EFFICIENCY OF FINNISH WATERWORKS IMPACT OF GOVERNANCE MODEL

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1 EFFICIENCY OF FINNISH WATERWORKS IMPACT OF GOVERNANCE MODEL M.Sc. Maila Herrala*, University of Oulu, Finland *corresponding author M.Sc. Heikki Huotari Prof. Harri Haapasalo, University of Oulu, Finland ABSTRACT Efficiency of water and sewage services has been studied worldwide for decades and it has also raised interest in Finland. Public authorities have deployed several different ways to organize these services, even in the national level. Governance of these models has also been distributed. Objective of this research is to find out if a governance model has an effect on the efficiency of waterworks in Finland. Data Envelopment Analysis (DEA) was used as a tool to measure the efficiency of 19 waterworks. The efficiency results were analyzed in a way to see possible differences between different governance models. Research results indicate some differences in efficiency levels between different governance models. These results are, however, only suggestive because of the small sample and different constraints encountered in the research process. This research was the first attempt to compare efficiency of waterworks governance models in Finland and it should be used as a foundation for improved and perfected models in the future. Keywords: efficiency, data envelopment analysis (DEA), waterworks INTRODUCTION Efficiency of waterworks has been studied worldwide for decades (Coelli & Walding, 2006; García-Sánchez, 2006; Thanassoulis, 2002). Efficiency has been very important issue when evaluating all kinds of productive activities, and research around the subject has produced a variety of evaluation techniques (Abbott & Cohen, 2009; Coelli, Rao, O'Donnell, & Battese, 2005; Corton & Berg, 2009). According to Abbot & Cohen (2009), the estimation of cost functions by various techniques has been the most commonly used method of determining the levels of efficiency in the water industry. Efficiency of waterworks is a complex thing to be defined and consists of various dimensions. There have been difficulties in getting comprehensive, comparable, and plausible results. The objective of waterworks is not to deliver as much water as possible, but rather to connect as many households as possible at efficient cost and quality. The general problems in measuring efficiency of water and sewage services concerns data quality issues such as differences in the valuation methods of capital, price deflators, influence of public funding and outsourcing of different activities (Coelli & Walding, 2006). The water sector is also characterized by a high degree of complexity

2 which requires a large number of observations while data availability is most often very restricted. Currently there has been a growing interest towards the subject also in Finland. New governance models have been presented and implemented in many municipalities as an attempt to solve different problems, but has this helped to improve the overall efficiency of water and sewage services? The information about the ideal properties of waterworks would be very welcome to the decision-makers of water sector, because at the moment there are some presuppositions about the goodness or badness of the different governance models, but it is not possible to reliably state the differences between these models. The objective of this study is to find out if the waterworks efficiency is related with waterworks governance models. The goal is to see if there are differences in efficiency levels between the different governance models. The problem is defined and approached with help of the following research question: Are there any differences in efficiency levels between waterworks different governance models? RESEARCH ENVIRONMENT From 5.3 million inhabitants of Finland, more than 90% belongs to the water system and around 80% to the sewage system of Finnish waterworks (Silfverberg 2007). Arranging water and sewage services for municipal residents is the fundamental obligation of municipalities (Act on Water Services 119/2001). Municipalities (336 in 2011) are self-administrative units of the Finnish government. Waterworks are governed by the law in Finland if it delivers drinking water or receives sewage more than 10m³ per day, or serves more than 50 inhabitants and at the same time serves more than one property (Act on Water Services 119/2001). According to the statistical system for Finnish waterworks (VELVET), there are currently approximately 1500 water and sewage service providers in Finland. Most of the waterworks are extremely small, providing water only to few dozens or hundreds of customers. 25 largest municipal waterworks provide about 60% of the total amount of water sold in Finland (Kuntaliitto 2007). According VELVET, the most common ownership and governance models in the Finnish water sector measured in number are respectively: private cooperative, municipal department (MD), municipal-owned enterprise (MOE), municipal-owned company (MOC) and federation of municipalities (FoM). In addition, there are hundreds of small water service associations and partnerships, which provide services to less than 50 people. According to the VELVET statistics, 222 of Finnish waterworks are organized as MDs, 54 as MOEs, 155 as MOCs and 886 as cooperatives. The rest of the roughly 1500 waterworks are organized as partnerships, limited partnerships and federations of municipalities. Waterworks as a municipal department (MD) is an integral part of municipal organization. They are part of the municipal budget and investment program. MDs have a defined income source (user fees), but it can receive additional income transfers from the state or the municipality. (Heikkinen, Tapio, Haikko, & Paloposki, 1996; Kähkönen, 1996) Reasons to include business activities, like waterworks, into municipality s budget can be e.g. temporary

