Pricing Policy Effectiveness is Domestic Water Demand Management

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The 2SLS technique was used to eliminate this bias. In the first stage, instrumental variables were created for all the three variables, by separately regressing them on all pre-determined variables as well as the three block prices. In the second stage, OLS was used to estimate equations 3, 4, 5, and 6, replacing AP, MP, and DV by their respective instrumental variables. 5. DATA DESCRIPTION Three sets of information were collected: household level data about consumption and household characteristics, tariff structures, and weather. Household-level information: Primary data for the study was obtained from WASA, Lahore. The data set contained the following information about 156 households: Location: Information about the town in which the connection is located. Consumption: Water consumption in gallons from January 2004 to December 2006. Meter reading is carried out by WASA with a back-lag of two months: that is, for the billing cycle March-April, the meter is read in the last week of February. For the subsequent cycle, May-June, the meter is read in the last week of April. In this way, the March-April bill is based on January-February consumption, and May-June bill is based on March-April consumption. The variable used in this study is the consumption during the billing period, the previous month’s meter-reading, rather than the meter-reading recorded in the billing period. Eighteen meter readings were recorded per household. Plot size: The plot size in marlas. Property Value: Property value in Rupees. Number of Residents: The number of residents living in a house. Meter-ownership: Information about whether the meter is owned by WASA or the consumer. Tariff Structure: The tariff structures for domestic metered connections since January 2004 were provided by WASA. Table 6.1: Descriptive Statistics Variable Mean Std. Deviation Minimum Maximum Consumption (1,000 gallons per billing period) 17.9 14.9 2.00 155.12 Average Price (Rs.) 17.7 2.4 9.2 26.0 Marginal PriceP (Rs.) 21.8 3.5 9.2 27.3 14
Household Size 6.3 1.8 2.0 11.0 Plot Size (marlas) 8.7 13.9 1 160 Property Value (Rs. lacs) 29.4 35.5 2.0 250.0 Water Expenditure (Rs) 340.1 373.6 18.4 4033.9 Temperature (Celsius) 30.4 6.2 18.5 38.6 Weather: Information about the weather in Lahore was provided by the Pakistan Meteorological Department. The data-set contained the maximum average monthly temperature recorded in degree Celsius from January 2004 to December 2006. The temperature values were averaged over two-month periods in line with WASA billing cycles. 6. RESULTS The estimated demand model explains 60 percent of the variation in water consumption and is statistically significant. 15 Price, plot size, household size, meter ownership, past water consumption, and the difference variable that captures the income effect have been found to significantly influence the household demand for water. The effect of weather is moderate while that of wealth, as measured by property value, is negligible. Whether water demand is more responsive to marginal or average price could not be established from the data. Therefore, both AP and MP models were estimated. The AP models showed a significant positive price-demand relationship, which is contrary to economic theory. Such contradiction is often attributed to statistical errors, such as multicollinearity or weak instrumental variables. However, no such error could be identified. Regardless of the reason for the discrepancy, the AP models were not suited for drawing inferences. All following discussions and conclusions are based on MP models. The results show that the marginal price of water has a significant impact on water use: a percentage increase in the marginal price brings a 2.33 percent decline in average water use. A 10 percent increase in the marginal price will bring down average water consumption by 23 percent. Table 7.1: Model Summary Statistic Value 15 Though this R 2 value is not very high, it is acceptable for panel data models with a large cross-sectional element. Cross-sectional data has high variance due to the diversity of the sampling units, and it is therefore difficult to fit a regression line that explains all the variation. 15
Page 1 and 2: Pricing Policy Effectiveness is Dom
Page 3 and 4: Relentless extraction of water may
Page 5 and 6: too much variability which can caus
Page 7 and 8: 3. WATER SUPPLY IN LAHORE Lahore is
Page 9 and 10: Tariffs for both metered and unmete
Page 11 and 12: about the house value was used as a
Page 13: elasticities of the variables, thus
Page 17 and 18: We may infer that low-income househ
Page 19 and 20: generate equally high revenue, they
Page 21 and 22: Table 5.1: Allocation of Sampling U
Page 23 and 24: T .101 .023 4.324 .000 NR .177 .399
Page 25 and 26: ln_Q_lag .649 .016 41.784 .000 IV.
Page 27: Lahore Development Authority Act 19

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