Demand function estimate for residential water in Oman

Abstract

Current subsidies to residential water users in Oman are estimated
at USD 314 million/y. This study estimates the demand function for
residential water in Muscat, Oman, for households living in villas. A
two-stage least squares econometric model with lagged average
water price was used with socio-economic variables. Price elasticity
for residential water in Muscat was estimated as –2.10. This high
price elasticity is explained by the large proportion of water used
for outdoor purposes. This study indicates that it may be possible
to manage water demand in Muscat through modifying the price of
water and reforming subsidies for residential water.

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Demand function estimate for residential water in
Oman
Hemesiri Kotagama, Slim Zekri, Rahma Al Harthi & Houcine Boughanmi
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(2016): Demand function estimate for residential water in Oman, International Journal of Water
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INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT, 2016
http://dx.doi.org/10.1080/07900627.2016.1238342
Demand function estimate for residential water in Oman
Hemesiri Kotagamaa, Slim Zekria, Rahma Al Harthib and Houcine Boughanmia
aDepartment of Natural Resource Economics, College of Agricultural and Marine Science, Sultan Qaboos
University, Muscat, Sultanate of Oman; bEconomic Researcher, Ministry of Manpower, Muscat, Sultanate of Oman
ABSTRACT
Current subsidies to residential water users in Oman are estimated
at USD 314 million/y. This study estimates the demand function for
residential water in Muscat, Oman, for households living in villas. A
two-stage least squares econometric model with lagged average
water price was used with socio-economic variables. Price elasticity
for residential water in Muscat was estimated as –2.10. This high
price elasticity is explained by the large proportion of water used
for outdoor purposes. This study indicates that it may be possible
to manage water demand in Muscat through modifying the price of
water and reforming subsidies for residential water.
Introduction
The rapid and signicant increase in demand for residential water in Oman is driven by
increasing population growth, improvements in the standard of living (associated with
increased household income) and rapid economic development, which has attracted large
numbers of migrants for employment (Alshawaf, 2008). The Public Authority for Electricity
and Water (PAEW, 2011) expects demand for residential water in Oman to reach 630 million
m³ by 2025, from 215 million m³ in 2011. The Governorate of Muscat (the capital of Oman)
utilizes 148 million m3/y of residential water, which represents 69% of total water usage in
the country. Because more than 90% of the residential water supply is desalinated, the
increase in water demand leads to increased dependence on non-renewable energy for
desalination and increased government subsidies. Residential water prices are subsidized
in Oman and other Gulf Cooperation Council countries; this has led to higher demand for
water, which has been primarily addressed through supply-side management (Abderrahman,
2000; Dawoud, 2011; Ouda, 2014).
The price of water in Oman is administered in a two-block structure which has not
changed since 1980, even though cumulative ination reached 48.8% between 2000 and
2013. The price is USD 1.14/m³ for water consumption of less than 23 m³/month and USD
1.43/m3 for consumption greater than 23 m³/month (OIFC, 2013). Government subsidy of
water supplies has reached 61.5% of the total cost of production and supply of water; this
subsidy totalled USD 314 million nationwide in 2012, with a 6% annual rate of increase. The
subsidy for water supplies in the Muscat Governorate reached USD 175 million in 2012. The
© 2016 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Water demand; residential
uses; outdoor uses; water
price elasticity; Oman
ARTICLE HISTORY
Received 12 January 2016
Accepted 10 September 2016
CONTACT Slim Zekri Slim@squ.edu.om

