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5.benchmarking of

  1. 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htmBIJ18,1 Benchmarking of North Indian urban water utilities Mamata R. Singh, Atul K. Mittal and V. Upadhyay86 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 andBenchmarking: An International urbanization has resulted in progressive decline in the per capita availability of waterJournal in Indian cities. In India, water supply to the consumer is inadequate, intermittent,Vol. 18 No. 1, 2011pp. 86-106 generally for low duration and of poor quality. Considering the growing water scarcityq Emerald Group Publishing Limited and poor services to the consumers, Indian urban water utilities need to instill1463-5771DOI 10.1108/14635771111109832 efficient practices for sustainable water supply services to the consumers. An attempt
  2. 2. towards benchmarking of Indian water utilities would serve as an important step in North Indianthis direction. urban water Though several benchmarking initiatives have been undertaken internationally(Table I), Indian urban water sector has hardly witnessed any benchmarking study. Most utilitiesof such initiatives do not view performance from sustainability dimensions and computeefficiency with major focus on cost-saving aspect. Also such studies have not endeavoredto estimate potential for reduction in parameters other than cost (for example, 87unaccounted for water (UFW, i.e. water loss) reduction, staff reduction, etc.). Attempts toestimate utilities’ performance in totality that encompass important criteria (such asservice sufficiency, service reliability, resource conservation, staff rationalization,business viability, etc.) of sustainable water supply services (referred as “sustainabilitycriteria” 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 forsustainability-based performance assessment of 35 North Indian urban water utilitiesusing data envelopment analysis (DEA) approach. The efficiency scores obtainedthrough DEA model may be used to rank the utilities and estimate potential for costsavings and other input reductions (such as UFW, i.e. water loss, staff size and operationand maintenance (O&M) expenditure). The study uses secondary data of 35 urban waterutilities (hereafter referred as decision-making units, i.e. decision making unit (DMUs) asper DEA terminology) pertaining to three states (Haryana, Punjab and Uttar Pradesh)and two union territories (Delhi and Chandigarh) provided by National Institute ofUrban Affairs (NIUA Report, 2005). The data are of the year 1999. This paper is divided into five sections including the present one. Section 2 discussesthe status and problems of Indian urban water sector and further reviews the literatureon benchmarking and DEA in water sector. Section 3 presents the methodology for thestudy. Section 4 discusses the benchmarking framework using DEA including the basisfor selection of input and output variables for assessment of technical and scaleefficiency (te and se) scores of the DMUs. Section 5 covers the analysis of DEA results for35 North Indian DMUs. Finally, Section 6 provides conclusions and recommendations.2. Literature reviewThis section initially discusses the status and problems of Indian urban water sectorcovering a range of issues, namely: per capita water supply, revenue receipts, waterquality, UFW, staff size and O&M expenditure including examples of few internationalstudies. The second part of this section introduces DEA as a benchmarking tool andreviews the benchmarking studies undertaken in water sector by various authors indifferent countries using DEA.2.1 Status and problems of Indian urban water sectorIn India about two-thirds of the cities have net per capita supply below the establishednorms 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 120a 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 ofwater supplied to the consumers is often in question as more than 50 percent of urbancenters in India do not monitor raw water quality and have inadequate laboratoryfacilities for testing water quality. For the remaining Indian cities, periodicity of water
  3. 3. 88 BIJ sector 18,1 Table I. using DEA in water Benchmarking studiesCountry Author(s) Inputs Outputs Sustainability criteria ignored/remarksPalestine 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 lossesPeru 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 serviceUganda 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 networkIndia 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 supplyBrazil 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 sewageUK Thanassoulis OPEX (operating expenditure) Water produced, number of connections Service reliability, resource (2000) and length of distribution network conservation, staff rationalization and business viabilityJapan 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 pipesGhana Akosa et al. Technical, financial, economic, Reliability, utilization and convenience Variables are at very abstract level (1995) institutional, social and environmental factors factorsUSA 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. 