Journal of Cleaner Production 29-30 (2012) 214e221
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The performance of private partners in the waste sector P. Simões*, N.F. Cruz, R.C. Marques Centre for Management Studies, Technical University of Lisbon, 1049-001 Lisbon, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 August 2011 Received in revised form 14 January 2012 Accepted 20 January 2012 Available online 31 January 2012
Private sector participation has proliferated uncontrollably throughout the world, often without devising the proper regulatory structures. This practice has special relevance in refuse collection services. Currently, there are some doubts about their actual benefits for the sector. In this article, the effects of private sector participation in the Portuguese waste sector are evaluated, including both ‘retail’ (refuse collection) and ‘wholesale’ (waste treatment) markets. The data were collected from 228 utilities, including 32 regional entities responsible for waste treatment and 196 local entities in charge of refuse collection. The results are based on a productivity analysis of waste treatment utilities (using Törnqvist and Malmquist productivity indexes to compute the total factor productivity), and on an efficiency analysis of refuse collection utilities (using traditional and robust non-parametric techniques). While in the ‘wholesale’ market the findings support the idea that benefits are ephemeral, in the ‘retail’ market private sector participation seems to increase the overall efficiency of the units. 2012 Elsevier Ltd. All rights reserved.
Keywords: Efficiency Private sector participation Productivity Refuse collection Waste treatment
1. Introduction For many years now, governments all over the world have heard the call for the privatization of public services.1 Arguably, the main reason for this option relies on the search for better performance (Megginson et al., 1994), although a wide set of other possible reasons can be advocated (Yarrow, 1986). Nonetheless, the conclusions on the effects of privatization are mixed (see the table provided in the Appendix). From a theoretical perspective, three main approaches have been used to examine the privatization phenomena:(1) Agency/Property Rights Theories, (2) Public Choice, and (3) organizational theories (vide Villalonga, 2000 for a detailed chronologic presentation of studies following these approaches). The Agency Theory elaborates on the issues around the problems and available solutions for each type of ownership structure. In brief, managers (the agent) of either public or private firms are assumed to give priority to the maximization of their own utility over that of the organization or its shareholders (the principal). Besides, this principaleagent relationship is usually bounded by important information asymmetries. However, in private firms, these conflicting pressures are reduced due to several factors * Corresponding author. Tel.: þ351 21 8417729; fax: þ351 21 8417979. E-mail address:
[email protected] (P. Simões). 1 The concept of privatization is often used to refer to different arrangements (e. g. full divestiture, outsourcing and long-term agreements with private partners such as concession contracts). Regarding waste services, privatization is also related to opening the market and allowing each dwelling to choose its provider (through single contracts), in light of what generally happens in the U.S. 0959-6526/$ e see front matter 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2012.01.027
(Villalonga, 2000). First of all, there is usually a market for ownership rights and the shareholders are able to opt out and sell their participation if managerial performance does not meet their expectations (although there is also the threat of takeover and/or bankruptcy); the Property Rights Theory mainly builds its defense of private ownership around this aspect. Moreover, the managerial labor market is more dynamic for privately-owned firms that are more concerned with the cost (Chaundy and Uttley, 1993) and the ability of managers than with discretionary or political decisions. Lastly, for publicly-owned firms, the principaleagent relationship is decomposed into two other agency interactions: the public (voters)epolitician and the politicianemanager relationships. On the other hand, the Public Choice theory gives emphasis to the idea that politicians tend to seek the maximization of their own utility rather than pursue the overall welfare (or the public interest). Hence, local decision-makers might promote strategic objectives to publicly-owned utilities that are more likely to lead to a reelection (Cavaliere and Scabrosetti, 2008) than to an efficient management. Finally, the research agenda of organizational theories is centered on the differences of the governance models of public and private firms. Specifically, the pro-privatization argument is often based on the better control and incentive mechanisms available for private firms as well as on the higher flexibility to manage human resources (Earle, 2006), although other variables, such as culture, objectives, accountability frameworks and reporting procedures, are also considered. Privatization in the waste sector has been increasing across the globe. In collection services private sector participation is relevant
P. Simões et al. / Journal of Cleaner Production 29-30 (2012) 214e221