3 financing of investments with taxes, avoiding extra costs that arise from creation of new administrative body, or keeping the pricing policy under political control (Myllyntaus, 2001). Municipal owned enterprise (MOE) is a business unit, for which municipal council has defined the state of (financial) independence. Although MOEs are fully owned by the municipality, their budget is separated from municipality s budget and they have their own financial statement including profit and loss statement, balance sheet, and funds statement. Public ownership ensures that the municipality can reap the benefits of operations and at the same time have democratic control over the operations. (Heikkinen et al., 1996; Jalkanen et al., 1996; Kähkönen, 1996; Myllyntaus, 2001) Because MOE is part of the municipality it cannot go bankrupt and it is not tax liable when operating within the municipality s territory (Heikkinen et al., 1996). Municipal owned company (MOC) is owned and controlled by one or several municipalities. It is a legal entity whose operations and management are regulated by Limited-liability Companies Act (624/2006). They operate as commercial businesses and do not have societal obligations like the MDs and MOEs. (Kähkönen, 1996) They can also be expected to make profit and to provide reasonable return on capital to the municipality (Jalkanen et al., 1996). MOC has its own budget, investment program and financial statement. MOC is subject to taxation at the tax rate on profit of 26 %. It is also liable to a property tax. MOCs can decide on investments independently and freely acquire financing from the financial markets. (Heikkinen et al., 1996) A cooperative is owned by its members, who usually are the customers of the service. Cooperatives are thus communities by nature, and although they are running business operations, their main purpose is to support their members welfare instead of sharing profit why cooperatives often operate on non-profit basis. Cooperative needs to comply with the Co-operatives Act (1488/2001) and they have the same tax on profit as the limited-liability companies, 26 %. In addition to loans provided by banks, financial institutions and insurance companies, the cooperatives can get financing from government and municipal subsidies. (Mynttinen & Taipale, 2007) METHODOLOGY Efficiency is often defined simply as the ratio of the output to the input of any system (Cooper et al., 2000). It means the amount of desired outcome that is produced with a certain amount of resources. When looking at the whole picture, typical approaches to efficiency tend to focus on subjects like cost reduction (reducing inputs), quality control (reducing waste), technology investment and time management (increasing productivity). The ultimate goal thereby is to reduce inputs, increase outputs, or both at the same time in order to improve profitability. (Witzel, 2002) Some of the most commonly used dimensions of efficiency and their definitions are: - allocative efficiency: refers to the responsiveness of service to public preferences (Leibenstein, 1966)

