Pricing Policy Effectiveness is Domestic Water Demand Management

Pricing Policy Effectiveness is Domestic Water Demand
Management
Estimation of Domestic Water Demand Function in Lahore
Tamkinat Rauf 1 *
and
M.Wasif Siddiqi**
ABSTRACT. This study examines the management of household water demand through a pricing
policy for achieving the objectives of cost recovery, efficient water use, and equitable allocation
of water resources. To this end, a demand function is estimated using household level data about
water consumption and socio-economic characteristics of 156 households supplied by WASA,
Lahore, in the period 2004-2006. The results show that domestic water demand is highly elastic
to price at the existing tariff rates. An average increase of up to 30% in the consumption-based
part of existing tariffs as well as an increasing non-volumetric rate based on property value or
the size of dwelling is recommended.
1 The writers are, respectively, Economic Analylist,Research Dept.State Bank of Pakistsn,Karachi and Associate
professor Govt. College University, Lahore. Tamkinat.rauf@gmail.com
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1. INTRODUCTION
The population of Lahore has roughly doubled over the past twenty years, and an increase of
two million is expected by the year 2020 (UN, 2005). This has important implications for city
planning as demand for housing, electricity, water, sanitation, public health, education, and
infrastructure grows accordingly.
WASA, the city’s official water supplier, has often responded to the growing demand by
offering the supply-side solution: augmenting supply capacity by exploiting new water resources. 2
Such investments are costly, but in view of the public good nature of water, WASA has kept tariffs
well below the cost-recovery level, relying on heavy loans and subsidies. While this arrangement may
have worked in the past, it is now becoming increasingly unsustainable, because 1) WASA is facing
severe financial constraints and which has led to poor service and underinvestment, and 2) the
environmental cost of extracting water is increasing.
With its low tariff rates and continually increasing costs, the WASA Lahore is unable to meet
even its O&M costs (WASA, 2007). WASA has been receiving financial assistance from the
provincial and Lahore district governments as well as international donors in the form of grants and
loans with the grant element gradually diminishing over the passage of time. WASA currently owes
Rs. 5.6 billion to these agencies and is in no position to make the repayment (WASA, 2007).
Deteriorating financial situation has also led to short-term planning, reactive operational strategy, and
underinvestment in asset maintenance, future capacity, IT equipment, management and accounting
information system, and training (IFC, 2005). Consequently, WASA has shown suboptimal
performance: low pressure and irregular supply, leakages, poor customer service, etc.
Secondly, indiscriminate and unplanned exploitation of water resources may result in severe
water shortages in future. Water supply in Lahore depends essentially on groundwater pumped
through privately or publicly-owned tubewells and hand pumps. However, groundwater is a limited
resource, recharged only once a year during the monsoon. Reportedly, the groundwater table has been
falling. 3 Falling water tables increase the cost of pumping, as more energy is required to pump deep
water. Furthermore, the International Water Management Institute has predicted severe water shortage
in the country by the year 2025, that will threaten even the sustainability of agriculture (IWMI, 2000).
2 In line with the demands of the growing population, WASA has continuously expanded its supply capacity in
the past. Currently, WASA produces 350 million gallons of water per day with its 400 tubewells. Over 25 more
tubewells have been approved under the 2007-08 budget of the agency (WASA, 2007).
3 Over the year 2002-03, the groundwater table in Punjab fell on average by 0.61 percent (Govt. of the Punjab,
2005). More recent estimates were not available.
2

Relentless extraction of water may also lead to an irreversible decline in the ability of the region to
store water in the ground (Gleick, 1998).
Water produced by WASA is not being efficiently utilised. The basic water requirement for
drinking, sanitation, bathing, and food preparation is 13.2 gallons pcd, while WASA produces 80
gallons pcd – an excess of over 66 gallons pcd (WASA, 2007). 4 Evidently, there is an excess demand
– people demand more quantities of water than they would if they were made to pay the true
environmental and supply cost of water. Clearly, the supply solution discussed earlier is not the best
answer to the apparently growing demand.
Instead, WASA should be looking at demand management that involves pricing policies and
rationing – notionally allocating a fixed amount of water to each household, based upon lifeline and
household size considerations. However, though rationing brings a definite change in demand, it is
difficult to implement and may not be widely acceptable. Pricing policies, on the other hand, have
been successfully implemented in countries like Brazil, Canada, France, Spain, the United Kingdom,
and the United States.
Through pricing policies existing demand patterns are modified to achieve various objectives,
such as cost recovery, conservation, and equitable allocation of water among different income groups.
To implement such a policy successfully, the value that consumers place on water must be known.
This value is reflected by the price elasticity of water demand – the percentage change in demand that
will be caused by a percentage change in price. If the demand is inelastic, it shows that at the existing
prices, the consumer highly values water and will be willing to pay a higher price in order to consume
the same amount of water. On the other hand, if elasticity is high, the consumer indicates a
willingness to reduce/increase the use of water with changes in its price. And if price is increased, he
would shift a part of his expenditures elsewhere. Clearly, this information is fundamental in deciding
the manner in which tariff should be structured.
Another important aspect of a pricing policy is that households with different socio-economic
settings, especially different income levels, will be affected differently, often giving rise to issues of
equity – fairness in the distribution of cost and conservation burdens. How water demand varies
across different types of household is central in estimating the implications of a pricing policy on
equity.
4 The internationally recommended lifeline supply is 50 litres pcd. 1 litre = 0.2642 gallons
3

