1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
BIJ
18,1 Benchmarking of North Indian
urban water utilities
Mamata R. Singh, Atul K. Mittal and V. Upadhyay
86 Indian Institute of Technology-Delhi, New Delhi, India
Abstract
Purpose – The purpose of this paper is to develop a suitable benchmarking framework that
encompasses multiple criteria of sustainable water supply services for assessing the performance of
select North Indian urban water utilities and also to arrive at potential for input reductions (or efficient
input levels).
Design/methodology/approach – The study considers 35 North Indian urban water utilities
pertaining to two union territories (Chandigarh and Delhi) and three states (Haryana, Punjab and Uttar
Pradesh) for sustainability-based performance assessment using input-oriented variable returns to
scale data envelopment analysis (DEA) model. Important criteria considered for sustainable water
supply services are service sufficiency, service reliability, resource conservation, staff rationalization,
and business viability which in turn address the key sustainability dimensions (social, environmental
and financial).
Findings – The approach when applied to a sample of 35 North Indian urban water utilities shows
low-performance levels for most of the utilities, with significant scope for reduction in operation and
maintenance expenditure, staff size and water losses. State/UT-wise analysis of sustainability-based
average efficiency presents the highest score for Chandigarh and the least score for Haryana, whereas
the rest of the three states/UT score in between them.
Research limitations/implications – Limited data availability has constrained the incorporation
of other sustainability criteria (such as services to the poor, tariff design, customer services, revenue
functions, etc.) for efficiency analysis of urban water utilities. Also, estimation of efficiency scores does
not encompass the effect of exogenous environmental factors which are beyond utilities’ managerial
control (such as topography, population density, water source, ownership status, etc.).
Practical implications – This framework would be useful for the regulator or operator of the facility
to rank the utilities and devise performance-linked incentive mechanism or price cap regulation.
Originality/value – This paper is a significant departure from the other international benchmarking
initiatives/studies as it develops a holistic framework for benchmarking in the water sector that
encompasses multiple criteria of sustainable water supply services using DEA as a tool.
Keywords India, Water industry, Urban regions, Benchmarking
Paper type Technical paper
1. Introduction
India has to support one-sixth of the world’s population with meager 1/50th of world’s
land and only 1/25th of the world’s water supply. Although the world water
development report ranked India 127th out of 180 nations for fresh potable water
availability to its citizens, India is the second largest consumer of water in the world
after China (Kapadia, 2005). Exponential growth of population, industrialization and
Benchmarking: An International urbanization has resulted in progressive decline in the per capita availability of water
Journal in Indian cities. In India, water supply to the consumer is inadequate, intermittent,
Vol. 18 No. 1, 2011
pp. 86-106 generally for low duration and of poor quality. Considering the growing water scarcity
q Emerald Group Publishing Limited and poor services to the consumers, Indian urban water utilities need to instill
1463-5771
DOI 10.1108/14635771111109832 efficient practices for sustainable water supply services to the consumers. An attempt
2. towards benchmarking of Indian water utilities would serve as an important step in North Indian
this direction. urban water
Though several benchmarking initiatives have been undertaken internationally
(Table I), Indian urban water sector has hardly witnessed any benchmarking study. Most utilities
of such initiatives do not view performance from sustainability dimensions and compute
efficiency with major focus on cost-saving aspect. Also such studies have not endeavored
to estimate potential for reduction in parameters other than cost (for example, 87
unaccounted for water (UFW, i.e. water loss) reduction, staff reduction, etc.). Attempts to
estimate utilities’ performance in totality that encompass important criteria (such as
service sufficiency, service reliability, resource conservation, staff rationalization,
business viability, etc.) of sustainable water supply services (referred as “sustainability
criteria” hereafter in this study) have not been made so far in the water sector. This study,
therefore, intends to fill this gap and evolves suitable benchmarking framework for
sustainability-based performance assessment of 35 North Indian urban water utilities
using data envelopment analysis (DEA) approach. The efficiency scores obtained
through DEA model may be used to rank the utilities and estimate potential for cost
savings and other input reductions (such as UFW, i.e. water loss, staff size and operation
and maintenance (O&M) expenditure). The study uses secondary data of 35 urban water
utilities (hereafter referred as decision-making units, i.e. decision making unit (DMUs) as
per DEA terminology) pertaining to three states (Haryana, Punjab and Uttar Pradesh)
and two union territories (Delhi and Chandigarh) provided by National Institute of
Urban Affairs (NIUA Report, 2005). The data are of the year 1999.
This paper is divided into five sections including the present one. Section 2 discusses
the status and problems of Indian urban water sector and further reviews the literature
on benchmarking and DEA in water sector. Section 3 presents the methodology for the
study. Section 4 discusses the benchmarking framework using DEA including the basis
for selection of input and output variables for assessment of technical and scale
efficiency (te and se) scores of the DMUs. Section 5 covers the analysis of DEA results for
35 North Indian DMUs. Finally, Section 6 provides conclusions and recommendations.
2. Literature review
This section initially discusses the status and problems of Indian urban water sector
covering a range of issues, namely: per capita water supply, revenue receipts, water
quality, UFW, staff size and O&M expenditure including examples of few international
studies. The second part of this section introduces DEA as a benchmarking tool and
reviews the benchmarking studies undertaken in water sector by various authors in
different countries using DEA.
2.1 Status and problems of Indian urban water sector
In India about two-thirds of the cities have net per capita supply below the established
norms as is evident from NIUA Report (2005). The status of revenue receipts is very poor.
For example, in certain Maharashtra towns, average revenue per connection is Rs 120
a year, as against expenditure of Rs 1,300 a year for each connection (Patwardhan, 1993).
