Potential efficiency effects of merging the Swedish district courts

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Socio-Economic Planning Sciences journal homepage: www.elsevier.com/locate/seps

Potential efficiency effects of merging the Swedish district courts Pontus Mattssona,∗, Claes Tidanåb a b

Department of Economics and Statistics, Linnaeus University, 351 95 Växjö, Sweden Financial Department, The Swedish National Court Administration (SNCA), 551 81 Jönköping, Sweden

ARTICLE INFO

ABSTRACT

Keywords: Benchmarking Courts Data envelopment analysis (DEA) Economies of scale Economies of scope Merger analysis

The Swedish district courts have undergone a substantial restructuring process in which the main reform has been to merge. As a result, the number of district courts has declined from 95 in 2000 to only 48 in 2009. All main arguments that support merging concern enhancements of efficiency. However, it has not yet been explicitly examined whether the mergers have the potential to increase efficiency ex ante. Thus, the expectation concerning higher efficiency was built on a subjective view. This paper investigates whether the mergers can be rationalized from a production economic point of view. Data envelopment analysis (DEA) is used to compute a production frontier where the conducted mergers are incorporated to identify the potential ex ante gains. Furthermore, the overall potential is decomposed into learning, scale, and harmony to investigate the source of the potential gain, e.g., an effect of adjusting to best practice or a pure merging effect such as scale. The results show diverse potentials, i.e., a number of mergers did not have the potential to gain in efficiency while others could gain substantially. A conclusion based on the analysis is that the potential production economic effects should be investigated before merger decisions are made in the future. This is also likely to be true beyond the Swedish district courts.

JEL Classification: D24 L11 P41 G34

1. Introduction A high level of efficiency in state-provided services is an explicitly specified goal in the Swedish Budget Law [1], 1 chapter 3§. In particular, efficiency is crucial to maintain short handling times, which are important from a law and order perspective [2,3]. The Swedish district courts have undergone a substantial restructuring process to enhance efficiency. The main reform has been to merge the district courts from 95 in 2000 to 48 in 2009. General theoretical arguments for merging include economies of scale, economies of scope, increased central control, and risk sharing [4]. Better performance in any of these aspects enhances efficiency, which generates more state-provided services and shorter handling times, given the level of public spending. Productivity in the Swedish district courts declined after 2010 [5,6]. Further, the studies centering on the mergers have only investigated qualitative aspects [7,8]. However, it has not been formally investigated whether the theoretical arguments are expected to be reached for the district courts in Sweden. This paper aims to investigate how well the assumed efficiency gains had the potential to be realised by estimating the potential production economic effects of the Swedish district court mergers, ex ante.

The contribution is, therefore, to evaluate whether the political decision to merge is well prepared, i.e., whether all mergers could be rationalized from an efficiency perspective. Otherwise, further enhancements of the decision basis for mergers should have been carried out. The point of departure is the data envelopment analysis (DEA) model of Bogetoft et al. [9] and Bogetoft and Wang [10]. An output perspective is carried out to investigate the potential gain of mergers, ex ante, based on the reason that district courts have a specific budget that they are supposed to spend optimally to maximize output. Using the output perspective, efficiency is defined as the maximum level of outputs that can be produced, given the inputs. One argument for using the applied model is that it does not only estimate the potential efficiency effect of the mergers, but also decomposes the potential gains into learning (LE), harmony (HA), and scale (SC) to isolate the sources. LE represents the gain from eliminating individual court inefficiency,1 and HA is the potential efficiency gain achieved from a more “powerful” input mix, i.e., a good combination of inputs that can achieve a large amount of output, or an output mix that is easier to produce. Finally, SC is the gain or loss that occurs by operating at a larger scale. The data are obtained from the Swedish National Court Administration (SNCA), consisting of all individual district courts,

Corresponding author. E-mail addresses: [email protected] (P. Mattsson), [email protected] (C. Tidanå). 1 Learning does not necessarily mean that, ex ante, inefficient courts learn something. This effect can be achieved in different ways, and we follow the most common name of this in the previous literature. Another alternative is to refer to this effect as technical efficiency. ∗

https://doi.org/10.1016/j.seps.2018.09.002 Received 30 January 2018; Received in revised form 2 June 2018; Accepted 9 September 2018 0038-0121/ © 2018 Published by Elsevier Ltd.

Please cite this article as: Mattsson, P., Socio-Economic Planning Sciences, https://doi.org/10.1016/j.seps.2018.09.002

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including the resigned, for the period 2000–2009. For each merger, the ex ante information of the individual courts is included in the DEA model. The data on outputs are collected in 14 categories that are aggregated into decided criminal cases, decided civil cases, and decided matters based on the broad categories stated by SNCA. Potential heterogeneity between the output variables is incorporated by weighting based on differences in spent resources between the 14 categories. Further, the inputs are separated into judges, law clerks, other personnel, and the area of the court. Diverse potential in efficiency gain is found among the mergers, i.e., a few indicate substantial positive effects while others indicate zero effect. In the analysis, a non-decreasing return to scale (NDRS) technology is assumed since most mergers are small and only a few comparison courts are large.2 The most positive merger indicates a potential gain of 47.8%. However, 31.0% of the mergers show an overall potential efficiency gain of less than 5%. If the gain that is obtained from eliminating individual inefficiency is excluded, 44.8% of the mergers show a potential efficiency gain of less than 5%. Even though a substantial number of mergers with small potential gains are present, there are other mergers with potential to be advantageous. For example, 27.6% of the mergers have the potential to gain more than 20.1% in efficiency. The paper proceeds as follows: The second part describes the Swedish court system and the mergers that have taken place in Sweden during the studied period. Third, previous research on mergers is briefly described. Fourth, a theoretical framework regarding mergers is presented and, fifth, the empirical strategy is outlined. The sixth part describes the data and the seventh reports the results. Finally, section eight concludes.

parts: 1) restructuring of the courts' organization, mainly by merging; 2) development of working methods and delegation within a court; 3) focusing the work tasks of the courts to concentrate the courts' activities to mainly judicial operations; and 4) improvements in the procedural code that, for instance, include the possibility of deciding a greater extent of cases on documents without a hearing. 2.2. Internal merging arguments According to the Ministry of Justice [11], the district courts' organization should be shaped, so that a number of goals will be achieved. It requires a strengthened drafting organization, which can define the role of a judge. If judges, who are the most expensive category of personnel, spend a greater proportion of working time judging while other staff works on legal drafting, organizational support, preparation of cases, and administrative duties, it will lead to a more efficient organization. This is easier to achieve in a larger district court than in a smaller district court. Further, the number of cases should be sufficiently large to achieve specialization. For instance, if a judge deals with a property case every day instead of once a month, it is obvious that he or she will handle a property case more quickly. Specialization is not possible to accomplish in small district courts because the number of some specific case categories will be too small [11]. A second argument for mergers is related to the possibilities to recruit qualified personnel. SNCA [14] notes that district courts in smaller places have had difficulties recruiting judges. Furthermore, it may be easier to find skilled management for larger district courts than smaller district courts. Thus, there is also an argument to establish larger district courts in bigger cities. The need for competence development must be fulfilled. In the smallest district courts, which are vulnerable if personnel are absent, it may be difficult to satisfy this need. For example, the staff may restrain some competence development since the district court's activities will be affected directly if someone is absent [11]. The geographic coordination with other authorities within the judicial system should be improved. The organization of the district courts also affects the efficiency of other authorities, such as the public prosecution service and correctional treatment. Both public prosecution and correctional treatment are organized in fewer but larger units compared to the district courts [8]. Detained persons must often be transported to different district courts, which entails high costs for correctional treatment. Thus, merging district courts will reduce costs for other authorities within the judicial system since the merged district courts to a larger extent will be located in the same place as other authorities, i.e., more centralized. Finally, the district courts have to be placed such that reasonable availability for citizens is achieved. This goal can be viewed as a constraint on the extent of mergers, i.e., there must be a limit on the maximum travel distance to the nearest district court [11]. According to the government, the strategy to achieve these goals is to create larger territorial jurisdiction of the courts. This means that the district courts' organization needs to be adjusted, so there will be fewer but larger district courts. A reform of the district courts' organization should be performed step by step, and regional differences in different counties should be taken into account, for instance the geographical distances between the district courts.

