Waste Management 30 (2010) 1130–1137

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Waste Management

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Regulatory structures and operational environment in the Portuguese waste sector

Pedro Simões a, Kristof De Witte b,c, Rui Cunha Marques a,*

a Center of Urban and Regional Systems (CESUR), Instituto Superior Técnico, Technical University of Lisbon, Avenida Rovisco Pais, 1049-001 Lisbon, Portugalb Center for Economic Studies, Faculty of Economics and Business, University of Leuven (KUL), HOG 02.161, Naamsestraat 69, 3000 Leuven, Belgiumc University of Maastricht, Faculty of Economics and Business Administration, Top Institute for Evidence Based Education, 6200 MD, Maastricht, The Netherlands

a r t i c l e i n f o

Article history:Accepted 16 December 2009Available online 15 January 2010

0956-053X/$ - see front matter � 2009 Elsevier Ltd.doi:10.1016/j.wasman.2009.12.015

* Corresponding author. Tel.: +351 218418319; faxE-mail address: rcmar@civil.ist.utl.pt (R.C. Marque

a b s t r a c t

This research computes the influence of the operational environment on the efficiency of the Portugueseurban solid waste services. A sample of 29 solid waste utilities encompassing the whole continentalcountry was used for this purpose. Particularly, we apply the non-parametric double bootstrap modelto estimate the effect of various explanatory factors on robust data envelopment analysis estimates. Ingeneral, we find a significant influence of the environmental context on the solid waste utilities’ perfor-mance. The environmental context is characterized by gross domestic product per capita, distance totreatment facilities, population density, regulation, type of management, composting and incinerationservices.

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1. Introduction

Municipal solid waste (MSW) services can be considered as animportant indicator of the development of a country. All aroundthe world, the MSW services have experienced dramatic changesover the last 20 years and have been subject to remarkableimprovement over the last decades (Kinnaman and Fullerton,1999). The increased waste production and the inadequate treat-ment facilities (in particular the uncontrolled landfills) pressuredPortugal to reform the waste sector in the 1990s (Pássaro, 2003).The reform resulted in a pioneering role of Portugal as it createdone of the few MSW economic regulators in the world: the Insti-tute for the Regulation of Water and Waste (IRAR).

MSW services are progressively more concerned with their eco-nomic efficiency and cost effectiveness. This can be attributed tothe external pressures from the EU (Lilja, 2009) and to increasedpublic awareness. Budget restrictions and the growing importanceof the sector in the economy are also pointed out as justificationsfor this concern (Delgado, 2005).

The cost efficiency has triggered a growing interest in privatemanagement. Unfortunately, this aspect has also been accompa-nied by new and complex regulatory problems (Massarutto,2006). The management and organizational system, the technolog-ical issues, the need for know-how (e.g., incineration and process-ing of hazardous waste) and the recovery of materials are someaspects which are related to private sector participation (Bucletand Godard, 2000; Bertossi et al., 1996).

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: +351 218409884.s).

The MSW services are usually regarded as monopolies and so,among other reasons, are characterized by reduced incentivestowards productive efficiency and innovation (Perotto et al.,2008). The evaluation of performance and, in particular, the useof benchmarking, can play an important role to invert this ten-dency. In this scope, our study intends to apply benchmarking tothe Portuguese MSW services taking into account their operationalenvironment. The efficiency studies concerning MSW services aremostly focused on performance evaluation, on the comparison be-tween public and private management and on the determination ofthe optimal market structure, analyzing the effects of the reformsrelative to the sector’s unbundling or horizontal integration.

As there is an upward trend towards private management, thedichotomy of public versus private management has become a rel-evant issue (Bel and Warner, 2008). Many studies have been car-ried out, often associated with divergent conclusions dependingon the sample and on the technique applied (for a survey see Haaset al., 2003). In spite of the opposition of some authors there is aconsensus that the incentives provided to the operators are moreimportant than the ownership (e.g., Bel and Costas, 2006). Otherauthors defend that the existence of regulation can be an adequatesubstitute for competition in the market (Marques and Simões,2008). However, even in this situation, regulation corresponds tomarket building. In fact, the presence of competition in the sector,unlike the assets or management ownership, becomes the mostconditioning factor. Indeed, the economic efficiency and, therefore,the waste sector with the presence of different firms reduce theprofit margin and monopoly power (Vickers and Yarrow, 1988).

