Measuring Potential Gains from Mergers
Of Italian Courts through Non-Parametric Model
Massimo Finocchiaro Castro∗
Department of Law and Economics, Mediterranean University of Reggio Calabria,
Via dei Bianchi, 2 - 89125 Reggio Calabria,
[email protected], tel. +39 0965 499360, fax +39 0965 499347.
Calogero Guccio
Department of Economics and Business, University of Catania,
Corso Italia, 55 – 95121 Catania,
[email protected] , tel. +39 095 7537744, fax +39 095 7537710.
Abstract
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 court
through merger of some courts and the abolition of 220 local courthouses. Two
aspects of merger analysis are the operational cost savings and the potential
production efficiency gains. This paper concentrates on the second aspect and uses a
non-parametric methodology to assess the potential effect of these mergers and
whether the mergers are efficiency enhancing. However, it has to be noted that the
reform under consideration suggests a merging procedure that should produce a
reallocation of input in an efficient way and not an aggregation of first instance courts
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. We compare the actual efficiency levels of observed
Italian courts, at the first instance level for the year 2011, with the merger of proposed
aggregated courts. Moreover, we make use of the bootstrapping methodology to
account for measurement errors. The preliminary results show small efficiency gains
from the proposed mergers.
JEL codes: D24, K41, K49
Keywords: first instance courts efficiency; non-parametric methods.
∗
Corresponding author.
1. Introduction
In most of the countries in the world the role of an efficient judiciary in the
achievement of economic and social growth has been widely acknowledged. An
efficient judicial system is certain, works out cases in a reasonable time frame and it
is accessible to the public (Dakolias, 2014). Recently, CEPEJ (2013) 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
services1. However, the proposed goal is still far from being reached. The general
Attorney of court of cassation, in his 2013 report on the judicial system, describes the
necessary goals to be achieved in order to improve the efficiency of Italian system
(Ministero della Giustizia, 2013). In particular, the main goals are the rational
distribution of judicial districts in Italy; the reform of the stiff and bureaucratic rules
regulating the use of financial resources; the introduction of tools able to filter the
relevance and importance of litigations before reaching the court or to solve the cases
before reaching the court (the attempt at conciliation is mandatory for several kinds of
cases).
Regardless of the importance of an efficient judiciary, the number of empirical
works investigating the performance of the Italian judicial system is still surprisingly
low (Marselli and Vannini, 2004; Coviello et al., 2009; Finocchiaro Castro and
Guccio, 2012). The lack of research may be due to the difficulty in gaining data for
the resources used to supply civil judicial services at the court level in Italy. However,
this is a common problem to several civil justice systems (Dakolias, 2014; CEPEJ,
2013).
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
through merger of 31 courts and the abolition of 220 local courthouses. Two aspects
of merger analysis are the operational cost savings and the potential production
efficiency gains. This paper concentrates on the second aspect and uses a nonparametric methodology to assess the potential effect of these mergers and whether
the mergers are efficiency enhancing. For this purpose, we run our efficiency analysis
on 160 Italian first instance courts for the year 2011. In details, our analysis refers to
1
In 2010, a tax on incoming cases has been established leading to a clearance rate of over 100%. In
other words, in Italy there were more settled cases than new cases for the year (CEPEJ, 2013).
the activity of first instance courts (Tribunali ordinari) falling into the areas over
which the judicial counties (Circondario di Tribunale Ordinario) have the
competence.
We empirically analyse potential gains from mergers in first instance courts using
the non-parametric Data Envelopment Analysis (DEA) with bias corrections by
means of bootstrapping. We apply a methodology developed by Bogetoft and Wang
(2005) to investigate whether these proposed mergers in the Italian first instance
courts promise potential efficiency enhancement. Bogetoft and Wang’s (2005)
methodology is a non-parametric measurement of the potential gains from mergers,
and we use it to compare two different comparative static equilibria: the existing
organisational structure of first instance courts and the new jurisdiction design, in
which the technical efficiency with which observed inputs are transformed to outputs
is evaluated relative to the existing structure.
Moreover, despite the increased use of non-parametric 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, 2012). In fact, the bootstrapping makes possible to run sensitivity
analyses on efficiency scores and scaling indicators (Simar and Wilson, 1998 and
2000). Hence, the model that is applied in determining the DEA production frontier in
this paper is the one outlined by Simar and Wilson, (1998, 2000). This enables us, to
overcome some traditional DEA limitations and to provide a robustness check of our
findings. More 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.
Our first results report the high levels of inefficiency reached by the first instance
courts 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 first instance courts. Finally, the preliminary findings show
considerable efficiency gains from the proposed mergers.
The analysis develops as it follows: in Section 2 a description of the reform of first
instance courts jurisdiction design in Italy is offered. Section 3 briefly reviews the
literature, whereas Section 4 describes the methodology and the data used. Section 5
provides technical efficiency estimates and Section 6 offers some concluding remarks.
2. The reform of first instance courts jurisdiction design in Italy
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 to
be reached. The most recent reform of judicial system focused on the positive effects
obtainable by designing new jurisdiction for first instance courts and local
courthouses. 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 first instance courts and local
courthouses, whereas the decree n.156/2012 deals with the reform of justice of the
peace offices (that will not be considered in our analysis). The main goals of the
reform were the reduction of first instance courts, their optimal distribution in order to
cut public expenditure and achieve efficiency gains, the optimal allocation of
available resources with respect to workloads. The reform posed two constraints to
the optimal redistribution of first instance courts: there should remain one first
instance court in each provincial capital and at least three first instance courts and
relative public prosecutor’s offices in each judicial district according to the provincial
capital existing on June 2011.