2. The Swedish court system and reformation of the district courts The role of the courts is to guarantee rule of law and security before the law. This function is of fundamental importance in a democratic society and means that the courts have a special position compared to other institutions and authorities [11]. There are three instances of general courts in Sweden: district courts, Courts of Appeal, and the Supreme Court. 2.1. Swedish district courts The district courts mainly handle cases, as the first instance, related to their catchment area, which corresponds to the surrounding geographical area. Within each court, there are different types of employees, which SNCA classifies into three categories: 1) judges consisting of chief judges, senior judges, and judges who are considered as permanent; 2) law clerks who are judges under education who work mainly in the preparation of cases; and 3) other personnel who work with, for example, human resources [12]. Cases are, by SNCA, separated into criminal cases, civil cases, and matters. Criminal cases are brought to justice by the prosecutor on behalf of the state or an individual. Civil cases, generally, fall under property or family law. The former relates to the inability of two parties to agree and the latter to, e.g., divorces or custody of children. Matters are regulated in the Court Matters Act [13] and usually require limited judge resources. SNCA separates these into four categories: (1) debt clearances, (2) debt enforcements, (3) bankruptcies or company reconstructions, and (4) other matters.3 During the spring of 2000 the government presented an official letter to reform the courts, and the Ministry of Justice [11] described the direction of the development. The reform consisted of four main

2.3. Reforming the district courts' organization: the outcome The practical work to develop the district courts' organization proceeds as follows. First, the SNCA investigates the district courts' organization in a specific region, and second, it submits a proposal for territorial jurisdiction of the district courts in the region. This means defining how many district courts should be in the region and which mergers are suitable. Third, the government makes decisions about the changes in the district courts' organization. This has resulted in 32

2

More arguments for this are provided in the empirical strategy section. Examples of other matters are estate administrators, parking remarks, heritages, and custodians. 3

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mergers, including two or more district courts during the years 2000–2009.4 The number of district courts during the same period decreased from 95 to 48, which is the current number. District courts that are adjacent have been merged. Typically there has been a relatively large district court in the region that has taken over one or more of the smaller, adjacent courts.

3.2. Ex ante literature on merging To the best of our knowledge, there has been no empirical investigation of the potential production gain of merging the Swedish district courts. Literature related to merging in the Swedish district courts is scarce. The Swedish National Audit Office [7] investigated the first mergers that took place using interviews regarding whether the governmental goals were expected to be reached. For instance, the geographic placement of the court, i.e., closeness to metropolitan areas and universities, was found to be crucial for recruiting qualified people.7 Further, the Swedish Agency for Public Management [8] qualitatively studied the mergers and stated areas where efficiency should be enhanced after the merging; to a large extent, this corresponded to the general arguments for merging stated above. This section, however, centers on the literature that considers mergers from an efficiency perspective, i.e., ex ante estimations of efficiency in production or cost.8 Literature regarding efficiency effects ex post will not be described here.9 From a parametric point of view, unexploited scope and scale economies, which are considered as arguments for merging, have been examined by, for example, Preyra and Pink [37] and Wagstaff and Lopez [38]. However, this area of literature does not attempt to measure the potential efficiency gain or its decomposed factors, which have been given less attention. The ex ante studies where decomposition is carried out are to a large extent based on Bogetoft et al. [9] and Bogetoft and Wang [10] due to the intuitive decomposition into LE, HA, and SC. Empirical applications have been conducted, ex ante, for local district offices of the Danish Forestry Extension Service [9], agricultural extension offices in Denmark [10], Danish hospitals [39], electricity distribution companies in Turkey [40], district courts in Italy [34], and hospitals in Greece [41]. The results indicate that inadequate structures are as costly as the inability of the individual units to produce on the frontier in some cases. In others there is no potential gain from a merger and the scale effect is generally negative under the variable returns to scale (VRS) technology.10 Halkos and Tzeremes [44] proposed a different model than Bogetoft and Wang [10] to investigate the efficiency gains of potential bank mergers. By using the Banker et al. [45] DEA model, they identified cost-efficient banks. Further, efficiency scores of virtual DMUs were obtained by estimating a new frontier including the summed inputs and outputs. However, this approach does not decompose the potential merging gains into three components. Only two alternative decomposition approaches have been found in an ex ante context. Saastamoinen et al. [46] applied the stochastic nonparametric envelopment of data (StoNED) proposed by Kuosmanen and Kortelainen [47]. StoNED, however, relies on estimation in the first stage, which will create uncertainty in the estimation, i.e., a noise term. This also requires more prior assumptions. Additionally, Lozano and Villa [48] proposed a premerging planning tool for costs, decomposed into technical efficiency, an allocative component, and a pure merger efficiency component. However, this is not applicable in our context since we only have access to production data.11 Brooks and Jones [51] highlighted the importance of economies of

3. Previous research Efficiency and total factor productivity (TFP) within courts have been investigated in several studies, starting with Lewin et al. [15] and Kittelsen and Førsund [16]. The benchmarking literature in courts is of importance for choosing relevant inputs and outputs. However, these studies have focused neither on whether the individual courts are structurally optimal nor if the mergers were optimal based on production economic arguments, ex ante, even though mergers have been extensively performed during the last 20 years. Thus, the structure itself might be suboptimal, but the standard individual efficiency literature does not take this into account [17]. However, the literature shows both a cost and benefit perspective, meaning that location models for social utility can be utilized [18]. One such consideration is that the demand for justice services may decline due to longer distance to the courts [19]. The present study focuses on investigating potential efficiency effects of merging. Thus, the next focus is to describe the TFP and efficiency studies for courts to obtain a background of input and output selection, followed by an overview of the ex ante potential efficiency literature targeting model choice. 3.1. Benchmarking within district courts Most of the literature investigating efficiency in district courts adopts the nonparametric technique DEA [20].5 All previous studies have included the number of employees as one or more inputs. Falavigna et al. [23], Ferrandino [24], Finocchiaro Castro and Guccio [25], and Falavigna et al. [26] measured employees as the number of judges, Silva [27] used administrative workers, and other studies have incorporated both these categories [28–30].6 Another common input is the caseload, consisting of pending and new cases [31,32]. The argument for inclusion is that performance will be underestimated for courts with low demand for justice services. Efficiency will, however, only be affected when other inputs are nonflexible, meaning that higher efficiency can be utilized by changes in, for example, the number of employees [6]. A measure of fixed inputs, i.e. capital, has to a large extent been omitted in the previous research on court performance. An exception is Elbialy and Garcia-Rubio [33], who used the number of computers as a capital proxy. Decided cases are generally used as the measure of outputs [23,32]. These are sometimes separated by category, e.g., criminal cases and civil cases [34]. Normally, the cases are simply added together, which ignores potential heterogeneity between cases. An exception is Santos and Amado [29], who used duration-based weight restrictions among their 43 output categories. Additionally, it can be argued to be a tradeoff between quality and efficiency. Falavigna et al. [26] incorporated the time needed to solve a case as a bad output. However, quality is mostly incorporated in a second stage, e.g., by judges' salaries and education level [31,35].