Though this study is old but the current situation has not yet improved. Also quality of
water supplied to the consumers is often in question as more than 50 percent of urban
centers in India do not monitor raw water quality and have inadequate laboratory
facilities for testing water quality. For the remaining Indian cities, periodicity of water
3. 88
BIJ
sector
18,1
Table I.
using DEA in water
Benchmarking studies
Country Author(s) Inputs Outputs Sustainability criteria ignored/remarks
Palestine Alsharif et al. Cost of water bought and energy costs, Total revenue Service sufficiency and service
(2008) maintenance and other expenses and reliability
staff salary and water losses
Peru Berg and Lin Operating costs, number of staff and Volume of water billed, number of Resource conservation and business
(2007) number of connections customers, service coverage and viability
continuity of service
Uganda Mugisha Labour, network length (as a proxy for Connections and water billed as a Service reliability, resource
(2007) capital) and operating expenses percentage of water delivered conservation and business viability
(stochastic frontier analysis (SFA)
technique used)
Canada Renzetti and Labour expenditure, materials Water delivered Service reliability, resource
Dupont expenditure and kilometers of conservation and business viability
(2007) distribution network
India Kulshrestha Operating expenditure and UFW Number of connections, length of Staff rationalization and business
(2005) distribution network and water viability
produced, population covered and
number of hours of supply
Brazil Tupper and Labour expenses, operational costs and Water produced, treated sewage, Resource conservation and business
Resende other operational costs population served by water and viability
(2004) population served by treated sewage
UK Thanassoulis OPEX (operating expenditure) Water produced, number of connections Service reliability, resource
(2000) and length of distribution network conservation, staff rationalization and
business viability
Japan Aida et al. Number of employees, operating Operating revenues and water billed (net Service reliability and resource
(1998) expenses before depreciation, net plant of leakage) conservation
and equipment and population and
length of pipes
Ghana Akosa et al. Technical, financial, economic, Reliability, utilization and convenience Variables are at very abstract level
(1995) institutional, social and environmental factors
factors
USA Lambert et al. Annual labour, energy used, materials Total water delivered Service reliability, resource
(1993) input and value of capital conservation (UFW control) and
business viability
4. quality monitoring (raw water or water at treatment plant or at distribution network) North Indian
varies from daily basis to once in a month to once in six months (NIUA Report, 2005). urban water
For water supply systems, UFW are attributed to line losses, fire hydrant losses, fire
fighting and evaporation, free supply to slum/J.J areas, billing and collection inefficiencies, utilities
theft, etc. In India, on an average about 40 percent of the consumers are not charged for the
water supply services due to poor billing and collection practices which eventually
encourage them to use water liberally and waste it. UFW in Indian cities range between 89
20 and 40 percent and is gradually increasing indicating substantial revenue loss
(Singh et al., 2005). An international study for UFW by Tynan and Kingdom (2002) for top
25 percent of developing countries recommend a target of 23 percent (or less). The mean
for developed countries is 16 percent. Average UFW in Singapore, Japan, the USA and
France are 6, 11, 12 and 15 percent, respectively, (Yepes and Dianderas, 1996).
Currently, most of the government organisations responsible for water supply are
overstaffed where number of employee per 1,000 connections ranges from 15 to 25
(Singh et al., 2005) whereas the recommended ratio of the developing countries is in the
range of five to ten (Kaaya, 1999). Owing to overstaffing, staff expenditure for Indian
cities is also very high (about 30 percent). A larger share of expenditure on establishment
considerably reduces the funds available for operation and maintenance of water supply
system. Expenditure on electricity, consumables, repairs and replacements and other
related expenses together constitute the operation and maintenance head. In India, about
half the total expenditure on water supply service is spent on O&M in most of the urban
centers (NIUA Report, 2005). O&M costs per cubic meter of water are Rs 13, 16 and 17 for
Chennai, Bangalore and Hyderabad, respectively, whereas typical prices charged to
consumers in India is about Rs 1.5-2.00 per cubic meter (Raghupathi and Foster, 2002).
Thus, consumers are charged for water supply below cost and many a times revenue
generated is not sufficient even to cover manpower cost.
The status and problems discussed so far indicate the overall position of Indian urban
water sector. The present study, however, focuses on urban water utilities of North Indian
region. The states (Punjab, Haryana and Uttar Pradesh) and union territories (Delhi and
Chandigarh) selected in the study fall in the north central part of India and are bordered
with mountains (Himalayas) on its north side and great plateau on its south side. This
region is almost dead flat, very fertile and one of the largest food producing baskets
accommodating a sizeable part of the Indian population. Water supply being a state
subject in India, the states and union territories considered for the analysis may have slight
differences in their policies; institutional arrangements, tariff structures, etc. but have great
similarities in terms of climatic conditions, topography, water supply practices and urban
inhabitants’ lifestyles and cultural values. Considering the similarities and the importance
of this region in terms of high population density and water resources availability
(due to abundance of rivers Satluj, Beas, Ravi, Ganga, Yamuna, Ramganga, Gomati,
Ghagra and Gandak) and also the want of reasonable sample size, the present study deals
with benchmarking of 35 North Indian urban water utilities using DEA approach.
2.2 Benchmarking using DEA
According to Tupper and Resende (2004), efficiency measurement studies have been
“relatively scarce” in the water supply sector. Lin (2005) and Berg (2006) also acknowledge
the fact that water sector has been given less attention and limited data availability is one
of the reasons for the same. For benchmarking, Berg (2006) has categorized many
5. BIJ alternative models into 11 analytic techniques arrayed in terms of the technical and
18,1 quantitative skills required for implementing the different approaches. Jamasb and Pollitt
(2001) have suggested that benchmarking methods should be treated as a decision aid tool,
need to be applied with care and regard to the context in which they are used and their raw
results should not be regarded as replacements for decision makers and their judgments.
A good review of benchmarking methods is available in Coelli et al. (1998, 2003).