in most countries. In fact, in most European countries, more than 50% of the population is supplied by private companies (Dijkgraaf and Gradus, 2008). In waste disposal the involvement of private sector is less pronounced but still relevant. While in waste collection services the rule is the short-term contracts, in disposal PPP arrangements (contractual or institutional) are the most common. According to recent findings, it seems that the reasons for privatizing local public services are mainly founded on cost concerns and budgetary constraints (interest or pressure groups might also be relevant in some occasions e for instance, see Bel and Fageda, 2009). In fact, several variables have been used by researchers to account for the causes of privatization (political ideology, lack of efficiency, effectiveness or quality, etc.), and they often lead to mixed conclusions. Regarding the effects, experience shows that privatization comes often with some sort of political costs and public contestation (especially regarding the so-called essential services, such as drinking water, energy and urban waste services). Moreover, without the proper contractual instruments to protect the public interest, the levels of quality of service (including the protection of the environment) are likely to decrease and the final costs to customers are bound to drift upward (Cruz and Marques, in press). Unfortunately, these poor outcomes are still frequent in several publiceprivate partnership (PPP) arrangements for delivering public services. Nevertheless, whenever the public authorities succeed in developing the adequate ex-ante studies (serious environmental impact assessments and economic viability studies e including a public-sector comparator) and in monitoring ex-post performance and compliance (effective contract management), privatization can depict good results (traditionally, higher costefficiency and a strict compliance with environmental and quality legal/contractual standards). In brief, the main advantages of privatization of public services are the mitigation of political patronage, the exploitation of private sector’s know-how and the cost-efficiency incentives due to competition for the market (Demsetz, 1968); the main disadvantages are the reduced flexibility/discretion of public authorities after the award of the services (since PPP contracts are “rigid by origin”, Spiller, 2008: 21), the impossibility of writing a complete long-term contract (that takes into account or predicts all future possibilities and events) and the potential negative political impacts of the decision to privatize. From an empirical perspective, several authors claim to have found indications that private sector participation in the provision of public services is beneficial (Ferris, 1986). This is supported by evidence in refuse collection operations (Savas, 1974; Cubbin et al., 1987), despite some signs that these benefits are ephemeral and tend to vanish over time (Dijkgraaf and Gradus, 2007; Bel and Warner, 2008). Other authors, however, emphasize the lack of facts and confirmations (Kemper and Quigley, 1976; Distexhe, 1993) or even that public production is actually preferable (Benito et al. 2010). These findings also vary across regions. In the U.S., research results clearly seem to be pro-privatization (Berenyi and Stevens 1988) or inconclusive (Collins and Downes, 1977; Callan and Thomas, 2001). In the UK, the evidence has been supportive of contracting out (Domberger et al., 1986; Szymansky and Wilkins, 1993), confirming benefits like cost reductions despite some progressive drawbacks that might occur. This idea of fewer benefits over time is also shared by Dijkgraaf and Gradus (2007) regarding refuse collection in the Netherlands and by Ohlsson (2003) in Sweden. The reasons for the success or failure may vary, depending on the maturity of the sector, the historical relationship with private investors, political ideology, among others. In the Portuguese waste sector, there are different types of private sector participation (see Section 2) and each model has its own strengths and rationale behind. For
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instance, Portuguese municipalities frequently opt for shortterm outsourcing contracts for the delivery of waste collection services. Besides searching for better performance and increased quality of service (the goals that should in fact prevail), local decision-makers consider other factors such as attractive project finance schemes (that allow them to provide new infrastructure off the balance sheet), and the potential for obtaining rents from the operators. This state of affairs brings about the need to examine the impacts of private sector participation on the waste sector (as for other public infrastructure services). For this purpose, the current paper presents an analysis of the whole sector. Indeed, different non-parametric benchmarking techniques are computed in this investigation to determine the productivity of waste treatment services and the efficiency of waste collection services in Portugal. By using productivity indicators to measure changes in a set of related variables, productivity is estimated as a ratio between an output index and an input index. Hence, besides contributing to the literature on performance evaluation in the waste sector, and being (to the best of the authors’ knowledge) the first application of a robust non-parametric approach (bootstrap) in the waste ‘retail’ market, the study also covers the effects of private participation in the ‘retail’ and ‘wholesale’ segments. After this brief introduction, the paper is organized as follows. Section 2 includes a description of the Portuguese market structure in the waste sector. The definition of the methodologies adopted to evaluate the performance of the sector is provided in Section 3, while the main results are presented in Section 4. Section 5 contains the analysis and discussion of the results and, finally, the main conclusions are drawn in Section 6. 