Efficiency and pricing of water supply and sewerage services in Japan

Utilities Policy 62 (2020) 100984

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Efficiency and pricing of water supply and sewerage services in Japan Junqing Liu, Mototsugu Fukushige * Graduate School of Economics, Osaka University, 1-7, Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan

A R T I C L E I N F O

A B S T R A C T

JEL classification: L95 Q25 D24

This paper investigates the technical efficiency of Japanese water supply and sewerage services using data envelopment analysis (DEA). We then apply regression analysis to explore the interactions between the effi­ ciencies of water supply and sewerage services on both their own prices and those of the other service. All other things being equal, the results indicate a positive relationship between the efficiency of water supply services and the prices for sewerage services, but no relationship between the efficiencies of sewerage services and the prices of water supply services.

Keywords: Efficiency Pricing Water supply Sewerage Data envelopment analysis

1. Introduction Throughout the world, there are many different price setting systems and structures for water and related services, for which Carvalho et al. (2012) and Rogers et al. (2002) provide a useful survey. Given that municipalities run water utilities in some countries and private com­ panies in others, recent research has focused on the benefits and chal­ �rez-Varela et al., 2017). lenges of privatization (Rogers et al., 2002; Sua Likewise, for wastewater and sewerage services, some countries may lack management systems, while others, even with such systems, may not charge their costs to residents or customers (Rogers et al., 2002). In Japan, different bureaus established by the one local municipality, but with separate accounting systems and under different legal acts, provide most water supply and sewerage services in an area. While these two systems operate separately for the most part, the calculation of water supply and sewerage service charges often depends on how much customers use the water supply. Further, while pricing systems differ between water supply and sewerage services and across municipalities, that sewerage service charges depend on customer use of the water supply is common to almost all sewerage services provided by Japanese municipalities. There has been much research on utilities in Japan using quantitative methods, mainly in the telecommunications, electric power, trans­ portation, and other industries. Among these, numerous empirical

studies attempt to either estimate production or cost functions, or analyze production or cost structures through measuring productivity. For example, Sueyoshi (1998) considered the privatization of Japanese telecommunications, while Besstremyannaya (2011) analyzed that of public hospitals and Hattori et al. (2005) electricity distribution. Goto and Takahashi (2017) examined the efficiency of Japanese electric power companies, Jitsuzumi and Nakamura (2010) and Guo et al. (2018) that of Japan’s railways, and Suzuki and Nijkamp (2011) private railways and public transportation authorities. However, there is rather less research in Japan on water utilities, especially concerning the efficiency of the industry, with most empirical production or cost structure studies being conducted since the late 1990s (e.g., Nakayama, 2003; Marques et al., 2014). Consequently, the pur­ pose of our analysis is to identify the efficiency of both water supply and sewerage utilities in Japan and examine the relationships between their efficiency and price setting. Existing studies in the water services industry have used a variety of different efficiency approaches, including stochastic frontier analysis (SFA) and data envelopment analysis (DEA), with most examining the efficiencies associated with economies of scale and scope. Specifically, previous studies suggest that the consolidation of small water utilities into larger units could be one solution to reduce unit costs and achieve scale economies. However, attention also needs to be paid to the situa­ tion where the entity could become too large, which may lead to

* Corresponding author. E-mail addresses: [email protected] (J. Liu), [email protected] (M. Fukushige). https://doi.org/10.1016/j.jup.2019.100984 Received 30 January 2019; Received in revised form 31 August 2019; Accepted 4 November 2019 Available online 25 November 2019 0957-1787/© 2019 Elsevier Ltd. All rights reserved.

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Utilities Policy 62 (2020) 100984

diseconomies of scale (Saal and Parker, 2000, 2001; Sauer, 2005; Torres and Morrison Paul, 2006; Nauges and Van den Berg, 2007, 2008; Iimi, 2008). In this regard, very few extant studies concern the efficiency of water utilities in Japan, with Berg and Marques (2011) identifying just six analyses of Japanese water utilities in total, all relating to cost or pro­ duction functions. This is in stark contrast to the approximately 190 studies concerning contexts outside Japan published between 1969 and 2009.1 Of the Japanese works, Mizutani and Urakami (2001) and Ura­ kami (2007) employ parametric techniques, but only one (Aida et al., 1998) involves nonparametric techniques (DEA), and in doing so pro­ vides a range-adjusted measure of efficiency to evaluate the perfor­ mance of water suppliers in Japan. In terms of the remaining Japanese studies, Marques et al. (2011) also use DEA in conjunction with a technique to include exogenous variables, while Phillips (2013) applies SFA to identify factors contrib­ uting to the inefficiency of the water supply industry. Yane and Berg (2013) examine the robustness of efficiency score rankings using a sample of 1221 water service organizations and a trans-log stochastic production frontier model. Marques et al. (2014) apply DEA to 1144 water supply (including small) utilities, alongside environmental factors. However, we are un­ able to identify any efficiency studies specifically concerning the sewerage sector in Japan. Moreover, even when studies explore effi­ ciency from the perspective of both water supply and sewerage services, the primary focus is invariably on the water supply, given its dominance in the existing literature and the easy availability of datasets. Accordingly, the purpose of the present paper is to use the nonparametric method of DEA to examine the efficiency of both water supply and sewerage services in Japan, and to use these results to help inform the relationship between their respective prices and efficiencies. From our survey of the empirical literature in Japan, we find no study that simultaneously analyzes water supply and sewerage service utili­ ties. We consider the agencies for water supply and sewerage services in Japan as related entities, especially as the efficiencies of water supply utilities could affect the pricing of sewerage services. This is particularly important when considering the reform of the water supply and sewerage services systems. If indeed there exists an interaction between the price settings for the water supply and sewerage services, we should consider this before proceeding to the privatization of the water supply system in Japan, a hotly debated topic, especially following the reform of Japanese water supply law.2 To do this, we examine the efficiency and pricing, and the interaction between efficiency and pricing, in the Japanese water supply and sewerage service sectors. As our method, we implement a two-stage analysis, involving the measurement of the relative efficiency of water supply and sewerage services using DEA in the first stage, and regression models to investigate the relationships between the prices and the estimated efficiency scores in the second stage.3 The remainder of the paper is structured as follows. Section 2 dis­ cusses the water supply and sewerage service systems in Japan. Section 3 details the DEA and regression methodologies employed in our twostage analysis and Section 4 provides the empirical results. Section 5