90 Most international initiatives on benchmarking limit themselves to
indicator-by-indicator comparisons and do not employ standard quantitative
techniques. Only very few studies have dealt with the most recent benchmarking
methods which use the most efficient utilities to form an efficiency frontier with respect
to which rest of the utilities are compared. These methods are called frontier methods.
One of the most used frontier method is DEA which stemmed from the concept of Pareto
optimality and states that, within the given limitations of resources and technology,
there is no way of producing more of some desired commodity without reducing output
of some other desired commodity (Zeleny, 1982). Charnes, Cooper and Rhodes (CCR) first
introduced the term DEA and received wide attention as it defined a simple measure of
firm efficiency accounting for multiple inputs and outputs (Charnes et al., 1978).
DEA in essence is a linear programming technique that converts multiple inputs and
outputs into a scalar measure of efficiency. The most efficient utilities are rated to have
an efficiency score of one, while the less efficient utilities score between zero and one. The
utilities lying on efficient frontier are identified as best practice utilities by DEA. CCR
considered constant returns to scale (CRS) model with input orientation whereas
subsequent works by Banker, Charnes and Cooper (BCC) proposed a variable returns to
scale (VRS) model with either input or output orientation (Banker et al., 1984). Both CCR
and BCC are most commonly used DEA formulations in the utility sector. After CCR and
BCC, there have been a large number of papers which have extended the application of
DEA methodology. Table I summarises few benchmarking studies undertaken in water
sector by various authors in different countries using DEA. It also lists the input and
output variables used for DEA in these studies along with the identification of
sustainability criteria that has been ignored under these studies.
3. Methodology
The study uses DEA as a benchmarking tool to estimate efficiencies of 35 DMUs under
consideration. Figure 1 presents the methodological sequence for the present study. The
first step consists of selection of DMUs that enter the analysis. Important criteria for
sustainable water supply services (namely, service sufficiency, service reliability,
resource conservation, staff rationalization, business viability, etc.) that address the key
sustainability dimensions (social, environmental and financial) are then identified
against which efficiencies of the selected 35 DMUs are to be evaluated. Next crucial step
for DEA consists of model specification and selection of input and output variables
(Table II) that address the above-identified sustainability criteria. DEAP (Version 2.0)
software is run to obtain te and se scores for each DMU. The study finally analyses the
DEA results to assess performance status of 35 DMUs.
3.1 DEA formulations
For water utilities input minimization is generally preferred option as output is
often exogenous and beyond managerial control at least in short to medium term.
6. Selection of 35 water North Indian
utilities (DMUs) for DEA urban water
utilities
Identification of important criteria for sustainable
water supply services
91
Model specifications and selection of input/output
variables representing the above criteria for DEA
Results using DEA software
Figure 1.
Analysis of DEA results Methodology
Inputs/outputs Sustainability criteria Sustainability dimensions
Inputs
1. UFW Resource conservation Environmental
2. Total staff Staff rationalization Financial
3. O&M expenditure Resource conservation Environmental
Outputs
1. Net per capita supply Service sufficiency Social Table II.
2. Total revenue receipts Business viability Financial Inputs, outputs and
3. Water treated Service reliability Social sustainability
Also, the analysis in the paper intends to suggest input benchmarks. Hence, the basic
DEA model discussed below has an input orientation. This section describes the DEA
formulation employed in the paper for analysis.
In case of CRS hypothesis as developed by Charnes et al. (1978), a proportional
increase of all input levels produces equi-proportional increase in output levels. The
CRS assumption is only appropriate when all firms are operating at an optimal scale.
Imperfect competition, constraints on finance, etc. may cause a firm to not operate at
optimal scale. Banker et al. (1984) suggested an extension of the CRS DEA model to
account for VRS situations, by adding a convexity constraint as shown in equation (3).
The efficiency score in the presence of multiple input and output factors is defined as:
weighted sum of outputs
Efficiency ¼ ð1Þ
weighted sum of inputs
Assuming that the chosen sample has z DMUs, each with m inputs and n outputs, the
relative efficiency score of a test DMU p is obtained by solving the model proposed by
Charnes et al. (1978):
Pn Pn
vk ykp vk yki
max Pk¼1 m s:t: Pk¼1
m # 1 ;i ð2Þ
j¼1 uj xjp j¼1 uj xji
7. BIJ where:
18,1 i ¼ 1 to z;
j ¼ 1 to m;
k ¼ 1 to n;
92 yki ¼ amount of output k produced by DMU i;
xji ¼ amount of input j utilized by DMU i;
vk ¼ weight given to output k; and
uj ¼ weight given to input j.
The fractional program in equation (2) is subsequently converted to a linear
programming format and a mathematical dual is employed as shown in equation (3), to
solve the linear program. The dual reduces number of constraints from z þ m þ n þ 1
in the primal to m þ n in the dual; thereby rendering the linear problem easier to solve:
X z Xz
minu;l u s:t: uxjp 2 li xji $ 0 ;j 2 ykp þ li yki $ 0 ;k
i¼1 i¼1 ð3Þ
Xz
li ¼ 1 ! Convexity constraint li $ 0 ;i
i¼1
where:
u efficiency score; and
li dual variables (weights in the dual model for the inputs and outputs of the
z DMUs).
The above problem is run z times for calculating the relative efficiency scores (u) of all the
DMUs. Each individual DMU in the sample requires the solution of linear program.
Distance of a DMU from the frontier measures its efficiency scores. A DMU is efficient if
it operates on the frontier and also has zero associated slacks. The slacks are output
shortfalls and input surpluses associated with the examined DMU, in addition to the
increase of all outputs or the decrease in all inputs by a factor equal to the efficiency
score. The technique also computes input and output targets that would turn an
inefficient unit into an efficient one. À Pz Á
Note that the convexity constraint i¼1 li ¼ 1 essentially ensures that
benchmarking of an inefficient firm is only against firms of a similar size. That is,
the projected point (for that firm) on the DEA frontier will be a convex combination of
observed firms. CRS case has no convexity restriction imposed. Hence, in a CRS-DEA,
benchmarking of an inefficient firm may be against firms of substantially larger
(smaller) size and the “l” weights will sum to a value greater than (less than) one.
