Benchmarking operationalefficiency in the integrated

water service provisionDoes contract type matter?

Corrado lo StortoDII – Department of Industrial Engineering,University of Naples Federico II, Naples, Italy

Abstract

Purpose – This is a benchmarking study and the purpose of this paper is to investigate if there is anyassociation between operational efficiency in the integrated water management industry in Italy andthe typology of service providers, and as a consequence, the nature of concession contract.Design/methodology/approach – The study is focussed on 38 optimal territorial areas (ATOs),e.g. a circumscribed geographical area where the provision of integrated water services is consideredefficient. It uses Data Envelopment Analysis (DEA) to calculate ATO efficiency and a stepwiseregression procedure performed to investigate the effect of contract type on the operational efficiencyrate of the ATO.Findings – This study shows that there are some inefficiencies in the water service supply industry inItaly. The estimated average pure technical and scale efficiency of ATOs are 92.62 and 93.91 percent,respectively, while the average technical efficiency is 87.61 percent and the lowest is slightly higherthan 13 percent. Operational inefficiencies might not be determined by size only. In fact, results showthat the water service provider and contract agreement typologies are associated with efficiency.In particular, operational efficiency is higher in those ATOs where the water service supply concessioncontracts that fit the schemes of the new legislative framework prevail or where the service is mostlyprovided by a private equity owned or by mixed public-private companies.Research limitations/implications – It was assumed that any incremental level of water qualitybeyond the minimum acceptable threshold as required by law is not important to increase theoperation efficiency score; henceforth, no variables measuring the water quality were introduced in theDEA model. The result of the study may be not fully representative of the Italian water service sector,because the unavailability of accurate and consistent public databank in Italy did not allowed to havea larger sample.Practical implications – This paper is one of the first in Italy to investigate the association betweenthe operational efficiency of the ATOs and the nature of water service providers and contractagreements used.Originality/value – This paper is one of the first in Italy to investigate the association between theoperational efficiency of the ATOs and the nature of water service providers and contract agreements used.

Keywords Benchmarking, Efficiency, Contracts, DEA, Water management, Service providers,Water service provision, Waste water

Paper type Research paper

1. BackgroundOver the past 20 years there has been increasing interest from both scholarsand policy-makers in measuring the productivity and efficiency of service provision,and in searching for optimal operational and business models in the water supply,sewerage, and wastewater treatment industries (Bruggink, 1982). The comparison ofwater utilities’ performance enables the construction of yardsticks that may helpdecision-makers – i.e. utility managers, industry regulators, policy-makers, etc. – to

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1463-5771.htm

Received 21 November 2012Revised 22 February 2013

Accepted 18 March 2013

Benchmarking: An InternationalJournal

Vol. 21 No. 6, 2014pp. 917-943

r Emerald Group Publishing Limited1463-5771

DOI 10.1108/BIJ-11-2012-0076

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identify weak and strong performers. Quantitative benchmarking procedures that allowanalysts to measure cost and productivity performance have now been implemented inseveral countries in order to have baselines for making comparisons across serviceproviders, to identify trends in key indicators, to identify performance gaps andproductivity improvements, to identify and share best practices, and to drive serviceimprovements by linking funding support to performance monitoring and improvementplanning. As Berg (2013) claims, performance comparison and benchmarking studies areindeed important tools that can be used for the development and implementation of watermanagement policies. For instance, in the UK, the national economic regulatory authorityfor water services management – the OFWAT – adopts mandatory benchmarkingpractices with yardstick competition to derive useful information from water managementutilities relative to water pricing.

Both in developed and under-developed countries the water service industries haveundergone substantial changes as a consequence of innovative regulatory frameworks,a pressure toward greater liberalization, and more pressing quality standards, with theaim to increase productivity, enhance efficiency, and to improve the financial andeconomical balance and environmental sustainability. The natural characteristics ofthe drinking water and sewerage service supply, the existence of different positive andnegative externalities, and welfare concerns rationally justify the service provision bythe public sector or, when the private sector is involved, the necessity for effectivegovernment regulation aimed at avoiding or limiting monopoly rents and ensuringwater quality (Abbott and Cohen, 2009).

In Italy, the estimated annual turnover of the water service industry in 2009 was about6.5 billion euro, for about 5.5 billion cubic meters of water distributed. There is, however,a great variance and fragmentation as to the water management system infrastructurestock, business models adopted, and performance. The Italian water service supplyindustry has been characterized by a complex and, sometimes, inconsistent normativeevolution by means of which the national legislator attempted to identify a more effectivemanagement and organizational framework in the changed economical, social andenvironmental context. Since Law no. 36/1994 (the Galli Law), the Italian watermanagement sector has undergone a number of reforms with the aim of improvingefficiency and service quality, and reducing the local fragmentation of local operators andnetworks. These reforms would heavily impact the structure of the sector, both on thedemand and supply side. Particularly, water services were organized on the basis ofoptimal territorial areas (ATO), key actors in the restructuring process. In the newframework, one single company has to be in charge of the whole water cycle, with theseparation between the planning and regulation role assigned to public authorities fromthe management and investment role assigned to specialized companies. According toLaw, every ATO had its own Authority which had to define norms and rules assuring aneffective and proper water management service, to check the status of existing plants andfacilities, performance and level of service, to define the organizational and managementmodel, define the tariff scheme, planning future infrastructure investment, verify theachievement of technical and service standards, and control financial management.

In April 2006, the Italian Government adopted the Legislative Decree no. 152/2006to reorganize the national environmental legislation. This decree replaced and“refined” Law 36/1994, without introducing, however, any significant change. Law152/99 enabled the integration of some quantity and quality issues related to watermanagement. More recently, Law no. 191/2009 (the Annual Public Finance Law)has formally suppressed the ATOs in order to reduce local government expenses and,

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at the same time, achieve a higher rate of simplification in the public administrationsector either by eliminating or decreasing the number of intermediate institutionalactors. In compliance with it, regional administrations have to transfer the ATO duties,tasks and functions to the province administrations.

Apparently, the water industry reform had an impact on the overall integratedservice provision framework. At the beginning of 2009, the number of water serviceproviders – both municipal departments, under direct municipal management (gestionein economia) and specialized firms – was 3,351, and 114 of them were concessionariesfor the integrated water service, while in 1999, before the reform, this number was7,826 and most of service was delivered by municipal departments with only a smallamount of it organized to be delivered by private companies (Gilardoni and Rome,2009; ISTAT, 2006, 2009). The emerging regulatory framework in Italy remains almostunique in its nature, as it combines together different regulatory mechanisms typical ofseveral countries. Anyway, the presence of public-private equity companies, localmunicipalities that own the infrastructure networks and investment models adoptedthat distribute responsibility between the private and the public actors, make theItalian system very similar to the French one.