2. Portuguese market structure 2.1. Introduction In Portugal waste services have been historically produced directly by public authorities. However, after a change in the national law, in 1993, private participation in the waste sector started to flourish, adopting several forms. Above all, the concession (contractual PPP) of waste treatment services was a major change in the sector. In fact, the opening of this market segment to private investors was followed by the creation of a sector-specific regulator to monitor these processes (nowadays known as The Water and Waste Services Regulation Authority e ERSAR, in the Portuguese acronym). Lately there has been an increasing (yet uncontrolled and still unregulated) private sector participation in refuse collection and urban cleaning services, mainly through short-term outsourcing contracts (from one to five years). 2.2. Retail services The waste sector in Portugal is clearly stratified in two different segments: the ‘retail’ market and the ‘wholesale’ market. The ‘retail’ segment comprises refuse collection (undifferentiated or selective) and, sometimes, other services like the collection of large volumes or even urban cleaning. Refuse collection is often provided directly by the local governments (in-house production). Currently, however, the majority chooses to contract-out the services, awarding short-term contracts to private operators. Some municipalities have semiautonomous utilities endowed with financial and administrative autonomy (around 5% of the market). Municipal companies cover 12% of the population; these entities can either be fully owned by local governments (66% of the cases) or consist in institutionalized
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PPPs which are mixed publiceprivate companies (joint ventures) where local governments hold the majority of the shares (to keep the dominant influence).2 Besides being in charge of waste services, sometimes these ‘retail’ utilities provide other services like water, wastewater or urban transportation. On balance, there are about 260 utilities operating in the ‘retail’ segment encompassing nearly 10 million inhabitants.
2.3. Wholesale services ‘Wholesale’ services include transfer stations (if they exist), waste transportation (between transfer stations, and treatment facilities) and waste treatment. There is a total of 32 ‘wholesale’ utilities operating at a regional level (29 in mainland Portugal and three on the islands). Within this group of entities, there are four different types of institutional arrangements: public companies (publicepublic partnerships e PuPs, where the central state has the majority of the shares and municipalities own the remainder),3 associations of municipalities (AMs), intermunicipal companies (where the AMs are always the majority shareholders) and municipal concessions (contractual PPPs established between the AMs and the private partners). Hence, in mainland Portugal, there are 11 concessions to PuPs and two concessions to private companies; three of the remaining systems are managed directly by the respective AMs while the other nine are managed by intermunicipal companies (three of them are 100% public and the other six are mixed companies). In a few particular cases, the wholesale utility is also in charge of refuse collection.
3. Methodologies implemented 3.1. Efficiency analysis In this paper, the efficiency of urban waste utilities is estimated by means of two different methodologies. The first one consists in the application of the traditional non-parametric technique of data envelopment analysis (DEA). One of the strong points of this method is the absence of an aprioristic particular production function to emulate production of outputs from the consumption of inputs in the waste utilities’ industry. However, its application often requires very large data sets to obtain reliable estimates and to avoid the so-called problem of the “curse of dimensionality”. Generally, this technique compares each decision-making unit (an urban waste utility, in this case) with the best practices that form the efficient frontier. Nonetheless, this feature makes DEA very sensitive to extreme observations (outliers) and noise in the data. The process of finding the efficient utilities to draw up the best practice frontier can be formulated as a linear programming problem. The efficiency of the n utilities corresponds to a set of n linear programming problems. In the following nomenclature, gi is a vector describing the weights of other observations, while X and Y are, respectively, the inputs and outputs of each observation. Equation (1) shows the DEA formulation of the input-oriented constant returns to scale (CRS) model
2 This process is often regarded as “partial privatization” (see Cruz and Marques, in press). 3 The central state capital participation is indirect. Strictly speaking, EGF, a subholding of Águas de Portugal (the central state company for the environmental sector), is the company that owns the majority of the shares (at least 51%).