The use of the CRS specification when not all firms are operating at the optimal scale,
results in measures of te confounded by se. The use of the VRS specification permits the
calculation of te devoid of these se effects and is most commonly used in the service
or utility sector. As the CRS contains VRS within its envelope, VRS model provides te
scores which are greater than or equal to those obtained under CRS model. If there is a
difference in the CRS and VRS te scores for a particular firm, then this indicates
8. that the firm has scale inefficiency. The DEA model solved may be useful to identify North Indian
whether a DMU on the VRS efficient boundary operates with constant, increasing or urban water
decreasing returns to scale (CRS, IRS or DRS).
utilities
4. Benchmarking framework using DEA
The important sustainability criteria incorporated into analysis are service sufficiency,
service reliability, resource conservation, staff rationalization and business viability. 93
Most of the output variables considered for analysis in the water sector are generally
exogenous and are beyond managerial control at least in short to medium term rendering
the exercise on output-oriented DEA model futile. Input orientation has, therefore, been
considered for DEA as the objective of the analysis is to suggest input benchmarks to
produce a given level of output. This is useful to estimate the potential for reduction in
inputs – O&M expenditure, UFW and staff size and hence potential for cost savings.
Percentage cost-saving potential (% CSP) of each DMU has been calculated as:
Actual Exp: 2 projected Exp:
% CSP ¼ £ 100
Actual Exp:
Or, potential for input reduction (%) of each DMU has been calculated as:
Actual input 2 projected input
¼ £ 100
Actual input
where, inputs may be O&M expenditure or UFW or staff size.
For utility or service sector, output levels cannot be raised equi-proportional to input
levels and hence VRS-DEA model is more appropriate. This paper, therefore, considers
input-oriented VRS-DEA model for analysis.
4.1 Selection of input and output variables
The input and output variables chosen for DEA have been determined on the basis of:
.
reference to the standard literature on whatever scarce work on benchmarking
has been carried out so far in the water sector (Table I);
.
analogy drawn from the variable selection in electricity sectors (as both water
and electricity sectors are essentially network industries with natural monopoly
characteristics);
.
ideas drawn from the variable selection for benchmarking by other service
sectors (namely, hospital, educational institutions, tourism, banks, etc.); and
.
data availability for the 35 DMUs under consideration from NIUA Report.
Suitability of the chosen input and output variables are further affirmed using Pearsons’
correlation method which checks the compliance with isotonicity relationship
(i.e. increase in input should result in increase in output). Number of input and output
variables is so determined that their sum total is less than one-third of the total number of
DMUs selected for DEA (Banker et al., 1989) in order to strengthen the discriminatory
power of DEA and avoid “degree of freedom” problems to occur.
Utilities which are not subjected to competition may compromise its service quality
(or reliability) for reducing costs and to increase profits. Service reliability criterion
therefore needs to be incorporated for efficiency estimation in order to effectively align
9. BIJ incentives with the reliability factors. UFW if controlled would enhance environmental
18,1 quality and assure long-term availability of water. This is of special significance as the
government policy now accords major emphasis on resource conservation.
Rationalisation of staff size and adequate revenue generation are the two most critical
issues which need to be given due consideration for business viability of water utilities.
The present study therefore considers UFW (in million litres’ per day (MLD), total staff
94 (nos) and operation and maintenance (O&M) expenditure (in Indian rupees, INR
millions/year) as three inputs and net per capita supply (in liters per capita per day – lpcd),
total revenue receipts (in INR millions/year) and water treated (as percentage of water
produced) as three outputs. Service sufficiency and service quality criteria address social
sustainability dimension and is represented by outputs net per capita supply and water
treated. Resource conservation criterion address environmental sustainability dimension
and is represented by inputs UFW and O&M expenditure (O&M expenditure serves as a
proxy for energy consumption in the absence of exclusive data on energy consumption for
the 35 DMUs). Staff rationalization and business viability criteria address financial
sustainability dimension and are represented by an input total staff and output total
revenue receipts, respectively. The inputs and outputs chosen for DEA are shown in
Table II.
5. Results and analysis
This section covers the results of efficiency analysis in terms of te scores, se scores,
returns to scale (RTS), benchmark DMUs, input and output slacks, percentage CSPs,
etc. for each DMU; ranking position, number of DMUs under different efficiency ranges
and cost-recovery analysis. This section further explores the scope for reduction in
O&M expenditure, UFW and staff size (Tables III and IV).
5.1 Efficiency analysis
te for 35 DMUs ranges from 0.268 to 1 with its average value as 0.814. se for 35 DMUs
ranges from 0.279 to 1 with its average value as 0.879 (Table III and Figure 2).
Percentage CSP for 35 DMUs ranges from 0 to 73 percent. Total CSP of all DMUs is
INR 410 millions/year (US$1.00 < 45.00 Indian rupees, INR) and is 10.65 percent of the
actual annual expenditure of all DMUs (Table IV).
For 14 DMUs se . te whereas for 13 DMUs te . se and for rest of the eight overall
efficient DMUs te ¼ se (Table III and Figure 2). DMUs with te . se need to place major
emphasis on improving their operational scale whereas the DMUs with se . te need to
focus on productivity and technology improvement. These measures would enhance
the operational efficiency of the DMUs.
Data on RTS show that 11 DMUs have se ¼ 1. More than 50 percent of the DMUs
(18 nos.), mostly large sized with higher population exhibit DRS and need to strive for
optimization of operational scale and productivity enhancement. Unbundling of water
supply functions may also help in optimal allocation of resources. On the other hand, less
than 20 percent of the DMUs (six nos. – Gurgaon, Pathankot, Faizabad, Mathura,
Rae Bareli and Rampur), mostly small sized with lesser population exhibit IRS and need
to focus on resource expansion. Also possibility may be explored to transfer the
resources from the DMUs operating at DRS to those operating at IRS within a state.