The urban waste collection service is more and more deliveredby private operators. The strong presence of the private sector,mainly in Spain, attracted a significant attention of scholars. In

Table 1Market structure of the Portuguese solid waste services.

Arrangement Number Population

Retail Directly by municipalities 220 7,565,058(82.7%)

Semi-autonomous utilities 5 490,674 (5.0%)Municipal companies 13 1,199,321

(12.2%)Intermunicipal utilities 6 596,371 (6.1%)

Total 244 9,851,424

Wholesale Multimunicipal concessionaires 15 5,774,347(58.6%)

Municipal concessionaries 2 257,607 (2.6%)Intermunicipal associations andservices

5 1,799,180(18.3)

Intermunicipal companies 7 2,020,290(10.5%)

Total 29 9,851,424

P. Simões et al. / Waste Management 30 (2010) 1130–1137 1131

Portugal, the urban waste management is starting to interest thecommunity since some recent papers have been produced (e.g.,Gaiola, 2002; Marques et al., 2006; Marques and Simões, 2009).The literature comprises studies in 13 countries, particularly theUSA (e.g., Callan and Thomas, 2001), Canada (e.g., McDavid,1985), Switzerland (e.g., Burgat and Jeanrenaud, 1990), Spain(e.g., Benito et al., 2005), UK (e.g., Szymansky and Wilkins, 1993),Ireland (e.g., Reeves and Barrow, 2000), Denmark (e.g., Christoffersenand Paldam, 2003), the Netherlands (e.g., Dijkgraaf and Gradus,2003), Sweden (e.g., Ohlsson 2003), Australia (e.g., Whorthingtonand Dollery, 2001), Portugal, Belgium (e.g., Distexhe, 1993) andFinland (e.g., Sarkis, 2000).

Techniques to estimate efficiency are often classified into para-metric and non-parametric methods. Sometimes they are furtherdivided into frontier and non-frontier models depending onwhether they compute the best practices or the average adjust-ment and into stochastic or deterministic if they consider the ran-dom error or not (Daraio and Simar, 2007). The urban wasteefficiency studies collected from the literature amount to 55 pa-pers, from which 18 use non-parametric methods (e.g., Cubbinet al., 1987; Jenkins and Anderson, 2003) and 37 draw on paramet-ric methods (e.g., Kemper and Quigley, 1976; Domberger et al.,1986). Studies that employ partial measures of productivity arenot included (as they are often too simplistic). In the class of thenon-parametric and parametric techniques, respectively, dataenvelopment analysis (DEA) and stochastic frontier analysis (SFA)are popular tools. As far as the DEA technique and the stochasticfrontiers (and deterministic) are concerned, besides the largernumber of works under development by academics, there are alsosome regulators and governmental organisms that apply thesetypes of methodologies (Spain, by Sindicatura de Comptes daCatalunha, Australia, by IPART, etc.). Many studies that appear inthe literature regard the performance of the municipalities as orga-nizations, including also in this scope the activities provided re-lated to MSW services.

Both parametric and non-parametric models require the speci-fication of variables (in non-parametric applications these arespecified in input and output variables). For the non-parametricmodels, the main variables for the inputs are the number ofemployees and the total cost of the service (OPEX plus CAPEX).The most applied output variables are the quantity of collectedwaste and the number of service users. Regarding the explanatoryfactors, the distance to the landfills and the population density arepopular. From the 18 studies that employ the DEA technique (e.g.,Vilardell and Riera, 1989; Distexhe, 1993; Bosch et al., 2001; Benitoet al., 2005 or Sanchez, 2008) 3 examine the influence of the scaleefficiency. From the remaining studies 10 consider only the con-stant returns to scale assumption, while the others assume variablereturns to scale. Whereas 12 of the 18 studies consider orientedmodels (i.e., either input minimization or output maximization),6 papers are non-oriented (both input reductions and outputexpansions). In Appendix 1 an extensive presentation of the stud-ies that applied DEA in MSW services can be seen.

