Measuring Potential Efficiency Gains from Mergers of Italian First Instance Courts through Nonparametric Model

Abstract

In several countries, economic development has boosted the mobility of population, changing the distribution of litigation. Hence, the increasing difference between the new demand of legal services and the old judicial maps has increased processing time and backlog, therefore, badly affecting judiciary efficiency. These issues are particular relevant for the Italian judicial system because of the Italian government’s proposal (decree n. 155/2012) of new judicial map of first instance courts. The courts’ reorganization has been achieved through the horizontal merger of some courts and the abolition of all local courthouses. This article represents the first attempts at measuring the potential performance improvements achieved by the enforced reform. For this purpose, we compare the efficiency levels of Italian first instance courts in 2011 with those reached by the new judicial map. Our results show not negligible efficiency gains from the proposed mergers under the variable returns to scale technology assumption.

Keywords

first instance courts horizontal mergers efficiency nonparametric methods bootstrapping

The efficiency, effectiveness, and quality of judicial system has become one of the main points of interest in the ongoing reform of public sector since judicial system is vital to the society for a number of reasons. With this aim, there also comes the discussion on the quality left aside by judges rushing to complete all the cases during the year. Thus, there has been an everlasting dispute within the judiciary itself on the responsibility of judges in terms of productivity and cutting delays, while at the same time granting the high quality of their judgments (Fabri, Langbroek, and Pauliat 2003).

On this matter, Staats, Bowler, and Hiskey (2005) suggest that the performance of judicial systems consists of several dimensions¹ in which efficiency is mainly concerned with the speed of judicial decision-making and the ability of a judicial system to avoid unreasonable delays and backlogs. However, court judgments will not be effective unless legal decisions are actually enforced. Hence, whereas efficiency is concerned with the duration of the judicial decision-making process, effectiveness is concerned with the ability of a legal system to enforce court decisions after being spelled out (Voigt and El-Bialy 2014). Moreover, a general problem in carrying out comparative analyses is the definition of a conceptually defensible measure of efficiency that can be equitably or fairly applied to different courts. On this matter, a large number of empirical contributions have dealt with the issue of assessing the courts performances and identifying its determinants (Voigt and El-Bialy 2014).

These issues are particularly relevant for the Italian judicial system. Recently, The European Commission for the Efficiency of Justice (CEPEJ 2014) has highlighted the overall low performance of the Italian judicial system despite the several reforms of civil sector aiming to achieve a quicker, less costly, and more efficient judicial services.² In particular, looking at the Italian judicial map, CEPEJ (2013) reports the excessive number of courts and the irrational distribution of resources and locations.

To start tackling all the above-mentioned issues, Italian judicial reform is entering a new phase through the Italian government’s proposal (decree n. 155/2012) of new jurisdiction design of first instance courts (Tribunali Ordinari, hereafter FICs) through the merger of thirty-one courts and the abolition of 220 local courthouses. The proposed two goals of new jurisdiction design of FICs are the operational cost savings and the potential production efficiency gains. This article focuses on the second one and applies a nonparametric methodology to assess the potential effect of FICs mergers on efficiency levels. For this purpose, we run our efficiency analysis on 160 FICs in the year 2011. In detail, our study refers to the activity of FICs falling into the areas over which the judicial counties (Circondario di Tribunale Ordinario) have the competence. We empirically analyze potential gains from mergers in FICs using a recent refinement of the nonparametric data envelopment analysis (DEA) developed by Bogetoft and Wang (2005). This approach is a nonparametric measurement of the potential gains from horizontal mergers, and we use it to compare two different comparative static equilibria: the existing organizational structure of FICs and the new jurisdiction design, where the technical efficiency with which observed inputs are transformed to outputs is evaluated relative to the existing structure.

Moreover, despite the increased use of nonparametric frontier to measure the efficiency of judicial systems, there are few studies that make use of the bootstrapping methodology to account for measurement errors in its estimates (Finocchiaro Castro and Guccio 2014, 2015). In fact, the bootstrapping makes it possible to run sensitivity analyses on efficiency scores and scaling indicators (Simar and Wilson 2008). Hence, we apply a consistent bootstrap estimation procedure (Simar and Wilson 1998) to obtain the sampling distribution of the efficiency scores and derive bias-corrected scores.

Our results report high levels of inefficiency reached by the FICs in Italy. Whereas inefficiency seems to be higher in the south of Italy than in the rest of the country, the huge civil caseload appears as a factor able to explain the inefficiency levels of FICs. Finally, we find evidence of not negligible efficiency gains from the proposed mergers under the variable returns to scale (VRS) technology assumption.

The analysis develops as follows: in the next section, a description of the reform of first instance courts judicial map in Italy is offered followed by a brief review of literature on the judicial system efficiency in the third section. The fourth section describes the methodology and data and the fifth section presents the empirical research findings. The article concludes in the sixth section with our concluding remarks.

Reshaping Italian Judicial Map

Several attempts at reforming judicial system in order to achieve efficiency gains have been put forward in Italy in the last decades. However, the goal seems still far out of reach. The most recent reform of judicial system has focused on the positive effects obtainable by designing new jurisdiction for FICs and local courthouses. In most European countries, judicial maps have become obsolete and inefficient in terms of dimension and competences. Hence, several projects aiming to reshape judicial systems have started, although from very different baselines.³ However, the main common difficulty in setting such a reform appears to be the correct balance between the access to justice, the minimum size of a court in order to ensure the presence of various competences and functions, the decrease in costs achieved through an optimal allocation of resources, and the increase in the quality of the service provided (CEPEJ 2013).

These issues are particularly relevant for the Italian judicial system because of the huge number of courts often characterized by irrational distribution of resources and locations. To reshape the judicial system, the technique used is the suppression of the smallest and less efficient structures in order to merge them with larger courts.

In order to improve the effectiveness and efficiency of the whole judicial system, the Italian government has issued two decrees (n.155/2012 and n.156/2012) to put into practice the legislative decree n.148/2012. The decree n.155/2012 refers to the design of new jurisdiction for FICs and local courthouses, whereas the decree n.156/2012 deals with the reform of justice of the peace offices (whose efficiency analysis is outside the scope of our study).⁴ The main goals of the reform were the reduction of FICs, their optimal distribution in order to cut public expenditure and achieve efficiency gains, and the optimal allocation of available resources with respect to workloads. The reform posed two constraints to the optimal redistribution of FICs: there should remain one FIC in each provincial capital and at least three FICs and relative public prosecutor’s offices in each judicial district according to the provincial capitals existing on June 2011.