summarizes our main findings, discusses the policy implications, details the limitations of the analysis, and provides some possible directions for future research. 2. Water supply and sewerage service systems in Japan Enacted in 1957, the purpose of Japan’s Waterworks Law is to pro­ tect water supply works and ensure a safe and clean water supply under the control of the Ministry of Health, Labor and Welfare. For its part, the role of the Sewerage Water Law in Japan, enacted in 1958, is to establish rules for the installation and management of sewerage outfalls in urban areas as a means to protect water quality in water zones under the guidance of the Ministry of Land, Infrastructure and Transportation. Within these laws, municipalities (including city, town, and village local governments) own water services in Japan (Urakami and Nakayama, 2003; Urakami, 2007).4 The water service operators actually producing these services fall into three categories: small, small private, and large water utilities. Both types of small water utilities typically service 101 to 5000 consumers. These mainly exist because of special conditions in some geographic areas and collectively service only about 10% of the population (Ura­ kami and Nakayama, 2003). Because of the difficulty in acquiring data on these utilities and given their relatively minor role at the national level, we do not include them in this analysis.5 In contrast, large water utilities supply areas with more than 5000 consumers and service more than 90% of the population (Urakami and Nakayama, 2003). These are the principal focus of our analysis. In terms of price setting in the Japanese water supply sector, the Waterworks Law regulates water rates. These vary according to the local government, but in most cases, they comprise a basic charge and a commodity charge. The first of these is a fixed monthly fee, charged regardless of usage and only determined by the caliber (size) of the water meter. Water meter calibers can be 13, 20, and 25 or more mil­ limeters (mm), with the rate of the basic charge increasing with diam­ eter. In the past, the most common water meter caliber was 13 mm, but most contracts nowadays are for 20 mm. The second component is a commodity charge reflecting the volume of water provided to customers where the rates vary depending on usage. As the basic charge is fixed, the commodity charge corresponding to water consumption determines most of the price fluctuation in water bills. With a metered rate, the unit price of the usage fee increases sys­ tematically with the amount of water used as part of a so-called pro­ gressive system, included as one of the primary objectives for the Waterworks Law. For its part, the Sewerage Water Law regulates prices in the Japanese sewage sector. In most cases, the assumption is that sewerage usage equals the use of the water supply, with a progressive system also applied to sewerage rates under the same principle as the water supply commodity charges. As discussed, in Japan, water supply and sewerage services are operated by separate bureaus in a system similar to the US and Germany, but unlike the UK where water supply and sewerage services are verti­ cally integrated such that water companies provide both potable water and sewerage services (Urakami and Parker, 2011; Saal et al., 2013). Nonetheless, we could question why it is necessary to separate the ownership of water supply and sewerage services in Japan, especially as sewerage charges are directly determined by water charges. In addition, we could also question why sewerage charges are determined by water supply usage given it is reasonable to consider that the pricing of sewerage services should merely reflect the consumption

1 In Abbott and Cohen’s (2009) survey of productivity and efficiency studies of the global water industry during the past 20 years, the only Japanese study is that of Mizutani and Urakami (2001). 2 In December 2018, the Diet approved a revision of the Waterworks Law to encourage the so-called concession method. This allowed local governments in Japan to entrust the management of water supply utilities to businesses while retaining ownership. 3 Most studies explain the efficiencies using explanatory variables, e.g., Ramalho et al. (2010), Mbuvi et al. (2012), and Su� arez-Varela et al. (2017), whereas we specify the efficiencies as an independent variable in price regressions.

4

Municipalities, including the local governments of cities, towns, and vil­ lages, also mainly manage sewerage services in Japan. However, an earlier revision of the Sewerage Water Law in 2014 permitted the concession method for sewerage services. 5 Village municipalities manage most small water supply utilities in Japan. 2

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Utilities Policy 62 (2020) 100984

and efficiency of the sewerage sector. Clearly, there is some prospect for a potential interaction between water supply and sewerage pricing and thence between pricing and efficiency in both the water supply and sewerage services systems.