The main objectives of this paper are threefold. Firstly, it in-tends to describe the ‘‘biological cycle” of the urban solid wasteservices in Portugal, opening the window about the regulation thatcharacterizes it all over the world. Secondly, the paper tries toencourage Portuguese operators to better performances, by bench-marking their efficiencies. Finally, we include the operational envi-ronment (explanatory factors) in the analysis. This innovativeaspect intends to measure the performance of solid waste utilities,by encompassing many explanatory factors, such as GDP per capi-ta, distance to treatment facilities (TF), population density, squarepopulation density, type of management, IRAR’s regulation and TF(sanitary landfill, composting or incineration) using a recent tech-nique based on a double bootstrap procedure.

The paper is organized into the following sections. Section 2presents briefly the context of the Portuguese MSW services. Sec-tion 3 determines the influence of the operational environmenton the MSW ‘wholesale’ operators’ performance in Portugal. Sec-tion 4 draws some conclusions.

2. The governance model in Portugal

2.1. Institutional framework

The MSW services are divided into three segments, i.e., primary,secondary and tertiary markets. The primary market (the retailfirms) is carried out by the municipalities. The secondary market(the wholesale firms) encompasses the transfer stations, the trans-portation of urban waste and their disposal in sanitary landfills orother appropriate treatment facility (TF). These services are pro-vided by regional or national firms. In the field of the tertiary mar-ket the Sociedade Ponto Verde (SPV) is responsible for promotingthe selective collection, take-back and recycling of packaging wastein the country (Magrinho et al., 2006).

The SPV is the main driving force of the tertiary market in Por-tugal. Its activity includes the management and the responsibilityfor the final destination of the waste produced by the packagingmanufacturers or distributors who finance the collection, sortingand recovery of packaging as a payback for the sales packaging ser-vice. The role of municipalities and regional firms within this sys-tem is just to conduct and sort the packaging waste originatingfrom the selective collection to SPV.

Continental Portugal counts 29 wholesale MSW services. 17 ofthem are concessions from which 14 are controlled by the Stateand 3 by private operators. The remaining 12 are intermunicipalsystems from which half of them became public–private partner-ships. Despite the increasing trend of private sector participationin the primary segment, activities such as non-selective collectionof urban waste or urban cleaning are usually carried out by themunicipality itself or by means of outsourcing contracts. Currently(at the end of 2008) there are 244 operators, either with intermu-nicipal systems or with direct management of the municipalitycharacterized by different frameworks. Table 1 shows the solidwaste market structure in Portugal.

2.2. Regulatory model

2.2.1. IntroductionPortugal has a regulatory entity for the MSW services, the afore-

mentioned IRAR. This is an atypical circumstance in the Europeansetting and, in a broader sense, even in the world. IRAR’s actions

1132 P. Simões et al. / Waste Management 30 (2010) 1130–1137

focus on the users’ interest protection by promoting the servicequality and tariffs control (Baptista et al., 2003). Given its goals,the main strategy adopted by the regulatory authority consists inthe public display and periodic comparison (benchmarking) of aset of performance indicators. In particular, IRAR determines aset of performance indicators for each operator and comparesand displays publicly the results (i.e., sunshine regulation). In thisway, the operators with a poor performance get ‘embarrassed’,while the best performers are put into the limelight. The sunshineregulation should trigger the race to the top.

2.2.2. IRAR2.2.2.1. Responsibilities. IRAR’s responsibilities include the imple-mentation of regulation, preparing guidelines for multimunicipaland municipal systems, and monitoring the activity of the opera-tors under its jurisdiction. The latter include the multimunicipaland municipal concessions for water supply, urban wastewaterand urban waste management. IRAR is also responsible for thetechnical standards issuing and for establishing relationships withsimilar institutions, either national or international, to improve itsown performance. Nevertheless, some factors restrain the role ofthis Institute, particularly its impossibility to apply sanctions andthe fact that the local administration bodies in charge of the urbanwaste direct management until now (January 2009) are not liableto IRAR’s action (Marques, 2005).

2.2.2.2. Tariffs. IRAR only intervenes in the concessionary compa-nies. Moreover, the price setting rules are established in the con-cession contracts and are based on the rate-of-return regulation.IRAR determines the prices annually (the cost of waste treatedper ton) according to the contracts and as such its role in thisscope, despite being positive, is limited (Pássaro, 2007).

