Assessing Effective Demand

This section illustrates how the CV data can be used to assess effective demand for WSS services. Using a single-bound, closed-ended CV question to elicit household preferences, the study asked households currently connected to piped water services to consider an increase in monthly consumption charges for improved water supply service. In addition to paying monthly consumption charges, households without access to

5 _{In contrast, Whittington et al. (2002) find that private sector involvement increased WTP values,}

implying that consumers in Nepal have less confidence in government-run water services.

6 _{For all variables in Table 5.3, including the regression constant but excluding the monthly}

consumption charge and connection charge, the mean is multiplied by the coefficient. The sum of the products is then divided by the coefficient on the monthly consumption charge (−0.002). Section A4.2.1 of Appendix 4.2 illustrates a slightly different procedure for the calculation.

piped water were asked additional questions on connection charges. Based on the proposed improvement, the survey sought consumer responses, either “yes” or “no”, to different water bills for improved water services. Table 5.4 shows that 83% of connected respondents and 57% of unconnected respondents answered “yes” when they were presented with the improved service with a Rs. 100 monthly bill.7 _{As the}

bid amount increased, the percentage of respondents (both connected and unconnected) answering “yes” gradually dropped.

Table 5.4 Distribution of “Yes” Responses to Closed-Ended CV Question

Water bill (bid) for improved service (Rs.) Connected (%) (n=680) Unconnected (%) (n=1,138) 100 83 57 200 74 36 300 63 35 400 47 29 500 42 28 600 29 22 800 33 9 1,000 14 15 Total 49 29

CV = contingent valuation, n = number of households. Source: Gunatilake, et al. (2006).

The responses to the elicitation question can be used to gauge the effective demand for improved water supply and sanitation services (Table 5.4). Demand analysis shows the relationship between price and quantity demanded and, as shown in Figure 5.2, a similar price–quantity relationship can be obtained from the responses to the elicitation question in Table 5.4.8 _{Figure 5.2 illustrates that, as expected, as the}

bid (monthly water bill) increases, acceptance (“yes” answers) by both connected and unconnected households decreases. However, acceptance of the bid is higher among those who are currently connected to piped

7 _{Unconnected households receive a connection charge together with the monthly bill. These}

connection charges were randomly assigned among the monthly bills.

8 _{Note that the y-axis here represents the percentage of households accepting a bid, and is therefore}

slightly different from the usual quantity measure. However, the percentage of households accepting a bid is a good proxy for the quantity demanded. Using average consumption data, these percentages can be readily converted to quantities.

water compared to those unconnected. This seems counterintuitive in that, usually, unconnected households might be expected to be willing to pay more because the economic cost of water (through direct purchase, time spent in collecting, or as expenditure on water-related diseases) for unconnected households is generally higher than that of connected households (UNDP 2006).

In this study, however, the situation is different for two reasons. One is the availability of cheap and good quality substitutes, mainly well water, and the other is the affordability of connection charges. As will be shown later, excluding the connection charge substantially increases the unconnected households’ WTP. Segregating the sample further into poor (first income quintile) and non-poor (fifth income quintile) groups shows substantially higher “yes” responses among the non-poor for both connected and unconnected groups.

Data organized as in Table 5.4 and Figure 5.2 is useful in summarizing information on demand in response to changes in charges. For example, if the monthly water bill is Rs. 200, about one-third of households currently without a private tap will be connected. If the water bill goes

90 60 30 0 0 200 400 600 800 1000 Connected HHs % Households want improved water service

Unconnected HHs Proposed monthly water bill (Rs.)

Figure 5.2 Household Demands for Improved Water Service in Sri Lanka

HH = household.

up to Rs. 400, then almost 30% of households without an individual tap would be connected. Thus the information generated through a CV study can be used in predicting effective demand for proposed WSS services under various combinations of prices and incomes.9 _{Such predictions}

enable the analyst to provide feedback to project engineers on optimal plant capacity.

Simulations are generally performed using income as a policy variable. Sometimes, a proxy for income may be needed when accurate income data are difficult to obtain. Proxy variables can be the number or value of the assets a household possesses and monthly household expenditure. This study uses poverty as a proxy:10 _{the sub-sample of}

poorest and richest were separated and their uptake rate was separately estimated.11

Table 5.5 shows the predicted uptake rates (or rates of acceptance) for two income groups (poor and non-poor). The results indicate that the predicted uptake rates are much lower than anticipated.12 _This

cast initial doubts on the viability of the proposed PPP since the PPP designers assumed that 95% of the population would get individual water connections. The investment plan; hours of supply; and consequently the capacity of the plants, revenue levels, and subsidy requirements were all dependent on this assumption. Removal of the connection charges showed a significant increase in uptake rates. Even with no connection and monthly charges, however, about 30% of the poor did not want to get connected. This implies that poor households may have a good substitute for piped water or they may be incurring certain implicit transaction costs when getting individual water connections.13

9 _{Similar analysis using prices and income for wastewater and solid waste projects are presented in}

Chapter 6.

10 _{See footnote 4 of Chapter 5 for the definition of poverty.}

11 _{In predicting the uptake rates, the mean value of the monthly bill for connected households and}

mean values of monthly bills and connection charges for unconnected households were used.

12 _{The uptake rates in Table 5.5 show the percentage of households in each category that are willing}

to get the connection/remain connected to receive improved water service with increased bills. 13 _{These transaction costs may be due to bureaucratic red tape or petty corruption involved in}

Table 5.5 Predicted Uptake Rates of Improved Water Supply for Poor and Non-Poor

Service area Uptake rates (%) Poor Non-poor Greater Negombo Connected Unconnected 4932 6447 Kalutara-Galle Connected Unconnected 4427 5942

Note: Poor households are defined as the bottom quintile of the sample and the non-poor are the upper quintile based on monthly per capita consumption.

Source: Gunatilake, et al. (2006).

5.5.3 Use of WTP Estimates and