JUDICIAL REFORM AND ITS EFFECT ON THE ECONOMY. THE CASE OF BULGARIA

Neli Borisova Ivanova* Student ID: 141400

Supervisor: Prof. Stefan Voigt

Date: 16th August 2014

Well-functioning institutions have long been discussed as crucial for economic development. This paper focuses on judicial systems as an important factor for economic growth. It contributes to the related literature with an empirical study of the judicial reform in Bulgaria. Unlike, the majority of the existing studies it not only estimates the efficiency of the judicial system in Bulgaria but also explores its effect on economic growth.

The analysis uses data for 112 District Courts in Bulgaria for the period 2005-2012. The selected time-frame covers both periods before and after Bulgaria has become a Member State of the European Union. The approach adopted to obtain efficiency scores for the Bulgarian judiciary is Data Envelopment Analysis. In addition, the linear and non-linear effects of the efficiency of the judicial system on economic growth are estimated. In order to address the endogeneity problem, a Two-Stage Least Squares methodology is used. The main findings of this paper are: (1) the judicial reform in Bulgaria after entering the EU has been beneficial to the efficiency of the judicial system and (2) there is a positive correlation between the efficiency of the judicial system and GDP after Bulgaria’s accession to the EU. These conclusions have both implications for the policy of the on-going judicial reform in Bulgaria as well as macroeconomic implications.

*Acknowledgement: I would like to express my heartfelt gratitude to my supervisor, Prof. Stefan Voigt for his valuable guidance and support. Special thanks to Mrs Sonia Naydenova, Representative of the Supreme Judicial Council.

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Authorship Declaration

I hereby declare and confirm that this thesis is entirely the result of my own work except where otherwise indicated. I acknowledge the supervision and guidance I have received from Prof. Stefan Voigt. This thesis is not used as part of any other examination and has not yet been published.

Neli Ivanova

16.08.2014

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CONTENTS

1. INTRODUCTION………………………………………………………………4

2. THE BULGARIAN JUDICIAL SYSTEM AND THE REFORM………...... …6

2.1 Structure of the Judicial System……………………………………………….6

2.2 Judicial Reform before Entering the EU…………………………...………….8

2.3 The Reform after 1st January 2007……………………………….…………. 11

2.4 Pending Issues……………………………………………….……………... 14

3. RELATED LITERATURE……………………………………………………. 16

3.1 Why Judicial Reform……………………………………………………….. 16

3.2 Judicial Efficiency…………………………………………………….…….. 17

3.3 Measuring Judicial Efficiency………………………………………….……. 20

4. EMPIRICAL ANALYSIS PART I ………………………………………...... 21

4.1 Methodology: Data Envelopment Analysis………………….……………… 21

4.1.1 Specifications of The Model: CRS vs VRS……………….…………. 26

4.1.2 Specifications of The Model: Output- vs Input-Oriented…….……... 26

4.2 Data……………………………………………………………………...… 26

4.3 Application and Results…………………………………………………….. 28

5. EMPIRICAL ANALYSIS PART II…………………………………...………... 33

5.1 Methodology…………………………………………………………...…... 33

5.2 Data……………………………………………..…………………………. 34

5.3 Model Specification and Results……………………...…………………….. 35

6. FURTHER DISCUSSION………………………………….…………………. 39

7. CONCLUSION……………………………………………….……………….. 41

8. APPENDICIES………………………………………………….…………….. 43

9. BIBLIOGRAPHY………………………………………………………………78

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1. INTRODUCTION

Literature on judicial reforms is of extreme importance due to the key impact it has on economic growth (Schumpeter, 1934). This paper presents an empirical study of the efficiency of the judicial system in Bulgaria and its correlation with economic activity. It provides new evidence of this relationship using data concerning the reforms in the judiciary in Bulgaria both before and after its accession to the EU.

Since the beginning of the 1990’s the link between judicial reform and economic growth has been explored in the literature (El Bialy and Garcia-Rubio, 2011). According to Posner

(1998), for a country to enjoy economic prosperity it has to have a legal system that protects property and contract rights. Furthermore, a reform towards such a legal infrastructure can increase economic growth and generate resources for other reforms with bigger impact (Posner, 1998). Messick (1999) holds that a well-functioning judicial system supports economic development by checking government abuse, by protecting the rule of law and by facilitating the exchange of goods and services between private individuals.

Other studies explore the correlation between the rule of law and economic growth, democratic governance and investment. Weder(1995) claims that economic development can only be achieved with a strong legal system.

This paper focuses on the case of Bulgaria. This is an interesting case as Bulgaria is a post- socialist country and one of the newest members of the EU. Having gone through many changes in its economic and political development has made judicial performance crucial for the reputation of the country. Bulgaria is still in a position where a sound judicial system can help it attract investment and improve many economic indicators. And reform is a proof of work in that direction. However, questions such as how successful the reform has been and whether it has an actual impact on the economy, remain.

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Even though work towards a more efficient judicial system has been done for years in

Bulgaria, upon its accession to the EU on 1 January 2007, it still had various weaknesses in its judicial system that needed attention. Accordingly, the European Commission undertook a Cooperation and Verification Mechanism in order to be able to verify the progress made against six benchmarks set for this purpose. However, the results of the reform in the Bulgarian Judicial system are neither fully obvious nor easily quantified.

It is considered that the judicial reform in Bulgaria has had the greatest impact on District

Courts. This is due to the fact that District Courts (First Instance Courts) in Bulgaria are considered to be the most active and dynamic part of the judicial system. Apart from descriptive statistics and progress reports, to the best of our knowledge, there are no empirical works on the actual impact of the reform on the efficiency of the District courts in Bulgaria. This study focuses on court efficiency as one of many aspects of court performance. Efficiency refers to when maximum output is produced with a certain given combination of inputs or when a given output is produced with minimum input.

The paper represents a within-country study of Bulgaria. This has the benefit that all features unique to a particular country do not need to be controlled for, leaving less scope for problems due to “omitted variables” bias (Voigt, 2014). It not only gives evidence of the efficiency of the Bulgarian District Courts but also reflects on the extent to which entering the EU has been a key factor for the improvement of efficiency and through that for the Bulgarian economy as a whole. Furthermore, it adds to the scarce literature in the field on post-socialist EU-member states (Murrell, 2001, Buscaglia and Dakolias, 2000,

Dimitrova-Grajzl et. al., 2010).

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This study answers two questions: (1) whether the efficiency of the judicial system in

Bulgaria has improved after entering the EU; and (2) whether the efficiency of the judicial system has had an effect on the economic growth.

As a first attempt to assess the efficiency of District Courts in Bulgaria, a significant part of the empirics rely on self-collected data not published earlier in this format. The study covers 112 District courts for a period of 8 years, covering a period before and after

Bulgaria’s accession to the EU. It uses a Data Envelopment Analysis to measure court efficiency. It then compares the technical efficiency before and after Bulgaria became a member of the European Union. In the second part of the empirical analysis a TSLS is performed to establish the effect of efficiency of the judicial system in Bulgaria on GDP.

The structure of this paper is as follows: Section 2 describes the Bulgarian judicial system, the judicial reform in Bulgaria and some pending issues of concern. Section 3 presents a literature review. Sections 4 and 5 focus on the empirical analysis. Section 6 provides a further discussion and Section 7 concludes the paper.

2. THE BULGARIAN JUDICIAL SYSTEM AND THE REFORM

2.1 Structure of the Judicial System

The structure of the Bulgarian Judiciary includes the Supreme Court of Cassation (SCC), the Supreme Administrative Court (SAC), Courts of Appeal, Provincial Courts and District

Courts (World Bank Report, 2008). (See figure 1) Independent from the judiciary body is the Constitutional Court in Bulgaria, which derives its powers directly from the constitution (Judicial Systems in Member States, 2014).

The Supreme Court of Cassation is the supreme instance for criminal and civil cases and its jurisdiction covers the entire country. The Supreme Administrative Court has the supreme - 6 - jurisdiction to overview the precise and equal application of laws by administrative courts

(The Constitution of Bulgaria Atr. 125(1)). The District Courts deal with all first instance cases, apart from cases with a specific category that is considered as relevant to and assigned to Provincial Courts.

The Bulgarian judiciary is governed by the Supreme Judicial Council (SJC) which is chaired by the Ministry of Justice. It represents the judiciary and ensures its independence. Its main functions are to negotiate and accept a draft budget of the judicial system; to monitor its execution; to appoint, promote or to release from duty the magistrates as well as organize their training; to impose disciplinary punishments according to the Judiciary System Act; to determine the number of judges, prosecutors and investigators in the individual courts, prosecutor offices and investigative offices; and other activities specified in the Judiciary

System Act (Creation of the SJC n.d.).

Figure 1 based on www.e-justice.europa.eu

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Upon its accession to the EU, the Judiciary in Bulgaria employed around 13, 650 people, out of which 8,529 were judges and corresponding staff, 3634 prosecution staff, 1362 investigators, 93 SJC staff and 50 National Institute of Justice staff (Ministry of Justice,

2007).

The Bulgarian judiciary as all judicial systems strives for efficiency, independence, better accountability, accessibility and effectiveness. It has been subject to a reform for many years. Judicial reforms are seen as transition towards a more market-friendly economy.

Some of the most common targets of judicial reforms are: improving the judicial branch, facilitating the access to dispute resolution mechanisms, shortening the period for processing cases and professionalizing of the agents (Messick, 2002). Reforms have to have both a cultural aspect and a systematic aspect in the delivery of justice and thus countries are better off implementing a reform as a process of multiple stages (Dakolias and Said,

1999). This is the case for Bulgaria, with the first wave of reforms being at 1989.

2.2 Judicial Reform before Entering the EU

The judicial reform has been one of the main challenges that Bulgaria is facing since its democratization in 1989. Many steps towards modernizing and improving the judicial system in Bulgaria have been made even before its accession to the European Union. This section summarizes some of the main achievements of the reform before 2007 with the focus on those that are closely related to the accession of Bulgaria in the European Union.

These include Constitutional, legislative and procedural changes. In addition to structural and functioning changes, there has also been an increase in the budget allocation which has led to an increase in salaries, training costs, costs related to premises and technology

(World Bank Report, 2008).

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A plan for amendments to the Constitution was made as part of the reform before entering the EU. The target of these changes was to improve the efficiency, independence and accountability of the judiciary.

Furthermore, work was done in favor of improving the quality of the staff, the quality of the selection and recruitment procedures and the ethical qualities of the judges. In 2004, for the first time centralized competitions were introduced as a recruitment procedure.

However, until 2006, direct appointments for specific vacancies were allowed. In 2006 an amendment in the Judicial Systems Act restricted the entry-level recruitment to general competitions. The European Commission recognizes the success of these competitions as part of the reform (COM, 2008). Training of the magistrates started being organized by

The National Institute of Justice ever since it was established in 2003 as an independent body from the judicial system. What is more, the immunity of the magistrates was lowered.

Constitutional amendments both in 2003 and in 2007 were specifically targeted towards restricting their immunity. Compulsory evaluation of the work of the magistrates was introduced before granting them the status of tenure. (World Bank Report, 2008)

In 2005, the Supreme Judicial Council issued rules, some of which focused on reporting of the workload of the magistrates. Some indices were established to measure the work of the magistrates in terms of quality and quantity. A few of the entries needed for these indices include time spent per case, number of pending cases and complexity of the cases. As a rule of thumb, in 2005 a post for a new magistrate in a court was opened whenever the average workload of a specific court was higher than the average on a country level for the same type of court. (Ivanova et al., 2008)

A new Penal Procedure Code was created in 2005 which targets the overlaps of functions between investigating magistrates, prosecutors and police investigators. A working group was established to monitor the implementation of the new code. The Code was a step - 9 - towards fighting organized crime through new investigation techniques. It also induced new penal procedures to improve the efficiency of the judicial system such as shortening the pre-trial phase of criminal cases. Prosecutors were assigned control over the investigation in the pre-trial phase. Another change in the penal procedures was the transfer of workload from investigating magistrates to police investigators. (COM, 2006)

In light of the increased number of administrative cases a new Administrative Procedure

Code was adopted. Furthermore, 315 administrative judges were appointed, prepared and trained.

In order to improve the enforcement of judicial decisions, private enforcement bailiffs as a supplement to public bailiffs were introduced (World Bank Report, 2008). A lot of work was also done in the area of combating corruption and organized crime.

A specific target of the reform was harmonization of Bulgarian law and judicial practices with European Union law and European Union best practices and standards. Various projects were undertaken to ensure and improve the administrative and judicial capacity for collaboration of Bulgaria in the area of justice with the other Member States (Todorova et al., 2008). Today, in terms of harmonization with European law, Bulgaria is in top ten

Member States.

Harmonization was also needed in the area of accountability and transparency of the judicial system. Some of the criticism that the judicial system faces in that period is that there is no objective criteria to establish the ability of magistrates to make decisions, their professional and analytical skills. The need for a more precise mechanism to decide and report on the workload of a particular court and each and every magistrate was considered.

Preparations were made to start unifying the way in which statistical data was collected.

Moreover, for the period 2005-2007 the quality of the professional and practical skills of

- 10 - the administration of the judicial system was improved. Thus, higher efficiency was ensured as well as uniformity of practices in different courts and more society-oriented working culture of the administration. (Todorova et al., 2008)

2.3 The Reform after 1st January 2007

After the accession of Bulgaria to the EU, it was still considered that there were many issues in the Bulgarian Judicial System. The transfer of successful policies and practices from the EU directly to Bulgaria has not been an easy task due to the fact that the judicial system is in its character very “national”. Due to the insufficient progress in the pre- accession preparations of Bulgaria, The European Commission officially introduced a

Verification and Cooperation Mechanism (VCM) with a Commission Decision of 30

December 2006 as post-accession monitoring tool. According to this mechanism, six benchmarks have to be addressed by Bulgaria:

Benchmark 1: Adopt constitutional amendments removing any ambiguity regarding the independence and accountability of the judicial system.

Benchmark 2: Ensure a more transparent and efficient judicial process by adopting and implementing a new judicial system act and the new civil procedure code. Report on the impact of these new laws and of the penal and administrative procedure codes, notably on the pre-trial phase.

Benchmark 3: Continue the reform of the judiciary in order to enhance professionalism, accountability and efficiency. Evaluate the impact of this reform and publish the results annually.

Benchmark 4: Conduct and report on professional, non-partisan investigations into allegations of high- level corruption. Report on internal inspections of public institutions and on the publication of assets of high- level officials.

Benchmark 5: Take further measures to prevent and fight corruption, in particular at the borders and within local government.

Benchmark 6: Implement a strategy to fight organised crime, focussing on serious crime, money laundering as well as on the systematic confiscation of assets of criminals. Report on new and ongoing investigations, indictments and convictions in these areas. Source: COMMISSION DECISION of 13/XII/2006

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Three of these benchmarks are dedicated to fighting corruption and organized crime. All benchmarks are inter-related, meaning that progress in one of them would have spillover effects on the other. According to the European Commission, improvement in these benchmarks will enhance the confidence of Bulgarians and the rest of the world in the rule of law in Bulgaria and will have a positive long-term effect on the economy (COM, 2008).

Some of the main goals of the reform of the judicial system since entering the EU have been improving the efficiency of the judicial system and the perception that the judicial system creates. Measures were taken to improve the management of the judiciary, to match the European Union standards for the quality of justice, to ensure the quality of the institutions and of the judicial staff (World Bank Report, 2008).

One of the main changes was related to the organization of the judiciary after the adoption of a new Law on the Judiciary. Emphasis has been put on the efficiency, independence and the accountability of the judicial system. New forms were introduced to ensure uniformity in the collection of statistics of the performance of the judicial system. These were issued by the Supreme Judicial Council (Todorova, 2008). As part of the European Union,

Bulgaria has started implementing changes that generate sustainable improvement for the judicial system. A Supreme Judicial Council was created that was accompanied by an

Inspectorate. The inspectorate and the administrative heads of courts have been surprisingly successful in detecting disciplinary violations and weaknesses of the judicial practice. The Code of Administrative procedure and new administrative courts became a functional part of the judicial system. (MOJ n.d)

Substantial problems that hinder the performance of the judicial system are attributed to corruption and organized crime and that is why more effort has been put into fighting them. In connection with this, a State Agency for National Security (SANS) has been set - 12 - up. Permanent teams of investigating magistrates, police officers and SANS staff have been set to deal with organized crime under the leadership of the prosecution. A law for conflict of interest has been adopted. Moreover, a National Anti-Corruption Strategy was adopted in 2009. (COM, 2010) Further changes to the Penal Procedure Code have been made. A specialized criminal court for dealing with organized crime was established ensuring its independency and effectiveness. Bulgaria has improved the law framework in the area of fighting corruption and crime. Transparency has increased as an increased number of cases that are related to fraud, corruption and organized crime are made public. What is more, a principle for random distribution of cases “Law Choice” is being implemented.

Efforts have been made to make the judicial system more publicly accountable. All meetings and decisions of the Supreme Judicial Court are published. Moreover, all courts have their webpages and publish their decisions on the respective webpages. An “e-Justice” initiative is ongoing. As part of it a Unified IT System for combating crime was established as well as a system for management of the database about prisons and detention. Public councils have been assigned both to the Ministry of Justice and the Supreme Judicial

Council to ensure the participation of non-governmental representatives in the evaluation of the judicial system and the judicial reform.

The ethical quality of the magistrates has been targeted through a code of ethics. The code is used as a basis for disciplinary sanctions and investigations. (COM, 2009) In addition, a principle of mandate for the leading positions in the judicial system has been adopted.

Progress has been made in increasing the professionalism within the judicial system. A common system for training and qualification of magistrates at national level was introduced as well as a compulsory training for entry positions for magistrates. The entry- level training is of duration six months. There is also a compulsory training for magistrates that are transferred from a lower instance court to a higher one. (MOJ n.d) Other - 13 - educational courses target specific needs and programs. Apart from training provided by the National Institute of Justice, there are also European projects and international initiatives that together with the support of the Ministry of Justice aim at improving the theoretical and practical preparation of the judicial staff (Ivanova et al, 2008).

In addition, administrative staff has started being subject to evaluations and being hired only through a competitive procedure, akin to all the magistrates. Following recommendations by the European Commission, more administrative staff was hired to accommodate the needs of the judiciary (Todorova, 2008).

In order to monitor the progress of the judicial reform and to motivate a more dynamic progress, a schedule with deadlines for specific targets, policy changes and parties involved has been put in place. Additionally, each change that has been introduced as part of the reform is being assessed for potential impact, public approval and institutional support

(MOJ n.d). Significant success was achieved with addressing procedural obstacles.

