Pricing Policy Effectiveness is Domestic Water Demand Management

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When missing variables differ significantly across areas or communities, this may give rise to a ‘differential intercept model’ – a model in which the regression line has varying intercepts for different population groups. To estimate these differences across groups, a ‘fixed effects model’ is used, based upon dummy variables assigned on the basis of geographical location. 13 Five intercept dummies have been introduced for Aziz Bhatti, Allama Iqbal, Ganj Baksh, Ravi, and Shalimar towns. Nishtar town is the benchmark category. Table 4.1: Notation and Variable Description Variable Indicator Qt AP MP DV W NR L T Dmo Dait Dabt Dgbt Drt Dst Description Quantity of water consumed in billing period t Average price Marginal price Difference variable Wealth Number of residents Plot size Average temperature in period t Meter ownership dummy Dummy for residence in Allama Iqbal Town Dummy for residence in Aziz Bhatti Town Dummy for residence in Ganj Baksh Town Dummy for residence in Ravi Town Dummy for residence in Shalimar Town Functional Form As discussed in chapter two, there is no theoretical basis for adopting any specific functional form. Past studies have typically used linear, semi-log, and double-log forms. The MacKinnon, White, and Davidson test (MWD test) was used to choose between linear and double-log forms. The results recommend a double-log functional form. However, there were no a priori grounds for choosing between semi-log and double-log functions, and therefore, both functions were estimated. Double-log models have the advantage that their estimators give the 13 Fixed Effect Model (FEM): A model in which the intercept term varies over sampling units but is constant over time. 12
elasticities of the variables, thus simplifying interpretation. Semi-log models are useful to estimate elasticity across different population groups, as the coefficients give ‘semi-elasticity’: elasticity of the dependent variable with respect to varying values of the regressors. The Estimated Models The following demand models were estimated: 1. MP Models DV = [P 1 Q 1 + P 2 Q 2 + P 3 (Q – Q 1 – Q 2 )] – P 3 Q [1] Semi-log: lnQ = β 0 + β 1 MP + β 2 T + β 3 W + β 4 NR + β 5 L + β 6 Dmo + β 7 Dabt + β 8 Dait + β 9 Dgbt + β 10 Drt + β 11 Dst + β 12 DVµ 3 [3] Double-log: lnQ = β 0 + β 1 lnAP + β 2 lnT + β 3 lnW + β 4 lnNR + β 5 lnL + β 6 Dmo + β 7 Dabt + β 8 Dait + β 9 Dgbt + β 10 Drt + β 11 Dst + β 11 D β 11 DV + µ 2 [4] 2. AP Models AP = [P 1 Q 1 + P 2 Q 2 + P 3 (Q – Q 1 – Q 2 )] / Q [2] Semi-log: lnQ = β 0 + β 1 AP + β 2 T + β 3 W + β 4 NR + β 5 L + β 6 Dmo + β 7 Dabt + β 8 Dait + β 9 Dgbt + β 10 Drt + β 11 Dst + µ 3 [5] Double-log: lnQ = β 0 + β 1 lnAP + β 2 lnT + β 3 lnW + β 4 lnNR + β 5 lnL + β 6 Dmo + β 7 Dabt + β 8 Dait + β 9 Dgbt + β 10 Drt + β 11 Dst + µ 2 [6] Estimation Technique Since price is endogenously determined by the model a ‘simultaneous equation bias’ is likely to arise. 14 Hausman test was used to check for if the simultaneity problem existed. The results showed that both price variables, as well as the difference variable created a simultaneous equation bias under OLS. 14 Simultaneous equations bias: Inconsistency of OLS estimators in a simultaneous equations system. 13
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: about the house value was used as a
Page 15 and 16: Household Size 6.3 1.8 2.0 11.0 Plo
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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