On this basis, 107 out of the 1662 first instance courts are sited in the provincial
capitals. Thus, the merging procedure suggested by the decree n.155/2012 has been
applied on the remaining 59 first instance courts 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 first instance courts those to be merged was the following. First, the 107
first instance courts sited in the provincial capitals have been used as benchmark.
However, five of them have been excluded from this analysis because they are located
in metropolitan areas (Rome, Milan, Naples, Turin, Palermo), leaving 102 first
instance courts as benchmarks. To do so, it have been computed the statistic averages
of four indexes: office workload, population served, judges productivity, and
dimension of the offices (number of judges) and they have been sequentially applied
to select the first instance courts, among the 59 already chosen, below the
benchmarks. The application of this methodology has shown that merging has been
2
However the court of Giugliano, established in 1999, has not been in service yet.
necessary for the smallest offices among the least productive. The outcome of the
reform should be the creation of new offices to be placed in the most productive class
according to the different dimensions. The methodology to be applied to local
courthouses closely resembles that used for first instance courts.
According to the above-mentioned methodology, the first version of the Decree
n.155/2012 required the suppression of 37 first instance courts and all the local
courthouses. However, the changes due to the Decree n.155/2012 with respect to the
first version of the reform of the distribution and the consistency of courts in the
Italian territory are given by the maintaining the six courts previously to be
suppressed sited in areas characterised by high levels of criminality - e.g. Sicily
(Caltagirone and Sciacca), Calabria (Castrovillari, Lamezia Terme and Paola) and
Lazio (Cassino). Moreover, the Costitutional 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 37 to 303, whereas
all the 220 local courthouses are still to be abolished. The reform is in effect since
September 2013.
Table 1 shows the outcome of merging procedure listing the Judicial District, the
merged courts and the resulting courts. The procedure has involved 17
JudiciaDistricts out of the 27 existing before the reform. Most of the mergers have
been done within the same District, with the only exception being the first instance
court 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 Table 1 reports that, in most of the cases, the merging procedure involved two
first instance courts, whereas in few cases the first instance courts merged have been
three. Overall, the reform that has been conducted on 55 first instance courts has lead
to the suppression of 30 'old' first instance courts and to the creation of 25 'new' ones.
Thus, it has affected a large part of the courts with significant potential effect on the
Italian judicial system.
3
The final suppressed courts are: Alba, Aqui Terme, Ariano Irpino, Avezzano, Bassano del Grappa,
Camerino, Casale Monferrato, Chiavari, Crema, Lanciano, Lucera, Melfi, Mistretta, Modica, Mondovì,
Montepulciano, Nicosia, Orvieto, Pinerolo, Rossano, Sala Consilina, Saluzzo, Sanremo, Sant'Angelo
dei Lombardi, Sulmona, Tolmezzo, Tortona, Vasto, Vigevano and Voghera
Table 1 – Proposed mergers of Italian courts by Judicial District
Judicial District
Ancona
Bari
Brescia
Caltanissetta
Catania
Catanzaro
Firenze
Genova
Genova
L’Aquila
L’Aquila
Messina
Milano
Napoli
Napoli
Perugia
Potenza
Salerno and Potenza
Torino
Torino
Torino
Torino
Torino
Trieste
Venezia
First instance courts merged
Camerino, Macerata
Foggia, Lucera
Crema, Cremona
Enna, Nicosia
Modica, Ragusa
Castrovillari, Rossano
Montepulciano, Siena
Chiavari, Genova
Sanremo, Imperia
Avezzano, L’Aquila, Sulmona
Chieti, Lanciano, Vasto
Mistretta, Patti
Pavia, Vigevano, Voghera
Ariano Irpino, Benevento
Sant'Angelo dei Lombardi, Avellino
Orvieto, Terni
Melfi, Potenza
Lagonegro, Sala Consilina
Alba, Asti
Acqui Terme, Alessandria, Tortona
Casale Monferrato, Vercelli
Cuneo, Modovì, Saluzzo
Pinerolo, Torino
Udine, Tolmezzo
Bassano del Grappa, Vicenza
New courts
Macerata
Foggia
Cremona
Enna
Ragusa
Castrovillari
Siena
Genova
Imperia
L’Aquila
Chieti
Patti
Pavia
Benevento
Avellino
Terni
Potenza
Lagonegro
Asti
Alessandria
Vercelli
Cuneo
Torino
Udine
Vicenza
Source: our computation on data provided by Ministero della Giustizia
3. Literature review
In the last decade the estimation of judicial system efficiency has been the subject of
many studies related to the evaluation of public sector performances. 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
(European Commission for the Efficiency of Justice) 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 2013 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 (11 judges each 100,000 citizens in Italy, 10.7 in France
and 10.2 in Spain). On the other hand, Italy appears at the end of the ranking for the
length of civil judicial procedures (493 days in Italy, 270 in France and 260 in Spain),
whereas Italy ranks first when we look at the numbers of lis pendens (more than 3.8
millions). The World Bank (2009) annually draws up the report Doing Business
ranking countries also according to efficiency of judicial system measured as the
length of civil judicial procedures. The 2009 report ranks Italy 156th out of 181
countries right below Angola, Gabon, Guinea and Sao Tome and above Gibuti,
Liberia, Sri Lanka and Trinidad, whereas the last European country (Spain) ranks
54th.