2 H. KOTAGAMA ET AL.
average subsidy per residential connection exceeded USD 1000 in 2012 (PAEW, 2012).
Paradoxically, households not connected to the urban water network pay higher prices for
water supplied by bourses and receive lower quality of service. On average, each household
pays USD 100 per/month for water supplied by bourses, while a family that is connected to
the urban water network receives greater volumes with higher reliability and pays approx-
imately USD 87 per/month. Thus, it is evident that households are willing to pay more for
water based on scarcity values.
Since May 2008, households have paid for sewage disposal, in addition to residential
water supply, in districts connected to sewage treatment plants (Zekri, Boughanmi, & Zairi,
2010). The service charge for sewage disposal is included in the same bill as the charge for
residential water as one total amount, thus appearing as a higher price for water. The sewage
disposal charge is USD 0.40/m3 of residential water consumed, regardless of the volume of
sewage disposed of (Haya, 2013). This charge is far below the cost of wastewater treatment
(Zekri et al., 2010). In fact, the subsidy for sewage treatment in the Muscat Governorate was
approximately USD 29 million in 2012 and will increase rapidly as additional residences are
connected to the sewage disposal network. The total subsidy to the water sector (for desal-
ination and sewage treatment) in the Muscat Governorate reached USD 204 million in 2012.
The cost of water supply and sewage treatment in Muscat reached USD 5.4/m3 on average
per household, while each household paid only USD 1.85/m3, for a 66% subsidy. These gures
do not take into account the fuel subsidy provided to desalination plants or the environ-
mental costs of brine and chemical disposal in the sea by desalination plants.
Oman continues to increase the supply of desalinated water to respond to growing
demand for residential water, and few if any initiatives have been undertaken to manage
the demand for residential water. A lack of information exists regarding the primary variables
aecting demand for residential water, and this lack of information hinders the ability to
design and implement strategies to manage demand for water. This article will bridge the
information gap and support the process of decision making to manage increasing demand
for residential water. This study estimates the residential water demand function for house-
holds living in villas (individual houses with garden space) in the Muscat Governorate; villas
represents the primary type of residential building in the city. The second section provides
a review of previous studies regarding estimation of the demand function for residential
water use, and the third section provides an explanation of the research method adopted
and the econometric model utilized to estimate the demand function for residential water.
The fourth section presents and discusses the results. The conclusions are presented in the
nal section.
Literature review
Determinants of water demand. Price of residential water is a principal determinant of demand
for residential water. Increasing block rate pricing introduces some diculties in econometric
model specications. Hussain, Thrikawala, and Barker (2002), and Mieno and Braden (2011),
utilized the marginal price with a dierence variable to deal with the non-linearity and
discontinuity of price imposed by increasing block pricing. The dierence variable was
dened as the dierence between actual total payments and the total amount consumers
would pay at the marginal price. Other specications of the price of water include average
price (Domene & Saurí, 2006; Kenney, Goemans, Klein, Lowrey, & Reidy, 2008; Musolesi &

INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 3
Nosvelli, 2007), marginal price (Mieno & Braden, 2011; Olmstead, Michael Hanemann, &
Stavins, 2007), and lagged average price (Arbues, Barberan, & Villanua, 2004; Arbués &
Villanúa, 2006).
Non-price determinants of demand for residential water considered in most studies
include household income, number of household members, ages of household members,
gender structure of a household, type of building structure, and other factors such as rainfall
and temperature (Corbella & Sauri Pujol, 2009).
Certain studies have demonstrated a positive relationship between household income
and the quantity of water consumed (Domene & Saurí, 2006; Harlan, Yabiku, Larsen, & Brazel,
2009; Schleich & Hillenbrand, 2009). However, studies have also reported that high-income
households are less sensitive to changes in the price of water (Mieno & Braden, 2011).
Schleich and Hillenbrand (2009) determined that income elasticity is positive and declines
as income increases. According to certain studies (Arbues et al., 2004; Gaudin, 2006; Hussain
et al., 2002; Olmstead, 2007), conducted in various locations representing a wide range of
water development status and socio-economic conditions, estimates using the log-log
demand function indicate that income elasticity of residential water is low and ranges
between 0.074 and 0.55.
Although studies have conrmed that households with more members consume more
water than households with fewer members (Arbues et al., 2004; House-Peters, Pratt, &
Chang, 2010), consumption per capita declines with increased numbers of household mem-
bers (Arbues et al., 2004; Schleich & Hillenbrand, 2009). It has been determined that sin-
gle-family houses (villas) consume more water, due to greater outdoor water use (Domene
& Sauri, 2006). Mukhopadhyay, Akber, and Al-Awadi (2001) and House-Peters et al. (2010)
indicated that outdoor water use depends upon the outdoor space and the level of education
of household members. In addition, these scholars demonstrated that ownership of a swim-
ming pool had a signicant eect on the quantity of residential water consumption.
Price elasticity of demand for residential water. Most studies (Arbues et al., 2004; Arbués &
Villanúa, 2006; Gaudin, 2006; Hajispyrous, Koundouri, & Pashardes, 2002; Hussain et al., 2002;
Martinez-Espineira, 2002; Musolesi & Nosvelli, 2007; Olmstead et al., 2007) indicate that
demand for residential water is price inelastic, with estimates ranging from –0.029 to –0.80.
A few studies (Klaiber, Smith, Kaminsky, & Strong, 2014; Yoo, Simonit, Kinzig, & Perrings, 2014)
have estimated high price elasticities, when outdoor uses are considered, ranging from –1.57
to –3.33. Olmstead et al. (2007) indicated that the demand for residential water is more price
elastic when block pricing is used than when uniform pricing is used.
Method of estimating the demand function of residential water. The most common econo-
metric model used to estimate the demand function for residential water when increasing
block prices are in place is the ordinary least squares (OLS) model. However, use of the OLS
model has been criticized by certain authors due to the endogeneity issue (Arbues et al.,
2004; Gaudin, 2006; Kenney et al., 2008; Mieno & Braden, 2011; Olmstead, Hanemann, &
Stavins, 2005).
Olmstead et al. (2007) tested dependence of price elasticity on the price structure. A
discrete continuous choice model was used to estimate the price elasticity for water, for
both increasing block prices and uniform marginal prices. Hussain et al. (2002) estimated
water demand for industrial, residential and commercial sectors of urban areas in Sri Lanka
using monthly data for water consumption at the country level. OLS was utilized to estimate
the model with linear and log-log forms. The log-log functional form performed better than