2.4 Pending Issues

The judicial reform in Bulgaria is a long-term process. Even though the reform has led to many improvements in the judicial system, there are still numerous pending issues. One of them is the low confidence of the public in the judiciary. A study by Open Society Institute

Sofia (2012) shows that only 16 percent of Bulgarians trust the Bulgarian Judicial System.

Furthermore, the independence of the judicial system still causes many doubts. According to the World Economic Forum in 2013 Bulgaria ranks in the bottom three Member States in terms of perception of the independence of the judicial system (BTA, 2013).

Political influences are one of the sources of concern. Even though the Supreme Judicial

Council is working in favor of improving the ethics in the judiciary, limited actual progress has been achieved. A lot of political pressure is still felt in elections within the judicial - 14 - system. For example, no direct elections by judges were allowed in the election of the judicial quota. This has left too much power in the hands of the existing leadership of the courts as opposed to representing a possibility for a new start. (COM, 2014)

A further issue is the fight with corruption and organized crime. Bulgaria ranks second highest among the European Union Member States in 2013 according to a corruption perceptions index by Transparency International (Center of the Study for Democracy,

2013). This has negative implications not only on the efficiency of the judicial system but also on the level of investment in Bulgaria. The fact that some of the wage rates of magistrates match that of other EU countries does not seem to have a significant positive effect on corruption. There are no single institutions that are fully responsible for the coordination of the fight against corruption and organized crime. As discussed earlier, numerous measures have been taken with regard to this problem including a reform in the

Penal Code. Nevertheless, in order to have an impact these need to be combined with a better law enforcement system and especially a better managed judiciary. (COM, 2014)

Better defined and more transparent standards for promotions and appraisals for judges, investigators and prosecutors are needed. Thus, the integrity and efficiency of the judicial system would be improved. Moreover, more transparency is required also in the area of case allocation. In particular, according to the European Commission the system of random allocation of case workload should be checked by independent experts to make administrative heads more accountable. In relation to this, still pending is the problem of uneven workload between courts and magistrates. (COM2014) Monitoring strategies of the judicial reform should be strengthened by including all relevant professional organizations and NGOs.

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Having discussed the judicial system and the on-going judicial reform in Bulgaria, this paper continues with a discussion of the relevant literature and work done on judicial reforms and economic activity as well as on judicial efficiency.

3. RELATED LITERATURE

3.1 Why Judicial Reform

A lot of literature has been dedicated to the necessity and beneficial effects of well- functioning judicial systems. This section discusses some of the previous work in the field.

It is not possible to assume as theory that judicial efficiency would necessarily lead to economic prosperity (Barro, 1991). One argument is that a poor economy might not be able to achieve an efficient judicial system but without a good judicial system it may never experience the desired economic growth (Posner, 1998). Addressing this paradox, Gray

(1997) suggests that the demand for a judicial reform would be higher if there is an economic reform ongoing in the country as well. Acknowledging the link between judicial systems and economic growth, many donor organizations, such as the World Bank, have financed a lot of projects to support judicial reforms.

Many economic studies show that a badly functioning and inefficient judicial system results in higher transaction costs and that in return stalls economic growth (Messick, 2002).

Moreover, efficiency of judicial systems is crucial for influencing the behavior of contracting parties. An inefficient judicial system would lower the discounted value of punishment and thus induce inefficient behavior from parties.

DeShazo and Vargas (2006) add to the literature that views a judicial reform towards a more efficient judicial system as a prerequisite for democracy and sustainable development.

They provide insights and policy recommendations in light of the latest wave of judicial

- 16 - reforms in Latin America. Their conclusions target specifically Argentina, Chile, Peru,

Colombia, Venezuela and Guatemala.

Chemin(2007) uses data for a period after a judicial reform in India and shows that the reform decreased the number of pending cases per judge in Lower Courts and increased the speed of the judiciary. As a second stage he estimates that the reform led to an increase in investment incentives, decrease in the probability of a breach of contract, improvement and facilitation of access to financial institutions and rental markets. In another study

(2009) he proves that weak judiciaries have an adverse effect on the overall economy as they constrain sectors such as registered manufacturing and agricultural development.

De Sousa and Schwengber (2005) estimate efficiency scores for courts in light of the relationship between the inefficiency of the judicial system in Brazil and economic development. Moreover, they see the judiciary as a crucial tool to enforce rules and guarantee contracts and overall as an important pre-requisite for economic governance.

Chemin (2007) discovers that judicial reforms affect economic activity and in particular entrepreneurship in Pakistan through two mechanisms. The first is through improving the confidence of entrepreneurs that their workforce would be protected and the second is through affecting the willingness of unemployed people to arrange financial resources and apply for loans.

3.2 Judicial Efficiency

There has always been lack of consensus concerning what judicial inefficiency is and what the best method to measure it is (Botero et al, 2003). Moreover, many different studies deal with different possible sources of inefficiency and use different methods to measure it.

This section looks into several of the determinants of efficiency that have been discussed in the literature.

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A branch of the literature on judicial systems deals with determinants of judicial output as sources of inefficiencies. Dimitrova-Grajzl et al. (2010) as a way to tackle the issue of ineffective judicial system in Slovenia, test how judicial staffing and caseload affect court output. They examine a panel of first instance courts over a time period of nine years. They show that output does not depend on the number of judges but that caseload has a significant impact on the number of resolved cases.

Other authors see lack of funding as a source of inefficiency. More resources mean more training, more judges and staff, better computer technology, more courts (Botero et al.,

2003). A study by Buscaglia and Dakolias (1996) shows that resources allocated to court staff are of significant importance for the effectiveness of the judicial systems in Argentina and Ecuador. They conclude that if resources are well targeted they can improve efficiency.

However, an increase in resources does not always lead to an increase in the efficiency of the judiciary as is the case proven by the study of Latin America made by Buscaglia and

Ulen (1997).

Kakalik ( 1997) shows that simplifying procedures for the case of the United States can lead to a decrease in costs and time, can have an impact of the satisfaction of the participants, can change the views on justice and ultimately can lead to an increase in efficiency. Furthermore, the more systematic the problem of corruption in a country is, the more effective the reduction of procedural complexities (Buscaglia and Dakolias, 1999).

What is more, Djankov et al. (2003) suggest a strong link between formalism and judicial inefficiency referring to a panel of 109 countries. Formalism can be detrimental to court performance. It can lead to less consistency, longer proceedings and can impede access to justice (Botero et al., 2003). On the other hand, according to a statistical study by Varela and Mayani (2001), the Dominican Republic does not have higher expectations for less complicated procedures. - 18 -

Some authors suggest that reducing the number of filed cases can increase efficiency through decreasing backlogs (Buscaglia and Dakolias, 1996). However, minimal direct systematic effect of shortening dispositioning time has been established by Mahoney et at.

(1985).

The efficiency of a judiciary depends on many actors. Amongst them are judges and judicial staff, lawyers and plaintiffs when it comes to civil cases, prosecutors and suspects when we talk about criminal cases (Voigt, 2014). Thus, incentives are also an important factor that should be discussed. For example, Posner (1993) suggests that judges can be efficiency – internalizing actors that maximize utility and that their utility is a function of income, leisure and judicial voting. Furthermore, the actors in the judiciary can be seen as agents that should maximize the welfare of the principal (possibly the society as a whole). That makes reputation a factor of relevance for judicial quality (Garoupa and Ginsburg, 2009).

Judicial reforms targeting incentives account for the fact that the actors of the judiciary are one of the sources of inefficient performance (Messick, 2002). In order to create positive incentives, payment of judges may be related to output or include a number of benefits that are not related to specific performance. On the other hand, efficiency can be targeted through negative incentives as well – penalizing inefficient behavior through sanctions or other form of penalty (Djankov et al, 2003).

Another related topic is the organizational structure of the judiciary. Together with incentives, it is a factor that influences the behavior of judges. Moreover, the efficiency of the judiciary can be affected by the size of courts as well. There is much controversy regarding what the best court size is. On the one hand, small courts bring about an indivisibility problem that can be efficiency reducing, but on the other hand, the larger the court, the easier it is for actors to cover their level of effort (Voigt, 2014). Voigt and El

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Bialy (2013), however, find no correlation in their study between court size and the resolution rate of courts.

Another way to target both expertise and efficiency is through court specialization.

However, it is crucial to have the right form and degree of court specialization.

Specialization may increase the complexity of coordination and communication. This may lead to loss of effectiveness. Other adverse effects could be unequal treatment of parties and corruption. (Reiling, 2003)

Court systems may be responsible for more than just deciding cases. The involvement of judges in activities beyond the decision of cases would affect their output productivity.

Keeping everything else constant, including the budget, it could transfer into a decrease of judicial efficiency (Voigt and El Bialy, 2013).

3.3 Measuring Judicial Efficiency

Different techniques are used to estimate judicial efficiency. Some of these methods include OLS, instrumental variable approaches, Data Envelopment Analysis, Stochastic

Frontier Analysis. As this list is not exhaustive, this section mentions some of the studies that measure judicial efficiency.

De Sousa and Schwengber’ study (2005) of Rio Grande do Sul, Brasil courts estimates efficiency scores using two techniques: Free Disposal Hull (FDH) approach and the order- m frontier. It concludes that production of judicial services is becoming an area of concern in particular for small courts as they cannot exploit economies of scale in the production of services and become a source of waste of resources. Both approaches determine a non- parametric efficiency frontier but the authors put more emphasis on the results obtained using order-m frontier as it removes the issue of dimensionality. Tulkens (1993), on the

- 20 - other hand, focuses on an FDH approach to compare productive efficiency results. What he tests is whether increasing efficiency scores can lead to a decrease in existing backlog.

Another technique is applied by Lewin et al. (1982) - Data Envelopment Analysis (DEA).

The analysis is done using data of 100 criminal superior courts in North Carolina in order to evaluate courts in terms of administrative efficiency. Besides this study, Garcia-Rubio and Rosales-Lopez (2010) also use DEA to estimate the technical efficiency of First

Instance Courts of Andalucia and a study by Elbialy and Garcia-Rubio (2011) on Egyptian

First Instance Courts.

Antonucci et al. (2013) apply a Stochastic Frontier Analysis in order to analyze the efficiency of 26 Italian Courts of Appeal. Using a comparison of approaches, Jacobs (2001) applies both DEA and SFA when measuring efficiency of English hospitals. He concludes that even though they provide different results it is the trend in efficiency that should be taken into account and not the actual differences in estimates.

The next two sections discuss the empirical analysis applied - the methodology, the data used by this study as well as the results obtained from the analysis.

4. EMPIRICAL ANALYSIS PART I

4.1 Methodology: Data Envelopment Analysis

When talking about efficiency of production units three types of efficiency are to be considered: technical efficiency, allocative efficiency and cost efficiency. Technical efficiency requires that there is no wasting of inputs when producing outputs relative to most efficient units in a particular sample of Decision Making Units (DMUs). The minimization of costs of production is a problem that allocative efficiency deals with.

Allocative efficiency ensures inputs are used where they are of highest value to society. A - 21 - producing unit can be cost efficient only if both technical efficiency and allocative efficiency are achieved. Cost efficiency is the product of technical and allocative efficiency scores. (Bhat et al, 2001)

The efficient frontier plots the best possible use of resources to produce the output quantity. If a DMU is producing at a point on the efficiency frontier it is considered as technically efficient. The budget line depicts the combinations of inputs (labor and capital) that have the same cost. Both technical and allocative efficiency are achieved at the point where the budget line is tangent to the efficient frontier. (Bhat et al, 2001) (Figure 2)

Figure 2 Source: Bhat et al., 2001

For the purposes of this work, the focus is only on technical efficiency (only input and output amounts are considered). As there is no market and given the uncertainty about prices from a social point of view, no consideration of prices is needed (Pedraja-Chaparro and Salinas-Jimenez, 1996). The two most commonly used methods to obtain an efficient frontier are DEA and Stochastic Frontier Anlaysis (SFA). SFA has been introduced by

Aigner (1979) and Meeusen van den Broeck (1977). It is a parametric approach and essentially adds to the efficient frontier an error term that allows for both technical inefficiencies and for inefficiencies that are a result of a random event to individual units

(Pereira et al., 2007). Efficiency scores produced through SFA are not compared with a benchmark and that makes them less sensitive to outliers (Voigt, 2014). However, SFA allows only for one output and requires a prior specification of the functional form of the - 22 - production function, which is not the case with DEA and this is one of the reasons for which this paper does not use this approach. The parameters are estimated in a way that they either lay on the frontier or below. In addition, SFA takes as given the independence between the explanatory variables and the error term. (Pedraja-Chaparro and Salinas-

Jimenez, 1996)

In order to measure efficiency of the District Courts in Bulgaria this study uses Data

Envelopment Analysis (DEA). Due to its less restrictive assumptions DEA accommodates better the above-mentioned uncertainty related to the judicial system production technology (Pedraja-Chaparro and Salinas-Jimenez, 1996). The technique has been introduced by Charnes et al. (1978) and it represents a non-parametric approach to estimate efficiency. DEA is a useful tool for measuring the relative performance of decision- making units (DMUs) especially in cases where there are multiple inputs or outputs, which makes comparisons complicated. In its essence it is a linear programming based technique. It compares the behavior of DMUs with best observed practices. In this way it is possible to distinguish the efficient units (the ones lying on the technological frontier) from the inefficient ones (the ones lying outside of the frontier).

In more formal terms, let us assume that there are n number of units (n DMUs) and that

each unit employs m inputs xij (i=1,…m) to produce s outputs yrj(r=1,..,s). The technology used to produce the inputs is considered to follow the properties suggested by Shephard

(1970) (Elbialy and Garcia-Rubio, 2011). Using Banker, Charnes and Cooper (1984)’ output- oriented model one can estimate the technical efficiency of a DMU solving the following maximization problem:

max  Subject to: j 0 0

- 23 -

n  jxx ij io i=1,…m j1

n jyy rj 00 r r=1,…s (1) j1

  0 j=1,….n

n  j 1 j1

where θ is the efficiency score, xio and yrj are the values for input i and output r for DMU0

and λj stands for the weight that DMUj has in the reference set representing the best observed practices. (Garcia-Rubio and Rosales-Lopez, 2010)

An efficient DMU is one that achieves the efficiency score of 1. Whenever, DMUSs are inefficient that is either due to excessive inputs or lack of sufficient output – input slack and output slacks. For example, in Figure 3 units A and C are efficient as they are on the frontier and points D and E are inefficient. The projection of point D on the efficiency frontier – D* gives us the slack CD*.

Figure 3 based on Elbialy and Garcia-Rubio (2011)

The way to improve efficiency is to reduce the number of inputs or to increase the output respectively. Efficient units can be relatively efficient (weakly efficient) and fully efficient.

For a DMU to be fully efficient it has to have both an efficiency score of one and no input

- 24 - or output slacks. (Ozcan, 2008) The gap between a Farrell- Debreu-type of efficiency (weak efficiency) and Pareto- Koopmans efficiency is described by the slacks (Tone and Tsutsui,

2013). Table 1 below displays the conditions for each type of efficiency.

Efficiency Condition All input slacks All output slacks Score

Fully Efficient 1.00 0 0

Weakly Efficient 1.00 at least one≠ 0 at least one≠ 0

Table 1 Source: Ozcan, 2008

By solving (1) we can obtain efficiency scores for each DMU. However, these scores represent weak efficiency. A second stage of the computation (2) must be introduced to compute efficiency scores satisfying Pareto – Koopmans efficiency. Problem (2) also estimates the slacks in inputs and outputs.

ms  Max sir00 s  j,,ss i00 r  ir11

n  Subject to:  jx ij s i0 x io i=1,…..,m j1

n  jy rj s r0 0 y r 0 r=1,…..,s (2) j1

 j  0 j=1,……,n

n  j 1, j1

  where s i0 and s r0 are input slacks and output slacks, respectively. (Elbialy and Garcia-

Rubio, 2011)

- 25 -

4.1.1 Specifications of The Model: CRS vs VRS

The standard DEA specification assumes constant returns to scale. That means that the differences in size of the units producing the output are not taken into account. There are no economies or diseconomies of scale and if we change all inputs by a factor this will lead to a change of the same factor in the output (doubling inputs will lead to a doubling in output). However, as in reality this may not be the case, a less restrictive option of the

DEA is assuming variable returns to scale. In that case technical efficiency can be estimated using the BCC model. It separates efficiencies into technical and scale efficiency and allows identifying whenever the best possible outputs is not obtained or a too big amount of inputs has been used. (Banker et al., 1984) Due to the big difference in the sizes of the

District Courts in Bulgaria, variable returns to scale are applied.

4.1.2 Specifications of The Model: Output- vs Input-Oriented

An input- oriented DEA shows by how much inputs can be optimized/reduced to achieve the same level of output. It is used whenever there is more control over the inputs rather than on the outputs. An output-oriented model maximizes outputs given a certain amount of input values. In this setting, whenever a unit is inefficient it needs to increase its output to achieve efficiency. (Ji and Lee, 2010) In this study an output-oriented technical efficiency is estimated.

4.2 Data

This research is based on data for 112 District Courts in Bulgaria for the period 2005-2012.

Currently, the number of District Courts in Bulgaria is 113. However, due to the fact that

Galabovo District Court starts functioning as a District Court in 2007, it is left out from the sample. The time period of 8 years is chosen such that it covers both before- and after accession of the country to the EU. The reason why only 2 years before entering the EU

- 26 - are covered is because data is being collected and published systematically by the Supreme

Judicial Court since 2005. Before that, the judicial system was much less publically accountable.

In order to perform a DEA, data for output and input variables is needed. Lewin et. al

(1982) use various inputs such as number of days of court held per year, number of attorneys, number of minor offences, caseload and size of white population. In their study of 30 judicial courts in North Carolina, they use resolved cases and cases pending for more than 90 days as output variables. Moreover, both Kittelsen and Forsund (1992) and

Perdraja –Chaparro and Salinas-Jimenez (1996) take as inputs judges and administrative staff. In their study on 107 Norwegian District Courts, Kittelsen and Forsund (1992) take different types of filed cases as outputs. Pedreaja-Chaparo and Salinas-Jimenez (1996) apply number of court decisions and other resolved cases as output. Based on court sentences and warrants is also the output variable used by Lopez (2008).

The data used in this paper to perform a Data Envelopment Analysis for District Courts in

Bulgaria includes three input factors and one output factor. All data is provided on a yearly basis. The selected input variables are number of judges, number of administrative staff and a capital variable – number of computers. The output variable used is number of resolved cases (Figure 4).