This study estimates household water demand in Lahore in order to explore the potential of a
pricing policy to increase revenues and discourage inefficient water use while ensuring a fair
distribution of water in the community.
The thesis is organised as follows: Section two reviews literature on water demand. Section
three gives a description of the study area. This is followed by the methodology: section four,
sampling framework; section five, theoretical framework; and section six, data description. Results
are presented in section seven, followed by policy recommendations in section eight, the last section.
2. LITERATURE REVIEW
The estimation of urban residential water demand has been an area of wide and growing
interest world-wide for the past three decades. However, most of the existing literature pertains to the
developed world: the United States and Europe (Agthe and Billings, 1987, Arbues and Villanua,
2006, Batchelor, 1975, Chicoine and Ramamurthy, 1986, Foster and Beattie, 1979, 1981, Hansen
1996, Headley, 1963, Hewitt and Hanemann, 1995, Nauges and Thomas, 2000, Neiswiadomy and
Molina, 1989, 1991, Renwick and Archibald, 1998, Wong, 1972). So far, no comprehensive study has
been conducted in Pakistan to estimate the urban residential water demand and neither has the
researcher come across any such study of similar-income countries. Therefore, the reviewed literature
has limited usefulness in many aspects. What follows is an analytical review of the methodologies and
data types used in previous studies and a brief assessment of the suitability of these methods in the
present context.
Water demand estimation studies have used various sorts of data: time-series (Agthe and
Billings, 1987, Hansen, 1996), panel (Arbues and Villanua, 2006, Nauges and Thomas, 2000,
Neiswiadomy and Molina, 1989, 1991, Renwick and Archibald, 1998) and cross-sectional (Chicoine
and Ramamurthy, 1986, Foster and Beattie, 1979, 1981, Headley, 1963, Wong, 1972). Time series
data is useful to study the effects of a policy change such as restructuring of block-rates, rationing of
water supply, or the introduction of a new water-related appliance. Time series data also captures the
effect of weather. A drawback of such data is that it is aggregate: summing or averaging quantities,
such as consumption, income, and prices, for the entire community. For this reason, results derived
from time series data have limited usefulness.
Cross-sectional data on the other hand can be collected for disaggregate units, such as
individuals, households, or localities. This data holds more information than time-series data, and is
appropriate for estimating demand across different groups. However, cross-sectional units may have
4

too much variability which can cause heteroscedasticity, in which case the OLS estimators have high
variances. 5
The most useful approach is perhaps the panel data, because it combines elements of both
cross-sectional and time-series: more variables can be studied while time effect is also captured. Panel
data also increases the number of observations, and hence the accuracy of the model. For these reason,
this study has used panel data.
Urban water is usually priced under ‘block-rate’ schedules: a volume-based rate consisting of
a sequence of marginal prices for different consumption blocks. Water use in each billing period is
divided into successive blocks with use in each ascending block charged at a different price. The
block rate schedules can be progressive or regressive with increased consumption. The schedules are
established to ensure efficient use of resource, as well as to achieve equity, environmental
conservation, cost recovery, and public acceptability. An important point of contention in water
demand studies has been the specification of the price variable in the model (Charney and Woodard,
1984, Chicoine and Ramamurthy, 1986, Foster and Beattie, 1981, Opaluch 1982, 1984). Water
demand studies use two alternative types of price specifications:
1. Marginal price of the block under which the consumer falls plus a ‘difference variable’ (following
Taylor, 1975 and Nordin, 1976). The difference variable is calculated as the difference between what
the consumer actually pays and what he would have been charged if all consumption units were
charged at the marginal price of the last unit of consumption. The difference variable is used along
with the marginal price.
2. Average price – the total water expenditure by the consumer in a billing period divided by the total
water consumed in that period.
Proponents of the difference variable specification (Agthe and Billings, 1987, Renwick and
Archibald, 1998) argue that the consumer is well-informed and therefore responds to the marginal
price and difference variable. On the other hand, those who favour average pricing argue that the
consumer does not devote time to studying the tariff structure and only has a rough idea of what he
pays for his consumption (Foster and Beattie, 1981).
Nieswiadomy and Molina (1991) and Opaluch (1982) have suggested statistical tests to
determine the price to which the consumers actually respond. The advantage of one test over the other
5 Heteroscedasticity is defined as non-constant variances of residuals. In the presence of heteroscedasticity, OLS
estimators remain unbiased and consistent but they no longer have minimum variance.
5