But, even though the restructuring of the sector is at an advanced stage ofimplementation, the results are very different, especially when comparing the Northernand Southern regions of Italy, and the role of ATOs as a means to achieve efficiency hasbeen largely debated (ANEA – UTILITATIS, 2008; Anwandter and Rubino, 2006;Benvenuti and Gennari, 2008; Carrozza, 2008; Giolitti, 2010). After nearly 18 years sinceLaw 36/1994, a few ATOs have still to complete the regulatory and organizationalprocess necessary to implement an integrated water supply service, while in manyATOs the drinking water and sewerage and water treatment service is still managedby a large number of small companies operating either on the basis of the in-house rule,or regimes of local exceptions. In general, several kinds of contractual agreements forthe supply of integrated water services co-exist in the ATO, with the involvementof different bodies (i.e. private companies, public companies, etc.) (OECD, 2007).The immediate outcome of this complex and variegated contractual framework is oftena lack of operational efficiency and inequality in tariff and service quality to thedetriment of the users. The long-term outcome is the difficulty of implementing soundand reliable planning of facilities and network investments. That adds to the fact thatin Italy the conditions across different geographical regions are rather uneven. Indeed,there are regions with a chronic need for water, i.e. Apulia, and other regions, i.e.Calabria, where the remarkable quantity of water available is unfortunatelycounteracted by the lack of distribution infrastructure and treatment plants. Inrecent years, statistics show that there has been a noticeable increase in contracting thesupply of integrated water services to private providers (i.e. full private equitycompanies or mixed private-public equity companies established for the purpose) insearch of alternative ways of providing the service more efficiently, and collectingfinancial resources for the rehabilitation and expansion of the infrastructure andfacilities (CONVIRI, 2010; COVIRI, 2008). The Art. 15 of the Legislative Decree no. 135/2009 has acknowledged the recommendations issued by the Court of Justice of theEuropean Commission to the Italian Government relating to the subject of relevanteconomical public services. Specifically, the Decree has ratified the possibility for theprivate sector to have a major role in the management of local public services, andwhen the provision of public services is entrusted to public-private companies, theequity share of the private partner should be 440 percent. The Decree also ratified

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that the in-house grant of service provision would be allowed only as an exception,after the positive advice issued by the Competition and Market Authority. According tothe Decree, all in-house grants of public services should have ceased by December 31,2011. However, the in-house grants still in operation would continue to exist until theirnatural expiration if the local public administrations would reduce their equity share atleast by 40 percent. Vice versa, as to the management of service entrusted to mixedpublic-private equity companies, the Decree also ratified the expiration of the contractsin advance if the selection of the private partner did not occurred according to qualitycriteria and the identification of a clear role of the private partner in the management ofthe service.

Even though the private sector participation in the provision of public service – and,particularly, in the integrated water service – has been a topic of discussion amongscholars and policy makers, it is still unclear if the involvement of a private body hasan effect on the efficiency rate of service supply. This paper aims at giving an answer tothis question. It reports main results of a performance analysis and benchmarkingstudy relative to 38 ATOs in the Italian water service industry. The operationalefficiency may be dependent on a large number of variables that cannot be all includedin the benchmarking model developed to measure it. Rather than investigating to whatextent variables internal to the benchmarking model directly influence efficiency,attention was focussed on external variables. The study has measured the ATOsefficiency by implementing Data Envelopment Analysis (DEA), and the associationbetween the ATO operational efficiency and the typology of water service provider(public vs private) and service concession contract agreement is specificallyinvestigated. The remainder of this paper is organized as follows. Section 2 reviewsthe literature on the influence of the ownership and legal status of the service operatorson the efficiency of water supply provision. Section 3 explains the analyticalframework adopted to measure operational efficiency of the ATOs and assess theweight of the contract type on the efficiency rate. Section 4 reports information relativeto sample and provides details relative to DEA based efficiency model and themethodology adopted to measure the effect of contract type on efficiency. Section 5reports the findings of the study. Finally, Section 6 presents main conclusions anddiscusses limits and further developments.

2. Literature reviewA major issue that has been investigated is that of private vs public ownership and/ormanagement and the consequent impact on efficiency levels, as competition increasespressure for cost savings and efficiency (Abbott and Cohen, 2009; Perard, 2009; Saaland Parker, 2001; Walter et al., 2009). Berg and Marques (2011), after performing an indepth literature survey which examined 190 quantitative analyses and benchmarkingstudies using cost or production functions to evaluate the water management utilitiesperformance, found that the water utilities ownership influence on efficiencies is one ofthe most investigated topic. As Hukka and Vinnari (2007, p. 86) emphasize: “Since thebeginning of the 1990s, the international discussion on the management of water andwastewater undertakings has largely focused on the public-private partnership (PPPs)as a method of improving water services delivery, within the wider frameworkpromoting the expansion of private sector participation in the sector. The mainassumption underlying this approach has been a critique of public sector utilities,which were deemed unable to expand coverage and improve the quality of the serviceswithout the financial and technical inputs of the private sector.” Similarly, Saal and

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Parker (2001, p. 66) point out: “[y] public ownership is usually associated withpolitical and economic goals that may conflict with the efficient use of factor inputs[y] at the same time, however, the extent of performance improvement resulting fromprivatization depends, at least in part, upon shareholders ability to monitormanagement effort in the pursuit of effecting gains [y].”

The participation of the private sector in the water service supply has recently beensubject to increasing criticism because it failed to get expected goals. Several empiricaleconometric and multiple case studies from across the USA, Europe and developingcountries have addressed this topic, but results are many times ambiguous andcontradictory, even showing that privatization does not necessarily provide better costservice delivery (Carvalho and Marques, 2011; Walter et al., 2009). Some case studieseven found that the private sector participation in the water provision led either to thecancellation of some services or an increase of water tariffs for the consumers withdetriment for some population groups (Kirkpatrik and Parker, 2005). Furthermore,the real meaning given to the “privatization” concept often remains itself unclear,sometimes indicating the transfer of asset ownership to a private body, other times theaward of a contract for the provision of water services to a private company, or boththem. Scholars also found that some factors may have a moderating effect on therelationship between ownership and efficiency. For instance, Bhattacharyya et al.(1995) found that the amount of output (i.e. size of operations, and, consequently, scaleeconomies) may have a non secondary weight or even be more important thanownership. In the same way, regulation may have a greater effect on efficiency thanprivatization (Saal and Parker, 2004). When comparing efficiency of public and privateowned utilities, Saal et al. (2007) found that the regulatory regime is as influent onefficiency as the ownership structure. As in other industries, i.e. gas, electricity, andrailways, an appropriate combination of incentives and governance mechanisms isnecessary to achieve efficiency (Olsen et al., 2005).

Perard (2009), in a review of 22 empirical tests and 51 case studies, found that theprivate sector participation per se in water supply does not systematically have asignificant positive effect on efficiency. Bel and Warner (2008) offer an in depth reviewof econometric studies that focussed on the privatization issue in the water servicesupply since the 1970s, without – however – finding an acceptable support for thehypothesis that privatization is leading to reduced costs. Indeed, only five of the 18studies discovered systematic cost savings with privatization. They even concludetheir paper emphasizing that “[y] because there is no systematic optimal choicebetween public and private delivery, [y] managers should approach the issue in apragmatic way” (Bel and Warner, 2008, p. 1343). Given the natural monopolycharacteristic of the water supply, “[y] the benefits from privatization would beexpected to erode over time” (Bel and Warner, 2008, p. 1339).