Given that 107 out of the 166⁵ FICs are sited in the provincial capitals, the merging procedure, as suggested by the decree n.155/2012, has been applied on the remaining 59 FICs only. It has to be noted that the above-mentioned constraints were not in force for the 220 local courthouses that have been all included in the merging procedure. The methodology applied to choose among the 59 FICs to be merged was the following. First, the 107 FICs sited in the provincial capitals have been used as benchmarks. However, 5 of them have been excluded from this analysis because they are located in metropolitan areas (Rome, Milan, Naples, Turin, and Palermo), leaving 102 FICs as benchmarks. To do so, the averages of four indexes—office workload, population served, judges productivity, and dimension of the offices (number of judges)—have been computed. Then, they have been sequentially applied to identify the FICs, among the 59 FICs already chosen, scoring lower than the averages of the four indexes. The application of this methodology⁶ has shown that merging has been necessary for the smallest offices among the least productive ones. Thus, the expected outcome of the reform should have been the creation of new offices to be placed in the most productive class according to the different dimensions.

As just reported, the first version of the decree n.155/2012 required the suppression of thirty-seven first instance courts and all the local courthouses. However, a new version of the decree n.155/2012 and the distribution of courts in the Italian territory lead to maintaining six courts, among those selected to be suppressed, sited in areas characterized by high levels of criminality—for example, Sicily (Caltagirone and Sciacca), Calabria (Castrovillari, Lamezia Terme, and Paola), and Lazio (Cassino). Moreover, the constitutional court has stopped the suppression of Urbino’s first instance court (decision n.237/2013) because the city of Urbino, like Pesaro, is provincial capital of the province of Pesaro and Urbino. Hence, the number of suppressed courts has been decreased from thirty-seven to thirty,⁷ whereas all the 220 local courthouses have not been abolished yet. The reform has been in effect since September 2013.

Table 1 shows the outcome of merging procedure by listing the judicial districts,⁸ the courts to be merged, and the new courts resulting from the merging. The procedure has involved seventeen judicial districts out of the twenty-seven existing before the reform. Most of the mergers have been done within the same district, with the only exception being the FIC of Lago Negro that has involved both judicial districts of Potenza and Salerno, whereas the district with the highest number of mergers has been the one of Turin. Also, in most of the cases, table 1 reports that the merging procedure involved two FICs, whereas in few cases the FICs merged have been three. Overall, the reform conducted on fifty-nine FICs has led to the suppression of thirty of them and to the creation of twenty-five “new” FICs. Thus, it has affected a large part of the courts with significant potential effect on the Italian judicial system.

Table 1.

Proposed Mergers of Italian Courts at Judicial District Level.

Judicial districts	First instance courts merged	New courts
Ancona	Camerino, Macerata	Macerata
Bari	Foggia, Lucera	Foggia
Brescia	Crema, Cremona	Cremona
Caltanissetta	Enna, Nicosia	Enna
Catania	Modica, Ragusa	Ragusa
Catanzaro	Castrovillari, Rossano	Castrovillari
Firenze	Montepulciano, Siena	Siena
Genova	Chiavari, Genova	Genova
Genova	Sanremo, Imperia	Imperia
L’Aquila	Avezzano, L’Aquila, Sulmona	L’Aquila
L’Aquila	Chieti, Lanciano, Vasto	Chieti
Messina	Mistretta, Patti	Patti
Milano	Pavia, Vigevano, Voghera	Pavia
Napoli	ArianoIrpino, Benevento	Benevento
Napoli	Sant’Angelo dei Lombardi, Avellino	Avellino
Perugia	Orvieto, Terni	Terni
Potenza	Melfi, Potenza	Potenza
Salerno and Potenza	Lagonegro, Sala Consilina	Lagonegro
Torino	Alba, Asti	Asti
Torino	AcquiTerme, Alessandria, Tortona	Alessandria
Torino	Casale Monferrato, Vercelli	Vercelli
Torino	Cuneo, Modovì, Saluzzo	Cuneo
Torino	Pinerolo, Torino	Torino
Trieste	Udine, Tolmezzo	Udine
Venezia	Bassano del Grappa, Vicenza	Vicenza

Source: Our computation on data provided by Ministero della Giustizia.

The decree n. 155/2012 has implied a wide and deep reforming process of Italian judicial map, although affected by several constraints and quantitative criteria to be met that may have seriously influenced the new territorial reshape of FICs. Moreover, it will clearly take some time before being able to fully assess the effect of such a reform in terms of efficiency levels achieved by judicial offices. For this reason, in the present article, we evaluate only the potential efficiency gains that may be obtained through the new judicial map.

Background

In the last decade, the estimation of the efficiency of judicial systems has been the subject of many studies (Voigt and El-Bialy 2014). The beneficial effects of an efficient judicial system of economic growth and competition are well established in the literature (Mauro 1995; Levine 1998; Messick 1999; Feld and Voigt 2003). At the same time, the public-good nature of the judicial system poses some problems in optimally allocating the available resources to secure the access to all citizens and to provide the judicial service efficiently. On this matter, the CEPEJ has recently undertaken an analysis of the functioning of the justice system in all European states to propose concrete solutions to improve fairness, quality, and efficiency of justice in Europe. Focusing on the Italian situation, the 2014 CEPEJ report, on the one hand, shows that the human resources allocated to justice in Italy are close to those allocated by more efficient European countries. On the other hand, the Italian justice system is well below European peers in terms of time needed to resolve administrative, civil, and commercial cases (CEPEJ 2014). Similar statistics from the Organization for Economic Cooperation and Development (OECD) show that the performance of the Italian justice system is well below European and OECD averages. For example, it takes an average of 1,185 days to enforce a contract in Italy, more than twice the OECD high-income country average (OECD 2013). Furthermore, the OECD average time needed to complete a civil case up to the Supreme Court level is 788 days, while it takes almost eight years in Italy (OECD 2013).