Figure 4: Based on dataset

- 27 -

The choice of variables follows the ones selected by Elbialy and Garcia-Rubio (2011) in their study of First Instance Courts in Egypt. The data for number of judges, administrative staff and resolved cases was provided by the Supreme Judicial Council. The entries for number of computers were collected on a court-by-court basis.

4.3 Application and Results

An output-oriented data envelopment model of variable returns to scale allows comparing the efficiency of the District Courts in Bulgaria before and after it has become a Member

State of the European Union. In order to make such a comparison this study is going to use the results of envelopment analysis performed for the years 2006 and 2012. As Bulgaria becomes a Member State on 1st January 2007, the year before is considered as representative of the pre-accession efficiency of the judicial system. The last year in our sample of observations is 2012 and it is used in this comparison in order to show whether the judicial reform after Bulgaria has become a Member State, has increased the efficiency of the judiciary. Table 2 shows descriptive statistics of the sample for both years.

, Inputs Output

Judges Computers Admin Resol

Mean 7,714286 43,03571 14,58929 2854,955 S.D 12,21536 69,4205 11,55368 6333,548 Pre-EU Minimum 1 8 1 182 Maximum 116 696 40 59536 Mean 8,053571 38 16,71429 4140,241 S.D 13,18055 63,64584 12,56 11175,85 Post-EU Minimum 1 8 1 259 Maximum 123 641 45 111718

Table 2 based on dataset

- 28 -

The analysis of the efficiency of District Courts in Bulgaria before entering the EU shows that from the 112 analyzed courts, 5 are efficient. Appendix A displays the efficiency score for each court and the corresponding slacks for each of the three inputs. The courts

Chepelare, Pernik, Plovdiv, Razlog and Sofia are considered efficient as they satisfy both the condition of an efficiency score of one and the condition of all input and output slacks being equal to zero. The rest of the courts are described as inefficient. Thus, as this is an output-oriented DEA they can increase their efficiencies by augmenting their output. The more the efficiency score is different from one, the more inefficient the unit is. In this case the least efficient courts are Ivailovgrad, Topolovgrad and Pomorie.

From the efficient courts, Plovdiv and Sofia District courts are located in the biggest cities in Bulgaria that are also financial centers. These are districts that have an impact on the economy, that have a good social infrastructure and from that point of view they are attractive to the best professionals in the area of justice. These and other factors can explain the efficiency of these courts. For example, District Court Sofia, has introduced a lot of changes as part of its reform in 2006. Among these are the establishment of a new software system for management of cases and the installation of new office technology.

Video recording was introduced in the registration units in the court and has targeted the problem of corruption.

District Courts Razlog and Chepelare, on the other hand, are courts famous for their good workload organization. District Court Chepelare has adopted a program for sustainable development of the judicial system in year 2003. Since then, numerous changes have been introduced in the working of the court. A new software programme for management of the cases has been introduced as well as a webpage of the court and an electronic database.

Furthermore, the court has participated in the testing of a methodology for criteria for the caseload of District Courts in Bulgaria. The reputation of Chepelare court in 2006 is of an

- 29 - innovative and striving for improvement one. The results from the Data Envelopment

Analysis are confirmed by the award “Model Court” that Chepelare District Court receives on 7th April 2006.

The total number of cases for the efficient courts is 126187 and around 76 percent of these have been resolved.

The least efficient District Courts ( Ivailovgrad, Topolovgrad and Pomorie ) are all quite small in size. This may lead to a consideration of the role of the size of the court for efficiency. The environment being less competitive could lead to less than best qualified staff. In particular, the annual report of Topolovgrad court (2006), states that the court has to do a significant amount of work to improve its results. It stresses the need for better qualified staff, administration and more advanced informational technologies. The court has 4 judges and that small number does not allow for judges to specialize in specific types of cases. What is more, the subsidy assigned to Topolovgrad court hardly covers the monthly expenses of the court.

The total number of filed cases for these 3 least efficient courts is 1548, around 55 percent of which have been resolved within the same year. However, work has been done in all these courts to improve their performance.

In 2012 – six years after the accession of Bulgaria to the EU - the number of efficient

District Courts is four. The efficient courts in this year are Drqnovo, Pernik, Sofia and

Varna. However, it should be noted that Plovdiv is very close to be considered as an efficient court. Appendix B shows the results for 2012. The least efficient courts are Malko

Tarnovo, Ivailovgrad and Ardino. The total number of filed cases in the most efficient courts is 191918 and 78 percent of them have been resolved in 2012. The total number of filed cases in the least efficient courts, on the other hand is 939.

- 30 -

In 2012, District Court Drqnovo, evaluates its performance in the annual report as “more than satisfactory”. Ninety-four percent of the resolved civil cases in that period and ninety percent of the resolved penal cases have been resolved in a three-month period.

Furthermore, during 2012 an evaluation of the staff of the court was performed. All members of the staff of District Court Drqnovo obtained a “Very good” or “Excellent” grade.

District Court Pernik is one of the courts with the biggest workload in Bulgaria in 2012.

The court has a very good organization and management of the caseload. New statistical forms contributed to the transparency and the accountability of the court as well as to its performance. That was enhanced by the daily updates of the webpage of the court.

An increase in the efficiency of District Court Sofia has been recorded compared to previous years regardless of the increased number of filed cases. This can partially be explained by the internal reallocation of staff from units with less workload to more dynamic ones. Furthermore, the court was equipped with new technology. During 2012, 51 students were hired as interns in the administration of the court.

The last efficient court in 2012 – District Court Varna - received an award “Model Court”.

It achieved a score of 87 percent for compliance with the 24 quality indicators of the

Supreme Judicial Council. Sofia District Court had especially improved the quality of its judicial judgments. The budget allocated to the court had increased in 2012 and, according to its annual report, the court had a satisfactory technology equipment.

The least efficient courts in 2012 face similar problems in their operation. As an example

District Court Ardino, has expressed the need for better facilities and better technological equipment. Another problem in 2012 of the same court is the fact the allocated budget does not allow to cover its expenses.

- 31 -

Comparing the results for 2006 and 2012, one can conclude that the efficiency results of

District Courts in 2012 are more assembled around their average. Essentially, this means that they are more closely distributed around the efficiency frontier. Nevertheless, the mean efficiency of the District Courts in Bulgaria in 2012 is still 52,32 % which leaves significant room for improvement. (Table 3)

Efficiency Statistics Mean 0,5023632 S.D 0,2126641 Pre-EU Minimum 0,1390672 Maximum 1 Mean 0,5232649 S.D 0,1908923 Post-EU Minimum 0,1426532 Maximum 1

Table 3 based on DEA analysis

In order to compare the pre – EU period with the post-EU one, a Malmquist Index which measures productivity changes, in computed (Appendix C). The index is divided between changes in efficiency and changes in technology. Looking at the changes in technical efficiency, one can conclude that efficiency has increased from 2006 to 2012 approximately

12 times.

As a step forward and in order to be able to use the efficiency scores for the second part of this analysis, a Data Envelopment Analysis is performed for the period 2005-2012 using the whole sample of 112 District Courts. The results of this analysis can be obtained from

Appendix D. Year 2007 has the largest number of efficient courts. The lowest efficiency score is attributed to Ilailovgrad District Court in 2008. Using these results this study continues with the second part of the methodology.

- 32 -

5. EMPIRICAL ANALYSIS PART II

5.1 Methodology

After having estimated the technical efficiency scores we proceed to the second stage of the analysis – estimating the linear and non-linear effects (namely the change in the direct effect due to a change in the environment – in this case entering the EU) that the efficiency of the judicial system has on economic growth. This is done using panel data. The relationship that we try to estimate is the following:

GDPit= α0 + β₁Efficiency_ Scoreit +εᵢt

An Ordinary Least Squares (OLS) Estimator cannot be used in this case as one of the conditions for it to generate unbiased and consistent estimators is that there should be zero correlation between the explanatory variables and the error term (E(εᵢ|Xᵢ)=0) If this condition is not satisfied there is a problem of endogeneity. Endogeneity is a problem of an edogenous explanatory variable – a variable that is correlated with the error term either because of a measurement error, an omitted variable or simultaneity (Woolridge, 2002) In equation (3) such a problem appears due to the variable “Efficiency_Score”.

In order to address the problem of endogeneity which is common to the related literature, a Two-Stage Least Squares (TSLS) procedure is applied. Using TSLS ensure an unbiased and consistent estimate. TSLS represent two consecutive regressions. An instrumental variable is needed for the first stage of the TSLS. A variable z is a good instrumental variable if it satisfies two conditions: (a) it is uncorrelated with the error term and (b) it is correlated with the independent variable (Woolridge, 2002).

Cov(z, ) 0 (a)

and

Cov(z,x) 0 (b)

- 33 -

In the first stage of the TSLS, we regress variable x (Efficiency_Scores) on the instrumental variable z. In this way we obtain the predicted values of Efficiency_Scores:

XZZZZXˆ  (')'1 . The first stage is followed by a second one in which y is regressed on

ˆ xˆ which gives the coefficient TSLS (Cameron and Trivedi, 2005). In more formal terms:

ˆ 1  1  1 TSLS [X'Z(ZZZXZZZ ' ) ' ] [X'Z( ' ) 'Y]

Considering the way the data has been obtained and the relationships estimated, three possible problems might occur: measurement error, endogeneity and fixed effect. As for the first stage the most significant problem that we need to account for is measurement error, we use a pooled OLS. For the second stage three estimators are computed: fixed effect, random effect and pooled OLS. Fixed effects estimator is used when the unobserved variables in the error term do not change over time. It is the estimator obtained by applying pooled OLS to a time-demeaned equation. Random effects model is another panel data model that is used when the unobserved effect is uncorrelated with the explanatory variables in each time period. (Woolridge, 2002)

Under the assumption that the variable Efficiency_Scores is sequentially exogenous, the error

term (εit) is not correlated with the current and the past values of Efficiency_Scores but might be correlated with its future values. That means that Efficiency_Scores is affected by GDP in the previous year but is not affected by GDP in the current year. That is the reason why, as an extension of this analysis, the second stage of the TSLS is also performed using the lagged value of Efficiency_Scores. Again fixed effects, random effects and pooled OLS are used.

5.2 Data

The main variable for the first stage of the TSLS is the technical efficiency scores obtained through the Data Envelopment Analysis. The instrumental variable (interaction_term) used is - 34 - an interaction term composed of a district fixed effect to control for the fact that the heterogeneity of courts may lead to biased coefficients and a variable that reflects the introduction of publishing the decisions of the cases on the website of the particular court.

The data for the year in which each court started posting decisions on its website has been collected either from the webpages of the courts or from the statistics units in each court.

The variable represents data for a sequentially introduced improvement in the judicial system in terms of making it more publically accountable.

For the second stage, data for GDP at a district level is needed. Unfortunately, no such data is being recorded. Due to unavailability of GDP data at district level in Bulgaria, two methods to compute such statistics are used. They are both based on using yearly statistics for GDP at regional level. For the purposes of statistics, Bulgaria is divided into 28 regions.

The data for GDP at regional level was kindly provided by the National Statistics Institute upon request. The first estimate uses GDP per capita at regional level. Based on the population in each district, the variable GDP1 at district level is computed. The second computation of GDP uses data for energy consumption at district level and GDP at regional level. The data for energy consumption was self-collected from reports issued by each district, the relevant districts, the National Statistics Institute and the 3 energy supplying companies in Bulgaria. No such compiled dataset exists in Bulgaria yet. GDP at

district level (GDP2) was computed using the formula: [(energy consumption of districtN/ energy consumption of regionN)* GDP regionN].

The following section explains in details the specificities of the models used and the obtained results.

5.3 Model Specification and Results

In formal terms the 1st stage of the 2SLS takes the following form:

- 35 -

Efficiency_scoresit = β1 interaction_termit + γ time_fixed_effect + εit (1)

where i= 1,2,…,n represents the number of courts (n=112) and t= 1,2,..8 represents the time period. A variable for time fixed effects controls for unexpected exogenous shocks in a certain year that might affect the efficiency of all courts the same way. In equation (1)

(interaction_term) is the instrumental variable. Staiger and Stock (1997) suggest that an instrumental variable is weak if the F-statistic on the test of excluded instruments is smaller than 10. Following their “rule of thumb” it can be concluded that the instrumental variable

(interaction_term) is a good instrumental variable (F-stat= 28.74) and can be used to address the endogeneity problem. (Appendix E) Through this first stage we compute the predicted efficiency scores.

Next, the relationship between the predicted efficiency scores and GDP is estimated. In order to account for both the linear and non-linear effects of the efficiency of the judicial system we also include an interaction term between the predicted efficiency score and a

dummy variable (DEU) that takes the value of 0 for the years in which Bulgaria is not a

Member State and the value of 1 for the rest.

GDPit = β1Efficiency_score_hatit + β2Efficiency_score_hat * DEU + γ time_fixed_effect + λ

region_fixed_effect + εit

The first set of regressions is using GDP1. Three techniques are used – fixed effect estimator, random effect estimator and pool estimator (Appendix F Table 1). Having accounted for district fixed effects while computing the predicted efficiency scores, here we include a regional fixed effect to control for region specific policies and characteristics that do not change over the time period considered. A time fixed effect is also introduced as when dealing with GDP we need to take out the effect of an exogenous shock at a country

- 36 - level. We cluster the error term at regional level due to the fact that the original data for

GDP has been collected at regional level to allow for serial correlation of the error term.

The most significant results are achieved through the random effects estimation. Both fixed effects and random effects specifications show a negative correlation between efficiency scores before entering the EU and a stronger and more significant positive correlation after the accession of Bulgaria to the EU. The results obtained from the pooled

OLS estimation show a positive correlation between the dependent variable and the independent variables. However, the positive coefficient of the efficiency score before entering the EU is statistically insignificant. In this case the positive correlation after entering the EU is also stronger and significant.

As discussed earlier in this stage three problems should be taken into consideration: measurement error problem, endogeneity and the correlation of the fixed effect with the independent variable. As the biggest of these problems concerning this study is the measurement error problem and it is fair to assume that the error is autocorrelated, using a first difference procedure or demeaning would enhance the bias in the coefficients produced by the measurement error. Hence, pool estimation and random effects estimation are considered to be best. Nevertheless, from statistical significance point of view, random effects estimation is the best.

As the negative coefficient before the accession of Bulgaria is not robust on the different estimations it should be taken with precaution. However, it can be explained through the fact that in the negotiations period for accession of Bulgaria to the European Union, a lot of new regulations and policies have been introduced. Some of these regulations specifically target the efficiency of the judicial system. Nevertheless, they could have a negative effect on economic growth until the economy adjusts itself to these regulations. In that sense the period 2005 – 2006 can be considered as an adaptation period in which even - 37 - if there has been an improvement in the efficiency of the judicial system, that might not have had a positive impact on the economy and especially on the level of GDP.

All three estimation techniques confirm the positive, statistically significant effect on economic growth of an improvement in the efficiency of the judicial system in Bulgaria after its accession to the EU. The significantly positive correlation between GDP and judicial system after 2007 can be accounted by the fact that some adaptation period from the first wave of the reform has already passed and the actual positive effects of the reform in the judicial system in Bulgaria on the economy can be observed.

Using GDP2 generates much less significant results which can be attributed to problems with the computation of GDP2 (Appendix F Table 2). A pooled OLS estimator shows a significant, at 5 per cent level of significance, positive correlation between judicial efficiency and economic growth before entering the EU. When using random effects estimation, what is significant at 10 per cent level of significance is the positive correlation between efficiency and GDP2 after entering the EU. The results generated using a fixed effects estimator do not exhibit statistical significance.

Buscaglia and Ulen (1997) state that “after a lag, a more efficient judiciary attracts additional demand from citizens and businesses that has previously been reluctant to use the courts due to delay and backlog” Following this statement, as a step further we will test for the case of Bulgaria if the performance of the economy is affected by the efficiency of

the judiciary in the previous year (Efficiency_scoret-1).

GDPit = β1 Efficiency_sore it-1 + β2 (Efficiency_score * DEU)it-1 + γ time_FE + λ region_FE + εit

First we estimate a relationship using GDP1 (Appendix G Table 1). A random effects model shows that the lagged values of the independent variables have a statistically

- 38 - significant effect on GDP. The economy responds negatively to a change in the efficiency scores in the previous year before being a Member State. However, after entering the EU, economic growth is positively correlated with the judicial efficiency in the previous year.

This positive correlation effect is stronger than the negative one. The same signs of the relationships are confirmed using a fixed effects setting but the obtained coefficients are significant at 5 percent level of significance. A pooled OLS, on the other hand shows contradiction. Nevertheless, only the positive correlation before entering the EU is significant at 5 percent level of significance.

Using GDP2 the results confirm the signs of correlation of the ones obtained with GDP1.

However, the only statistically significant coefficients are the ones establishing the effect on

GDP of a change in the previous year’s efficiency score before Bulgaria became a Member

State.

6. FURTHER DISCUSSION

The analysis of the efficiency of the judicial system in Bulgaria showed that there has been an increase in its efficiency after Bulgaria has become a Member State. However, it also showed that the average efficiency of District Courts as of 2012 is still quite low. This could be explained with the pending issues that the reform still has to deal with.

A study on the functioning of judicial systems in the EU (2014) concludes that the clearance rate and the dispositioning time in First Instance Courts in Bulgaria are satisfactory. Nonetheless, it also suggests that no system for regular evaluation of the activity of each court exists. In addition, there are no quantitative targets for each court and no quantitative performance targets for each judge. An interesting fact is that Bulgaria allocates substantial resources to its judicial system. It ranks in top three of the EU

- 39 -

Member States in terms of share of GDP allocated for justice and even though in actual terms Bulgaria cannot rank high, the sums it allocates are higher than of countries like

Cyprus and Greece which have higher GDP (BTA, 2014). This addresses a paradox linked to public financing of courts and judicial institutions. Namely, in judicial reforms, the lack of efficiency may attract higher economic investments in inefficient judicial structures.

Therefore, the weaker and less motivated courts and other institutions might get more financing than those who have organized their work effectively. In short, the question of how helpful and well-targeted the financing has been for improving the efficiency of the

Bulgarian Judicial system arises. Moreover, addressing the low average efficiency of courts in Bulgaria is a discussion on the need for further simplification of procedures in order to deal with corruption. An open question also remains around the possibility to establish quality targets for the performance of the judicial system.

This analysis also shows that the efficiency of the judicial system has a positive relationship with GDP after Bulgaria has become a Member State of the European Union. In the past decade Bulgaria went through many reforms to generate macroeconomic stability and economic growth. And even though there is controversy in the opinions about the efficiency of the Bulgarian economy after its accession to the EU, some facts should be taken into account. There have been many improvements in the structure of the economy.