8 <
9 n n P P = pþq g g g g ðx;yÞ˛< Y ;x X for ð ; .; Þ i i i i 1 n þ y ^ j DEACRS ¼ i¼1 i¼1 : ; such that gi 0; i ¼ 1; .; n (1) To allow for variable returns to scale (VRS), and therefore take into account the different scale levels among the utilities, the n X gi ¼ 1 is added to the previous formuconvexity constraint of i¼1
lation (Banker et al., 1984). In practical terms, the difference between the CRS and VRS models is the fact that the latter considers the effects of economies (or diseconomies) of scale on efficiency. To overcome the drawbacks of traditional non-parametric approaches (and to strengthen the findings), the efficiency of ‘retail’ utilities is also estimated through the robust non-parametric approach of bootstrap. The idea behind the DEA-bootstrap approach, introduced by Efron (1979), is to resample the estimator in order to enable statistical inference. It approximates the sampling distributions of the estimator by using the empirical distribution of resampled estimates obtained from a Monte Carlo simulation (for a detailed description of the technique, see Simar and Wilson, 2000).
3.2. Productivity analysis Productivity accounts for the relationship between the outputs produced and the inputs consumed over time and it is a crucial concern of any firm. There is a wide set of approaches to measure productivity. The methodologies vary between a range of too simplistic approaches, such as analysis on single performance indicators, and highly complex methods that require mathematical programming skills. Nowadays, the total factor productivity (TFP) is a valuable technique for productivity estimation. The TFP index is often defined as the increase in total output not caused by the variation of inputs (or, in the present case, the decrease in total input not explained by the variation of outputs). The TFP is an extension of partial measures of productivity (as in the case of performance indicators) and can be measured as a ratio between the weighted sum of all the outputs and the weighted sum of all the inputs. Productivity indexes are used to assess changes in prices and quantities over time, as well as to measure their differences between firms, industries, regions or countries (Coelli et al., 2005). Taking into account that (almost always) firms deal with multiple outputs and/or inputs, it is necessary to aggregate them to obtain a global indicator, so that a single input index and a single output index can be computed. These aggregate indexes can be calculated by the weighted sums of the individual inputs and individual outputs. Usually, the coefficients used to weigh output indexes correspond to the output revenues, whereas input indexes are weighted by input costs. The Laspeyres, Paasche, Fisher and Törnqvist indexes are among the most used for the computation of the TFP. Particularly, the Törnqvist productivity index (TPI) has been widely implemented for this purpose. The TPI is a weighted geometric mean of the quantities q, with the weights given by the simple mean of the value shares at period s and period t. The Törnqvist price index (QT) is therefore given by
QstT
¼
N Y qit i¼1
qis
Wis þWit 2
(2)
P. Simões et al. / Journal of Cleaner Production 29-30 (2012) 214e221
where wis and wit present the value share of the i-good or service in N X the base period s:pis qis = pis qis . i¼1
These indexes have a few drawbacks. They are very demanding considering the data needed and do not allow for their decomposition (for instance, to measure explicitly the contribution of technical efficiency). To mitigate these problems, this research also uses the Malmquist productivity index (MPI). The MPI was initially developed by Caves et al. (1982), based on the notion of distance function introduced by Malmquist and Shephard in the early 1950s, but its first empirical application was carried out by Färe et al. (1989). The MPI can be computed through a parametric or a nonparametric methodology. In this study DEA is used to estimate the efficiency scores between periods and, therefore, make no behavioral assumption regarding the production function. MPI has several interesting properties (Marques, 2008). Firstly, computation can rely on quantity data and does not require input and output price values (as the TPI does); secondly, being a frontier method, it is possible to separate productivity variations due to technical efficiency changes (the so-called “catching-up”) and due to technological progress (shifts in the frontier itself). Caves et al. (1982) developed the MPI by comparing two vectors of inputs/outputs in period t for a reference technology, and using radial contraction of inputs (considering an input orientation), as follows:
MPIti ¼
Dti ytþ1 ; xtþ1 Dti ðyt ; xt Þ
(3)
In the spirit of the Fisher index, Färe et al. (1989) suggested the computation of the geometric mean for the t and t þ 1 reference technologies to avoid the problem of its influence over the MPI scores, resulting in the following formulas:
tþ1
MPIi y
;x
tþ1
t
;y ;x
t
" ¼
! Dti ytþ1 ; xtþ1 Dti ðyt ; xt Þ tþ1 tþ1 !#1=2 Dtþ1 y ;x i Dtþ1 ðyt ; xt Þ i
(4)
Making some algebraically transformations (following Nishimizu and Page, 1982, and Färe et al., 1989), the MPI can be decomposed in two distinct terms
MPIi ¼
tþ1 tþ1 ! Dtþ1 y ;x i
"
Dti ðyt ; xt Þ !#1=2 Dti yt ; xt Dtþ1 ðyt ; xt Þ i
! Dti ytþ1 ; xtþ1 Dtþ1 ytþ1 ; xtþ1 i
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produce a set of outputs in the production process. As for the case of EffCh, a value below the unit for the TechCh term represents the decline of the production technology (e.g. for the introduction of new strict regulations), while a value above the unit represents technological progress. Consequently, increases in productivity result in MPIs greater than the unit (and, conversely, lower productivities show MPIs under the unit). A MPI score equal to one, shows signs of productivity stagnation. Note that the balance between the increases and decreases in each part of equation (7) leads to positive or negative changes in the overall productivity index (e.g. a 10% improvement in EffCh compensates for a 5% decline in TechCh). In other words, MPI describes the change in productivity of a utility as the product of EffCh by TechCh. Improvements in EffCh scores are interpreted as “catching-up” (the utility gets closer to the efficient frontier), while improvements in TechCh usually represent innovation. 4. Private sector participation and its impacts on performance 4.1. Data collection In this study, the aim is to evaluate the impacts of private sector participation in the Portuguese waste sector. With this purpose, the data set was collected from utilities operating in the ‘retail’ (refuse collection) and ‘wholesale’ (transfer stations, waste transportation and treatment) markets. While the performance evaluation of ‘retail’ utilities consists in inefficiency measurement, a productivity analysis is carried out for the ‘wholesale’ market. Hence, this investigation includes all 32 operators in charge of the waste treatment in Portugal (29 in the mainland and three on the islands) for the period between 2001 and 2008, and 196 operators in charge of refuse collection (including 15 utilities on the islands) for the year of 2008, covering about 84% of the Portuguese population. The data on ‘retail’ utilities was collected by means of a questionnaire sent to all 308 Portuguese municipalities. To clarify some particular items, direct contact with the utilities (municipal departments, municipal services, municipal companies, etc.) was sometimes necessary. Concerning the ‘wholesale’ utilities, the information required was mostly available on their annual reports. Occasionally, the quality of the data was not adequate and some simplifications were needed. The sector-specific regulator was also consulted to validate the data in very particular cases. 4.2. Model specification
(5)
Hence, the contribution of the technical efficiency change (EffCh) between the periods t and t þ 1 is measured by the first term on the left side (outside the square brackets). This represents the change in the factors of production in relation to the lowest amount of inputs that still produce the desired outputs (efficient frontier) in the timeframe considered. It may have a value above, equal or below the unit, representing the improvement, maintenance or decline in technical efficiency. The other term (inside the square brackets), represents the technological change (TechCh) between the periods t and t þ 1. TechCh can be induced by an increase (or decrease) of the rate of transforming inputs into outputs, although there are no changes in their proportions. On the other hand, this change can also result from changes in the proportions of inputs needed to
The selection of variables is a critical stage in any performance evaluation model. An erroneous mix of variables can lead to biased results, since the selected set of inputs and outputs will “speak” on behalf of the utilities. Therefore, the features of each market segment were taken into account in the process of choosing the most suitable variables. Concerning the orientation of the models, the matter is quite consensual in both segments. Even though the entities in charge of waste services should strive to be sustainable from an economic standpoint, one must bear in mind that, as other public services (supposed to be universal, affordable, equitable, safe and with adequate quality standards) mainly where the demand side management policies are relevant (e.g. waste, water or energy), the main objective should be to reduce costs (inputs) and not to increase revenue (outputs). Hence, an input orientation was used in all models (minimization of the inputs consumed, keeping the same level of outputs produced).