4 H. KOTAGAMA ET AL.
the linear form according to the signs, signicance, reliability and adequacy of estimated
coecients. Arbues et al. (2004) concluded that consumers react to the lagged average price.
Arbués and Villanúa (2006) utilized dynamic panel data for households to estimate the
demand function for residential water in Spain. This analysis indicated that the linear demand
function was more appropriate than the log-log and semi-log functional forms. Musolesi
and Nosvelli (2007) estimated the residential water demand in a dynamic context in Italian
cities. In the econometric analysis, the generalized method of moments estimator was uti-
lized to overcome the endogeneity issue.
Gaudin (2006) used aggregate cross-sectional community-level data, and estimated water
price elasticity coecients using the OLS and two-stage least squares methods of analysis.
The log-log functional form was utilized because the linear functional form forces price
elasticity to decrease along the demand curve and the log-log form facilitates convenient
interpretation of estimated parameters. Mieno and Braden (2011) used municipal panel data
on water consumption and a xed eect model to reduce bias that could be caused by OLS
because data regarding swimming pools and lot sizes for each municipality were not avail-
able. The data were aected by autocorrelation, which was addressed by using Newey-West
consistent estimation for the variance-covariance matrix of coecients. Polycarpou and
Zachariadis (2013) analyzed residential water demand in Cyprus, using a two-stage least
squares model with an instrumental variable in two stages. The rst regression was between
the suspected endogenous variable and other exogenous variables, which are not related
to the error term. Then the predicted value of the endogenous variable obtained in the rst
stage was used in the second stage as an exogenous variable in the OLS regression.
Research method
This study estimates the demand function of residential water for two districts in the Muscat
Governorate: Al-Seeb and Al-Qurm (Figure 1). These two districts represent households with
dierent socio-economic characteristics. Al-Qurm includes high-income households, and
Al-Seeb represents middle-income households. The dominant building structure in the
Muscat Governorate is the villa vis-à-vis apartment. Villas are singular houses that include a
garden space. The sample includes villas exclusively. The monthly residential water con-
sumption per household and the amount paid for residential water and sewage disposal
services were collected from the water billing company. The sample framework included a
list, provided by the company, that included homeowners’ names and addresses of house-
hold water users. In addition, the list contained monthly data on the quantity of water con-
sumed and payments made by the households from January 2010 through December 2012.
The sample was a stratied random sample. Households were stratied as either (1) house-
holds that pay for both residential water and sewage disposal, or (2) households not con-
nected to sewage disposal network, which pay for residential water only. Thus, for an identical
amount of residential water used, the households in (1) paid higher prices for water than
households in (2). The sample included 266 households, of which 144 were in (1) and 122
were in (2). A questionnaire was utilized that included questions regarding household char-
acteristics, indoor water use and outdoor water use. The survey was conducted from 1 July
1 through 21 August 21 2013. Trained interviewers visited the villas and conducted personal
interviews.

INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 5
Consumers are billed monthly for water use. However, in this article quarterly averaged
data regarding water use and payments were utilized. In fact, it has been observed that
many consumers pay their bills late, which might aect water consumption in the following
months. The econometric model utilized for this study estimates the demand function for
residential water use as:
where:
Q = volume of water consumed (average of three months, in m3)
LAP = lagged average (three-month) price of water consumed, in OMR/m
3
(OMR 1 = USD
2.6), calculated by dividing the total value of the water bill by the quantity of water
consumed
I = monthly household income (OMR/month)
D = dummy for water price blocks: D = 0 if quantity of water consumed was less than
23 m³/month; D = 1 if quantity of water consumed was more than 23 m³/month
HHM = number of residents in the household
AREA = extent of irrigated area in the garden (m2)
POT = number of potted plants
MIRR = use of modern irrigation system in the garden (dummy variable,
yes = 1, no = 0)
CAW = car washes per week (times per week each car is washed, multiplied by number
of cars)
SPOOL = swimming pool in household (dummy variable, yes = 1, no = 0)
ε = error term.
ln
(
Q
)
=∝
0
+∝
1
ln
(
LAP
)
+∝
2
ln
(
I
)
+∝
3
D+∝
4
ln
(
HHM
)
+∝
5
ln
(
AREA
)
+∝
6
ln
(
POT
)
(1)
+∝
7
MIRR+∝
8
ln (CAW)+∝
9
SPOOL +𝜀
(2)
Figure 1.Location of the Seeb and Qurm Districts in Muscat, Oman.

6 H. KOTAGAMA ET AL.
Results and discussion
Figure 2 shows the quantity of water consumed and price paid by households included in
the sample. Certain elements of the data do not align with the basic demand theory of an
inverse relationship between price and quantity of water consumed. These households pay
only for residential water use. Thus, it is observed that at very low water prices, households
are not responsive to water prices. The data elements that align with the basic demand
theory correspond to households that pay for both sewage and residential water and are
wealthier than the previous group. Households connected to the sewage disposal network
pay USD 0.62/m3 more than households not connected, given an equal quantity of water
consumed. Consequently, the econometric analysis included only households that pay for
both residential water and sewage (144 observations). Because data were collected over
three years, 12 data points were available for each household, for a total of 1728 data points.
The estimated correlation matrix of the independent variables indicated a high correlation
between number of potted plants, size of the irrigated area and use of a modern irrigation
system. Thus, only the number of plotted plants was retained as an independent variable.
Endogeneity was veried by the Hausman (1976) test based on the method suggested by
Hill, Griths, and Lim (2008). The calculated statistic for the Hausman test was –26.27 and
was highly signicant at the 5% level, indicating endogeneity between the price variable
and the error term. Therefore, the lagged average price was considered the most appropriate
model to estimate the water demand function. To overcome the problem of endogeneity,
certain studies have applied a two-stage least squares model using the lagged average price
Figure 2.Relationship between water price and volume of water consumed.

INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 7
(Gaudin, 2006; Polycarpou & Zachariadis, 2013), because the lag eliminates the endogeneity
(Arbues et al., 2004; Arbués & Villanúa, 2006). The study described in this article utilized a
lagged average price to estimate the demand function for residential water.
The demand function was estimated for four functional forms to evaluate the best model
t: linear, log-linear, linear-log and log-log. The log-log functional form provided the best t
to the data, with the highest coecient of determination (R
2
), and its estimates are provided
in Table 1. The log-log functional form has also been used in certain residential water demand
studies (Gaudin, 2006; Hussain et al., 2002).
The price variable possesses a negative sign and is highly signicant, with an estimated
coecient of –2.10, indicating that the demand for residential water is price elastic. The
estimated price elasticity of –2.10 for household demand of residential water falls within the
range of price elasticities of –0.081 to –3.33, as per past studies. This high elasticity for house-
holds living in villas is due to the use of a signicant portion of the water for ‘non-essential’
outdoor purposes such as irrigating gardens (45% of households), washing cars (40% of
households) and lling swimming pools (6% households). Use of water for these non-
essential purposes could be reduced if prices increase in contrast to essential water use for
domestic purposes. The variables for household income (I), frequency of car washing at
home (CAW), number of members in the household (HHM), price block (D1), number of
potted plants in the garden (POT) and presence of a swimming pool (SPOOL) are all statis-
tically signicant and aect the demand for residential water. In addition, all variables possess
the expected sign. The elasticity of the total number of members in the household is 0.056,
indicating that increase in water consumption is less than proportional to an increase in the
number of residents in household. These results are supported by the studies of Arbués and
Villanúa (2006), and Schleich and Hillenbrand (2009). In alignment with March and Sauri
(2010), this study has demonstrated that the presence of a swimming pool and use of water
for garden irrigation are signicant variables aecting household water demand. As would
be expected, the frequency of car washing at home was also signicant in determining the
quantity of water use.
The average annual water consumption per household was estimated as 519 m3/y for
the Al-Qurm District and 440 m3/y for the Al-Seeb District. The daily per capita average
consumption was estimated as 0.289 m3 and 0.173 m3 for Al-Qurm and Al-Seeb Districts,
respectively. The average per capita consumption for the entire sample was 0.197 m3/day,
much more than the international average of 0.090 m3/day. On average, payment for water
(residential and sewage disposal) represented 3.8% of the total income of a household. The
Table 1.Estimates of the log-log lagged average price model.
**Significant at 5% level.;
***Significant at 1% level.
Variables Coecient Std. error t-Statistic
Intercept 1.510 12.90 12.90
LAP −2.10*** −13.08 -13.08
I0.040** 2.62 2.62
CAW 0.037*** 3.42 3.42
HHM 0.056** 2.68 2.68
D1 1.110*** 41.29 41.29
POT 0.048*** 5.77 5.77
SPOOL 0.190*** 4.23 4.23
R2-squared 0.700
S.E. of regression 0.396
F-statistic 527.827