As to the USA, Canada and the countries of Latin America, a large number ofstudies conducted since the 1970s did not discover any discernible difference betweenpublicly and privately owned firms entrusted of service provision, either in terms ofcosts or efficiency (Bhattacharyya et al., 1994; Byrnes et al., 1986; Feigenbaum andTeeples, 1983). Studies based on data collected from the American Water WorkersAssociation that implemented either parametric or non-parametric techniques wereunable to ascertain whether the private operators perform better than public onesas to efficiency. Mann and Mikesell (1976) found that private operator ownedutilities had higher costs than government utilities. Results from a number of empiricalstudies support the idea that public providers are more efficient (Bruggink, 1982;

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Lambert et al., 1993; Shih et al., 2006). On the contrary, some studies revealedthat private operators are more efficient, having lower costs than public ones(Bhattacharyya et al., 1995; Crain and Zardkoohi, 1978; Morgan, 1977).

In order to investigate the efficiency rate of the Brazilian water and sewerageindustry operators, Sabbioni (2008) compared operators providing services at the local(municipal) level. Findings from his research show that the legal status of the providerwas associated to its operations costs. Particularly, he found that local public providersthat were organized similarly to a corporate firm were more cost efficient than localpublic providers operated like not-for-profit organizations. He also found that privatelyowned firms were high efficient. da Silva e Souza et al. (2007), implementing astochastic cost frontier approach, estimated the relative efficiency of Brazilian publiclyand private owned water utilities but no significant differences emerged between thetwo types of operators. Seroa da Motta and Moreira (2006) found that ownership doesnot have any effect on efficiency gains for local municipal services; vice versa, localprivate operators have moved faster than public ones toward the efficient frontier sincethe time of privatization. Studying the case of the Buenos Aires concessions in thewater services industry, Casarin et al. (2007, p. 245) explain that “the [y] privatizationof water supply services was motivated by a general discontent with the public sectorperformance as revealed by under investment, sluggish system expansion, poorservice quality and long-standing operating deficits. Private sector involvement isaimed at overcoming government difficulties to impose service coverage [y].”Scholars also found that (Casarin et al., 2007, p. 246) “[y] the remarkable increase inprofits were originated almost exclusively by tariff increases, as the contribution oftotal factor productivity improvements and of input process have been negligible.”Similarly, Rais et al. (2002), conducting multiple case study research in Argentina,found that the introduction of the private sector in the water service supply had anegative impact on the industry performance.

Ambiguous conclusions also emerged from empirical research conducted in Europe.For instance, Ashton (2000a) showed that the efficiency level is positively influenced byprivatization, but, on the contrary, Saal and Reid (2004) underline that there are nodifferences in terms of productivity and efficiency between operators belonging to thepublic and private sectors. Shaoul (1997) came to the same conclusion after conductingfinancial analysis of operators in the water industry in England and Wales. Dore et al.(2004) – comparing the experience of UK and France in the privatization of the deliveryof the drinking water service, point out that the evidence from these countries does notsupport the case that the private sector has absolute advantage over the public inachieving higher efficiency. Particularly, the scholars affirm (Dore et al., 2004, p. 49):“[y] although water quality improvements were associated with privatization, there isno demonstrable evidence that privatization resulted in lower prices. In fact, theevidence in both countries indicates higher prices because of privatization. It should benoted that the experience in both countries is similar to the privatization of local hydroutilities in the Province of Ontario, Canada, where costs increased significantly due to asimilar private sector tenet of maximizing shareholder value. It seems that theregulated system in England and France did not work satisfactorily. With naturalmonopolies in water, private production requires adequate regulation. In the twocountries examined, it is not possible to find that the private sector demonstratedabsolute efficiency advantage.” In Spain, Garcia-Sanchez (2006), measuring efficiencyof the municipal water services, did not find any significant difference between publicand private owned firms. Investigating the influence of the operational environment on

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the efficiency of sixty-six water utilities in Portugal, Carvalho and Marques (2011)found that ownership has an ambiguous effect on efficiency. Vinnari and Hukka(2007) studied the impact of privatization of Tallin water in Finland and found thatthe major effect of privatization was a substantial increase of water service tariff, thusreducing social efficiency for users.

As to countries in Asia and Pacific regions, Estache and Rossi (2002) found thatboth public and private water operators have similar efficiency levels. Dumol (2000)also found mixed results in Philippines developing multiple case study analysis. InAfrica, by applying parametric analysis techniques, Estache and Kouassi (2002) foundthat private operators are more efficient than public ones. On the contrary, using bothparametric and non-parametric techniques, and a larger size data set, Kirkpatrick et al.(2006) show that service providers belonging to the public sector achieve higherefficiency levels. A recent econometric analysis of the private sector participation inChina urban supply carried on by Wang et al. (2011) has showed that the introductionof the private sector participation significantly improved the production capacity ofurban water supply and water coverage rate of the water supply industry in thedeveloped eastern cities, but no significant effect in the less developed central andwestern cities. Further, they also found that the participation of the private sector hasno significant effect on fixed asset investment.

As a final conclusion, it is clear that a lot of ambiguity relative to the impact of theprivate actor participation in the water services supply still remains.

3. The analytical framework for measuring efficiency based on DEADifferent methodologies have been used to measure efficiency in the public utilities,and, more specifically, in the water service industry. Several scholars provided moreor less extended review of these methodologies (Abbott and Cohen, 2009; Coelliet al., 2003; Corton and Berg, 2009; Gonzalez and Rubio, 2008). These methodologiesinclude: partial productivity indicators (Essential Services Commission, 2012;Europe Economics, 1998; WSAA, 2003); total factor productivity indexes (Ashton,2000b; Casarin et al., 2007; Kendrick, 1982); parametric techniques, i.e. econometricmethodologies (Antonioli and Filippini, 2001; Ashton, 2000b; Bruggink, 1982; Feigenbaumand Teeples, 1983; Fraquelli and Giandrone, 2003; Garcia and Thomas, 2001; Picazo-Tadeoet al., 2008; Renzetti, 1999; Sauer, 2005; Tynan and Kingdom, 2005) and stochastic frontiertechniques (Aubert and Reynaud, 2005; Bhattacharyya et al., 1995; Estache and Rossi,2002; Saal et al., 2007); non parametric techniques, i.e. DEA (Anwandter and Ozuna, 2002;Byrnes et al., 2010; Coelli and Walding, 2005; Cubbin and Tzanikadis, 1998; Erbetta andCave, 2006; Garcia-Sanchez, 2006; Herrala et al., 2012; Romano and Guerrini, 2011; Singhet al., 2011; Thanassoulis, 2000a, b).