was not readily apparent. This study has used the more recent Nieswiadomy and Molina (1991) price
specification test to determine the correct price specification.
It has been further argued that ill-informed consumers react to past rather than current prices
(Charney and Woodard, 1984), and hence the appropriate specification of price would be the lagged
(average) price. The lagged-price specification has not been used in the present study because the
WASA tariff schedule is rather uncomplicated (only three blocks) and has been revised only once
since 1998. However, past bills may have some impact on the consumers’ decision-making; a lagged
consumption variable has therefore been added as a regressor.
With multi-part block rates, prices are endogenously determined by the quantity demanded,
and hence a the model is based on simultaneous equations. Under simultaneity, the OLS method
yields biased and inconsistent estimates. Most water demand studies have used either instrumental
variables (Nauges and Thomas, 2000, Neiswiadomy and Molina, 1989, 1991) or two-stage least
squares (Agthe and Billings, 1987, Renwick and Archibald, 1998) to remove the ‘simultaneity bias’.
Arbues and Villanua (2006) have used a ‘dynamic panel model’ which is applicable to cases where
the price is lagged to a degree such that it is no longer correlated with the error term in the current
period. Hewitt and Hanemann (1995) have used a complex discrete/continuous model that builds on
the discontinuous nature of the budget constraint faced by the consumer under block-pricing. The
OLS method has been used in studies where uniform rates were charged (Hansen, 1996) or the
demand function was formulated under restrictive assumptions (Chicoine and Ramamurthy, 1986,
Foster and Beattie, 1979, Headley, 1963, Wong, 1972). This study has used two-stage least squares
method of estimation.
Different functional forms have been used in domestic water demand studies, including linear
(Agthe and Billings, 1987, Batchelor, 1975, Renwick and Archibald, 1998, Nauges and Thomas,
2000, Neiswiadomy and Molina, 1989), double-log (Foster and Beattie, 1979, 1981, Hewitt and
Hanemann, 1995, Neiswiadomy and Molina, 1991, Wong, 1972) and semi-log models (Hansen,
1996). However, there is no evidence to indicate which is the most appropriate form. Linear models
are easy to estimate while double-log models are useful because the coefficients give estimates of
elasticities. Following Arbues and Villanua (2006), in order to establish the most adequate functional
form, this study has estimated all three types of specifications: linear, double-log, and semi-log. The
most statistically and theoretically sound model has been selected for drawing conclusions.
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3. WATER SUPPLY IN LAHORE
Lahore is one of the oldest cities of South Asia and is the provincial capital of Punjab. The
Lahore district spreads over an area of 1,772 square kilometres with a population density of over
3,566 persons per thousand square kilometres (Govt. of the Punjab, 2005). Population-wise, Lahore is
the second largest city in Pakistan, fifth-largest in South Asia, and 23rd in the world (World
Gazetteer, 2007). The current urban population is over 6.6 million and is expected to exceed eight
million by the year 2020 (UN, 2004).
The Lahore Development Authority (LDA) is the chief municipal body responsible for
preparation and implementation of schemes for environmental improvements, housing, slum
improvement, solid waste disposal, transportation and traffic, health and education facilities, and
water supply and sewerage in Lahore City area under the LDA Act, 1975. The chief water supplier in
urban Lahore is WASA, formed under the Act.
The WASA service area extends to 350 square kilometres, supplying water and sewerage
services to a population of over five million (IFC, 2005). Other private water suppliers also exist in
Lahore City, but there is no official record of their number and coverage. For administrative purposes,
the area covered by WASA is divided into six blocks called ‘towns’: Allama Iqbal Town, Aziz Bhatti
Town, Ravi Town, Shalimar Town, Ganj Baksh Town, and Nishtar Town. Each town is further
divided into O&M sub-divisions.
Though located along the bank of River Ravi, water supply in Lahore depends on
groundwater, the river being the most polluted in the entire country. 6 For Lahore, groundwater is an
ideal source of water because it is relatively free of impurities and therefore little or no treatment of
the water is needed before it is put to household use.
Table 3.1: WASA Administrative Division
Town
Allama Iqbal Town
Aziz Bhatti Town
Ganj Baksh Town
O&M Sub-divisions
Allama Iqbal Town, Samanabad, Johar Town, Ichhra
Taj Pura, Mustafabad
Ravi Road, Krishan Nagar, Shimla Hill, Mozang, Gulberg
6 River Ravi is the most polluted river in Pakistan, receiving 47 percent of the total municipal and industrial
pollution discharged into all rivers in the country. According to the World Wildlife Fund, extreme pollution has
destroyed around 42 fish species that the river was home to. Even contact with the river water has been reported
to cause severe skin diseases. The water is certainly unfit for drinking. (EDC News:
http://www.edcnews.se/Cases/PakRaviriver.html)
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Nishtar Town
Ravi Town
Shalimar Town
Green Town, Industrial Area, Township, Garden Town
Shahdara, Data Nagar, City, Farkhabad, Misri Shah,
Shadbagh
Baghbanpura, Mughalpura
Water Pricing under WASA
WASA water tariffs are apparently based on cost-considerations, but are well below the cost
recovery level. The objective is seemingly to recover an acceptable portion of the cost rather than the
full costs, that being a compulsion due to political considerations. 7
Table 3.2: ARV-based Billing Structure for Domestic Unmetered Connections
ARV (Rs.)
Rate (Rs. per month)
January 1998 May 2004
Up to 400 70.55 98.77
401-500 108.80 152.32
501-720 185.30 259.42
721-1000 323.00 452.20
10001-1500 455.00 637.84
1501-2388 479.40 671.16
2389-4370 510.00 714.00
4371-4499 533.00 747.32
4500 and above 84% of ARV 84% of ARV
Table 3.3: Water Tariff Structure for Domestic Metered Connections
Consumption (gallons)
Rate (Rs. per 1,000 gallons per month)
January 1998 May 2004
Up to 5,000 9.20 12.88
5,001-20,000 14.90 20.86
20,001 and above 19.50 27.30
Source: WASA, Lahore.
7 The City District Govt. has not allowed WASA to raise tariffs for the past three years in spite a 10 percent
increase in the electricity rates (WASA, 2007).
8