For the outputs, out of all DMUs, 14 DMUs have slack for net per capita supply
whereas only four DMUs have slack for total revenue receipts and three DMUs have
10. North Indian
urban water
utilities
95
Table III.
DEA results: efficiencies,
ranking and targets for
35 DMUs
14. 1.1
te se North Indian
1
0.9 urban water
Efficiencies
0.8
0.7
0.6
utilities
0.5
0.4
0.3
0.2
0.1
0 Gurgaon 99
Delhi
Kanpur
Lucknow
Ludhiana
Varanasi
Ambala
Faridabad
Hisar
Karnal
Rohtak
Amritsar
Bathinda
Hoshiarpur
Jalandhar
Moga
Pathankot
Patiala
Agra
Aligarh
Allahabad
Bareilly
Faizabad
Ghaziabad
Gorakhpur
Haldwani
Hapur
Jhansi
Mathura
Moradabad
Muzaffarna
Rae Bareli
Rampur
Saharanpur
Chandigarh
Figure 2.
te and se of 35 DMUs
Cities
slack for percentage water treated. For the inputs, out of all DMUs, 14 DMUs have
slack for UFW whereas three DMUs have slack for total staff and one DMU has slack
for O&M expenditure. Thus, there is a scope for increasing average net per capita
water supply provision by 9.4 percent and reducing UFW by 10.74 percent of their
respective actual values of all the DMUs due to slacks, in addition to the decrease in all
inputs by a factor equal to the efficiency score. However, scope for increase in rest of
the outputs and decrease in rest of the inputs of all the DMUs is almost negligible on
account of slacks.
Average of projected net per capita water supply of all DMUs is 121.7 lpcd as against
their actual average value of 111.3 lpcd. This would require an additional 381 MLD of
water to meet the projected demand for all the DMUs.
Agra is found to be the most frequent benchmark DMU (for nine inefficient DMUs)
followed by Haldwani and Chandigarh (for seven inefficient DMUs each) (Table III).
The inefficient DMUs are of similar size and scale as of their respective efficient
benchmark DMUs (i.e. Agra, Haldwani and Chandigarh).
5.2 Ranking position and number of DMUs under various efficiency ranges
About 18 DMUs rank first on te scores whereas 12 DMUs rank first on se scores. All eight
overall efficient DMUs rank first on te and se scores. Delhi, Karnal Ambala and
Jalandhar rank first on te score whereas they rank 35th (last), 34th, 33rd and 31st,
respectively, on se score. These four DMUs need to focus on improving their operational
scale in order to be overall efficient. Jhansi, Gorakhpur and Ghaziabad rank first on se
scores but they rank 31st, 30th and 22nd, respectively, on te scores. These three DMUs
need to shift their focus towards productivity enhancement and technology upgradation
in order to be overall efficient. Pathankot, Mathura and Muzaffarnagar rank close to
each other on te and se scores.
More than 50 percent DMUs (18 nos.) have 100 percent te and only 14 percent DMUs
(five nos. – Ludhiana, Faridabad, Gurgaon, Faizabad and Jhansi) have te , 50 percent.
About 70 percent of the DMUs have te . 75 percent. Approximately, one-third
DMUs (11 nos.) have 100 percent se and only two DMUs (Delhi and Karnal) have
se , 70 percent and for rest of the 22 DMUs, se ranges between 70 and 100 percent.
5.3 Cost-recovery analysis
Faridabad, Gurgaon, Faizabad and Jhansi have higher potential for increasing (by more
than 60 percent) their actual cost recovery (Table IV and Figure 3).
15. BIJ 120 Actual cost recovery (%)
110 Projected cost recovery(%)
18,1 100
90
80
70
% 60
50
40
30
100 20
10
0
Gurgaon
Delhi
Kanpur
Lucknow
Ludhiana
Varanasi
Ambala
Faridabad
Hisar
Karnal
Rohtak
Amritsar
Bathinda
Hoshiarpur
Jalandhar
Moga
Pathankot
Patiala
Agra
Aligarh
Allahabad
Bareilly
Faizabad
Ghaziabad
Gorakhpur
Haldwani
Hapur
Jhansi
Mathura
Moradabad
Muzaffarna
Rae Bareli
Rampur
Saharanpur
Chandigarh
Figure 3.
Actual vs projected
percentage cost recovery
Cities
Projected annual revenue receipts of all DMUs as obtained from DEA show only
0.15 percent increase in the actual annual revenue receipts of all DMUs whereas
projected cost recovery (49.8 percent) of all the DMUs when calculated using projected
expenditure data shows 5.4 percent increase in actual cost recovery (44.4 percent) of all
DMUs. Thus, actual cost recovery can be increased by 5.4 percent (if all inefficient
utilities reach efficient frontier) though potential for increasing actual annual revenue
receipts is only by 0.15 percent. Increased cost recovery would help in improving
service coverage and hence increased consumer satisfaction.
5.4 UFW analysis
Projected UFW is maximum for Kanpur and Allahabad (30 percent each) though they
are 100 percent technically efficient and is # 5 percent for 17 DMUs (Table IV and
Figure 4).
Actual UFW of all utilities is 23.23 percent whereas projected UFW is 18.05 percent
of the total water produced of all utilities.