4 - technical efficiency: measures a firm s success in producing maximum output from a given set of inputs (Farrell, 1957; Leibenstein, 1966) - price efficiency: measures a firm s success in choosing an optimal set of inputs (Farrell, 1957) - cost efficiency: refers to the least amount of inputs used to produce a fixed level of output(s) at minimum possible cost (Coelli et al., 2002) - scale efficiency: tells about the optimal size and scale of operations of an entity under efficiency analysis (Cubbin & Tzanidakis, 1998; Coelli et al., 2005). In this research, Data Envelopment Analysis (DEA) was selected as a tool to measure the efficiency of waterworks. It has been used in various studies to measure waterworks efficiency in different countries (Abbot & Cohen, 2009). As a non-parametric method, DEA concentrates on variations in performance among entities under comparison. It describes the subjects of efficiency evaluation as decision making units (DMU). These DMUs are thought to be any kind of entities, e.g. waterworks, that convert multiple inputs into multiple outputs, and the estimate of relative efficiency is derived from comparison of the ratios of total inputs used to total outputs produced by each DMU (Cooper et al., 2004). DEA uses linear programming techniques, which create an efficiency frontier that is used as a benchmark when comparing the DMUs with each other (Coelli et al., 2005). This frontier in a way envelopes the data leaving the measured efficiency values below the plotted curve in the graph (e.g. Cubbin and Tzanidakis, 1998). In the case of single input and output, the constant returns to scale (CRS) assumption is based on thought that all observed production combinations can be scaled up or down proportionally. This kind of DEA model is named as CCR-model (due to Charnes, Cooper, and Rhodes, 1978) and it is one of the two most widely used DEA models together with BCCmodel (due to Banker, Charnes, and Cooper, 1984). BCC-model uses variable returns to scale (VRS) assumption, which allows the returns to scale vary when observed production combinations are scaled up and down (i.e. it does not have to result in equivalent changes in efficiency level). (Cullinane et al., 2006) As an example of these two assumptions, there are 6 points (A, B, C, D, E, and F) in Figure 1 representing 6 companies, which each has different combinations of one input and one output. The straight line from origin represents the efficiency frontier under CRS assumption and the piecewise line going via points A, B, C, D, and E forms the efficiency frontier under VRS assumption. Only the company C is CRS-efficient, but all the companies A, B, C, D, and E are VRS-efficient. When comparing the efficiency scores derived from both methods, it is possible to separate the effects of technical efficiency and scale efficiency and get information about their proportions. Let us take a look at company F, which is inefficient under both CRS and VRS assumptions. The input orientated technical inefficiency of the company F under the CRS is the distance FH, but under the VRS the same technical efficiency is only the distance FG, while the difference between these two, the distance GH, is considered as scale inefficiency. So at its current size, company F should improve its position at least to the point G, if it would like to become technically efficient under the VRS and further improvement would require changes in the company s size. (Cubbin & Tzanidakis, 1998; Coelli et al., 2005).

5 C D VRS E B H Scale inefficiency A G Technical inefficiency F Figure 1: CRS vs. VRS, single input and single output example. Input oriented CRS model Assume there are data on N inputs and M outputs for each I firms. For the i-th firm these are represented by the column vectors xi and qi, respectively. The N I input matrix,, and the M I output matrix, Q, represent the data for all I firms. For each firm, a measure of the ratio of all outputs over all inputs is needed, let that be u qi /v xi, where u is an M 1 vector of output weights and v is a N 1 vector of input weights. (Coelli et al., 2005). The optimal weights are obtained by solving a mathematical programming problem: max u,v (u qi /v xi), subject to u qj /v xj 1, j = 1,2,,I, u, v 0. (1) This involves finding values for u and v, such that the efficiency measure for the i-th firm is maximized, subject to the constraints that all efficiency measures must be less than or equal to one. The linear programming problem is solved for each of the I firms in the sample, so each of these firms is assigned a set of weights that are most favorable to them. One problem in this formulation is that it has infinite number of solutions. (Coelli et al., 2005). To avoid this, one can impose a constraint v xi = 1, which provides: max µ,v (µ qi), subject to ν xi = 1, µ qj - ν xj 0, j = 1,2,, I, µ, ν 0, (2)