The World Bank (2015) annually draws up the report Doing Business ranking countries also according to efficiency of judicial system measured as the length of civil judicial procedures.⁹ The 2015 report ranks Italy 147th out of the 189 countries reporting 1,185 days, on average, for dispute resolution.

A different source of inefficiency seems to be the presence of unexploited economies of scale. The dimension of the geographical areas of 72 percent of Italian judicial districts is suboptimal (less than twenty judges), leaving room for relevant economies of scale. Marchesi (2003) suggests that the optimal dimension of judicial districts can be reached by putting small courts together according to some parameters derived from data analysis and supported by experts in public sector management. Moreover, higher efficiency can also be reached exploiting economies of specialization and adopting better organizational schemes to increase the productivity of judges (Marchesi 2003).¹⁰

There is also a growing number of empirical studies that assess the efficiency of Italian courts performance and examine the factors affecting their performance as well as the impact of judicial efficiency on economic performance using microfunded empirical tools.¹¹ Efficiency assessment represents a first step toward the evaluation of a coordinated judiciary system and constitutes one of the basic means of audit for the rational distribution of human and economic resources (Voigt and El-Bialy 2014).

Following this line of inquiry, this article assesses the potential efficiency gains achieved by the new judicial map of FICs created according to the decree n. 155/2012. However, the analysis of efficiency of judicial system poses other difficulties besides data availability. First, the courts can be seen as production units producing not just a single service but also a mix of different services. Furthermore, the activity of the courts is characterized by low substitution rate between inputs such as judges, clerks, and different kinds of courts. Given the above-mentioned difficulties, the technical efficiency of judiciary has been investigated frequently applying the DEA nonparametric technique that overcomes all the problematic assumptions required by a parametric production function based on parametric techniques (see, for instance, the survey by Voigt and El-Bialy, 2014).

On this line of research, some papers have investigated the technical efficiency of Italian courts, largely confirming the presence of room for performance improvements (Marselli and Vannini 2004; Finocchiaro Castro and Guccio 2014, 2015; Ippoliti 2014). Marselli and Vannini (2004) have investigated the efficiency in Italian judicial districts looking at both civil and criminal cases using DEA. Then, environmental variables are introduced into the second stage to study the determinants of inefficiency. The authors find high levels of inefficiency mainly due to an excessive caseload that the caseload accumulated through years cannot be resolved by increasing the efficiency and that some judicial districts are affected by strong VRS. Finocchiaro Castro and Guccio (2014) employ the two-stage approach to investigate technical efficiency in Italian judicial districts by focusing on civil cases in 2006. The authors report that technical efficiency is explained by demand factors and that opportunistic behavior from both claimants and lawyers negatively affects technical efficiency in Italian judicial districts. Whereas, using a different empirical prospective, Ippoliti (2014) find a positive effects of numbers of lawyers on the efficiency score achieved by first instance. Finally, Finocchiaro Castro and Guccio (2015) assess technical efficiency of Italian first instance courts in the period 2010–2011 distinguishing between managerial (in)efficiency and (in)efficiency due to the nondiscretionary caseload. The authors find that the presence of bottlenecks, as the caseload, plays a role in the assessment of level of courts’ inefficiency. However, this effect has been relatively low compared with the inefficiency due to the lack of managerial efficiency in particular in the south of the country.¹²

Other papers, not based on DEA technique, focus the attention on the role of different factors in explaining the efficiency of the Italian judicial system. For instance, Coviello, Ichino, and Persico (2015) show that the average efficiency depends on organizational features of judges’ activity and in particular a higher efficiency may be achieved if judges work on cases sequentially instead of starting a new case in parallel with others.¹³

Di Vita (2010) reports a significant level of correlation between the average length of civil proceedings and the indicator of complexity of legal system. The author concludes that a relevant portion of resources seems to be dedicated to enforce the huge number of Italian laws leading to low efficiency of the judicial system. Finally, Buonanno and Galizzi (2014) raise the attention on the application of the supplier-induced-demand hypothesis to the analysis of determinants of judicial system. Their main common result is that the rising quantity of lawyers causes an increase in the number of unnecessary civil trials (due to asymmetric information, imperfect agency relation between lawyer and client, tougher competition, and uniform minimum fees for service) lowering the efficiency of the judicial system.

Methods and Data

Methodological Framework

Following several papers reviewed in the previous section, we use DEA (Charnes, Cooper, and Rhodes 1978) to measure efficiency of Italian FICs. The aim of DEA is to measure productive efficiency through the estimation of a frontier envelopment surface for all decision-making units (DMUs) by using linear programming techniques. In doing so, DEA allows for the identification of best practices and for the comparison of each DMU with the best possible performance among the peers rather than just with the average.¹⁴

In order to facilitate the interpretation of the results in the next sections, it is useful to recall that in the output-oriented DEA model, considering n DMUs to be evaluated, an efficiency score θ _i is calculated for each DMU by solving the following program, for i = 1, …, n, in the case of constant returns to scale (CRS):

\begin{array}{l} {Max}_{λ, θ_{i}} θ, \\ subject to \; x_{i} \geq X λ, \\ θ_{i} y_{i} \leq Y λ, \\ λ \geq 0, \end{array}

where x_i and y_i are, respectively, the input and output of ith DMU; X is the matrix of inputs and Y is the matrix of outputs of the sample; and λ is a n × 1 vector of weights which allows to obtain a convex combination between inputs and outputs. Solving equation (1), DMUs with an efficiency score equal to 1 are located on the frontier, and therefore their outputs cannot be further expanded without a corresponding increase in inputs.¹⁵

Based on the estimated DEA frontier, it is possible to analyze hypothetical cases of horizontal merger between FICs. On this matter, Bogetoft and Wang (2005) propose a simple direct pooling of the inputs and outputs set used by each FICs involved into the merging procedure. To analyze hypothetical cases of horizontal merger, Bogetoft and Wang (2005) use the technology set (equation [1]), estimated before any merger, as the reference set. Then, the authors decompose the efficiency gains from DMU mergers into technical efficiency gains, synergies from joint operation, and size gains. Thus, it is possible to measure both the overall potential gains from mergers and the separate role of the three effects on the implementation of Italian government’s proposal (decree n. 155/2012) of new jurisdiction design of FICs.