The Bulgarian economy has put emphasis on optimization of the use of resources. As part of the competitive EU market, Bulgarian businesses have been striving for higher efficiency and competitiveness. Furthermore, in the period 2001 – 2010 the average growth rate in

Bulgaria reached 4.7% and the level of per capita income as a share of the average for the

European Union increased from 22% to 44%. A step forward to increase economic growth would be ensuring the efficiency of the judiciary.

- 40 -

7. CONCLUSION

This paper looks at the judicial reform in Bulgaria. It examines the change in efficiency of the Bulgarian judicial system before and after its accession to the European Union due to the Judicial Reform. The focus is on efficiency as one of the indicators of performance of a judicial system. The analysis uses a Data Envelopment Analysis to obtain efficiency scores for the District Courts in Bulgaria. It proves that the on-going judicial reform has an impact on the efficiency of the judicial system. It shows that there has been an increase in the technical efficiency of District Courts in Bulgaria after entering the EU. This study can be added to the literature in support of the progress made by the Bulgarian Judicial System after becoming a Member State country of the EU.

The analysis of DEA shows that even after Bulgaria has been implementing a reform as a member of the EU for more than 6 years, courts achieve on average around 52 percent of technical efficiency. This has significant policy implications and shows that even though the reform has improved the efficiency of the judiciary in Bulgaria, more changes need to be introduced. Further work could establish the drivers of these inefficiencies. However, based on the recommendations from the European Commission and on discussions on the topic, improvements need to be done in the areas of transparency, corruption and accountability of the judicial system. What is more, in order to improve the efficiency better workload management and distribution techniques should be applied. The uneven distribution of number of cases among courts should be addressed. A particular point of concern for the judiciary of Bulgaria is the public trust in the judicial system.

Well-functioning institutions and especially well-functioning judicial systems have a significant impact on economic indicators. (COM, 2014) This paper tries to prove this statement by estimating the relationship between the efficiency of the Bulgarian District

- 41 -

Courts and GDP. The findings of this empirical analysis are in line with the literature that points out the effect of institutions on economic performance. A thematic result is the fact that the efficiency of the judicial system has a positive impact on GDP after entering the

European Union. This implies that since it has become a Member State, having a more efficient judicial system, would play a key role in restoring the trust in the stability of

Bulgarian economy and in supporting economic growth. A judicial system that provides justice that is predictable, on-time and easily accessible creates an attractive business environment (COM, 2014). And this is what Bulgaria is trying to achieve.

The findings of this paper have policy implications for Bulgaria as they suggest that the country should put extra effort in solving the remaining issues of its judicial system and through that it will achieve progress not only in the judiciary but in the economy overall.

Priority should be given to improving the efficiency of the judicial system, its predictability and independence as also suggested by the European Commission (2014). However, we should not forget that both the economy and the judicial system in Bulgaria are very specific in their nature.

- 42 -

APPENDIX A:

Data Envelopment Analysis for Bulgarian District Courts, 2006

Input slacks Court Efficiency Judges computers Admin Aitos 0,672258 0,00000 0,00000 0,00000 Ardino 0,199373 0,00000 0,00000 6,31435 Asenovgrad 0,49548 0,13682 0,00000 0,00000 Balchik 0,300461 0,16623 0,00000 0,00000 Belogradchik 0,382216 0,00000 0,00000 0,00000 Berkovica 0,45607 0,00000 0,00000 0,00000 Blagoevgrad 0,669701 0,14249 0,00000 0,75519 Botevgrad 0,528061 0,76389 0,00000 0,00000 Bqla 0,500496 0,00000 0,00000 0,00000 Bqla Slatina 0,302454 0,02440 0,00000 0,00000 Breznik 0,197458 0,00000 0,00000 0,00000 Burgas 0,76898 0,00000 2,19709 21,53140 Chepelare 1 0,00000 0,00000 0,00000 Cherven Briag 0,363335 0,00000 0,00000 0,00000 Chirpan 0,321919 0,00000 0,00000 0,00000 Devin 0,402041 0,00000 0,00000 0,00000 Devnia 0,324358 0,17945 0,00000 0,00000 Dimitrovgrad 0,666881 0,00000 0,25405 1,44491 Dobrich 0,53618 0,93011 0,00000 0,50335 Drqnovo 0,502547 0,00000 1,92279 0,00000 Dulovo 0,307373 0,00000 0,00000 0,00000 Dupnica 0,573959 0,00000 0,95660 9,85296 Elena 0,538645 0,00000 0,00000 17,41620 Elhovo 0,233116 0,23465 0,00000 0,00000 Elin Pelin 0,342467 0,00000 0,00000 0,00000 Etropole 0,275157 0,63498 0,00000 8,82619 Gabrovo 0,643845 0,00000 1,22637 6,97499 Gen. Toshevo 0,346326 0,00000 0,00000 0,00000 Goce Delchev 0,578989 0,11317 0,00000 0,00000 Gorna Orqhovica 0,722372 0,00000 1,37596 8,54807 Harmanli 0,749242 0,00000 0,00000 0,00000 Haskovo 0,758089 0,00000 4,94563 8,08628 Ihtiman 0,573696 0,00000 0,00000 0,00000 Isperih 0,350419 0,00000 0,00000 0,00000 Ivailovgrad 0,17584 0,00000 0,00000 5,45104 Kardjali 0,52834 0,00000 1,33343 4,57895 Karlovo 0,519447 0,71131 0,00000 0,00000 Karnobat 0,377724 0,34340 0,00000 0,00000 Kavarna 0,287828 0,32143 0,00000 0,00000

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Input slacks Court Efficiency Judges computers Admin Kazanlak 0,599272 0,00000 2,56831 8,98908 Kneja 0,291081 0,00000 0,00000 0,00000 Kostinbrod 0,449715 0,00000 0,00000 0,00000 Kotel 0,385373 0,00000 1,47447 0,00000 Kozlodui 0,772309 1,25518 0,00000 0,00000 Krumuvgrad 0,275185 0,00000 0,00000 8,89765 Kubrat 0,411876 0,00000 0,00000 0,00000 Kula 0,267599 0,00000 0,00000 8,65235 Kustendil 0,773606 0,00000 0,99464 10,83050 Levski 0,404427 25,88180 0,00000 0,00000 Lom 0,502991 0,00000 3,59279 5,28141 Lovech 0,614349 0,01307 0,00000 0,74506 Lukovit 0,356289 0,00000 0,00000 0,00000 Madan 0,273331 0,00000 0,00000 0,00000 Malko Tarnovo 0,246713 0,00000 0,00000 7,15467 Mezdra 0,354262 0,00000 0,00000 0,00000 Momchilgrad 0,362592 0,01351 0,00000 0,00000 Montana 0,635028 0,00000 0,00000 0,71610 Nesebar 0,80235 0,26511 0,00000 0,00000 Nikopol 0,281643 0,00000 0,00000 0,00000 Nova Zagora 0,534184 0,00000 0,93482 0,66773 Novi Pazar 0,587511 0,00000 1,02814 0,14688 Omurtag 0,297268 0,14597 0,00000 0,00000 Orqhovo 0,353692 0,00000 0,26527 0,08842 Panaguirishte 0,335363 0,00000 0,00000 0,00000 Parvomai 0,302332 0,00000 0,00000 0,00000 Pavlikeni 0,431329 0,00000 0,00000 0,00000 Pazardjik 0,762581 0,00000 1,85198 14,10780 Pernik 1 0,00000 0,00000 0,00000 Peshtera 0,373522 0,16018 0,00000 0,00000 Petrich 0,969519 0,99676 0,00000 0,00000 Pirdop 0,332755 0,00000 0,00000 0,00000 Pleven 0,687266 0,96778 0,00000 8,68200 Plovdiv 1 0,00000 0,00000 0,00000 Pomorie 0,139067 0,00000 0,00000 0,00000 Popovo 0,434239 0,00000 0,00000 0,86848 Provadia 0,422267 0,19701 1,73695 0,00000 Qmbol 0,506321 0,00000 1,10908 10,0420 Radnevo 0,436238 0,00000 0,00000 0,00000 Radomir 0,736098 0,00000 2,76037 2,39232 Razgrad 0,524831 0,18728 0,00000 0,00000 Razlog 1 0,00000 0,00000 0,00000 Ruse 0,901996 0,00000 6,74349 24,2035

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Input slacks Court Efficiency Judges computers Admin Samokov 0,503816 0,00000 0,00000 0,00000 Sandanski 0,514059 1,55548 0,00000 0,00000 Sevlievo 0,367165 0,00000 0,27974 1,59105 Shumen 0,588252 0,15207 0,00000 3,54952 Silistra 0,496171 0,00000 0,94087 5,37518 Sliven 0,674853 0,00000 0,00000 3,98163 Slivnica 0,392442 0,44386 0,00000 0,00000 Smolqn 0,353931 0,00000 1,33146 1,88763 Sofia 1 0,00000 0,00000 0,00000 Sredec 0,405766 0,00000 0,00000 12,8493 Stara Zagora 0,896024 3,20618 0,00000 4,76051 Svilengrad 0,578616 0,00000 0,86793 0,86793 Svishtov 0,433842 0,21304 0,00000 0,00000 Svoge 0,387901 0,00000 0,00000 0,00000 Targovishte 0,635967 0,03924 0,00000 0,74422 Tervel 0,270025 0,10386 0,00000 8,49540 Teteven 0,431746 0,00000 0,00000 0,00000 Topolovgrad 0,148763 0,00000 0,00000 0,00000 Tran 0,222769 0,00000 0,00000 7,20286 Troqn 0,500173 0,45472 0,00000 0,00000 Trqvna 0,521642 0,00000 1,99585 0,00000 Tsarevo 0,874763 0,00000 0,00000 10,78870 Tutrakan 0,278922 0,13696 0,00000 0,00000 V.Tarnovo 0,725796 1,24377 0,00000 0,00000 Varna 0,942435 0,00000 19,47700 32,67110 Veliki Preslav 0,430849 0,00000 0,00000 0,00000 Velingrad 0,503217 0,00000 1,50965 0,50322 Vidin 0,570725 0,00000 1,52193 9,22671 Vraca 0,691673 0,07358 0,00000 0,73582 Zlatograd 0,320859 0,00000 0,00000 0,00000 (Source: Author’s calculations)

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APPENDIX B:

Data Envelopment Analysis for Bulgarian District Courts, 2012

Input slacks Court Efficiency judges computers admin Aitos 0,499462 0,00000 0,00000 1,44930 Ardino 0,142653 0,00000 0,36765 6,33589 Asenovgrad 0,472496 0,00000 0,00000 3,97680 Balchik 0,280235 0,02290 0,00000 0,00000 Belogradchik 0,288987 0,26528 0,00000 0,00000 Berkovica 0,328844 0,00000 0,00000 0,36211 Blagoevgrad 0,666317 0,00000 0,00000 3,73214 Botevgrad 0,760055 0,00000 0,71680 10,28860 Bqla 0,616887 0,00000 3,31012 5,62721 Bqla Slatina 0,458418 0,00000 0,00000 2,87599 Breznik 0,176215 0,00000 0,41690 1,07878 Burgas 0,869922 6,78688 0,00000 9,24200 Chepelare 0,59337 0,00000 5,21490 1,01307 Cherven Briag 0,444261 0,00000 0,06863 3,47824 Chirpan 0,364427 0,00000 0,42072 3,21763 Devin 0,526298 0,00000 3,98788 3,90230 Devnia 0,532019 0,00000 1,14622 5,22935 Dimitrovgrad 0,609682 0,00000 0,00000 8,06732 Dobrich 0,549277 1,82529 0,00000 0,50703 Drqnovo 1 0,00000 0,00000 0,00000 Dulovo 0,402371 0,00000 0,95195 2,46329 Dupnica 0,596657 0,00000 0,00000 9,64233 Elena 0,611054 0,00000 0,96378 26,52870 Elhovo 0,284677 0,10928 0,00000 0,00000 Elin Pelin 0,427423 0,20331 0,00000 0,00000 Etropole 0,252633 0,02609 0,00000 0,00000 Gabrovo 0,690497 0,00000 0,90382 16,33620 Gen, Toshevo 0,279966 0,00000 0,00000 1,53646 Goce Delchev 0,426392 0,00000 0,00000 1,22460 Gorna Orqhovica 0,484568 0,00000 0,00000 3,58664 Harmanli 0,835143 0,00000 2,81097 5,94784 Haskovo 0,646713 0,00000 0,00000 8,64970 Ihtiman 0,517307 0,00000 0,00000 1,90754 Isperih 0,534762 0,00000 1,26517 3,27379 Ivailovgrad 0,170673 0,00000 0,09852 7,23904 Kardjali 0,745287 0,00000 0,00000 12,65200 Karlovo 0,515006 0,00000 0,00000 0,96409 Karnobat 0,417838 0,00000 0,00000 1,86905 Kavarna 0,267775 0,05270 0,00000 0,00000

- 46 -

Input slacks Court Efficiency Judges computers admin Kazanlak 0,603396 0,00000 0,00000 10,83770 Kneja 0,37353 0,00000 0,51019 1,16614 Kostinbrod 0,390445 0,06775 0,00000 0,00000 Kotel 0,60829 0,00000 2,78429 0,86051 Kozlodui 0,534838 0,00000 0,00000 1,97218 Krumuvgrad 0,236816 0,00000 0,61033 0,09819 Kubrat 0,352377 0,00000 0,12892 0,39535 Kula 0,478059 0,00000 3,24536 21,37270 Kustendil 0,561496 0,00000 0,00000 10,97580 Levski 0,491439 0,00000 0,56735 4,33905 Lom 0,389035 0,00000 1,36953 8,53980 Lovech 0,582848 0,00000 0,00000 3,39818 Lukovit 0,389042 0,00000 0,92042 2,38169 Madan 0,467593 0,00000 3,07547 2,99944 Malko Tarnovo 0,174403 0,00000 0,00000 5,85494 Mezdra 0,399373 0,00000 0,00000 1,86610 Momchilgrad 0,387346 0,00000 1,30375 2,75866 Montana 0,529966 0,00000 0,00000 0,34069 Nesebar 0,724975 0,00000 0,00000 3,82332 Nikopol 0,407786 0,00000 0,96476 2,49644 Nova Zagora 0,815261 0,00000 6,00509 8,25203 Novi Pazar 0,512317 0,00000 3,15304 8,10960 Omurtag 0,363723 0,00000 0,05618 2,84768 Orqhovo 0,484969 0,00000 2,11731 3,93890 Panaguirishte 0,479613 0,00000 2,09392 3,89539 Parvomai 0,775691 0,00000 7,59294 2,87573 Pavlikeni 0,637655 0,00000 2,14625 4,54135 Pazardjik 0,679116 0,00000 0,00000 9,57826 Pernik 1 0,00000 0,00000 0,00000 Peshtera 0,426006 0,00000 0,00000 5,30220 Petrich 0,487003 0,00000 0,00000 0,41743 Pirdop 0,794114 0,00000 5,22307 3,50572 Pleven 0,683738 2,45912 0,00000 5,98417 Plovdiv 0,992868 9,19840 0,00000 0,00000 Pomorie 0,425796 0,08380 0,00000 0,00000 Popovo 0,432359 0,00000 1,79622 5,54685 Provadia 0,728722 0,00000 0,00000 2,11455 Qmbol 0,461178 0,00000 0,00000 5,31173 Radnevo 0,394799 0,00000 0,45578 3,48578 Radomir 0,548806 0,00000 1,61518 9,07537 Razgrad 0,532784 0,00000 0,00000 0,88321 Razlog 0,57029 0,16657 0,00000 0,00000 Ruse 0,729144 0,99214 0,00000 7,03593

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Input slacks Court Efficiency Judges computers admin Samokov 0,402983 0,00000 0,00000 0,76038 Sandanski 0,871424 0,00000 0,00000 5,46708 Sevlievo 0,512354 0,00000 0,00000 7,29183 Shumen 0,546722 0,00000 0,00000 5,31259 Silistra 0,444067 0,00000 0,00000 7,53189 Sliven 0,732526 0,00000 0,00000 18,33910 Slivnica 0,356893 0,24484 0,00000 0,00000 Smolqn 0,458552 0,00000 0,00000 6,69671 Sofia 1 0,00000 0,00000 0,00000 Sredec 0,633546 0,00000 0,99925 27,50520 Stara Zagora 0,730171 0,65778 0,00000 5,70407 Svilengrad 0,815097 0,00000 1,75610 9,64200 Svishtov 0,506733 0,00000 0,00000 1,86855 Svoge 0,445344 0,00000 1,49896 3,17172 Targovishte 0,441551 0,14079 0,00000 0,00000 Tervel 0,291889 0,00000 1,04416 0,12103 Teteven 0,463873 0,00000 0,53553 4,09566 Topolovgrad 0,251864 0,00000 1,40470 0,86002 Tran 0,296798 0,00000 0,00000 12,32590 Troqn 0,520317 0,00000 0,49071 7,56363 Trqvna 0,280324 0,00000 1,00278 0,11623 Tsarevo 0,47836 0,00000 0,17501 0,53670 Tutrakan 0,330725 0,00000 0,38181 2,92006 V. Tarnovo 0,672347 0,84359 0,00000 0,64936 Varna 1 0,00000 0,00000 0,00000 Veliki Preslav 0,385633 0,00000 0,83084 3,40486 Velingrad 0,578613 0,00000 1,12430 8,41106 Vidin 0,494166 0,12354 0,00000 0,37945 Vraca 0,693032 0,00000 0,00000 1,94089 Zlatograd 0,644199 0,00000 7,59421 4,96505 (Source: Author’s calculations)

