Information on residential water demand

Applied Economics, 2006, 38, 383–393
Effect of price information on
residential water demand
S. Gaudin
Department of Economics, Oberlin College, Oberlin, OH 44074, USA
E-mail: [email protected]
Microeconomic theory predicts that people decrease consumption when
price increases, the magnitude of the effect depending on price elasticity.
The law of demand, however, implicitly assumes that consumers know
prices, an assumption that is not always satisfied in markets with ex post
billing. When prices are not transparent, elasticity estimates are potentially
lower than their full information potential. Evidence of low price elasticity
abounds in residential water demand studies, limiting the effectiveness and
desirability of using price signals as a conservation tool. It is hypothesized
that resident’s sluggish response to price is partly due to the absence
of price information on water bills. Differences in the informational
content of bills are documented for the first time on the basis of sample
bills collected from 383 utilities across the USA. A standard aggregate
water demand model is augmented with qualitative variables describing
differences in billing information, allowing such variables to affect the
intensity with which consumers respond to price signals. No evidence
is found that non-price information items affect price elasticity but there is
a statistically significant effect in the case of price-related information;
in our sample, price elasticity increases by 30% or more when price
information is given on the bill.
I. Introduction
The power of price signals in motivating demand
responses is put into question when price elasticity
is low and when people have imperfect information
about prices. When it comes to residential energy and
water use, evidence of low price elasticity of demand
abounds.1 Recent empirical analyses have also
found evidence that, within price ranges observed,
there is a large amount of water use under which
demand is insensitive to price (Gaudin et al., 2001;
Martı´nez-Espin˜eira and Nauges, 2004). The focus
of most studies has been to estimate intrinsic
parameters of water demand in order to assess the
relative merits of price versus non-price demand
reduction policies. However, existing research has
not considered the possibility that price elasticity
itself could be influenced by policy. We hypothesize
1The average estimate of price elasticity from 18 studies of annual residential water demand reported in Hanemann (1997)
is 0.46 (absolute value) with a mean elasticity of 0.36 for winter demand and 0.70 for summer demand (computed
from Hanemann, 1997, Table 2.5, pp. 67–72). Espey et al. (1997), in a meta-analysis of residential water demand studies,
report a median short run price elasticity of 0.38 and a median long-run price elasticity of 0.64.
Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online 2006 Taylor & Francis 383
http://www.tandf.co.uk/journals
DOI: 10.1080/00036840500397499
that price elasticity results are not exogenous to the
level of information provided to consumers. The
issue of price transparency has been advanced in
past research to explain demand responses and price
perception in utility demand (Foster and Beattie,
1981; Shin, 1985; Nieswiadomy and Molina, 1991),
but variation in information across utilities has not
been documented. While water utilities are legally
required to provide price information, there are no
specific regulations about the way the information
is to be provided, creating potential differences
across communities in the cost of acquiring price
information. In particular, although information
costs become minimal to consumers when they can
find the information they need on their utility bill,
water utilities are not required to give price information on bills. Would price elasticity increase with
better price information on water bills?
Historically, growing cities in the USA have relied
on expanding supply to satisfy rapidly increasing
water demands. As new supply sources become more
and more difficult to secure, measures to encourage
conservation have become an important part of
water management and planning in areas subject to
drought and/or experiencing high population growth.
The effectiveness and desirability of price instruments in demand management, however, depends on
the magnitude of the price elasticity of demand. The
lower the price elasticity, the greater the price increase
needed to absorb a given shortage. In practice, large
price increases are unlikely because of distributional
implications and political pressures. Higher price
elasticity would increase the effect of price signals
and, consequently, the attractiveness of price-based
policies. If weak sensitivity to price stems from the
intrinsic nature of the good, then no economic policy
instrument can change it; e.g. if the water we use is
a necessity to life, as is often implicitly assumed,
price elasticity cannot be changed, short of finding
substitutes for water. However, very little of the water
used by residential consumers can be considered
a necessity.2 Basic economic theory points to at
least two other reasons why consumers would not be
responsive to price in their decision to consume
water: water bills constitute a small portion of
their budgets, and price information is imperfect.
This paper is concerned with the latter. In particular,
we hypothesize that residents’ sluggish response to
price is partly due to the fact that the information
necessary to make informed decisions is not conveniently available to them. If this hypothesis is
valid, including clear price information on water bills
should increase price elasticity.
The Environmental Protection Agency (EPA)
guidelines for water conservation recommend the
use of an ‘understandable’ and ‘informative’ water
bill.3 Best Management Practices produced by state
agencies started to include the layout and information
content of bills as a tool to foster conservation
(e.g. Envision Utah, 2002). However, cross sectional
variations in billing practices and, in particular
differences in the informational content of bills have
not been considered in empirical studies of residential
water demand. The experience of gas and electric
utilities gives some indication that the content and
presentation of bills matter. For example, Fast (1990)
found a significantly larger price elasticity of residential electricity demand after the 1985 change
to ‘plain language’ bills in the state of New York.