Tariffs for both metered and unmetered connections have been revised thrice over the past
decade: in July 1997, in January 1998, and then in May 2004. No annual inflation adjustments are
made.
Water is charged volumetrically where the connection is metered, while unmetered
connections are charged on the basis of the annual rental value (ARV) of the house. 8 The ARV is
divided into nine bands ranging from Rs.400 to Rs.4, 500 and above. However, since ARV-based
charges do not directly affect consumption, we have selected only metered households for this study.
Currently only 30 percent of WASA connections are metered but WASA is making
substantial efforts to meter all existing connections (WASA, 2007). 9 No new unmetered connections
have been issued since January 1997. Metered connections are charged with a two-part tariff: a
variable volume-based part and a fixed part. The fixed part includes monthly connection fee of Rs.12
plus a flat charge of Rs.3 per month. The volume-based charges are divided into three ascending
volumetric blocks, that is, consumption in each succeeding block is charged higher than the previous
block.
4. A MODEL FOR WATER DEMAND
The domestic demand for water arises from its use for sanitation, bathing, washing clothes,
cleaning homes and cars, cooking and drinking, watering lawns, cooling, and recreational activities.
Like other commodities, the demand for water is expected to fall with price and increase with income.
Some other factors, such as the prices of water-related appliances, household size, house size, and
weather, etc. are also expected to have some influence on water demand. Based on these
considerations, a model for domestic water demand is presented below.
As discussed in chapter two, the price effect under block-rate pricing enters the demand
equation indirectly. If consumers are well-informed of the price structure, they respond to the
marginal price (MP), that is, the price of their final consumption block. But fully informed consumers
are also aware of the benefit that they gain by having paid less on the initial blocks. This benefit
enters the demand equation as the ‘difference variable’ – the difference between what the consumers
actually pay and what they would have paid had all units been priced at the marginal price. The
difference variable (DV) is computed as follows:
8 Annual Rental Value (ARV) is defined as the gross annual rent at which a land or a building might be expected
to be let from year to year, less deductions for repair and maintenance. (World Bank and The Urban Unit,
Lahore, 2006.)
9 WASA allocated Rs.29 million for the procurement of domestic water meters in 2007-08. (WASA budget
document, 2007.)
9

DV = [P1Q1 + P2Q2 + P3 (Q-Q1-Q2)] – [P3Q] [1]
Where P1, P2, and P3 are the respective prices charged under the successive blocks. Q1 and Q2 are
the respective consumption limits for the first two blocks.
Alternatively, if the consumers are not fully aware of the water charges, they approximate the
average price of water by dividing the total water expenditure in a billing period by the quantity of
water consumed in that period. The average price (AP) is computed as:
AP = [P1Q1 + P2Q2 + P3 (Q-Q1-Q2)] / Q [2]
The correct specification of price is largely a circumstantial question. For example, in
Zaragoza, Spain, where the tariff rate consists of 205 consumption blocks, it is reasonable to assume
that consumers cannot be fully aware of such a complex tariff structure, and therefore AP
specification can be used. 10 In the present case there are only three blocks and, analogously, it can be
argued that the marginal price specification is correct. However, a more rigorous approach can also be
adopted.
Under increasing block rates, the perceived price (P*) must lie somewhere between the
average and marginal prices. Let ‘k’ be a parameter that measures price perception. We may write,
P* = MP (AP/MP) k
The value of ‘k’ will lie somewhere between zero and one. If ‘k’ is found to be zero, then MP is the
perceived price. If it is equal to one, the perceived price is AP. If neither of these cases holds, then the
perceived price lies between AP and MP.
To test the value of ‘k’, the following equation is estimated:
lnQ = a 0 + a 1 lnMP + a 1 kln(AP/MP)+ ΣaiXi + µ [3]
Where Xi denotes predetermined variables.
Standard t-test can be applied to test the value of ‘k’.
Household water use is expected to increase with increase in income. More affluent
households are likely to use water less vigilantly than low income households. They are also more
likely to use water for washing cars, watering lawns, and swimming pools. A strong relationship has
also been found between use of water-based appliances and household water demand (Batchelor,
1975). However, reliable data of these indicators can be difficult to obtain. Therefore, information
10 Arbues, Fernando and Villanua Inmaculada (2006). Potential for Pricing Policies in Water Resource
Management: Estimation of Urban Residential Water Demand in Zaragoza, Spain.
10