6 The change of notation from u and v to µ and ν is used to stress that this is a different linear programming problem. The form of the DEA model in linear programming problem 2 is known as the multiplier form. (Coelli et al., 2005). Anyhow, the generally preferred form to solve this problem is called the envelopment form: minθ,λ θ, subject to -qi + Qλ 0, θxi λ 0, λ 0, (3) Here θ is a scalar and λ is a I 1 vector of constants. The value of θ obtained is the efficiency score of the i-th firm. The linear programming problem must be solved I times, once for each firm in the sample. A value of θ is then obtained for each firm. (Coelli et al., 2005). Input oriented VRS model In a case when the firms face imperfect competition, government regulations, constraints on finance etc. the VRS assumption is more appropriate, because the CRS assumption is mainly for situations where all firms are operating at optimal scale. To separate the effects of scale efficiency and technical efficiency, the CRS linear programming problem can be modified to account for VRS by adding the convexity constraint: I1 λ = 1 to equation 3 to provide: minθ,λ θ, subject to -qi + Qλ 0, θxi λ 0, I1 λ = 1, λ 0, (4) where I1 is an I 1 vector of ones. Technical efficiency scores provided by VRS are greater or equal to those provided by CRS, thereby the number of efficient firms is often higher in VRS. (Coelli et al., 2005). Calculation of scale efficiency can be done by conducting both a CRS and a VRS DEA for all firms in the sample, and then decomposing the technical efficiency scores obtained from the CRS DEA into two components, one due to scale inefficiency and due to pure technical inefficiency. Figure 1 helped to explain the inefficiencies as distances, now let us take a look at how to express the technical and scale efficiencies as ratio measures. TE CRS is technical efficiency given by CRS, TE VRS is technical efficiency given by VRS, and SE is scale efficiency. (Coelli et al., 2005) These expressed as ratios from Figure 1 are the following: TE CRS = YH / YF TE VRS = YG / YF SE = YH / YG So the scale efficiency can be expressed as:

7 SE = TE CRS / TE VRS. (Coelli et al., 2005). Finally, in order to know if the firm is operating in an area of increasing or decreasing returns to scale, one has to run an additional DEA problem with non-increasing returns to scale (NIRS) imposed. This is done by substituting the I1 λ = 1 restriction in eq. 4 with I1 λ 1, to provide: minθ,λ θ, subject to -qi + Qλ 0, θxi λ 0, I1 λ 1, λ 0, (5) By comparing the inefficient firms VRS technical efficiency scores with technical efficiency scores obtained with NIRS, one can determine the nature of returns to scale. If a firm has an equal technical efficiency (TE) score from both the VRS and NIRS, decreasing returns to scale exist for that firm, i.e. the firm could become more efficient by decreasing its size when the productivity of its inputs would increase. If TE scores from VRS and NIRS are unequal, increasing returns to scale apply and the company could improve efficiency by increasing its size again. (Coelli et al., 2005). In a case where scale inefficiency exists and the company is otherwise technically efficient, the efficiency improvement is achievable only through a change in the size of a company, or otherwise it cannot achieve the maximum productivity for its inputs (Jaforullah & Whiteman, 1999). DEA model and explanation of its variables RESEARCH RESULTS The selection of the input and output variables that are to be included in a DEA model is a complicated exercise. Variables for this study were selected according to the previous waterworks efficiency evaluation studies which have used the DEA method. Different variables used in the past 20 years are listed by Abbot & Cohen (2009) and summarized in Table 1. Table 1: Variables used in water sector DEA studies (summarized from Abbot & Cohen 2009). INPUT variables personnel (numbers) / labour (p/t and f/t) operating expenditure energy length of mains capital expenditure chemicals other costs materials specific wastewater treatment costs proportion of water delivered to non-households management cost OUTPUT variables volume of water delivered number of properties connected/supplied population served length of mains annual water consumption sewage treated potable water volume of delivered potable and non-potable water physical amount of wastewater primary treatment secondary treatment