Fast’s study provided evidence of the overall effect
of the new bill but could not identify the
specific effect of price information. Other evidence
in energy demand relates to consumption feedback
rather than price information (Egan et al., 1996;
Matsukawa, 2004).4
Growing cities, especially those in the West and
Southwest of the USA but also in water scarce areas
around the world, would benefit from understanding
whether a slight modification on their water bills
could reinforce the effectiveness of price signals,
allowing utilities to rely more on prices to reflect
changes in scarcity rather than rationing methods,
thereby reducing the welfare impact of shortages.
This study provides the first quantitative measure
of the impact of price information on household’s
sensitivity to price variation in utility demand
analyses.
Billing information was collected directly from
utilities across the USA to document the variation
in billing practices and to test the impact of price
information. In our sample, 17% of the utilities
clearly indicated marginal prices next to unit
2Baumann and Boland (1997) dismiss the ‘water is a necessity’ argument as a water management myth (p. 21).
3The Safe Drinking Water Act Amendments of 1996 mandated EPA to publish guidelines for water conservation to be used
by public systems. These guidelines are purely informative and available on the EPA website. ‘Understandable’ refers to the
inclusion of price and quantity information on the bill; ‘informative’ refers to additional quantity information such as
comparison with previous usage and the inclusion of conservation tips.
4Egan et al. (1996) compare the effectiveness of different graphical displays to provide feedback on own energy use and find
that consumers react differently to different displays. Matsukawa (2004) finds that the use of monitors providing continuous
feedback to customers on their energy consumption promotes energy conservation, and that the more the monitor is used,
the greater the price elasticity of demand.
384 S. Gaudin
consumed on their water bill while 78% gave no price
information other than total amount due. We used
this data along with utility and community level data
from secondary sources in a standard water demand
model where features of the bill may affect price
elasticity. The analysis indicates that the presence of
marginal price information on the bill has a
statistically significant impact on price elasticity
while other types of information are not found to
affect price elasticity.
II. Water Demand Model and Data
Model specification
Basic aggregate annual residential water demand
model. We use an aggregate water demand model
to be estimated using cross-sectional community
level data. The use of aggregate cross-sectional data
is motivated by the nature of our quest. Indeed,
features of water bills scarcely change over time and
are identical for all consumers in the service area of
a given utility, therefore this research would not gain
from using microeconomic or pooled cross-sectional
data.5 Our choice of demand variables, functional
form, and estimation procedure are guided by
insights provided by a large number of published
studies, data availability, and interpretability of the
coefficients of interest to this study.6 We base our
analysis on the following aggregate water demand
model:
Q ¼ f ðAP, I, H, D, AAP, T90Þ
where Q is per capita annual water consumption,
AP is average price, I is income, H is average
household size, D is density, AAP is average annual
precipitation, and T90 is the number of days when
temperature exceeded 90F. While AAP is a 30-year
average and as such will capture structural differences
between communities mostly related to outdoor
water use and practices, T90 is specific to the survey
year and will capture both structural and temporary
features of water demand. It is likely that a high level
of T90 will not only increase outdoor water use due
to higher evaporation but also possibly increase
indoor water use due to more frequent washing and
showering, both effects that cannot be captured
by AAP.
The choice of an average price specification
is driven by specificities of our data set. Several
studies show that consumers tend to respond to
average prices for water and electricity demand
rather than marginal prices (Foster and Beattie,
1979, 1981; Shin, 1985; van Helden et al., 1987;
Griffin and Chang, 1990). Others find that neither
an average price specification, nor a marginal price
specification can be rejected in favour of the other
(Williams and Suh, 1986). A few studies have shown
that, in some circumstances, marginal prices are
more appropriate (Nieswiadomy and Molina, 1991;
Taylor et al., 2004).7 When using marginal prices,
however, one must be aware of the effective price
structure used, as people are likely to respond
differently to marginal prices depending on whether
price schedules are increasing, decreasing or uniform
(Nieswiadomy and Molina, 1991; Olmstead et al.,
2003). Unfortunately, although we know the type
of rate structure that utilities used in 1995/6 – as
reported in the American Water Works Association
(AWWA) database – we do not know the full price
schedule and cannot identify the amount of fixed
and variable charges for each utility.8 Without
knowledge about fixed fees and free allowances, we
cannot distinguish between cases when the marginal
price is less, equal to, or greater than the average
price therefore, using a marginal price specification
with heterogeneous price structures would create
additional estimation and interpretation problems.
Although the use of marginal prices rather than
average prices may affect elasticity estimates (Espey
et al., 1997), the magnitude of the effect of information on price responses should not be affected.
Finally, although it is often argued that billing
information may affect the appropriateness of a
given price specification (Foster and Beattie, 1981;
Shin, 1985), the limited information on marginal
prices does not allow us to test for the appropriate
price specification; we leave this enquiry for further
research and focus our contribution on the impact
of information on price elasticity.
Data constraints also motivate the exclusion
of other prices in the demand equation. While most
aggregate residential water demand models in the
5Espey et al. (1997) found no significant differences between long-run elasticity estimates calculated using single period cross
sectional data and other types of data.