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APPENDIX C

Table 1: Malmquist Index for Bulgarian District Courts for 2006 and 2012

Period dmu tfpch effch Techch pech sech 1. 2006-2012 Lovech 1,46742 2,21708 0,661866 1,81168 1,22377 2. 2006-2012 Nesebar 0,90053 1,31534 0,684644 1,49689 0,87871 3. 2006-2012 Harmanli 1,02781 1,48181 0,693617 1,51987 0,97496 4. 2006-2012 Ardino 0,43414 0,60750 0,714638 0,73794 0,82323 5. 2006-2012 Pernik 1,55319 1,99841 0,777213 1,90007 1,05176 6. 2006-2012 Panaguirishte 0,35144 0,47764 0,735797 0,43982 1,08598 7. 2006-2012 Kneja 0,28761 0,41823 0,687685 0,48600 0,86055 8. 2006-2012 Kustendil 0,76918 1,11621 0,689101 0,96398 1,15791 9. 2006-2012 Radomir 2,11526 2,74659 0,770144 2,01335 1,36419 10. 2006-2012 Petrich 1,01264 1,45119 0,697795 1,43318 1,01257 11. 2006-2012 Kula 0,39241 0,53848 0,728734 0,63621 0,84638 12. 2006-2012 Ihtiman 0,70308 1,02568 0,685482 1,03431 0,99165 13. 2006-2012 Tran 0,29797 0,38690 0,77014 0,39286 0,98483 14. 2006-2012 Kardjali 0,53447 0,71522 0,747284 0,60414 1,18386 15. 2006-2012 Sandanski 0,55832 0,81713 0,683261 0,89422 0,91379 16. 2006-2012 Trqvna 0,35937 0,51552 0,697107 0,60582 0,85095 17. 2006-2012 Ruse 1,39355 2,08084 0,669706 1,65494 1,25735 18. 2006-2012 Orqhovo 0,36770 0,53798 0,683488 0,56191 0,95740 19. 2006-2012 Sliven 1,30465 1,69801 0,768338 1,41432 1,20058 20. 2006-2012 Svoge 0,39252 0,51801 0,757751 0,61784 0,83842 21. 2006-2012 Bqla Slatina 0,90832 1,20688 0,752671 1,02455 1,17789 22. 2006-2012 Nova Zagora 0,60502 0,84123 0,719209 0,77383 1,08711 23. 2006-2012 Kazanlak 1,21342 1,68366 0,720702 1,51763 1,10944 24. 2006-2012 Krumuvgrad 0,38188 0,51093 0,747424 0,37855 1,34969 25. 2006-2012 Silistra 0,59343 0,87377 0,679217 0,85876 1,01739 26. 2006-2012 Peshtera 0,25340 0,36403 0,696081 0,39511 0,92135 27. 2006-2012 Chirpan 0,56805 0,75551 0,751881 0,72561 1,04122 28. 2006-2012 Slivnica 0,62118 0,85824 0,723784 0,99736 0,86051 29. 2006-2012 Mezdra 0,82780 1,17346 0,705442 1,04217 1,12597 30. 2006-2012 Pavlikeni 0,75609 1,12618 0,671381 1,18275 0,95216 31. 2006-2012 Plovdiv 0,92614 1,36224 0,679873 1,34383 1,01377 32. 2006-2012 Kostinbrod 0,94360 1,28585 0,733833 1,22085 1,05324 33. 2006-2012 Breznik 0,22936 0,33240 0,690008 0,38000 0,87473 34. 2006-2012 Montana 0,94506 1,41684 0,667023 1,29752 1,09196 35. 2006-2012 Troqn 0,68981 0,95589 0,721648 0,95475 1,00119 36. 2006-2012 Lukovit 0,65084 0,89816 0,724641 0,95916 0,93640 37. 2006-2012 Teteven 0,69785 1,01608 0,686812 1,02519 0,99111 38. 2006-2012 Nikopol 0,67793 0,89179 0,760194 0,71131 1,25373 39. 2006-2012 Kavarna 0,70001 0,94774 0,738603 1,07777 0,87936 40. 2006-2012 Pirdop 0,49289 0,73747 0,668352 0,77438 0,95233

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41. 2006-2012 Karlovo 0,67935 0,99574 0,682256 1,04733 0,95076 42. 2006-2012 Dupnica 0,79154 1,15431 0,685735 0,91619 1,25989 43. 2006-2012 Radnevo 0,68377 0,89947 0,760194 0,44168 2,03643 44. 2006-2012 Dobrich 0,61241 0,88381 0,692928 0,86833 1,01782 45. 2006-2012 Velingrad 0,52033 0,76437 0,680726 0,72162 1,05924 46. 2006-2012 Goce Delchev 0,89178 1,24356 0,717126 1,07286 1,15917 47. 2006-2012 Shumen 0,59198 0,83211 0,711418 0,78285 1,06293 48. 2006-2012 Svilengrad 1,11438 1,57765 0,706356 1,54925 1,01833 49. 2006-2012 Sofia 0,76295 1,09763 0,695091 1,36182 0,80600 50. 2006-2012 Novi Pazar 0,83701 1,09752 0,762642 0,95439 1,14997 51. 2006-2012 Svishtov 0,81068 1,21296 0,668352 1,09951 1,10318 52. 2006-2012 Lom 0,45100 0,58885 0,765893 0,64844 0,90810 53. 2006-2012 Cherven Briag 0,72669 1,03184 0,704263 0,76636 1,34641 54. 2006-2012 Popovo 0,33563 0,43581 0,770144 0,43423 1,00362 55. 2006-2012 Karnobat 0,36853 0,54044 0,681913 0,58266 0,92752 56. 2006-2012 Madan 0,86744 1,12634 0,770143 0,87529 1,28682 57. 2006-2012 Botevgrad 0,71256 1,05051 0,678305 1,01581 1,03416 58. 2006-2012 Burgas 0,59749 0,88725 0,673421 0,78235 1,13409 59. 2006-2012 Drqnovo 0,40584 0,52697 0,770140 0,65322 0,80672 60. 2006-2012 Aitos 2,19999 2,98184 0,737768 2,66444 1,11914 61. 2006-2012 Elena 1,00644 1,30683 0,770141 1,51202 0,86429 62. 2006-2012 Veliki Preslav 0,82885 1,09223 0,758865 1,06576 1,02484 63. 2006-2012 Levski 0,83609 1,13123 0,739102 1,02091 1,10805 64. 2006-2012 Chepelare 2,10174 3,04431 0,690382 1,00000 3,04431 65. 2006-2012 Isperih 0,40123 0,57035 0,703491 0,58943 0,96763 66. 2006-2012 Balchik 0,43327 0,62972 0,688035 0,61166 1,02952 67. 2006-2012 Sredec 0,65344 0,93374 0,699814 1,22357 0,76313 68. 2006-2012 Vidin 1,40321 2,04348 0,686677 1,57468 1,29771 69. 2006-2012 Belogradchik 0,64769 0,85475 0,757751 0,98426 0,86841 70. 2006-2012 Kozlodui 0,59610 0,86772 0,686977 1,18466 0,73246 71. 2006-2012 Berkovica 0,99043 1,42153 0,696737 1,19593 1,18864 72. 2006-2012 Pleven 1,94487 2,67672 0,726560 1,82466 1,46697 73. 2006-2012 Blagoevgrad 1,15854 1,73036 0,669538 1,35415 1,27782 74. 2006-2012 Devnia 0,65574 0,96156 0,681960 0,90785 1,05916 75. 2006-2012 Targovishte 1,02518 1,46212 0,701157 1,22444 1,19455 76. 2006-2012 Smolqn 0,46114 0,63062 0,731262 0,36695 1,71855 77. 2006-2012 Kubrat 0,57133 0,85028 0,671935 0,92070 0,92351 78. 2006-2012 Tervel 0,32231 0,46590 0,691810 0,66740 0,69808 79. 2006-2012 Provadia 0,71122 1,03333 0,688500 1,12556 0,91776 80. 2006-2012 Malko Tarnovo 0,54125 0,72417 0,747420 2,17034 0,33366 81. 2006-2012 V.Tarnovo 0,51912 0,75565 0,686977 0,77266 0,97799 82. 2006-2012 Varna 1,32647 1,80759 0,733833 1,56442 1,15544 83. 2006-2012 Qmbol 0,76648 1,11187 0,689370 0,94676 1,17439 84. 2006-2012 Tsarevo 1,68477 2,39115 0,704585 2,15483 1,10967 85. 2006-2012 Topolovgrad 0,19884 0,29185 0,681295 0,32499 0,89803 86. 2006-2012 Stara Zagora 1,01722 1,62104 0,627515 1,23731 1,31013

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87. 2006-2012 Zlatograd 0,91550 1,28517 0,712361 1,21533 1,05749 88. 2006-2012 Dimitrovgrad 1,02888 1,36782 0,752143 1,30353 1,04932 89. 2006-2012 Gabrovo 0,77963 1,14921 0,678410 1,17765 0,97585 90. 2006-2012 Momchilgrad 0,59710 0,87588 0,681720 0,91528 0,95695 91. 2006-2012 Haskovo 1,27739 1,83178 0,697345 1,54528 1,18544 92. 2006-2012 Omurtag 0,23159 0,34652 0,668352 0,36626 0,94610 93. 2006-2012 Samokov 0,82295 1,20045 0,685536 0,96922 1,23865 94. 2006-2012 Elhovo 0,45326 0,62030 0,730707 0,55064 1,12655 95. 2006-2012 Devin 0,62825 0,92292 0,680726 1,08037 0,85426 96. 2006-2012 Asenovgrad 0,46200 0,68008 0,679331 0,64742 1,05044 97. 2006-2012 Pomorie 0,17258 0,24623 0,700902 0,29107 0,84594 98. 2006-2012 Etropole 0,35763 0,47421 0,754149 0,61193 0,77494 99. 2006-2012 Kotel 1,71429 2,22594 0,770140 1,80996 1,22983 100. 2006-2012 Bqla 0,74616 1,06268 0,702158 0,98740 1,07623 101. 2006-2012 Gen. Toshevo 0,50021 0,71807 0,696601 0,74393 0,96523 102. 2006-2012 Elin Pelin 0,81645 1,21353 0,672795 1,17102 1,03632 103. 2006-2012 Razgrad 0,70214 0,92593 0,758312 0,86475 1,07075 104. 2006-2012 G. Orqhovica 1,21362 1,71449 0,707861 1,47291 1,16402 105. 2006-2012 Pazardjik 1,36497 2,01001 0,679086 1,80332 1,11462 106. 2006-2012 Ivailovgrad 0,47982 0,71062 0,675209 0,83747 0,84853 107. 2006-2012 Dulovo 0,45273 0,64055 0,706839 0,51580 1,24175 108. 2006-2012 Tutrakan 0,33382 0,47593 0,701416 0,45826 1,03855 109. 2006-2012 Vraca 1,54544 2,16661 0,713300 1,78453 1,21411 110. 2006-2012 Razlog 1,19983 1,55794 0,770140 1,55232 1,00362 111. 2006-2012 Parvomai 0,47831 0,70265 0,680726 0,71966 0,97636 112. 2006-2012 Sevlievo 1,45995 2,02783 0,719957 0,90725 2,23513 Total effect on TE 12,39428 (Source: Author’s Calculations)

- 51 -

APPENDIX D

Table 1: Data Envelopment Analysis of Bulgarian District Courts, 2005 - 2012 year Court VRS_TE CRS_TE SCALE RTS 2005 Aitos 0.555885 0.450553 0.810514 Irs 2006 Aitos 0.434513 0.373424 0.859407 Irs 2007 Aitos 0.466932 0.391754 0.838995 Irs 2008 Aitos 0.063166 0.063166 1.000000 - 2009 Aitos 0.410631 0.357316 0.870163 Irs 2010 Aitos 0.551390 0.479799 0.870163 Irs 2011 Aitos 0.843149 0.703649 0.834549 Irs 2012 Aitos 0.539743 0.469665 0.870163 Irs 2005 Ardino 0.107811 0.107811 1.000000 - 2006 Ardino 0.146355 0.146355 1.000000 - 2007 Ardino 0.257028 0.257028 1.000000 - 2008 Ardino 0.034445 0.034445 1.000000 - 2009 Ardino 0.156922 0.156922 1.000000 - 2010 Ardino 0.131700 0.131700 1.000000 - 2011 Ardino 0.270647 0.246085 0.909249 Irs 2012 Ardino 0.135898 0.135898 1.000000 - 2005 Asenovgrad 0.510064 0.473365 0.928051 Irs 2006 Asenovgrad 0.341240 0.322115 0.943952 Irs 2007 Asenovgrad 0.320576 0.307647 0.959670 Irs 2008 Asenovgrad 0.061676 0.061676 1.000000 - 2009 Asenovgrad 0.329622 0.321921 0.976638 Irs 2010 Asenovgrad 0.429355 0.417708 0.972874 Irs 2011 Asenovgrad 0.536087 0.510397 0.952077 Irs 2012 Asenovgrad 0.454974 0.442632 0.972874 Irs 2005 Balchik 0.324423 0.279653 0.862001 Irs 2006 Balchik 0.209937 0.188499 0.897883 Irs 2007 Balchik 0.279693 0.257327 0.920035 Irs 2008 Balchik 0.053770 0.053770 1.000000 - 2009 Balchik 0.306517 0.290926 0.949133 Irs 2010 Balchik 0.377051 0.357872 0.949133 Irs 2011 Balchik 0.331117 0.314274 0.949133 Irs 2012 Balchik 0.288449 0.273776 0.949133 Irs 2005 Belogradchik 0.255590 0.255590 1.000000 - 2006 Belogradchik 0.254773 0.240212 0.942846 Irs 2007 Belogradchik 0.338296 0.338296 1.000000 - 2008 Belogradchik 0.051531 0.051531 1.000000 - 2009 Belogradchik 0.249395 0.249395 1.000000 - 2010 Belogradchik 0.307421 0.307421 1.000000 - 2011 Belogradchik 0.367782 0.367782 1.000000 - 2012 Belogradchik 0.283206 0.283206 1.000000 -

- 52 -

year Court VRS_TE CRS_TE SCALE RTS 2005 Berkovica 0.281547 0.256594 0.911372 Irs 2006 Berkovica 0.347160 0.317709 0.915165 Irs 2007 Berkovica 0.257540 0.235159 0.913099 Irs 2008 Berkovica 0.039115 0.039115 1.000000 - 2009 Berkovica 0.322054 0.294067 0.913099 Irs 2010 Berkovica 0.376278 0.342930 0.911372 Irs 2011 Berkovica 0.397664 0.363107 0.913099 Irs 2012 Berkovica 0.350082 0.319055 0.911372 Irs 2005 Blagoevgrad 0.549978 0.539255 0.980502 Irs 2006 Blagoevgrad 0.448623 0.433189 0.965598 Irs 2007 Blagoevgrad 0.446921 0.440902 0.986533 Irs 2008 Blagoevgrad 0.419723 0.418826 0.997862 Irs 2009 Blagoevgrad 0.337622 0.336900 0.997862 Irs 2010 Blagoevgrad 0.613477 0.610870 0.995751 Irs 2011 Blagoevgrad 0.671519 0.670832 0.998976 Irs 2012 Blagoevgrad 0.644597 0.643219 0.997862 Irs 2005 Botevgrad 0.449184 0.389726 0.867630 Irs 2006 Botevgrad 0.310586 0.289187 0.931099 Irs 2007 Botevgrad 0.404481 0.383906 0.949133 Irs 2008 Botevgrad 0.067035 0.067035 1.000000 - 2009 Botevgrad 0.520981 0.487954 0.936605 Irs 2010 Botevgrad 0.705767 0.664438 0.941441 Irs 2011 Botevgrad 0.785193 0.743160 0.946468 Irs 2012 Botevgrad 0.722037 0.659152 0.912906 Irs 2005 Bqla 0.496272 0.427787 0.862001 Irs 2006 Bqla 0.363876 0.336334 0.924310 Irs 2007 Bqla 0.323725 0.298117 0.920896 Irs 2008 Bqla 0.062483 0.062483 1.000000 - 2009 Bqla 0.324418 0.293881 0.905872 Irs 2010 Bqla 0.570835 0.515677 0.903373 Irs 2011 Bqla 0.585423 0.538131 0.919217 Irs 2012 Bqla 0.580104 0.524266 0.903745 Irs 2005 Bqla Slatina 0.408714 0.352312 0.862001 Irs 2006 Bqla Slatina 0.226460 0.211843 0.935455 Irs 2007 Bqla Slatina 0.261003 0.237691 0.910684 Irs 2008 Bqla Slatina 0.072997 0.072997 1.000000 - 2009 Bqla Slatina 0.407708 0.371293 0.910684 Irs 2010 Bqla Slatina 0.532369 0.486106 0.913099 Irs 2011 Bqla Slatina 0.440925 0.403342 0.914762 Irs 2012 Bqla Slatina 0.452760 0.414168 0.914762 Irs 2005 Breznik 0.140095 0.140095 1.000000 - 2006 Breznik 0.122577 0.122528 0.999598 Irs 2007 Breznik 0.103379 0.103379 1.000000 - 2008 Breznik 0.024939 0.024939 1.000000 -

- 53 -

year Court VRS_TE CRS_TE SCALE RTS 2009 Breznik 0.126344 0.126344 1.000000 - 2010 Breznik 0.142155 0.142155 1.000000 - 2011 Breznik 0.195726 0.184333 0.941793 Irs 2012 Breznik 0.187763 0.171013 0.910792 Irs 2005 Burgas 0.712479 0.672557 0.943967 Drs 2006 Burgas 0.486621 0.479564 0.985498 Irs 2007 Burgas 0.551110 0.508574 0.922818 Drs 2008 Burgas 0.564111 0.504712 0.894705 Drs 2009 Burgas 0.468348 0.414844 0.885760 Drs 2010 Burgas 0.899824 0.785285 0.872709 Drs 2011 Burgas 0.915844 0.798186 0.871530 Drs 2012 Burgas 0.867195 0.756293 0.872114 Drs 2005 Chepelare 0.172673 0.172673 1.000000 - 2006 Chepelare 1.000000 1.000000 1.000000 - 2007 Chepelare 0.202013 0.202013 1.000000 - 2008 Chepelare 0.087058 0.087058 1.000000 - 2009 Chepelare 0.242695 0.226543 0.933448 Irs 2010 Chepelare 0.268615 0.259223 0.965035 Irs 2011 Chepelare 0.319762 0.313964 0.981870 Irs 2012 Chepelare 0.593370 0.511389 0.861838 Irs 2005 Cherven Briag 0.325076 0.280216 0.862001 Irs 2006 Cherven Briag 0.276571 0.253108 0.915165 Irs 2007 Cherven Briag 0.342705 0.295424 0.862034 Irs 2008 Cherven Briag 0.061565 0.061565 1.000000 - 2009 Cherven Briag 0.435246 0.377633 0.867630 Irs 2010 Cherven Briag 0.521082 0.461463 0.885585 Irs 2011 Cherven Briag 0.670730 0.591080 0.881249 Irs 2012 Cherven Briag 0.444433 0.385603 0.867630 Irs 2005 Chirpan 0.326276 0.283087 0.867630 Irs 2006 Chirpan 0.248798 0.227691 0.915165 Irs 2007 Chirpan 0.397309 0.350128 0.881249 Irs 2008 Chirpan 0.074754 0.074754 1.000000 - 2009 Chirpan 0.266403 0.239307 0.898291 Irs 2010 Chirpan 0.342748 0.297379 0.867630 Irs 2011 Chirpan 0.362759 0.321254 0.885585 Irs 2012 Chirpan 0.355688 0.314992 0.885585 Irs 2005 Devin 0.319965 0.300328 0.938630 Irs 2006 Devin 0.302108 0.291384 0.964503 Irs 2007 Devin 0.298344 0.278489 0.933448 Irs 2008 Devin 0.048098 0.048098 1.000000 - 2009 Devin 0.333898 0.311677 0.933448 Irs 2010 Devin 0.433347 0.404507 0.933448 Irs 2011 Devin 0.558291 0.487717 0.873589 Irs 2012 Devin 0.468385 0.437214 0.933448 Irs