2.2.2.3. Sunshine regulation. Sunshine regulation represents one ofthe main regulatory tools adopted by IRAR (Marques, 2006). In thislight-handed regulation IRAR shows the results of the utilities per-formance in a report published annually. The document includesan overall evaluation of a utility’s performance, a comparison be-tween utilities and an individual qualitative and quantitativeassessment of each operator. IRAR aims at raising the awarenessabout the utilities’ performance by means of public pressure, i.e.,through representative groups, media, politicians and NGOs.

Poor performing operators are embarrassed and should improvetheir performances by learning from the best performing utilities.Although this method does not set tariffs and its coercive poweris limited, positive results are obtained with the public displayand discussion of the regulated firms’ behavior, introducing com-petitiveness between them and leading to a progressive rise of per-formance in the respective market. This reality is noticed in theurban waste sector in Portugal (Marques and Simões, 2008).

3. Performance evaluation of the Portuguese MSW services

3.1. Methodologies

3.1.1. DEA methodThe non-parametric frontier technique (which does not assume

any a priori assumption on the production model by estimating rel-ative efficiency against a set of best practice firms constituting thefrontier) of DEA is based on mathematical programming to mea-sure the relative efficiency of Decision Making Units (DMU) whichpresent a homogeneous set of inputs and outputs. Its various appli-cations are explained by the several advantages of the procedure(e.g., Cooper et al., 2004). In particular, the identification of bestpractices that can be followed by other operators, the computation

of the operators optimal size, the estimation of potential efficiencyearnings, the accomplishment of marginal rates of substitution be-tween production factors, the computation of productivity changethrough time of each operator, the identification of the most effi-cient operators at each point in time and the determination ofthe most efficient organizational structure (e.g., public versus pri-vate or bundling versus unbundling) are some of them.

The principle of DEA is relatively simple (see, e.g., Cooper et al.,2004). It first determines the best practice observations as the obser-vations which are undominated in the input (i.e., for a given amountof outputs, no observations are using less inputs) and output (i.e., fora given amount of inputs, no units produce more outputs) orienta-tions. These best practice observations constitute the best practicefrontier and obtain a relative efficiency score of 1. Inefficient units(i.e., observations which are dominated in either the input or outputorientation) are evaluated relative to this best practice frontier. Inthis paper, we will focus on the horizontal input shortfall. As such,we look at the maximum input reduction which is possible withthe observation’s given output, if it would produce as efficiently asits best practice observations (or a convex combination of its refer-ence partners). Inefficient observations arrive at values smaller than1, representing the potential input reduction expressed in percent-age. One of the main advantages of DEA is related to the fact that un-like other benchmarking (parametric) techniques it does not needan a priori specification to each input/output weight and does not re-quire judgments about the production function.

Charnes et al. (1978) developed a linear programming (LP)model which estimates under constant returns to scale (CRS) theefficiency of entities. Consider n entities which use p inputs x toproduce q outputs y. In an input-oriented model (i.e., for a givenamount of outputs, minimize the inputs), the LP model maximizesthe efficiency as (see Cooper et al., 2004 for an extensive discussionon the model):

Min h

s:t:

Yk� Y0 P 0hX0 � Xk P 0X

k ¼ 1

h free; k P 0

where k variables are non-negative weights or intensity variablesdefining frontier points and h corresponds to the efficiency of theentity under analysis. Banker et al. (1984) extended the CRS modelto variable returns to scale (VRS) by adding an additional restric-tion:

Pk ¼ 1.

As the CRS model represents the optimal long run scale, whilethe VRS estimates the short run improvements, by comparing theestimates, we can compute the scale efficiency (SE) and the puretechnical efficiency (PTE). If there is a difference between thetwo TE for the same operator, it means that the operator revealsscale inefficiency and that it can be calculated by means of the re-sults achieved with the different methods. The SE accounts for thedegree of savings attained if the operator was operating at an opti-mal scale. In other words, the SE gives us the idea of how many in-puts utilities could reduce if they circumstantially provided theirMSW services to an ‘‘ideal” population (optimal dimension).

The generalized idea of ‘‘big is always better” in the local utili-ties is not an absolute truth. Many studies have been recentlydeveloped studying this matters (e.g., Marques and De Witte,2008; Brynes et al., 2002), and, like in our study, the large dimen-sion is not a synonym of better performance.