Furthermore, to account for DEA traditional limitations, which do not allow for any statistical inference and measurement error, Simar and Wilson (1998, 2000) introduced a bootstrapping methodology to determine the statistical properties of DEA estimators. The idea underlying the bootstrap procedure is to approximate the sampling distributions of efficiency scores by simulating their data generating process (Simar and Wilson 2008).¹⁶

The model that has been applied to determine the DEA production frontier in this article is the one outlined by Simar and Wilson (1998, 2000). It enables us to overcome some traditional DEA limitations and to provide a robustness check of our findings. In particular, we employ a consistent bootstrap estimation procedure (Simar and Wilson 1998) to obtain the sampling distribution of the efficiency scores and derive bias-corrected scores.

Data

In Italy, the administration of justice is articulated into judicial districts (Distretto di Corte di Appello) and judicial counties (Circondario di Tribunale Ordinario). The twenty-nine judicial districts are usually located in the main town of the region, although the most populated regions have two, whereas the judicial counties are 165¹⁷ and are distributed in the territory. In each judicial county, FICs are organized according to their field of specialization and may be subdivided into different benches. However, for statistical purpose, the data on resources (mainly judges and administrative staff) of judicial counties are collected at aggregate level only, whereas data on judicial counties activities are disaggregate in civil and criminal services. This poses a severe problem to the measurement of the efficiency in judicial sector. Only in some cases is it possible to obtain estimates able to disaggregate the data on the resources adopted to provide civil and criminal services.¹⁸

Hence, our analysis refers to the activity of 165 FICs (Circondario di Tribunale Ordinario) in Italy for the year 2011. The employed data set has been obtained from Italian Department of Justice (Ministero della Giustizia–Direzione Generale di Statistica). Unfortunately, our model specifications have been limited by data availability, for example, the data set does not include cost and input factor prices. Thus, as a consequence, only the FICs’ technical efficiency will be assessed.

Table 2 reports all the variables employed in the efficiency analysis and their descriptive statistics as well as the two estimated models. Specifically, our analysis includes the number of judges (judges), administrative staff (adm_staff), and size of the caseload—both civil (caseload_civil) and criminal (caseload_criminal) cases as inputs. In our computation, the caseload is the sum of the number of filed cases during year t and the number of pending cases at the beginning of year t. Regarding the output, we employ the number of civil and criminal cases resolved during a given year (res_civil_cases and res_criminal_cases), without distinguishing between cases resolved through the full legal process and other resolved cases.

Table 2.

Summary Statistics (Year 2011) and Estimated Models.

Variables	Observations	M	SD	Minimum	Maximum	Model 1	Model 2
Inputs
Judges	165	30.68	48.81	6.00	379.00	♦	♦
Adm_staff	165	99.36	136.96	18.00	1,198.00	♦	♦
Caseload_civil	165	37,138.56	53,577.68	2,853.00	409,190.00		♦
Caseload_criminal	165	15,373.67	18,062.76	793.00	136,979.00		♦
Outputs
Res_civil_cases	165	16,387.03	24,706.64	1,235.00	199,304.00	♦	♦
Res_criminal_cases	165	7,659.07	9,452.46	427.00	65,268.00	♦	♦

Note: Diamond symbol (♦)\rm\;represents\;a\;way\;to\;signal\;which\;of\;all\;the\;listed\;variables\;is\;included\;in\;each\;of\;the\;two\;models.\;SD= standard deviation. Adm_staff = administrative staff; caseload_civil = size of the caseload for civil cases as input; caseload_criminal = size of the caseload for criminal cases as input; res_civil_cases = the number of civil cases resolved during a given year as output; res_criminal_cases = the number of criminal cases resolved during a given year as output.

Source: Our computation on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

From the analysis of the data, it appears that the FICs differ considerably in terms of personnel—ranging from 6 to 379 judges and from 18 to 1,198 members of administrative staff, of both civil and criminal caseload, in the number of resolved cases.

Results and Discussion

In this section, we first calculate average efficiency estimates for the nonmerged courts and assess the inefficiency as well as the impact of model specification and bootstrap bias correction on FICs’ performance. Second, we show the efficiency gains from mergers under both constant and VRS.

Benchmark Analysis

Here we report the technical efficiency scores achieved by the different production function specifications shown in the last two columns of table 2. We choose the output orientation so that the output efficiency is measured for a given level of input and use a Shepherd (1970) output-oriented distance function. Consequently, efficiency scores assume values between 0 and 1, which is the reciprocal of Farrell (1957) distance function. The output orientation is justified by very large backlog of cases of the Italian courts that calls for higher case resolution rates, given the existing staff levels.

First, we evaluate the best possible input–output specifications. The first input–output specification with judges (judges) and administrative staff (adm_staff) as an input and the number of civil and criminal cases finished during a given year (res_civil_cases and res_criminal_cases) is the most common one, because it accounts for the main courts’ activities. The second input–output specification, which includes both civil (caseload_civil) and criminal (caseload_criminal) cases, takes into account the different aspects of the demand of justice exerted by the area in which each court operates.

As shown in the second section, the literature investigating the efficiency of justice services has mainly adopted the one-stage-production model. Under this approach, it is assumed that the justice institution disposes of a set of resources (e.g., judges, support staff equipment, expenditure, etc.) used exclusively to provide judicial services (basically the resolution of disputes) to the litigants. In such context, each DMU controls both the outputs, which are mainly related to the level of services provided to the litigants, and the inputs, which are related to the level of resources used for that purpose. Excluding those variables connected to the operating environment, the efficiency analysis is, thus, confined to managerial aspects. However, there are cases in which it might be important to consider nondiscretionary variables in order to take into account the differences among justice institutions in the operating environment.¹⁹

In what follows, we employ the one-stage approach considering both discretionary and nondiscretionary inputs, since it allows for the potential identification of the effects of operating environment in the production system.²⁰

Moreover, as robustness test of our results, we use the subsample of FICs excluding the courts located in some metropolitan areas (Rome, Milan, and Napoli).²¹

Table 3 also shows the efficiency scores of subsamples obtained according to geographical macro areas in the country.