Considering the available resources, the weight of public spending for justice on
the Italian balance changed from 1.22% in 2006 to 1% in 2009, corresponding to 7.56
billions. Thus, the Italian commission for the public spending review, in its final
report, investigated the efficiency of the Italian justice system (Ministero
dell’Economia, 2007a and 2007b). The main sources of inefficiency were found to be
the presence of economies of scale, the delay in adopting new information and
communication technologies to conduct the proceedings faster and how legal fees are
determined especially for civil proceedings.
On the same line of research, Antonelli and Marchesi (1999) point out that the
dimension of the geographical areas of 72% of Italian judicial districts is sub-optimal
(less than 20 judges) leaving room for relevant economies of scale. The authors
suggest 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 specialisation and adopting better
organisational schemes to increase the productivity of judges4.
Surprisingly, few works have investigated the efficiency and the factors causing
dysfunction of judicial systems from all over the world, mainly due to the lack or
incompleteness of available data. 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 (Marselli and Vannini, 2004). Second, the activity of the courts is
4
This solution cannot be applied to small court where the same judge often deals with both civil and
criminal cases.
characterised by low substitution rate between inputs such as judges, clerks and
different kinds of courts (Marchesi, 2003). Finally, the public decision-maker can
only vary the area under the control of a specific judicial district tailoring the caseload
on the quality and quantity of the demand of justice originated by the territory
(Antonelli and Marchesi, 1999).
Given the above-mentioned difficulties, the technical efficiency of judiciary has
been investigated mostly applying the DEA non-parametric technique5 that
overcomes all the problematic assumptions required by a parametric production
function based on parametric techniques. Lewin et al. (1982) study the efficiency of
judicial districts of North Caroline, whereas Kittelsen and Føresund (1992) investigate
the Norwegian courts of first instance. Tulkens (1993) looks at the system of judges
of peace in Belgium and Pedraja-Chaparro and Salinas-Jiménez (1996) analyse the
efficiency levels of administrative courts in Spain. More recently, Marselli and
Vannini (2004) have investigated the efficiency Italian JDs looking at both civil and
criminal cases. 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
variable returns to scale. Schneider (2005) focuses on the performance of German
labour courts of appeal and shows that employing judges with Ph.D. increases the
productivity although the Federal Labour Court more often overrules their decisions.
The author also finds that courts employing judges with higher ex-ante promotion
probabilities are less productive and write decision that are less often confirmed.
Gorman and Ruggiero (2009) apply the DEA to the analysis of the impact of
environmental variables on the efficiency of prosecutor offices in the United States,
showing that prosecutor offices are more efficient in socio-economically
disadvantaged counties. Finally, Finocchiaro Castro and Guccio (2012) 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 behaviour from both claimants
and lawyers negatively affects technical efficiency in Italian judicial districts.
Other papers, not based on DEA technique, focus the attention on the role of
5
An exception is offered by Rosales-López (2008). The author investigates the performance of first
instance courts in Spain and determines whether achieving low reversal rates and a high level of output
are incompatible goals in the judiciary system applying parametric techniques.
different factors in explaining the efficiency of the Italian judicial system. For
instance, Coviello et al. (2009) show that the average efficiency depends on
organisational features of judges’ activity. In details, a higher efficiency may be
achieved if judges work on cases sequentially instead of starting a new case in parallel
with others. Di Vita (2010) reports 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 some works (Sobbrio et al., 2009; Carmigniani and Giacomelli, 2010;
Buonanno and Galizzi, 2010) raise the attention on the application of the supplierinduced-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.
4. Methods and data
4.1. Methodological framework
Effective government service provision, such as judicial service, benefits from the
support of rigorous measurement techniques. In this study, we focus on technical
efficiency of Italian first instance courts (our Decision Making Units - DMUs), whose
measurement requires the comparison between the actual performance of each DMU
and the optimal performance of DMUs located on the relevant frontier (the best
practice frontier). This approach is based on the efficiency measures proposed by
Koopmans (1951) and Debreu (1951) and empirically applied by Farrell (1957). Two
main analytical approaches are available to estimate efficiency frontiers: parametric
and non-parametric.6
In details, we apply the non-parametric frontier method developed by Charnes et
al. (1978) that generalized Farrell’s single input/output measure into a multipleinput/multiple-output technique. The aim of this approach is to measure productive
efficiency through the estimation of a frontier envelopment surface for all DMUs by
6
For a more extensive discussion, see Cooper et al. (2007) and Fried et al. (2008).
using linear programming techniques. By constructing envelopment unitary isoquants
corresponding to comparable DMUs across different situations, DEA identifies as
productive benchmarks those DMUs that exhibit the lowest technical coefficients, i.e.
the lowest amount of inputs to produce one unit of output. 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. Once
the reference frontiers have been defined, it is possible to assess the potential
efficiency improvements available to inefficient DMUs if they were producing
according to the best practice of their benchmark peers. From an equivalent
perspective, these estimates identify the necessary changes that each DMU needs to
undertake in order to reach the efficiency level of the most successful DMU.