3.1.2. DEA bootstrap approachThe traditional DEA approach has been deemed a non-paramet-

ric technique in which the traditional statistical inference is not

P. Simões et al. / Waste Management 30 (2010) 1130–1137 1133

possible to do. In order to reverse that assumption, a new approachconsists in the bootstrap methodology application to the DEA esti-mators. The bootstrap allows us for bias estimates and for statisti-cal inference about the DEA results. Though this procedure wasdeveloped by Efron (1979), only in the mid-1990s did the firstapplications associated with DEA appear. It was only after the re-cent work of Simar and Wilson (1998, 2000) that the results at-tained started to make sense in the real world by using abootstrap smooth algorithm based on a data generation process(DGP).

The bootstrap is especially convenient to estimate the bias inthe data (because the non-parametric process estimates the effi-ciency scores relative to the sample observations). By construction,there are always non-observed entities which are beyond the effi-cient boundary. The bootstrap estimates the bias arising from thisassumption. Therefore, Simar and Wilson (1998) propose a boot-strap procedure which replicates the estimates and perturbs them(i.e., sampling variation). By disturbing the efficiency scores, statis-tical inference on the noise term can be deduced. A second stage ofthe bootstrap examines the influence of the operational environ-ment. To do so, it estimates by a bootstrapped left truncatedregression the effect of exogenous influences on the efficiencyscores. After B bootstrap replications a positive or negative signfor each of the exogenous influences is obtained. A negative signindicates a favorable environment, while a positive indicates anunfavorable environmental characteristic (for an extensivedescription, see Daraio and Simar, 2007; Fried et al., 2008 or Simarand Wilson, 1998).

The idea behind bootstrapping is to simulate the sampling dis-tribution of interest by mimicking the DGP (Balcombe et al., 2008).The bootstrap estimates create a pseudo frontier which providesestimations of the sampling distribution of the bias term h ðx; yÞ -h ðx; yÞ, as it is possible to observe through Fig. 1.

Fig. 1. Context of pseudo frontier in the real world.

Table 2Statistical features of the Portuguese MSW services sample adopted.

Variables Mean

Inputs OPEX (€) 6,130,248CAPEX (€) 3,437,071

Outputs Treated solid waste (ton) 187,570Recycled waste (ton) 12,483

Explanatory factors GDP per capita (€) 11,839Distance to TF (km) 14.1Population density (inh km�2) 308.7(Population density)2 (inh km�2)2 332,917Management 0.17Regulation 0.59Composting 0.24Incineration 0.07

3.2. Efficiency of the Portuguese solid waste services

3.2.1. DEA resultsThe data was collected from the 2007 annual reports of the solid

waste operators. The research encompassed the services related tothe secondary market, i.e., the urban waste treatment service pro-vided to whole continental country by the 29 utilities. Table 2 pre-sents a statistics summary.

Following the literature, e.g., IPART (1998), the model comprisestwo inputs (i.e., operational (OPEX) and capital (CAPEX) expendi-tures) and two outputs (i.e., treated and recycled waste). The CA-PEX component presents reports regarding depreciation andinterest expenses. For the OPEX component, it includes the internalmanpower costs, external services, energy, chemicals, other con-sumables and materials for maintenance and repair and otheroperating costs. The model also includes eight explanatory factors,in particular the population density and square population density,the GDP per capita (per region encompassed by the utility) and theaverage distance to TF (landfills, composting and incinerationplants). To better characterize the operational environment, weadd it with dummy variables that illustrate the management ofthe service (private versus public), the regulation and the servicesof composting and incineration.

This point gains more importance, because as time goes by, to-gether with the growing of solid waste production, municipalwaste flow then had to be redirected from landfilling to othertreatments (Eriksson et al., 2005), these institutional changes ledto major changes in the Portuguese waste management.

We expect to find a positive effect from GDP per capita to effi-ciency, a negative effect from the distance to the treatment facili-ties and an ambiguous effect from population density. Indeed,the latter is expected to contribute negatively to efficiency for bothsparse and dense populated areas. To test this effect, a quadraticterm of population density is included in the analysis. Regardingthe variables of management and regulation, we are completelyopen mind about it, without any preconceived idea about the re-sults. The same is not applied to the variables of composting andincineration, because, to our best knowledge, composting mighthave a negative relationship with efficiency, considering its largeinvestment and difficulty of selling the product (fertilizer). Con-trarily, the incineration should improve the performance of utili-ties, but only in specific conditions of population provided (e.g.,at least 1 million inhabitants, see Hogland and Marques, 2007).The input orientation seems the most appropriate as the utilitiesare obliged to serve the urban waste users and as there is a demandminimization policy. The results are presented in Table 3.

