Production cost structure of the water and sewage industry An econometric cost analysis of urban water supply and sewage treatment, with an application to a section of Swedish communities
MADELENE MALMSTEN
Master of Science Thesis Stockholm, Sweden 2008
Production cost structure of the water and sewage industry An econometric cost analysis of urban water supply and sewage treatment, with an application to a section of Swedish communities
Madelene Malmsten
Master of Science Thesis INDEK 2008:64
KTH Industrial Engineering and Management Industrial Management SE-100 44 STOCKHOLM
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Master of Science Thesis INDEK 2008:64
Production cost structure of the water and sewage industry An econometric cost analysis of urban water supply and sewage treatment, with an application to a section of Swedish communities
Madelene Malmsten
Approved
Examiner
Supervisor
2008-09-08
Thomas Sandberg
Thomas Sandberg
Commissioner
Contact person
Dimitrios F. Lekkas
Dimitrios F. Lekkas
Abstract In this thesis, I have studied how we can capture the financial characteristics of the underlying production technology for water and wastewater systems. More broadly, I focus on how we can get hold of the cost structure of a water and wastewater system or total industry. After determining the (or a) cost structure of a system, the system can be analysed in terms of e.g. efficiency, technology, growth, capacity, expenses etc. This thesis may serve as a bridge between mathematical models on the one hand and the water and wastewater industry on the other hand. Foremost, the research has been established on the basis of literature studies. The literature review is related to an empirical study of Swedish utilities. The estimation procedure is based on a multivariate regression approach, using a Transcendental Logarithmic cost function. The estimated cost structure in this thesis makes it possible for one to assess a few characteristics of the Swedish industry. The cost structure may be relevant for other systems of the same manner as for those that have been chosen for this study. The efficiency characteristics of the Swedish industry are expressed in terms of Economies of production output density, Economies of customer density and Economies of scope. This thesis urges that the fundamental statistics are missing for the Swedish case. In order to be able to create a more throughout analysis, the quality level of information [3]
has to be increased. It is an ongoing process for the Swedish Water & Wastewater Association to establish the underlying statistical framework. A few conclusions can be drawn from the study. For instance, there exists Economies of scope between water and wastewater in the industry. There are cost advantages of a joint production of water and wastewater. The estimations are based on a sample of utilities (25 of them) serving 1 community. The larger utilities exhibit Diseconomies of production output density and Diseconomies of customer density, while the smaller utilities in the sample exhibit Economies of production output density and Economies of customer density.
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Table of Contents Abbreviations
7
Preface
8
Acknowledgements
9
1. Chapter 1: Introduction 1.1. Background 1.1.1. New features of the water and wastewater industry 1.1.2. Two concepts: Actual cost estimation and cost structure representation 1.2. Problem statement 1.3. Purposes of the study 1.4. Outline 2. Chapter 2: Methodology 2.1. Ontological and epistemological platforms 2.2. Research approach 3. Chapter 3: Characterization and modelling of the water and wastewater services 3.1. The econometric analysis 3.2. The water and wastewater service system 3.3. Water as an input and output variable 3.4. Cost categories 3.5. Challenging features of the cost structure analysis 3.6. Modelling under real life conditions: a step-by-step approach 3.7. Identification of possible cost and scale drivers: The explanatory variables 3.8. Structural-conceptual representation of the service system 3.8.1. Perspectives 3.8.2. Production growth 3.8.3. A few important concepts 3.8.4. Production patterns 3.9. A mathematical framework 3.9.1. An approach to derive cost functions 3.9.2. The Transcendental Logarithmic cost function 4. Chapter 4: Empirical study and analysis 4.1. Capture a representation of the total costs: procedure and limitations [5]
10 10 10 12 13 13 13 15 15 16 18 18 19 21 22 23 24 26 29 29 30 31 34 37 37 40 42 43
4.2.
Modelling the structure of variable costs, an application to a sample of Swedish communities 4.2.1. The structural representation of the model 4.2.2. Data description 4.2.3. Data sorting and selection 4.2.4. Estimation procedure with a multivariate regression approach 4.2.5. Parameter estimates 4.2.6. Analysis: Understanding the parameter figures. Estimating Economies of production output density, Economies of customer density and Economies of scope 5. Chapter 5: Summary and conclusions 6. References
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44 44 45 48 50 52
54 61 63
Abbreviations OFWAT: The Office of Water Services in England SWWA: The Swedish Water and Wastewater Association TC: Total cost TVC: Total variable cost WFD (The Directive): European Union Water Framework Directive
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Preface This thesis has been conducted in cooperation with the Department of Statistics and Actuarial-Financial Mathematics at the University of the Aegean, Karlovasi, Greece and the Department of Industrial Engineering and Management at the Royal Institute of Technology, Stockholm, Sweden. It constitutes the final part of a Master of Science Degree in Industrial Engineering and Management at the Royal IT. The assignment was essentially carried out between the 23rd of January and the 30th of June 2008. The formation of this paper progressed during discussions between the author and the commissioner about how to build a robust mathematical representation of the water and wastewater services for the main purposes of 1) understanding the underlying production technology, 2) capture the cost structure of the services, 3) estimating the total costs of the services and 4) measuring the “behaviour” of the costs when the service system changes in size and structure. This thesis focuses on (1), (2) and (3), since (4) is beyond the means of this paper. It requires much information and a lot of time for investigation. The initial idea and desired venture was to capture the economic characteristics of the underlying production technology for one or several Greek utilities, or for the Greek water and wastewater industry as a whole. The analysis would thereby reflect the municipal units of water and wastewater under the local conditions. Due to limitations in data quantity and quality, the application could not be carried out successfully. However, the numbered topics shown above are of interest for the global water and wastewater industry. For that reason, the initial idea was not neglected totally. As an alternative, the water and wastewater industry in Sweden was chosen for the empirical study. The Swedish Water & Wastewater Association holds cost analysis as one of the main points on its list of research priorities. The issues, viewpoints and findings presented in this thesis may therefore contribute to earlier works in the research area of water and wastewater services, especially for Sweden.
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Acknowledgements I would like to send my appreciation to Dimitrios F. Lekkas at the University of the Aegean for initialising this project and for offering a lot of time to discuss the features of the water and wastewater industry. Additional thanks to my supervisor Thomas Sandberg at the Royal Institute of Technology for the helpful comments in order to successfully establish this thesis. Furthermore, I would like to express my gratitude to the Swedish Water & Wastewater Association (Svenskt Vatten AB) and personnel for providing essential data samples needed for the analysis. Not to fail noticing, thanks to the two parties in Greece for being helpful and interested in providing data, although the information was not enough to make an empirical study. Finally, thanks to my family and friends for all the love and support.
Stockholm, June 10th 2008 Madelene Malmsten
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Chapter 1 Introduction 1.1 Background 1.1.1 New features of the water and wastewater industry During recent years, essentially during the last seven-eight years in Europe since the establishment of the European Union Water Framework Directive (WFD), the protection, improvement and usage of water sources have become important subjects of discussion. This has initiated stronger quality demands from regulators on the operations of water and wastewater establishments. The Directive was produced by the European Commission and updates the exiting water legislation. Its key objective is to drive the achievement of sustainable water resource management, which involves an aim to achieve at least “good status” of all waters by the year 2015. The strong environmental awareness spreading around the world and the proceeding infrastructural changes due to globalization have caused the need for deeper understandings about how water and wastewater establishments should be organised, re-designed, re-built and expanded. In some parts of the world, for example in China, a large percentage of the water and wastewater facilities are old and the life time of the technology is reaching an end (City Paper, Nanchang Municipal Government). At least parts of the total system are in need for improvements or replacements. We can expect to find the same conditions in other urban water and wastewater facilities around the world due to the fact that many of them were first developed several years ago, as in the beginning of the 20th century. The infrastructural changes may be one of the 21 st century’s biggest challenges, by listening to concerned parties (see for example Clark R. M. et al (2002), eNewsUSA, European Public Health Alliance among others). The supply of water to water consumers and the treatment of wastewater should preferably be performed in an effective and environmentally friendly manner. The new standard requirements affect the supply and treatment plants in many ways, i.e. technologically, organisationally and economically. The changes may well mean higher supply and treatment costs, leading to higher service rates to customers.
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There are many new forces behind the ongoing development of water supply and wastewater treatment. Private investors have shown their interest in the industry. The extents to which these forces are active differ from country to country. In England, the water and wastewater facilities are privately owned and are mainly regulated by the OFWAT authority (The Office of Water Services). In France, the individual communities are responsible for controlling the facilities and many of the communities (75 percent) have chosen to let private actors run the operations (Thomasson et al, 2003). Quite the opposite holds for Greece, where most of the facilities (60-75 percent) are municipally owned and controlled (E.Y.D.A.P, 2006). In Sweden, there are a few privately owned facilities but most of the organisations are operated by the individual municipality (Thomasson et al, 2003). Communities intend to cooperate more and more across borders. Potential cost savings can be made from the consolidations, especially for small water and wastewater organisations that have difficulties keeping up with the new quality and investment demands. The size of a water and wastewater system is determined by the needs and demands from the surrounding environment. In order to reach economic and financial efficiency, the services have to be operated within an appropriate size and structure. Among other issues, the Directive stipulates cost analysis of the water and wastewater services. This includes estimations of the economic impacts that water and wastewater services have on the environment. Moreover, it includes studies of financial costs of the water and wastewater services (Camp Dresser & McKee Ltd, 2004). Not only regulators are interested in the analysis of costs. A matter of course is that the water and wastewater organisations want to have information about their costs in order to control their activities. This motivates the importance of cost analysis and cost estimation. There are of course many other reasons why organisations are interested in estimating the costs of supply and treatment. One of these reasons is pricing principles. The two and only effective pricing principles are 1) prices based on the costs of water and wastewater services and 2) prices based on market pricing (FAO, 1996). As written earlier, the water and wastewater industry is in a changing era. The growth creates new infrastructural patterns. How do we capture the economic and financial impacts on the water and wastewater utilities due to the growth? How do we know in which dimension the growth will scatter? As a first priority, are the existing water and wastewater systems (or the industry as a whole) operating efficiently and can the systems handle the rising changes successfully?