Potential for UFW reduction of all DMUs is 305 MLD and is 22.3 percent of the actual
total UFW of all DMUs, if all inefficient DMUs reach efficient frontier. Thus, 305 MLD of
additional water may be made available to the consumers of all DMUs if UFW is brought
50
Actual UFW (%)
45
Projected UFW (%)
40
35
30
% 25
20
15
10
5
0
Gurgaon
Delhi
Kanpur
Lucknow
Ludhiana
Varanasi
Ambala
Faridabad
Hisar
Karnal
Rohtak
Amritsar
Bathinda
Hoshiarpur
Jalandhar
Moga
Pathankot
Patiala
Agra
Aligarh
Allahabad
Bareilly
Faizabad
Ghaziabad
Gorakhpur
Haldwani
Hapur
Jhansi
Mathura
Moradabad
Muzaffarna
Rae Bareli
Rampur
Saharanpur
Chandigarh
Figure 4.
Actual vs projected UFW
(in percentage)
Cities
16. down to the projected level as obtained from DEA model. This would help in achieving North Indian
the target for average net per capita water supply provision to a great extent.
Annual additional revenue receipts potential of all DMUs when percentage UFW is
urban water
brought down to projected level (through additional water sale) is INR 109.9 millions/year utilities
and is 6.4 percent of the actual annual revenue receipts of all DMUs and 2.85 percent of
the total annual expenditure of all DMUs. Thus, reducing the UFW would improve
service coverage and revenue receipts status. 101
5.5 Staff analysis
Potential for reducing staff size of all DMUs is 2,999 nos. and is 8.42 percent of the total
number of actual staff of all DMUs (Table III), if all inefficient utilities reach efficient
frontier. This would result in cost saving of INR 156.27 millions/year and is 9.5 percent
of the actual annual staff expenditure of all DMUs and 4.1 percent of the total annual
expenditure of all DMUs.
Actual average of staff per 1,000 connections of all DMUs is 7.72 as against projected
average of 5.82. Though the analysis shows potential for reducing staff size, average
staff per 1,000 connections is well within the range (five to ten) obtained from
international average of developing countries (Kaaya, 1999). Thus, focus may be shifted
towards reducing staff expenditure and not on staff size at the initial instance.
5.6 O&M expenditure analysis
CSP of all DMUs on account of reduction in O&M expenditure, if all inefficient DMUs
reach efficient frontier, is INR 253.8 millions/year and is 11.52 percent of the actual
annual O&M expenditure of all DMUs and 6.6 percent of the total annual expenditure
of all DMUs (Tables III and IV). Cutting down the electricity expenditure would reduce
O&M expenditure to a greater extent and therefore suitable measures are required to
be taken in this direction.
Thus, it is evident that there is significant scope for cost savings on account of UFW
control, staff rationalization and O&M cost reduction.
5.7 State-wise performance analysis
This section carries out performance analysis for the two union territories (Delhi and
Chandigarh) and three states (UP, Haryana and Punjab). Calculations of efficiencies
and other variables (staff size, UFW, O&M expenditure, etc.) for each of the three states are
based on the respective average values of variables of all the utilities belonging to the state
under consideration. Out of 35 utilities under consideration, two utilities are of union
territories (Chandigarh and Delhi), seven utilities belong to Haryana, seven utilities belong to
Punjab and rest of the 19 utilities belongs to UP. The analysis highlights the following facts:
.
Delhi and Chandigarh have 100 percent te. te of UP, Punjab and Haryana are,
respectively, 0.92, 0.81 and 0.67. Thus, Haryana has to relatively focus more on
improving its technical efficiency.
.
Chandigarh has 100 percent overall efficiency and Delhi has the least overall
efficiency (0.28). Overall, efficiencies of UP, Punjab and Haryana are,
respectively, 0.76, 0.78 and 0.51.
.
Chandigarh has 100 percent se and Delhi has the least se (0.28). se of UP, Punjab and
Haryana are, respectively, 0.93, 0.85 and 0.84. Thus, Delhi has to place major emphasis
on improving its se which in turn would also improve its overall efficiency.
17. BIJ .
Delhi exhibits DRS and Chandigarh has 100 percent se. In Haryana, all the utilities
18,1 except Gurgaon (with IRS) exhibit DRS. In Punjab, Bhatinda and Moga have
100 percent se, Pathankot exhibits IRS and rest of the four utilities exhibit DRS.
In UP, eight utilities have 100 percent se, four utilities exhibit IRS and rest of the
seven utilities exhibit DRS. Suitable strategies need to be evolved to transfer the
resources from utilities operating at DRS to those utilities operating at IRS within
102 the state in order to optimize the operational scale.
.
CSP[1] is nil for Delhi and Chandigarh, highest for Haryana (58.4 percent), very
less for Punjab (6 percent) and 23.4 percent for UP. Within Haryana, Faridabad,
Gurgaon and Ludhiana have very high CSP (. 65 percent). Within Punjab,
Pathankot and Hoshiarpur have higher CSP (. 35 percent) and within
UP, Faizabad, Jhansi and Bareilly have higher CSP (. 50 percent).
.
Potential for UFW reduction (as a percentage of water produced) is nil for Delhi
and Chandigarh and is highest for Haryana (14.59 percent) followed by Punjab
(13 percent) and UP (8.2 percent).
.
Potential for staff reduction (as a percentage of total number of actual staff) is nil
for Delhi and Chandigarh and is highest for Haryana (54.87 percent) followed by
UP (19.9 percent) and Punjab (10.9 percent).
.
Potential for reduction in O&M expenditure (as a percentage of total expenditure)
is nil for Delhi and Chandigarh and is highest for Haryana (41 percent) followed
by UP (12 percent) and Punjab (3 percent).
.
Potential for increasing the annual cost recovery is nil for Delhi and Chandigarh
and is highest for Haryana (36 percent) followed by UP (23 percent) and Punjab
(4.3 percent).
6. Conclusions
Presently, most of the utilities have failed to provide adequate service and connection
coverage with wide supply demand gap. UFW of most of the utilities are very high along
with high O&M expenditure and oversized and untrained staff. The range of problems
prevalent in Indian urban water sector clearly establishes the need for benchmarking.