If the Portuguese MSW services would produce as efficient astheir best practices, they could, on average, improve their effi-ciency by 34.2% from which 18.9% correspond to SE earnings. Thismeans that, on average, each operator could reduce the inputs con-

Std. deviation Median Minimum Maximum

7,786,607 2,644,453 600,089 29,722,1964,100,753 1,868,950 337,932 18,209,990

205,169 96,901 13,893 774,43015,257 5,869 1,113 59,999

3,997 23,423 7,522 10,7328.6 37.5 4.2 11.4496.1 1,993.6 14.8 85.6863,488 3,974,234 219 7,3280.38 0.00 0.00 1.000.50 1.00 0.00 1.000.44 0.00 0.00 1.000.26 0.00 0.00 1.00

Table 3Results for the Portuguese MSW services.

Values obtained TE PTE SE

Average 0.658 0.813 0.811Minimum 0.203 0.305 0.458Weighted* 0.699 0.895 0.785Weighted** 0.694 0.871 0.800

* Weighted by treated urban waste.** Weighted by population served.

Table 4Double bootstrap (VRS) results summary.

Variables Estimate Lower bound Upperbound

t-Value

Intercept 2.062 0.98536 3.0167 58.271GDP per capita �2.0317e�05 �0.00010183 5.402e�05 �7.322Distance to

landfill0.0014314 �0.031901 0.028364 1.3686

Populationdensity

�0.0054709 �0.0073358 �0.00376 �84.376

(Populationdensity)2

1.1536e�05 1.0075e�005 1.3103e�05 204.91

Management �0.3577 �1.1819 0.22003 �15.088Regulation 0.14524 �0.40922 0.49413 9.1744Composting �0.31323 �1.1174 0.23052 �11.895Incineration �17.51 �20.736 �15.234 �175.79Std. deviation 0.13569 0.059127 0.25978 35.643

1134 P. Simões et al. / Waste Management 30 (2010) 1130–1137

sumed (OPEX and CAPEX) by 34.2% while still producing the samequantity of outputs (treated and recycled urban waste). In mone-tary terms, this indicates a potential reduction of 61 million Euros,considering the CRS model (and thus working on the optimalscale), and 49 million for the VRS model. According to the modelresults the optimal size would be about 300,000 inhabitants foreach Portuguese ‘‘wholesale” solid waste utility. This result is inline with previous studies carried out in Portugal (Marques and Si-mões, 2009).

3.2.2. DEA bootstrap resultsThe results of the described bootstrap methodology (Simar and

Wilson, 1998) are displayed in Fig. 2. A 95% significance level and2000 bootstrap replications are adopted. Fig. 2 shows the bootstrapresults and their confidence intervals, proving the usefulness ofthis procedure.

By construction, the efficiencies obtained by bootstrapping de-crease considerably, comparing to the ones achieved with DEA-VRS. From this new procedure, on average, solid waste utilities de-picted worse performances, with a corresponding efficiency valueof 0.708. As previously explained, this value means that, on aver-age, each utility can reduce about 29% their inputs consumed.

3.3. Examining the operational environment

The capacity to transform the resources into products does notonly depend on the operators’ technical efficiency, but also on theoperational environment that characterizes them. Entities whichare working in favorable environments could generate the prod-ucts more easily (i.e., it requires less inputs), and vice versa forunfavorable environments (i.e., it requires more inputs). Therefore,it is necessary to explain the efficiency scores and explore thedeterminants of the efficiency (see Fried et al., 1999). One of themost adopted procedures for explaining the operational environ-ment in the analysis consists in the two-stage methodology.