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1.1.2 Two concepts: Actual cost estimation and cost structure representation To capture the cost structure of a single system (or a whole industry) is not necessarily the same as to capture the actual costs raised by the system. The cost structure should preferable be seen as the “behaviour” of the underlying technology. The cost structure can be used for the purpose of measuring the “monetary behaviours” when for example a service system changes in structure and/or in size. As will be shown in the theoretical framework and in the empirical study, the cost structure is represented by a parametrical expression, a so-called cost function.
Definition of cost structure: “A cost structure is the distribution of costs among the elements of the process. The term ‘cost’ can be used generally to refer not only to particular financial costs, but the use of resources generally. Roughly determining the cost structure of a process involves understanding the individual steps and resources that are consumed by the process, and characterizing how they are allocated and how they scale with size of the task. Determining the cost structure is a useful step in any effort to make a task more efficient, because it helps to focus attention or technology on the places where it can be most effective” (Palo Alto Research Centre, 2008) The cost structure of a system can be analyzed in order to assess efficiencies and inefficiencies in the system. The information obtained from the analysis can be useful in many other situations, e.g. 1) When planning new construction and/or replacement projects. It can be seen as a complement to actual cost estimations. 2) When pricing the water and wastewater services. 3) When evaluating the financial situation of a system (or a total industry) On the other hand, actual costs are more accurately estimated by using bottom-up procedures. This means that the costs of activities are estimated separately and added up to a total cost. Ramirez (2001) has used the bottom-up approach to estimate total cost of a water and wastewater utility. He estimated the costs for each activity of the water and wastewater system. Each part of the system was evaluated in terms of costs; the investments of the constructions and the operational costs of working the system. The model developed by Ramirez (2001) has been practically implemented and is a part of the research programme “Sustainable Urban Water Management”, by CIT Urban Water Management AB in Sweden. The project ended in June 2006. The model exists now as an Excel-file in Swedish where the user can choose inputs of infrastructure, water and wastewater treatment, recycling etc.
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1.2 Problem statement The research question is formulated as follows: How can an economic/mathematical model be created and designed in order to 1) assess the (or a) cost structure of the water and wastewater services and 2) measure the “behaviour” of the costs when the service system changes in size and structure? The question is both of a theoretical and a practical nature. For that reason, the question will be approached and discussed from theoretical and practical points of view. There is room for personal opinions and judgement, since the question does not impose any absolute truths. Additional characteristics of the research question are mentioned in Chapter 2: Methodology.
1.3 Purposes of the study The study has several objectives. The main purposes are to examine how the economic/mathematical model can be created and designed, to understand and describe how we can assess the (or a) cost structure of the water and wastewater services. This includes forming an understanding and an opinion about what variables should be included in the model. Another purpose of the study is to try to create a bridge between the mathematical models presented in this study and the water and wastewater services. To my knowledge, there is not much literature written about the relationship between the mathematical models so often used in the econometrical literature and the theoretical framework underlying the models. This thesis is also a direct contribution to the Swedish research field of water and wastewater. The empirical study applies parts of the theoretical framework on a section of Swedish communities, each responsible for the local water and wastewater system.
1.4 Outline This thesis is organized as follows: Chapter 1 introduces the topics of this thesis, states the research question and purposes of the study. Chapter 2 describes the methodology in terms of ontology, epistemology and the principles of the research approach. Chapter 3 presents theory in order to understand the industry and the following reasoning in Chapter 4. Chapter 4 holds the empirical study, which estimates a cost structure for a section of Swedish water and wastewater organisations. The subsequent chapter also [13]
contains an analysis of the estimations. Chapter 5 summarizes the conclusions of the study.
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Chapter 2 Methodology This chapter specifies the methodological choices underlying the thesis. This includes a description of ontology, epistemology and research approach. Ontology is the fundamental assumptions about the nature of the world and epistemology refers to the way one chooses to collect knowledge.
2.1 Ontological and epistemological platforms The aims of this study are, firstly, to recognize the courses of action (to understand) and, secondly, to answer the research question (to explain). The analysing tools are modelling and interpretation. The aims have been accomplished by looking at the objectives partly from a positivistic point of view and partly from a hermeneutic point of view. I use the following example (1) to illustrate how my research question could have been formulated if I had possessed either a positivistic view or a hermeneutic view and finally how my research question has been formulated.
Example 1: The research question A strictly positivistic influenced question could be: “How is an economic/mathematical model created and designed in order to 1) assess the (or a) cost structure of the water and sewage services and 2) to measure the “behaviour” of the costs when the service system changes in size and structure?” On the other hand, a hermeneutic influenced question could be: “How does performer number 1, 2, 3, etc. believe that an economic/mathematical model should be created and designed in order to 1) assess the (or a) cost structure of the water and sewage services and 2) to measure the “behaviour” of the costs when the service system changes in size and in structure?” My research question (influenced by the two perspectives): “How can an economic/mathematical model be created and designed in order to 1) assess the (or a) cost structure of the water and sewerage services and 2) measure the “behaviour” of the costs when the service system changes in size and structure?
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The positivistic viewpoint issues an objective reality and a materialistic ontology. Thus, a model is a reflection of the reality. This viewpoint is traditionally followed by quantitative methods. The epistemological platform is “the experience”. In contrast, the hermeneutic viewpoint is based on subjective opinions, a non-material ontology and the analysis is mostly qualitative. The epistemological platform is the sense, “the reasoning”. Due to the fact that I do not see the reality with an explicit positivistic or hermeneutic point of view, my research question is influenced by both of the aspects. It is probably not very unique to possess this two-folded point of view. I do not apply a rigorous critical theory, which means that I do not seek total renovation with my analysis. Nevertheless, I do have the ambition to make a contribution to the research field by adding perspectives that I believe have not been addressed (sufficiently enough) in earlier studies. The empirical applications add to this value creation.
2.2 Research approach I have approached the problem on a base of deductive reasoning i.e. I have taken part of existing knowledge through literature studies and I have related the review to an empirical study. However, I have not strictly abided by the deductive approach. Some parts of the thesis have its roots in theory and others in practise, some parts are more quantitative and others more qualitative. Johansson – Lindfors (1993) calls this approach “The golden mean”, which stands for a more open relationship to one’s choice of method. Statistical and mathematical techniques have been combined with economic theory and methodology. The problem statement results in both qualitative and quantitative analysis. Considering the formulation of the question, one might simply settle with a qualitative analysis. However, this approach does not give an answer to if the model can be applied practically on a real-world case. If the model contains certain characteristics that can be best understood by measuring them, it is worth doing so i.e. to “operationalize” (Eriksson & Wiedersheim – Paul, 2006). I stand by the belief that it is possible and necessary to quantify my qualitative statements in order to make any useful conclusions. To summarize my choice of method, I herewith answer the three main methodological questions stated by Johansson – Lindfors (1993): How? Where? Why? The following scheme includes these questions with my corresponding answers. A forth question has been added and refers to how I have used, analysed and interpreted information. [16]
• How have I searched for information?
HOW?
By conducting a literature study (=> “the approach”), reviewing the findings and by speaking to some of the industry authorities in order to take part of existing information (=> “the direct information collection method”).
• Where have I searched for information?
WHERE?
Essentially, I have searched in research papers, articles, consulting reports, journals, books and other forms of literature on the internet and in nearby libraries. Keywords in the study are for example Water and Wastewater Industry, Science , Technology and Management, Cost Modelling and Analysis. Some of the references are strictly industry specific and others refer to a wider public. The numerical data was collected by contacting the authorised individuals in the industry.
• Why have I chosen this reseach approach?
WHY?
The problem statement is of a complex nature and for that reason the reliance on existing papers were a preferable choice. Most of the needed information was seeked out in the literature, mainly by reviewing research papers. I believe that this method brings about the best quality of this thesis, taking into account the underlying resources of this project.
• How have I used, analysed and interpreted information? INTERPRETATION
The numerical information have been used, analysed and interpreted by applying statistical and econometrical methods. Some shortcomings in the reviewed papers have been identified in order to try to give new theoretical contributions to this research field. The practical applicable process goes hand in hand with the theory developing process.
Figure 1: Summary of the choice of method. Source: Author.
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Chapter 3 Characterization and modelling of the water and wastewater services This chapter is a theoretical framework, which is the result of a literature review on the characteristics of water and wastewater services. It presents the emerging costs of proving the services. The chapter intends to give the reader a brief introduction to the field and in the same time it focuses on what needs to be understood in order to produce a cost structure model that could be applied to a number of water and wastewater systems. The chapter highlights a discussion about how one might choose to represent and analyse a water and wastewater system.