8 H. KOTAGAMA ET AL.
estimated net annual subsidy was USD 1846 and USD 1565 per household for Al-Qurm and
Al-Seeb Districts, respectively. On average, this is equivalent to a subsidy of USD 3.56 per m3
of water.
Total elimination of water subsidies in Oman would result in a 195% increase in the price
of water, from USD 1.83/m3 to USD 5.4/m3. Such a substantial price increase is not politically
feasible; therefore, water price reform for the elimination of subsidies should be implemented
gradually. Our econometric model suggests that a 10% increase in the price of water will
lead to 21% less water being consumed, for annual water savings of 109 m3 per household.
However, this model cannot be used to predict the quantity of water saved with a total
elimination of subsidy, as the price increases in this case by 195%, and this is substantially
beyond the data range used to generate the econometric model. Furthermore, a ban on
irrigation of gardens may not be operationally feasible considering the high transactional
cost of implementation. However, higher freshwater prices may encourage households to
reduce wasteful water use and provide an incentive to recycle domestic grey water for garden
irrigation (Ahmed, Al-Buloshi, & Al-Maskary, 2012).
Conclusions and recommendations
The demand for water in the Sultanate of Oman has increased faster than supply. This increas-
ing demand is associated with population growth, rural-to-urban migration, rising standard
of living and growth in the industrial and service sectors. The sultanate has adopted a sup-
ply-side approach that consists of increasing the desalinated water supply, while few or no
strategies have been adopted to manage the increasing demand for water. The absence of
information regarding the demand for water impairs the ability of policy makers to utilize
water demand management tools. This article is the rst study to analyze the demand for
residential water in Oman using household-level data. This study is based on a random
sample of households selected from the Al-Seeb and Al-Qurm Districts of the Muscat
Governorate. A random sample of water users was stratied into those paying for both
residential water and sewage disposal and those paying only for residential water. However,
only the data for high-income households that pay for both residential water and sewage
disposal were utilized in the estimation of the demand function for residential water. All
interviewed households resided in villas. The secondary data, regarding the water bill and
volume of water consumed by each household, were provided by the Oman Investment
and Finance Company and PAEW.
A two-stage least squares econometric model with lagged average water price and other
household socio-economic variables was utilized to estimate the demand function for res-
idential water. The econometric results indicate that the lagged average price is a signicant
variable determining demand for water. The demand for water is price elastic, with a price
elasticity of –2.10. This result is consistent with results of international studies that included
outdoor water uses. Other variables utilized in the model included household income, num-
ber of residents in the household, number of potted plants in the garden, presence of a
swimming pool, and frequency of washing cars; all these were determined to be signicant
in determining the demand for residential water. As most of the households use water for
outdoor purposes such as irrigating a garden, washing cars and lling swimming pools, this
use may be adjusted as the price of water increases, thus explaining the high price
elasticity.

INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 9
The current price paid by households for residential water and sewage disposal and
treatment is estimated as USD 1.85/m
3
, and the cost of the water supply is estimated as USD
5.4/m3. Thus, the current subsidy is estimated as USD 3.56/m3. There is a need to consider
changes to residential water and sewage pricing in Oman to enhance the nancial sustain-
ability of water supplies and reduce the demand for desalinated water, which depends
entirely on non-renewable fossil fuel for production. Subsidies for the residential water sup-
ply could be targeted exclusively to low-income households. This study demonstrates the
feasibility of reducing residential water demand among high-income households through
increasing the price of water. This price increase will also encourage reduction of water use
for outdoor activities, such as washing of cars and irrigating of gardens, and may encourage
recycling of grey water for outdoor water use.
Disclosure statement
No potential conict of interest was reported by the authors.
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