In the last decade, DEA has been extensively used by scholars to measure efficiencyrates in the water industry and address several issues, i.e. the existence and measure ofscale economies and scope economies, industry regulation, design of incentives andprice-cap mechanisms, etc. (see for instance, Byrnes et al., 2010; Cubbin andTzanikadis, 1998; Estache and Rossi, 2002; Garcia-Valinas and Muniz, 2007; Lin andBerg, 2008; Ord�onez de Haro and Martınez, 2008; Raju and Kumar, 2006; RodrıguezDıaz et al., 2004; Shih et al., 2004, 2006; Thanassoulis, 2000a, b; Tupper and Resende,2004). As Abbott et al. (2012, p. 55) emphasize “[y] measurements tools like DEA areuseful in situations where markets are distorted by regulated prices, subsidies and alack of general market contestability. In these cases the usual market indicators ofperformance, like profitability and rates of return, cannot be used to gauge an

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institution’s economic performance accurately. In these situations DEA provides acomparative monitoring that identifies variations and hence provides encouragementand direction for performance improvement.”

Like the stochastic production frontiers, DEA estimates the maximum potential outputfor a given set of inputs. However, DEA provides efficiency relative measurements of aspecific unit by estimating an empirical production function frontier from multiple inputsand outputs relative to a sample of homogeneous units implementing a linearprogramming (LP) technique (Cooper et al., 2006). The production frontier is indeedgenerated solving a sequence of LP problems, one for each unit included in the sample,while the relative efficiency score of a unit is measured by the distance between the actualobservation and the frontier obtained from all the units under examination, adopting theFarrell measure of technical efficiency (TE) as a measure for the unit efficiency score.Efficiency is thus evaluated as the classical engineering ratio of outputs to inputs. Giventhe set of units, the model determines for each unit the optimal set of input weights andoutput weights that maximize its efficiency score. DEA is a flexible technique as it doesneither require any explicit assumption about the underlying relation between inputs andoutputs as required by statistical analysis such as regression nor the a priori knowledgeof weights to be assigned to assess efficiency.

A unit is efficient if TE¼ 1, but if TEo1 a unit is considered technically notefficient. In this case, inputs should be equiproportionally reduced to produce a level ofoutput allowing to achieve the efficient frontier and being consequently efficient. A unitis said to display total TE if it produces on the boundary of the production possibilityset, i.e. it minimizes inputs with given outputs and a given production technology.

The envelopment frontier will differ depending on the scale assumptions thatunderpin the model. Generally, two scale assumptions are made, constant returns toscale (CRS), and variable returns to scale (VRS) that encompasses both increasing anddecreasing returns to scale. The CRS assumption reflects the idea that the outputs willchange by the same proportion as inputs are changed. On the contrary, the VRSassumption reflects the idea that the production technology may exhibit increasing,constant and decreasing returns to scale.

DEA models can be either input or output oriented. In the input-oriented DEA, theLP model is aimed at determining how much the input used by a unit could contract ifused efficiently in order to achieve the same output level. In the output-oriented DEA,the LP model is aimed at determining the unit potential output given its inputs if itwere operated efficiently as the units that are on the efficient frontier.

To illustrate how DEA works, let us take an example of three units, Unit A, Unit B,and Unit C. Each unit produces two outputs O1 and O2 and uses only one input I1. Letus assume that each unit uses the same amount of input I1 and that the measure of suchamount is 10. The measures of the output produced by the three units are as follows:

Unit A :O 1 ¼ 180 and O2 ¼ 35 Unit B : O1 ¼ 90 and O2 ¼ 45 Unit C :O1 ¼ 40 and O2 ¼ 105

DEA attempts to determine whether it is possible to create a virtual unit that performbetter than one or more of the real three units in the example. Figure 1 showsgraphically how DEA measures efficiency.

Under the assumption of convexity, the line segment that connects Unit A to Unit Cis called the efficient frontier. It is easy to see that it defines the maximumcombinations of outputs that can be produced for a given set of inputs. Indeed, thesegment AC lies beyond both the segment AB that can be drawn between Units A and B

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and segment BC drawn between Units B and C. Consequently, a convex combination ofUnits A and C will generate the most output for a given set of inputs. Units A and C areefficient since they are on the efficient frontier, but as Unit B lies under the efficient frontier,it should be considered inefficient and its efficiency (or, inefficiency) can be measured asthe ratio OB/OV, where V is a virtual unit formed through a combination of Units A and C.The efficiency score of B is 70.7 percent, while it is 100 percent for both A and C.

4. Efficiency measurement in the Italian water service industryThe study included the following steps (see Figure 2): the selection of the ATOssample; the collection of data and the measurement of the operational efficiency of theATOs; the investigation of the effect that the water service provision contract has onthe operational efficiency rate.

4.1 The sampleSample includes 38 ATOs that correspond to 42 percent of total ATOs. In all, 17 ATOsare localized in the North of Italy, 10 and 11 ATOs are, respectively, localized in Centraland Southern Italy. On average, the Northern Italy ATOs have a longer infrastructure

development of theATO operationalefficiency model

sample selection anddata collection on

(inputs and outputs)

data collection oncontract types

calculation of theATO efficiency

score

measurement of theeffect of contract type

on ATO efficiency

Figure 2.Steps followed

in the study

18040 90 O1

O2

A

V

B

C

45

105

35

O

Figure 1.How DEA works

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network (7,515 km) that serves a higher number of municipalities. Vice versa, CentralItaly is characterized by a greater fragmentation of the distribution infrastructure, asevery ATO delivers its services to 47 municipalities. Northern ATOs also differ fromthe rest of sample because water service to final users is provided by a greater numberof concessionaires (Table I).

4.2 The measurement of the ATO operational efficiencyThe ATO efficiency was measured by implementing an input-oriented DEA modelconstructing the production function by searching for the maximum possibleproportional reduction in input usage, while output levels are held fixed. This choicewas largely justified as infrastructure investments in the water service industry needcontinuous maintenance to keep service quality at given standards and demand remainsalmost steady, while a major objective of managers and administrators is to reduce costs.

As the sample includes ATOs with different size, TE was calculated adopting theconceptualization suggested by Banker et al. (1984) to compare small and large ATOs,thus assuming VRS (BCC model). An input-oriented BCC LP model is defined as:

Min Y þ eXm

i¼1

S�i þXs

r¼1

Sþr

" #

s:t:Xn

j¼1

ljyrj � Sþr ¼ yrj ; r ¼ 1;:::; s

Xn

j¼1

ljxij þ S�i ¼ Yxij; i ¼ 1;:::;m

Xn

j¼1

lj ¼ 1

ljX0 j ¼ 1;:::; n

Sþr ; S�i X0 r ¼ 1;:::; s; i ¼ 1;:::;m

whereP

l j ¼ 1 is the convexity constraint added to the CCR model (Charnes et al.,1978) that assumes CRS, and Sr

þ and S i- are slack variables.