- 54 -

year Court VRS_TE CRS_TE SCALE RTS 2005 Devnia 0.365923 0.327063 0.893803 Irs 2006 Devnia 0.226633 0.203490 0.897883 Irs 2007 Devnia 0.197849 0.183556 0.927762 Irs 2008 Devnia 0.029454 0.029454 1.000000 - 2009 Devnia 0.425166 0.376521 0.885585 Irs 2010 Devnia 0.614959 0.560456 0.911372 Irs 2011 Devnia 0.635076 0.579887 0.913099 Irs 2012 Devnia 0.501692 0.452566 0.902079 Irs 2005 Dimitrovgrad 0.473544 0.435914 0.920536 Irs 2006 Dimitrovgrad 0.499230 0.470649 0.942751 Irs 2007 Dimitrovgrad 0.045857 0.045857 1.000000 - 2008 Dimitrovgrad 0.061189 0.061189 1.000000 - 2009 Dimitrovgrad 0.465766 0.428754 0.920536 Irs 2010 Dimitrovgrad 0.548726 0.511385 0.931949 Irs 2011 Dimitrovgrad 0.641203 0.603655 0.941441 Irs 2012 Dimitrovgrad 0.580781 0.538653 0.927463 Irs 2005 Dobrich 0.505931 0.494059 0.976534 Irs 2006 Dobrich 0.331444 0.329881 0.995287 Irs 2007 Dobrich 0.417725 0.387847 0.928475 Irs 2008 Dobrich 0.458025 0.436035 0.951988 Irs 2009 Dobrich 0.450769 0.418501 0.928415 Irs 2010 Dobrich 0.709518 0.643013 0.906268 Drs 2011 Dobrich 0.745339 0.675477 0.906268 Drs 2012 Dobrich 0.546555 0.495325 0.906268 Drs 2005 Drqnovo 0.324070 0.324070 1.000000 - 2006 Drqnovo 0.320199 0.320199 1.000000 - 2007 Drqnovo 1.000000 0.513757 0.513757 Irs 2008 Drqnovo 0.083967 0.083803 0.998049 Irs 2009 Drqnovo 0.402566 0.338345 0.840471 Irs 2010 Drqnovo 0.352494 0.322191 0.914034 Irs 2011 Drqnovo 0.421787 0.353877 0.838995 Irs 2012 Drqnovo 1.000000 0.861838 0.861838 Irs 2005 Dulovo 0.278349 0.253654 0.911281 Irs 2006 Dulovo 0.231661 0.205515 0.887140 Irs 2007 Dulovo 0.268615 0.237555 0.884373 Irs 2008 Dulovo 0.068300 0.068300 1.000000 - 2009 Dulovo 0.365708 0.318225 0.870163 Irs 2010 Dulovo 0.468532 0.407699 0.870163 Irs 2011 Dulovo 0.627132 0.523372 0.834549 Irs 2012 Dulovo 0.412396 0.344165 0.834549 Irs 2005 Dupnica 0.467343 0.445783 0.953866 Irs 2006 Dupnica 0.381755 0.369606 0.968175 Irs 2007 Dupnica 0.398971 0.384634 0.964065 Irs 2008 Dupnica 0.458196 0.457727 0.998976 Irs

- 55 -

year Court VRS_TE CRS_TE SCALE RTS 2009 Dupnica 0.477522 0.477033 0.998976 Irs 2010 Dupnica 0.604224 0.594165 0.983352 Irs 2011 Dupnica 0.691099 0.684038 0.989783 Irs 2012 Dupnica 0.578896 0.567456 0.980238 Irs 2005 Elena 0.472045 0.429206 0.909249 Irs 2006 Elena 0.430062 0.395416 0.919438 Irs 2007 Elena 0.422416 0.384082 0.909249 Irs 2008 Elena 0.044075 0.044075 1.000000 - 2009 Elena 0.355934 0.321719 0.903871 Irs 2010 Elena 0.557147 0.504671 0.905812 Irs 2011 Elena 0.788920 0.524263 0.664533 Irs 2012 Elena 0.786507 0.522660 0.664533 Irs 2005 Elhovo 0.238155 0.215724 0.905812 Irs 2006 Elhovo 0.154797 0.143065 0.924214 Irs 2007 Elhovo 0.165457 0.159623 0.964739 Irs 2008 Elhovo 0.028721 0.028721 1.000000 - 2009 Elhovo 0.213343 0.202491 0.949133 Irs 2010 Elhovo 0.288591 0.277066 0.960061 Irs 2011 Elhovo 0.301729 0.286141 0.948338 Irs 2012 Elhovo 0.285942 0.271170 0.948338 Irs 2005 Elin Pelin 0.285021 0.285021 1.000000 - 2006 Elin Pelin 0.258110 0.228980 0.887140 Irs 2007 Elin Pelin 0.257743 0.233466 0.905812 Irs 2008 Elin Pelin 0.062996 0.062996 1.000000 - 2009 Elin Pelin 0.276740 0.254611 0.920035 Irs 2010 Elin Pelin 0.410950 0.378088 0.920035 Irs 2011 Elin Pelin 0.462755 0.425751 0.920035 Irs 2012 Elin Pelin 0.425133 0.401568 0.944569 Irs 2005 Etropole 0.282188 0.256579 0.909249 Irs 2006 Etropole 0.210092 0.189896 0.903871 Irs 2007 Etropole 0.223040 0.223040 1.000000 - 2008 Etropole 0.031076 0.031076 1.000000 - 2009 Etropole 0.200224 0.200224 1.000000 - 2010 Etropole 0.302500 0.302500 1.000000 - 2011 Etropole 0.350491 0.350491 1.000000 - 2012 Etropole 0.260410 0.260410 1.000000 - 2005 Gabrovo 0.503386 0.487885 0.969206 Irs 2006 Gabrovo 0.435772 0.420973 0.966038 Irs 2007 Gabrovo 0.436595 0.412125 0.943952 Irs 2008 Gabrovo 0.400095 0.379536 0.948614 Irs 2009 Gabrovo 0.312827 0.299921 0.958745 Irs 2010 Gabrovo 0.574169 0.541988 0.943952 Irs 2011 Gabrovo 0.771285 0.728056 0.943952 Irs 2012 Gabrovo 0.670574 0.636116 0.948614 Irs

- 56 -

year Court VRS_TE CRS_TE SCALE RTS 2005 Gen. Toshevo 0.325843 0.319935 0.981870 Irs 2006 Gen. Toshevo 0.260857 0.232587 0.891626 Irs 2007 Gen. Toshevo 0.181919 0.169834 0.933567 Irs 2008 Gen. Toshevo 0.034458 0.034458 1.000000 - 2009 Gen. Toshevo 0.271733 0.246140 0.905812 Irs 2010 Gen. Toshevo 0.343045 0.295705 0.862001 Irs 2011 Gen. Toshevo 0.377676 0.325558 0.862001 Irs 2012 Gen. Toshevo 0.278263 0.252054 0.905812 Irs 2005 Goce Delchev 0.398705 0.381305 0.956358 Irs 2006 Goce Delchev 0.407747 0.384051 0.941885 Irs 2007 Goce Delchev 0.309136 0.303168 0.980696 Irs 2008 Goce Delchev 0.032066 0.032066 1.000000 - 2009 Goce Delchev 0.352816 0.342905 0.971909 Irs 2010 Goce Delchev 0.413373 0.403549 0.976236 Irs 2011 Goce Delchev 0.440495 0.430027 0.976236 Irs 2012 Goce Delchev 0.372371 0.363522 0.976236 Irs 2005 G.Orqhovica 0.457958 0.455591 0.994830 Irs 2006 G.Orqhovica 0.488921 0.472317 0.966038 Irs 2007 G.Orqhovica 0.426554 0.407541 0.955428 Irs 2008 G.Orqhovica 0.030985 0.030985 1.000000 - 2009 G.Orqhovica 0.415905 0.404931 0.973615 Irs 2010 G.Orqhovica 0.496390 0.492967 0.993104 Irs 2011 G.Orqhovica 0.536944 0.533241 0.993104 Irs 2012 G.Orqhovica 0.468378 0.463592 0.989783 Irs 2005 Harmanli 0.681242 0.479999 0.704594 Irs 2006 Harmanli 0.412390 0.377443 0.915258 Irs 2007 Harmanli 0.644719 0.542450 0.841373 Irs 2008 Harmanli 0.068762 0.068762 1.000000 - 2009 Harmanli 0.535346 0.446773 0.834549 Irs 2010 Harmanli 0.674519 0.562919 0.834549 Irs 2011 Harmanli 0.753730 0.646854 0.858204 Irs 2012 Harmanli 0.807040 0.692606 0.858205 Irs 2005 Haskovo 0.449497 0.438996 0.976638 Irs 2006 Haskovo 0.498248 0.490464 0.984376 Irs 2007 Haskovo 0.508806 0.482660 0.948614 Irs 2008 Haskovo 0.533454 0.509677 0.955428 Irs 2009 Haskovo 0.397086 0.385316 0.970359 Irs 2010 Haskovo 0.540400 0.522662 0.967177 Irs 2011 Haskovo 0.687175 0.677921 0.986533 Irs 2012 Haskovo 0.626217 0.609694 0.973615 Irs 2005 Ihtiman 0.459356 0.383355 0.834549 Irs 2006 Ihtiman 0.387917 0.352077 0.907607 Irs 2007 Ihtiman 0.346478 0.315840 0.911575 Irs 2008 Ihtiman 0.052907 0.052907 1.000000 -

- 57 - year Court VRS_TE CRS_TE SCALE RTS 2009 Ihtiman 0.312500 0.285863 0.914762 Irs 2010 Ihtiman 0.461322 0.422000 0.914762 Irs 2011 Ihtiman 0.566949 0.517681 0.913099 Irs 2012 Ihtiman 0.479726 0.438038 0.913099 Irs 2005 Isperih 0.363469 0.305178 0.839627 Irs 2006 Isperih 0.263940 0.235336 0.891626 Irs 2007 Isperih 0.250742 0.228374 0.910792 Irs 2008 Isperih 0.045979 0.045979 1.000000 - 2009 Isperih 0.574957 0.483754 0.841373 Irs 2010 Isperih 0.499479 0.428655 0.858205 Irs 2011 Isperih 0.575754 0.494115 0.858204 Irs 2012 Isperih 0.546446 0.456036 0.834549 Irs 2005 Ivailovgrad 0.077165 0.077165 1.000000 - 2006 Ivailovgrad 0.129077 0.129077 1.000000 - 2007 Ivailovgrad 0.082764 0.082764 1.000000 - 2008 Ivailovgrad 0.005445 0.005445 1.000000 - 2009 Ivailovgrad 0.092565 0.092565 1.000000 - 2010 Ivailovgrad 0.131237 0.131237 1.000000 - 2011 Ivailovgrad 0.217977 0.217977 1.000000 - 2012 Ivailovgrad 0.168795 0.168795 1.000000 - 2005 Kardjali 0.413754 0.387086 0.935547 Irs 2006 Kardjali 0.360127 0.348181 0.966828 Irs 2007 Kardjali 0.401011 0.380333 0.948437 Irs 2008 Kardjali 0.336048 0.319944 0.952077 Irs 2009 Kardjali 0.243249 0.230402 0.947185 Irs 2010 Kardjali 0.499112 0.472751 0.947185 Irs 2011 Kardjali 0.637035 0.603390 0.947185 Irs 2012 Kardjali 0.720216 0.683079 0.948437 Irs 2005 Karlovo 0.386283 0.371196 0.960944 Irs 2006 Karlovo 0.328001 0.316728 0.965631 Irs 2007 Karlovo 0.339453 0.332900 0.980696 Irs 2008 Karlovo 0.080824 0.078554 0.971909 Irs 2009 Karlovo 0.362175 0.361640 0.998523 Irs 2010 Karlovo 0.488941 0.479502 0.980696 Irs 2011 Karlovo 0.471606 0.460398 0.976236 Irs 2012 Karlovo 0.448153 0.437503 0.976236 Irs 2005 Karnobat 0.363618 0.332624 0.914762 Irs 2006 Karnobat 0.254460 0.234901 0.923138 Irs 2007 Karnobat 0.216575 0.204982 0.946468 Irs 2008 Karnobat 0.028357 0.028357 1.000000 - 2009 Karnobat 0.297287 0.279878 0.941441 Irs 2010 Karnobat 0.347503 0.327154 0.941441 Irs 2011 Karnobat 0.493301 0.468209 0.949133 Irs 2012 Karnobat 0.406792 0.382970 0.941441 Irs

- 58 - year Court VRS_TE CRS_TE SCALE RTS 2005 Kavarna 0.229576 0.229576 1.000000 - 2006 Kavarna 0.183779 0.183779 1.000000 - 2007 Kavarna 0.165326 0.165326 1.000000 - 2008 Kavarna 0.042471 0.042471 1.000000 - 2009 Kavarna 0.201798 0.201798 1.000000 - 2010 Kavarna 0.279782 0.279782 1.000000 - 2011 Kavarna 0.296452 0.296452 1.000000 - 2012 Kavarna 0.278897 0.259518 0.930516 Irs 2005 Kazanlak 0.403928 0.391955 0.970359 Irs 2006 Kazanlak 0.394357 0.384959 0.976168 Irs 2007 Kazanlak 0.506598 0.484018 0.955428 Irs 2008 Kazanlak 0.076809 0.074288 0.967177 Irs 2009 Kazanlak 0.570052 0.551341 0.967177 Irs 2010 Kazanlak 0.630574 0.613936 0.973615 Irs 2011 Kazanlak 0.607398 0.578359 0.952191 Irs 2012 Kazanlak 0.585065 0.567724 0.970359 Irs 2005 Kneja 0.198257 0.198257 1.000000 - 2006 Kneja 0.180696 0.180623 0.999598 Irs 2007 Kneja 0.160000 0.160000 1.000000 - 2008 Kneja 0.027032 0.027032 1.000000 - 2009 Kneja 0.331676 0.278484 0.839627 Irs 2010 Kneja 0.509265 0.470698 0.924269 Irs 2011 Kneja 0.566552 0.476171 0.840471 Irs 2012 Kneja 0.408580 0.343055 0.839627 Irs 2005 Kostinbrod 0.456680 0.427984 0.937165 Irs 2006 Kostinbrod 0.330488 0.292668 0.885563 Irs 2007 Kostinbrod 0.332051 0.308978 0.930516 Irs 2008 Kostinbrod 0.081108 0.081108 1.000000 - 2009 Kostinbrod 0.321298 0.295605 0.920035 Irs 2010 Kostinbrod 0.474149 0.411301 0.867449 Irs 2011 Kostinbrod 0.493842 0.428383 0.867449 Irs 2012 Kostinbrod 0.411218 0.378335 0.920035 Irs 2005 Kotel 0.258528 0.235066 0.909249 Irs 2006 Kotel 0.245541 0.245541 1.000000 - 2007 Kotel 0.241352 0.238031 0.986239 Irs 2008 Kotel 0.027265 0.027248 0.999346 Irs 2009 Kotel 0.260640 0.257053 0.986239 Irs 2010 Kotel 0.397397 0.348050 0.875825 Irs 2011 Kotel 0.520079 0.455498 0.875825 Irs 2012 Kotel 0.636305 0.557291 0.875825 Irs 2005 Kozlodui 0.443706 0.375720 0.846777 Irs 2006 Kozlodui 0.384112 0.332386 0.865337 Irs 2007 Kozlodui 0.354117 0.346761 0.979227 Irs 2008 Kozlodui 0.043318 0.043318 1.000000 -

- 59 -

year Court VRS_TE CRS_TE SCALE RTS 2009 Kozlodui 0.569655 0.504478 0.885585 Irs 2010 Kozlodui 0.422243 0.400429 0.948338 Irs 2011 Kozlodui 0.439809 0.417087 0.948338 Irs 2012 Kozlodui 0.495984 0.452882 0.913099 Irs 2005 Krumuvgrad 0.224990 0.224990 1.000000 - 2006 Krumuvgrad 0.202011 0.202011 1.000000 - 2007 Krumuvgrad 0.150589 0.150589 1.000000 - 2008 Krumuvgrad 0.018365 0.018365 1.000000 - 2009 Krumuvgrad 0.172543 0.172543 1.000000 - 2010 Krumuvgrad 0.080188 0.080188 1.000000 - 2011 Krumuvgrad 0.250519 0.250519 1.000000 - 2012 Krumuvgrad 0.237799 0.237799 1.000000 - 2005 Kubrat 0.307187 0.257922 0.839627 Irs 2006 Kubrat 0.314759 0.270506 0.859407 Irs 2007 Kubrat 0.399414 0.338995 0.848730 Irs 2008 Kubrat 0.064932 0.064932 1.000000 - 2009 Kubrat 0.398269 0.334397 0.839627 Irs 2010 Kubrat 0.438985 0.368306 0.838995 Irs 2011 Kubrat 0.544263 0.458447 0.842326 Irs 2012 Kubrat 0.374475 0.342283 0.914034 Irs 2005 Kula 0.221424 0.221424 1.000000 - 2006 Kula 0.196442 0.196442 1.000000 - 2007 Kula 0.195189 0.195189 1.000000 - 2008 Kula 0.025186 0.025186 1.000000 - 2009 Kula 0.296464 0.296464 1.000000 - 2010 Kula 0.357708 0.323322 0.903871 Irs 2011 Kula 0.299500 0.272320 0.909249 Irs 2012 Kula 0.378670 0.378670 1.000000 - 2005 Kustendil 0.546350 0.535617 0.980354 Irs 2006 Kustendil 0.520586 0.502572 0.965397 Irs 2007 Kustendil 0.541314 0.517187 0.955428 Irs 2008 Kustendil 0.593336 0.580178 0.977823 Irs 2009 Kustendil 0.336172 0.319276 0.949740 Irs 2010 Kustendil 0.662341 0.640601 0.967177 Irs 2011 Kustendil 0.673892 0.651773 0.967177 Irs 2012 Kustendil 0.545163 0.527269 0.967177 Irs 2005 Levski 0.399893 0.344708 0.862001 Irs 2006 Levski 0.284477 0.258851 0.909919 Irs 2007 Levski 0.320257 0.292418 0.913072 Irs 2008 Levski 0.041002 0.041002 1.000000 - 2009 Levski 0.387147 0.353356 0.912718 Irs 2010 Levski 0.494951 0.438321 0.885585 Irs 2011 Levski 0.476813 0.422259 0.885585 Irs 2012 Levski 0.476813 0.422259 0.885585 Irs