3.1 The econometric analysis Econometrics is the study of estimation and inference for economic (or financial) models using economic (or financial) data (Hansen, 2008). The main difference between an economic analysis and a financial analysis is that the first one aims to improve the social well being of society in terms of income and the second one aims to improve the financial situation of the operating entity (Asian Development Bank, 1999). This means that the economic analysis estimates the economic impact on the country’s economy and the financial analysis estimates the financial impact on the operating enterprise. Financial analyses ignore to value externalities that capture the additional social costs, such as the environmental impacts of the enterprise’s operations (Gordon, 2005) Externalities are valued in the economic analysis in order to sustain the health of society and environment. A core part of the economic analysis is to identify the extent to which economic values are reflected in pricing policies (Taylor, Stone and Webster Consultants). I have made a financial analysis, by applying the direct cost approach. Direct costs are the investments and operating costs incurred by the responsible agency in production of the service (Gordon, 2005). The analysis in this study involves an application of econometric tools on the financial data. [18]
3.2 The water and wastewater service system The framework of this thesis includes the following parts of any water and wastewater organisation: I.
Water Treatment Plant, WTP (including buildings, machinery etc.)
II.
Distribution, DN (including pipes, pressure and pumping stations, reservoirs, valves, fire hydrants and other apparatus)
III.
Sewerage, SN (including sewage pipes, storm water pipes [combined or separated], pumping stations, valves and other apparatus)
IV.
Wastewater Treatment Plant, WWT (including buildings, machinery etc.)
Figure 2: A conceptual framework of the water and wastewater service, with a few descriptive variables of the complex system. Source: Author.
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The following figure (3) is an example of how to simplify the representation of a service system, developed by Ramirez (2001).
Figure 3: System structure, Ramirez (2001) Urban Waters
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3.3 Water as an input and output variable Water can be seen as 1) a final physical good and/or 2) a service that is provided to markets (customers) having certain demand characteristics (Kim, 1991). In this thesis, water is regarded as a service rather than a physical good. Except this, water can be mentioned in terms of input and output. The notions output, input and water consumer might be confusing in the bigger context. Down follows an explanation to how the terms are defined in this paper. Water is an output from the production process to the water distribution system and it is later in its impure form an input to the purification system (wastewater treatment). The customers are the water consumers. First, water is produced and distributed through networks to connected customers. Second, sewage water from connected customers is collected through wastewater networks to the purification process. For a joint water and wastewater organisation the resulting production output is therefore the water and wastewater service. Literally, by service we mean the procedure of supplying water and treating wastewater. Numerically, as will be shown in the mathematical framework, volumes of supplied water and treated wastewater are the production outputs, i.e. they are variables that represent the service.
Customers = water consumers Water Treatment Plant
Sewage water input = sewage water collected Sewerage
Water output = water delivered Distribution
Wastewater Treatment Plant
The Water and Wastewater services Figure 4: Clarification of a few production terms. Source: Author.
Water delivered: estimated volume of water supplied to the boundary of each customer’s property (OFWAT report 2005-06).
Sewage water collected: estimated volume of water returned from customer’s properties to the wastewater network (OFWAT report 2005-06).
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3.4 Cost categories The costs of water supply and wastewater treatment can be divided into essentially three main elements: financial, environmental and resource costs.
Financial costs: the costs of producing and distributing water, as well as the costs of managing the wastewater treatment. The financial costs consist of
Capital costs (incurred by purchased assets) -
Depreciation charges (based on life-expectancy of assets) Interest rate costs
Operational & maintenance costs, O&M (incl. or excl. improvements, emergency and preventive maintenance) - Labour/Employment costs - Electricity/Energy costs - Material costs - Other contracted services
Environmental costs: basically the costs of any environmental deterioration expressed as monetary value. Environmental externalities are much more difficult to assess then the financial statements.
Recourse costs: opportunity costs, i.e. the costs incurred by choosing one option over an alternative option.
Other relevant cost categories that are used in economic analysis are fixed, variable, average, marginal, unit, short- and long-run costs.
Fixed costs are costs that do not vary in line with the production. Variable costs are costs that vary in line with the production. Average costs of supply and treatment can be estimated by dividing total cost of supply and treatment with the total volume of water produced and treated.
Marginal cost of water supply and treatment is calculated as the derivative of the total cost with respect to water quantity. The marginal cost is the change in total cost when the quantity of water changes by one unit.
Unit cost is simply a cost per unit, for example €/quantity, €/connection, €/hour, €/kWh etc. Depending on policies, the costs can be determined on a short- or long term basis. Residential and non-residential water consumers are charged a price (tariff) based on the costs per unit of consumed water and an on additional fixed cost per year (a flat rate or fixed rate). The former reflects the short term use and the later the long term
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use. The charges cover the costs of operations, maintenance and capital to some extent and the rest is put on local authorities and financed through taxes. The industry of providing water is very capital-intensive, second to the energy sector (Donald, 2003). Salaries represent one of the largest groups of expenses. Expenses that vary in line with the volume of produced water are for example expenses for energy (in pumping) and chemicals (for treatment). Due to modern technical interventions and improvements the costs of desalination and electricity are expected to be reduced in the coming years.
3.5 Challenging features of the cost structure analysis C. Barucq et al. (2006) state a number of difficulties in analyzing and comparing costs of water and wastewater services across different cities: 1) “In the case of not having accounts separating water and wastewater services, assumptions have to be made in order to distribute expenses between the two services, introducing a margin of error in the reassignment of costs.“ 2) “Certain investment financings by a municipality, region, State and/or European fund could not always be clearly identified or precisely estimated”. 3) “Accounting structures vary from one capital to another.” 4) “When several players are involved in managing the services consolidating on total costs is complex and sometimes difficult to achieve. “ 5) “Differences in production factor costs among public and private operators are a product of national features and cannot easily be interpreted in an international system” The British regulator OFWAT makes an important observation (report 2005-06) concerning capital charges, namely that there exists different accounting practices between water companies. This may complicate the procedure of making comparisons of costs between companies and communities. In Sweden, according to SWWA Department of Development (report 2007-13) the capital costs are in most cases reported as an entirety cost, i.e. no separate accounting for each separate part, such as the distribution. SWWA has introduced a fictitious/theoretical capital cost for the pipe network based on the average age of the pipe system, its structure, climate conditions and topography. SWWA uses the same depreciation and interest rate for every community. In need of financial figures, consulting reports are accessible information sources. In these reports, many figures are approximations and not actual real values. The reports [23]
are quite easy to get in hand, but they often include a lot of weaknesses (Kärrman, Olin et al. 2006), i.e.
They are only judgements of the costs, often made before a project has started. Environmental circumstances might be missing. They include preliminary technical forecasts, such as amount of customer connections. The O&M costs are often just presented as outlines (as a percentage of total investments). They have not always taken into account the regulation requirements from authorities. They are based on the individual knowledge of the planner or investigator.
There are important facts to keep in mind when determining statistical models of water and wastewater systems, or of any other systems for that matter (John Cubbin, 2004): A) B) C) D) E)
Even with totally accurate data and models, estimates are just estimates. Estimates are subject to sampling errors as long as there are limited observations. There may be errors in the measurement of the dependent variable. There may be variables excluded from the analysis. The explanatory factors may themselves be proxies and/or subject to errors of measurement. F) The wrong mathematical form may have been chosen to approximate the relationship between costs and their drivers. Cubbin (2004) also states that simplicity is a premium when there are few observations, as is often the case in water industry econometrics. This means that one have to have knowledge about the industry in order to not have too many omitted variables and determining factors.
3.6 Modelling under real life conditions: a step-by-step approach The British regulator OFWAT presents a practical step-by-step approach to derive statistical cost models (Report 2005-06, Appendix 1). Noticeably, my study does not contain such a sophisticated step-by-step procedure, because the needed resources for that kind of endeavour are missing (i.e. excessive time and expert judgment). Some steps are not being considered in this study.
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The OFWAT approach
A modified approach for this study Modified: Expert reviews are represented by the studied research reports.
Performed
Modified: System structure analysis.
Performed
In practice, there are a number of iterations through steps 5, 6 and 7 as we develop and refine the models
Modified: A) Statistical analysis to generate relationships between expenditure and a few explanatory factors (a cost structure). B) Assessing economic measures. C) Conclusions and judgments.
Figure 5: A step-by-step approach to derive statistical models. Source: OFWAT (Report 2005-06, Appendix 1).