4. quality monitoring (raw water or water at treatment plant or at distribution network) North Indianvaries 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, firefighting and evaporation, free supply to slum/J.J areas, billing and collection inefficiencies, utilitiestheft, etc. In India, on an average about 40 percent of the consumers are not charged for thewater supply services due to poor billing and collection practices which eventuallyencourage them to use water liberally and waste it. UFW in Indian cities range between 8920 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 top25 percent of developing countries recommend a target of 23 percent (or less). The meanfor developed countries is 16 percent. Average UFW in Singapore, Japan, the USA andFrance are 6, 11, 12 and 15 percent, respectively, (Yepes and Dianderas, 1996). Currently, most of the government organisations responsible for water supply areoverstaffed 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 therange of five to ten (Kaaya, 1999). Owing to overstaffing, staff expenditure for Indiancities is also very high (about 30 percent). A larger share of expenditure on establishmentconsiderably reduces the funds available for operation and maintenance of water supplysystem. Expenditure on electricity, consumables, repairs and replacements and otherrelated expenses together constitute the operation and maintenance head. In India, abouthalf the total expenditure on water supply service is spent on O&M in most of the urbancenters (NIUA Report, 2005). O&M costs per cubic meter of water are Rs 13, 16 and 17 forChennai, Bangalore and Hyderabad, respectively, whereas typical prices charged toconsumers 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 revenuegenerated is not sufficient even to cover manpower cost. The status and problems discussed so far indicate the overall position of Indian urbanwater sector. The present study, however, focuses on urban water utilities of North Indianregion. The states (Punjab, Haryana and Uttar Pradesh) and union territories (Delhi andChandigarh) selected in the study fall in the north central part of India and are borderedwith mountains (Himalayas) on its north side and great plateau on its south side. Thisregion is almost dead flat, very fertile and one of the largest food producing basketsaccommodating a sizeable part of the Indian population. Water supply being a statesubject in India, the states and union territories considered for the analysis may have slightdifferences in their policies; institutional arrangements, tariff structures, etc. but have greatsimilarities in terms of climatic conditions, topography, water supply practices and urbaninhabitants’ lifestyles and cultural values. Considering the similarities and the importanceof 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 dealswith benchmarking of 35 North Indian urban water utilities using DEA approach.2.2 Benchmarking using DEAAccording to Tupper and Resende (2004), efficiency measurement studies have been“relatively scarce” in the water supply sector. Lin (2005) and Berg (2006) also acknowledgethe fact that water sector has been given less attention and limited data availability is oneof the reasons for the same. For benchmarking, Berg (2006) has categorized many
  5. 5. BIJ alternative models into 11 analytic techniques arrayed in terms of the technical and18,1 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. 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 MethodologyInputs/outputs Sustainability criteria Sustainability dimensionsInputs1. UFW Resource conservation Environmental2. Total staff Staff rationalization Financial3. O&M expenditure Resource conservation EnvironmentalOutputs1. Net per capita supply Service sufficiency Social Table II.2. Total revenue receipts Business viability Financial Inputs, outputs and3. Water treated Service reliability Social sustainabilityAlso, the analysis in the paper intends to suggest input benchmarks. Hence, the basicDEA model discussed below has an input orientation. This section describes the DEAformulation employed in the paper for analysis. In case of CRS hypothesis as developed by Charnes et al. (1978), a proportionalincrease of all input levels produces equi-proportional increase in output levels. TheCRS 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 atoptimal scale. Banker et al. (1984) suggested an extension of the CRS DEA model toaccount 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 inputsAssuming that the chosen sample has z DMUs, each with m inputs and n outputs, therelative efficiency score of a test DMU p is obtained by solving the model proposed byCharnes 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. 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. 8. that the firm has scale inefficiency. The DEA model solved may be useful to identify North Indianwhether a DMU on the VRS efficient boundary operates with constant, increasing or urban waterdecreasing returns to scale (CRS, IRS or DRS). utilities4. Benchmarking framework using DEAThe important sustainability criteria incorporated into analysis are service sufficiency,service reliability, resource conservation, staff rationalization and business viability. 93Most of the output variables considered for analysis in the water sector are generallyexogenous and are beyond managerial control at least in short to medium term renderingthe exercise on output-oriented DEA model futile. Input orientation has, therefore, beenconsidered for DEA as the objective of the analysis is to suggest input benchmarks toproduce a given level of output. This is useful to estimate the potential for reduction ininputs – 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 inputwhere, inputs may be O&M expenditure or UFW or staff size. For utility or service sector, output levels cannot be raised equi-proportional to inputlevels and hence VRS-DEA model is more appropriate. This paper, therefore, considersinput-oriented VRS-DEA model for analysis.4.1 Selection of input and output variablesThe 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 outputvariables is so determined that their sum total is less than one-third of the total number ofDMUs selected for DEA (Banker et al., 1989) in order to strengthen the discriminatorypower 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 criteriontherefore needs to be incorporated for efficiency estimation in order to effectively align
  9. 9. BIJ incentives with the reliability factors. UFW if controlled would enhance environmental18,1 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 staff94 (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. 10. North Indian urban water utilities 95 Table III.DEA results: efficiencies, ranking and targets for 35 DMUs
  11. 11. BIJ18,196Table III.
