Pricing Policy Effectiveness is Domestic Water Demand Management

APPENDIX
I. Sampling Framework
The sampling frame consists of all domestic households that were metered prior to the beginning of the
study period, i.e., January 2004. The correct number of such households was not available, but an estimate was
made using the available information. Currently, WASA provides 480,000 domestic water connections of which
only 30 percent are metered. Therefore, the sampling frame comprises of 144,000 sampling units.
Sample Size: WASA Lahore covers 90 percent of the households under its official jurisdiction while the
remaining 10 percent being provided by other (private) sources (WASA, 2007). In a randomly selected sample,
the probability of a household having a WASA connection is, therefore, 90 percent. The sample size (n) was
computed in the following way:
n = PQ/SE(P) = 150
where P is the percentage of households having a WASA connection and Q the percentage of households
provided by others. If a six percent standard error is acceptable, a sample of 150 households is likely to contain
90 percent households with a WASA connection. For this study, the sample size was increased to 156
households in order to achieve a better allocation of sampling units.
Allocation of Sampling Units: The sampling frame was stratified on the basis of WASA’s administrative
division. As discussed earlier, the WASA area is divided into six towns which are further divided into O&M
units. Information about the exact population of these divisions was not available; therefore sampling units were
allocated proportionately according to a rough estimate of the population density under each subdivision.
Finally, the units were selected randomly using a random number table.
Time Frame: It was intended that the time frame should include at least one tariff change, in order to better
capture the effect of price on consumption. However, as mentioned earlier, tariffs have remained unchanged
since May 2004. Before that, tariffs were last increased in January 1998. In order to include both (or more) tariff
changes, the initial timing would have to be set at (or before) 1998. However, it was not practical to stretch the
study period that long.
As we go back in time, the number of metered connections, and hence the sampling frame, becomes
smaller. Moreover, socio-economic and demographic characteristics, such as household size and income, would
have significantly changed for each sampling unit over such a long time frame. Variations in these variables are
difficult to measure and would have created considerable errors in the data.
Because of these considerations, the time frame of the study was limited to the last three years, 2004 to
2006, to include one tariff change while allowing for consistency of socioeconomic and demographic variables
of each unit over this period.
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