about the house value was used as an indicator of wealth, a proxy for income, hoping that it would
also take into account some of the other water-related variables. 11
The size of the dwelling and the number of residents is also expected to influence water use.
Larger houses use more water for cleaning and irrigation of lawns, thus water use should increase
with the house size. The relationship of demand with the number of residents is, however, not so
direct. Generally, high income families are smaller than low income families. Per capita water
consumption may, therefore, be lower in low income families, in which case there may be a negative
effect of household size on consumption.
Three water-related activities are predominantly influenced by weather: watering lawns, using
room-coolers, and bathing. As temperatures rise, these activities become more frequent. A weather
variable has been included in the demand model to capture this effect.
Past bills, and therefore consumption patterns, are likely to have some influence a consumer’s
decision-making in the future. Moreover, this effect is likely to decline with time: older bills wielding
lesser influence than the ones that follow. In such a situation, Koyck (1954) has proposed substituting
the lagged explanatory variables by one single lagged dependent variable that appears among the
regressors. 12 Following this proposition, a lagged consumption term has been included. The
coefficient of this variable must be positive and must not exceed one.
Domestic water-meters are owned by either WASA or the consumers. Entitlement to the
meter does not directly influence demand but it may influence the quality and accuracy of the meter:
if the meter overestimates consumption, the consumer would make sure that the fault is fixed. If, on
the other hand, WASA meters under-read consumption, WASA will ensure accuracy. A dummy
variable has been used to capture this affect. The dummy takes a unit value if meter is owned by
consumer, and a zero value if owned by WASA. Possibly, this dummy will be negatively related with
the recorded consumption.
Fixed Effects
As discussed above, many factors cause variability in demand patterns across households:
household income, per capita income, ages of residents, lawn size, car ownership, and use of waterrelated
appliances. However, not all these variable could be included in the demand model. The effect
of missing variables is reflected in the intercept term.
11 Following Batchelor (1975), and Nieswiadomy and Molina (1989, 1991).
12 See: Koutsoyiannis, A. (1972). Theory of Econometrics (2 nd ed.). pp.304-6.
11

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

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

R 2 0.600
F-Statistic 304.163*
Variable Coefficient t-ratio
Intercept 0.54 (1.725)**
Meter-ownership (Dummy) -0.06 (-2.180)*
Difference Variable 2.89 (8.292)*
Plot Size 0.04 (3.297)*
Marginal Price -2.33 (-5.473)*
Household Size -0.14 (-4.380)*
Lagged Consumption 0.38 (10.402)*
Temperature 0.05 (1.457)***
Property Value -0.01 (-1.066)
Location Dummies
Aziz Bhatti Town -0.064 (-2.510)*
Allama Iqbal Town -0.128 (-3.901)*
Ganj Baksh Town 0.023 (0.942)
Ravi Town -0.070 (-2.751)*
Shalimar Town 0.010 (0.442)
* = significant at 5% level.
** = significant at 10% level.
*** = significant at 15% level.
The size of the plot, which presumably affects water use because of higher cleaning and lawn
watering requirements, has a small but significant impact on domestic water use. For every percentage
increase in the size of the plot, water consumption goes up by 0.041 percent. However, this
information does not tell us about the individual impact of lawn size and house size and water use
separately attributed to each variable. Possibly, water demand for one of the purposes may be more
elastic than the other.
The relationship of water demand and the number of residents living in a household was
found negative, contrary to what was expected. As household size increases by one percent,
consumption falls by 0.14 percent. Apparently small, this connection is statistically significant and
also has important implications for policymaking.
Commonly, low income families are larger as compared to high income families. If large
families consume less than small families, then there must be some underlying income connection.
16