A “benchmark” is a reference or measurement standard used for comparison whereas
“benchmarking” is the continuous activity of identifying, understanding and adapting
best practice and processes that will lead to superior performance. Benchmarking can be
a useful mechanism to help each utility focus on improvement opportunities by
comparing its practices with the other utilities and accordingly make suitable changes to
some of its procedures and working methods which in turn will lead to continuous
improvement. However, commitment for improvement at the top level is the necessary
prerequisite to realize the benefits of benchmarking.
To fulfill the commitments of the millennium development goals which incorporate the
target of “reducing by half the proportion of people without sustainable access to safe
drinking water by year 2015” (Johanesburg Summit, 2002), governments will need to
develop suitable sustainability-based benchmarking framework for assessing the relative
performance of utilities which in turn would facilitate efficient practices by water utilities
towards sustainable water supply services to its consumers. Hardly any benchmarking
initiative has been undertaken systematically in Indian urban water sector.
18. Benchmarking framework developed in the present study therefore serves as an North Indian
important milestone in this direction. urban water
Efficiency analysis of the selected 35 urban water utilities using DEA approach
shows substantial scope for reduction in UFW, staff size and O&M expenditure and utilities
hence, significant potential for cost savings. The study projects the percentage CSP on
account of reduction in UFW, staff size and O&M expenditure as 2.85, 4.1 and 6.6 percent,
respectively, of the total annual expenditure of all DMUs. Such results may be useful for 103
the water utilities to prioritize their improvement strategy. Though potential for
additional revenue receipts is almost negligible, the study shows potential for increased
cost recovery due to potential for cost savings (or reduced expenditure). The additional
water available through UFW control (305 MLD) would help in meeting projected per
capita water requirement (381 MLD) to a great extent. As average number of staff per
1,000 connections is within the international range (five to ten) of developing countries as
found by Kaaya (1999), utilities may place more emphasis on reducing the staff
expenditure than staff downsizing to curtail operating expenses. Possibility needs to
be explored by the utilities to minimize electricity expenses in order to bring down O&M
expenditure. The study also suggests that 50 percent (14 nos.) of the inefficient utilities
(with te . se) need to focus on improving their operational scale whereas about rest
50 percent (13 nos.) of the inefficient utilities (with se . te) need to strive for productivity
and technology improvement (Table III). Thus, the utilities striving to reach the efficient
input levels as projected by DEA model would eventually lead to sustainable water
supply services.
State/UT-wise performance analysis as regards efficiencies and other variables
(staff size, UFW, O&M expenditure, etc.) broadly present their status from best to worst in
the order of Chandigarh, Delhi, Punjab, UP and lastly Haryana. The analysis, thus, shows
maximum scope for improvement in Haryana. Chandigarh exhibits the best performance
in spite of the fact that water supply services in Chandigarh are managed by municipal
bodies. On the other hand, Haryana exhibits relatively worst performance though water
supply services in Haryana are managed by state government body (Public Health
Department). Water supply services in Delhi, Punjab and UP are managed by specialist
agencies (autonomous body/water boards) and their performance is in the middle order.
The benchmarking framework developed in the present study would be useful for the
regulator or operator of the facility to rank the utilities under their control for their
performance and accordingly devise suitable incentive mechanism or price cap
regulation. As water supply is essentially a state subject in India, setting up of an
independent regulatory body at state level will almost certainly become a mandatory
requirement to execute such benchmarking scheme. The scheme would also help water
managers to identify suitable benchmarks, estimate performance targets and devise
appropriate measures to remedy underperformance. Governments need to develop
uniformly acceptable template for data collection and its standardization in order to
facilitate effective implementation of such benchmarking scheme. The results of
benchmarking exercise, whenever attempted should be made public which in turn
would enable concerned stakeholders to act as pressure groups and facilitate efficient
practices by non-performing utilities. Internal efficiencies of water supply services when
improved would effect internal savings for greater expansion of service coverage,
reduced UFW, reduced electricity consumption and therefore increased revenue
generation. This would eventually lead to sustainable urban water supply services.
19. BIJ The scope of the present analysis could not be widened to incorporate additional
18,1 sustainability criteria (such as, services to the poor, tariff design, customer services,
revenue functions, etc.) due to limited data availability. Also efficiency analysis did not
take into account the impact of non-controllable environment factors (such as
topography, population density, water source, ownership status, etc.). However, there is
considerable scope for further research on this subject. Urban water utilities of other
104 developing as well as developed countries may also be included for DEA in order to draw
useful lessons from the international best practices. Also availability of data on resources
(materials, manpower, etc.) and their respective prices would enable cost efficiency
analysis of the utilities. Similar benchmarking studies may be undertaken using other
techniques, such as SFA, regression analysis, etc. and results may be compared with the
current analysis to gain greater insights. Efficiency analysis can also be performed using
time series data to estimate change in productivity levels of the utilities.
Note
1. Cost-saving potential of Haryana, Punjab and UP is calculated as difference between the
total actual and total projected expenditure divided by total actual expenditure of all the
utilities belonging to that state.
References
´
Aida, K., William, W.C., Jesus, T.P. and Toshiyuki, S. (1998), “Evaluating water supply services
in Japan with RAM: a range-adjusted measure of inefficiency”, Omega, International
Journal of Management Science, Vol. 26 No. 2, pp. 207-32.
Akosa, G., Franceys, R., Barker, P. and Jones, T.W. (1995), “Efficiency of water
supply and sanitation projects in Ghana”, Journal of Infrastructure Systems, Vol. 1 No. 1,
pp. 56-65.
Alsharif, K., Feroz, E.H., Klemer, A. and Raab, R. (2008), “Governance of water supply systems in
the Palestinian Territories: a data envelopment analysis approach to the management of
water resources”, Journal of Environment Management, Vol. 87 No. 1, pp. 80-94.