  12. 12. North Indian(continued ) urban water utilities 97 Table IV. DEA results: cost-savings potential, cost recovery and slacks for 35 DMUs
  13. 13. BIJ18,198Table IV.
  14. 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 Citiesslack for percentage water treated. For the inputs, out of all DMUs, 14 DMUs haveslack for UFW whereas three DMUs have slack for total staff and one DMU has slackfor O&M expenditure. Thus, there is a scope for increasing average net per capitawater supply provision by 9.4 percent and reducing UFW by 10.74 percent of theirrespective actual values of all the DMUs due to slacks, in addition to the decrease in allinputs by a factor equal to the efficiency score. However, scope for increase in rest ofthe outputs and decrease in rest of the inputs of all the DMUs is almost negligible onaccount of slacks. Average of projected net per capita water supply of all DMUs is 121.7 lpcd as againsttheir actual average value of 111.3 lpcd. This would require an additional 381 MLD ofwater 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 efficientbenchmark DMUs (i.e. Agra, Haldwani and Chandigarh).5.2 Ranking position and number of DMUs under various efficiency rangesAbout 18 DMUs rank first on te scores whereas 12 DMUs rank first on se scores. All eightoverall efficient DMUs rank first on te and se scores. Delhi, Karnal Ambala andJalandhar 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 operationalscale in order to be overall efficient. Jhansi, Gorakhpur and Ghaziabad rank first on sescores but they rank 31st, 30th and 22nd, respectively, on te scores. These three DMUsneed to shift their focus towards productivity enhancement and technology upgradationin order to be overall efficient. Pathankot, Mathura and Muzaffarnagar rank close toeach 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-thirdDMUs (11 nos.) have 100 percent se and only two DMUs (Delhi and Karnal) havese , 70 percent and for rest of the 22 DMUs, se ranges between 70 and 100 percent.5.3 Cost-recovery analysisFaridabad, Gurgaon, Faizabad and Jhansi have higher potential for increasing (by morethan 60 percent) their actual cost recovery (Table IV and Figure 3).
  15. 15. BIJ 120 Actual cost recovery (%) 110 Projected cost recovery(%)18,1 100 90 80 70 % 60 50 40 30100 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 ChandigarhFigure 3.Actual vs projectedpercentage 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 ChandigarhFigure 4.Actual vs projected UFW(in percentage) Cities
  16. 16. down to the projected level as obtained from DEA model. This would help in achieving North Indianthe 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 waterbrought down to projected level (through additional water sale) is INR 109.9 millions/year utilitiesand is 6.4 percent of the actual annual revenue receipts of all DMUs and 2.85 percent ofthe total annual expenditure of all DMUs. Thus, reducing the UFW would improveservice coverage and revenue receipts status. 1015.5 Staff analysisPotential for reducing staff size of all DMUs is 2,999 nos. and is 8.42 percent of the totalnumber of actual staff of all DMUs (Table III), if all inefficient utilities reach efficientfrontier. This would result in cost saving of INR 156.27 millions/year and is 9.5 percentof the actual annual staff expenditure of all DMUs and 4.1 percent of the total annualexpenditure of all DMUs. Actual average of staff per 1,000 connections of all DMUs is 7.72 as against projectedaverage of 5.82. Though the analysis shows potential for reducing staff size, averagestaff per 1,000 connections is well within the range (five to ten) obtained frominternational average of developing countries (Kaaya, 1999). Thus, focus may be shiftedtowards reducing staff expenditure and not on staff size at the initial instance.5.6 O&M expenditure analysisCSP of all DMUs on account of reduction in O&M expenditure, if all inefficient DMUsreach efficient frontier, is INR 253.8 millions/year and is 11.52 percent of the actualannual O&M expenditure of all DMUs and 6.6 percent of the total annual expenditureof all DMUs (Tables III and IV). Cutting down the electricity expenditure would reduceO&M expenditure to a greater extent and therefore suitable measures are required tobe taken in this direction. Thus, it is evident that there is significant scope for cost savings on account of UFWcontrol, staff rationalization and O&M cost reduction.5.7 State-wise performance analysisThis section carries out performance analysis for the two union territories (Delhi andChandigarh) and three states (UP, Haryana and Punjab). Calculations of efficienciesand other variables (staff size, UFW, O&M expenditure, etc.) for each of the three states arebased on the respective average values of variables of all the utilities belonging to the stateunder consideration. Out of 35 utilities under consideration, two utilities are of unionterritories (Chandigarh and Delhi), seven utilities belong to Haryana, seven utilities belong toPunjab 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. 