We may infer that low-income households consume less water per capita. However, more information
is needed to substantiate this inference.
The difference variable has an effect of increasing demand by 2.89 percent with every
percentage increase. In essence, the difference variable reflects the apparent increase in income from
having paid less for some units of a commodity under increasing block rates – what is known in
microeconomic theory as the ‘income effect’. As expected, this income effect is positive. 16 This
means that as more discriminatory tariffs are charged for consumption in successive blocks, the
income effect induces consumers to increase water expenditure. However, as a policy variable, DV
has limited value as it cannot be directly manipulated.
Weather has a modest influence on domestic water consumption. A one percent rise in
temperature increases water demand by 0.05 percent. This means that water demand is higher in
summer than in winter. 17
The intercept term reflects the average water consumption for Nishtar Town when all other
variables are set to zero is 860.6 gallons per household per month if the meter is owned by WASA
and 813.7 gallons if the meter is owned by the consumer. That means on average meter-ownership
reduces water consumption by 46 gallons per month.
According to the model, average household consumption of Nishtar Towm is statistically
similar to that of Ganj Baksh and Shalimar towns. Consumption in Aziz Bhatti and Ravi towns differ
from Nishtar Town by about 50 gallons per month, while Allama Iqbal Town households consume
around 100 gallons less water than households in Nishtar Town.
7. POLICY RECOMMENDATIONS
The results of this study show that there is considerable potential to use demand management
pricing policies. Therefore, a calculated re-arrangement of the tariff structure can be instrumental in
encouraging efficient use of water, improving revenues, and ensuring universal lifeline supply.
Conservation: Tariffs can be effectively used to induce efficient water use. At the current rate,
household water use is extremely elastic to changes in prices. A small ten percent increase in the
overall tariffs will cause an average household to conserve 2,000 gallons of water per month. A 30
16 Note that the difference variable was originally negative. Natural log of the absolute values was included in
the computations. However, the positive relationship of the variable consumption was also found in linear and
log-linear models (See Appendix). We have therefore interpreted the coefficient as positive.
17 The weather coefficient was significant at 15 percent level. If one adheres to the conventional five percent
level, then the coefficient is statistically zero.
17

percent increase will bring down water use to 2,700 gallons – close to the level of lifeline
consumption. (See table 8.1). 18
A drawback of these computations is that they are based on average consumption patterns,
and do not give any indication of how the tariff increase will affect households with varying
socioeconomic settings, particularly income levels. Taking into account these considerations may
make an overall equal increase in tariffs undesirable.
If an overall increase is not desirable, effective conservation can still be achieved by
increasing the tariff of the final block – monthly consumption of over 20,000 gallons. Currently,
average consumption in this block is over 1,800 gallons. A 30 percent increase in the price of this
block will induce average households to cut down over 1,000 gallons of monthly consumption. A
larger increase can virtually eliminate per household water consumption beyond 20,000 gallons. (See
table 8.2)
Table 8.1: Impact of Price Increase on Monthly Water Use
Current
Tariff Increase
10% 20% 30%
Avg. MP (Rs.) 21.8 23.98 26.16 28.34
Avg. Monthly Use (gallons) 8,900 6,900 4,800 2,700
Table 8.2: Impact of Price Increase on Water Use in Block 3
Current
Tariff Increase
30% 40% 50%
Block 3 Price (Rs.) 27.30 35.49 38.22 40.95
Avg. Monthly Use in Block 3
(gallons)
1,820 545 125 -300
Cost Recovery: Typically, a tariff structure that is meant for budget balancing consists of a large fixed
part coupled with declining block rates (Montginoul, 2006). Declining block rates promote
consumption leading to higher revenues and faster cost recovery on investments, while the fixed part
increases the certainty of revenue projections.
Though leading to immediate cost recovery, decreasing block rates are not compatible with
promoting efficient water use, and assuming that conservation is more important of the two
objectives, declining blocks rate are not advised. Moreover, though increasing block rates do not
18 Daily per capita lifeline is 13.21 gallons. The average household size is taken to be 7 (see Chapter 6 for
descriptive statistics on household size). Lifeline water supply is calculated as: 13.21*7*30= 2,800 gpm.
18

generate equally high revenue, they do lead to a cut-down in O&M costs while at the same time
easing the need for new investments to increase supply capacity. Therefore, the management will not
be ill-advised to continue following increasing block rates.
The non-volumetric part can also be effectively used to increase revenues. Currently the
monthly non-volumetric charge consists of a connection fee of Rs.3 and meter charge of Rs.12, that
is, all households indiscriminately pay fixed charges of Rs.15 per month. The connection fee can be
increased, uniformly or discriminately, varying increasingly with the household’s ability to pay. The
best measure of ability-to-pay is household income, but it is difficult to oblige people to provide
correct information about their incomes. Alternatively, non-volumetric charges can be based on
property value, ARV, or plot size.
Allocation: The effect of a pricing policy may not be uniformly felt across all income groups. Past
studies have found that indiscriminate tariff increase causes greater reduction in water use in lower
income households than in higher income households (Agthe and Billings, 1987, Renwick and
Archibald, 1998). If this disparity gets large enough, it can threaten the lifeline supply of vulnerable
households.
As a rule, water expenditure should not exceed five percent of the household income (WASA,
2005). The lifeline water requirement for a seven-member household (2, 800 gallons per month) costs
Rs.36.06. If the monthly income is Rs.5, 000, this expenditure amounts to a bare 0.72 percent. If the
tariff in the first block is increased by 50 percent, the lifeline consumption cost will amount to only
1.1 percent of the expenditures of a household with above-mentioned features.
Table#: Impact of Block 1 Price Increase on Lifeline Consumption
Current
Tariff Increase
10% 20% 50%
Price in Block 1 (Rs.) 12.88 14.17 15.46 19.32
Cost of Avg. Household Lifeline
Supply (Rs.)
36.06 39.68 43.29 54.10
Summing the discussion on pricing policies, one can see that there is significant room for
increasing tariffs to achieve conservation and cost recovery objectives without risking lifeline supply.
19