6 See Renzetti (2002) for a review of the literature.
7 Nieswiadomy and Molina (1991) found evidence that, for a true increasing block rate structure – i.e. without large quantity
allowances included in the fixed fee – the role of marginal prices dominated average price; a recent article by Taylor et al.
(2004) attributes the response to average price to the magnitude of fixed fees.
8We do know the cost to consumers of 3750 gallons and 7500 gallons per month but a pseudo-marginal price calculated from
this information is not likely to be the appropriate marginal price for the level of water consumed.
Effect of price information on residential water demand 385
literature ignore prices of other goods, sewer charges
are often included in the average price specification
(Renzetti, 2002). While we do not know sewer prices,
we do know whether the utility charged for sewer,
electricity, or other utilities on the same bill and we
are able to test whether the inclusion of such charges
on the bill affect price elasticity in the transformed
model.
Modified demand model. Price and quantity-related
billing information is assumed to enter the demand
equation through its effect on price elasticity. The
presence of such information is incorporated in the
estimation using qualitative dichotomous variables
interacted with the price variable (slope dummies).
Other type of information, such as messages aimed at
sensitizing consumers to the importance of conservation, may affect demand independently of prices
through their potential effect on consumer’s preferences, and are therefore included in the estimable
equation as intercept dummy variables. Our choice
of a log–log functional form as opposed to a linear
form is motivated by previous research where it was
shown that forcing price elasticity to decrease along
the demand curve is not an appropriate specification
for water demand (Gaudin et al., 2001).9 As opposed
to other functional forms, the log–log functional
form facilitates interpretation of the coefficient as
elasticity estimates and allows direct comparison with
the existing literature.10 The following relationship
is assumed:
lnðQÞ ¼ 0 þ 0 lnðAPÞ þ iði lnðAPÞXiÞ þ 1 lnðIÞ
þ 2 lnðDÞ þ 3ðHÞ þ 4 lnðAAPÞ þ 5T 90
þ jjZj, i ¼ 1, … , x; j ¼ 1, … , z
where the Xi and Zj are qualitative dichotomous
variables representing the different features of the
water bill: the Xs represent billing features that do not
directly affect consumer’s utility but are likely to
change consumers’ sensitivity to price; the Zs are
variables likely to directly affect consumers preferences for water. Price elasticity in a standard low
information water bill is 0 while price elasticity on
a bill that includes information item i is 0 þ i.
Data
Primary source data on water bills. The American
Water Works Association (AWWA) periodically
collects data on member utilities across the USA.
In 1996, AWWA surveyed a total of 3200 utilities
(the smallest utilities were excluded) and received
898 responses with 501 utilities serving residential
customers.11 Utilities with un-metered (flat fee) or
seasonal rates were dropped, leaving us with
495 utilities to locate and contact by phone during
the summer of 2003. The goal was to find out what
information was given on a residential water bill that
could potentially affect water use. Since most utilities
used December 1995 as the end date for the yearly
data they provided to the AWWA, we asked about
bills as they were in 1995.12 We obtained usable
information from 383 utilities of which 130 reported
charging decreasing block rates, 104 increasing block
rates, and 149 uniform per unit rates.13 Although
the sample may not be considered representative
of US utilities, sample selection cannot be related
in any systematic manner to variables in the demand
equation and therefore may be considered random
for our analysis.14
9 A better specification in cases when researchers are interested in changes in price elasticity along the demand curve would
require functional forms such as a Stone-Geary form or flexible forms that allow decreasing price elasticity with decreasing
quantity and increasing price (Gaudin et al., 2001). In terms of demand elasticities at the mean, however, Espey et al. (1997)
find that the choice of functional form does not systematically affect price elasticity estimates.
10 Coefficients on household size and number of hot days are left in their raw form to be conveniently interpreted as semielasticities. A similar regression with the two variables in their log form reduced the overall fit but did not affect the level and
significance of other parameter estimates.
11 AWWA membership in 1996 consisted of about 4000 utilities. Although there are approximately 56 000 utilities in the USA,
most of them are small utilities with a customer base lower than 500. Over two-thirds of the 500 largest utilities are represented
in the AWWA data. Although another survey was conducted in 1999, it did not include enough information for our analysis.
12When a copy of a 1995 bill could not be found but someone with an accurate recollection of the information could be
located, we asked the utility to answer a simple questionnaire. If there was any doubt about the accuracy of the information,
the observation was dropped.
13The price structure is as reported by the utility in the AWWA survey. As indicated in the previous section, we do not know
the details of the price schedule. In our phone interview we asked utilities that still had a decreasing block structure the
quantity level that would push consumers into the lower priced second block and found that in most cases, the second block
was too high to be reached by any single-family household. Such a price structure is effectively similar to the uniform rate for
the household.
14 Sixty utilities had either changed ownership (most due to the American Water Company merger in 2000) or could not be
located; 24 did not have records or recollection of their 1995 bill; 5 had bills changed in the 1995–96 period; 3 were removed
for miscellaneous reasons, such as a utility serving only summer homes. Twenty utilities refused to participate.