θCCR xi Xλ � 0; yi Yλ � 0; λ � 0; where θCCR is the minimization task as the objective function, and which represents the total efficiency rating of the DMUs being evaluated by DEA. λ represents the N � 1 nonnegative vector. The overall efficiency scores are obtained from θ with respect to λ. θ only operates relative to the inputs in the constraint. We need to solve this problem for each DMU N times. The model minimizes input consumption by keeping output constant, which is the dual problem commonly conducted rather than the primary problem of maximizing output. Subsequently, the CCR model further developed into the BCC model. BCC is an extension of the CCR model where the addition of a convexity restriction provides overall efficiency as the product of technical efficiency and scale efficiency (Banker, 1984; Tupper and Resende, 2004). Therefore, the BCC model can address the situation where we can improve efficiency through varying scale instead of the assumption of the CCR model that scale cannot be altered. The BCC model is as follows:

3. Method 3.1. First-stage analysis: DEA scores of technical efficiencies As noted, DEA and SFA are the two main approaches to measuring relative performance in water utilities. In this paper, we employ DEA given its several advantages over SFA. To start, SFA requires specific assumptions on the production frontier (Byrnes et al., 2010). It is then easier to obtain better results through DEA than SFA, as the re­ quirements and assumptions of the latter may be difficult to satisfy. Specifically, with DEA, the measurement of efficiency is with refer­ ence to an efficiency frontier calculated using linear programming, with no strong assumptions regarding the parameters of the production frontier. In addition, because in Japan municipal bureaus operate most water supply and sewerage services, it is not easy to assume that these are profit-maximizing entities. Therefore, the textbook assumption of a cost function usually assumed in economics is questionable. In addition, while there is the possibility of a correlation between inputs in DEA models, these should not meaningfully affect results. Similarly, the DEA approach allows multiple outputs in the one model, unlike most classic economic models. Moreover, there is the potential for heteroscedasticity in the parametric analysis as the sizes of water utilities vary markedly according to the distributed water volume and the number of consumers (Marques et al., 2011). The DEA approach can address this by restricting the analysis to water utilities of a similar size.6 We apply DEA models stipulating constant and variable returns to scale assumptions (constant return to scale and variable return to scale, respectively). The CRS model, also known as the CCR model after Charnes et al. (1978), is a flexible measurement approach to relative efficiency given the assumption of CRS. The VRS model, or BCC model after Banker et al. (1984) and Banker (1984), extends the CCR model to an assumption of VRS. The efficiency thus measured by the CCR model represents overall (in)efficiency, which can then be decomposed into pure technical (in)efficiency using the BCC model. Assume there are K inputs and M outputs for a sample of N decisionmaking units or DMUs (here, enterprises) in the water industry. The total input vector is X ¼ K � N and the total output vector is Y ¼ M � N, comprising the whole dataset. The column vectors for one specific entity such as the ith enterprise, are represented as xi for the input vector and yi for the output vector. The CCR model considers the following optimi­ zation problem:

minθBCC subject to: θBCC xi Xλ � 0; yi Yλ � 0; e’ λ ¼ 1; λ � 0: As shown in the BCC model, the only difference is that an additional constraint e’ λ ¼ 1 is added to the CCR model, where e’ is an N � 1 vector of ones. This restriction provides a relatively more convex and tighter envelopment frontier where the assumption is VRS rather than CRS, hence yielding equal or even better efficiency scores than with the BCC model. The DEA efficiency measures in both the CCR and BCC models lie between zero and one, with an efficiency score of one (less than one) indicating an efficient (less than efficient) unit. These are also relative efficiency scores comparing all reference units, as it is desirable that the DMUs involved must be comparable. The input orientation model we use is also well suited to our situation and is common to most research involving water utilities, as under water law and policy, all customers in Japan must be served (Marques et al., 2011). Before explaining the second-stage analysis, we note a problem in model selection. Because DEA is a nonparametric approach, we do not have any good criterion to choose a better model. In fact, our only testing method considers whether the calculated efficiency rankings are similar. We discuss this in detail in Section 4.

minθCCR

3.2. Second-stage analysis: regression analysis of prices

subject to:

At the second-stage analysis, we use the estimated efficiencies of the water supply and sewerage services to examine the relationships be­ tween the prices and the estimated efficiencies and their interactions between the water supply and sewerage sectors. Given the possibility of nonlinear relationships, we estimate the following four models:

6 Of course, DEA has some shortcomings. From an economic viewpoint, because DEA is a nonparametric method used to calculate the production or cost frontier, we cannot interpret let alone estimate the marginal productivities of each marginal cost of output for each input using the calculated frontier or efficiency scores. Conversely, from the viewpoint of statistical inference, it is difficult to apply a significance testing or interval estimation, so we cannot evaluate the differences in results when using different data or methods. This affects the choice of the better model between the BCC and CCR models in this paper. Instead, we merely consider that these two methods will yield different results using Wilcoxson’s rank-sum test. Additionally, DEA results are sensitive to outliers, such as when there are outliers for whom DEA sometimes provides extreme or difficult to interpret results. The maximum likelihood estimation of the SFA mitigates this difficulty by introducing additional error terms, but its maximization process sometimes does not converge.