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3.7 Identification of possible cost and scale drivers: the explanatory variables As been mentioned in the introduction, it is important for the reader to understand the different concepts of Cost Structure Representation and Cost Estimation. With the term “cost model” or “cost function” we mean a model that captures the technological and structural characteristics of a system, in terms of costs. We do not aim at a model that estimates the actual costs (capital costs and variable costs) of a system, at least not as a first resort. The model can be used for the purpose of studying in what direction and to what extent the costs of a business get affected by a system change. For example, by holding a representative mathematical cost structure of a system and by knowing that the marginal cost depends on the production quantity, we most certainly have enough information in order to calculate numerically how the marginal cost gets affected by the production quantity. We (simply) take the derivative of the total (annual) costs with respect to the (annual) quantity. A well behaved cost model can also capture how other components affect the marginal cost, e.g. how prices and operational variables (distance, network, density etc.) affect the marginal cost. An important step in modelling is to take a stand on critical components. Unit costs have here been recognized as critical components, e.g. Cost/hour (Labour force), Cost/kWh (Energy), Cost/material index (Material) and Cost/capital index (Capital). As will be shown in the empirical study, these unit costs enter into the cost model but they are expressed as unit prices instead: Price/hour, Price/kWh, Price/material index and Price/capital index. Principally, we assume that the water and wastewater utilities are price takers on the factor market. The prices are fixed and consequently the arising costs. (Stone and Webster Consultants Ltd, 2004) Except prices, there are other components that affect the costs for the water and wastewater utilities: structure and length of the pipe networks, the type of treatment plants, how closely people live to each other (Customer density), the distribution of customers (Industrial users, Domestic users and Public users), the quality of the water etc. It is desirable to construct a model that captures all of these components. However, it seems reasonable to believe that it is difficult to construct such a model. The amount of components is thousands. If we wish to characterize the water and wastewater services by modelling, it might be necessary to separate the services in different parts and construct models for these individual parts. This case is the normal case in Cost Estimation. Reasonably, it is not a big deal. However, complications arise when we want to represent the business in simply one model, which may be the case when we want to fit a cost structure for the total industry. For example, Hanemann (1998) says that the economic connection between water and wastewater can be neglected if the systems are analysed separately:”The systems may involve different technological choices, but [26]
the choices are inter-dependent: the costs of water supply are likely to affect decisions on waste disposal, just as the costs of waste disposal are likely to affect decisions on water intake and use.” Figure (6) is taken from OFWAT and contains the critical components that they use for benchmarking the English water and wastewater firms. These critical components represent expert judgments of potential cost drivers in the industry. A few of these components have been used in the empirical study. Generally, what variables we choose to include in the model is a judgment between variables we want to get explained by the model and variables we have to include in the model in order to fulfill its constraints.
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Operational expenditure
Explanatory variables included in the model
Capital maintenance expenditure
Water distribution model
Resident population (scale)
Water distribution model
Length of main (scale)
(log model)
Small/Big mains (cost drivers)
(log unit model)
Connected properties/length of main (cost driver)
Water resources and treatment model
Resident population (scale)
Water distribution model (noninfrastructure)
Pumping station capacity (scale)
(linear model)
Supplies from boreholes (cost drivers)
(log unit model)
Explanatory variables included in the model
Tower and service reservoir capacity Ratio of storage capacity to pumping station capacity
Number of sources Distribution input Water power model (log linear model)
Water business activities model
Water management and general model
Terrain Power Pumping head & Distribution input (cost drivers)
(log model)
Billed properties (cost driver)
Sewage treatment model
(log linear model)
(log unit model)
Sewerage Network (incl. power) model
Sewer length (scale)
(log linear model)
Area of sewer district
Sewage infrastructure model (log unit cost model)
Resident population
Billed number of customers (scale) Billed properties that are non-household (cost driver) Total load (scale) Load treated by sewage treatment works Total length of sewers (scale) Combined sewer overflows (cost driver)
Holiday population Large sewage treatment works model
Load, type of treatment & effluent consents (cost drivers)
(log linear model)
Figure 6: Potential cost drivers. Source: Author, based on OFWAT’s Appendix 1 in report 2005-06
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3.8 Structural-conceptual representation of the service system Structural conceptions are frequently used in organisational theory. Structures can be more or less visible, but we are anyhow often in the need to specify the underlying structures of a process (Bakka, 2001). A structural representation of a system can make it possible for the researcher to identify and understand its characteristics. This may be helpful when controlling the system. A traditional organisational analysis means that we step “inside” the entity in order to specify its structural characteristics. Bakka (2001) has specified two common characteristics of big organisations: complexity and formalization. An urban water and wastewater organisation is expected to possess both of the mentioned characteristics. The water and wastewater organisation is complex in the meaning of having many components that interrelate to one another and it is formalized due to certain rules and organisational plans.
3.8.1 Perspectives When studying the characteristics and efficiency of a system we can start by analysing it from different dimensions. For example, the water and wastewater system (or the total industry) can be analysed vertically or horizontally. We can also analyse the system in terms of product, function, customer or area, as well as any combination of them all. By using the structural perspective we can better understand the processes of the system. A system is often analysed in the same direction as the system is broken down. The researcher holds the responsibility to specify how the system is broken down and in what dimension it is analysed. If this is not specified in the study or obvious by itself, then there might be difficult to interpret and understand the analysis.
V E R T I C A L
Water Treatment Plant
Industry
Billed
Domestic In - leakages Public
HORIZONTAL Figure 7: Vertical or horizontal perspectives of analysing a system. Source: Author.
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Water Treatment Plant
--------------------------------------------------------------------------------------------------------------1. Vertical analysis: Separate analysis of services, broken up into e.g. (1) water supply and (2) wastewater treatment. 2. Horizontal analysis: Combined analysis of services, which means a joint analysis of the water supply and wastewater treatment. --------------------------------------------------------------------------------------------------------------3. Product analysis: Focuses on the study of production outputs, i.e. water supplied, water treated, and/or water charged, wastewater charged plus out (in) leakages. 4. Function analysis: A functional break down into water supply, water distribution, sewage collection and sewage treatment. 5. Customer analysis: Analysing the distribution of customers, i.e. industrial users, domestic users and public users. 6. Area analysis: Focuses on the characteristics of the area, i.e. formation, size, environment etc. --------------------------------------------------------------------------------------------------------------The water and wastewater technology can better be understood if it is modelled as a multiple output production process (Kim, 1995). The multiple outputs can be supplied water and treated wastewater. This is an example of the Horizontal-product analysis. If the outputs are regarded as supplied water and treated wastewater to customers, i.e. water to industry, households and public uses, we focus on a customer analysis. This analysis can be horizontal or vertical, i.e. horizontal if we regard both water and wastewater, vertical if we regard only water or only wastewater. One of the most essential steps in the cost analysing process is to define the underlying functions of the system that is being studied. To my knowledge (based on what has been found in the econometric literature) it seems to be a lack of explicit structural representations of the systems that have been modelled. This can cause confusions, since a water and wastewater service system is of a very complex structure with many variables.
3.8.2 Production growth “Growth occurs from an existing situation, with an existing infrastructure, land use and ownership.” (Hopkins et al. 2003) A water and wastewater service system is not a fixed state in time, it is steadily changing. Nowadays, we can expect a metropolitan water and wastewater service system to grow bigger and bigger in production scale. This is mainly a cause of globalization, consolidations and growing populations.
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Example: Resemblance 1) Increasing water Increasing production demand per customer capacity
Stable relationships: Customers/service area
2) Increasing customers per service area 3) Proportional expansion of service area and amount of customers 4) Proportional expansion of customers and number of local communities
Demand/customer Demand/customer
Demand/customer, Customers/community
Figure 8: Examples of production growth. Source: Author.
3.8.3
A few important concepts
In order to understand the economic state of an existing service system or a total industry, one might want to study if the systems are being operated efficiently. In that way, one can understand if the existing systems can handle the growing changes or if the systems are in need for re-investments. Efficiency can be measured in terms of e.g. Economies of scale, Economies of production output density, Economies of customer density and Economies of scope.
Economies of Scale: are achieved when it is profitable to enlarge the business i.e. to produce more units of goods or services, increase the network size and the number of customers. The quantity of water demanded by customers and the density of customers remain stable. The opposite, diseconomies of scale, occurs when the production requires more input than produced output i.e. it is not profitable to enlarge the business. Economies of scale refer to the decrease in unit cost as the production scale increases. The changing variables are normally said to be the production output, the amount of customers and the network size. The network size is a geographical representation of the service region e.g. the areal of the service region in km2 or/and pipe line distance from the plants to the end consumers or/and distance from raw water source or/and amount of communities.
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Decreasing variables
Increasing variables
Unit costs
The whole production scale: i.e. proportional increase of production output + amount of customers + network size etc.
Figure 9: Economies of scale. Source: Author.
Economies of production output density: are achieved when it is profitable for the production to increase for a given system size and a given amount of customers (Garcia and Thomas, 2001). The only changing variable is the production output, due to the increasing demand of water volume per customer.
Decreasing variables
Increasing variables
Unit costs
Production output
Figure 10: Economies of production output density. Source: Author.
Economies of customer density: are achieved when it is profitable for the production to increase with the amount of new customers (Stone and Webster Consultants, Ltd, 2004). The system network size is held constant while the amount of water volume and the amount of connections increase proportionally. Hence, the changing variables are the production output and the amount of connections.
Decreasing variables
Increasing variables
Unit costs
Production output + customers/connections.
amount
of
Figure 11: Economies of customer density. Source: Author.
Economies of scope: are achieved when a single firm can produce several goods or services at a smaller cost than would several firms specializing in each separate commodity (Garcia and Thomas, 2001).
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Decreasing variables
Increasing variables
Unit costs
Numerous/type of goods
Figure 12: Economies of scope. Source: Author.
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3.8.4
Production patterns
The structural representation of a water and wastewater network can become complicated. In order to build a cost structure model for (parts of) the Swedish industry (in this case) we need to understand how the model should be constructed so we can get hold of the things we want to get explained by the model. In this thesis, the model is constructed through a system of equations (a statistical regression with each of the firms), why it is important to control for different production patterns. Some patterns can be difficult to control for numerically. For example, if we choose to include more than one community in the framework, we have to control for different production output patterns between utilities. We need to understand that outflows from one utility might be greater than another utility. For instance, the first one might distribute water to two communities (or more) and the second one only to one community. Different output patterns are being shown in the following schematic examples. In order to diminish the complications, I chose to model utilities that serve simply one community. Yet, the determining reason for making this choice was that I did not possess information about how many communities every utility operates. The model can be modified later on by including how many communities every utility operates. The new information should enter the model as one or several operational variables. The boundary of urban service systems is thus in this thesis and at this point systems that do not service outlying communities.