The total TECRS was decomposed into pure TEVRS and scale efficiency (SE). SE wasmeasured adopting the method suggested by Coelli et al. (1998). This method assumesthat SE can be measured by dividing the CRS total TE by VRS pure TE:

SE ¼ TECRS

TEVRS

Network length (km) Population served Municipalities (no.)ATO (no.) Mean SD Mean SD Mean SD

North 17 7,515.2 4,465.8 640,176 523,254 102 76Center 10 6,813.2 3,880.5 806,583 1,040,037 47 20South 11 6,613.9 6,858.2 693,690 1,188,276 73 73

Table I.Sample characteristics bygeographical area

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. if SE¼ 1, then an ATO is scale efficient, both under CRS and VRS; and

. if SEo1, then an ATO is not scale efficient, as the combination of inputs andoutputs does not allow the ATO to be on the efficient frontier.

As the standard development of the DEA model produces an efficiency measure whichis between 0 and 1, and does not generate a ranking of units, Andersen and Petersen(1993) – modifying the original CCR model – introduced the concept of super-efficiencythat makes the efficiency analysis more discerning and provides a full not censoredranking of units efficiency (AP DEA model). An efficiency score of an ATO k 4100percent may thus be measured (Lovell and Rouse, 2003; Zhu, 1996).

4.3 The operational efficiency model variablesThe operational efficiency model includes five inputs and three outputs. These arebased on physical (i.e. network length, water amount), economical (i.e. operationalcosts), and management data (i.e. number of employees) (Ball et al., 1986). Consistentlywith literature, the efficiency model embodied a measure of quality service. Indeed, asPicazo-Tadeo et al. (2008, pp. 30-31) suggest that: “[y] omitting quality might offer abiased picture of performance. Conventional quantity-based measurement of efficiencymight lead to perverse outcomes penalizing utilities that produce higher qualityservices.” That is necessary because “[y] maintaining high levels of quality requiresthe use of resources endowed with an opportunity cost, i.e. resources that could otherwisebe devoted to augment the quantity of the service produced. Accordingly, firms devotingsmaller amounts of resources towards quality will tend to display, on equal terms, greaterscores of efficiency.” Some studies on water service efficiency have explicitly accounted forservice quality (see for instance, Saal and Parker, 2001; Lin, 2005). This paper usesunaccounted water as a proxy of the quality services as suggested in several papers (seefor instance, Antonioli and Filippini, 2001; Garcia and Thomas, 2001; Lin, 2005). Waternetwork losses are considered a non-desirable output produced jointly with the service ofwater delivered, i.e. a cost that the network has to bear. Consequently, this reduction ofservice quality is included in the model as an input.

Variables with various units have been included in the efficiency model as in DEAimplementation input and output variables do not need to be commensurate with eachother. Variables were selected having as a reference the DEA models presented in thereviewed literature. Anyway, the definitive selection of input and output variables toperform DEA analysis, as common in studies like this, was also influenced by samplesize and the availability of accurate data in the ATO web sites and technical reports(ANEA – UTILITATIS, 2008, 2009, 2010; COVIRI, 2008; CONVIRI, 2010). Indeed, theDEA outcome may be influenced by the excessively high number of variables comparedto the number of units. Variables used as inputs and outputs are as follows[1]:

Inputs

. Input 1: number of employees working in the ATO water service providers(no. of people).

. Input 2: loss of water (“amount of water introduced in the network”�“amount ofwater effectively distributed to consumer premises”) (cubic m).

. Input 3: length of aqueduct network (km).

. Input 4: length of sewerage network (km).

. Input 5: operative costs (h).

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Outputs

. Output 1: invoiced amount of water (cubic m).

. Output 2: number of municipalities served (no. of units).

. Output 3: population of municipalities served (no. of people).

4.4 Additional variables. According to art. 113, c. 5, of Legislative Decree no. 267/2000, theright of provision of water services may be assigned to different types of concessionairesadopting a number of contractual schemes:

(1) To a private equity company after a public tendering procedure.

(2) To a joint private-public equity company in which the private partner has beenselected after a public procedure.

(3) To a public equity company adopting an in-house procedure. The provision ofintegrated water services may be assigned to companies whose equity isowned by municipalities and local administrations that are located inside thegeographical area of the ATO.

However, the transitory regime created by the evolving legislative framework has leftstill in use the old contractual schemes that were allowed by the Law no. 36/1994, i.e.:

(1) Pre-existing concessions (concessioni preesistenti, according to the ex art. 10 c. 3,Law no. 36/94). Companies and consortia that had been concessionaires of theservice when Law 36/1994 came into force were allowed to provide service tillthe end of the contract. safeguarded management agreements (gestionisalvaguardate, according to the ex art. 9 c. 4, Law 36/94). With the aim tosafeguard the business models and the management capabilities of the oldbodies, the municipalities and province administrations were allowed toprovide water services after the identification of a main legal subject capableto coordinate service provision by integrating a number of different actorsproviding several functions of the integrated service.

(2) Special public companies, agencies and consortia (aziende speciali, enti andconsorzi pubblici, according to the ex art. 10 c. 1, Law 36/94). These bodies wereallowed to provide water services until the achievement of a full and effectiveorganization of the integrated water service according to art. 9 of Law 36/1994.

(3) Water services are provided by the municipalities themselves (gestioni ineconomia) when the establishment of a company or a managing body was notviable either due to the small size or to the characteristics of the service.

The following additional variables have been henceforth included in the study in orderto investigate the effect of the water service provision contract type on the ATOoperational efficiency. In particular, for every ATO the relative number of contractualagreements for the provision of the integrated water service belonging to apredetermined typology was measured. Specifically, two classification have beenadopted to identify contract types, the first one (letters a, b and c of following scheme)being more detailed than the latter (letters d and e):

(a) in-house: this variable measures the number of contract agreements associated tocompanies fully owned by the regional or municipal administrations that provide“in-house” the integrated water management service in the ATO. There is nocompetition at all in this case;

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(b) mixed&private: this variable accounts for the number of contracts assigned eitherto public-private equity companies in which the private partner was selected bytendering (public-private partnership) or contracts assigned to private companiesselected after a public tendering procedure (including companies quoted on theItalian Stock Exchange). In all cases, there is competition either for market orequity shares and the concession of rights to a private or mixed equity companyfor local water service provision is done through a competitive bid;

(c) other: this variable measures the number of contracts relative to water serviceprovision mostly performed by municipalities themselves, consortia and theso-called special companies;

(d) sii_mgmt: this variable accounts for all contractual agreements involving serviceoperators in the ATO that are consistent with the new legislative framework forpublic services provision (Legislative Decree no. 267/2000, and Law no. 290/03); and

(e) no_sii_mgmt: this variable measures all old-type contracts associated to waterservice supply in the ATO that were allowed by the Law no. 36/1994.

4.5 The measurement of the effect due to contract type on the ATO operational efficiencyA stepwise regression procedure was performed to investigate the effect of contracttype on the operational efficiency score of the ATO (Darlington, 1990; Stevens, 1996).This regression method allows to find the variables that account for the highestproportion of the observed variance. As many scholars point out, the stepwiseregression method is often the best compromise to find an equation capable to predictthe maximum variance for a specific data set (Darlington, 1968; Hocking, 1976).Particularly, in the study the forward stepwise method was implemented, combiningthe procedures used in the forward entry and backward removal stepwise regressionmethods[2]. Two regression models have been implemented, both of them adopting thesuper-efficiency score as dependent variable. Regression model 1 included the “inhouse,” “mixed&private” and “other” contract types as independent variables, whilemodel 2 included “sii_mgmt” and “no_sii_mgmt” as independent variables. Thenumber of municipalities served by the ATO was also included in both regressionequations as a control variable[3]. Furthermore, visual data analysis has beenperformed to corroborate the stepwise regression analysis findings for the technicalinefficient ATOs.