Yi ¼ α þ βXi þ γZi þ ei ;

(1)

Yi ¼ α þ βlnXi þ γlnZi þ ei ;

(2)

lnYi ¼ α þ βXi þ γZi þ ei ;

(3)

lnYi ¼ α þ βlnXi þ γlnZi þ ei ;

(4)

where Yi are the prices for water supply or sewerage services, Xi is their own estimated efficiency, and Zi are additional explanatory variables being the other system’s estimated efficiency used to capture the 3

J. Liu and M. Fukushige

Utilities Policy 62 (2020) 100984

interaction between water supply and sewerage services.7 Importantly, in our two-stage approach, we first focus on the relationships between the technical efficiencies and their prices, so there is no endogenous relationship between efficiency and the error terms in the price equa­ tions. This is because we do not assume cost minimization in calculating the efficiency of the water supply and sewerage services. This assumption introduces some difficulties in selecting inputs in the DEA and explanatory variables in the regression analysis. Of course, the total cost of operations contains useful information about price setting. If prices are set at marginal cost as in the situation of perfect competition, we could estimate the cost function with prices to evaluate the efficiencies of the utilities. However, if prices are set at average cost with some margin, prices and costs are simultaneously determined. In this situation, we should focus on the determinants of the marginal cost. In both cases, it is difficult to interpret the estimated coefficient for total costs or average costs in the price equation. Consequently, in this analysis, we do not include total costs or average costs in either the DEA or the regression equations.

Corporations. In the first stage of our analysis, we consider the technical effi­ ciencies, so we specify capital and labor as inputs, and water yield and the population serve as outputs. In this stage, we do not consider cost or cost function-related variables like maintenance or operational costs or workers’ wages as inputs because these may not reflect optimization, e. g., cost minimization. This follows similar work to that of Urakami and Nakayama (2003). For the outputs, as our study explores both the water supply and sewerage sectors, the outputs should consider different measurements for each context. Following Molinos-Senante et al. (2016) and Urakami and Nakayama (2003) for the water supply sector, the outputs include annual total water delivery in thousands of cubic meters (1000 m3 ) to gauge the volume of water treated into the delivery network and the population served.10 For the sewerage service sector, the outputs are the annual total water yield (1,000 m3 ) and the number of customers with access to sewerage services. In the third context comprising the com­ bination of water supply and sewerage services, the inputs and outputs are a combination of both types of utilities and, therefore, there are four inputs and four outputs. Table 1 details the variable data sources and definitions. In the second stage, we analyze price setting. Here we specify the water supply and sewerage prices (in yen) as dependent variables, as pertains to a 20 mm water meter caliber and 20 m3 of sewerage. The limited availability of these price data restricts our analysis to 215 bu­ reaus (areas).

3.3. Data We use 2014 data for water supply and sewerage utilities in Japan. Given the separate direct ownership of these utilities in Japan, the sample selected for the analysis is public sewerage operators that are law-applicable enterprises with corresponding water supply operators in the same area. To ensure the accuracy and consistency of the study, we remove from the sample non-law-applicable enterprises involved in both the water supply and sewerage sectors, small water suppliers, and un­ matched enterprises in the water supply and sewerage sectors. As noted, data on small enterprises are difficult to acquire and the share of the population supplied by these enterprises is small. Following this sample refinement, we obtain 215 utilities, as a sup­ plementary Appendix 1. The data are from The Yearbook of Local Public Enterprises (Chihou Kouei Kigyo Nenkan, in Japanese) published online by the Ministry of Internal Affairs and Communications, which reports the financial and management information and statistics of all water utilities in Japan (excluding some small private water utilities).8 Some data are also from the website of the Ministry of Internal Affairs and Communi­ cations.9 To meet our research aim, we divide the measurement of ef­ ficiency into three cases: the efficiency of the water supply sector, the efficiency of the sewerage sector, and the combined efficiency of the water supply and sewerage sectors. The model specification includes two inputs and two outputs. Following previous studies on the efficiency and performance of the Japanese water industry (Urakami and Nakayama, 2003), the inputs are labor and capital. The amount of capital is tangible fixed assets or property, plant and equipment (PP&E) from The Yearbook of Local Public Corporations. This reflects the fact that investment in tangible assets accounts for a large proportion of total capital, which we measure in monetary units (1000 yen). Labor is simply the total number of staff or the number of persons employed and is from The Yearbook of Local Public