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Indeed, in both models used to compute the efficiencies (DEA and DEA-Bootstrap) the linear programming problems assign scores by judging the input consumption level against the efficient frontier. In other words, the distance to the efficient frontier is calculated through the necessary change (i.e. decrease) in input consumption for each unit under analysis. The models assume that the level of outputs is fixed (in each year or timeframe under analysis); i.e. it is assumed that there is a certain amount of waste that is generated in the area of influence of each utility, and the utilities have to collect/treat it, having no control over that amount. If, for a certain waste utility, the amount of waste generated increases, than the inputs consumed by that utility will be compared with the efficient frontier in a different “zone” (it is not straightforward that the efficiency score would necessarily increase e this is even more evident for the VRS scores where utilities are compared solely with others that have a similar scale). The rationale is the same for TFPeMPI. The input-orientation issue is, therefore, crucial for the interpretation of the results. 4.2.1. Retail services Three inputs and one output were considered for utilities in charge of ‘retail’ services. The following variables were used as inputs: staff, vehicles and other operational expenditures (OOPEX e other operational costs subtracted from staff costs). The residential waste collected is used as the main output of ‘retail’ services. Even though there are some cases where local governments bundle several types of services (e.g. street cleaning, selective collection, water supply, etc.) in the same department (or any other governance model), the data used here corresponds to the resources allocated specifically to refuse collection. Accordingly, the variable staff includes the number of employees that carry out administrative tasks and the elements of the waste removal teams. Usually, these teams are composed by three elements (one driver and two waste removal employees), though recently there has been some innovation in this field (e.g. truck teams composed by only two elements, with more technological support). In the same fashion, this study only considered the number of vehicles and OOPEX (in Euros) allocated to refuse collection services. In line with most literature (e.g. for a state of the art review vide Simões et al., 2010), the quantity of waste collected by the utilities (in t) was used as the single output. The main statistics of the data collected from the 196 urban waste utilities for the year of 2008 are presented in Table 1. 4.2.2. Wholesale services To carry out the performance evaluation of ‘wholesale’ utilities and compute the TFP and the MPI, three inputs and two inputs were set. Inputs include staff (number of full-time employees), capital expenditures (CAPEX e net assets in Euros, encompassing intangible, tangible and financial assets) and OOPEX (in Euros). The cost of the input staff is determined by staff costs divided by the number of employees. The weighting of the second input is obtained through the capital cost (i.e. the sum of the depreciation plus the interest expenses, conveyed as a percentage of the net assets). The OOPEX input is weighted by an implicit price deflator that reflects the consumer price index (CPI). Both the CAPEX and the OOPEX Table 1 Data statistics for the year of 2008.
Average Str. Dev. Median Min. Max.
quantities are measured in monetary units with 2008 reference values. Regarding the outputs, the tonnage of waste (both for treatment and for recycling) dealt by the utilities was adopted. Each output is weighted by its revenue (fees charged for the waste treatment and any proceedings that might derive from selling recycled material). Table 2 summarizes the main statistics of the variables used in the models.
5. Results and discussion 5.1. Retail services Taking into account the market structure of the waste sector in Portugal, waste utilities performance was evaluated according to their operating segment. The performance of refuse collection services in Portugal was first measured by the application of DEA. Thus, two models were computed, respectively CRS and VRS models (which also capture the scale effects). Table 3 provides the average efficiency estimates. Concerning the VRS model, the results present an average efficiency score of 0.615, whereas the CRS model depicts an average score of 0.487. The latter incorporates the scale inefficiency contribution as well. The results obtained mean that on average the municipalities could reduce their inputs in 38.5% in the VRS model (or about 51% in the CRS model) producing the same level of outputs (residential waste collected). The results also point out the inefficiency induced by scale diseconomies (i.e. ratio between VRS and CRS). This means that the services could save on average about 19% of the inputs consumed if they operated at an optimal scale. As mentioned before, in Portugal there are different types of management models in charge of refuse collection service. The services directly provided by the municipalities (hereafter Mun), semi-autonomous utilities (SaU) and municipal companies (MC) are the most representative types of management in the mainland. Besides the different levels of autonomy that distinguish the different management models, the levels of inefficiency also differ among them. According to the figures presented in Table 4, it is possible to infer that municipal companies (MC) have the best performance (arguably due to their higher level of autonomy). At first glance, the services with more autonomy from the local government (i.e. SaU and MC), seem to depict better performances than the municipalities on their own. However, what seems to be an unequivocal conclusion is that private operation is more efficient than the direct (in-house) provision of the service (see Table 4). The better results obtained through outsourcing (or partial privatization) might be related to two main reasons besides the higher level of expertise of the private sector. The first one has to do with competition, also in line with Bosch et al. (2000), since local governments/public utilities can frequently put their service on the market (i.e. for open competition) as a consequence of the short duration of the contracts. The second one is associated with the economies of scale that private companies can achieve with the Table 2 Statistical summary of the variables. Variables
2001 Quantity
Staff (no.)
