can be applied to derive mean WTP for specific target groups by replacing the average value for each variable X (for example, RMB 24,501 for income) with the specific X value for the group concerned (for example, RMB 20,000 for the very poor).
service alternatives (Pattanayak et al. 2006). Such simulations will help the analyst predict the service coverage and output of the WSS plant with reasonable accuracy under the most probable future scenarios. This information can then be fed back to engineering designs to avoid under- capacity or excess capacity issues in designing water supply plants. The predicted uptake rates with the most plausible policy scenario answer the effective demand question directly and provide additional information on financial sustainability, and overall viability of the WSS project.
The estimated WTP values have a number of important uses such as calculating the benefits of the proposed improvements to the water supply system, setting tariffs, and making informed decisions on related policy issues. Calculating project benefits using WTP estimates is straightforward. Once the analyst gets reasonable confidence about the estimated mean WTP value, it can be readily used in project economic analysis. The mean WTP multiplied by the number of households served by the project provides the total gross benefit of the project.
The use of WTP estimates in setting tariffs is also reasonably straightforward, but some understanding is required to avoid its misinterpretation and misuse. CV surveys provide measures of the maximum WTP for proposed improvements in WSS in the context of the existing or proposed institutional regime. The WTP is related but not equal to the future demand or monthly bill paid by the households to the water utility. Although future demand and WTP contain similar behavioral information on household preferences, WTP is different because it is an ex ante measure of welfare change associated with the improved WSS. It will not show how much water will be consumed when services are improved, or how many households will be connected to the improved service with a revised tariff and connection charges. Therefore, WTP cannot be used to estimate revenue directly, because households will pay only a proportion of the maximum WTP expressed in the CV study. Moreover, basic economic principles suggest that monthly charges should be equal to or less than mean WTP. Therefore, a tariff that charges above mean WTP will lead to welfare losses and is likely to discourage households from connecting to the water services. Therefore the WTP29
should be treated as the upper bound of the tariff. Furthermore, tariff setting requires additional information because it requires meeting a set of goals combining financial sustainability, economic efficiency, and
29 The bids and number of "yes" answers can be used to predict the revenue although the mean WTP
distributional equity (Dole 2003, Dole and Bartlett 2004, Dole and Balucan 2006). In addition to WTP therefore, information on the cost of delivery, capital replacement requirements, and various social considerations should be used in setting tariffs.
Finally, the estimated WTP functions can be used to analyze policy issues related to designing WSS projects such as the choice of provider, the design of spatially based pro-poor service delivery, the affordability of the poor, and the characterization of low WTP groups.
4.8 Concluding Remarks
CV studies are widely used in designing WSS projects. The application of the CV method in developing countries is a cause for concern because poorly designed and administered CV studies produce unreliable WTP estimates. Unreliable CV results are largely due to poor study design, poor survey implementation, and failure to undertake a variety of tests to examine the validity of responses to the different CV scenarios. The CV method has undergone significant improvements during the last 20 years. Advances in econometric analysis, survey research methods, sampling and experimental design, and policy simulations in the last two decades have been remarkable.
This chapter aims to motivate practitioners to use these improved methods to obtain reliable WTP estimates. It recommends a number of good practices that can be applied in the design, survey implementation, and data management and analysis stages of a CV study. Table 4.2 provides a checklist that a CV study team can use in examining whether a CV study has applied best practice methodology to estimate WTP. If answers to the questions in the table are satisfactory, it is expected that the resulting study will be useful for policy purposes. The next chapter provides an example of the application of the good practices described in this chapter.
Table 4.2 Quality Checklist
Attributes/Procedures Relevant Questions 1. Design Issues 1.1 Pre-characterization of WSS 1.2 CV Scenario 1.3 Commodity Definition 1.4 Elicitation Method 1.5 Bid Distribution 1.6 Sample
1.1 Has the study team undertaken adequate pre-characterization activities?
1.2 Does the study use a realistic CV scenario? 1.3 Does the commodity definition provide a
complete and precise account of improved WSS?
1.4 Does the study use referendum elicitation format?
1.5 Does the study use reasonable bids with adequate range to understand the demand? 1.6a Is the sample size adequate?
1.6b Is the sampling frame and method reasonable?
1.6c Has the study team strictly implemented the sampling strategy?
1.6d Was there a replacement method? 2. Survey Instrument
2.1 Focus Group Discussions 2.2 Pre-testing
2.3 Quality of Survey Instrument
2.1 Has the team undertaken enough focus group discussions and have they been used to refine the instrument?
2.2 Has the team undertaken enough pre-tests and have the findings been used to refine the instrument?
2.3 Is the overall quality of survey instrument satisfactory?
3. Potential Biases 3 Has the team considered bias minimizing measures in designing an conducting the study?
4. Survey Implementation 4.1 Enumerator Training 4.2 Field Supervision
4.1 Have the enumerators been given
adequate training emphasizing the accuracy of the data and minimizing biases?
4.2 Has there been adequate effort to ensure quality of data through supervision of field work?
5. Data Management 5.1 Quality Checks 5.2 Preliminary Analysis
5.1 Have adequate quality checks been incorporated while entering data? 5.2 Do the descriptive statistics tally with
secondary administrative data?
Table 4.2 Quality Checklist
Attributes/Procedures Relevant Questions
6. Validity Tests 6 Has a validity test been undertaken? Do results confirm positive income elasticity, negative price elasticity, and other theoretical expectations?
7. Estimation of Mean WTP 7 Has appropriate econometric modeling been undertaken to estimate mean WTP? Does the estimated value fall within similar previous estimates?
8. Demand Analysis 8 Does the analysis demonstrate that there is effective demand for proposed improved WSS services?
9. Reporting 9 Does the CV study report contain adequate information to answer the above questions?
CV = contingent valuation, WSS = water supply and sanitation, WTP = willingness to pay. Source: Gunatilake, et al. (2007).