7 An effect evaluation could not be performed since the report was performed so close in time after the mergers, i.e., most of the results are based on beliefs from the interviewed people. 8 Merging issues relating to financial performance and competition are questions that will not be described here. 9 The literature considering the actual effects, ex post literature, regarding how to integrate firms to make the merger successful (e.g., Knilans [36]) will not be further described in this paper. 10 Bogetoft and Grammeltvedt [42] and Agrell et al. [43] investigated the potential gains, ex ante, and the realised gains, or losses, ex post. 11 Comparative studies between SFA and DEA can be found in, for example, Hjalmarsson et al. [49] and Silva et al. [50].

4

The mergers in the Stockholm region in 2007 are calculated as one merger. A survey of judicial efficiency has been conducted by Voigt [21], and a broader overview of the extensive use of DEA in many areas of research can be found in Emrouznejad and Yang [22]. 6 Silva [27] did not include judges, since each bench within a court is represented by one judge, and no aggregation to court level was performed. In addition, the main objective was to link inputs to outputs for Portuguese courts where the linkages go through cases received and cases solved in different categories. 5

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effect when CRS is assumed).13

scale in the merging process; however, the merger only became a success when relatively small hospitals merged together, i.e., two small hospitals together. Furthermore, Lynk [52] concluded that merging small clinical departments substantially reduces relative variability and thus the costs. This is a potential gain that cannot directly be disentangled by the Bogetoft and Wang model. To summarize, careful evaluation is necessary before a merging decision takes place. Empirical studies to measure potential gains can be found for some sectors and not for others, even though the theoretical arguments for doing mergers to a large extent relate to efficiency. The Bogetoft and Wang [10] ex ante DEA model requires few prior assumptions, has an intuitive decomposition using production data, and is the standard approach to investigate potential efficiency effects. There is no consensus that mergers create a more efficient structure. Thus, it is not necessarily the case that there were production economic arguments to perform the mergers in the Swedish district court system, i.e., the merging decisions in Sweden were made on scarce information.

4.2. Learning Bogetoft and Wang [10] separated a learning effect that cannot be directly interpreted as a cause of the merger. Learning best practices can occur by transferring managerial control to the more efficient unit if the source of inefficiency is lack of managerial talent. On the other hand, becoming fully efficient is a more unlikely occurrence if each individual unit in the merging process is highly inefficient. Individually this can be achieved by increasing the incentives to perform well. However, this effect can partly become an effect of the merger based on two theoretical arguments. First, the risk related to volatility for demand of justice services and employees on sick leave will be shared that will decrease the vulnerability for merged units. For example, if the incoming cases to a small district court decrease in the short run, assuming a small backlog, the efficiency for that district court will decline. The reason is that adjustments in the inputs to temporary variations in the incoming cases are difficult.14 Second, merging will lead to a higher degree of centralization, meaning that courts to a lower extent will be present in rural areas. Internal arguments to enhance productive efficiency described in Section 2 are related to this, i.e., the ability to recruit qualified personnel is crucial. Unqualified management is often interpreted as a source of persistent inefficiency [54]. Recruitment of qualified personnel in the countryside is, at least in Sweden, a known difficulty [55]. Finally, it could also be easier to recruit a manager to the merged unit since a manager may prefer being responsible for a larger number of people.

4. Theoretical framework The economic literature identifies two main areas where merging gains have their source. The first is gaining market power, which is not of relevance for district courts and thus is not considered further here. The second argument is economies of scale, economies of scope, risk sharing, and scarce managerial skills, which are all related to productive efficiency [10]. 4.1. Scale and scope effects

5. Empirical strategy

A merger leads to a new unit that operates at a larger scale than the initial units. This may, based on production economic reasons, be advantageous, disadvantageous, or neutral, depending on the scale properties of the technology. Related to the scale is the internal argument that the number of cases has to be large enough to make suitable specializations. Further, the effect of economies of scope, i.e., harmony, is achieved by either a more productive mix of inputs or a more easily produced output mix.12 The scale and scope effects are described in Fig. 1 below. In the graph on the left-hand side of Fig. 1, units A and B can produce y1 and y 2 outputs using x1 and x2 inputs, respectively. At this stage they are efficient, i.e., they operate on the frontier. If the outputs are pooled together, the produced output is the same as before, i.e., y1 + y 2 . However, this point is not on the frontier, i.e., not efficient, since outputs are produced at level y1 + y 2 and input usage is x1 + x2 . Thus, a merged unit between A and B can produce the output F*(y1 + y 2 ) using the same aggregated inputs. Similarly, using an input perspective, the efficient merged unit can use E*(x1 + x2) to produce y1 + y 2 . This is generated by the scale effect. A scale effect can, for instance, be related to the administration of courts, since a fairly large organization is necessary for an efficient drafting organization. This is also an internal argument described in Section 2. Bogetoft and Wang [10] also provided arguments that scale effects are obtained if, for example, a large amount of fixed capital is necessary or if specialized personnel are required. This is when the scale effect is positive, i.e., increasing returns to scale (IRS). However, it can of course also be the opposite. Furthermore, on the right-hand side, both A and B produce y using different input mixes. Adding outputs, they produce 2y together according to the solid isoquant. However, the added inputs x1 + x2 are not a tangency of the L(2y) isoquant, meaning that the input mix can be changed to produce the same output using less input at point C, or similarly, using an output perspective, produce the output on the isoquant L(F 2y) on the right (this exemplifies the harmony

To measure efficiency, the analysis can be performed using parametric and nonparametric frontier methods. The parametric method is based on stochastic frontier analysis (SFA) developed by Aigner et al. [56] and Meeusen and Van Den Broeck [57]. The nonparametric methods refer to DEA [20] or free disposable hull (FDH) [58]. Furthermore, StoNED can be seen as a hybrid between parametric and nonparametric models [47]. The merging approach developed by Bogetoft and Wang [10] does not rely on SFA or DEA and can be performed with each. DEA has the advantages, in comparison to SFA and StoNED, of relying on fewer prior assumptions and does not require as many observations. Additionally, in comparison to SFA, DEA incorporates multiple outputs and inputs easily; only feasible mergers are included, and information on input and output prices is not necessary. Further, DEA is particularly advantageous when the products are not sold on a market [17], which makes it suitable in our application. Given that DEA is chosen, there is, to the best of our knowledge, only the Bogetoft and Wang [10] model that allows decomposing the potential gain into these intuitive components using production data. The Bogetoft and Wang model is described with an output perspective.15 First, it is assumed that x i inputs can produce y i outputs, for court i, in the production possibility set S, stated as equation (1).