386 S. Gaudin
The kind of information given on water bills from
city to city varied along several dimensions. All bills
included meter readings, quantity used, and total
amount due for water (separate from other charges).
There was significant variation on whether the bill
included marginal price information and history
of use. Table 1 summarizes our survey results on
information variables that were found to differ
significantly across utilities.
Only 17% of utilities in our sample indicated price
per unit next to consumption and an additional 3%
indicated the price schedule somewhere on the bill.
For history of use, a simple comparison to the same
period last year was the most common (23% of
utilities overall), while only 6% gave more extensive
historical data (multiple months, generally presented
with a graph). Although price information and
consumption history are positively correlated
( ¼ 0.27, p-value ¼ 0.00), many utilities with price
information did not include history and vice versa.15
There is no evidence of correlation between price
information and conservation messages on the bill
( ¼ 0.03, p-value ¼ 0.53).16 A break down of the
sample by regions and by rate structure revealed that
a disproportionate share of utilities in the West
included different types of information on the bill.
The same is true for utilities using increasing block
rates. The empirical analysis will take account of
these features of the data.
Another feature of bills that may affect water
use although not related to their informational
content is whether they include utility charges other
than water. The effect on water use could be two-fold:
on the one hand, including other utilities reduces the
consumer’s ability to understand the cause of changes
in the total bill, especially if the customer only looks
at the bottom line and there is little breakdown,
possibly reducing price elasticity; on the other hand,
consumers are more likely to pay attention to a larger
total bill and price elasticity tends to be higher for
goods that constitute a larger portion of income.
Other utilities were included in about three-quarters
of the bills (286 included sewer charges and 59
included energy charges, of which 55 included both
sewer and energy). Bills with sewer and/or energy
charges were evenly distributed among the different
regions and across rate structures.
Finally, billing frequency may influence water
consumption through price perception. Arbues et al.
(2003) recommend that billing frequency be included
as a relevant variable in residential water demand
models. Two opposite forces could be at play: on the
Table 1. Prevalence of information on water bills by region and by rate structure (1995)
Regiona Rate structureb
Full sample MW NE SE SW W DB IB UR
Number of collected bills 383 109 64 77 31 102 130 104 149
Percent of bills with
Price per unit consumedc 17.2 11.0 23.4 3.9 19.4 29.4 8.5 26.9 18.1
Price scheduled 2.9 2.8 3.1 5.2 0 1.2 2.3 2.9 3.4
Consumption history Ie 22.7 11.0 7.8 15.6 28.6 50.0 10.8 39.4 21.5
Consumption history IIf 5.7 1.8 0 3.9 16.1 11.7 2.3 14.4 2.7
Conservation messages 9.7 4.6 3.1 6.5 6.5 22.6 3.0 18.3 9.4
Other non-price infog 10.2 3.7 7.8 6.5 9.7 21.6 3.9 17.3 10.7
Notes: a US Census definition. b DB ¼ declining block rate; IB ¼ increasing block rate; UR ¼ uniform rate. c
Rate per unit indicated next to units consumed. With block pricing, the price per unit is next to quantity consumed for
all relevant consumption blocks.
dBills that include information about the rate schedule on the bill but not next to consumption.
e
Consumption in the same period in the previous year is included for comparison with current period.
f
Consumption for all periods in the previous year is included (the information is either presented in a table or a graph).
Note that these bills are a subset of the bills reported to include Consumption history I.
g Includes features used by a small number of utilities that provide information on quantity including: benchmark
comparisons, daily consumption, and % age changes in consumption.
15 Among utilities that gave price information on the bill, 32% gave simple history and 13% gave more detailed history
(compared to 23 and 6% in the full sample), while 40% of all bills with consumption history included price information
(compared to 20% in the full sample).
16Eight per cent of the bills with price information gave conservation messages (compared to 10% for the full sample) while
16% of the bills with messages gave price information (compared to 20% for the full sample).
Effect of price information on residential water demand 387
one hand, frequent bills are a reminder that water
is not free and may create a better understanding on
the part of consumers of the price structure and the
relation between consumption and cost, increasing
price elasticity; on the other hand, more frequent
billing causes smaller overall bills, which would
dampen price elasticity.17 Over half of the utilities
in our sample used monthly billing.18
Qualitative (dummy) variables were created to
record the presence of the different types of information. To avoid low sample problems, we do not
create dummy variables for information items used
by fewer than 30 utilities. We are most interested in
the presence of price information, theoretically most
likely to increase price elasticity. Quantity information may also affect price elasticity by attracting
the attention of individuals to prices in an effort
to figure out whether changes in total bills are due
to quantity or price movements.19 The presence
of conservation messages is more likely to affect
consumers’ preferences independently of prices and
is therefore allowed to enter directly in the demand
function.
Qualitative variables used in the model are
separated into two groups. One group includes billing
features that may affect demand through price
responses (Xis); the other includes billing feature
that may affect demand by altering preferences (Zjs).
A single variable, message, is included in the latter
group and set equal to 1 if the utility commonly
included conservation related messages on bills.