Following the literature reviewed in the previous section we employ an outputoriented approach7
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 θi is calculated for each DMU by solving the following program,
for i=1,…., n, in the case of constant returns to scale (CRS):
where xi and yi are, respectively, the input and output of i-th DMU; X is the matrix of
inputs and Y is the matrix of outputs of the sample; λ is a n×1 vector of weights
which allows to obtain a convex combination between inputs and outputs. Solving
(1), DMUs with an efficiency score equal to one are located on the frontier and
therefore their outputs cannot be further expanded without a corresponding increase in
inputs8. Banker et al. (1984) modified the model (1) to account for variable returns to
7
From an output-oriented perspective efficiency is defined as the ratio of a DMU’s observed output to
the maximum output, which could be achieved given its input levels (Farrell, 1957).
8
We assume an output-oriented model to maximize the outputs that could be produced given the
inputs. Moreover, we assume a Shephard (1970) output-oriented distance function and, consequently,
scale (VRS) by adding the convexity constraint: eλ=1, where e is a row vector with all
elements unity, which allows to distinguish between Technical Efficiency (TE) and
Scale Efficiency (SE). This change is crucial for our merge analysis given that VRS
means that there are likely to be disadvantages of being either too small or too large.
Based on the estimated DEA frontier, it is possible to analyse hypothetical cases of
horizontal integration between first instance courts. In fact, Bogetoft and Wang
(2005) propose a simple direct pooling of the inputs and outputs set used by
individual first instance courts to be merged. For the analysis of hypothetical cases of
horizontal integration, Bogetoft and Wang (2005) use the technology set (1) estimated
before any merger as the reference set. Then, the authors decompose efficiency gains
from DMU mergers into technical efficiency gains, synergies from joint operation,
and size gains. Doing so we can measure both the overall potential gains from
mergers and the separate role of the three effects on Italian Government’s proposal
(Decree n. 155/2012) of new jurisdiction design of first instance court.
As mentioned above, the deterministic nature of non-parametric methods has
caused the traditional literature to describe them as non-statistical methods. Moreover,
DEA estimators have received some criticisms because they rely on extreme points
and may be very sensitive to data selection, aggregation, model specification, and data
errors. However, a series of recent developments made it possible to determine
statistical properties of non-parametric frontier estimators in a statistical model (Simar
and Wilson, 2008). Nowadays, statistical inference based on non-parametric frontier
approaches in measuring the economic efficiency is available basically by using
asymptotic sampling distributions or by using bootstrap (Simar and Wilson, 2008). 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 - DGP
(Simar and Wilson, 2008) 9.
efficiency scores assume values between zero and one that is the reciprocal of Farrell (1957) distance
function.
9
Some major issues remain regarding the use of asymptotic results and bootstrap: first, the high
sensitivity of non-parametric approaches to extreme value and outliers and, second, the way to allow
stochastic noises in non-parametric frontiers. Alternative approaches provide robust measures of
efficiency at extreme data points based on partial frontiers and the resulting partial efficiency scores. A
The model that is applied in determining the DEA production frontier in this paper
is the one outlined by Simar and Wilson (1998, 2000) that 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.
4.2. Data
In Italy, the administration of justice is articulate into judicial districts (Distretto di
Corte di Appello) and judicial counties (Circondario di Tribunale Ordinario). The 29
Judicial districts are usually located in the main town of the region, although the most
populated regions have two, whereas the judicial counties are 16510 and are
distributed in the territory. In the judicial counties, courts of first instance are
organised according to their field of specialization and may sub-divided 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 penal services. This
poses a severe problem to the measurement of the efficiency in judicial sector. Only
in some cases, it is possible to obtain estimates able to disaggregate the data on the
resources adopted to provide civil and penal services. For instance Carmignani and
Giacomelli (2009) estimate the number of judges and administrative staff at district
level employing data achieved by the self-governing body of ordinary judges
(Consiglio Superiore della Magistratura). However, such computation poses sever
difficulties and large likelihood of systematic bias.
Our analysis refers to the activity of 165 first instance courts (Circondario di
Tribunale Ordinario) in Italy for the year 2011The dataset employed in our empirical
analysis has been obtained from Italian Department of Justice (Ministero della
Giustizia - Direzione Generale di Statistica).
Our model specifications have been limited by data availability, e.g., the dataset
does not include cost and input factor prices. Thus we examine only the first instance
detailed survey of these approaches can be found in Simar and Wilson (2008). See also Wilson (2012)
for a discussion on these approaches and for a proposed extension of order-m estimator obtained by
Cazals et al. (2002).
10
However the court of Giugliano, established in 1999, has not been in service yet.
courts’ technical efficiency.
Table 2 reports all the variables employed in efficiency analysis as well as their
descriptive statistics. Specifically, our analysis includes the number of judges
(JUDGES) administrative staff (ADM_STAFF), size of the caseload - both civil
(C_LOAD_CIVIL) and criminal (C_LOAD_CRIMINAL) cases as inputs and . In our
computation the caseload is the sum of the number of filed cases during the year and
the number of pending cases at the beginning of that year.
Regarding the output we employ the number of civil and criminal cases finished
during a specific period (RES_CIVIL_CASES; RES_CRIMINAL_CASES) without
distinguishing between cases resolved through the full legal process and other
resolved cases.
Table 2 – Summary statistics of the variables used in the DEA models for the Italian courts of first
instance – year 2011
Variables
Obs
Mean
Std. Dev.