Example 2: Co-operational production output flows. I
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Example 3: Co-operational production output flows. II
Example 4: Co-operational production output flows. III
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Additionally, other operational variables that might be difficult to control for numerically can be treated as dummy variables (takes values 0 or 1). For example, a dummy variable is included in the model and takes the value 1 if the corresponding utility distributes chemically treated water and the value 0 otherwise. Féres and Reynaud (2006) have included dummy variables that control for licence agreements (yes/no) and environmental regulations (yes/no). Urakami (2005) has included a dummy for dam water and one for groundwater. Martins, Fortunato and Fernando (2006) use one dummy for ownership. Bottasso and Conti (2003) constrain their mathematical function by including a dummy for wastewater (yes/no). By reviewing the literature, we can see how different authors incorporate different and various variables in their mathematical formulas. We recognize the importance of identifying the underlying system structures that is being studied in every case. Again, variables we choose to include in the model is a judgment between what we want to get explained by the model and what we have to include in the model in order to fulfill its constraints. This choice may be more or less of one’s own free will. Information availability is a critical issue for any industry analysis.
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3.9 A mathematical framework 3.9.1 An approach to derive cost functions Following Hanemann (1998) we can consider an establishment (in this case a water and wastewater utility) producing water (output 1) and cleaning wastewater (output 2) through the use of certain N inputs, e.g. capital, labour, electricity and material. The price of input k (where k = 1, ..., N) is denoted by wk and the amount of the input k is denoted by xk. The total cost of production and treatment for the water and wastewater utility is then given by the function
N
TC wk xk w1 x1 w2 x2 ... w N x N
Equation (1)
k 1
The volume of produced and treated water/wastewater by the utility at a specific unit of time is denoted by y. It is relevant to relate the output y to the N inputs in order to get the production function for the utility. The literature review makes obvious that the so- called Cobb-Douglas production function was one of the most frequently used production functions in the applied field of water supply and treatment in the 1960s and 1970s. The Cobb-Douglas function has however been abandoned in favour for other production models, such as the Constant Elasticity of Substitution (CES) production function. For the moment we hold on to the Cobb-Douglas production function in order to understand the concept of a cost function that includes prices and outputs rather than production quantity. The Cobb-Douglas production function is given by the following expression,
N
y A x kak Ax1a1 x 2a2 ... x Na N
A 0, a k 0, k 1,..., N
Equation (2)
k 1
where A, a1, ..., aN are parameters that could be given or that should be estimated. They depend upon the relationship between the output and the inputs.
The CES production function has a somewhat more complex parametric structure than the Cobb-Douglas function. If we suppose that a water and wastewater utility can be represented by a Cobb-Douglas production function and the utility wants to minimize its total costs, a suitable problem to solve is the following (where x1, … , xN are the optimal input levels),
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N
N
k 1
k 1
min TC w k x k subject to y A x kak
Equation
(3)
Without questioning the microeconomic proofs referred to in the notations of Hanemann we introduce a set of functions, so-called long-run conditional input demand functions, which give the optimal choice of inputs,
x k g k (w1 , w 2 ,..., w N , y)
k 1,..., N
Equation (4)
That is, the gk function represents the demand for input xk.
This yields the total cost of production for the utility at the optimum,
N
TC ( w1 , w 2 ,..., w N , y) w k g k ( w1 , w 2 ,..., w N , y )
Equation (5)
k 1
If we, on the other hand, are interested in the short-run total cost of production at the optimum we fix the capital input at a level of xN, where N stands for the input N (= capital). The other 1, …, N-1 inputs are variables (e.g. labour, electricity and material). The total cost is denoted by TC, the total variable cost by TVC and the total fixed cost by TFC (= wN xN).
TC ( w1 , w 2 ,..., w N , x N , y ) TVC ( w1 , w 2 ,...w N 1 , y ) w N x N N 1
w k g k ( w1 , w 2 ,..., w N 1 , x N , y ) w N x N
Equation (6)
k 1
As we see in the above expression the short-run-case gives demands for inputs conditioned on the levels of the fixed inputs, i.e. x k g k (w1 , w 2 ,..., w N 1 , x N , y) for k=1,…,N. In the long-run-case, the demands for inputs are conditioned on the prices of the fixed inputs, i.e. x k g k (w1 , w 2 ,..., w N , y) for k=1, …, N.
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We can now derive the cost function TC(w1, w2, ..., wN, y) for N = 2 in the cases of a Cobb-Douglas production function. We get the following results with the use of Shephard's lemma1:
1
a1
y a1 a2 w1 a1 a2 w 2 TC ( w1 , w 2 , y ) w k g k ( w1 , w 2 , y ) (a a a 2 ) A k 1 a1 a2 2
y where x1 g 1 ( w1 , w 2 , y ) A
1 a1 a 2
a2
a1 a2
1
a1 w 2 a1 a2 , x 2 g 2 ( w1 , w 2 , y ) a 2 w1
Equation (7)
The same approach yields for the CES production function. The resulting cost function is
TC(w1 ,w 2 ,y)
1 μ
y δ1ζ w1ζρ δ2ζ w 2ζρ A
1 ζρ
where μ 0 , ζ
1 0 1 ρ
Equation (8)
where the δ are share parameters, ρ depends on the degree of substitutability of the inputs and A and μ depend upon the units in which the outputs and inputs are measured.2
1
For detailed information, see Andersson F, 2007, “Kostnadsminimering och Shephards Lemma”, Lecture Notes: March 26. 2
www.minneapolisfed.org/research/prescott/macro_theory/Lectures/CESProdFn.pdf
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3.9.2. The Transcendental Logarithmic cost function Nowadays, the Transcendental Logarithmic (Translog) cost function is often used by researchers for the purpose of representing the production technology (see for example Reynaud, 2006, and Martins, 2006, et al.) The Translog cost function is presented in this section. The Translog cost function seems to be the most popular type of cost function used in the water and wastewater industry. Stone and Webster Consultants Ltd (2004) used the both the Translog function and a function called the generalised quadratic specification, the last being more ad-hoc than the first. The formulas can be recognized as equally complex in mathematical form. I use the Translog cost function in my empirical study.
Conditions: The Translog cost function is often mentioned in the literature of water supply costs, but its practical use is not restricted to the water industry and it has been applied within other industries such as the banking industry and the electricity industry. The Translog cost function has the advantage of being flexible in its form i.e. it has few restrictions on its input data. For this reason researchers have chosen the Translog cost function in favour for other more constrained cost functions. The Cobb-Douglas cost function restricts the inputs to be only substitutes and the CES cost function restricts the inputs to be substitutes and/or complements. The Translog cost function relaxes these constraints and allows the degree of complementarity and substitution to be different between different sets of inputs. It also allows inferior inputs (see Hanemann, 1998)
Substitutes, complements, normal and inferior inputs: The meaning of inputs being substitutes can be described as the following: two inputs i (e.g. electricity) and j (e.g. labour) can be categorized as substitutes if an increase in the price of input i leads to an increase of the demand for input j. Described in the same sense, two inputs i (e.g. electricity) and j (e.g. labour) can be categorized as complements if an increase in the price of input i leads to a decrease of the demand for input j. Typically, the demand of inputs increases when the production output increases. However, there may be cases when the demand for some inputs decreases as the production output increases. In these cases we define these inputs as inferior (secondrated) rather than normal. For example, this usually happens when an increase of production output (volumes of water) causes an increase in the demand for certain inputs/input (electricity) in exchange for other inputs/input (labour). The stated example illustrates a typical technological change when the production process gets further mechanized and the demand for labour decreases in favour of the industrial development.
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Type Substitutes Complements Normal input j Inferior input j
Price of input i (electricity) Increase Increase -
Demand of input j (labour) Increase Decrease Increase Decrease
Production output Increase Increase
Table 1: Visualization of substitutes and complements, normal input and inferior input
Formula: The Translog cost function has the advantage of being flexible in its form, i.e. it has the fewest restrictions on the input data. 2 1 ln TC(w1 ,w2 ,y) ln β0 β y ln y βk ln wk δ yy ( ln y)2 2 k 1 2 2 2 1 δky ln wk ln y δki ln wk ln wk 2 k 1 i 1 k 1
Equation (9)
where β ' s and δ' s are the coefficien ts to be estimated and δki δik
Notice: The exact mathematical restrictions on the Translog cost function are stated in the empirical study.