Vehicles (no.)
OOPEX (V)
Refuse collected (t)
33.33 91.33 12.00 3.00 976.00
7.94 14.72 3.00 1.00 153.00
611,179.73 1,130,153.29 164,210.00 3858.00 8,789,904.00
18,853.09 30,577.95 8507.66 380.00 275,469.00
Input CAPEX (103 V)/(e) Staff (no.)/[V/(no. year)] OPEX (103 V)/(e) Output Solid waste treated (t)/(V/t) Solid waste recycled (t)/(V/t)
5852.42 88 5001.85 287,155.82 9847.49
2008 Price 0.131 22,698 0.844 22.07 66.73
Quantity 3174.98 82.25 4696.68 179,050.94 13,328.50
Price 0.179 20,274 1.000 35.51 153.85
P. Simões et al. / Journal of Cleaner Production 29-30 (2012) 214e221
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Table 3 Efficiency scores per type of management model. DEA
Overall Mun SaU MC
DEA-bootstrap
CRS
VRS
CRS
VRS
0.487 0.481 0.537 0.559
0.615 0.652 0.630 0.686
0.434 0.432 0.484 0.494
0.550 0.585 0.568 0.604
possibility of providing their services in different and near municipalities. This assessment has some limitations regarding incentives for pollution prevention (waste reduction). For instance, if a certain waste utility carried out campaigns for waste prevention (and, in a way, if one accepts that this is a responsibility of waste utilities), this assessment would not take that effort directly into account. Nevertheless, it is reasonable to state that the less the waste generated, the less the costs (or resource/input consumption) involved in waste management. 5.2. Wholesale services As mentioned previously, one objective was related to the productivity measurement and its evolution regarding wholesale utilities. In this scope, two different approaches were computed (whose results are presented in Table 5). Cumulative scores are obtained by consecutively multiplying the binary indexes (relative to two consecutive years); these values account for a cumulative change in productivity since the first year under analysis. This means that they refer to the base date (2001 in this case). The average values in Table 5 correspond to the average productivity change between two consecutive years. In general, at the end of the eight-year period, cumulative values of the productivity change below 0.88 were observed, either by TPI or by MPI. This means that ‘wholesale’ utilities ended up, after eight years, with a productivity decline of more than 12%. There was only one year with a positive change in productivity, which was observed in 2005 (TPI ¼ 1.049 and MPI ¼ 1.020). One might also note that the year of 2008 was the Table 4 Efficiency scores per type of management model: detail. DEA
Mun e In Mun e Out SaU e In SaU e Out MC e In MC e Out
DEA-Bootstrap
CRS
VRS
CRS
VRS
0.425 0.571 0.540 0.532 0.527 0.595
0.616 0.712 0.642 0.598 0.667 0.708
0.381 0.513 0.485 0.481 0.463 0.529
0.555 0.635 0.572 0.556 0.590 0.619
In e In-house; Out e Outsourcing contracts (institutionalized PPPs for MC). Table 5 Productivity cumulative indexes at the period 2002e2008. Year
2002 2003 2004 2005 2006 2007 2008 Average
TPI
MPI
Average values
Cumulative values
Average values
Cumulative values
0.995 0.944 0.987 1.049 1.004 0.988 0.933 0.986
0.995 0.941 0.929 0.971 0.955 0.942 0.876
0.989 0.959 0.962 1.023 0.973 0.978 0.952 0.977
0.989 0.955 0.925 0.949 0.899 0.896 0.863
Fig. 1. Cumulative MPI values for private and public utilities.