Min
Max
JUDGES
165
30.68
48.81
6.00
379.00
ADM_STAFF
165
99.36
136.96
18.00
1198.00
C_LOAD_CIVIL
165
37138.56
53577.68
2853.00
409190.00
C_LOAD_CRIMINAL
165
15373.67
18062.76
793.00
136979.00
RES_CIVIL_CASES
165
16387.03
24706.64
1235.00
199304.00
RES_CRIMINAL_CASES
165
7659.07
9452.46
427.00
65268.00
Source: our computation on data provided by Ministero della Giustizia - Direzione Generale di Statistica
From the analysis of the data it appears that the first instance courts 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 and, in the
number of resolved cases. Table 3 shows the inputs and the outputs employed in the
four different models we have estimated.
Table 3 – The estimated models
Variable
Mod_1
Mod_2
JUDGES
♦
♦
ADM_STAFF
♦
♦
inputs
C_LOAD_CIVIL
♦
C_LOAD_CRIMINAL
♦
outputs
RES_CIVIL_CASES
♦
♦
RES_CRIMINAL_CASES
♦
♦
Source: our elaboration
5. Results and Discussion
In this Section, we first calculate average efficiency estimates for the unmerged courts
and analyse the inefficiency as well as the impact of model specification and
bootstrap bias correction. Second, we present merger gains under constant and
variable returns to scale.
5.1 Benchmark analysis
Here we report the technical efficiency scores achieved by the different production
function specifications as shown in Table 3. We choose the output orientation so that
output efficiency is measured for a given level of input and use a Shephard (1970)
output-oriented distance function. Consequently, efficiency scores assume values
between zero and one, which is the reciprocal of Farrell (1957) distance function. The
output orientation is justified by very large back log of cases of the Italian courts that
calls for higher case resolution rates, given the existing staff levels.
We now evaluate the best possible input-output specifications. The first inputoutput specification with judges (JUDGES) and administrative staff (ADM_STAFF)
as a inputs and the number of civil and criminal cases finished during a specific
period (RES_CIVIL_CASES; RES_CRIMINAL_CASES) is the most common
because the variables incorporate the main function of the courts. In comparison to
the second input-output specification including both civil (C_LOAD_CIVIL) and
criminal (C_LOAD_CRIMINAL) cases it takes into accounts the different condition
of the demand of justice in which courts operate. Thus, the focus of this model on the
demand side is justified in an economic perspective.
More in general the literature investigating the efficiency of justice services,
reviewed in section 2, 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 non-discretionary
variables in order to take into account the differences among justice institutions in the
operating environment.11
In what follows, we employ the one-stage approach considering both discretionary
and non-discretionary inputs, since it allows for the potential identification of the
effects of operating environment in the production system12.
Moreover, for comparison purpose, we also use the subsample of first instance
courts excluding the courts located in some metropolitan areas (Rome, Milan,
Napoli)13.
Table 4 reports the benchmark analysis of the whole sample of 165 first instance
Italian courts in order to indicate the relative performance of each of the different
courts.
Table 4 - The descriptive statistics of the models outcome
Sample
Obs.
CRS
Average
St. dev.
VRS
Min
Max
Average
St. dev.
Min
Max
Model 1
All sample
165
0.6332
0.1676
0.2324
1.0000
0.7124
0.1807
0.2332
1.0000
North
64
0.6978
0.1490
0.4294
1.0000
0.7725
0.1436
0.4786
1.0000
Centre
38
0.6713
0.1380
0.3739
1.0000
0.7782
0.1327
0.5348
1.0000
South
63
0.5445
0.1648
0.2324
1.0000
0.6115
0.1953
0.2332
1.0000
Model 2
11
More in general, the one-stage-production model might directly include uncontrollable variables
in its linear functions, along with traditional inputs and outputs use of the capability of DEA to
accommodate multiple variables (Banker and Morey, 1986). From this approach, the DMU can decide
on some controllable factors internal to production activities, while the impact of the uncontrollable
factors is out of the control of the DMU. Conversely, studies that have constructed models using
controllable factors only, implicitly assume that all the inefficiencies of DMUs are caused by bad
management and could underestimate the evaluation of those DMUs. To take into account the impact
of the uncontrollable factors in one-stage framework, Lewin et al., (1982) and Schneider (2005) have
used the courts' caseload as a non-discretionary input. A different point of view is provided by the socalled two-stage approach, which starts with a standard DEA model based on traditional inputs and
outputs in the first stage, and regresses11 the efficiency scores of the first stage against a set of selected
uncontrollable variables in the second stage (Finocchiaro Castro and Guccio, 2012).
12
However, the efficiency estimates basically do not change also in the case in which we employ
Banker and Morey, (1986)s’ algorithm since we use an output oriented approach.
13
The district of Naples represents a special case in which all the local courthouses have been unified
with the district of Giugliano (not in service yet) that has been renamed as Court of Northern Naples.
We included into the analysis Turin and Palermo because of the specific characteristics of the two
districts.