Banker, R.D., Charnes, A. and Cooper, W.W. (1984), “Some models for estimating technical and
scale inefficiencies in data envelopment analysis”, Management Science, Vol. 30 No. 9,
pp. 1078-92.
Banker, R.D., Charnes, A., Cooper, W.W., Swarts, J. and Thomas, D.A. (1989), “An introduction to
data envelopment analysis with some of its models and their uses”, Research in
Governmental and Nonprofit Accounting, Vol. 5, pp. 125-63.
Berg, S.V. (2006), “Survey of benchmarking methodologies: executive summary”,
working paper, World Bank, Public Utility Research Centre, Washington, DC, available at:
www.purc.org
Berg, S.V. and Lin, C. (2007), “Consistency in performance rankings: the Peru water sector”,
Journal of Applied Economics, Vol. 40 No. 6, pp. 93-805.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision making
units”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429-44.
Coelli, T., Rao, D.S.P. and Battese, G.E. (1998), An Introduction to Efficiency and Productivity
Analysis, Kluwer, London.
Coelli, T.J., Estache, A., Perelman, S. and Trujillo, L. (2003), A Primer on Efficiency Measurement
for Utilities and Transport Regulators, The World Bank, Washington, DC.
20. Jamasb, T. and Pollitt, M. (2001), “Benchmarking and regulation: international electricity North Indian
experience”, Utilities Policy, Vol. 9, pp. 107-30.
urban water
Johanesburg Summit (2002), “Johanesburg Summit – Secretary General calls for global action on
water issues”, available at: www.johanesburgsummit.org/html/media_info/pressrelease_ utilities
prep2/global_action_water_2103.pdf (accessed 2002).
Kaaya, J.A. (1999), “Experience of autonomous water and sewerage authorities in Tanzania”,
Proc. 25th WEDC Conference, Addis Ababa, Ethiopia, 30 August-3 September. 105
Kapadia, K. (2005), “Sustainable water supply in urban development: case studies of Gurgaon,
Dwarka and Greater Noida”, Journal of Indian Buildings Congress ( IBC ), Vol. 12 No. 1.
Kulshrestha, M. (2005), “Performance assessment and efficiency evaluation for the urban water
operations of Indian utilities”, PhD thesis, Indian Institute of Technology, Delhi.
Lambert, D., Dichev, D. and Raffiee, K. (1993), “Ownership and sources of inefficiency in
provision of water services”, Water Resources Research, Vol. 29, pp. 1573-8.
Lin, C. (2005), “Incorporating service quality & prospects of benchmarking: evidence from the
Peru water sector”, Utilities Policy, Vol. 13, pp. 230-9.
Mugisha, S. (2007), “Effects of incentive applications on technical efficiencies: empirical evidence
from Ugandan water utilities”, Utilities Policy, Vol. 15 No. 4, pp. 225-33.
NIUA Report (2005), “Status of water supply, sanitation and solid waste management in urban
areas for year 1999”, Main Report & Statistical Volume I (Water Supply & Water Tariff),
MoUD, Government of India, New Delhi.
Patwardhan, S.S. (1993), “Financing urban water supply scheme”, Journal of IWWA,
October-December.
Raghupathi, P.U. and Foster, V. (2002), “A scorecard for India : Paper 2”, Water Tariff &
Subsidies in South Asia, December.
Renzetti, S. and Dupont, D. (2007), “Measuring the technical efficiency of municipal water
suppliers: the role of environmental factors”, Journal of Land Economics, Vol. 85 No. 4,
pp. 627-36.
Singh, M.R., Upadhyay, V. and Mittal, A.K. (2005), “Urban water tariff structure & cost recovery
opportunities in India”, Water Science and Technology, Vol. 52 No. 12, pp. 43-51.
Thanassoulis, E. (2000), “The use of data envelopment analysis in the regulation of UK water
utilities: water distribution”, European Journal of Operational Research, Vol. 126,
pp. 436-53.
Tupper, H.C. and Resende, M. (2004), “Efficiency and regulatory issues in the Brazilian water and
sewage sector: an empirical study”, Utilities Policy, Vol. 12, pp. 29-40.
Tynan, N. and Kingdom, B. (2002), “A water scorecard: setting performance targets for water
utilities”, Public Policy for the Private Sector, Group Private Sector and Infrastructure
Network, Public Policy Journal, Note No. 242, The World Bank, Washington, DC.
Yepes, G. and Dianderas, A. (1996), Indicators, Water and Waste Water Utilities, 2nd ed.,
Water and Sanitation Division of the Transportation, Water and Urban Development
Department, TWUWS, The World Bank, Washington, DC ( published informally by
International Bank of Reconstruction and Development).
Zeleny, M. (1982), Multiple Criteria Decision Making, McGraw-Hill, New York, NY.
About the authors
Mamata R. Singh is a Master of Engineering (Building Engineering and Management) and
submitted a PhD thesis in July 2008. She is a Research Scholar at the Indian Institute
21. BIJ of Technology-Delhi, New Delhi, India and a Lecturer at the Directorate of Training and Technical
Education, New Delhi, India. Her areas of specialization include urban infrastructure (water),
18,1 project management and quality management systems (including ISO-9000 series).
Mamata R. Singh is the corresponding author and can be contacted at: mamatarsingh@yahoo.com
Atul K. Mittal holds a PhD (in Waste Water). He is Associate Professor, Environmental
Engineering, in the Department of Civil Engineering at the Indian Institute of Technology-Delhi,
New Delhi, India. His areas of specialization include water and wastewater design and treatment,
106 urban infrastructure, environmental engineering and management.
V. Upadhyay holds a PhD in Economics. He is Professor in the Department of Humanities and
Social Sciences, Indian Institute of Technology-Delhi, New Delhi, India. His areas of
specialization include development economics, economic theory and econometrics.
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