5. ResultsTable II reports summary statistics of input and output variables of the DEA models.Both standard deviation and maximum-minimum range values clearly point out thatthe ATOs greatly differ as to input and output measures. In some cases, the standarddeviation value is rather larger than mean and, when lower than mean it is very close toits value. Particularly, ATOs differ as to their size and the size of their operations(i.e. the amount of water delivered to consumers, the size of population served, theoverall length of water and sewerage network, etc.). Indeed, sample includes ATOs thatdeliver integrated water services to 3 (small) municipalities only and ATOs thatprovide services to more than 300 municipalities. This large sample variance justifiesthe decision to implement a VRS DEA model to compare the ATOs and measure theirefficiency rate.

ATO efficiency was calculated by running three DEA models, e.g. VRS, CCR andAP-super-efficiency. In particular, CCR DEA was performed to rate SE, while AP DEAmade it possible to have a full ranking of ATOs. Figure 3 illustrates in a graphic shape

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the outcome of the implementation of DEA analysis as to the VRS and CCR DEAmodels[4]. Data show that ATOs greatly differ as to their efficiency measurements. Theaverage index of TE is situated at 87.61 percent, with a standard deviation of 17.46percent, while pure TE is at 92.62 percent (standard deviation of 14.93 percent), and SE

Variable Mean SD Maximum Minimum

Number of employeesin the ATO waterservice providers 379 336.06 1,627 53Loss of water 38,211,719 53,455,164 284,506,690 1,921,071Length of aqueduct network 4,785.81 3,318.39 15.891.00 445.00Length of sewerage network 2,317.14 1,865.73 9,534.00 236.00Operative costs 54,379,699.67 67,414,788.17 297,666,667.00 5,774,884.29Amount of water invoiced 56,042,966.39 82,991,178.93 446,900,000.00 3,454,803.00Number of municipalities served 80 70 315 3Population of municipalities served 711,714 910,973 4,069,869 52,172

Table II.Summary statistics ofinputs and outputs

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

ATO1ATO2

ATO3

ATO4

ATO5

ATO6

ATO7

ATO8

ATO9

ATO10

ATO11

ATO12

ATO13

ATO14

ATO15

ATO16

ATO17

ATO18ATO19

ATO20ATO21

ATO22

ATO23

ATO24

ATO25

ATO26

ATO27

ATO28

ATO29

ATO30

ATO31

ATO32

ATO33

ATO34

ATO35

ATO36

ATO37ATO38

CRS-IN VRS-IN

Figure 3.Graphs of CCR and VRSDEA efficiency scores

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is at 93.9 percent (standard deviation of 11.4 percent) (see Table A1 for details). Afterdecomposing TE into pure technical and SE, 23 ATOs result efficient (the 60.52 percentof sample) and 15 (the 39.47 percent of sample) are technical inefficient. Most of thistechnical inefficiency is in the form of scale inefficiency as SE mean is higher than CCRefficiency mean. These efficiency scores measure, however, only relative efficienciesrather than absolute efficiencies, consistently with the DEA technique features. Thus,an increase of the efficiency rate of ATO “n” might be consequent either to an increaseof the efficiency of the same ATO, or an efficiency reduction of the remaining ATOs inthe sample under examination, or, finally, a combination of both.

Tables III and IV show the output of the stepwise regression analysis performedto measure the effect of the contractual agreement type on the operationalefficiency score, adopting the operational super-efficiency score as dependent variable.In particular, Table III reports the output of the regression performed with the“in house,” “mixed&private” and “other” contract types as independent variables,and Table IV reports the output of the regression that included “sii_mgmt” and“no_sii_mgmt” as independent variables.

Data reported in both tables clearly show that there is an effect of the contractualtype on the level of operational efficiency achieved by the ATO. Particularly, inTables III and IV both the “mixed&private” and “sii-mgmt” contract types havea positive effect on the operational efficiency score, even though less influential thanthe intercept effect. Finally, Figure 3 illustrate the plot of the VRS efficiency scoresvs the normalized amount of concession contracts entrusted either to public-private

Variable StepsF to

removep to

removeF toenter

p toenter

Effectstatus Comment Coeff. Prob.

in-house Step 1 1.115 0.299 Outother 0.008 0.929 Outmixed&private 5.873 0.021 Enteredln municipality 0.111 0.741 Outmixed&private Step 2 5.873 0.021 In 0.175 0.072other 0.301 0.587 Out Pooledin-house 1.298 0.263 Out Pooledln municipality 0.007 0.932 Out Pooledintercept 1.040 0.083

Notes: r2, 0.389; F, 5.873; prob., 0.021

Table III.Stepwise regressionanalysis – model 1

Variable StepsF to

removep to

removeF toenter

p toenter

Effectstatus Comment Coeff. Prob.

ln municipality Step 1 0.111 0.741 Outsii_mgmt 6.665 0.014 Enteredno_ sii_mgmt 0.201 0.657 Outsii_mgmt Step 2 6.665 0.014 In 0.149 0.058ln municipality 0.432 0.516 Out Pooledno_ sii_mgmt 0.216 0.646 Out Pooledintercept 0.932 0.101

Notes: r2, 0.409; F, 6.665; prob., 0.014

Table IV.Stepwise regressionanalysis – model 2

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equity companies in which the private partner was selected by tendering or to fullprivate companies selected by a public tendering procedure (mixed&private); onlytechnically inefficient ATOs (No. 15) are considered. Operational efficiency increaseswhen the amount of this type of contracts increases (Figure 4).

6. ConclusionThe results that emerged from the study are consistent with findings reported inprevious studies (Carrozza, 2008; Giolitti, 2010) and technical reports (i.e. ANEA, 2010;CONVIRI, 2010). Indeed, this study has showed that there are some inefficiencies in thewater service supply industry in Italy. The estimated average pure technical and SEof ATOs are 92.62 and 93.91 percent, respectively, while the average TE score is87.61 percent and the lowest is slightly higher than 13 percent. In all, 23 ATOs in thesample are 100 percent efficient and the remaining 15 ATOs are inefficient. Thisinefficiency is not only due to the scarcely efficient use of inputs (i.e. the amount ofemployees, the amount of operative costs, etc.) but also to an unbalanced size of theATOs. The sample indeed contains a number of ATOs which are inefficient due to theirsize. Findings support the idea that there might be an optimal size of ATOs associatedto higher efficiency scores, and large scale operations and ATO sizes are associated tolower efficiency rates. Anyway, operational inefficiencies might not be determined bysize, only. In fact, findings also show that the operational efficiency might be associatedto the service provision contract types and the providers nature itself. In particular,data reveal that the ATOs in which the integrated water service supply contracts areconsistent with the new modern legislative framework or the service is delivered bya full private owned or by mixed public-private equity companies achieve a higheroperational efficiency rate. The visual analysis relative to 15 technically inefficientATOs also supports the widespread belief that public-private partnership mightpositively influence the efficiency of public service supply.