S= {(x i , y i) xi can produce y i},

(1)

The output based Farrell [60] efficiency, given the technology, is 13

See Bogetoft and Wang [10] or Bogetoft and Otto [17] for further description using different scale assumptions. 14 The reason why this ends up as a learning effect is based on the assumption of a maximum of output that is possible to be produced, and variability will never be larger than that. Thus, variability will always generate a lower average, i.e., inefficiency during the years with a lower demand, and individual inefficiency is captured as a potential gain in learning by the Bogetoft and Wang model. 15 The model is performed by applying the “benchmarking” package in R developed by Bogetoft and Otto [59].

12 See, for example, Baumol et al. [53] for a more formal definition of economies of scope.

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Fig. 1. Scale and scope effects (Based on Bogetoft and Wang [10]).

defined as equation (2):

Max FJ FJ , z s.t.

(2)

Fi = Max{F R 0 (xi, Fy i) S}

where convexity and disposability of the inputs and outputs are assumed. Fit represents the maximum expansion of the aggregated output given the input for the individual unit. Constant returns to scale (CRS) [20] or VRS developed by Banker et al. [45] is often used in DEA. In this study, we utilize NDRS technology, discussed by Leleu [61], among others. The argument for not using the more flexible VRS is related to several potential issues. First, assuming VRS generates a limited number of peers in the efficiency evaluation. Therefore, the mergers will most likely get a negative scale effect that gives a too pessimistic picture [17]. A second disadvantage with the VRS assumption is that feasible solutions are not obtained, under some circumstances, for the linear program problem presented in equation (4) below. This occurs if the merged unit becomes too large, so it ends up outside the feasible region. Further, if the initial size differs, the VRS technology may be ambiguous [43]. This suggests that a NDRS technology with or without bias-corrected efficiency scores is preferred. The present study applies the Bogetoft and Wang model under the NDRS technology to evaluate the ex ante potential efficiency effects of the mergers from 2001 to 2009.16 Results based on the VRS technology are, however, reported in the appendix. The output-based measure of the potential gain from merging is, by following Bogetoft and Wang [10], expressed in equation (3):

xj

j J

FJ

j J i

zi

j I

yj

zi x i

j I

1 (NDRS),

zi y i i

zi = 1 (VRS),

(4)

where is a vector of intensity variables (weights). The obtained maximum gain FJ is intuitively carried out similarly as the standard efficiency scores, with the exception that the inputs and outputs of the merged units are aggregated. Further, FJ includes several effects that are relevant to separate for policy purposes as described in the theory. How these are obtained will now be described. In order to separate the individual inefficiencies, the original units are projected to the production possibility frontier to calculate adjusted efficiency gains from the merger, i.e., merging gains that ignore the gain obtained from eliminating the individual inefficiency. This problem can, by following Bogetoft and Wang [10], be expressed as equation (5).

zi

F

J

x j, F

= Min F R 0 j J

F jy j j J

S (5)

where the initial is projected on the efficient frontier. In a DEA problem, this is stated as equation (6).

yj

FJ = Min F R 0

x j, F j J

yj j J

S

Max F F, z s.t.

(3)

where FJ represents the maximal proportional expansion of the aggregated output in the merged unit J that can be carried out using the aggregated inputs. If FJ is above unity, production gains can be achieved by merging, and if FJ is below unity, the merger is costly.17 The interpretation of the decomposed factors described below is similar. This is, in Bogetoft and Wang [10], evaluated as:

j J

F

xj

j J i

zi

j I

F jy j 1 (NDRS),

zi x i

j I i

zi y i zi = 1 (VRS)

(6)

Letting LEJ = FJ /F J , we get F J = FJ /LEJ as the individual efficiency adjusted merging gain. This means that the overall efficiency gain, executed using equation (4), may not be interpreted as a pure merging effect and should be considered as an upper limit described in the theory. A direct effect of the mergers is the harmony that is related to economies of scope, i.e., a more productive input mix or an output mix that is easier to produce (see Fig. 1). The harmony effect is captured by examining how much more of the average output can be produced given the amount of average inputs. Averages are used to eliminate the effect of expansion of size, i.e., to isolate the harmony effect from the

16

Exceptions are the mergers in 2007 that only consisted of parts of different courts, which made it impossible to separate how inputs and outputs belonged to the different merged district courts. 17 It should, however, be noted that the overall merging gain is weakly positive under the NDRS and convexity assumption. 5

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size effect [10]. This is, in particular, relevant when the different units are not too different in size. However, if the merged units operate under different scales, the harmony and size effect can be difficult to separate. The linear programming problem of the harmony effect is defined in equation (7):

procedure that identifies super-efficient units, i.e., a unit that substantially pushes the frontier. Banker and Chang [68] argued that the method of super-efficiency performs well in practical applications using experiments.19 The present study identifies and eliminates super-efficient units from the analysis as a sensitivity analysis. A potential outlier is identified if the super-efficiency DEA score, assuming NDRS, is either infeasible or below 0.75, i.e., the observation is above the frontier.20 This limit is used in the robustness investigation of best-practice models by Agrell and Niknazar [63] as well as empirical applications, e.g., Edvardsen et al. [70]. Finally, if an observation is identified as superefficient based on the above criteria, the observation is individually evaluated on whether there is any argument to believe that it is an outlier. This argument is based on Simar [71]. Evaluation of whether the individual courts that are identified as outliers, based on their super-efficiency scores, are likely to drive the frontier during the specific year is, finally, performed using correspondence with SNCA.

Max HA HA, z s.t. J

1

j J

1

HA J i

zi

xj

[

j J

i I

zixi]

F jy j

[

1 (NDRS),

i

i I

zi y i ]

zi = 1 (VRS)

(7)

where J is the number of elements in J, i.e., number of merged units ex ante. Finally, the maximum size gain is achieved by capturing how much more can be produced when operating at full scale. This can be defined as the linear programming problem in equation (8).

6. Data Identification of relevant inputs and outputs is fundamental in DEA and, therefore, chosen based on several sources. The choice takes its departure from economic theory, previous research on efficiency and TFP within courts, and interviews with representatives from the courts and SNCA. The data used were obtained from SNCA and contain all individual district courts, including the closed courts, for the period 2000–2009.21 Labor costs correspond to approximately 70% of the total cost, and all previous research incorporated at least one measure of employees. This paper incorporates labor divided into three categories based on broad categorizations by SNCA as they are argued to stand for different competencies and perform different working tasks, i.e. they cannot be used interchangeably. These are judges, law clerks, and other personnel.22 Most previous research on court efficiency ignored capital as an input [24,34].23 However, we argue that office space as a capital proxy is relevant based on two arguments. First, rental cost represents around 13% of the total cost. Second, office area and other capital variables are highly correlated in service production; for example, it is almost proportional to the number of computers and other equipment according to SNCA. Excluding a capital proxy from the analysis will, therefore, ignore possible substitution between labor and capital. Additionally, there are some courts that experience constraints in the number of hearing rooms, according to SNCA, and office area corresponds to operational costs such as costs for maintenance, heating, and insurance.24 The raw data on outputs consist of decided cases and decided matters divided into 14 categories. A simple aggregation into chosen main categories could be performed. On the one hand, complexity differences are presented between different court cases according to, for