The first group of qualitative variables includes
three categories: price information variables, quantity
information variables, and variables related to other
aspects of billing. For price information we test the
model with two measures: mpinfo and priceinfo. The
former is set equal to 1 if marginal price is indicated
clearly next to units consumed; the latter includes
all the utilities with mpinfo ¼ 1 plus the ones with
full information about the price schedule somewhere
else on the bill. To describe quantity information
we use the quantityinfo variable, equal to 1 when
the bills include simple or advanced consumption
history or other detailed quantity information such as
daily average use.20 Other variables related to the
size of the bill and the rate structure are:
CombinedBilling ¼ 1 if sewer charges, electricity
charges, and/or gas charges were included together
with water charges on the same bill; MonthlyBill ¼ 1
if water was billed monthly; and IB ¼ 1 if the utility
reported using an increasing block rate structure.21
Secondary data sources. All variables pertaining
to price and quantities and other characteristics of
the utilities come from the 1996 AWWA survey.
Per capita quantity was calculated as the ratio
of volume of residential sales to retail population
served and average price as the ratio of total revenue
to total volume of residential water sales. Density
was calculated as size of the service area divided by
the retail population served. Census data on average
income, median income and household size was
aggregated to match the utilities’ service areas.22
Finally, the Annual Climatological Summary of the
National Oceanic and Atmospheric Administration
(NOAA) was used to locate the closest weather
station with 1995 data on temperatures and normal
rainfall. Table 2 presents summary statistics and
description for the variables used in the estimation.
III. Estimation and Results
Endogeneity issues
Concerns about endogeneity arise when an explanatory variable is potentially correlated with the error
component of the dependent variable, thereby
violating a basic assumption of ordinary least squares
(OLS) and creating bias in coefficient estimates.
Two potential sources of endogeneity need to be
addressed in regards to the estimation of our water
demand model. One source is related to the specification of the price variable; the other, recently highlighted in the water demand literature, comes from
the possibility that there are unobserved communityrelated components of per capita consumption
that may be correlated with community-related
17 Stevens et al. (1992) find that higher frequency billing decreases price elasticity while Kulshreshtha (1996) did not find
conclusive results on billing frequency (see Arbue´s et al., 2003). 18Billing frequency in our sample varied from twice a year (only two utilities) to 12 times a year (200 utilities); 92 utilities
billed four times a year and 88 billed six times a year. There were no clear differences in billing frequency by rate structure.
19 However, the presence of detailed quantity information on the bill could also complicate the bill and obscure price
information, leading to a reverse effect.
20 Variables were also created for simple history, advanced history, and others quantity related information to test individual
effects.
21 Additional dummy variables were created to test the individual effects of sewer charges and energy charges as well as
different billing frequencies.
22The 2000 US Census was used because the 1990 Census did not allow us to match a good number of service areas because
of changes in zip codes or names of places.
388 S. Gaudin
components of price or, in our case, to the choice
of billing information in a specific community
(Nauges and Thomas, 2000).
The existing literature on residential water demand
recognizes different reasons for believing that the
price variable in a residential water demand model
may be endogenous (Renzetti, 2002).23 In particular,
endogeneity may arise when using an average price
specification in a sample where some utilities have
non-linear price structures. The use of aggregate data
mitigates the problem generated by the simultaneous
determination of price and quantity in consumer
choices, thus reducing the likelihood of a simultaneity
bias (Shin, 1985). Models of water demand that
have estimated price elasticity using different price
specifications with aggregate data have not found
significant differences (Griffin and Chang, 1989;
Martı´nez-Espin˜eira, 2003). The endogeneity in this
model is therefore more likely to come from the fact
that marginal price is usually not equal to average
price. If marginal price would be a better specification
in some communities, measurement errors may result
in biased OLS estimates. Following Hausman (1978),
we test for endogeneity of the price variable by
running both OLS and two-stage least squares (2SLS)
on the same demand model and comparing the
coefficient estimates, assuming that in the absence
of endogeneity, 2SLS estimates are consistent but
inefficient. We perform the test using the standard
(base) demand model where average price is the
only variable that needs to be instrumented. In the
two-stage procedure, the log values of the total
charges for 3750-gallon and 7500-gallon monthly bills
are used in addition to the other exogenous variables
to instrument price.24 Testing for the equality of OLS
and 2SLS coefficient estimates, we find no evidence
of a systematic bias in the OLS coefficients with a

2 value of 3.46 (p-value ¼ 0.75).
The second source of endogeneity is more problematic in the absence of instruments for price
information variables. If the quantity of water use
per capita motivates utility managers to include more
information on their bills (i.e. if they believe it can
foster conservation), the coefficients on information
variables will be biased. It is also possible that
community characteristics – e.g. environmental consciousness of the population or familiarity with water
scarcity issues – motivate both lower water use
and informative billing. In the absence of good
instruments for billing information, this potential
source of bias cannot be tested formally. Political data
about environmental consciousness of the population
could be obtained for some communities but the
number of utilities for which we could obtain data was
too small to carry a meaningful analysis. Several
factors, however, lead us to believe that OLS results
are appropriate to make meaningful conclusions.