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Chapter 4 Empirical study and analysis This empirical study includes an application of the theoretical framework on a few water and wastewater organisations in Sweden. The chapter specifies the estimation procedure, a structural conceptual representation that underlies the mathematical model, a description of the data samples and the resulting analysis. A model has been fitted for the total variable cost (TVC) of the production. If one wishes to fit a model of the total cost (TC) of the services, the price of capital has to be included in the model. Due to data limitations, the model was restricted to only capture the variable cost. However, this is not a problem for the structure analysis. Either total cost or variable cost modelling can be used to capture the cost structure of the underlying technology (Stone and Webster Consultants, Ltd. 2004). If the variable cost function is minimized with respect to the capital stock then it will yield the same economically relevant information about the underlying technology as the total cost function. But of course, the variable cost function is a representation of the variable costs in monetary terms and nothing else. It is the concepts of Economies of scale, Economies of customer density, Economies of production output density and Economies of scope that can be assessed in the same manner by using either a variable cost function or a total cost function. It is assumed that the utilities aim to minimize their costs. TC (output, prices; Operational environment) = = TVC (output, prices; Capital stock, Operational environment) + + FC (price, Capital stock) The capital factors (= pipe length in my application) are treated as quasi-fixed inputs in the short-run, which mean that the capital is not fully under control of the individual organisation because of external quality investment demands (Stone and Webster Consultants, Ltd. 2004). I have used the pipe line network of water and wastewater as a proxy for the capital stock. The capital prices are hereby based on the network. Other relevant proxies for the capital stock are the amount of plants, pumps, pressure stations etc. To reduce the expressions in the cost function, I settled with only the pipe network. However, it was more the fact that the data material was incomplete that limited the use of variables. There were too many missing data in the samples. The simplification of the capital stock makes it rather inappropriate to estimate Economies of scale, Economies of production output density and Economies of customer density in the longrun. The long-run version of the measurements requires a more accurate representation [42]
of the capital stock. We can agree on the fact that simply the length of the pipe network is not a genuine estimate of the total capital stock. A more truthful estimate of the capital stock might be the book value of the utilities. Nonetheless, this choice of a proxy can be seen as legitimate as a last resort, when we have limited amount of data. By revising earlier relevant studies we can observe that a proxy for the capital stock is commonly used (see for example Garcia and Thomas, 2001). Antonioli and Filippini (2001) used the number of water wells as a simplified representation of the capital stock. However, the non-identifiable inputs are not completely ignored. The inputs are enclosed in a joint constant (see Parameter estimates). Furthermore, these mislaid inputs can be separated from the constant and can be incorporated as operational variables when more information has been collected in the future. The cost structure can thus be interpreted accurately although the measures are valuable only in the shortrun.
4.1 Capture a representation of the total costs: procedure and limitations In total cost modelling, the capital price has to enter the cost function together with the price of labour, material and electricity. The capital price for the water and wastewater utility is usually measured as the cost of capital, comprising the interest rate on longterm debts and the depreciation rate (Kim, 1995). However, there are often variations in construction costs between the utilities together with different depreciation principles between communities. According to Tagesson (2001), we can see a clear difference between the Swedish communities and their capital costs. Reasons for low capital costs may be:
Buy from other community Large-scale production Topography Technology
Communities sometimes use different principles of calculating capital costs. This will complicate the modelling when the capital costs have not been estimated under the same principle for all the communities. Then, the capital price is not only a function of technology and operating environment. It is also a function of the depreciation principle, which is difficult to represent in the model. Tagesson (2001) states that the different principles may lead to different prices of the water service among different communities, during different time periods and generations (due to high life expectancy of water facilities). [43]
-
Depreciation principle: is the depreciation based on the historical acquisition value or a present acquisition value? Depreciation time: what life-time do the assets have? Depreciation time distribution pattern: Linearly, progressive or digressive?
Figure 13: Depreciation principle. Source: Author.
4.2 Modelling the structure of variable costs, an application to a sample of Swedish communities In this section, I have modelled the structure of the total variable costs of production, distribution, collection and treatment for a few water and sewage organisations in Sweden by using an econometric methodology and terminology. I have approximated the variable cost function by using the Translog form. The availability of data has influenced the study to simply look at the “simultaneously horizontal” process of providing water and cleaning wastewater. This means that I have created a model for the joint service system of water and wastewater. It was not possible to distinguish between the two types of services and to model them separately. If we want to study sub-activities individually, we need to have separate accounting information i.e. separate economical, administrational, technical and environmental figures for water supply, wastewater treatment, distribution and sewerage.
4.2.1 The structural representation of the model A fundamental but core part of the analysing procedure is to understand the underlying structure of the service system. As been described earlier, there are many different dimensions of the system that can be analysed and the researcher holds the responsibility for trying to specify what he is studying. An over-viewing description of the multivariate structure of the industry is informative in the big context, but equally important in order to fully understand the system is to describe what dimensions of the system the present study is actually focusing on. This may at least avoid conceptual confusions due to the fact that researchers tend to focus on different parts of the system. A good representation may also limit the risk of forgetting or overlooking some aspects of the system. It seems to be a tendency among the econometric papers not to
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clearly present the research perspectives and this may underestimate the complexity of the system. The following figure (14) is the structural conceptual representation of the water and wastewater system underlying the mathematical model. The model is representative for one community, producing water to consumers and collecting the wastewater from consumers. I have included the amount of connections as an operating variable. I wished to include more operational variables e.g. the capacity of the system (m3/hour) and the distance to the users (km), but I did not possess such information. The distance to the users demonstrates the size of the network.
Figure 14: Structural representation of a water supply and sewage treatment system attributed to one community
4.2.2 Data description The data used in this study comes from SWWA and contains operational data for about 200 communities, corresponding to the individual water and sewerage organisation. The data material is extensive but there are many “gaps” (missing values) and unreasonable values in the sample. For that reason, I had to go over the data and check for possible errors. SWWA is gradually trying to increase the quality level of the information. It is an ongoing but long process to establish the underlying statistical framework and to assure that all existing organisations give their adequate contribution. Quality level 1, out of 3, [45]
has been reached. The results in this study should therefore be used for perspicuous purposes only. Table (2) presents the overall descriptive statistics for the water and sewage organisations used in this study. Out of 200 communities, only a sample of 25 communities were selected and used in the study. The sorting procedure was made with care and the procedure is described with further details in the following section. The systems range from very small systems producing about 700 cubic metre of water per day and treating about 1100 cubic metre of wastewater per day to bigger systems producing about 50 000 cubic metre water per day and treating about 52 000 cubic metre of wastewater per day. The specified numbers in Table (2) are annual values for the year 2005, which was the only accessible year with relevant data. The large variation between minimum and maximum values simply reflects the different sizes between the systems, but it also shows that there are organizational differences. This can be observed by looking at the different prices of labour & material and electricity. Price nr 1 is a “combined” unit price for labour and material and can be seen as a function of produced water and treated wastewater in cubic metres. A more expected unit price for labour is the price per total hours worked, usually obtained by dividing total wage expenses by total hours worked. A unit price for material is recognized as difficult to establish, due to lack of homogeneity in this input. A common way to handle this is to construct a price index for input materials (see for example Garcia and Thomas, 2001). I have followed this approach since I cannot separate the labour price from the material price in a satisfactory way. Both labour and materials are thus influenced by the production and sewage variables. The labour & material price is therefore said to be endogenous (explained within the model).
Table 2:
Sample statistics, 25 observations Annual values, year 2005
Variable Total Variable Cost Production Sewage Price1: Labour & Material Price2: Electricity Total network length Connections Cost Share1: Labour & Material Cost Share2: Electricity
Notation TVC y1 y2 w1 w2 Length Connections S1 S2
Unit Mean Std. Deviation Minimum Maximum SEK 28,446,856.72 23,045,851.80 3,946,000.00 109,213,000.00 m3 3,257,550.32 4,240,649.17 244,233.00 18,351,000.00 m3 4,428,141.92 4,642,821.84 396,980.00 19,150,000.00 SEK/m3 3.99 1.37 2.01 7.78 SEK/kWh 0.75 0.11 0.44 1.00 Km 567.57 355.78 96.00 1482.00 --68,933.24 8,2476.08 5,145.00 340,200.00 --0.87 0.03 0.80 0.96 --0.13 0.03 0.04 0.20
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Communities used in the study are: Bollnäs, Borlänge, Essunga, Falkenberg, Gislaved, Hagfors, Hallstahammar, Halmstad, Herrljunga, Hjo, Härnösand, Kristinehamn, Lycksele, Mark, Mora, Norrköping, Nybro, Olofström, Ronneby, Tierp, Tingsryd, Ulricehamn, Uppsala, Vetlanda and Ängelholm. Cost Data: The total (annual) variable cost is the total sum of annual electricity and other annual operational costs for plants, distribution networks, pressure stations, reservoirs, storm water networks and pumps. Other annual operational costs are defined by the SWWA as the costs of labour and material (plus cost of external services) Cost Share Data: The cost shares are calculated as the cost of each individual shareinput divided by the total variable cost.
Cost Share1
Cost of labour & material Total variable cost
Cost Share 2
Cost of electricit y Total variable cost
Equation (10)
Equation (11)
Price Data: The price of labour & material is obtained by dividing total annual operational costs with the volume of water produced and wastewater treated during the year. The price of energy is determined by dividing total annual electricity costs by the total use of kWh. Output Data: The output data corresponds to the annual volume of water produced and wastewater treated. Domestic, industrial and public users constitute the main users of water and sewage. Capital stock Data: The capital stock data is represented by the length of the distribution network and length of the sewerage in kilometres. This is a rather limited representation of the capital stock. Technical/Operational data: The technical data corresponds to the amount of total connections to the water distribution network and the sewerage.
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4.2.3 Data sorting and selection The communities, and so the utilities, was sorted and selected with respect to the following criteria: Included: utilities having complete cost information (O&M, electricity, labour & material). Included: utilities having complete information about the selected operational variables (connections, pipe length). Included: utilities having complete information about the volume of produced water and treated wastewater. Included: utilities having similar urban customer structure (50-80 % domestic, 10-30 % industrial and 1-10 % public) -
Excluded: utilities performing services across communities and utilities letting other communities operate all/parts of the services. Excluded: utilities having irrational operational estimates, e.g. 0 percent out (in) leakage. Excluded: utilities having unreasonable input prices, i.e. extremely high or low prices.