Several studies point out the underdevelopment and the criticalities of the Italianintegrated water management infrastructure (ISTAT, 2006, 2009; CONVIRI, 2010). Themean age of the adduction and distribution pipes is, respectively, about 40 and 38 years(CONVIRI, 2010; ANEA, 2010). According to some estimates, the water service supplyindustry in Italy will need about 65 billion euro of investment in the next thirty years,most of which needed to keep the operating infrastructure in efficiency. Furthermore,the obsolescence and the scarce infrastructure network recovery works imply that thepublic sector has to allocate in budget a great amount of financial resources to deal

20

40

60

80

100

0.0 0.2 0.4 0.6 0.8 1.0

effic

ienc

y sc

ore

(%)

mixed&private

Figure 4.Plot of the efficiency scorevs amount of “mixed &private” contracts (onlytechnically inefficientATOs were considered)

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with unplanned maintenance of the water service supply assets (ANEA – UTILITATIS,2008). It is clear that, in this context in which the necessary investment is greater than theavailable public resources, and the regulatory framework is extremely articulated and stillevolving, it is important to stimulate and support the entrance into the water servicesupply industry of private actors, adopting new participative models more oriented tocompetition and market. The improvement of the efficiency and quality of serviceprovision, investment in technological innovation, the reduction of operational costs, andthe availability of resources from the financial markets may be perfectly consistent withthe need to preserve the nature of water as a public good. The entrance into the market ofprivate actors might be the most effective (and, probably, the only) way to increaseoperations efficiency and the amount of financial resources available for investment.The survey presented in a recent Blue Book on the water service in Italy (ANEA, 2010)has indeed showed that the amount of investment is lower in those cases in whichthe water services are provided through in-house management as a consequence ofthe difficult search for financial resources. Vice versa, the Blue Book data show thatinvestment is greater in the case of public-private companies, which – however – adopta higher tariff regime. Three years after the first financial planning stage, for all“in-house” service provision contracts it was necessary to modify the estimates relativeto investment, reducing the average facilities and plants depreciation rate andequity yield, by respectively, 63 and 54 percent in 2009. In both private equity and mixedprivate-public equity companies, the need for making corrections to financial plans wasless demanding, reducing depreciation rate and equity yield by 13 and 20 percent only.That might be due to a more sound and effective planning activity performed by privateand public-private companies.

But, according to recent data relative to all Italian ATOs, in 2009 there were onlyseven private equity companies entrusted of the provision of the integrated waterservice out of 114 providers in operation. In Italy, the integrated water service remainsstill scarcely appealing to private providers for several reasons, i.e. the legal andregulatory uncertainty, the steadiness of the tariff regime, the still unsolved conflict ofinterest between the in-house providers and the ATO Authorities, and the unclear riskallocation in which the private providers have not to support demand uncertaintyrisks. There is no doubt that the effective implementation of the water service reformand the shift toward higher rate of service efficiency implies also a cultural revolution,as the service providers are requested to pay greater attention to the environmentissues as a whole, and, the citizens are themselves requested to consider water nolonger a “no- price” and limitless resource, but a resource that should be used bothefficiently and effectively.

The Italian water service provision industry is still undergoing a deep evolution andfar from achieving a stable arrangement, as a consequence of the recent PublicFinance Law no. 191/2009 as well. The abolition of the ATOs and the National WaterService Regulatory Authority with the transfer of its duties and responsibilities to theRegulatory Authority for Electricity and Gas (Aeeg) by the Art. 21 of the Decree Lawno. 201/2011 have introduced further uncertainty and ambiguity in the industry. Hence,even though this study provides useful data about the efficiency of the industry and theimpact of contract types, they cannot be considered as definitive, but rather a stimulusfor policy makers to develop and implement a sound benchmarking framework in thefuture. This benchmarking framework should necessarily include a number of relevantcontextual variables, such as the nature of the service operators and the legalarrangement of the service provision contract, while the performance measurement

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should generate more than one ranking to take into account major uncontrollable orunavoidable differences across utilities under evaluation.

7. Limitations of this paper are as followsA major limitation of this paper is due to the output and input set of variables and samplesize (Zhu, 1996). The output used to evaluate efficiency in the study does not include anyvariables related to water quality attributes. Here the assumption was done that theincremental level of water quality beyond the minimum acceptable threshold (accordingto standard defined by law) is not important to increase the efficiency score. Furthermore,the lack of consistent data available in public databanks and technical literature for allATOs made it impossible to include a greater number of inputs and outputs, and – at thesame time – of a larger number of ATOs, making this efficiency study not fullyrepresentative of the Italian integrated water service industry. Empirical experience andliterature on productivity and efficiency measurement report that the result of efficiencymeasurements crucially depends on the availability of adequate, accurate and timely data,and that the efficiency scores might be sensitive to the choice of model, input and outputvariables, and the size of sample. Indeed, the deterministic approach in DEA assumesthat there are neither atypical observations, nor measurement errors in the sample.Consequently, the outcome of DEA efficiency analysis might be seriously influenced bya number of factors, primarily the presence of outliers and the quality of data since thetechnique assumes that there are no errors. As Rossi and Ruzzier (2000. p. 84) point out,the efficiency measures obtained with DEA can be very sensitive to the number ofvariables included in the model “[y] as the ratio number of variables/sample size grows,the ability of DEA to discriminate among firms is sharply reduced, because it becomesmore likely that a certain firm will find some set of weights to apply to its outputs andinputs which will make it appear as efficient. [y] That is to say a lot of firms might belabeled as 100% efficient not because they dominate other firms, but just because thereare no other firms or combinations of firms against which they can be compared whenthere are so many dimensions.” In the study, special attention was put on the modelspecification in order to achieve an acceptable robustness as suggested in literature (see,for instance, Pedraja et al., 1999). As it was underlined in Section 4.3, due to sample sizeand data availability, DEA model included only eight input and output variables topreserve stability of efficiency results. The data used have been carefully checked foreliminating possible measurement errors. In the super-efficiency analysis, three ATOsemerged as potential outliers. DEA was run dropping these ATOs, but the efficiencyscores of the undropped ATOs remained almost stable and, for this reason, all ATOshave been considered in the regression analysis. As a further proof of robustnessof efficiency results, the sample size was randomly reduced by dropping four ATOs(equivalent to 10 percent of sample size) repeatedly, performing DEA on a 34 unitssample, without observing significant changes. The conventional DEA methodshave been criticized as they do not incorporate any random effect and are unableto discriminate between technical inefficiency and stochastic shocks that affect thetransformation of inputs into outputs (Schmidt, 1985). However, Banker (1993) hasprovided a formal statistical basis of the method by identifying conditions under whichDEA estimators are statistically consistent and likelihood maximizing. The statisticalfoundation of DEA is now well accepted and new extensions of DEA have been proposedto improve its robustness to data errors and outliers, many of them based onbootstrapping (Banker et al., 1991; Banker and Natarajan, 2008; Gstach, 1998; Simar andWilson, 2000; Simar, 2007; Zhu, 1996). However, as Kuosmanen and Johnson (2010)

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observe, these approaches remain still non-statistical and do not allow for a genuineprobabilistic treatment of stochastic noise in data. In addition, Zervopoulos (2012)emphasizes that the DEA bootstrap method even though provides the efficiency scoreswith stochastic properties, is inappropriate when the sample size is small, particularly ifcompared to the number of input and output variables. In this respect, Chernick (2008)suggests that a minimum sample of 50 observations is necessary for estimating reliablescores. The analysis of results has to take into account this shortcoming, as the pattern ofefficiency of the investigated sample and the efficiency score of the individual ATOsmight change when the DEA model is altered and, more important, a large number of newunits are added to the old sample.