First, correlations between informational features of
the bill are low or insignificant as reported earlier. For
example many utilities that include conservation
messages on the bill do not provide detailed price
information. The EPA guidelines about the importance of understandable and informative bills were
not published until the late 1990s while the bills we
collected date back to 1995. We found no evidence
while collecting the data that bill formats were chosen
with water conservation strategies in mind. When
asked about the motivation for including or not
including different features on the bills the utility staff
that provided us with the information on billing
features either did not know or made reference to
software capabilities, mailing costs, and attempt to
Table 2. Summary statistics
Variables Description Mean Std. dev. Min. Max. Na
Q Per capita residential consumption in thousand gal./yr 32.48 15.94 7.47 112.6 378
AP Average price of water in US$ per thousand gal. 2.33 1.04 0.13 6.30 380
Income Per capita income in thousand US$ 22201 8584 9480 98643 383
HHsize Avg. number of household members in housing units 2.56 0.37 1.71 4.91 383
Density Population density in persons per square mile 2246 2033 8.07 11923 353
AAP 30-year average annual precipitation in inches 35.08 13.28 1.66 79.49 383
T90 Number of days with temperature >90F. 41.00 33.40 0 172 383
aMissing entries in the AWWA database reduce the sample size for Q, AP, and Density.
23These reasons are somewhat different from usual simultaneity issues in demand estimation since the supply side of the
market is regulated to balance revenues and expenses each period. In effect, this means that a higher price cannot be
interpreted as an incentive for utilities to supply more water.
24To gauge the appropriateness of the instruments, we use them to obtain predicted values for the average price variable.
Correlation between the predicted and the original average price variable is 0.65. A linear regression of the predicted value of
AP on all the predetermined endogenous variables of the demand equation produces residuals that do not significantly
explain the variation in Q (p-value of 0.57).
Effect of price information on residential water demand 389
reduce consumer enquiries. More importantly, water
bills are rarely changed. Most utilities included in our
survey were still using the same bill as in 1995 in 2003.
In cases when the bill format was changed since 1995,
all utilities included indicated that the previous bill
had been in effect for as long as they could remember.
Since the format of water bills cannot be changed
easily in response to changes in current quantity
demanded, it is more likely to be correlated with
structural variables already included in the model
(such as average annual precipitation or density) than
to the dependent variable.25 While we cannot quantitatively assess the extent to which omitted, community
related variables bias coefficient estimates on price
and price information variables, the same type of bias
is expected for price and price information (lower
quantity use and higher price with greater price
information) in ‘water aware’ communities.
However, a simple calculation of the correlation
coefficient between average price and the price
information dummy did not reveal any significant
relationship between price and price information
( ¼ 0.04, 95% confidence interval from 0.06 to 0.1).
Results and discussion
Results of the OLS regressions are given in Table 3.
The first column gives results of the simplest model
with variables most commonly used in the literature.
In order to clarify the role of billing information
relative to other billing features added to the model
as determinants of price elasticity, another base
model is estimated with the same variables as in the
standard model plus non-information-related billing
variables (Base Modelþ). In the information models,
we use two definitions of price information: INFO I
is the more restrictive model where only bills with
unit price given next to units consumed are included
in the price information variable (mpinfo); INFO II
includes in the price information variable all bills with
the full price information on them, whether next to
consumption or somewhere else on the bill (priceinfo).
To address problems highlighted in the data section
concerning the higher prevalence of information for
utilities in the West and Southwest and those using
increasing block price schedules, we run INFO II on
sub-samples of the data excluding (a) all utilities in
the West (column 5), (b) all utilities in the West and
Southwest (column 6), and (c) all utilities with
increasing block (IB) pricing (last column).26
Results from the base model (standard aggregate
model with no information variables) compare
favorably to the literature. We find a price elasticity
of 0.37 and an income elasticity of 0.30.27 The
coefficients on average rainfall and high temperatures
are of expected sign and significant. Density, used
as a proxy for describing the housing stock and
size of yards, has the expected negative effect on
per capita water use. The significant and positive
effect of household size on per capita consumption
is more surprising but the result is likely a feature
of using aggregate data: household consumption is
clearly positively related to household size and in
our sample, per capita and per household usage
are highly correlated ( ¼ 0.93). Overall, the adjusted
R-squared of 0.44 is similar to other studies of water
demand that use aggregate cross-sectional data. In
the extended Base Model in the second column of
Table 3, neither billing frequency nor combination
billing is found to have a significant effect on price
elasticity in our sample. We also ran the model (and
subsequent regressions) with sewer and power as
separate variables and included lower frequency
billing in addition to monthly billing. In all cases,
we found no significant individual effects.