8 maintenance and operation costs capital replacement cost outside services groundwater, surface water, purchased water treatment plants storage capacity analyses performed number of assessments (services to properties) average pumping head average peak size of area served capacity of pumping water quality index water service index In this research, DEA model by Coelli and Walding (2006) has been applied. They used operating and capital expenditures as inputs and number of properties connected and volume of water delivered as outputs. Figure 2 shows the variables used in this research. Inputs Operating expenditure (Decision Making Unit = DMU) Waterworks Outputs Volume of delivered water (billed) Volume of treated sewage (billed) Capital expenditure Figure 2: DEA model used in this study. Number of water customers Number of sewage customers Operating expenditure variable as an input includes all operating costs of waterworks. It is formed by summing up the following income statement items: materials and supplies, change in stock value, outsourced services, wages and salaries, pension expenses, other indirect employee costs, and other operating expenses. Capital expenditure variable is formed by summing up the following income statement items: depreciations to plan and write-downs, interest expenses (all of them including interest expenditure to municipality, interest expenditure to others, income transfers to municipality), compensation for capital invested, and other financial expenses. Output variables include volume of billed water, volume of billed sewage, number of water customers and number of sewage customers. The idea was to create a DEA model that takes into account both of the two dominating factors in the water industry, economies of scale and density (Walter et al., 2009). When the capital expenditure as input is related mostly to the network density (sparsely populated areas usually have higher amounts of pipeline capital relative to the operating costs), it should ensure that the waterworks with highly dense networks are benchmarked with other similar high density waterworks, low density firms with other low density companies etc. (Coelli & Walding, 2006). In addition, the capital costs of waterworks and the length of water and sewage mains are tied strongly together (Coelli & Walding, 2006), so instead of placing the length of mains as a variable, it was included in the model without excluding the capital costs caused by other sources in a similar way. The number of water and sewage service customers as an output together with the measures for two basic services water delivery and sewage treatment (which can be seen as the main tasks for any waterworks) is

9 also related to the scale of operations. The bigger the ratio of the capital costs to the number of customers is, the more sparse tend the network structure be as well. Since one of the main goals of the study is the ranking of waterworks according to their efficiency, the total number of variables has to be kept low enough in order to avoid the loss of discrimination power the DEA method offers (Ajodhia et al., 2003; Tone & Tsutsui, 2009), because the comparison group is known to be relatively small. Data collection and DEA solver program The required data for the study is collected from the financial statements of the waterworks from the year 2008 (or the financial statement of a municipality in a case when the waterworks is operating as a municipal department). Choosing the financial statements as the primary source of data partly relates to the data quality requirement set by the DEA method. Common and tightly controlled rules for accounting are seen as the best insurance available against the errors in data. If all the necessary information was not found this way, the missing info was obtained from the waterworks Internet pages or by asking directly from the waterworks themselves. An efficiency analysis was carried out for a selected group of 19 waterworks (see Table 2). Waterworks from the most common ownership and governance models (municipal department, municipal owned enterprise, municipal owned company, and cooperative) were included in the study and for comparability all waterworks provide all core functions of water and sewage services (water acquisition and treatment, water delivery, sewage collection and sewage treatment). The results are analyzed so that the possible differences between different governance models efficiency can be identified. Table 2: Size and governance models of waterworks included in the DEA analysis. Number of water customers Municipal owned company (MOC) Municipal owned enterprise (MOE) Municipal department (MD) Cooperative (COOP) MOC 1 MOC 2 MOC 3 MOC 4 MOC 5 MOE 1 MOE 2 MOE 3 MOE 4 MOE 5 MOE 6 MOE 7 MD 1 MD 2 MD 3 MD 4 MD 5 COOP 1 COOP 2 < > 60000