Table 3.

The Descriptive Statistics of the Models Outcomes.

Sample	Observations	CRS				VRS
Sample	Observations	Average	SD	Minimum	Maximum	Average	SD	Minimum	Maximum
Model 1
North	64	.6978	.1490	.4294	1.0000	.7725	.1436	.4786	1.0000
Center	38	.6713	.1380	.3739	1.0000	.7782	.1327	.5348	1.0000
South	63	.5445	.1648	.2324	1.0000	.6115	.1953	.2332	1.0000
All samples	165	.6332	.1676	.2324	1.0000	.7124	.1807	.2332	1.0000
Model 2
North	64	.8805	.0934	.5832	1.0000	.9182	.0819	.6907	1.0000
Center	38	.8115	.0940	.6407	1.0000	.8557	.0988	.6448	1.0000
South	63	.6911	.1452	.4721	1.0000	.7210	.1511	.5021	1.0000
All samples	165	.7923	.1427	.4721	1.0000	.8285	.1455	.5021	1.0000

Note: CRS = constant returns to scale; VRS = variable returns to scale; SD = standard deviation.

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

The results reported in table 3 support the common perception of the inefficiency as a major problem of the Italian FICs and that it varies significantly across the country. The average aggregate technical efficiency score of 63.32 percent in model 1, under CRS assumption, indicates that the FICs are, on average, largely technically inefficient in the provision of judicial services. The quite large standard deviation as well as the large difference between the minimum and maximum efficiency scores indicate, however, that there are considerable differences in the aggregate technical efficiency of Italian FICs. Also, strong differences among FICs exist in relation with the geographical macro areas (north, center, and south).²² Table 3 also includes estimates for the case of VRS, which show results generally overlapping with those under CRS.

In the lower part of table 3, we show estimates for model 2 with uncontrollable inputs, where caseload—both civil (caseload_civil) and criminal (caseload_criminal) cases—is considered an input external to managerial control although affecting technical efficiency. As expected, the average levels in our sample increase when we consider such an input, reaching the efficiency scores of 79.23 percent in the model 2 with CRS.

Given the importance of scale efficiency for our analysis, table 4 shows the distribution of returns to scale of the sample. It can be noted that in model 1 more than 75 percent of first instance courts have increasing returns to scale, whereas only 13 percent of them have decreasing returns to scale. The results change significantly when we consider model 2, in which the 43 percent of first instance courts show diseconomies of scale being the productive scale is too high.²³

Table 4.

Distribution of Returns to Scale.

Returns to scale	Model 1		Model 2
Returns to scale	Observations	%	Observations	%
IRS	124	75.15	61	36.97
Eff_scale	19	11.51	33	20.00
DRS	22	13.34	71	43.03
Total	165	100.00	165	100.00

Note: Eff_scale = efficient scale; IRS = increasing returns to scale; DRS = decreasing returns to scale.

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

These results are relevant especially with respect to the effects of nondiscretionary variables on efficiency variation. Moreover, not taking into account the caseload in the assessment of efficiency gains from the merging procedure may have relevant effects on the higher performance to be achieved with the reform. However, Finocchiaro Castro and Guccio (2015), using comparable data, find that caseload plays a marginal role in the FICs’ inefficiency levels compared with the inefficiency due to the lack of managerial efficiency.

Finally, in order to test the sensitivity of the efficiency estimates, we employ the bias-corrected efficiency scores. In fact, the DEA efficiency estimate measures performance relative to an estimation of the true and unobservable production frontiers and provides point estimates of performance. Since estimates on the frontier are based on finite samples, DEA measures, based on these estimates, are subject to sampling variation of the frontier. To address this problem, we implement a bootstrap procedure, with 2,000 bootstrap draws as described by Simar and Wilson (1998), to correct the bias in DEA estimators and obtain their confidence intervals. Table 5 reports the bias uncorrected and corrected average values of technical efficiency at FIC level, estimated with different models under CRS and VRS assumptions. The results show that, from the perspective of sensitivity analysis, only the efficiency estimates in model 1 under CRS assumption are quite robust with respect to sampling variation since there are only small differences between biased and bias- corrected efficiency estimates. Under VRS assumption, the bootstrap bias correction has relatively strong effects on efficiency estimates.²⁴

Table 5.

Uncorrected and Bias-corrected Estimate.

Models		Observations	M	SD	Minimum	Maximum
Model 1	CRS	165	.6332	.1676	.2324	1.0000
	Bias-corrected CRS estimate	165	.5947	.1549	.2192	.9234
	VRS	165	.7124	.1807	.2332	1.0000
	Bias-corrected VRS estimate	165	.6335	.1475	.2133	.9440
Model 2	CRS	165	.7923	.1427	.4721	1.0000
	Bias-corrected CRS estimate	165	.7342	.1258	.4419	.9636
	VRS	165	.8285	.1455	.5021	1.0000
	Bias-corrected VRS estimate	165	.7519	.1210	.4720	.9366

Note: CRS = constant returns to scale; VRS = variable returns to scale; SD = standard deviation.

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

The specification of returns to scale of the reference technology is important to evaluate the merger possibilities, because, by definition, a merged group of courts is a rescaling of the individual resources in the group. To check the importance of economies of scale, we perform the Banker test (1996) on both models. The results show that we can reject the null hypothesis of CRS at any conventional level of significance.²⁵

This result is not surprising since, in our sample, courts vary considerably in size, which can be an important factor in determining the organization and the production of services by courts. However, Bogetoft and Wang (2005) demonstrate that only under CRS reference technology, there are the necessary and sufficient conditions to ensure a feasible solution to the DEA linear program. In fact, under VRS reference technology, these conditions may not hold and it is possible that there may be no feasible solution to the linear program. Hence, they suggest using the CRS technology as reference point to evaluate the potential efficiency gains of mergers. CRS technology assumption is typically discussed because it may not consider differences in the DMU dimension; nevertheless, it has some potential advantages as reference point. In fact, CRS assumption identifies overall inefficiency (one due to scale inefficiency and one due to “pure” technical inefficiency), while VRS only analyzes technical and managerial efficiency, ignoring scale efficiency.²⁶

However, since the estimations of the above-mentioned models with different scale assumptions provide the opportunity to better assess the potential gain that different FICs involved in the analysis could achieve through the horizontal merger, we provide both estimates. In particular, we first discuss in deep the estimates under CRS technology used as baseline estimates to assess the potential efficiency gain of horizontal mergers of Italian FICs proposed by decree n. 155/2012. Then, we provide the estimates under VRS technology.