17. BIJ . Delhi exhibits DRS and Chandigarh has 100 percent se. In Haryana, all the utilities18,1 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 within102 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. 18. Benchmarking framework developed in the present study therefore serves as an North Indianimportant milestone in this direction. urban water Efficiency analysis of the selected 35 urban water utilities using DEA approachshows substantial scope for reduction in UFW, staff size and O&M expenditure and utilitieshence, significant potential for cost savings. The study projects the percentage CSP onaccount 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 103the water utilities to prioritize their improvement strategy. Though potential foradditional revenue receipts is almost negligible, the study shows potential for increasedcost recovery due to potential for cost savings (or reduced expenditure). The additionalwater available through UFW control (305 MLD) would help in meeting projected percapita water requirement (381 MLD) to a great extent. As average number of staff per1,000 connections is within the international range (five to ten) of developing countries asfound by Kaaya (1999), utilities may place more emphasis on reducing the staffexpenditure than staff downsizing to curtail operating expenses. Possibility needs tobe explored by the utilities to minimize electricity expenses in order to bring down O&Mexpenditure. 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 rest50 percent (13 nos.) of the inefficient utilities (with se . te) need to strive for productivityand technology improvement (Table III). Thus, the utilities striving to reach the efficientinput levels as projected by DEA model would eventually lead to sustainable watersupply 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 inthe order of Chandigarh, Delhi, Punjab, UP and lastly Haryana. The analysis, thus, showsmaximum scope for improvement in Haryana. Chandigarh exhibits the best performancein spite of the fact that water supply services in Chandigarh are managed by municipalbodies. On the other hand, Haryana exhibits relatively worst performance though watersupply services in Haryana are managed by state government body (Public HealthDepartment). Water supply services in Delhi, Punjab and UP are managed by specialistagencies (autonomous body/water boards) and their performance is in the middle order. The benchmarking framework developed in the present study would be useful for theregulator or operator of the facility to rank the utilities under their control for theirperformance and accordingly devise suitable incentive mechanism or price capregulation. As water supply is essentially a state subject in India, setting up of anindependent regulatory body at state level will almost certainly become a mandatoryrequirement to execute such benchmarking scheme. The scheme would also help watermanagers to identify suitable benchmarks, estimate performance targets and deviseappropriate measures to remedy underperformance. Governments need to developuniformly acceptable template for data collection and its standardization in order tofacilitate effective implementation of such benchmarking scheme. The results ofbenchmarking exercise, whenever attempted should be made public which in turnwould enable concerned stakeholders to act as pressure groups and facilitate efficientpractices by non-performing utilities. Internal efficiencies of water supply services whenimproved would effect internal savings for greater expansion of service coverage,reduced UFW, reduced electricity consumption and therefore increased revenuegeneration. This would eventually lead to sustainable urban water supply services.
  19. 19. BIJ The scope of the present analysis could not be widened to incorporate additional18,1 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 other104 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. 20. Jamasb, T. and Pollitt, M. (2001), “Benchmarking and regulation: international electricity North Indian experience”, Utilities Policy, Vol. 9, pp. 107-30. urban waterJohanesburg 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. 105Kapadia, 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 authorsMamata R. Singh is a Master of Engineering (Building Engineering and Management) andsubmitted a PhD thesis in July 2008. She is a Research Scholar at the Indian Institute
  21. 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

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