10 The actual analysis has been carried out with the help of DEA solver program called Win4DEAP (version 1.1.2) which is developed by Michel Deslierres. The original DEAP program is written by Tim Coelli at the Centre for Efficiency and Productivity Analysis in University of New England. In final comparison, the input oriented model is selected along with the variable returns to scale assumption and DEA (multi-stage) calculation method. With these selections the program calculates the constant returns to scale (CRS) results also. This is important considering that under the variable returns to scale (VRS) assumption the inefficient waterworks are often benchmarked only against waterworks of similar size, but in a CRS DEA the waterworks can be benchmarked against substantially larger or smaller waterworks as well (Coelli et al., 2005). Results The final efficiency scores are shown in Table 3. The abbreviation crste means CRS technical efficiency under CRS assumption, vrste means technical efficiency under VRS assumption, scale means scale efficiency, and final column tells if the waterworks was considered to have increasing (irs) or decreasing returns to scale (drs) in its current situation. The crste scores compare all the waterworks with each other assuming that every one of them is able to reach the same efficiency level no matter their size. Vrste, instead, allow different levels of returns to scale occur, thereby resulting in better efficiency scores. Table 3: DEA efficiency scores for reviewed waterworks. Waterworks crste vrste scale irs/drs/- MOC 1 0,954 0,968 0,986 drs MOC 2 0,539 0,782 0,690 drs MOC 3 0,834 1,000 0,834 drs MOC 4 0,719 0,761 0,946 irs MOC 5 0,810 0,810 1,000 - MOE 1 1,000 1,000 1,000 - MOE 2 1,000 1,000 1,000 - MOE 3 1,000 1,000 1,000 - MOE 4 0,950 1,000 0,950 drs MOE 5 0,870 0,895 0,972 drs MOE 6 0,934 1,000 0,934 drs MOE 7 1,000 1,000 1,000 - MD 1 0,893 1,000 0,893 irs MD 2 0,811 0,826 0,982 irs MD 3 0,830 1,000 0,830 irs MD 4 0,861 0,998 0,863 irs MD 5 0,571 0,619 0,922 drs COOP 1 1,000 1,000 1,000 - COOP 2 0,712 0,793 0,897 irs The scale efficiency score is derived from the crste and vrste scores. So when e.g. MOE 4 has a crste value of 0,950, it means that it is reaching 95% of the efficiency that it should achieve, i.e. it should be able to reduce its use of inputs by 5% and still be able to produce the same amount of inputs. MOE 4 seems to get a vrste score of 1, so its inefficiency can be seen to result solely because of its wrong size (drs = too big). However, waterworks are not

11 necessarily the best targets for specific size determination, because they have their area of operation and they usually cannot change it radically in the short run. Consequently, this research concentrates mainly on crste and vrste efficiency values which are shown for reviewed waterworks in Figures 3 and 4. Figure 3: crst scores of reviewed waterworks. Figure 4: vrst scores of reviewed waterworks. Research results seem to indicate, that municipal owned enterprises (MOE) are clearly best performers among the reviewed waterworks. The efficiency scores of MOEs are generally very good and it seems that MOE is the only governance model getting consistently good scores. Presupposition was that the municipal owned companies (MOC) would be generally efficient because of their greater freedom and independence compared to MOE and municipal department (MD), but the results showed poorer overall performance compared to MOEs. MD s showed systematically inferior scores compared to MOEs and MOCs although the municipalities may have their ways to make MD s look like more efficient than they really are by means of creative accounting.