Estimation of Potential Gains of the Merging Procedure: Baseline Estimates

In this section, we present the estimates of the potential efficiency gains from mergers and the decompositions of the efficiency gains into three components: technical efficiency gains, synergies from joint operation, and size gains (Bogetoft and Wang 2005).

The overall potential efficiency gain (E_J ) is the simple efficiency evaluation of a hypothetical DMU using the sum of inputs of the premerger DMUs to produce the sum of the premerger outputs. A merger is assumed to be beneficial for E_J < 1 (e.g., a value of E_J = 0.9 indicates a potential for output increasing of 10 percent through merging the DMUs). For E_J > 1, a merger is assumed to have a negative impact on efficiency. However, the potential overall gains (E_J ) from merging still include inefficiencies of the individual DMUs from before merging that cannot be attributed to a merger. To correct the overall potential gains from merging, we need to project each DMU into the efficient premerger DEA frontier using their efficiency scores. The performance measure, $E_{J}^{*}$ , represents the corrected overall potential gains from merging, being beneficial for $E_{J}^{*}$ < 1. Hence, it is more meaningful to represent the potential gains from merger by the corrected $E_{J}^{*}$ .

We now turn to investigate the efficiency gains of the mergers and their decomposition into technical efficiency effects (or learning effects), synergies from joint operation (or scope) effects, and scale effects. We first look at the efficiency levels achieved by the courts involved in the merging procedure. As described in the second section, only thirty courts out of the fifty-five under analysis have been suppressed and merged into twenty-five new FICs. Table 6 reports the average efficiency levels of the two models presented in previous section distinguishing merged from not merged courts under both CRS and VRS assumption. It can be noted that, under the CRS assumption, the merged courts have lower average efficiency levels than not merged ones, leading to potential higher efficiency gains. This result holds for both models. Differently, under VRS assumption, the differences in efficiency levels between the two groups of courts are smaller. For instance, model 2 does not report any significant difference between merged and not merged courts under VRS assumption.

Table 6.

The Descriptive Statistics of the Model Outcomes.

Samples	Observations	CRS				VRS
Samples	Observations	Average	SD	Minimum	Maximum	Average	SD	Minimum	Maximum
Model 1
Not merged	110	.6562	.1686	.2324	1.0000	.7078	.1812	.2332	1.0000
Merged	55	.5872	.1571	.2773	1.0000	.7215	.1809	.3498	1.0000
All samples	165	.6332	.1676	.2324	1.0000	.7124	.1807	.2332	1.0000
Model 2
Not merged	110	.7964	.1392	.4973	1.0000	.8285	.1411	.5021	1.0000
Merged	55	.7840	.1505	.4721	1.0000	.8286	.1552	.5035	1.0000
All samples	165	0.7923	.1427	.4721	1.0000	.8285	.1455	.5021	1.0000

Note: CRS = constant returns to scale; VRS = variable returns to scale; SD = standard deviation.

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

Then, we use model 1 to calculate the overall potential merger effects. In table 7, we report the merger efficiency gains derived from our sample. The potential gain from each merger is indicated by the difference between unity and the relevant number in each column.

Table 7.

Efficiency Scores and Decomposition for Proposed Court Mergers (Model 1—Constant Returns to Scale) by Judicial District.

New courts	First instance courts merged	Overall potential efficiency gain	Individual technical efficiency gain	Learning effects	Scope effect	Size effect
Macerata	Camerino, Macerata	.7458	1.0000	.7458	1.0000	1.0000
Foggia	Foggia, Lucera	.7708	.9975	.7727	.9975	1.0000
Cremona	Crema, Cremona	.6783	.9577	.7083	.9577	1.0000
Enna	Enna, Nicosia	.3836	.9965	.3850	.9965	1.0000
Ragusa	Modica, Ragusa	.5204	1.0000	.5204	1.0000	1.0000
Castrovillari	Castrovillari, Rossano	.4075	.9817	.4151	.9817	1.0000
Siena	Montepulciano, Siena	.5304	1.0000	.5304	1.0000	1.0000
Genova	Chiavari, Genova	.4517	.9891	.4567	.9891	1.0000
Imperia	Sanremo, Imperia	.6134	.9619	.6377	.9619	1.0000
L’Aquila	Avezzano, L’Aquila, Sulmona	.5704	1.0000	.5704	1.0000	1.0000
Chieti	Chieti, Lanciano, Vasto	.7070	1.0000	.7070	1.0000	1.0000
Patti	Mistretta, Patti	.5677	.8977	.6324	.8977	1.0000
Pavia	Pavia, Vigevano, Voghera	.5774	.9663	.5976	.9663	1.0000
Benevento	Ariano Irpino, Benevento	.8011	.8939	.8962	.8939	1.0000
Avellino	Sant’Angelo dei Lombardi, Avellino	.6495	.9960	.6521	.9960	1.0000
Terni	Orvieto, Terni	.5580	1.0000	.5580	1.0000	1.0000
Potenza	Melfi, Potenza	.4380	1.0000	.4380	1.0000	1.0000
Lagonegro	Lagonegro, Sala Consilina	.4477	.9586	.4670	.9586	1.0000
Asti	Alba, Asti	.6551	.9666	.6777	.9666	1.0000
Alessandria	Acqui Terme, Alessandria, Tortona	.5662	.9239	.6128	.9239	1.0000
Cuneo	Cuneo, Modovì, Saluzzo	.4759	.9483	.5018	.9483	1.0000
Torino	Pinerolo, Torino	.6317	1.0000	.6317	1.0000	1.0000
Vercelli	Casale Monferrato, Vercelli	.7067	1.0000	.7067	1.0000	1.0000
Udine	Udine, Tolmezzo	.6425	.9987	.6433	.9987	1.0000
Vicenza	Bassano del Grappa, Vicenza	.7496	1.0000	.7496	1.0000	1.0000
Mean efficiency		.5939	.9774	.6086	.9774	1.0000
Average efficiency gain		.4061	.0226	.3914	.0226	.0000