As a first requirement, the utilities had to possess complete cost information, taking the cost categories into consideration. Second, the operational variables were selected based on the total distribution of operational variables. Two of the most comprehensive operational variable samples were the amount of connections and the length of pipe lines. I settled on the opinion to not include more than these two operational variables in the model, due to defective data. More variables would result in templates rather than true estimates. With respect to the limited amount of information, it seemed most reasonable to keep the model simple but as realistic as possible. The selection of operational variables involves a judgement about how important the variables are for the model’s accuracy and what we want the model to tell us. In order to control for different urban environments, the municipalities were selected based on the similarity of structure. The 25 selected utilities operate in similar community structures, bearing upon the distribution of households, industry and public services. Utilities that operate across communities were excluded. The collected statistical information did not clarify how many communities were served by the individual districts. It was not possible to set apart if the utilities in the sample were serving 2, 3, 4 or 5 (etc.) communities. It was only possible to spot if the utilities were serving 1
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community. For this reason, only utilities serving 1 community was grouped and selected. Other ways of grouping and selecting municipalities could be based on: -
Community size (amount of connections, size of the production, area size etc.) Geographical conditions Operational logistics (distance to pole municipality, quality of infrastructure etc.)
I am interested to capture how the cost structure gets affected by the size of the system. No size grouping was performed. We will eliminate the size effect if all of the observations in the selected sample have the same or very similar size. The amount of observations does not allow us to include too many variables, because then the system will be undetermined. We need to have at least one condition attributed to every parameter.
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4.2.4
Estimation procedure with a multivariate regression approach3
The following expression follows a Translog approximation form. It represents a cost structure for the utilities chosen in this study (thus a cost structure for a part of the Swedish water and wastewater industry):
ln TVC Constant a1 ln w1 a 2 ln w 2 b1 ln y1 b2 ln y 2 c ln Length d ln Connections 0.5a11 ln w1 ln w1 a12 ln w1 ln w 2 0.5a 22 ln w 2 ln w 2 0.5b11 ln y1 ln y1 b12 ln y1 ln y 2 0.5b22 ln y 2 ln y 2 0.5cc ln Length ln Length cd ln Length ln Connections 0.5dd ln Connection ln Connections e11 ln w1 ln y1 e12 ln w1 ln y 2 e 21 ln w 2 ln y1 e 22 ln w 2 ln y 2 f1c ln w1 ln Length f 2 c ln w 2 ln Length g1d ln w1 ln Connections g 2d ln w 2 ln Connections h1c ln y1 ln Length h1d ln y1 ln Connections h2c ln y 2 ln Length h2d ln y 2 ln Connections
Equation (12)
where w1 and w2 are the prices for labour & material and electricity, respectively. Y1 and y2 are the produced water and treated wastewater, respectively. The Length and Connections variables are the included operational variables in the model. The Constant is one of the parameter estimates and it encloses other omitted variables, which for all the reasons could not be caught in separate variables. The explicit parameter estimates are a1, a2, b1, b2, c, d, a11, a12, a22, b11, b12, b22, cc, cd, dd, e11, e12, e21, e22, f1c, f2c, g1d, g2d, h1c, h1d, h2c, h2d. The above expression, together with the so-called Cost Share equations S1 and S2 forms a multivariate regression system. The Cost Shares are the first derivative of ln(TVC) with respect to the individual prices w1 and w2. Thus, we get two Cost Share equations and the system of equations to be estimated is: ln(TVC i ) lnTVC ( yi , wi , CS i , Z i ) εTCVi
, where i = 1, ..., 25.
S j,i S j,i ( yi , wi , CS i , Z i ) ε Sharej,i
, where j = 1, 2 and i =1, ..., 25.
3
The computer program used for this section was MATLAB 7.5.
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εTVC i and εSharej,i are, respectively, the residuals associated with the cost equation i and the cost share equation j for utility i. The system of equations is estimated by Ordinary Least Squares (OLS). The numerical values of the parameter estimates are presented in the next section. In order to estimate the regression system above, all the variables are normalized by their sample means. The Translog cost function needs a reference point (Garcia and Thomas, 2001). This means that the first-order parameters are most accurately representative for the sample mean of the total selection of 25 utilities. For example, that is to say that if the price for electricity (w2) increases by 1 percent, the total variable cost will increase by a1 percent for the “average” utility (i.e. an utility with mean[TVC], mean[y1], mean[y2], mean[w1], mean[w2] etc). In addition, it can be confirmed that the mean of the estimated ln(TVC) given by equation (12) equals the mean of actual ln(TVC). Figure (15) illustrates this statement. There are certain restrictions on the model: Homogeneous of degree one in factor prices, concave in factor prices and symmetry in certain parameters (a12 = a21, b12=b21). The Cost Shares are not allowed to be negative. The homogeneity in input prices is fulfilled when the following parameter restrictions hold:
a1 a 2 1 a11 a 21 a12 a 22 0 e11 e 21 e12 e 22 0 f 1c f 2c g1d g 2 d 0
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4.2.5 Parameter estimates
Table 3: Estimated parameters No. Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Constant a1 a2 b1 b2 c d a11 2*a12=2*a21 a22 b11 2*b12=2*b21 b22 cc cd dd e11 e12 e21 e22 f1c f2c g1d g2d h1c h1d h2c h2d
Corresponding variable Constant ln w1 ln w2 ln y1 ln y2 ln Length ln Connections ln w1*ln w1 ln w1*ln w2 ln w2*ln w2 ln y1*ln y1 ln y1*ln y2 ln y2*ln y2 ln Length*ln Length ln Length*ln Connections ln Connections*ln Connections ln w1*ln y1 ln w1*ln y2 ln w2*ln y1 ln w2*lny2 ln w1*ln Length ln w2*ln Length ln w1*ln Connectons ln w2*ln Connections ln y1*ln Length ln y1*ln Connections ln y2*ln Length ln y2*lnConnections
29 R-squared 30 R-squared 31 R-squared
Cost equation Cost Share1 (Labour & Material) Cost Share2 (Electricity)
32 N
Observations
Estimates OLS (White) Std. Error 17.3584 0.0132 0.8848 0.0048 0.1156 0.0049 0.5116 0.0976 0.4240 0.1013 (-0.0099) 0.0504 0.0086 0.0691 0.1087 0.0130 (-0.1016) 0.0121 0.0900 0.0259 0.0727 0.8252 (-0.0744) 0.4158 0.6191 0.3223 0.0789 0.1904 0.1360 0.2494 (-0.1984) 0.0840 0.0423 0.0177 (-0.0010) 0.0165 (-0.0392) 0.0182 0.0008 0.0175 (-0.0297) 0.0136 0.0276 0.0138 (-0.0026) 0.0080 0.0019 0.0082 (-0.5378) 0.3635 0.4901 0.4864 0.3225 0.3565 (-0.6311) 0.5951 0.998 0.689 0.690 25
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Figure 15: Estimated ln(TVC) and actual ln(TVC)
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Real ln(Total Variable Cost) Normalized
15 15
15.5
16
16.5
17
17.5
18
18.5
19
15.5
16
16.5 17 17.5 Estimated ln(Total Variable Cost) Normalized
18
18.5
19
4.2.6 Analysis: Understanding the parameter figures. Estimating Economies of production output density, Economies of customer density and Economies of scope In this section, I implement the theoretical concepts of structural characterization, system analysis, critical factors and efficiency for the Swedish sample of utilities. Subsequently, I present and put into practice the mathematical measurements of Economies of production output density, customer density and scope. The meaning of Economies of scale is presented on a theoretical stage. The economies are first evaluated at the sample mean of variables, i.e. the final estimates capture the state of an “average” utility. Second, the economies are evaluated for “small” utilities [Total production: 6.4∙105-5.0∙106 m3] and “large” utilities [Total production: 5.1∙106-3.8∙107 m3] in the sample. This section is consequently the final session of the empirical study.
Measure
Decreasing return to size
Constant return to size
Increasing return to size
Economies of production output density*
1
Economies of customer density*
1
Economies of scale *
1
Measure
Joint production
Separate production
Economies of scope**
0
Table 4: Definition of efficiency measures. Sources: * Garcia and Thomas (2001), ** Hajargasht, Coelli and Rao (2006).
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Horizontal-product analysis of Economies of production output density and Economies of customer density: Economies of production output density in the short-run (for the joint production of water and wastewater) = 1
ln TVC ln TVC ln y ln y 1 2 * (b1 b11 ln y 1 b12 ln y *2 e11 ln w *1 e 21 ln w *2 h1c ln Length * h1d ln Connections * b2 b22 ln y *2 b12 ln y *1 e12 ln w *1 e 22 ln w *2 h´2 c ln Length * h2 d ln Connections * ) 1 (0.5116 0.0727 ln y *1 0.5 0.0744 ln y *2 0.0423 ln w *1 0.0392 ln w *2 0.5378 ln Length * 0.4901ln Connections * 0.4240 0.6191 ln y *2 0.5 0.0744 ln y *1 0.0010 ln w *1 0.0008 ln w *2 0.3225 ln Length * 0.6311ln Connections * ) 1 (0.9616 0.0355 ln y *1 0.5819 ln y *2 0.0413 ln w *1 0.0384 ln w *2 0.2153 ln Length * 0.1410 ln Connections * ) 1 {for the " average" utility} 0.5116 0.4240 1.06 1
Equation (13) Notation in the formula (*): Normalized variables.