major contributor to the utilities productivity decline (TPI ¼ 0.933 and MPI ¼ 0.952). Following the general trend, for both methodologies, public and private utilities attain similar results, in spite of the slightly higher productivity scores of the public ones. The cumulative results for the MPI approach are shown in Fig. 1. Even though the results for public utilities are somewhat close to the scores obtained for private utilities, their evolution over the eight-year period of analysis is completely different. As predicted by the literature, better initial results were observed for the private utilities with a slight increase of productivity, which can be explained by the natural incentives induced by the competition for the market. However, after this (short) period of improvement, privately-owned utilities lose those incentives, resulting in a productivity decline year after year, reaching out to a total decrease of about 15%. With regard to public utilities, the evolution of productivity is quite different although, ultimately, the results are similar. The year 2005 represented, in fact, an atypical circumstance on a general tendency of productivity decrease. This might have to do with the implementation of an effective regulatory model by the waste regulator during this year e ERSAR.4 However, after this first year of “alarm” for utility managers, the decrease tendency can also be related to the trade-off between the lack of incentives induced by the economic regulatory model adopted by ERSAR (based on rateof-return) and the high demanding standards for quality of service (resulting in a significant change in the production frontier). 6. Conclusions Besides the main goal of analyzing the influence of private sector participation in the waste sector, this study also evaluates the performance of the whole sector in Portugal. In general, ‘wholesale’ waste utilities showed relevant productivity losses during the eight-year period of analysis and retail utilities stressed high levels of inefficiency. Regarding the old (and still open-ended) discussion on public versus private service production, the results were mixed considering the ‘retail’ and the ‘wholesale’ market. Although the results can be regarded as quite clear for the ‘retail’ market, the same is not as evident for the waste sector as a whole. One should notice that
4 For a detailed explanation of the Portuguese regulatory framework, see Marques and Simões (2008).
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the duration of contracts and consequently the positive influence of competition in the short-run contracts can be determinant for the sound results of privatization in retail utilities. Therefore, it seems that positive outcomes can derive from private or public production, depending on several other important factors. For example, results should be contextualized regarding location, time, and political ideology, among many others variables. Nevertheless, this study was capable of identifying some empirical evidence that supports the decision for privatization, especially in the ‘retail’ market. Indeed, the incentives that come with privatization are only there for a short period, being substituted with “quiet-life” over time. The short-term contracts in the ‘retail’ segment that stimulate competition for the market and the potential for economies of scale available for private investors (gains obtained from the provision in multiple municipalities) are the basis for the outperformance of privately managed utilities. As argued above, the use of short-term outsourcing contracts in the ‘retail’ market seems to be a worthy strategy (still, the actual decision is always political rather than a technical one e albeit the results obtained in this research could influence positively local decision-makers). In fact, the recurrent pressure from competition for the market proves to be effective in curbing costs. Regarding the ‘wholesale’ market, two key actions could be carried out: (1) ERSAR could evolve from its current rate-of-return regulation to a pricecap regulation providing more incentives to become more efficient and innovative; concerning quality, minimum standards should be clearly defined by law/contract and ERSAR could monitor the compliance of operators (and keep its current ‘sunshine regulation’). (2) The contracts firmed with operators (regardless of their public, private or mixed ownership) should include outcome-based performance indicators that could be linked to the payment schemes (undertaken by the respective local governments). The analysis carried out in this study is mainly concerned with economic efficiency and productivity. Nevertheless, one of the best incentives for waste prevention at the source is using price-signals. If the retail utilities would raise tariffs (i.e. reduce the cost for taxpayers and increase the cost for ratepayers) this could encourage waste prevention. Thus, analyzing the mix between ratepayers and taxpayers contributions could be factored in the performance analysis. However, to avoid abuses, the first step would be to find the “efficient price”; and this is where the analysis presented in this paper contributes to the discussion.
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Appendix
Public more efficient
Private more efficient
Non-significant differences
Spain Benito et al. (2010) Sweden Ohlsson (2003) U.S. Pier et al. (1974)
Belgium Lawarrée (1986). Canada Kitchen (1976); McDavid (1985); Tickner and McDavid (1986); Pelletier (1986). Ireland Reeves and Barrow (2000). Netherlands Boorsma (1982); Dijkgraaf and Gradus (2003, 2007) Switzerland Pommerehne (1976); Pommerehne and Frey (1977); Burgat and Jeanrenaud (1990). UK Hartley and Huby (1985); Domberger et al. (1986); Cubbin et al. (1987); Chaundy and Uttley (1993); Knox and Young (1995); Bello and Szymanski (1996); Szymansky (1996). U.S. Savas (1974, 1977b,c,d, 1980); Edwards and Stevens (1978); Petrovic and Jaffee (1978); Stevens (1978); Stevens and Savas (1978); Bennet and Johnson (1979); Berenyi (1981); Dubin and Navarro (1988), Berenyi and Stevens (1988); Haas et al. (2003); Bae (2010). Portugal Marques and Simões (2009); Simões et al. (2010).
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