- 60 -

year Court VRS_TE CRS_TE SCALE RTS 2005 Lom 0.295022 0.283124 0.959670 Irs 2006 Lom 0.331969 0.327858 0.987615 Irs 2007 Lom 0.293781 0.277315 0.943952 Irs 2008 Lom 0.044599 0.044599 1.000000 - 2009 Lom 0.494361 0.468251 0.947185 Irs 2010 Lom 0.472160 0.449698 0.952428 Irs 2011 Lom 0.458549 0.436735 0.952428 Irs 2012 Lom 0.377533 0.359573 0.952428 Irs 2005 Lovech 0.463300 0.457994 0.988548 Irs 2006 Lovech 0.430517 0.408394 0.948614 Irs 2007 Lovech 0.566211 0.530273 0.936528 Irs 2008 Lovech 0.437191 0.426978 0.976638 Irs 2009 Lovech 0.317616 0.300841 0.947185 Irs 2010 Lovech 0.548544 0.533664 0.972874 Irs 2011 Lovech 0.603755 0.587377 0.972874 Irs 2012 Lovech 0.552089 0.539191 0.976638 Irs 2005 Lukovit 0.347572 0.291831 0.839627 Irs 2006 Lukovit 0.272279 0.233998 0.859407 Irs 2007 Lukovit 0.359602 0.301932 0.839627 Irs 2008 Lukovit 0.040800 0.040800 1.000000 - 2009 Lukovit 0.789909 0.719586 0.910974 Irs 2010 Lukovit 0.379874 0.339533 0.893803 Irs 2011 Lukovit 0.495643 0.413639 0.834549 Irs 2012 Lukovit 0.372301 0.332764 0.893803 Irs 2005 Madan 0.230927 0.227749 0.986239 Irs 2006 Madan 0.184544 0.182086 0.986682 Irs 2007 Madan 0.387085 0.369330 0.954131 Irs 2008 Madan 0.101399 0.101399 1.000000 - 2009 Madan 0.276191 0.259241 0.938630 Irs 2010 Madan 0.384909 0.355760 0.924269 Irs 2011 Madan 0.378546 0.346307 0.914835 Irs 2012 Madan 0.416507 0.381035 0.914835 Irs 2005 Malko Tarnovo 0.178305 0.178305 1.000000 - 2006 Malko Tarnovo 0.181114 0.181114 1.000000 - 2007 Malko Tarnovo 0.108527 0.108527 1.000000 - 2008 Malko Tarnovo 0.011387 0.011387 1.000000 - 2009 Malko Tarnovo 0.137880 0.137880 1.000000 - 2010 Malko Tarnovo 0.152843 0.152843 1.000000 - 2011 Malko Tarnovo 0.220563 0.220563 1.000000 - 2012 Malko Tarnovo 0.170799 0.170799 1.000000 - 2005 Mezdra 0.230420 0.224818 0.975689 Irs 2006 Mezdra 0.234853 0.220632 0.939448 Irs 2007 Mezdra 0.289563 0.281050 0.970601 Irs 2008 Mezdra 0.038586 0.038586 1.000000 -

- 61 - year court VRS_TE CRS_TE SCALE RTS 2009 Mezdra 0.297253 0.285643 0.960944 Irs 2010 Mezdra 0.353578 0.339768 0.960944 Irs 2011 Mezdra 0.361027 0.346927 0.960944 Irs 2012 Mezdra 0.390825 0.375561 0.960944 Irs 2005 Momchilgrad 0.326916 0.298812 0.914034 Irs 2006 Momchilgrad 0.283972 0.240371 0.846459 Irs 2007 Momchilgrad 0.301747 0.269702 0.893803 Irs 2008 Momchilgrad 0.080218 0.080218 1.000000 - 2009 Momchilgrad 0.380789 0.355446 0.933448 Irs 2010 Momchilgrad 0.287759 0.261135 0.907478 Irs 2011 Momchilgrad 0.454780 0.390295 0.858205 Irs 2012 Momchilgrad 0.362607 0.329058 0.907478 Irs 2005 Montana 0.461479 0.459864 0.996499 Irs 2006 Montana 0.425396 0.410762 0.965597 Irs 2007 Montana 0.398313 0.398301 0.999970 Irs 2008 Montana 0.391987 0.391975 0.999970 Irs 2009 Montana 0.282718 0.282710 0.999969 Irs 2010 Montana 0.469698 0.468053 0.996499 Irs 2011 Montana 0.497549 0.495807 0.996499 Irs 2012 Montana 0.490329 0.490314 0.999969 Irs 2005 Nesebar 0.509379 0.439052 0.861935 Irs 2006 Nesebar 0.459065 0.407255 0.887140 Irs 2007 Nesebar 0.380396 0.359311 0.944569 Irs 2008 Nesebar 0.017122 0.017122 1.000000 - 2009 Nesebar 0.450746 0.411404 0.912718 Irs 2010 Nesebar 0.746353 0.671297 0.899436 Irs 2011 Nesebar 0.767343 0.690176 0.899436 Irs 2012 Nesebar 0.602172 0.549613 0.912718 Irs 2005 Nikopol 0.207600 0.195516 0.941793 Irs 2006 Nikopol 0.189149 0.184973 0.977924 Irs 2007 Nikopol 0.290002 0.243493 0.839627 Irs 2008 Nikopol 0.049059 0.049059 1.000000 - 2009 Nikopol 0.320880 0.286804 0.893803 Irs 2010 Nikopol 0.516562 0.431096 0.834549 Irs 2011 Nikopol 0.509305 0.425040 0.834549 Irs 2012 Nikopol 0.417946 0.348796 0.834549 Irs 2005 Nova Zagora 0.351397 0.311181 0.885553 Irs 2006 Nova Zagora 0.402138 0.384348 0.955761 Irs 2007 Nova Zagora 0.462059 0.424733 0.919217 Irs 2008 Nova Zagora 0.071077 0.071077 1.000000 - 2009 Nova Zagora 0.451290 0.405907 0.899436 Irs 2010 Nova Zagora 0.552132 0.488960 0.885585 Irs 2011 Nova Zagora 0.636304 0.572315 0.899436 Irs 2012 Nova Zagora 0.769551 0.718114 0.933160 Irs

- 62 - year court VRS_TE CRS_TE SCALE RTS 2005 Novi Pazar 0.518734 0.468610 0.903373 Irs 2006 Novi Pazar 0.443391 0.422717 0.953373 Irs 2007 Novi Pazar 0.368169 0.335614 0.911575 Irs 2008 Novi Pazar 0.079957 0.079957 1.000000 - 2009 Novi Pazar 0.440021 0.398603 0.905872 Irs 2010 Novi Pazar 0.586776 0.531544 0.905872 Irs 2011 Novi Pazar 0.523985 0.474664 0.905872 Irs 2012 Novi Pazar 0.488947 0.459138 0.939035 Irs 2005 Omurtag 0.294164 0.261066 0.887483 Irs 2006 Omurtag 0.210812 0.191296 0.907427 Irs 2007 Omurtag 0.325717 0.280779 0.862035 Irs 2008 Omurtag 0.054178 0.054178 1.000000 - 2009 Omurtag 0.259058 0.231966 0.895421 Irs 2010 Omurtag 0.336013 0.297569 0.885585 Irs 2011 Omurtag 0.376326 0.326512 0.867630 Irs 2012 Omurtag 0.364289 0.316068 0.867630 Irs 2005 Orqhovo 0.263368 0.240833 0.914435 Irs 2006 Orqhovo 0.273184 0.257408 0.942250 Irs 2007 Orqhovo 0.523831 0.489674 0.934794 Irs 2008 Orqhovo 0.030233 0.030233 1.000000 - 2009 Orqhovo 0.332873 0.304390 0.914435 Irs 2010 Orqhovo 0.623343 0.563934 0.904693 Irs 2011 Orqhovo 0.821537 0.743239 0.904693 Irs 2012 Orqhovo 0.464382 0.409236 0.881249 Irs 2005 Panaguirishte 0.247144 0.223386 0.903871 Irs 2006 Panaguirishte 0.259997 0.236981 0.911477 Irs 2007 Panaguirishte 0.270695 0.245650 0.907478 Irs 2008 Panaguirishte 0.105335 0.105335 1.000000 - 2009 Panaguirishte 0.347870 0.315685 0.907478 Irs 2010 Panaguirishte 0.510544 0.449916 0.881249 Irs 2011 Panaguirishte 0.602079 0.530582 0.881249 Irs 2012 Panaguirishte 0.459253 0.404716 0.881249 Irs 2005 Parvomai 0.212016 0.193103 0.910792 Irs 2006 Parvomai 0.214269 0.203041 0.947600 Irs 2007 Parvomai 0.252220 0.236741 0.938630 Irs 2008 Parvomai 0.045960 0.045960 1.000000 - 2009 Parvomai 0.233531 0.212698 0.910792 Irs 2010 Parvomai 0.249350 0.234836 0.941793 Irs 2011 Parvomai 0.359172 0.301571 0.839627 Irs 2012 Parvomai 0.668091 0.608614 0.910974 Irs 2005 Pavlikeni 0.293306 0.272926 0.930516 Irs 2006 Pavlikeni 0.318306 0.282382 0.887140 Irs 2007 Pavlikeni 0.353412 0.314342 0.889448 Irs 2008 Pavlikeni 0.043144 0.043144 1.000000 -

- 63 - year court VRS_TE CRS_TE SCALE RTS 2009 Pavlikeni 0.407636 0.354710 0.870163 Irs 2010 Pavlikeni 0.527636 0.454823 0.862001 Irs 2011 Pavlikeni 0.531557 0.458203 0.862001 Irs 2012 Pavlikeni 0.625374 0.536699 0.858205 Irs 2005 Pazardjik 0.507939 0.505980 0.996143 Irs 2006 Pazardjik 0.498080 0.484367 0.972468 Irs 2007 Pazardjik 0.571837 0.568030 0.993343 Irs 2008 Pazardjik 0.500436 0.497791 0.994715 Irs 2009 Pazardjik 0.394047 0.386192 0.980066 Irs 2010 Pazardjik 0.690479 0.674976 0.977548 Irs 2011 Pazardjik 0.752332 0.735440 0.977548 Irs 2012 Pazardjik 0.662308 0.657117 0.992161 Irs 2005 Pernik 0.485512 0.475872 0.980144 Irs 2006 Pernik 0.659608 0.647816 0.982123 Irs 2007 Pernik 0.397479 0.388325 0.976970 Irs 2008 Pernik 0.715878 0.713117 0.996143 Irs 2009 Pernik 0.616440 0.613130 0.994631 Irs 2010 Pernik 1.000000 0.991853 0.991853 Drs 2011 Pernik 0.885018 0.877367 0.991355 Drs 2012 Pernik 0.995253 0.979338 0.984010 Drs 2005 Peshtera 0.277314 0.252736 0.911372 Irs 2006 Peshtera 0.268648 0.246490 0.917522 Irs 2007 Peshtera 0.366741 0.334248 0.911400 Irs 2008 Peshtera 0.115161 0.115161 1.000000 - 2009 Peshtera 0.338437 0.308210 0.910684 Irs 2010 Peshtera 0.445619 0.403673 0.905872 Irs 2011 Peshtera 0.490156 0.444019 0.905872 Irs 2012 Peshtera 0.404732 0.366635 0.905872 Irs 2005 Petrich 0.915227 0.852945 0.931949 Irs 2006 Petrich 0.632208 0.601911 0.952077 Irs 2007 Petrich 0.467405 0.466183 0.997386 Irs 2008 Petrich 0.045161 0.044272 0.980331 Irs 2009 Petrich 0.430484 0.422017 0.980331 Irs 2010 Petrich 0.511028 0.483092 0.945334 Irs 2011 Petrich 0.472974 0.448379 0.948000 Irs 2012 Petrich 0.440535 0.438258 0.994830 Irs 2005 Pirdop 0.362609 0.304457 0.839627 Irs 2006 Pirdop 0.246867 0.218616 0.885563 Irs 2007 Pirdop 0.274218 0.249890 0.911281 Irs 2008 Pirdop 0.041117 0.041117 1.000000 - 2009 Pirdop 0.556124 0.485793 0.873533 Irs 2010 Pirdop 0.704791 0.615658 0.873533 Irs 2011 Pirdop 0.854009 0.746005 0.873533 Irs 2012 Pirdop 0.764105 0.642722 0.841144 Irs

- 64 - year court VRS_TE CRS_TE SCALE RTS 2005 Pleven 0.416406 0.396064 0.951148 Irs 2006 Pleven 0.429977 0.425706 0.990066 Irs 2007 Pleven 0.522737 0.496411 0.949639 Irs 2008 Pleven 0.587803 0.555630 0.945265 Drs 2009 Pleven 0.428617 0.412362 0.962076 Irs 2010 Pleven 0.071680 0.067439 0.940845 Irs 2011 Pleven 0.079137 0.073018 0.922680 Irs 2012 Pleven 0.681177 0.618866 0.908526 Drs 2005 Plovdiv 1.000000 1.000000 1.000000 - 2006 Plovdiv 0.716712 0.684456 0.954994 Irs 2007 Plovdiv 0.730595 0.725747 0.993364 Irs 2008 Plovdiv 0.731128 0.723768 0.989932 Drs 2009 Plovdiv 0.533569 0.527681 0.988964 Irs 2010 Plovdiv 0.771676 0.762502 0.988111 Drs 2011 Plovdiv 0.985809 0.974089 0.988111 Drs 2012 Plovdiv 0.897175 0.886466 0.988064 Drs 2005 Pomorie 0.311729 0.284931 0.914034 Irs 2006 Pomorie 0.087400 0.087400 1.000000 - 2007 Pomorie 0.213456 0.213456 1.000000 - 2008 Pomorie 0.058957 0.058957 1.000000 - 2009 Pomorie 0.288823 0.268755 0.930516 Irs 2010 Pomorie 0.438428 0.371251 0.846777 Irs 2011 Pomorie 0.553841 0.468980 0.846777 Irs 2012 Pomorie 0.487338 0.412666 0.846777 Irs 2005 Popovo 0.298394 0.272406 0.912906 Irs 2006 Popovo 0.318712 0.307574 0.965051 Irs 2007 Popovo 0.245321 0.222230 0.905872 Irs 2008 Popovo 0.065369 0.065369 1.000000 - 2009 Popovo 0.252414 0.230430 0.912906 Irs 2010 Popovo 0.430899 0.392796 0.911575 Irs 2011 Popovo 0.468419 0.426999 0.911575 Irs 2012 Popovo 0.409687 0.377279 0.920896 Irs 2005 Provadia 0.444410 0.376316 0.846777 Irs 2006 Provadia 0.292973 0.262924 0.897434 Irs 2007 Provadia 0.311448 0.283845 0.911372 Irs 2008 Provadia 0.084651 0.084651 1.000000 - 2009 Provadia 0.358549 0.326772 0.911372 Irs 2010 Provadia 0.667441 0.579092 0.867630 Irs 2011 Provadia 0.721375 0.621826 0.862001 Irs 2012 Provadia 0.623933 0.542923 0.870163 Irs 2005 Qmbol 0.432667 0.419432 0.969411 Irs 2006 Qmbol 0.327910 0.319127 0.973214 Irs 2007 Qmbol 0.358686 0.356603 0.994194 Irs 2008 Qmbol 0.367366 0.365424 0.994716 Irs

- 65 - year court VRS_TE CRS_TE SCALE RTS 2009 Qmbol 0.252516 0.250537 0.992162 Irs 2010 Qmbol 0.464715 0.464694 0.999954 Irs 2011 Qmbol 0.547985 0.545089 0.994716 Irs 2012 Qmbol 0.449348 0.446973 0.994716 Irs 2005 Radnevo 0.415651 0.368081 0.885553 Irs 2006 Radnevo 0.330732 0.306688 0.927300 Irs 2007 Radnevo 0.256401 0.230323 0.898291 Irs 2008 Radnevo 0.037843 0.037843 1.000000 - 2009 Radnevo 0.276404 0.248291 0.898291 Irs 2010 Radnevo 0.399768 0.346850 0.867630 Irs 2011 Radnevo 0.379975 0.336500 0.885585 Irs 2012 Radnevo 0.383049 0.339223 0.885585 Irs 2005 Radomir 0.371336 0.334974 0.902079 Irs 2006 Radomir 0.529855 0.517858 0.977358 Irs 2007 Radomir 0.346324 0.328267 0.947862 Irs 2008 Radomir 0.069217 0.069217 1.000000 - 2009 Radomir 0.526011 0.498585 0.947862 Irs 2010 Radomir 0.584090 0.542066 0.928051 Irs 2011 Radomir 0.565758 0.525052 0.928051 Irs 2012 Radomir 0.524451 0.486718 0.928051 Irs 2005 Razgrad 0.442700 0.441392 0.997047 Irs 2006 Razgrad 0.355062 0.338087 0.952191 Irs 2007 Razgrad 0.413627 0.390246 0.943474 Irs 2008 Razgrad 0.344293 0.331772 0.963631 Irs 2009 Razgrad 0.318962 0.299648 0.939448 Irs 2010 Razgrad 0.513456 0.505483 0.984471 Irs 2011 Razgrad 0.526557 0.518380 0.984471 Irs 2012 Razgrad 0.479226 0.471784 0.984471 Irs 2005 Razlog 0.566406 0.527867 0.931958 Irs 2006 Razlog 0.789269 0.734058 0.930048 Irs 2007 Razlog 1.000000 1.000000 1.000000 - 2008 Razlog 0.049906 0.049906 1.000000 - 2009 Razlog 0.398947 0.396929 0.994941 Irs 2010 Razlog 0.560021 0.546407 0.975689 Irs 2011 Razlog 0.604451 0.586681 0.970601 Irs 2012 Razlog 0.450452 0.437209 0.970601 Irs 2005 Ruse 0.627907 0.624258 0.994189 Irs 2006 Ruse 0.562617 0.554154 0.984958 Irs 2007 Ruse 0.586236 0.571666 0.975146 Irs 2008 Ruse 0.479488 0.467571 0.975146 Irs 2009 Ruse 0.475736 0.474015 0.996384 Irs 2010 Ruse 0.799846 0.779967 0.975146 Drs 2011 Ruse 0.863237 0.841782 0.975146 Drs 2012 Ruse 0.726779 0.699515 0.962486 Drs