Results from the models with information variables
indicate that price information has a significant
positive impact on price elasticity. The presence
of marginal price information on the bill next to
quantity consumed increases price elasticity by a
factor of 1.4 (price elasticity is 0.36 for areas that do
not include the information and 0.51 for areas that
do). Assuming constant elasticity, this means that
for any given quantity reduction target, required price
increase can be close to 30% lower with price
information on the bill; for example, a 10% decrease
in quantity requires a price increase of approximately
20% when price information is on the bill, compared
to 29% otherwise. Accounting for the presence of
price information anywhere on the bill (Info II) yields
a larger coefficient (although not significantly so)
and lower standard error. We interpret this effect
as further evidence that individuals find it costly to
seek simple price information outside of the bill
but do react more strongly when the price system
is transparent. We cannot reject the hypothesis that
all other types of information have no effect on the
price elasticity with probability values equal to 0.5
for messages and 0.4 for quantity related information
25For example, it is likely that consumers in areas with low annual precipitation are more sensitized to water conservation
than those in water abundant areas.
26 Note that the sample size decreases significantly, thus affecting standard errors and test statistics. 27The estimation was run with median income instead; other parameter estimates were not affected significantly and the
income elasticity was 0.24.
390 S. Gaudin
interacted with average price. We also ran the model
with history and other information separately with
no significant individual effects. The joint significance
of non-price billing features was tested using different
linear combinations and the null hypothesis could
not be rejected in all cases.28 Given such high
probabilities that the coefficients may be zero,
we cannot make inferences from their signs. As in
the Base Modelþ results, the evidence on the impact
of larger bills (through the inclusion of sewer and/or
power on the same bill) is also inconclusive.
Similarly, there is no evidence that, controlling for
information on the bill, the use of increasing
block rates has a significant impact on price
elasticity.29 Despite the low significance levels of all
billing and price structure dummies, inclusion of
Table 3. Estimations results
Dependent variable
ln(Q)
Base
model
N ¼ 349
Base
modelþ
N ¼ 349
Info I
N ¼ 349
Info II
n ¼ 349
West
excluded
n ¼ 254
W & SW
excluded
n ¼ 226
IB
excluded
n ¼ 252
ln(AP) 0.37*** 0.38*** 0.36*** 0.35*** 0.28*** 0.28*** 0.37***
(0.039) (0.053) (0.056) (0.055) (0.066) (0.071) (0.066)
ln(Income) 0.30*** 0.30*** 0.31*** 0.31*** 0.30*** 0.25*** 0.30***
(0.057) (0.060) (0.060) (0.060) (0.075) (0.084) (0.073)
HHsize 0.25*** 0.25*** 0.27*** 0.27*** 0.24*** 0.34*** 0.33***
(0.048) (0.049) (0.050) (0.050) (0.092) (0.11) (0.059)
ln(Density) 0.048*** 0.047*** 0.044*** 0.042*** 0.026 0.019 0.036*
(0.016) (0.016) (0.016) (0.016) (0.020) (0.022) (0.020)
ln(AAP) 0.23*** 0.23*** 0.23*** 0.22*** 0.23*** 0.14 0.19***
(0.035) (0.035) (0.036) (0.035) (0.063) (0.10) (0.044)
dt90 0.0015*** 0.0015*** 0.0014*** 0.0015*** 0.0023*** 0.0018* 0.0023***
(0.0005) (0.0005) (0.0005) (0.0005) (0.0007) (0.0010) (0.0007)
ln(AP) mpinfo 0.15***
(0.055)
ln(AP) priceinfo 0.16*** 0.21*** 0.22*** 0.14**
(0.051) (0.060) (0.065) (0.066)
ln(AP) QuantityInfo 0.045 0.043 0.025 0.018 0.018
(0.049) (0.048) (0.060) (0.065) (0.060)
ln(AP) 0.0014 0.013 0.009 0.0068 0.014 0.038
CombinationBilling (0.045) (0.045) (0.045) (0.052) (0.057) (0.054)
ln(AP) 0.0041 0.0015 0.002 0.036 0.039 0.013
HighFrequencyBill (0.044) (0.044) (0.044) (0.050) (0.054) (0.050)
ln(AP) IB 0.048 0.057 0.064 0.028 0.055
(0.044) (0.046) (0.046) (0.060) (0.072)
Message 0.043 0.040 0.054 0.052 0.023
(0.058) (0.058) (0.090) (0.10) (0.083)
Intercept 1.07* 1.13* 0.93 0.89 0.81 0.82 0.60
(0.59) (0.63) (0.63) (0.63) (0.76) (0.86) (0.77)
Adjusted R-squared 0.441 0.439 0.446 0.452 0.314 0.256 0.427
F-test 46.84 31.19 24.35 24.89 10.67 7.46 18.02
Hausman test 2 3.46
(w/instruments for AP) ( ¼ 0.75)
Notes: *** statistically significant at the 1% level or better; ** significant at the 5% level; * significant at the 0% level.
Standard errors are in parentheses.
28For all models with information we test the joint significance of (1) all non-price variables interacted with ln(AP) with and
without IB, (2) history and otherinfo interacted with ln(AP) with and without IB, (3) power and sewer interacted with ln(AP),
(4) all non-price information variables interacted with ln(AP) plus the message. We found no evidence of joint significance as
F-test values ranged between 0.01 and 0.92 with probability values from 0.99 to 0.43.