Fig. 2. Bootstrap confidence intervals a

In a first stage, this technique solves the bootstrap DEA algo-rithm without taking into account the explanatory factors. In a sec-ond stage, the influence of the operational environment on theefficiency estimates is examined. A (semi-parametric) regressionanalysis was carried out to determine the influence of environmen-tal variables on the bias-corrected efficiency scores. Simar and Wil-son (2007) describe a statistical model (i.e., a DGP) that is logicallyconsistent with regressing non-parametric DEA efficiency esti-mates in a second stage regression on covariates (environmentalvariables) that are different from the inputs in the first stage. Theyspell out the separability conditions that allow for a two-stage pro-cedure. DGP is appropriate for the two-stage approach and ac-counts for the censoring of the dependent variable (theestimated efficiency scores) that is due to lumpiness (many valuesof 1). The second stage regression involves a generated dependentvariable but, more importantly, the estimated efficiency scores areserially correlated in an unknown fashion. Standard inference istherefore not appropriate (Alexander et al., 2007). Likewise, an or-dinary (naïve) bootstrap is inconsistent.

The operational environment is characterized by the GDP percapita, the distance to TF (landfill, incineration, etc.), the popula-tion density, the square population density and four dummies,such as type of management (the private utilities take the valueof 1 and 0 for the public ones), IRAR’s regulation (the utilities reg-ulated take the value of 1 and 0 for the utilities non-regulated),composting and incineration (both they take the value of 1 if thesetreatment facilities exist and 0 in the opposite situations).

The sign obtained for the explanatory factors indicates the influ-ence on efficiency and its importance can be examined through

nd corrected DEA-VRS efficiencies.

P. Simões et al. / Waste Management 30 (2010) 1130–1137 1135

statistical tests (t-value in this case). The number of explanatoryfactors and their type (continuous or categorical) are not relevantto the analysis (i.e., we can estimate all types of variables withouta curse of dimensionality). The results obtained from double boot-strap are shown in Table 4.

By adding an intercept to the regression, by construction, itcaptures everything which is not captured by the other environ-mental variables. From the results, as expected, GDP and popula-tion density have a positive relationship with efficiency and thedistance to the sanitary landfill has a negative influence. Thismeans that, in general, solid waste utilities can obtain better re-sults (performance) with higher GDP and population density,and, in contrast, a higher distance to the landfill has a negative ef-fect on efficiency.

This study tried to explore particularly the fact that highly ur-ban areas served do not always reflect a benefit to the utility. Thispoint was studied by the population density and square populationdensity variables. From the negative sign, the simple analysis ofthe population density variable points to the idea that utilitiesperformance gets worse with the increase of population density.The inclusion of the square population density variable proves thatthe utilities performance also has a positive relationship with thepopulation density. In short, the positive and negative signs ofthe explanatory factor of population density show that bad perfor-mances can be obtained both in sparse areas (rural) and congestedareas (urban).

The negative sign for management and the positive one for reg-ulation indicate that the private utilities (in order to be linked tothe value of 1) seem to have better performance than the publicones and the regulated utilities (by the same reasoning) appearto be less efficient than the non-regulated ones, but with less sta-tistical significance.

Regarding the composting and incineration variables, the nega-tive sign could be an indication of a positive relationship with theperformance of the utilities. Although this is well accepted con-cerning the incineration, the same cannot be stated about com-posting. However, there are two main reasons that can explainthis result. One is associated with the fact that the implementationof composting depends on deep studies about its economical and

Appendix 1

Paper País Method Inputs

Cubbin et al. (1987) UK DEA Employers; vehicles

Vilardell and Riera(1989)

Spain DEA TOPEX; staff

Burgat and Jeanrenaud(1990)

Switzerland FDH Work time; vehiclescapacity; distance tolandfill

Distexhe (1993) Belgium DEA Staff; vehicles capacityFDH

Sindicatura de Comptesda Catalunha (1996)

Spain FDH Staff; no. vehicles; no.containers; OPEX; CAPEX

IPART (1998) Australia DEA TOPEX

Outpu

Indexand frcolleccollecpaperabandcollecCostu

Tonnafrequ

Solidcollec

Tonna

Tonnatonnatonna

financial viability and the other is related to the European Unionfunds used in the construction of this facilities, reducing their costof capital. The latter point widely depicts the real cost of the ser-vice in its performance.

4. Concluding remarks

This study explored the Portuguese regulatory and organiza-tional model of the solid waste sector. The strong regulatory modelwith an application of benchmarking has a positive role as it hascontributed significantly for the improvement of the quality of ser-vice provided.