All sample
165
0.7923
0.1427
0.4721
1.0000
0.8285
0.1455
0.5021
1.0000
North
64
0.8805
0.0934
0.5832
1.0000
0.9182
0.0819
0.6907
1.0000
Centre
38
0.8115
0.0940
0.6407
1.0000
0.8557
0.0988
0.6448
1.0000
South
63
0.6911
0.1452
0.4721
1.0000
0.7210
0.1511
0.5021
1.0000
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
The results reported in Table 4 support the common perception of the inefficiency
as a major problem of the Italian first instance courts and that it varies significantly
across the country. In details, the average aggregate technical efficiency score of
63.32%, in the Model 1 with constant returns to scale (CCR), indicates that the DMUs
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 indicates, however, that there are
considerable differences in the aggregate technical efficiency of Italian First instance
courts. Table 4 also shows the efficiency scores of sub-samples obtained according to
years and geographical macro areas in the country. Strong differences among DMUs
exist in relation with the geographical macro areas (North, Centre and South). In all
models and with different scale assumptions, the result confirms the geographical gap
between the Northern and Southern areas. Table 4 includes also estimates for the case
of variable returns to scale (VRS), which show results generally overlapping with
those under constant returns to scale (CRS).
In the lower part of Table 4, we show estimates for the models with uncontrollable
inputs,
where
caseload
-
both
civil
(C_LOAD_CIVIL)
and
criminal
(C_LOAD_CRIMINAL) - is considered an input external to managerial control that
affects technical efficiency though. As expected, average levels of efficiency of
DMUs in our sample increase when we consider such an input, reaching the
efficiency scores of 79.23% in the Model 2 with constant returns to scale (CCR).
However, observation of the score distribution in subsamples reveals regularities with
respect to the previous models.
Given the importance of scale efficiency for our analysis, Table 5 shows the
distribution of returns to scale of the sample. It can be noted that in Model 1 more
than 75% of first instance courts have increasing returns to scale, whereas only 13%
of them have decreasing returns to scale. The results change significantly when we
consider Model 2, where considering the caseload the 43% of first instance courts
show diseconomies of scale, being the productive scale too high. Tables 6 and 7
report no changes when we exclude, from the analysis the first instance courts
belonging to the metropolitan areas of Milano, Rome and Naples respectively.
Table 5 – Distribution of returns to scale
Mod_1
Returns to scale
Mod_2
Obs.
%
Obs.
%
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%
IRS
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
Table 6 - The descriptive statistics of the models outcome for subsample excluding courts in
metropolitan area
Sample
Obs.
CRS
Average
St. dev.
VRS
Min
Max
Average
St. dev.
Min
Max
Model 1
All sample
162
0.6326
0.1690
0.2324
1.0000
0.7095
0.1792
0.2332
1.0000
North
63
0.6976
0.1502
0.4294
1.0000
0.7726
0.1442
0.4786
1.0000
Centre
37
0.6730
0.1395
0.3739
1.0000
0.7736
0.1301
0.5348
1.0000
South
62
0.5426
0.1654
0.2324
1.0000
0.6073
0.1909
0.2332
1.0000
All sample
162
0.7916
0.1436
0.4721
1.0000
0.8257
0.1448
0.5021
1.0000
North
63
0.8799
0.0940
0.5832
1.0000
0.9169
0.0819
0.6907
1.0000
Centre
37
0.8106
0.0952
0.6407
1.0000
0.8518
0.0971
0.6448
1.0000
South
62
0.6904
0.1463
0.4721
1.0000
0.7173
0.1482
0.5021
1.0000
Model 2
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
Table 7 – Distribution of returns to scale for subsample excluding courts in metropolitan area
Returns to scale
Mod_1
Mod_2
Obs.
%
Obs.
%
124
76.54
61
37.65
EFF_SCALE
17
10.49
30
18.52
DRS
21
12.96
71
43.83
Total
165
100.00
165
100.00%
IRS
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 in explaining efficiency variation. Moreover, not taking into
account the caseload in the merging procedure may have relevant effects on the
efficiency gains to be achieved with the reform. Finally, in order to test the sensitivity
of the efficiency estimates relative to the variables used we employ the biased
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 8
reports the average values of technical efficiency at DMU level, estimated with
different models under CRS and VRS assumption. The reported results show that,
from the perspective of sensitivity analysis, only the efficiency estimate in Model 1
under CRS assumption are quite robust with respect to sampling variation since there
are only small differences due to bootstrapping efficiency estimates. Under VRS
assumption the bootstrapped bias correction has relatively strong effects on efficiency
estimates.
Table 8 – Uncorrected and bias corrected estimate
Models
CRS
Bias corrected CRS estimate
Mod 1
VRS
Bias corrected VRS estimate
CRS
Mod 2
Bias corrected CRS estimate
VRS
Bias corrected VRS estimate
Obs
Mean
Std. Dev.
Min
Max
165
0.6332
0.1676
0.2324
1.0000
165
0.5947
0.1549
0.2192
0.9234
165
0.7124
0.1807
0.2332
1.0000
165
0.6335
0.1475
0.2133
0.9440
165
0.7923
0.1427
0.4721
1.0000
165
0.7342
0.1258
0.4419
0.9636
165
0.8285
0.1455
0.5021
1.0000
165
0.7519
0.1210
0.4720
0.9366
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
In order to test more thoroughly the efficiency scores before and after the biased
correction between the four models we used kernel density estimates of the efficiency
scores that rely on the reflection method (Simar and Wilson, 2008). In such a way we
are able to avoid the problems of bias and inconsistency at the boundary of support. In
Figures 2 and 3, we report the univariate kernel smoothing distribution (Wand and
Jones, 1995) and the reflection method to determine densities for the performance
estimates, respectively under CRS and VRS assumption. The criterion for bandwidth
selection follows the plug-in method proposed by Sheather and Jones (1991). The
kernel density functions, reported in Figures 2 and 3, allow us to confirm the abovementioned results.