29 Recall that utilities that report using and increasing block rate structure may have free allowances that would effectively
create a decreasing rate structure for lower consumption levels. Again, the structure of the AWWA data does not allow us to
identify such nuances in rate structures.
Effect of price information on residential water demand 391
these variables was important to make sure that price
information results were not capturing other features
of the bill or utility.30
The magnitude and significance of our results
appear robust to sample selection and not solely
driven by the fact that we included utilities from the
West and Southwest – where attitudes toward water
are likely to be different than in the rest of the
country – or utilities that opted for increasing block
prices that may have opted for the price structure
to promote conservation. The regression without
Western states reveals a larger increase in elasticity
with price information (although the coefficients
are not statistically different from each other).
While total price elasticity is approximately the
same (0.49), elasticity for utilities without price
information is only 0.28. Such numbers imply that
the percentage price increase required to obtain
any size reduction in quantity is 40% less than what
it would need to be without the price information
(for example, a 10% decrease in quantity requires
a 20% increase in price instead of 35%). Excluding
the Southwest in addition to the West reduces the
magnitudes and significance levels of some of the
coefficients, but results on price elasticity and
information variables are virtually unchanged.
Finally, excluding utilities across the USA that
reported using increasing block rates slightly reduces
the estimated effect of price information on price
elasticity but not significantly so.
IV. Conclusions
While issues of price information have been suggested
before as possible factor contributing to low price
elasticity in water and electricity demand, the existing
literature did not provide any quantitative analysis
of the impact of price information on household’s
sensitivity to price variation. This paper constitutes
a first attempt at quantifying the effect of providing price information on water bills. We started
by asking the question whether the demand for
residential water could be made more elastic by
providing consumers with more informative water
bills. In order to assess differences in billing information across the US, we surveyed all utilities with
residential water sales included in the American
Water Works Association’s 1996 database. We
found enough variation in the content of bills (whether
they included price information, history of use,
and/or conservation messages) to test our hypothesis.
We merged the data thus collected with existing
data sets to estimate parameters of a simple residential
water demand model where price and quantity
information on bills is allowed to affect price elasticity
and where the presence of conservation messages may
affect preferences independently of prices.
Our results provide evidence in support of the
hypothesis that price information increases the price
elasticity of demand but the inclusion of additional
information aside from simple price information
is not found to significantly affect water demand.
The magnitude and statistical significance of the
price information effect is large enough to merit
notice from researchers and utility managers. All other
factors held constant, a utility that gives marginal
price information on the water bill can attain the same
level of conservation with a 30 to 40% lower rate
increase.
The significance of the price information variable
should be taken into account when interpreting elasticity results from the literature. Elasticity
estimates calculated using microeconomic data are
conditional on billing formats. Results from cross
sectional studies that do not take account of the
existence of price information on the bill are likely
to be biased downward for utilities that provide such
information. Data limitation did not allow us to test
the effect of billing information on price perception.
For future research, data on billing information
needs to be collected along with full price schedules
for a cross section of utilities that use identical price
structures. With this new data set, we can investigate
whether Shin’s price perception parameter (Shin,
1985) decreases when consumers receive more informative bills, providing a direct test of whether the
price perception parameter is indeed a function of
information costs.31 Finally, this study constrains the
price information to have the same impact on price
elasticity at all price levels. Further research using
a more flexible functional form and possibly a large
microeconomic data set that spans utilities with
different billing practices could test whether people
pay more or less attention to the information on the
bill depending on the size of the bill.
30 At the suggestion of a referee, we ran the same model taking account of the possibility that price elasticity was not constant
for all income levels. We created dummy variables for four income levels around the mean. All results were largely statistically
insignificant except for communities in the highest 10% of the income distribution for which the price elasticity was reduced
by 0.12 (with a standard error of 0.07 and a p-value of 0.083). 31 In Shin’s article (1985) individuals respond to a perceived price P ¼ MP (AP/MP)k
, where MP is marginal price and
AP is average price. The price perception parameter k is between 0 and 1. A decrease in k indicates that individuals’ price
perception is closer to marginal price than average price.
392 S. Gaudin
No one likes the prospect of water rationing and
no one likes exorbitant price increases; policies to
increase the price elasticity in residential water
demand have the potential to reduce reliance on
rationing by reducing the level of price increases
necessary to absorb shortages. We hope the results
of this research will encourage water utilities in the
USA and other countries to use bills to their full
potential by incorporating information that is most
likely to make a difference for their community by
increasing price sensitivity and foster water conservation at low cost. More generally, our results provide
evidence in support of the intuitive but often ignored
fact that the predictions of economic theory (that
people respond to price signals) are necessarily
weaker when prices are imperfectly observed.
Acknowledgements
I wish to thank Adam Greeney for invaluable
assistance in the data collection process and the
Mellon Foundation for financial support. I also
thank Barbara Craig, Hirschel Kasper, Kenneth
Kuttner, Celine Nauges, Steven Renzetti, John
Swinton, seminar participants at McMaster
University and the Lawrence Berkeley National
Laboratory, as well as two anonymous referees for
comments on earlier drafts. All remaining errors
are mine.
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