Max SC SC, z s.t. j J

xj

SC HJ i

zi

[

j J

i I

F jy j

1 (NDRS),

zix i] [

i I i

zi y i ]

zi = 1 (VRS)

(8)

Using each of the components, we obtain the decomposition FJ = LEJ *HAJ *SC J , where FJ symbolizes the total merging effect, LEJ the effect of eliminating the individual inefficiency, HAJ refers to the harmony effect, and SC J represents the scale effect. If two units are equally large and each of them produces y , their added production is 2y. The potential production for the merged unit is FJ 2y. 5.1. Potential limitations An issue in the merging literature is that the result can vary depending on model choice, i.e., for different frontier estimators or scale assumptions [15]. Different results depending on scale assumptions are found in several empirical investigations, e.g., Kristensen et al. [39] for hospital mergers, Bagdadioglu et al. [40] for electricity distributors, and Finocchiaro Castro and Guccio [34] for Italian district courts. Since there is a variation, the model in the present study is applied using different technology assumptions. The applied technologies are NDRS and VRS; the results using the latter are only reported in the appendix. Furthermore, DEA has drawbacks in that the assumption of the frontier cannot be tested, and noise cannot be separated from inefficiency [62]. Agrell and Niknazar [63] discussed the robustness of different best practice models and argued that a nonparametric approach is preferred; however, outliers might be an issue. In DEA applications outliers are particularly problematic if they substantially drive the frontier, i.e., their peers will be considered highly inefficient and, in our context, have large learning potentials [17].18 However, there are no generally accepted definitions of what an outlier is, and, therefore, no generally accepted method to identify it [64]. This paper attempts to handle outliers by identifying and eliminating them as a test of robustness. Different procedures for this issue are present in the DEA literature (e.g., Wilson [65]; Kapelko and Oude Lansink [66]). Banker and Gifford [67] proposed an outlier detection

19 The super-efficiency procedure can also be performed for ranking efficient units, i.e., discriminating between units with an efficiency score equal to unity. This ranking procedure, however, does not perform well according to Banker and Chang [68]. 20 Infeasible values are not surprising [69]. 21 During the period January 2001 to May 2008, seven district courts were also land registration authorities. The outputs and inputs related to this are excluded because the output data are not complete for the number of decided matters. 22 The labor cost is approximately 70% of total cost during each year of the studied period except 2000–2001, when it is 60%. This is extracted from SNCA annual reports 2001–2017 (in Swedish). 23 Elbialy and García-Rubio [33], however, are an exception since they incorporate computers as a measure of capital. 24 Andersson et al. [72], who used the same data using another time period, tested office area and concluded that it is a more relevant capital proxy than the number of computers.

18 To our knowledge, the only study on potential ex ante efficiency effects of mergers in district courts, Finocchiaro Castro and Guccio [34], did not investigate the presence of outliers.

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Table 1 Descriptive statistics separated by mergers and non-mergers.b Ex ante year

Outputs

Inputs a

2000 2001 2003 2004 2005 2008

Mergers Non-mergers Mergers Non-mergers Mergers Non-mergers Mergers Non-mergers Mergers Non-mergers Mergers Non-mergers

a

a

Obs.

Criminal cases

Civil cases

Matters

Judges

Law clerks

Other personnel

Area

400.9 (318.7) 786.1 (1079.0) 464.5 (359.2) 851.0 (1169.4) 679.2 (592.1) 1002.5 (1393.0) 636.4 (544.7) 1151.7 (1604.4) 493.5 (147.6) 1260.1 (1605.9) 1196.2 (1310.8) 1708.0 (1328.2)

283.3 (231.8) 639.4 (1162.8) 354.6 (248.6) 679.6 (1226.7) 294.5 (148.4) 761.9 (1209.3) 363.2 (240.5) 922.6 (1399.8) 544.1 (71.23) 915.4 (1349.8) 894.0 (1249.1) 1114.0 (1211.9)

165.6 368.4 191.3 387.4 239.9 454.2 204.7 535.5 226.6 564.6 371.4 536.3

3.722 8.157 4.478 8.434 5.966 8.972 4.967 10.08 5.462 10.06 9.385 11.83

3.470 5.988 4.274 7.101 4.481 7.802 4.200 8.313 4.665 8.601 8.770 12.07

8.233 16.06 8.748 16.20 8.846 17.58 7.932 19.71 8.339 20.32 20.73 24.08

1827.1 (751.1) 2851.5 (4414.6) 1568.3 (757.3) 2932.7 (4637.4) 1904.2 (868.8) 3110.5 (4551.2) 1925.4 (942.7) 3468.1 (5224.5) 1744 (223.9) 3566.6 (5328.5) 3330.9 (4495.8) 3752.7 (3499.2)

(121.1) (647.4) (109.1) (693.1) (156.9) (825.1) (118.1) (865.7) (62.41) (941.1) (518.1) (535.2)

(3.337) (14.60) (2.869) (14.94) (4.915) (14.28) (4.348) (15.44) (1.237) (14.89) (13.46) (10.43)

(2.274) (8.414) (2.310) (10.35) (2.473) (9.910) (2.742) (10.26) (0.856) (10.26) (10.03) (11.19)

(4.925) (26.72) (5.850) (29.04) (4.815) (29.02) (4.871) (32.42) (1.903) (31.94) (32.90) (23.68)

24 67 9 61 8 64 18 46 4 46 9 44

a

The weighted means are reported as the outputs. Standard errors are shown in parentheses. The courts that merged during the ex ante year are eliminated from the data based on the reason that inputs and outputs are difficult to assign to the specific units in the data. Further, these are likely to either be inefficient since they were a part of a merger in the middle of the studied year according to the SNCA. b

merger are reported in Table 1.29 Table 1 reports the descriptive statistics for each year prior to when a merger is reported, i.e., year 2000 includes data for 2000 where mergers represent the mergers during 2001. Non-mergers include all courts that are present during the entire year of 2000.30 Looking at the numbers, it can be observed that the merged courts are smaller than the non-merged on average. This is of course not surprising since small courts are most likely to merge. In the right column the number of observations for mergers and non-mergers is reported. The mergers vary between 4 and 24 courts, ex ante, and the non-mergers vary between 44 and 67 courts. Finocchiaro Castro and Guccio [34] performed their model both with and without caseload, concluding that inefficiency is smaller when the caseload is incorporated.31 Unlike them, we do not incorporate the caseload as an input based on the argument that volatility in caseload is a source of inefficiency if inputs are not flexible.

example, Santos and Amado [29]. On the other, inclusion of all output categories is, in our case, problematic due to low discrimination power according to, for example, [73,74]. Therefore, we follow the majority of the literature and perform aggregation of the outputs.25 This is performed into the three main output classes, categorized by SNCA. These are decided civil cases, decided criminal cases, and decided matters.26 In contrast to the majority of the literature that perform aggregation, potential heterogeneity within these categories is controlled for by making use of differences in spent resources among the 14 categories. This is performed by weighting the outputs based on resource intensity. The spent resources are approximated by self-reported time consumption obtained from the court's time-recording system. All employees record the time allocation to different cases and matters for two months a year. This is considered to be a good approximation of resource consumption and also generally accepted within courts and used internally, e.g., in the allocation of resources among the district courts.27 This strengthens our study because the potential heterogeneity in output between categories is rarely considered in the benchmarking literature described in Section 3 and not in the only previous study on ex ante gains from merging courts, i.e., Finocchiaro Castro and Guccio [34]. Ignoring this could introduce biased efficiency scores if the different categories are not equally distributed between courts.28 Descriptive statistics of the average level of inputs and weighted outputs for the courts included in the mergers and courts not included in a