4. Results and discussion 4.1. Results of DEA Table 2 presents the efficiency scores for water supply, sewerage, and water supply–sewerage for the CCR and BCC models. Ews (CCR) and Ews (BCC) are the efficiencies calculated using only the water supply data and the CCR and BCC models, respectively. Ess (CCR) and Ess (BCC) are the efficiencies calculated using only the sewerage service data and the CCR and BCC models, respectively. Lastly, Eb (CCR) and Eb (BCC) are the efficiencies calculated using both the water supply and sewerage service data and the CCR and BCC models, respectively. We can see that the average BCC efficiency scores are higher than those for the CCR because the BCC model is a measure of pure technical efficiency. For instance, there is an efficiency of 61.81% in the combi­ nation case with the BCC, but just 51.49% for the CCR model. This difference is natural in that the BCC model assumes variable returns to scale production technology, which is rather more flexible than the assumption of constant returns to scale in the CCR model. However, regardless of whether we use the CCR or BCC approach, the efficiencies of the water supply sector exceed those of the sewerage sector. This could relate to the fact that all of the coefficients of variation for the sewerage inputs and outputs are smaller than that for the water supply, as shown in Table 1. In other words, there are smaller variations among cities and towns in the production characteristics of sewerage services than for the water supply. In addition, there are more efficient entities with the BCC model than the CCR model. Specifically, in the CCR model for sewerage services, there are 18 efficient enterprises, compared with just six efficient en­ tities for the BCC model. In the case of water supply–sewerage, there are 19 efficient enterprises, 12 more than both services alone. In addition,

7 For example, Renzetti (1999) evaluated the pricing policies of water supply and sewerage service using estimated cost functions. We did not estimate a cost function, so instead include the calculated efficiencies directly as an indepen­ dent variable in explaining prices. More recent studies focus on the effects of governance structures or the ownership of water and sanitation corporations on prices or tariffs; see Barbosa and Brusca (2015) for Brazil, Porcher (2017) for France, and Silvestre and Gomes (2017) for Portugal. All these studies analyze the price settings directly and, unlike our analysis, do not consider the effects of technical efficiency on prices. Su� arez-Varela et al. (2017) analyze the rela­ tionship between ownership and technical efficiency using DEA while Gonz� alez-G� omez and García-Rubio (2018) survey studies on the relationship between ownership and price. 8 http://www.soumu.go.jp/main_sosiki/c-zaisei/kouei_kessan.html. 9 http://www.soumu.go.jp/main_sosiki/c-zaisei/kouei.html.

10 An anonymous referee suggested the importance of leakages in the water supply. Owing to data availability, we use water yields in the DEA. Of course, while water yield is an important measure of the amount of water supplied, leakage in the water supply is also an important problem when we consider efficient water supply to the consumers. If we were able to obtain leakage data, we could compare the efficiencies with and without leakage.

4

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Utilities Policy 62 (2020) 100984

Table 1 DEA inputs and outputs and selected statistics. Data Input Labor (Water Supply): number of persons employed (persons) Labor (Sewerage): number of persons employed (persons) Capital (Water Supply): tangible fixed assets or property, plant, and equipment (billion yen) Capital (Sewerage): tangible fixed assets or property, plant, and equipment (billion yen) Output Water yield (Water Supply): annual total water delivery (1,000,000 m3 ) Water yield (Sewerage): annual total water yield (1,000,000 m3 ) Population (Water Supply): population served (1000 persons) Population (Sewerage): number of customers (1000 customers)

Mean

Standard Deviation

Coefficient of Variation

Minimum

Maximum

114.455 66.465

314.812 181.685

2.750 2.733

4 2

3603 2124

51.263

168.228

3.281

2.307

2320.29

136.899

403.654

2.948

3.623

5098.19

34.901

108.933

3.121

0.684

1457.797

30.865

94.332

3.056

0.288

1197.764

321.524

979.721

3.047

7.120

13089.824

275.548

745.657

2.706

4.643

9130.363

Note: Total number of observations is 215 and the source is The Yearbook of Local Public Corporations. Table 2 Results of DEA. Mean

Table 3 Wilcoxson’s rank-sum tests. Standard Deviation

Water Supply CCR 0.5149 0.1687 model: Ews (CCR) BCC 0.6181 0.2005 model: Ews (BCC) Sewerage CCR 0.4209 0.1781 model: Ess (CCR) BCC 0.5059 0.2074 model: Ess (BCC) Water Supply & Sewerage CCR 0.6318 0.1719 model: Eb (CCR) BCC 0.7301 0.1842 model: Eb (BCC)

Maximum

Minimum

# of Efficient Observations

1

0.2117

6

1

0.2632

18

1

0.1192

3

1

0.1863

10

1

0.3512

17

1

0.3586

36

Ews (BCC) Ews (CCR) Ews (BCC) Ess (CCR) Ess (BCC) Eb (CCR)

6.658** – – – –

Ess (CCR) 6.280** 10.885** – – –

Ess (BCC)

Eb (CCR)

Eb (BCC)

1.296 4.802** 6.830** – –

7.305** 0.284 11.591** 5.095** –

12.748** 6.339** 14.603** 10.266** 7.490**

Note: Total number of observations is 215. * and ** denote statistical signifi­ cance at the 5% and 1% level, respectively.

results for the efficiency scores of the BCC and CCR models and for the water supply and sewerage sectors. First, the latter suggests dissimilar efficiencies for water supply and sewerage services in the same local government area, suggesting that the inefficiencies of these services do not greatly dependent on local government characteristics or customs. Therefore, we have scope to analyze the interaction between water supply and sewerage service utilities. Second, the former implies that the estimated efficiencies from the BCC and CCR models are mutually different. As discussed in Subsection 3.1, there is no good model selec­ tion criterion for selecting between the BCC and CCR models. In the following subsection, we employ both calculated efficiencies as an explanatory variable for price setting. When only one of the esti­ mated coefficients for the BCC or CCR efficiencies in the regression equations is statistically significant, we cannot conclude that the effi­ ciencies affect the price setting significantly. However, when both the coefficients in the corresponding regression equations are significant, we can conclude that there is a significant relationship between the ef­ ficiencies and the prices, even when one of the calculated efficiencies using either the BCC or CCR models are not properly specified.