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 (1996) test for both Models and the
results show that we can reject the null hypothesis of CRS at any conventional level
of significance14. This result is not surprising since the courts in our sample vary
considerably in size, and size 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 programme. In fact, under VRS reference technology,
these conditions may not hold and it is possible that there will fail to be a feasible
solution to the linear programme. This suggests to use the CRS technology as
reference point to evaluate the potential efficiency gain of mergers.
3
2
Density
1.5
0
0
.5
1
1
Density
2
2.5
3
Figure 2 – Kernel densities estimates of the CRS and CRS bias corrected scores distribution for the
estimated models
.2
.4
.6
.8
Model 1 - CRS density estimate
1
.4
.6
.8
Model 2 - density estimates (CRS)
1
Eff scores - CRS density estimate
Eff scores - density estimate CRS
Bias corrected eff scores - CRS density estimate
Bias corr Eff scores - density estimates CRS
1.2
Notes: Plots show respectively the Model 1 and Model 2 kernel estimates under CRS assumption. CRS bias
corrected scores estimated with the procedure proposed by Simar and Wilson, (1998). Univariate kernel
smoothing distribution (Wand and Jones, 1995), estimated through reflection method. The criterion for bandwidth
selection followed the plug-in method proposed by Sheater and Jones (1991).
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
14
Results are available upon request.
3
2
Density
1.5
0
0
.5
1
1
Density
2
2.5
4
Figure 3 – Kernel densities estimates of the VRS and VRS bias corrected scores distribution for the
estimated models
0
.2
.4
.6
.8
Model 1 - VRS density estimate
1
.2
.4
.6
.8
Model 2 - VRS density estimate
Eff scores - VRS density estimate
Eff. Scores - VRS density estimate
Bias corrected eff scores - VRS density estimate
Bias-Corrected eff scores - VRS density estimate
1
Notes: Plots show respectively the Model 1 and Model 2 kernel estimates under VRS assumption. VRS bias
corrected scores estimated with the procedure proposed by Simar and Wilson, (1998). Univariate kernel
smoothing distribution (Wand and Jones, 1995), estimated through reflection method. The criterion for bandwidth
selection followed the plug-in method proposed by Sheater and Jones (1991).
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
5.2 The efficiency gains of the courts mergers
In this section, we present our estimates of the potential gains from mergers and
our decompositions of the efficiency gains. As above-mentioned, we apply the
methodology proposed by Bogetoft and Wang (2005) to estimate the overall potential
gain from our sample and to decompose the overall potential gain into constituent
components. In particular the authors suggest a decomposition of the overall potential
gain merger efficiency into three components: technical efficiency gains; synergies
from joint operation; and size gains.
The overall potential efficiency gains (EJ) is the simple efficiency evaluation of a
hypothetical DMU using the sum of inputs of the pre-merger DMUs to produce the
sum of the pre-merger outputs. A merger is assumed to be beneficial for EJ < 1. (e.g. a
value of EJ = 0.9 indicates a potential for output increasing of 10% through merging
the DMUs). For EJ > 1, a merger is assumed to have a negative impact on efficiency.
However the potential overall gains (EJ) 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 the individual
DMUs into the efficient pre-merger DEA frontier using the efficiency scores of the
individual DMUs. The performance measure E*J represents the corrected overall
potential gains from merging. As before, a merger is evaluated as being beneficial for
E*J < 1. For the previous consideration it is more meaningful to represent the
potential gains from merger by the corrected E*J.
We turn now 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 report each of these with
respect to the CRS reference technology since we observed that they represent a
reference point of the organizational structure and can therefore be regarded as a
benchmark in evaluation of efficiency gain of proposed mergers.
We start the analysis looking at the efficiency of courts involved in the merging
procedure. As described in Section 2, the courts under analysis have been 55 of which
30 have been suppressed and merged into 25 new first instance courts. Table 9 reports
the efficiency levels of the models presented in paragraph 5.1 distinguishing merged
from not merged courts. 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. In fact, Model 2 does not report any significant difference between merged
and not merged courts.
Table 9 - The descriptive statistics of the models outcome
Sample
Obs.
CRS
VRS
Average
St. dev.
Min
Max
Average
St. dev.