The expression shows us that the Economies of production output density for the Swedish utilities “overall” are dependent mostly on the treatment of wastewater, the capital stock (represented by Length) and the amount of connections. Above all, wastewater is a critical factor among the utilities, especially for utilities with large production of treated wastewater. Wastewater has a large effect on the total variable cost of production. This may be a cause of having expensive treatment procedures or/and too much in-leakage (over flow). In-leakage is water that searches its way into the sewage network although it does not belong in the system, for example rainwater, groundwater or surface water that forces themselves into wells and pipes. The amount of in-leakage water in the system can vary between 0 and several hundred percent of the normal water flow (Weglert T, 2005) On the other hand, the effect of wastewater for the average firm is not that critical as it is for larger production. A force of direction, if the volume of water supplied to customers increases by 1 percent (all other things unchanged) then the total variable cost increases by approximately 0.51 percent for the average firm. If the volume of wastewater increases by 1 percent (all other things unchanged) then the total variable cost increases by approximately 0.42 percent. Conclusively, water and wastewater are about equally affecting the total variable cost for the average utility. For the industry “as [55]
a whole” we can conclude that especially wastewater has a large impact on the variable cost. The price of electricity has a higher effect (it is a sensitivity factor) on the total variable cost when the amount of wastewater increases than when the amount of water increases. On the other hand, the price of labour & material has a higher effect on the total variable cost when the amount of water increases than when the amount of wastewater increases. The estimated Economies of production output density are 1.06 for the average utility. This leads us to conclude that the average utility exhibits Economies of production output density (returns to size are increasing, because Economies of production output density > 1). It would be profitable to produce more units of goods with the existing network and the amount of existing customers (i.e. to have an increasing demand from the existing users to produce water and to clean wastewater). On the contrary, it would not be profitable for the large utilities to produce more units of goods with their existing network and their amount of existing customers.
Measure
“Small” utilities in the sample
The “average” utility
“Large” utilities in the sample
Economies of production output density
= 1.34
= 1.06
= 0.86
Marked increasing return to size
Increasing return to size (roughly constant return to size)
Decreasing return to size
Table 5: Estimated Economies of production output density for different utility sizes. Source: Author.
Economies of customer density in the short run (for the joint production of water and wastewater) =
ln TVC ln TVC ln TVC ln y 2 Connections ln y1
1
0.5116 0.4240 0.0086 1.03 1
Equation (14) A 1 percent increase of the amount of connections (all other things unchanged) results in approximately a 0.0086 percent increase of the total variable cost for the average utility. A proportionally increase of 1 percent respectively of the volume of water, the
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volume of wastewater and the amount of connections brings about an 0.97 percent increase of the total variable cost for the average utility.
Measure
“Small” utilities in the sample
The “average” utility
“Large” utilities in the sample
Economies of customer density
= 1.04
= 1.03
= 0.87
Increasing return to size (roughly constant return to size)
Increasing return to size (roughly constant return to size)
Decreasing return to size
Table 6: Estimated Economies of customer density for different utility sizes. Source: Author
The estimated economies of customer density are 1.03 for the average utility. This leads us to conclude that the average utility exhibits economies of customer density (returns to size are increasing, because economies of customer density > 1). It would be profitable for the average utility and for the small utilities to serve more customers (thus to also produce more units of goods) in their existing network. On the contrary, it would not be profitable for the large utilities to serve more customers with their existing network. The larger utilities does not benefit from customer growth when having their existing network.
Horizontal-product analysis of Economies of Scale: A theoretical measure of Economies of scale in the short run (Equation 15) and in the long run (Equation 16): ln TVC ln TVC ln TVC ln TVC ln y ln y ln Connection s ln Network size 1 2
ln TVC ln Capital stock ln TVC ln TVC ln TVC ln TVC ln y1 ln y 2 ln Connections ln Network size
1
Equation (15)
1
Equation (16)
Network size stated in the previous formulas is a geographical representation/measure of the service region. The size of a network can be measured in e.g. [57]
a) Km (the length of the total pipe network) b) Km2 (the area of the service region) c) Communities (the amount of communities that form a service region) How we choose to represent the network size in the formulas depends on how the business is enlarged. Here is a short example. By the definition of Economies of scale the density of customers has to remain unchanged once the production has varied in size. If the network size is represented only by (a) then the proportionally varying variables are the supplied water, the treated wastewater, the amount of customers and the length of the total pipe network. Thus, the resulting measure of customer density (Customers/Km) remains unchanged. However, we do not control for any other variations by this measurement. Since we have not included a representation for the area, the measure of customers per areal (Customers/Km2) may have increased, remained the same or decreased. In the same manner, since we have not included a representation for the amount of communities, the measurement of customers per community (Customers/community) may have increased, remained the same or decreased. In order to control for an area variation (e.g. an enlargement), we include one more expression in our equation. In order to control for community consolidations, we include yet one more expression in our equation. All of the possible size variations should preferably be included in the mathematical formula in order for the desired customer density to remain unchanged once the production has varied in size (e.g. Customers/[Km & Km2 & community]). Here is a general formula for the short-run case with more than one network size representation: ln TVC ln TVC ln TVC ln TVC ln TVC ln TVC () () ()... ln y 2 ln Connections ln (a ) ln (b) ln (c ) ln y1
Equation (17) I did not possess any information about the area (e.g. in km2) of the service regions or the amount of served communities, which meant that I could not tag on the proper measurement of Economies of scale. The furthest to reach at this point are the measurements of Economies of production output density and Economies of customer density. Economies of scope (water and wastewater) =
2 log( TVC ) 0.0744 b12 0 log Y1 log Y2 2 Equation (18)
There are Economies of scope between water and wastewater in the Swedish industry, i.e. it appears to be cost advantages with the joint production of water and wastewater.
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1
An autonomous example; Horizontal-customer analysis of Economies of production output density and Economies of scope: This is simply a short example of a horizontal-customer analysis, in comparison to the horizontal-production analysis. The parameter estimates are based on estimation procedures like the previous ones. The estimates in this subdivision should be taken more slightly, because they rely on an approximation procedure that is not that rigorous. This is mainly due to missing values. This small section serves as an illustration to how the industry analysis can be approach from another point of view. Economies of production output density in the short run (for the joint production of water and wastewater to domestic users and industry users) = 1
ln TVC ln TVC 1 0.5428 0.4225 1.04 ln y ln yindustry domestic
Equation (19)
For the simplicity to calculate, I have excluded public users. It is a small part of the total production. It seems logical that the measurement of Economies of production output density with the customer perspective gives almost the same result as the analysis with the product perspective. They represent the same thing, using two different perspectives. The result of 1.04 is a little less than the previous 1.06, which may be the result of not including the public users. We can conclude that domestic users have a large effect on the total variable cost of production. A 1 percent increase of the total water and wastewater demand from domestic users (all other things unchanged) brings about a 0.54 percent increase of the total variable cost. A 1 percent increase of the total water and wastewater demand from industry users (all other thing unchanged) brings about a 0.42 percent increase of the total variable cost.
Economies of scope (domestic and industry) =
2 log( TVC ) 0.6725 0 log YDometic log YIndustry 2 Equation (20)
The estimate of Economies of scope is greater than 0 which indicates that it is not profitable for the average utility to serve both domestic and industry users. It seems to be more profitable to specialize in different production units. This contradicts a study made by Kim (1995) that found cost advantages of operating with a combination of residential and residential outputs for a cross-section of 60 utilities for the year 1973 in the United States. If we exclude the possibility of estimation errors, the individual industry analysis can be seen as case-specific, depending on the underlying production
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technology and environment. The analysis can be spit up into different product, customer or function groups.
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Chapter 5 Summary and conclusions In this thesis we have seen how an economic/mathematical model for the water and wastewater industry can be constructed by using a multivariate regression approach. The accurate mathematical formula (including explanatory variables) we choose to apply on the statistical data is a choice based on the desired venture. A second (alternative) approach is the stochastic frontier analysis. While the regression method generates an expression of the average cost, the stochastic frontier method determines how close each utility is to the expenditure achieved by the best of the industry. The second approach is often used for the purpose of benchmarking (OFWAT, 2005). Each estimated parameter in the mathematical formula describes the relationship between the total (variable) cost and the corresponding system input. The parameters represent a cost structure for a few Swedish utilities in the industry. The results from the empirical study could be of interest for authorities in the Swedish water and wastewater industry (especially). In order to properly build a mathematical model of a single system (or total industry) we need to form an understanding about the underlying production technology. A critical step is to clarify which structural-conceptual representation is underlying the mathematical model. This step can be approach from a dimensional perspective. The Translog cost function is better interpreted as a “cost structure function” for the existing industry rather than a “cost estimation function” for new constructions. The Translog cost function has been normalized in order to be easily interpreted for an average utility. The efficiency measures have been derived from the variable cost formula and have been calculated for different sizes of the utilities (“small”, average and “large”). We can also see that the coefficient of the capital stock has a negative sign, which goes along the lines of cost theory (Filippini, 2007). It is important to be critical to one’s own choice of method. Thus, the estimations need to be refined with more sophisticated econometrical methods because the parameter restrictions stated for w1 and w1 is not perfectly fulfilled (the parameters a1+a2 does not summarize perfectly to 1). Moreover, the data used for this study is cross-sectional data (i.e. data-sets that do not reflect difference in time). More interesting would be the use of panel-data (i.e. data-sets that reflects changes in time).
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Can the estimates really be verified in reality? The estimated cost structure is an approximation of the actual state of the industry. The efficiency measures can be estimated for every utility in the study, although the measures are most accurately interpreted for the average utility. The average approach is a common way of representing the conditions of an industry. However, the average utility is a fictive utility based on actual values of the existing industry and cannot be identified independently. As an alternative to the average approach, we can use the median approach. This study points out the need for high quality methods of collecting information and presenting statistics about the industry. It is vital in order to make any further analysis.
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