Note: Total number of observations is 215.

the average efficiency of the combined case (water supply and sewerage) is higher than in the separate cases (water supply or sewerage). Of course, as these combined cases include four inputs and four outputs, they are more flexible than water supply or sewerage services only, which only include two inputs and two outputs. The average scores are 0.7301 and 0.631 for the BCC and CCR models, respectively. We use Wilcoxson’s rank-sum test11 to assess the differences between the efficiencies in Table 3. As shown, there are significantly different

4.2. Results of regression estimation After examining the efficiencies of the water supply and sewerage sectors, we estimate Models (1)–(4) in subsection 3.2 for water supply and sewerage services to consider the potential linkage between the price settings of water supply and sewerage services. We estimate each model using ordinary least squares (OLS) and conduct White’s (1980) test for heteroscedasticity (White’s test) and Ramsey’s (1969) test for misspecification with second-order fitted values (RESET2) as diagnostic tests. We use four models and two diagnostic tests to improve the robustness of the statistically significant estimated coefficients. In all cases, we include two additional explanatory variables to check the

11

An anonymous referee suggested the Kolmogorov–Smirnov test and another suggested the Mann–Whitney U test. According to Lehmann and D’Abrera (1975) and Linebach et al. (2014), Wilcoxson’s rank-sum test is equivalent to Mann–Whitney’s U test, while we consider the former a better test than the Kolmogorov–Smirnov test. 5

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Utilities Policy 62 (2020) 100984

compare the magnitudes of the estimated coefficients for Ess and Ews, the estimated coefficients for Ews are greater than half of the estimated coefficients for Ess in the CCR cases while those for Ews are similar to or greater than the estimated coefficients for Ess in the BCC cases. This suggests that we cannot ignore the magnitude of the relationships be­ tween a one-unit change in the efficiencies of the water supply system and the calculated changes in the prices of sewerage services.

robustness of the regression results, namely, capital and labor inputs for water supply and sewerage services in each regression equation. In the log-linear regression equations, we employ their logarithmically trans­ formed values. Tables 4 and 5 provide the results for the price of the water supply (Pws) for the CCR and BCC efficiency scores, respectively. In each case, we estimate the regression model without the other efficiency. In most cases, the coefficients of the efficiency indexes for water supply (Ews) are statistically significant at the 1% level and their signs are negative. This suggests that water supply prices fall when water supply efficiency improves. In some cases, White’s test or RESET2 is statistically signifi­ cant at the 5% level, but in all cases, when the additional explanatory variable is included, the estimated coefficients of the efficiency of the sewerage system or the integrated efficiency of water supply and sewerage system are statistically insignificant. This implies that there is no interaction between the price setting of the water supply and the efficiency of the sewerage system. Tables 6 and 7 detail the results for the price of sewerage services (Pss) with the CCR and BCC efficiency scores, respectively. In each case, we again estimate the regression model without the other efficiency. Once again, in all cases, the coefficients of the efficiency indexes for sewerage (Ess) are statistically significant at the 1% level and their signs are negative. This indicates that the price of sewerage services falls when efficiency in sewerage services improves. While White’s test or RESET2 is statistically significant at the 5% or 1% level in some cases, in all cases, when the additional explanatory variable is included, the estimated coefficients of the efficiency of the water supply system or the integrated water supply and sewerage system are statistically significant at the 1% level and their signs are negative. As discussed in the previous subsec­ tion, even when one of the calculated efficiencies using either the BCC or CCR models is not correct, in both cases the estimated coefficients are statistically significant. This provides a robust evidence that there is a significant interaction between price setting in the sewerage system and the efficiency of the water supply system. In addition, the signs of the estimated coefficients suggest that given the other variables specified as explanatory variables in the regression analysis, there is a positive relationship between the inefficiency of the water supply system and the price for sewerage services. When we

5. Conclusion This paper measured the relative efficiency of the water supply and sewerage sectors in Japan using DEA, and then investigated the re­ lationships between efficiency and price setting using regression models. In the first stage, we calculated the technical efficiencies for water supply and sewerage services, and in the second stage, we explained the prices for water supply and sewerage services using the efficiencies calculated in the first stage. Unlike most previous research that merely focuses on one side of the water industry or is very well documented in countries such as the US and UK, we applied the nonparametric tech­ nique of DEA to investigate both water supply and sewerage services in Japan, and additionally considered the combination of water supply and sewerage services in the one analysis. The major findings are as follows. First, based on the measurement of relative efficiency scores using the DEA approach, the efficiency of water supply (sewerage) services negatively influences prices in the water supply (sewerage) sectors, which indicates better efficiency can induce lower charges. However, there is no apparent influence of sewage effi­ ciency on water supply charges. This is a surprising result in that the efficiency of the water supply system in Japan can determine charges for the sewerage system. This suggests a mismatch and a degree of unfair­ ness in actual usage given the bills for sewerage services are calculated using water supply usage. Therefore, just as we questioned the price setting method for sewerage services at the beginning of our paper, it is likely necessary to reconsider the setting of sewage prices in the future. One solution could be installing sewage meters, although this may not be possible given the cost and level of technical difficulty involved. Of course, recent reforms to the Waterworks Law in Japan allow a concession method in the water

Table 4 Regression of water supply prices on efficiency index (CCR). Dependent V.