Min
Max
Model 1
All sample
165
0.6332
0.1676
0.2324
1.0000
0.7124
0.1807
0.2332
1.0000
Not merged
110
0.6562
0.1686
0.2324
1.0000
0.7078
0.1812
0.2332
1.0000
Merged
55
0.5872
0.1571
0.2773
1.0000
0.7215
0.1809
0.3498
1.0000
Model 2
All sample
165
0.7923
0.1427
0.4721
1.0000
0.8285
0.1455
0.5021
1.0000
Not merged
110
0.7964
0.1392
0.4973
1.0000
0.8285
0.1411
0.5021
1.0000
Merged
55
0.7840
0.1505
0.4721
1.0000
0.8286
0.1552
0.5035
1.0000
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
In this preliminary assessment we calculate the overall potential merger effects for
CRS absent non-discretionary input (Model 1). In Table 10 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 10 - Efficiency scores and decomposition for proposed court mergers (CRS), by Judicial District
New courts
First instance courts merged
Overall
potential
Eff. Gain
Individual
technical
efficiency
gain
Learning
effects
Scope
effect
Size effect
Macerata
Camerino, Macerata
0.7458
1.0000
0.7458
1.0000
1.0000
Foggia
Foggia, Lucera
0.7708
0.9975
0.7727
0.9975
1.0000
Cremona
Crema, Cremona
0.6783
0.9577
0.7083
0.9577
1.0000
Enna
Enna, Nicosia
0.3836
0.9965
0.3850
0.9965
1.0000
Ragusa
Modica, Ragusa
0.5204
1.0000
0.5204
1.0000
1.0000
Castrovillari
Castrovillari, Rossano
0.4075
0.9817
0.4151
0.9817
1.0000
Siena
Montepulciano, Siena
0.5304
1.0000
0.5304
1.0000
1.0000
Genova
Chiavari, Genova
0.4517
0.9891
0.4567
0.9891
1.0000
Imperia
Sanremo, Imperia
0.6134
0.9619
0.6377
0.9619
1.0000
L’Aquila
Avezzano, L’Aquila, Sulmona
0.5704
1.0000
0.5704
1.0000
1.0000
Chieti
Chieti, Lanciano, Vasto
0.7070
1.0000
0.7070
1.0000
1.0000
Patti
Mistretta, Patti
0.5677
0.8977
0.6324
0.8977
1.0000
Pavia
Pavia, Vigevano, Voghera
0.5774
0.9663
0.5976
0.9663
1.0000
Benevento
Ariano Irpino, Benevento
0.8011
0.8939
0.8962
0.8939
1.0000
Avellino
0.6495
0.9960
0.6521
0.9960
1.0000
Terni
Sant'Angelo dei Lombardi,
Avellino
Orvieto, Terni
0.5580
1.0000
0.5580
1.0000
1.0000
Potenza
Melfi, Potenza
0.4380
1.0000
0.4380
1.0000
1.0000
Lagonegro
Lagonegro, Sala Consilina
0.4477
0.9586
0.4670
0.9586
1.0000
Asti
Alba, Asti
0.6551
0.9666
0.6777
0.9666
1.0000
Alessandria
0.5662
0.9239
0.6128
0.9239
1.0000
Cuneo
Acqui Terme, Alessandria,
Tortona
Cuneo, Modovì, Saluzzo
0.4759
0.9483
0.5018
0.9483
1.0000
Torino
Pinerolo, Torino
0.6317
1.0000
0.6317
1.0000
1.0000
Vercelli
Casale Monferrato, Vercelli
0.7067
1.0000
0.7067
1.0000
1.0000
Udine
Udine, Tolmezzo
0.6425
0.9987
0.6433
0.9987
1.0000
Vicenza
Bassano del Grappa, Vicenza
0.7496
1.0000
0.7496
1.0000
1.0000
Mean efficiency
0.5939
0.9774
0.6086
0.9774
1.0000
0.0226
0.0000
Average efficiency gain
0.4061
0.0226
0.3914
Source: our elaboration on data provided by Ministero della Giustizia - Direzione Generale di Statistica
The first column reports the potential overall gains (EJ), 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 EJ.
However, since those results still include the individual inefficiencies within courts
before merging, we must consider the projections of the individual courts and
calculate the corrected potential gains E*J. The merger gains drop down dramatically
after correcting for individual inefficiencies. On average, we find low potential
merger gains of 2.26%. In Table 10 after calculating overall gains from merging, we
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% of the overall merger gains EJ could be realized by
improving efficiency within the individual courts. Thus, significant potentials for
efficiency increase arising from a better management in the individual production
plans of the different courts. Such efficiency improvement potentials are usually not
attributable to a merger since efficiency could be improved by, for example, sharing
best practices between individual courts. On average, less than 3% of the efficiency
could be gained by reallocating the inputs in the integrated courts. The scope (or
synergy) effect thus inhibits the weak potential for efficiency increases. We can
further only find weak scale effects for all merger cases. Considering the median,
there are already no efficiency gains from an increase in courts size.
However, some limitations of our results have to be noted. First, the reform under
consideration suggests a merging procedure that should produce a reallocation of
input in an efficient way and not an aggregation of first instance courts 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, when checking the efficiency gains from merging procedure we do
not consider all the remaining estimated models described in paragraph 5.1 and we
also do not study the effects of caseload on efficiency gains. Finally, we do not
account for potential bias of efficiency scores and we do not perform estimation with
bootstrapping procedure when considering the effects of merging.
6. Conclusions
In this paper, by using DEA technique and focusing on the performance of 165
Italian first instance courts in the year 2011, we found evidence that there is
considerable scope for efficiency improves in judicial services. The empirical analysis
reports higher level of inefficiency across the country. To the best of our knowledge,
this is the first paper to use non-parametric techniques, proposed by Bogetoft and
Wang (2005), to analyse Italian first instance court mergers.
Our research provides a preliminary investigation of the influence of some
obstacles, geographical location and the civil caseload, on measured efficiency. In
details, while the level of inefficiency appears to be higher in the South of Italy rather
than in the Centre 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. Hence, given the relevant impact on efficiency of
the context in which the DMUs operate, policy-makers should focus the attention of
the re-shaping of the territorial distribution of first instance courts. The importance of
such aspect is also confirmed by a new reform of first instance courts that, among
other interventions, calls for the merging of most of the existing courts in order to
gain more efficiency. Our preliminary results show considerable efficiency gains from
the proposed mergers.
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