12 Research results were tested for statistical significance with the Kruskal-Wallis and Mann- Whitney tests. The Kruskal-Wallis test showed that there exist statistically significant differences between some of the groups. Mann-Whitney tests revealed that the relation between MOEs and MDs is statistically significant, which means that MOEs are more efficient than MDs when measuring them with the DEA model. Despite these results, the MOCs seem to be doing worse than MOEs as well, which could have been verifiable with a larger sample size. DISCUSSION It was noticed during the research process, that there are numerous factors affecting the efficiency of waterworks which were not completely included in examination. Most of these exclusions were unwanted though, and caused by the lack of recorded information or homogeneity constraints of the group. The literature and managerial interviews clearly pointed out that the comparisons of waterworks should take into account also quality and operation reliability factors. In order to achieve a balanced and applicable DEA model, these factors were however excluded since the number of variables was already at its maximum limit. This is a consequence of the small size of the comparison group of waterworks, which in turn is caused by homogeneity, representativeness, and applicability constraints. By excluding quality and reliability issues from the comparison, the reasons behind the different costs at different waterworks are not completely clear. Anyhow, inclusion of the quality and reliability issues in the same DEA model could have resulted in failure, because their cost correlation is not clear. Before there is evidence how to balance different quality factors in a way that their effect on total costs would be equal, it is hard to use them on similar grounds for cost related evaluation purposes. The quantity, quality and availability of information are also somewhat hindering the implementation of accurate and plausible benchmarking studies. Despite the changes in law to improve transparency of waterworks there are still problems with its application. According to the research, it seems that in some cases all the costs are not recorded in waterworks departments balance sheets, but instead in the balance sheets of municipality thereby blurring the real financial situation of the waterworks. In addition, there may be difficulties in defining the accurate cost allocation of billed and delivered water and billed and treated sewage since many waterworks sell water and/or sewage treatment services to other waterworks. Also in reality, for example rain waters increase (often more than double) the amount of sewage treated thereby causing costs that are not directly covered by sewage fees. A similar effect can be seen in the amount of billed water, which does not include the unaccounted-for-water which is lost and never gets to the customer. CONCLUSIONS The objective of this study was to find out if there are any differences in efficiency levels between waterworks governance models. Data envelopment analysis (DEA) was selected as a research method since it has been widely used in previous waterworks benchmarking

13 studies and seemed to offer the best tool for assessing efficiency of different governance models. Previous literature has introduced dozens of different variables for DEA studies from which the most suitable ones were selected by taking into account the constraints of information availability and homogeneity of comparison group. Input variables used in this study were operating expenditure and capital expenditure and output variables volume of billed water, volume of billed sewage, number of water customers and number of sewage customers. The results of this study indicate that there are differences in waterworks governance models efficiencies. Statistically significant difference between the reviewed waterworks was found to exist among municipality owned enterprises and municipal departments, where municipality owned enterprises were generally more efficient ones. Municipality owned companies had very unequal efficiency results and the sample group of cooperatives was very small, why statistical difference could not be found regarding these groups. Furthermore, because the sample group included only 19 waterworks, these results cannot be generalized to the national level or too general level. During the research it became clear that waterworks efficiency research has a lot of constraints to overcome of which availability of transparent and accurate information is the most prevailing one. Perhaps this has been contaminated by the heterogeneous public management policy. The efficiency scores from this DEA study are thus suggestive and they cannot be taken as an absolute truth because of many influencing factors in the background. This research was the first attempt to compare efficiency of waterworks governance models in Finland. It gave interesting and somewhat promising results which should encourage researchers to investigate the matter further. This research should be used as a foundation for improved and perfected models in the future, since the findings seem to be logical. Future research could for example investigate how strongly each quality and reliability factor correlates with the total costs in order to ease the future efficiency evaluations and add also quality aspect into it. In addition, larger sample groups and more data should be collected in order to get more reliable and generalizable results. REFERENCES 1. Abbott, M., & Cohen, B. (2009). Productivity and efficiency in the water industry. Utilities Policy. 17 (3-4), Ajodhia, V., Petrov, K. and Scarsi, G.C. (2003). Benchmarking and its Applications. Zeitschrift für Energiewirtschaft. 27 (4). 3. Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research. 2 (6), Coelli, T.J., Rao, P., O Donnell, C.J. and Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis. New York: Springer. 5. Coelli, T., & Walding, S. (2006). Performance measurement in the Australian water supply industry. In T. Coelli, & D. Lawrence (Eds.), Performance measurement and regulation of network utilities. Cheltenham: Edward Elgar Publishing. 6. Coelli, T. J., Rao, P. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York: Springer.

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