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.
20

Table 5.1: Allocation of Sampling Units
Town Sampling Units O&M Sub-division Sampling Units
Allama Iqbal Town 25
Allama Iqbal Town 5
Ichhra 5
Johar Town 5
Sabzazar 5
Samanabad 5
Aziz Bhatti Town 25
Taj Pura 13
Mustafabad 12
Ganj Baksh Town 25
Gulberg 5
Krishan Nagar 5
Mozang 5
Ravi Road 5
Shimla Hill 5
Nishtar Town 26
Garden Town 6
Green Town 6
Industrial Area 7
Township 7
Ravi Town 44 30
City 5
Data Nagar 5
Farkhabad 5
Misri Shah 5
Shadbagh 5
Shahdara 5
Shalimar Town 25
Baghbanpura 13
Mughalpura 12
Total 156 156
II. Hausman Test
Ho: Coefficient of residuals is statistically zero; no simultaneity problem
H1: Coefficient of residuals is not statistically zero; simultaneity problem exists
Level of Significance: α = 0.05
Test Statistic: t-ratio
Computations and Conclusions:
21

Variable
Regression 1 Regression 2
Residual
Conclusion
Dependent Independent Dependent Independent
Coefficient
1. AP AP Q_lag, P3, Dst, T,
Q
RES, PRE, T, Dabt,
5.953
Reject Ho.
W, Dmo, Drt, L,
Dmo, Dgbt, NR, L,
(76.009)
Conclude that
Dabt, NR, Dgbt,
Dst, Dait, W, Q_lag,
simultaneity
Dait
Drt
problem exists.
2. MP MP Q_lag, P3, Dst, T,
Q RES, PRE, Dst, T,
1.727
Reject Ho.
W, Dmo, Drt, L,
Dmo, Dgbt, L, Dabt,
(28.804)
Conclude that
Dabt, NR, Dgbt,
NR, Dait, W, Q_lag,
simultaneity
Dait
Drt
problem exists.
3. DV DV Q_lag, P3, Dst, T,
Q RES, PRE, Dst, T,
-0.077
Reject Ho.
W, Dmo, Drt, L,
Dabt, P2, Dmo, Dgbt,
(- 30.451)
Conclude that
Dabt, NR, Dgbt,
L, NR, W, Drt, Dait
simultaneity
Dait
problem exists.
Details
1. AP
Regression 1 Residual Statistics
Min Max Mean SD N
PRE 11.7150 29.1553 17.9508 1.80555 2652
RES -8.19803 8.07184 .00000 1.13266 2652
Regression 2 Coefficients
Predictors B SE t Sig.
(Constant) -3.347 1.420 -2.357 .018
T .101 .015 6.752 .000
NR .205 .255 .803 .422
Dmo -1.605 .365 -4.393 .000
L .051 .007 7.005 .000
W 6.39E-009 .000 .191 .848
Dgbt 1.197 .378 3.166 .002
Drt .003 .742 .003 .997
Dst .617 .413 1.495 .135
Dabt .503 1.086 .464 .643
Dait .496 .414 1.199 .230
Q_lag .791 .009 90.491 .000
PRE .201 .071 2.841 .005
RES 5.953 .078 76.009 .000
2. MP
Regression 1 Residuals Statistics
Min Max Mean SD N
PRE 13.8864 33.3924 22.1529 2.22821 2652
RES -13.73344 6.95036 .00000 2.30573 2652
Regression 2 Coefficients
Predictors B SE t Sig.
(Constant) -3.110 2.113 -1.472 .141
22

T .101 .023 4.324 .000
NR .177 .399 .444 .657
Dmo -1.628 .570 -2.856 .004
L .051 .011 4.509 .000
W 1.07E-009 .000 .021 .984
Dgbt 1.182 .589 2.007 .045
Drt -.059 1.157 -.051 .960
Dst .590 .644 .916 .360
Dabt .432 1.692 .255 .799
Dait .358 .656 .546 .585
Q_lag .793 .013 62.615 .000
PRE
.160 .088 1.823 .068
RES 1.727 .060 28.804 .000
3. DV
Regression 1 Residuals Statistics
Min Max Mean SD N
PRE -351.0884 -2.2660 -78.4510 39.70316 2652
RES -175.04536 186.92253 .00000 53.85123 2652
Regression 2 Coefficients
Predictors B SE t Sig.
(Constant) 53.504 2.567 20.845 .000
T -.148 .023 -6.287 .000
NR -2.745 .395 -6.956 .000
Dmo -4.854 .557 -8.720 .000
L -.028 .011 -2.457 .014
W -3.68E-007 .000 -7.136 .000
P2 -1.742 .101 -17.265 .000
Dgbt 2.117 .580 3.650 .000
Drt -5.569 1.143 -4.873 .000
Dst -3.566 .636 -5.606 .000
Dabt -5.237 1.670 -3.136 .002
Dait -13.675 .663 -20.616 .000
PRE -.383 .005 -79.960 .000
RES -.077 .003 -30.451 .000
III. Price Perception Test
Ho: k=1; consumers respond only to average price.
H1: k≠1; consumers do not respond only to average price.
Level of Significance: α= 0.05
Test Statistic: t* = (β1 – β2)/ S.D β1-β2
Critical Region: t*> 2 19
19 The degrees of freedom of the β population is unknown, therefore the rule of thumb is used.
23