7. Results The potential ex ante gain is decomposed into LE, HA, and SC based on the theoretical arguments presented above. Courts that merged during the year of investigation are excluded from the analysis. To exemplify, when potential gains from merging using the actual mergers in 2001, data from 2000 are analyzed. If there were any mergers in the ex ante year, here 2000, these are eliminated from the comparison. Only a table consisting of the share of courts within different intervals of potential efficiency gain is reported in the main text to improve the readability of the results. The results under the NDRS technology are reported here. However, a more flexible scale assumption, i.e., VRS, is

25 A potential alternative to increase discrimination power is inclusion of weight restrictions as performed by, for example, Santos and Amado [29]. Weight restrictions, however, have pitfalls according to, for example, Dyson et al. [75]. 26 In the aggregation, property cases and environmental cases are included in the category criminal cases. The reason is that property and environmental cases are not present in as many courts. Thus, we wanted to avoid a lot of zerovalue outputs. Furthermore, weighting is performed so the potential heterogeneity in comparison to the criminal cases is not a problem. 27 Without the weighting, both summary offenses and environmental cases are aggregated, i.e., they have a weight of one. However, the average time consumption for handling an environmental case is 5.7 times larger than the average time for a summary offense. Therefore, an environmental case receives the weight of 5.7 compared to a summary offense, meaning that one environmental case generates 5.7 times more output. 28 There is no available quality measure for our studied time period. However, Andersson et al. [84] found zero correlation between efficiency score and rate of change in higher instance for a later time period within Swedish district courts. This can be considered as a quality measure. However, it has the limitation that most changes occur due to new evidence (and not bad quality), according to SNCA. Furthermore, quality measures are barely considered in any of the previous studies described in Section 3.

29 Each court that used personnel from the backup force, who served different district courts, has been assigned to these hours equivalent to the time the judges served the specific court. A backup force served courts in the Stockholm region during the period 2008–2010, and during the period 2013–2016 a backup force served nationally. 30 This means that courts that merged or were created during 2000 are eliminated. Further, 2002 and 2007 are not in the analysis because there were no mergers during 2003 and 2008. Finally, the ex ante year 2006 is also eliminated from the analysis based on the reason that all mergers during 2007 consisted of parts of different courts, making it impossible to distinguish which part to evaluate ex ante. A full description of when the different mergers took place can be sent upon request. 31 The conclusion in Finocchiaro Castro and Guccio [34] can be a result of a flexibility problem; however, higher efficiency scores can also occur since the ability to discriminate between courts declined when adding inputs, i.e., a larger share of the courts are potentially unique.

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inefficiency is an effect of scarce managerial skills in one of the merged units, the merger can achieve higher efficiency by letting the manager with the highest skills control the whole merged court. If each individual unit in the merge is inefficient, recruitment of a new manager can lead to higher efficiency of the merged unit. Finding a skilled manager is potentially easier for a larger court if, for example, the court is in a larger city or a manager prefers to control a large court. Further, new working methods to develop the court's drafting organization may also be easier to implement in a new merged district court than in district courts that are not affected by mergers pointed out by representatives from SNCA. From this perspective, mergers can support learning, as also stated by Kristensen et al. [39]. Finally, it is possible that the variability in workload is smaller for large units. The potential gain from that is not taken into account in the Bogetoft and Wang model but will be a part of the learning effect since variability results in negative differences in comparison to the efficient frontier described in the theory. However, variability in workload can, at least partly, be handled by more flexible inputs, e.g., by developing the backup force as concluded by Mattsson et al. [6].36 To summarize, column 3 in Table 2 reports the share of mergers in different intervals of pure merging gains. These are the effect when the overall gain is adjusted by the LE component, since that may not be an effect achieved by the merger itself, as described in the theory. This gives indications that all mergers were not necessary based on production economic arguments. To exemplify, a substantial share of the mergers indicate a potential gain of less than 5.0%. However, an extensive part of the mergers also points to large potential gains. Thus, diverse effects between the individual mergers in relation to the potential efficiency gain can be observed.

Table 2 Distributions of the overall potential gain and the components of the mergers. Intervals of potential efficiency effects

Non-decreasing returns to scale (NDRS) FJ (2)

F*J (3)

LEJ (4)

HAJ (5)

SCJ (6)

1.000–1.050 1.051–1.100 1.101–1.200 1.201 < Min Max

0.310 0.069 0.345 0.276 1.000 1.478

0.448 0.172 0.345 0.034 1.000 1.239

0.552 0.103 0.241 0.103 1.000 1.265

0.586 0.241 0.172 0.000 1.000 1.190

0.793 0.138 0.069 0.000 1.000 1.118

reported in Table A1, and the full results including each merger, using both scale assumptions, appears as A2 in the appendix. The share of mergers within different intervals is reported in Table 2.32 Table 2 shows the share of district courts within different intervals under the NDRS technology.33 The different columns represent overall efficiency symbolized as FJ; individual efficiency adjusted merging effect, i.e., the merging effect when individual inefficiency is not eliminated (F∗J), the efficiency gain obtained if the courts become individually efficient (LEJ), the efficiency effect from economies of scope referred to as harmony (HAJ) and the merging effect by operating at a larger scale (SCJ). When interpreting the results, it should be kept in mind that each component is weakly positive.34 In Table 2, it can be observed that substantial differences are present in the overall potential gain between mergers. Of the mergers, 31.0% indicate a potential gain of less than 5%, and 27.6% have an overall potential gain above 20%, as observed in column 2. However, the majority of the latter have a large gaining potential that is partly due to individual, ex ante, inefficiency. The reason is that only 3.4% of the mergers have a potential gain above 20% if the effect of eliminating individual efficiency is not incorporated as a merging effect, observed in column 3. Similarly, this column also shows that 44.8% of the mergers have a potential efficiency gain below 5%. The most positive scale effect is 11.8%.35 However, the effect of scale (SCJ) is, in most cases, a small source of efficiency gain. For example, as reported in column 6, 79.3% of the mergers have a potential gain from scale between 0% and 5%. Therefore, most of the potential efficiency gain is obtained from the other two sources: harmony and learning. The harmony effect has its source in economies of scope, i.e., generated from advantages achieved by a more powerful input mix or an output mix that is easier to produce. Harmony is reported in column 5 and the range of the merging effect is 0.0%–19.0%, where 17.2% of the mergers have a potential gain of merging larger than 10.1%. An output allocation closer to optimal may be achieved also without the mergers, for example, by reallocating cases. However, a positive harmony effect is unlikely to be fully obtained by restructuring within the court but can be achieved by a merger. Learning is the third component, and potential gains related to this should be considered as an upper bound. Column 4 in Table 2 indicates that the effects on efficiency attributed to learning are between 0.0% and 26.5%. This can be achieved without the merger by learning best practices, i.e., studying and copying peer units. However, if the ex ante