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

The first column reports the potential overall gains (E_J ), whereas the second column reports the corrected overall potential gains from merging $(E_{J}^{*})$ . In all cases, a merger between the courts would be beneficial when looking at the potential overall gains, E_J . However, given that the overall potential gains, E_J , may still include the individual inefficiencies of FICs before merging, we must consider the projections of the each FICs and calculate the corrected potential gains $E_{J}^{*}$ . The merger gains drop down dramatically after correcting for individual inefficiencies. On average, the potential gains from mergers turn out to be quite low (2.26 percent). In table 7, we also provide the decomposition into the technical efficiency effects (or learning effects), synergies from joint operation (or scope) effects, and scale effects. On average, around 39 percent of the overall merger gains, E_J , could be realized by improving efficiency of each FIC. Thus, higher efficiency levels can be achieved through a better management of the individual production plans of the different courts. Such efficiency improvements are usually not attributable to a merger since efficiency could be improved, for example, by sharing best practices between individual courts. On average, less than 3 percent of the efficiency could be gained by reallocating the inputs in the merged FICs. The scope (or synergy) effect, thus, inhibits the weak potential for efficiency increases. We can only find weak scale effects for all merger cases. Considering the median, there are no efficiency gains from an increase in courts size.²⁷

Potential Efficiency Gains under VRS Assumption

In table 8, we report the merger efficiency gains derived from model 1 under VRS technology assumption, whereas table 9 shows the merger efficiency gains obtained by model 2. Each table reports the merger efficiency gains and the decomposition into the technical efficiency effects (or learning effects), synergies from joint operation (or scope) effects, and scale effects. The potential gain from each merger is indicated by the difference between unity and the relevant number in each column.

Table 8.

Efficiency Scores and Decomposition for Proposed Court Mergers (Model 1— Variable Returns to Scale) by Judicial District.

New courts	First instance courts merged	Overall potential efficiency gain	Individual technical efficiency gain	Learning effects	Scope effect	Size effect
Macerata	Camerino, Macerata	.7639	.8273	.9234	.8955	.9238
Foggia	Foggia, Lucera	.7267	.8438	.8612	.9614	.8777
Cremona	Crema, Cremona	.7708	.9711	.7938	.9979	.9731
Enna	Enna, Nicosia	.7564	.8798	.8598	1.0000	.8798
Ragusa	Modica, Ragusa	.4322	.6277	.6885	.9758	.6433
Castrovillari	Castrovillari, Rossano	.5601	.8507	.6584	.9988	.8517
Siena	Montepulciano, Siena	.4354	.8425	.5168	.9777	.8617
Genova	Chiavari, Genova	.5976	.7641	.7821	.9344	.8178
Imperia	Sanremo, Imperia	.4517	.9575	.4718	.9784	.9786
L’Aquila	Avezzano, L’Aquila, Sulmona	.8440	.8885	.9500	.9838	.9031
Chieti	Chieti, Lanciano, Vasto	.5818	.7616	.7639	.9493	.8023
Patti	Mistretta, Patti	.7168	.8251	.8687	.9831	.8393
Pavia	Pavia, Vigevano, Voghera	.6128	.7075	.8662	.8112	.8721
Benevento	ArianoIrpino, Benevento	.5903	.7933	.7440	.9960	.7965
Avellino	Sant’Angelo dei Lombardi, Avellino	.8157	.8679	.9399	.9162	.9472
Terni	Orvieto, Terni	.5151	.9082	.5672	.9955	.9122
Potenza	Melfi, Potenza	.5985	.7482	.7999	.9171	.8159
Lagonegro	Lagonegro, Sala Consilina	.4580	.8575	.5340	.9699	.8841
Asti	Alba, Asti	.4836	.7162	.6753	.9790	.7316
Alessandria	AcquiTerme, Alessandria, Tortona	.7040	.8932	.7882	.9938	.8988
Cuneo	Cuneo, Modovì, Saluzzo	.5860	.6865	.8536	.8839	.7767
Torino	Pinerolo, Torino	.7439	.8612	.8638	.9471	.9093
Vercelli	Casale Monferrato, Vercelli	.5983	.7687	.7783	.9488	.8101
Udine	Udine, Tolmezzo	.6317	.9804	.6444	.9811	.9993
Vicenza	Bassano del Grappa, Vicenza	.6508	.8911	.7304	.9383	.9496
Mean efficiency		.6250	.8288	.7569	.9566	.8662
Average efficiency gain		.3750	.1712	.2431	.0434	.1338

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

Table 9.

Efficiency Scores and Decomposition for Proposed Court Mergers (Model 2—Variable Returns to Scale) by Judicial District.