Pws

const

4192.698** 4215.148** (22.445) (21.344) 1744.48** 1683.88** (–5.138) (–4.428) – 128.06 – (–0.356) – – – – Labor & Capital (Water Supply) 0.1203 0.1167

4333.531** (19.586) 1126.52 (–1.810) – – 724.485 (–1.184)

13.723 5.131

Additional Variables Adjusted R2

8.797 11.031 5.462 5.099 Pws 2588.892** 2570.511** (18.186) (15.292) 986.607** 969.994** (–5.404) (–4.854) – 31.668 – (–0.206) – – – – Labor & Capital (Water Supply) 0.1306 0.1266

White’s test RESET2

8.708 1.266

Ews Ess Eb Additional Variables Adjusted R2 White’s test RESET2 Dependent V. const ln(Ews) ln(Ess) ln(Eb)

ln(Pws)

11.888 1.253

8.291001** (14.649) 0.66284** (–6.663) – – – – log(Labor) & log(Capital) 0.2108

8.294241** (14.623) 0.68218** (–6.078) 0.038867 (0.374) – – (Water Supply) 0.2075

8.333996** (14.739) 0.45287* (–2.541) – – 0.2486 (–1.418)

19.226 1.652

19.281 1.644

7.739413** (13.640) 0.38609** (–6.568) 0.029512 (0.663) – – (Water Supply) 0.2204

7.681347** (13.712) 0.26158** (–2.821) – – 0.15535 (–1.421)

0.1319

17.337* 1.491 ln(Pws) 7.688816** (13.693) 0.3696** (–6.946) – – – – log(Labor) & log(Capital) 0.2225

14.392 1.828

15.665 0.143

20.034 0.120

19.718 0.004

0.1220

2593.35** (18.225) 677.812** (–2.088) – – 439.384 (–1.150)

Note: t-values in parentheses. Dependent V. is dependent variable. * and ** denote statistical significance at the 5% and 1% level, respectively. 6

0.2145

0.2262

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Utilities Policy 62 (2020) 100984

Table 5 Regression of water supply prices on efficiency index (BCC). Dependent V.

Pws

const

4397.412** 4362.735** (23.982) (22.0425) 1874.38** 1934.14** (–6.407) (–6.055) – 146.6525 – (0.470) – – – – Labor & Capital (Water Supply) 0.1715 0.1684

4476.938** (19.365) 1613.03** (–2.951) – – 329.415 (–0.566)

11.031 1.165

Additional Variables Adjusted R2

8.600 11.129 1.464 1.422 Pws 2634.522** 2678.191** (22.741) (18.550) 1140.15** 1176.2** (–6.524) (–6.227) – 79.419 – (0.508) – – – – Labor & Capital (Water Supply) 0.1764 0.1735

White’s test RESET2

7.796 0.004

Ews Ess Eb Additional Variables Adjusted R2 White’s test RESET2 Dependent V. const ln(Ews) ln(Ess) ln(Eb)

ln(Pws)

11.263 0.002

7.684471** (13.639) 0.57543** (–6.826) – – – – log(Labor) & log(Capital) 0.2175

7.664923** (13.620) 0.62695** (–6.722) 0.115567 (1.282) – – (Water Supply) 0.2199

7.71584** (13.623) 0.48969** (–3.060) – – 0.10797 (–0.630)

19.447 2.212

18.846 3.188

7.200009** (12.599) 0.3802** (–6.888) 0.056016 (1.234) – – (Water Supply) 0.2254

7.173325** (12.519) 0.32724** (–3.388) – – 0.0375 (–0.312)

0.1726

16.240 3.407 ln(Pws) 7.169515** (12.542) 0.35291** (–6.970) – – – – log(Labor) & log(Capital) 0.2234

9.740 0.002

15.330 0.465

19.095 0.100

18.631 0.507

0.1688

2636.032** (22.662) 1080.2** (–3.288) – – 88.0269 (–0.215)

0.2152

0.2201

Note: t-values in parentheses. Dependent V. is dependent variable. * and ** denote statistical significance at the 5% and 1% level, respectively. Table 6 Regression of sewerage price on efficiency index (CCR). Dependent V.