Computations:
Regression
Dependent Variable: lnQ
Independent Variables: lnQ_lag, Dgbt, lnT, ln_MP_IV, ln(AP/MP), Dmo, Dabt, lnNR, Dst, lnL, Dait, , Drt, lnW
Variable Tested Coefficient S.E. Variance
0.756
MP 20 (3.514)
-0.576
AP/MP
(-6.677)
0.215 0.046225
0.086 0.000740
β1 – β2 = 0.756 – (-0.576)
= 1.332
S.D β1-β2 = (Var β1 + Var β2 – 2Cov β1, β2 )½
= 0.230913 21
t* = 1.332/0.230913
t* = 5.768
Conclusion: t* > 2 ; reject Ho.
Conclude that k≠1. Consumers do not respond only to average price.
[Note that a similar test cannot be used for the hypothesis that consumers respond only to MP (k=0). If the
alternate hypothesis (k≠0) is false, it would not imply that consumers do not respond to AP. Average price has
been introduced in the regression equation as a ratio of AP and MP. If the alternate hypothesis is false, we can
only conclude that consumers do not respond to this ratio: a conclusion with little practical significance.]
Details
Coefficients
B SE t Sig.
(Constant) -.588 .287 -2.050 .040
Dmo -.031 .025 -1.235 .217
Dgbt .042 .025 1.719 .086
Drt .002 .024 .090 .929
Dst .047 .023 2.085 .037
Dabt .010 .023 .407 .684
Dait .036 .024 1.457 .145
lnT .156 .030 5.278 .000
lnNR .020 .024 .850 .396
lnW -.010 .011 -.907 .365
lnL .087 .012 7.446 .000
ln_MP_IV .756 .215 3.514 .000
lnAP_MP -.576 .086 -6.677 .000
20 The positive sign of MP coefficient is possibly due to multicollinearity between MP and AP/MP variables.
21
Cov β1, β2 = -rσ 2 /[(1-r 2 )√(Σx 1 2 Σx 2 2 )]
24

ln_Q_lag .649 .016 41.784 .000
IV. Summary of Estimated Models
Statistic
AP Models
MP Models
Semi-log Double-log Semi-log Double-log
R 2 0.569 0.590 0.569 0.600
F-Statistic 290.669 315.841 290.669 304.163
D-W Statistic 1.911 2.315 1.911 2.289
Variables
Constant
1.658
(17.415)*
0.010
AP
(1.901)**
Dmo 0.017
(0.644)
DV
Dabt
Dait
Dgbt
Drt
Dst
L
MP
NR
Qt-1
T
W
--
0.082
(3.504)*
0.170
(6.755)*
-0.036
(-1.504)***
0.069
(2.903)*
0.122
(5.263)*
0.002
(4.218)*
--
0.034
(8.451)*
0.022
(35.285)*
0.005
(5.049)*
5.57E-009
(2.331)*
* = significant at 5% level.
** = significant at 10% level.
*** = significant at 15% level.
-0.494
(-1.826)**
.255
(3.431)*
-.026
(-1.006)
--
0.033
(1.411)
0.066
(2.721)*
0.049
(1.986)*
0.018
(0.760)
0.054
(2.365)*
0.079
(6.697)*
--
0.044
(1.876)**
0.648
(39.479)*
0.155
(5.206)*
-0.003
(-0.270)
2.331
(9.550)*
-- --
-.069
(-2.643)*
1.292
(40.825)*
-0.128
(-5.379)*
-0.251
(-9.373)*
-0.023
(-0.932)
-0.126
(-5.247)*
-0.010
(-0.431)
-3.69E-005
(-0.069)
-1.428
(-13.914)*
-0.042
(-9.374)*
--
(excluded)
-.001
(-1.037)
-5.33E-009
(-2.222)*
.543
(1.725)**
-.056
(-2.180)*
2.886
(8.292)*
-.064
(-2.510)*
-0.128
(-3.901)*
.023
(0.942)
-0.070
(-2.751)*
0.010
(0.442)
0.041
(3.297)*
-2.327
(-5.473)*
-.139
(-4.380)*
0.380
(10.402)*
.047
(1.457)***
-0.012
(-1.066)
25

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