New courts	First instance courts merged	Overall potential efficiency gain	Individual technical efficiency gain	Learning effects	Scope effect	Size effect
Macerata	Camerino, Macerata	.8518	.8685	.9808	.8759	.9915
Foggia	Foggia, Lucera	.8539	.9148	.9334	.9446	.9685
Cremona	Crema, Cremona	.8862	.9961	.8897	.9964	.9997
Enna	Enna, Nicosia	.7878	.8604	.9155	1.0000	.8604
Ragusa	Modica, Ragusa	.5349	.7587	.7049	.9688	.7831
Castrovillari	Castrovillari, Rossano	.6354	.8449	.7521	.9475	.8917
Siena	Montepulciano, Siena	.4662	.8130	.5735	.9016	.9017
Genova	Chiavari, Genova	.7021	.8461	.8298	.9738	.8689
Imperia	Sanremo, Imperia	.7772	.9987	.7781	.9987	1.0000
L’Aquila	Avezzano, L’Aquila, Sulmona	.8547	.8992	.9505	.9856	.9123
Chieti	Chieti, Lanciano, Vasto	.6561	.8016	.8185	.8904	.9002
Patti	Mistretta, Patti	.8300	.9181	.9041	.9658	.9506
Pavia	Pavia, Vigevano, Voghera	.6248	.7279	.8584	.8112	.8973
Benevento	ArianoIrpino, Benevento	.7606	.9005	.8446	.9415	.9564
Avellino	Sant’Angelo dei Lombardi, Avellino	.9193	.9538	.9638	.9788	.9744
Terni	Orvieto, Terni	.6195	.9701	.6387	.9701	1.0000
Potenza	Melfi, Potenza	.7900	.9296	.8498	.9506	.9779
Lagonegro	Lagonegro, Sala Consilina	.4989	.8923	.5591	.9662	.9235
Asti	Alba, Asti	.5459	.7752	.7042	.9471	.8185
Alessandria	AcquiTerme, Alessandria, Tortona	.8970	.9573	.9370	.9929	.9642
Cuneo	Cuneo, Modovì, Saluzzo	.8177	.9065	.9020	.9368	.9677
Torino	Pinerolo, Torino	.9224	.9409	.9803	.9846	.9557
Vercelli	Casale Monferrato, Vercelli	.8347	.9158	.9114	.9657	.9484
Udine	Udine, Tolmezzo	.9289	.9913	.9371	.9913	1.0000
Vicenza	Bassano del Grappa, Vicenza	.8188	.9349	.8758	.9349	1.0000
Mean efficiency		.7526	.8926	.8397	.9528	.9365
Average efficiency gain		.2474	.1074	.1603	.0472	.0635

Source: Our elaboration on data provided by Ministero della Giustizia–Direzione Generale di Statistica.

Not surprisingly, under the VRS assumption, we observe a significant increase in the potential efficiency gains obtainable from merging procedure. In details, looking at model 1, the efficiency gains, corrected for individual inefficiencies, turns out to be not negligible (17.2 percent). In table 8, we also provide the decomposition into the technical efficiency effects (or learning effects), synergies from joint operation (or scope) effects, and scale effects. On average, around 24 percent of the overall merger gains, E_J , could be realized by improving efficiency of each FIC. Thus, higher efficiency levels can be achieved through a better management of the production plans of the different courts. Such efficiency improvements are usually not attributable to a merger, since efficiency could be improved, for example, by sharing best practices between courts. On average, around 4 percent of the efficiency could be gained by reallocating the inputs in the merged FICs. The scope (or synergy) effect, thus, inhibits the weak potential for efficiency increases. Finally, we report an average size effect of 13.4 percent. Table 9 shows the merger efficiency gains and the decomposition into the technical efficiency effects (or learning effects), synergies from joint operation (or scope) effects, and scale effects regarding model 2. The results are very similar to those reported in table 8 with the main difference being the lower average efficiency gain achieved (10.7 percent), when taking into account both civil and criminal caseload into the analysis. This finding points to the crucial role that the level of caseload may play in the attempt at improving the efficiency of FICs. Summing up, our additional exercise has shown that if the technology is modeled using a VRS DEA model, the gains from merging FICs are considerably higher than those under CRS. We can, thus, conclude that the scale effects generally seem to work in favor of the horizontal mergers proposed by decree n. 155/2012.

Conclusions

Given the relevant impact on efficiency of the context in which the FICs operate, most of European countries have focused the attention of the reshaping of their territorial distribution. In Italy, the importance of such aspect is also confirmed by a new reform of FICs that, among other interventions, calls for the merging of most of the existing courts in order to gain more efficiency and reduce public expenditure.

In this article, we focus on the performance of 165 Italian FICs in the year 2011 providing considerable scope for efficiency improvement in judicial services. In detail, our results show that while the level of inefficiency appears to be higher in the south of Italy rather than in the center or in the north of Italy, the level of civil caseload strongly affects the performance of first instance courts, especially in those areas where the demand for civil judicial services is higher. Also, we found evidence of small efficiency gains from the proposed mergers being slightly higher when both civil and criminal caseloads are taken into account. To the best of our knowledge, this is the first article to use nonparametric techniques, proposed by Bogetoft and Wang (2005), to analyze Italian FICs merger procedure.

Our findings call for closer attention to be paid by policy makers to some aspects of the merging procedure and to the periods afterward in order to secure the targeted efficiency levels. First, the new judicial map should lead to higher quality of justice rather than focusing only on cost saving. Second, policy makers should monitor closely the relevant cost of implementing the reform to ensure the net return of new judicial map in the medium and long run. Finally, in the first periods of the reform, some primary goals should be achieved such as ensuring the continuity of judicial services, monitoring the transfer of staff from old to new staff, and organizing the logistics of new offices (CEPEJ 2013).

However, some limitations of our results have to be noted. First, the reform under consideration suggests a kind of merging procedure that should produce a reallocation of input in an efficient way and not an aggregation of FICs keeping constant the total amount of human resources. The available data, unfortunately, do not let us investigate the effects of a potential reallocation of input as suggested by the reform. Second, being a wide and deep reform of judicial map, it will take some more years before it would be possible to assess the real increase in the efficiency levels of judicial offices due to their optimal territorial reshaping. Nowadays, it is only possible to evaluate the potential, rather than the real, efficiency gains reached by the reform. Third, it has to be noted that the new judicial map implies the reshaping of two pillars of judicial system, the FICs and the local courthouses. In other words, two reforms of Italian judiciary have taken place. Unfortunately, to the best of our knowledge, there are no available data to assess the overall effect of merger both for FICs (decree n.155/2012) and local courthouses (decree n.156/2012). Furthermore, it has to be noted that even getting the data, the results of the efficiency analysis that pooled FICs and local courthouses may be severely biased due to the relevant differences in two courts. Thus, it would have been interesting to analyzing the effects of both reforms on efficiency levels, with special attention to eventual effects of one reform on the other. Hence, we have confined our investigation to the FICs reform only, providing policy makers with useful insights based on robust econometric techniques though.

Footnotes

Acknowledgments

Thanks are due to Professor James Alm, Professor Massimo Bordignon, Professor Paolo Liberati, and four anonymous referees for their helpful and constructive comments. The authors would also like to thank Professor Alessandro Petretto and the participants at the Annual Meetings of Italian Public Economic Association for their helpful discussion and comments. Any remaining errors are solely the authors’ responsibility.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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