8. Conclusion and policy recommendation This paper aimed to investigate whether the performed mergers for Swedish district courts from 2001 to 2009 could be rationalized based on production economic arguments. The investigation was performed by an ex ante analysis developed by Bogetoft and Wang [10], which not only estimated the potential efficiency gains of the conducted mergers but also decomposed the estimates into LE, HA, and SC. Thus, this paper contributes empirically by investigating whether the political decisions should have been further evaluated before a merger decision was taken. In particular, efficiency is an important area to study, since the Swedish Budget Law [1] states that governmental services should be provided efficiently. Regardless of its importance, little attention has been given to these issues even though merging has been extensively performed to achieve better utilization of public resources. In contrast to most previous efficiency literature on district courts, this study accounts for complexity differences by weighting outputs based on time consumption in 14 categories. The conclusion based on the analysis is that it was possible to enhance structural efficiency in the sector of Swedish district courts. However, a substantial number of the mergers had a low potential efficiency effect. For example, 31.0% of the mergers had an overall potential gain below 5%. Therefore, critiques could be raised concerning the expected effectiveness of the mergers. At the same time, a considerable share (27.6%) indicates a potential gain of more than 20.1%. Thus, there exist substantial differences among different mergers related to potential efficiency gains. Based on the results, merged courts with low or zero potential gains could not be rationalized from an efficiency point of view. As a policy conclusion, we recommend that the individual courts that are considering merging should be evaluated based on production economic theory before a merging decision is taken in the future. In

32 Based on the rule of thumb suggested by Simar and Wilson [76], the bias is 2 1 not corrected, since s2 > 3 (BiasB [ ˆ (x t , yt ]) where s2 is the sample variance of the bootstrapped values and the parentheses on the right-hand side represent the bias of the efficiency scores. 33 The full results, when the outliers are eliminated, can be made available upon request. 34 The harmony component is weakly positive due to the convexity assumption. Scale is weakly positive when NDRS is assumed and learning represents the elimination of individual inefficiency, which cannot be below zero. 35 This is the merger between Karlskrona, Karlshamn, Ronneby, and Sölvesborg, reported in Table A2 in the appendix.

36 The backup force is judges who serve nationally, e.g., where the demand for justice services is high.

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addition, mergers have been extensively performed in many areas of the public sector in many countries. However, a limited number of studies have examined the potential effects, ex ante. Further, a substantial part of the potential gain is attributed to eliminating individual inefficiency, which may be accomplished without a full-scale merger, i.e., by learning best practices from peer units. Small potential gains of actual mergers are possibly also true for many actual mergers beyond the Swedish district court system, i.e., the decision support to merge is likely to also have the potential to improve other sectors. Our study is limited to only examining the potential efficiency gain in district courts by combining the merged courts from a production economic point of view. However, the potential efficiency gains are not guaranteed to be achieved. Additionally, the potential gains are bounded upwards by their annual best practice, and it should be made clear that efficiency gains for the judicial sector as a whole may occur when courts are centralized, i.e., they end up closer to other centralized authorities, such as public prosecution services. This is an internal argument for

merging, however, not accounted for in this study. Furthermore, quality may be enhanced with a higher degree of specialization. Finally, we do not examine the cost of merging in the judicial sector. For example, questions related to social utility and the increased cost of acquiring justice services occur with a new judicial map, which may generate a lower demand for justice services. Thus, quantitative and qualitative aspects of merging need to be analyzed extensively together with the social cost to improve the decision support for mergers in the future. Acknowledgement We are grateful to the insightful comments provided on an earlier version of this paper by Dr. Marijn Verschelde. Furthermore, we would like to thank everyone for received comments during the seminars at Linnaeus University, the Swedish National Court Administration (SNCA), and the SWEGPEC workshop in Stockholm 2017. Finally, we would like to thank the anonymous referees for constructive comments.

Appendix In Table A1, it can be observed that the scale effect is to some extent negative. However, as described in the main text, we rely on the NDRS technology. Furthermore, Table A1 and Table 2 in the main results only include the distribution of the share of courts within different intervals of potential efficiency gains and, therefore, no information for the individual mergers. The results including each merger are reported in Table A2. Sensitivity analysis The courts that are identified as potential outliers based on their output super-efficiency scores are, for each ex ante year of investigation, reported with the super-efficiency score (SE-score) in parentheses in Table A3. In Table A3, it can be observed that a substantial number of the super-efficiency scores are infeasible, which is expected when an output perspective is carried out [77]. This can be corrected for using the Cook et al. [77] transformation; however, the same district courts are detected as potential outliers. Thus, the infeasible values obtain a super-efficiency score below our limit, i.e., below 0.75. The output-oriented super-efficiency scores are equal to 1/(SE-score), e.g., the outputs need to be decreased by 1/(SE-score) to reach the frontier [77]. These are only potential outliers; however, Simar [71] mentioned that an investigation should be performed into whether an identified potential outlier in fact drives the frontier. In correspondence with SNCA, the courts that are identified as super-efficient are likely to drive the frontier during the specific year. The results when these are eliminated are similar to the reported results. These can be made available upon request. Table A1 Shares within different intervals of potential gain under the VRS assumption. Intervals of potential efficiency effects

FJ (2)

F*J (3)

LEJ (4)

HAJ (5)

SCJ (6)

< 0.900 0.900–0.999 1.000–1.050 1.051–1.100 1.101–1.200 1.201 < Min Max

0.034 0.310 0.138 0.172 0.172 0.172 0.870 1.472

0.069 0.414 0.241 0.069 0.207 0.000 0.860 1.164

0.000 0.000 0.552 0.103 0.241 0.103 1.000 1.265

0.000 0.000 0.586 0.241 0.172 0.000 1.000 1.190

0.241 0.552 0.103 0.069 0.034 0.000 0.834 1.101

Table A2 All mergers that took place during the time-period. Merger (year)

Non-decreasing returns to scale (NDRS) J

Köping-Sala-Västerås (2000) Linköping-Mjölby-Motala (2000) Örebro-Hallsberg (2000) Karlskrona-Karlshamn-Ronneby-Sölvesborg Falun-Hedemora-Ludvika (2000) Mora-Leksand (2000) Skövde-Falköping (2000)

*J

J

Variable returns to scale (VRS)

F (2)

F (3)

LE (4)

HA (5)

SC (6)

FJ(7)

F*J(8)

LEJ(9)

HAJ(10)

SCJ(11)

1.162 1.306 1.121 1.394 1.309 1.404 1.025

1.031 1.102 1.121 1.239 1.088 1.145 1.025

1.127 1.185 1.000 1.124 1.203 1.225 1.000

1.031 1.088 1.059 1.109 1.047 1.040 1.008

1.000 1.013 1.058 1.118 1.040 1.101 1.017

0.970 1.092 1.107 1.246 1.132 1.404 1.005

0.860 0.923 1.107 1.105 0.940 1.145 1.005

1.128 1.184 1.000 1.127 1.203 1.225 1.000

1.031 1.088 1.059 1.109 1.047 1.040 1.008 (continued on

0.834 0.848 1.045 0.996 0.899 1.101 0.996 next page)