Pss

const

3619.554** 3893.006** (34.973) (29.052) 1856.96** 1518.88** (–8.337) (–6.234) – 803.761** – (–3.122) – – – – Labor & Capital (Sewerage) 0.3044 0.3321

3977.703** (26.445) 1224.31** (–4.172) – – 982.355** (–3.220)

9.309 13.956**

Additional Variables Adjusted R2

6.848 11.804 7.442** 14.498** Pws 2098.504** 1886.2** (20.379) (16.363) 776.086** 620.335** (–7.825) (–5.902) – 507.362** – (–3.703) – – – – Labor & Capital (Sewerage) 0.2833 0.3240

White’s test RESET2

5.311 0.991

Ews Ess Eb Additional Variables Adjusted R2 White’s test RESET2 Dependent V. const ln(Ews) ln(Ess) ln(Eb)

ln(Pss)

9.886 3.943*

6.715424** (12.975) 0.86247** (–11.236) – – – – log(Labor) & log(Capital) 0.4876

6.926206** (13.717) 0.72313** (–8.713) 0.33127** (–3.795) – – (sewerage) 0.5182

7.12266** (14.115) 0.56593** (–5.687) – – 0.45942** (–4.427)

17.973 7.018**

19.718 6.803**

5.837103** (10.975) 0.28576** (–7.718) 0.21238** (–4.456) – – (sewerage) 0.4892

6.073359** (11.545) 0.20279** (–4.640) – – 0.35271** (–5.286)

0.3298

15.230 6.605* ln(Pws) 5.745057** (10.357) 0.3526** (–9.980) – – – – log(Labor) & log(Capital) 0.4436

7.956 7.154**

16.824 16.803**

17.874 19.233**

19.815 13.231**

0.3340

2027.371** (20.036) 462.244** (–3.714) – – 755.7** (–3.955)

0.5291

0.5066

Note: t-values in parentheses. Dependent V. is dependent variable. * and ** denote statistical significance at the 5% and 1% level, respectively.

supply, although privatizing the water supply provides some difficulty for price setting in sewerage services. However, if we were able to take advantage of concessions in both the water supply and sewerage ser­ vices, we could consider the combined management of the water supply and sewerage services and their privatization as a means to establish efficient or optimal price setting rules. Finally, we note some limitations with our analysis. The first con­ cerns how we consider the quality of the service. While Lin (2005) and Picazo-Tadeo et al. (2008) focus on service quality in the water supply, we cannot obtain data about the quality of either the water supply or the sewerage services. We hope to rectify this shortcoming in future

research. A second problem concerns the scope of the water supply bureaus. Because of limited price availability, in this analysis we were only able to analyze large water supply bureaus. As Urakami (2007) points out, there are many different types of small water supply bureaus or companies, and many are engaged in water intake and purification or water yield only. A final problem is that our analysis only focuses on the static efficiencies of water supply and sewerage services, and we were unable to adopt a dynamic perspective through applying dynamic efficiency models such as Kao’s (2013). This limitation was pointed out by an anonymous referee and it is certainly one of the most important problems remaining for future research in this 7

J. Liu and M. Fukushige

Utilities Policy 62 (2020) 100984

Table 7 Regression of sewerage price on efficiency index (BCC). Dependent V.

Pss

const

3567.763** 4039.487** (32.900) (29.717) 1525.24** 1066.89** (–7.405) (–5.015) – 1166.43** – (–5.261) – – – – Labor & Capital (Sewerage) 0.2660 0.3484

4139.306** (25.597) 690.112** (–2.578) – – 1367.8** (–4.603)

9.155 2.281

Additional Variables Adjusted R2

7.939 10.040 2.126 2.841 Pws 2244.663** 2015.99** (23.378) (20.251) 732.549** 506.23** (–6.873) (–4.668) – 719.6** – (–5.416) – – – – Labor & Capital (Sewerage) 0.2444 0.3339

White’s test RESET2

7.622 0.638

Ews Ess Eb Additional Variables Adjusted R2 White’s test RESET2 Dependent V. const ln(Ews) ln(Ess) ln(Eb)

ln(Pss)

10.376 0.026

6.480617** (11.822) 0.6782** (–9.612) – – – – log(Labor) & log(Capital) 0.4304

6.420986** (12.169) 0.5402** (–7.174) 0.34401** (–4.234) – – (sewerage) 0.4727

6.670846** (12.494) 0.43141** (–4.572) – – 0.40373** (–3.797)

20.691 0.000

22.750 0.039

5.554988** (10.049) 0.26289** (–6.757) 0.21922** (–4.489) – – (sewerage) 0.4552

5.808474** (10.513) 0.19777** (–4.126) – – 0.31094** (–4.238)

0.3186

18.711* 0.005 ln(Pws) 5.808603** (10.114) 0.33272** (–8.933) – – – – log(Labor) & log(Capital) 0.4057

8.496 0.209

18.812* 1.319

21.059 1.165

21.879 1.118

0.3301

2218.884** (24.295) 301.364** (–2.246) – – 1000.48** (–4.897)

0.4645

0.4500

Note: t-values in parentheses. Dependent V. is dependent variable. * and ** denote statistical significance at the 5% and 1% level, respectively.

area. We should particularly note the limitations of the regression approach in this paper. Our model only investigates the price settings for water supply and sewerage service by specifying only the estimated efficiencies and the capital and labor inputs for each service as explan­ atory variables. As discussed in Section 3, we do not employ any cost variables, whether operational or maintenance, as explanatory vari­ ables. By considering the simultaneous determination of prices and marginal or average costs and omitting the cost minimization behavior of the utilities, we, thus, do not include any cost-related factors, which could potentially provide important information about price setting behavior. Accordingly, in future research we will attempt to address this deficiency.

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