As a final remark, the study adopted a conventional static perspective to model theproduction function of the Italian integrated water management industry, by collectinginput and output data relative to a fixed period of time. Consequently, the efficiencymeasures obtained from the implementation of DEA provide only a snapshot of theproduction function of the ATOs belonging to sample. Future research could benefitfrom the adoption of a dynamic approach to measure changes in efficiency over timeand to explore to what extent assigning concessions either to private or public-privatecompanies may affect operational efficiency of ATOs or different research units.

Even with these limitations in mind, this paper provides some interesting findings thatare consistent with the literature and suggest promising streams for further research.Thus, the proposed framework should be considered open for further development.A major contribution would come from the investigation of the association betweenthe efficiency score measured for the specific contractual relationship “service provider –infrastructure owner” and the contract type, assuming this relationship as the researchunit rather than the ATO. Furthermore, this study has considered the efficiency from thesupply perspective only. Adding the user perspective in the analysis of the water serviceprovision would contribute to understand more in depth the critical determinants toproductivity. As Perard (2009) points out, most theories on private sector participation inthe water supply are based on the sole supposed difference in terms of efficiency betweenthe private and the public sector. But, the choice between public and private waterprovision cannot be only a matter of efficiency.

Notes

1. In brackets measuring units have been indicated.

2. In the forward stepwise method, at step 1 the procedures for forward entry are performed. Atany subsequent step where 2 or more effects have been selected for entry into the model,forward entry is performed if possible, and backward removal is performed if possible, untilneither procedure can be performed and stepping is terminated. Stepping is also terminatedif the maximum number of steps is reached.

3. Due to the great variance of this variable, the logarithm of the variable was used in theregression analysis.

4. Table A1 in the Appendix reports both efficiency scores for all three DEA modelsimplemented and scale-efficiency indications for the ATOs.

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Further reading

Estache, A. and Rossi, M.A. (2002), “How different is the efficiency of public and private watercompanies in Asia”, The World Bank Economic Review, Vol. 16 No. 1, pp. 139-148.

Saal, D. and Parker, D. (2001), “Productivity and price performance in the privatized water andsewerage companies of England and Wales”, Journal of Regulatory Economics, Vol. 20No. 1, pp. 61-90.

Saal, D., Parker, D. and Weyman-Jones, T. (2007), “Determining the contribution of technical,efficiency and scale change to productivity growth in the privatized English andWelsh water and sewerage industry: 1985-2000”, Journal of Productivity Analysis, Vol. 28,pp. 127-139.

(The Appendix follows overleaf.)

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Appendix

Efficiency scoreDMU Code CRS (%) VRS (%) AP (super-efficiency) (%) SE

ATO1 PIE-2 100.00 100.00 186.80 1.000ATO2 PIE-3 100.00 100.00 441 1.000ATO3 PIE-5 100.00 100.00 190.63 1.000ATO4 PIE-6 72.01 76.04 76.04 0.947ATO5 LOM-BG 100.00 100.00 111.67 1.000ATO6 LOM-BS 82.72 87.42 87.42 0.946ATO7 LOM-MN 100.00 100.00 198.47 1.000ATO8 VEN-AV 100.00 100.00 144.68 1.000ATO9 VEN-B 82.36 100.00 107.11 0.824ATO10 VEN-V 88.14 99.85 99.85 0.883ATO11 VEN-VC 100.00 100.00 243.51 1.000ATO12 LIG-GE 100.00 100.00 108.03 1.000ATO13 LIG-SP 91.72 100.00 100.87 0.917ATO14 EMR-1 94.86 99.93 99.93 0.949ATO15 EMR-4 92.27 95.76 95.76 0.964ATO16 EMR-5 100.00 100.00 110.82 1.000ATO17 EMR-7 100.00 100.00 118.74 1.000ATO18 TUS-1 TN 62.71 62.82 62.82 0.998ATO19 TUS-2 BV 90.29 93.15 93.15 0.969ATO20 TUS-3 MV 82.66 89.91 89.91 0.919ATO21 TUS-6 O 72.57 75.48 75.48 0.961ATO22 UMB-1 75.80 77.33 77.33 0.980ATO23 UMB-2 98.91 100.00 105.37 0.989ATO24 UMB-3 94.23 100.00 124.45 0.942ATO25 LAZ-1 LN 100.00 100.00 123.55 1.000ATO26 LAZ-2 RM 100.00 100.00 441 1.000ATO27 LAZ-4 LT 100.00 100.00 114.86 1.000ATO28 ABR-3 AS 63.38 100.00 129.25 0.634ATO29 ABR-4 PE 87.49 89.89 89.89 0.973ATO30 ABR-5 TE 88.33 90.12 90.12 0.980ATO31 ABR-6 CH 100.00 100.00 102.16 1.000ATO32 CAM-3 SV 100.00 100.00 158.35 1.000ATO33 CAM-4 SE 13.15 24.16 24.16 0.544ATO34 PUG-UN 80.49 100.00 441 0.805ATO35 BAS-UN 71.13 72.62 72.62 0.979ATO36 CAL-3 59.72 100.00 174.04 0.597ATO37 SIC-5 100.00 100.00 151.82 1.000ATO38 SIC-6 84.14 85.25 85.25 0.987Mean 87.61 92.62 0.939Minimum 13.15 24.16 0.544No. of efficient ATOs 16 23No. of inefficient ATOs 22 15

Table AI.DEA efficiency scores

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About the author

Corrado lo Storto is an Associate Professor in Economics and Engineering Management with theSchool of Engineering of the University of Naples Federico II, Italy where he teaches Economicsand Business Organization. He obtained his Laurea in Aeronautical Engineering, MBA, and PhDin Science of Industrial Innovation from the University of Padua. His research and professionalinterests include technology and innovation management, knowledge management, complexproject analysis and evaluation, and on these topics he has published several papers in refereedinternational journals and conference proceedings. He is a Member of AiIG, IEEE, ASCE and isthe Chair of the Italian Chapter of the IEEE-Technology Management Council. Associate ProfessorCorrado lo Storto can be contacted at: corrado.lostorto@unina.it

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