9

J

J

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Table A2 (continued) Merger (year)

Helsingborg-Klippan-Ängelholm (2000) Simrishamn-Ystad (2000) Boden-Luleå-Piteå (2001) Härnösand-Sollefteå-Örnsköldsvik (2001) Eslöv-Landskrona-Lund (2001) Gävle-Sandviken (2003) Sveg-Östersund (2003) Strömstad-Uddevalla (2003) Trollhättan-Vänersborg (2003) Kalmar-Oskarshamn-Västervik (2004) Arvika-Karlstad-Kristinehamn-Sunne (2004) Lindesberg-Örebro (2004) Bollnäs-Hudiksvall (2004) Jönköping-Värnamo (2004) Ljungby-Växjö (2004) Enköping-Tierp-Uppsala (2004) Trelleborg-Ystad (2005) Mölndal-Stenungsund (2005) Linköping-Mariestad-Skövde (2008) Karlskoga-Örebro (2008) Katrineholm-Nyköping (2008) Gothenburg-Mölndal (2008)

Non-decreasing returns to scale (NDRS)

Variable returns to scale (VRS)

FJ(2)

F*J(3)

LEJ(4)

HAJ(5)

SCJ(6)

FJ(7)

F*J(8)

LEJ(9)

HAJ(10)

SCJ(11)

1.146 1.478 1.263 1.167 1.031 1.044 1.340 1.159 1.027 1.195 1.247 1.043 1.000 1.101 1.030 1.107 1.058 1.071 1.164 1.121 1.034 1.014

1.131 1.169 1.085 1.106 1.026 1.044 1.190 1.139 1.014 1.076 1.130 1.043 1.000 1.027 1.030 1.063 1.000 1.034 1.159 1.082 1.034 1.014

1.012 1.265 1.164 1.055 1.005 1.000 1.126 1.017 1.013 1.111 1.103 1.000 1.000 1.072 1.000 1.041 1.058 1.035 1.005 1.037 1.000 1.000

1.128 1.096 1.066 1.041 1.026 1.018 1.190 1.128 1.005 1.047 1.113 1.042 1.000 1.019 1.012 1.055 1.000 1.034 1.067 1.055 1.000 1.014

1.003 1.067 1.018 1.063 1.000 1.025 1.000 1.009 1.009 1.028 1.016 1.001 1.000 1.008 1.017 1.008 1.000 1.000 1.086 1.025 1.034 1.000

0.987 1.472 1.234 0.996 0.969 0.971 1.241 1.057 0.964 1.141 1.081 1.004 0.870 1.073 0.970 0.976 1.047 1.053 1.163 1.108 1.034 0.963

0.975 1.164 1.060 0.946 0.964 0.971 1.102 1.039 0.950 1.028 0.979 1.004 0.870 1.001 0.970 0.938 0.990 1.017 1.158 1.069 1.034 0.963

1.012 1.265 1.164 1.053 1.005 1.000 1.126 1.017 1.014 1.110 1.104 1.000 1.000 1.072 1.000 1.041 1.058 1.036 1.005 1.037 1.000 1.000

1.128 1.096 1.066 1.041 1.026 1.018 1.190 1.128 1.005 1.047 1.113 1.042 1.000 1.019 1.012 1.055 1.000 1.034 1.067 1.055 1.000 1.014

0.865 1.062 0.995 0.909 0.940 0.954 0.926 0.920 0.946 0.982 0.880 0.964 0.870 0.983 0.958 0.888 0.990 0.983 1.086 1.013 1.034 0.949

Table A3 Observations identified as potential outliers based on the super-efficiency scores. 2000 Enköping (0.648) Karlshamna (Infeasible) Malmö (0.631) Mjölbya (0.360) Norrtälje (0.468) Sollefteå (Infeasible) Stockholm (0.463) Strömstad (Infeasible) Sveg (Infeasible) Örnsköldsvik (0.688) a

2001 Enköping (0.648) Hudiksvall (0.471) Mariestad (0.655) Norrtälje (Infeasible) Sollefteåa (Infeasible) Stockholm (0.428) Strömstad (0.688) Sveg (Infeasible) Örnsköldsvika (0.640)

2003

2004 a

Enköping (0.654) Hudiksvall (0.712) Karlskoga (0.623) Ljungby (0.736) Norrtälje (Infeasible) Stockholm (0.315) Strömstada (Infeasible) Svega (Infeasible)

Enköping (Infeasible) Karlskoga (Infeasible) Kristinehamna (Infeasible) Lindesberga (Infeasible) Ljungbya (Infeasible) Mora (Infeasible) Norrtälje (Infeasible) Oskarshamna (Infeasible) Stockholm (0.370) Växjöa (0.619)

2005

2008

Hässleholm (Infeasible) Karlskoga (Infeasible) Mariestad (Infeasible) Mora (Infeasible) Norrköping (0.729) Norrtälje (Infeasible) Stockholm (0.330) Södra Roslagen (0.650)

Karlskogaa (Infeasible) Mariestada (Infeasible) Norrtälje (Infeasible) Stockholm (0.489) Södertälje (0.705) Södertörn (0.701)

Indicates if the specific court is included in a merge during the following year.

References [5] [6]

[1] SFS 2011:203. Den svenska budgetlagen, kapitel 1, 3§ (The Swedish Budget Law, 1 chapter 3§). Stockholm: Ministry of Justice. (In Swedish). [2] SNCA. Budgetunderlag 2017–2019 (budget request 2018–2020). 2016. [Jönköping. (In Swedish)]. [3] SNCA. Årsredovisning 2016 (Annual report 2016) 2017. [Jönköping. (In Swedish)]. [4] Pinheiro R, Aarrevaara T, Berg L, Nordstrand GL, Torjesen DO. Strategic mergers in the public sector. In: Tarpa SY, Cooper SCL, Sarala RM, Ahammad MF, editors.

[7] [8]

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Mergers and acquisitions in practice. New York: Routledge, Taylor & Francis Group; 2017. p. 44–68. SNCA. Årsredovisning 2011 (Annual report 2011) 2012. [Jönköping. (In Swedish)]. Mattsson P, Månsson J, Andersson C, Bonander F. A Bootstrapped Malmquist index applied to Swedish district courts. Eur J Law Econ 2018:1–31. Swedish National Audit Office. Tingsrätter i förändring (District courts under reformation). 2002. 9. Stockholm, 2002. Swedish Agency for Public Management. Sammanslagna tingsrätter – en utvärdering (Merged district courts – an evaluation). 2007. 2007:09. (In Swedish).

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Pontus Mattsson is a Ph.D. candidate in Economics at Linnaeus University, Sweden. He holds a licentiate degree in economics from Linnaeus University and an M.Sc in business administration and economics from Linköping University, Sweden. From 2014 and onwards, he has been active as a teaching assistant in several courses at the Linnaeus University. His research includes efficiency, productivity and policy evaluation. Currently, he is a visiting scholar at Stern School of Business, New York University. Claes Tidanå is an economist employed by National Courts Administration in Jönköping, Sweden. He works with financing of the courts and analysis of the courts performance. He holds an M.Sc in business administration and economics from Gothenburg University, Sweden. Former employers are National Insurance Board and Ministry of Finance.

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