Secondly, the performance of the Portuguese solid waste ser-vices was evaluated by the application of the non-parametricbenchmarking techniques (in particular DEA) and its bootstrapvariant. The models developed, admitting CRS and VRS, allowedto conclude that the Portuguese operators’ inefficiency levels forthe year 2007 were relatively significant. For instance, if the utili-ties operated in an efficient way there would be a cost reduction ofabout 61 million Euros for the CRS model and of 49 million Eurosfor the VRS model. The study carried out has shown that the sys-tems have a theoretical optimal size if they comprise about300,000 inhabitants.

Thirdly, the influence of the operational environment on effi-ciency has also been analyzed. Regarding the explanatory factorsincorporated in the model, we can conclude that all the variableshave a significant influence on the operators’ performance. Whileefficiencies increase with GDP, they also decrease with distanceto landfill. In particular, from the square population density vari-able, we conclude that MSW utilities can obtain worse perfor-mances either providing sparse areas (rural) or high densityareas (urban). The results also showed that, for this sample, privateutilities denote better performances than public ones and non-reg-ulated utilities seem to have better performances than regulatedones.

Finally, against the expectations, a positive effect of compostingon the operators’ efficiency was observed. The same occurred withincineration, but in this case it was more predictable.

ts Explanatory factors Main conclusions

of collection pointsequency; index oftion points andtion methods;collected;oned vehiclested

Collection points density;relation between domesticand industrial collectionpoints

Private managementdenotes higher efficienciesthan public management

mers; tonnage – Insignificant differences inefficiency scores betweenthe different kinds ofmanagement

ge; collectionency

– Better performance whenservices are outsourced

waste collected;tion frequency

Dummy to representmanagement; distance tolandfill

Higher efficiencies withoutsourcing

ge – High levels of efficiency

ge collected;ge deposited;ge recycled

Recycling ratio; tonnagecollected/tonnage sold toretailers; dummy torepresent management

High levels of efficiency

(continued on next page)

Appendix 1 (continued)

Paper País Method Inputs Outputs Explanatory factors Main conclusions

Bosch et al. (1998,2000)

Spain DEA Containers; no. vehicles;staff

Organic solid wastecollected

Population density;stationary population

No differences inperformance betweenpublic and privatemanagement; competitionseems to be more importantthan public–privatedichotomy

FDH

Sarkis (2000) andJenkins andAnderson (2003)

Finland DEA TOPEX; global effects;sanitary effects; pollutantemissions; effluentsproduction

Tonnage; staff; technicalpossibilities

– Different efficiencies fromthe model adopted

Whorthington andDollery (2001)

Australia DEA TOPEX Solid waste collected;solid waste recycled

Costumers; occupationratio; population density;population distribution;deposition costs

Potential inputs reductionof about 65%; inefficiencycan be explained bycongestion and highpopulation indexes

Bosch et al. (2001) Spain DEA Containers capacity;vehicles capacity; worktime

Organic waste collected;weekly collectionfrequency

Distance to landfill;seasonal population

In general, the environmentslightly affects theperformance

Segal et al. (2002) EUA DEA TOPEX; staff Costumers – RankingGaiola (2002) Portugal DEA OPEX; CAPEX; staff Tonnage; selective

tonnage; costumers– High levels of inefficiency

Lozano et al. (2004) Spain DEA Glass containers;population; no. of barsand restaurants

Recycled glass – Improve the glass recyclingsector

Haas et al. (2003) EUA DEA TOPEX; waste produced Waste recycled;costumers

– Efficiencies diverge fromthe model

Delgado (2005) Spain DEA Containers capacity;vehicles capacity; worktime

Organic solid waste – Efficiencies diverge fromthe modelFDH

Benito et al. (2005) Spain DEA OPEX; CAPEX; grants Domestic tonnage; no.industries, commercialsand households housesserved

– Efficiency has a positiverelationship withmunicipality area andnegative with populationdensity; the performance ofpublic management isslightly higher than theprivate one

Moore et al. (2005) EUA DEA TOPEX and staff Population served – Better performance inservices managed byspecialized staff than byelected posts

Sanchez (2008) Spain DEA Staff; vehicles; containers Tonnage, collectionpoints, collection pointdensity; kilometers ofsurface area washing

Tourist index Municipalities can reducethe resources used inrendering this service by8%; no significant differencebetween public and privatemanagement

1136 P. Simões et al. / Waste Management 30 (2010) 1130–1137

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