- 66 - year court VRS_TE CRS_TE SCALE RTS 2005 Samokov 0.550875 0.474818 0.861935 Irs 2006 Samokov 0.386841 0.349241 0.902805 Irs 2007 Samokov 0.346798 0.319066 0.920035 Irs 2008 Samokov 0.036994 0.036994 1.000000 - 2009 Samokov 0.372180 0.340456 0.914762 Irs 2010 Samokov 0.475096 0.449663 0.946468 Irs 2011 Samokov 0.459788 0.436389 0.949109 Irs 2012 Samokov 0.376867 0.356692 0.946468 Irs 2005 Sandanski 0.422586 0.385133 0.911372 Irs 2006 Sandanski 0.329908 0.304905 0.924214 Irs 2007 Sandanski 0.426245 0.404224 0.948338 Irs 2008 Sandanski 0.083502 0.083502 1.000000 - 2009 Sandanski 0.485029 0.460357 0.949133 Irs 2010 Sandanski 0.689009 0.629134 0.913099 Irs 2011 Sandanski 0.562529 0.533915 0.949133 Irs 2012 Sandanski 0.726481 0.664558 0.914762 Irs 2005 Sevlievo 0.316606 0.293640 0.927463 Irs 2006 Sevlievo 0.264950 0.252067 0.951376 Irs 2007 Sevlievo 0.332437 0.309814 0.931949 Irs 2008 Sevlievo 0.062154 0.062154 1.000000 - 2009 Sevlievo 0.453248 0.420371 0.927463 Irs 2010 Sevlievo 0.547380 0.507675 0.927463 Irs 2011 Sevlievo 0.506691 0.469937 0.927463 Irs 2012 Sevlievo 0.488067 0.452664 0.927463 Irs 2005 Shumen 0.580824 0.570925 0.982956 Irs 2006 Shumen 0.379186 0.375701 0.990809 Irs 2007 Shumen 0.449099 0.428100 0.953242 Irs 2008 Shumen 0.413586 0.396715 0.959208 Irs 2009 Shumen 0.600780 0.576813 0.960108 Irs 2010 Shumen 0.542933 0.542513 0.999227 Irs 2011 Shumen 0.578687 0.576795 0.996731 Irs 2012 Shumen 0.533608 0.533195 0.999227 Irs 2005 Silistra 0.327170 0.320743 0.980354 Irs 2006 Silistra 0.335822 0.324417 0.966038 Irs 2007 Silistra 0.361215 0.347541 0.962145 Irs 2008 Silistra 0.422151 0.406170 0.962145 Irs 2009 Silistra 0.364527 0.349147 0.957806 Irs 2010 Silistra 0.436083 0.421769 0.967177 Irs 2011 Silistra 0.525527 0.509950 0.970359 Irs 2012 Silistra 0.430576 0.417814 0.970359 Irs 2005 Sliven 0.449422 0.445761 0.991853 Irs 2006 Sliven 0.440198 0.433863 0.985607 Irs 2007 Sliven 0.687993 0.672173 0.977006 Irs 2008 Sliven 0.558673 0.542406 0.970882 Irs

- 67 - year court VRS_TE CRS_TE SCALE RTS 2009 Sliven 0.423143 0.422516 0.998517 Irs 2010 Sliven 0.703134 0.695295 0.988852 Irs 2011 Sliven 0.815698 0.806604 0.988852 Irs 2012 Sliven 0.717596 0.703244 0.980001 Irs 2005 Slivnica 0.308335 0.291244 0.944569 Irs 2006 Slivnica 0.253007 0.235013 0.928878 Irs 2007 Slivnica 0.204031 0.199134 0.976000 Irs 2008 Slivnica 0.046544 0.046544 1.000000 - 2009 Slivnica 0.260114 0.249725 0.960061 Irs 2010 Slivnica 0.327833 0.314740 0.960061 Irs 2011 Slivnica 0.371783 0.356934 0.960061 Irs 2012 Slivnica 0.344650 0.330886 0.960061 Irs 2005 Smolqn 0.242136 0.230495 0.951922 Irs 2006 Smolqn 0.246165 0.238564 0.969125 Irs 2007 Smolqn 0.415444 0.383512 0.923138 Irs 2008 Smolqn 0.283145 0.267140 0.943474 Irs 2009 Smolqn 0.232867 0.217255 0.932956 Irs 2010 Smolqn 0.410213 0.385260 0.939173 Irs 2011 Smolqn 0.580558 0.545244 0.939173 Irs 2012 Smolqn 0.440757 0.414068 0.939448 Irs 2005 Sofia 0.862763 0.809729 0.938531 Drs 2006 Sofia 0.600536 0.575135 0.957702 Irs 2007 Sofia 0.593386 0.573356 0.966245 Drs 2008 Sofia 0.713471 0.699342 0.980196 Drs 2009 Sofia 0.674339 0.653413 0.968967 Drs 2010 Sofia 0.911428 0.882660 0.968436 Drs 2011 Sofia 0.943053 0.914956 0.970206 Drs 2012 Sofia 1.000000 1.000000 1.000000 - 2005 Sredec 0.486010 0.439291 0.903871 Irs 2006 Sredec 0.325734 0.297863 0.914434 Irs 2007 Sredec 0.286709 0.257548 0.898291 Irs 2008 Sredec 0.035392 0.035392 1.000000 - 2009 Sredec 0.537265 0.510448 0.950086 Irs 2010 Sredec 0.536267 0.484716 0.903871 Irs 2011 Sredec 0.896683 0.595875 0.664533 Irs 2012 Sredec 0.815458 0.541899 0.664533 Irs 2005 Stara Zagora 0.452947 0.425378 0.939133 Irs 2006 Stara Zagora 0.551499 0.523916 0.949985 Irs 2007 Stara Zagora 0.585449 0.562357 0.960557 Irs 2008 Stara Zagora 0.521738 0.501159 0.960557 Irs 2009 Stara Zagora 0.392843 0.381396 0.970861 Irs 2010 Stara Zagora 0.681277 0.681238 0.999944 Irs 2011 Stara Zagora 0.772592 0.772549 0.999944 Irs 2012 Stara Zagora 0.727174 0.702263 0.965742 Drs

- 68 - year court VRS_TE CRS_TE SCALE RTS 2005 Svilengrad 0.333554 0.295391 0.885585 Irs 2006 Svilengrad 0.428591 0.414852 0.967944 Irs 2007 Svilengrad 1.000000 1.000000 1.000000 - 2008 Svilengrad 0.096539 0.096539 1.000000 - 2009 Svilengrad 0.382365 0.348476 0.911372 Irs 2010 Svilengrad 0.568836 0.511631 0.899436 Irs 2011 Svilengrad 0.556507 0.509072 0.914762 Irs 2012 Svilengrad 0.756424 0.682355 0.902079 Irs 2005 Svishtov 0.358587 0.311121 0.867630 Irs 2006 Svishtov 0.307665 0.279183 0.907427 Irs 2007 Svishtov 0.306570 0.279929 0.913099 Irs 2008 Svishtov 0.026626 0.026626 1.000000 - 2009 Svishtov 0.547520 0.484876 0.885585 Irs 2010 Svishtov 0.485403 0.443221 0.913099 Irs 2011 Svishtov 0.492113 0.449348 0.913099 Irs 2012 Svishtov 0.469920 0.429084 0.913099 Irs 2005 Svoge 0.275358 0.275358 1.000000 - 2006 Svoge 0.258563 0.243785 0.942846 Irs 2007 Svoge 0.229093 0.221004 0.964690 Irs 2008 Svoge 0.084082 0.084082 1.000000 - 2009 Svoge 0.449320 0.419417 0.933448 Irs 2010 Svoge 0.686577 0.577667 0.841373 Irs 2011 Svoge 0.679970 0.594014 0.873589 Irs 2012 Svoge 0.440838 0.378329 0.858205 Irs 2005 Targovishte 0.466035 0.455900 0.978253 Irs 2006 Targovishte 0.419883 0.404794 0.964065 Irs 2007 Targovishte 0.474246 0.463167 0.976638 Irs 2008 Targovishte 0.291805 0.289202 0.991079 Irs 2009 Targovishte 0.215764 0.213839 0.991079 Irs 2010 Targovishte 0.420314 0.418141 0.994830 Irs 2011 Targovishte 0.410395 0.408273 0.994830 Irs 2012 Targovishte 0.388001 0.387487 0.998674 Irs 2005 Tervel 0.170528 0.170528 1.000000 - 2006 Tervel 0.197963 0.178201 0.900174 Irs 2007 Tervel 0.226262 0.213489 0.943548 Irs 2008 Tervel 0.055072 0.055072 1.000000 - 2009 Tervel 0.335131 0.335131 1.000000 - 2010 Tervel 0.431909 0.431909 1.000000 - 2011 Tervel 0.480575 0.480575 1.000000 - 2012 Tervel 0.293101 0.293101 1.000000 - 2005 Teteven 0.327860 0.284461 0.867630 Irs 2006 Teteven 0.328645 0.300764 0.915165 Irs 2007 Teteven 0.442532 0.383954 0.867630 Irs 2008 Teteven 0.063763 0.063763 1.000000 -

- 69 - year court VRS_TE CRS_TE SCALE RTS 2009 Teteven 0.367678 0.325610 0.885585 Irs 2010 Teteven 0.454986 0.402929 0.885585 Irs 2011 Teteven 0.504789 0.447033 0.885585 Irs 2012 Teteven 0.450067 0.398573 0.885585 Irs 2005 Topolovgrad 0.144792 0.144792 1.000000 - 2006 Topolovgrad 0.097702 0.097702 1.000000 - 2007 Topolovgrad 0.129142 0.129142 1.000000 - 2008 Topolovgrad 0.016233 0.016233 1.000000 - 2009 Topolovgrad 0.135995 0.135995 1.000000 - 2010 Topolovgrad 0.214704 0.207123 0.964690 Irs 2011 Topolovgrad 0.220763 0.212968 0.964690 Irs 2012 Topolovgrad 0.231586 0.227388 0.981870 Irs 2005 Tran 0.230344 0.230344 1.000000 - 2006 Tran 0.163533 0.163533 1.000000 - 2007 Tran 0.230869 0.230869 1.000000 - 2008 Tran 0.075209 0.075209 1.000000 - 2009 Tran 0.109517 0.109517 1.000000 - 2010 Tran 0.119650 0.119650 1.000000 - 2011 Tran 0.140306 0.140306 1.000000 - 2012 Tran 0.294003 0.271648 0.923965 Irs 2005 Troqn 0.384357 0.363782 0.946468 Irs 2006 Troqn 0.336949 0.311051 0.923138 Irs 2007 Troqn 0.355732 0.334900 0.941441 Irs 2008 Troqn 0.054625 0.054625 1.000000 - 2009 Troqn 0.415864 0.391512 0.941441 Irs 2010 Troqn 0.479785 0.449369 0.936605 Irs 2011 Troqn 0.507249 0.475092 0.936605 Irs 2012 Troqn 0.494290 0.451241 0.912906 Irs 2005 Trqvna 0.259920 0.259920 1.000000 - 2006 Trqvna 0.332366 0.332366 1.000000 - 2007 Trqvna 0.238905 0.238905 1.000000 - 2008 Trqvna 0.052988 0.052953 0.999346 Irs 2009 Trqvna 0.248650 0.245229 0.986239 Irs 2010 Trqvna 0.355040 0.355040 1.000000 - 2011 Trqvna 0.370524 0.370524 1.000000 - 2012 Trqvna 0.281488 0.281488 1.000000 - 2005 Tsarevo 0.527989 0.477234 0.903871 Irs 2006 Tsarevo 0.519597 0.475138 0.914435 Irs 2007 Tsarevo 0.470621 0.470621 1.000000 - 2008 Tsarevo 0.046811 0.046811 1.000000 - 2009 Tsarevo 0.544146 0.476577 0.875825 Irs 2010 Tsarevo 0.636137 0.448218 0.704594 Irs 2011 Tsarevo 1.000000 0.561729 0.561729 Irs 2012 Tsarevo 0.659467 0.464657 0.704594 Irs

- 70 - year court VRS_TE CRS_TE SCALE RTS 2005 Tutrakan 0.337680 0.292981 0.867630 Irs 2006 Tutrakan 0.197802 0.179491 0.907427 Irs 2007 Tutrakan 0.229850 0.213246 0.927762 Irs 2008 Tutrakan 0.049011 0.049011 1.000000 - 2009 Tutrakan 0.297228 0.269233 0.905812 Irs 2010 Tutrakan 0.334829 0.290508 0.867630 Irs 2011 Tutrakan 0.482926 0.425578 0.881249 Irs 2012 Tutrakan 0.322794 0.285862 0.885585 Irs 2005 V.Tarnovo 0.507302 0.498432 0.982514 Irs 2006 V.Tarnovo 0.445369 0.444619 0.998315 Irs 2007 V.Tarnovo 0.438523 0.428233 0.976534 Irs 2008 V.Tarnovo 0.496989 0.494575 0.995143 Irs 2009 V.Tarnovo 0.403538 0.383193 0.949583 Irs 2010 V.Tarnovo 0.697238 0.649611 0.931691 Drs 2011 V.Tarnovo 0.788079 0.739890 0.938853 Drs 2012 V.Tarnovo 0.668737 0.632280 0.945485 Drs 2005 Varna 0.591932 0.548254 0.926211 Drs 2006 Varna 0.564476 0.563828 0.998852 Irs 2007 Varna 1.000000 1.000000 1.000000 - 2008 Varna 0.737556 0.736450 0.998501 Irs 2009 Varna 0.504991 0.504594 0.999213 Irs 2010 Varna 0.936221 0.935941 0.999701 Irs 2011 Varna 1.000000 1.000000 1.000000 - 2012 Varna 0.871610 0.869074 0.997090 Irs 2005 Veliki Preslav 0.603288 0.517695 0.858122 Irs 2006 Veliki Preslav 0.326765 0.301675 0.923217 Irs 2007 Veliki Preslav 0.314891 0.284464 0.903373 Irs 2008 Veliki Preslav 0.026408 0.026408 1.000000 - 2009 Veliki Preslav 0.338473 0.299747 0.885585 Irs 2010 Veliki Preslav 0.450091 0.404828 0.899436 Irs 2011 Veliki Preslav 0.427601 0.384600 0.899436 Irs 2012 Veliki Preslav 0.366430 0.329580 0.899436 Irs 2005 Velingrad 0.376286 0.333233 0.885585 Irs 2006 Velingrad 0.375925 0.359585 0.956532 Irs 2007 Velingrad 0.409400 0.377015 0.920896 Irs 2008 Velingrad 0.054973 0.054973 1.000000 - 2009 Velingrad 0.494537 0.447987 0.905872 Irs 2010 Velingrad 0.544429 0.493183 0.905872 Irs 2011 Velingrad 0.733980 0.682637 0.930048 Irs 2012 Velingrad 0.551329 0.507518 0.920536 Irs 2005 Vidin 0.401714 0.400007 0.995751 Irs 2006 Vidin 0.377025 0.366265 0.971461 Irs 2007 Vidin 0.374197 0.352522 0.942076 Irs 2008 Vidin 0.348958 0.347263 0.995143 Irs

- 71 - year court VRS_TE CRS_TE SCALE RTS 2009 Vidin 0.240343 0.234410 0.975316 Irs 2010 Vidin 0.503023 0.465658 0.925720 Irs 2011 Vidin 0.467336 0.431840 0.924047 Irs 2012 Vidin 0.478071 0.470135 0.983399 Irs 2005 Vraca 0.413858 0.387815 0.937072 Irs 2006 Vraca 0.459237 0.447789 0.975072 Irs 2007 Vraca 0.376705 0.365191 0.969435 Irs 2008 Vraca 0.413478 0.397727 0.961906 Irs 2009 Vraca 0.345932 0.332754 0.961906 Irs 2010 Vraca 0.844437 0.837447 0.991723 Irs 2011 Vraca 0.776175 0.774701 0.998101 Irs 2012 Vraca 0.677474 0.674184 0.995143 Irs 2005 Zlatograd 0.217665 0.217665 1.000000 - 2006 Zlatograd 0.223521 0.217153 0.971509 Irs 2007 Zlatograd 0.198588 0.198588 1.000000 - 2008 Zlatograd 0.049060 0.049060 1.000000 - 2009 Zlatograd 0.251970 0.235201 0.933448 Irs 2010 Zlatograd 0.297829 0.278008 0.933448 Irs 2011 Zlatograd 0.539578 0.533907 0.989492 Irs 2012 Zlatograd 0.447473 0.442771 0.989492 Irs

- 72 -

APPENDIX E

First Stage TSLS (IV)

Source SS Df MS Number of obs = 896 F(119, 777) = 28.74 Model 145.497374 119 1.22266701 Prob > F = 0.0000 Residual 33.0560945 777 .042543236 R-squared = 0.8149 Adj R-squared = 0.7865 Total 178.553469 896 .199278425 Root MSE = .20626

VRS_TE Coef. Std. Err. t P>|t| [95% Conf. Interval] 2006 .3453122 .0195009 17.71 0.000 .3070315 .383593 2007 .3656406 .0195336 18.72 0.000 .3272957 .4039855 2008 .1614822 .0212208 7.61 0.000 .1198252 .2031391 2009 .374638 .0239156 15.67 0.000 .3276912 .4215848 2010 .5043525 .0271016 18.61 0.000 .4511515 .5575535 2011 .5640189 .0286315 19.70 0.000 .5078147 .6202231 2012 .5195718 .0310793 16.72 0.000 .4585625 .580581

int_1 .0568032 .0941513 0.60 0.546 - .1280178 .2416243 int_2 -.3168536 .10533 -3.01 0.003 -.5236187 -.1100886 int_3 -.0531358 .10533 -0.50 0.614 -.2599008 .1536293 int_4 -.1971088 .1214517 -1.62 0.105 -.4355211 .0413036 int_5 -.1965846 .1478306 -1.33 0.184 -.4867793 .0936102 int_6 -.1277741 .0941513 -1.36 0.175 -.3125951 .057047 int_7 .0761584 .10533 0.72 0.470 -.1306067 .2829234 int_8 .1353899 .0941513 1.44 0.151 -.0494312 .3202109 int_9 .0605319 .2085885 0.29 0.772 -.3489319 .4699956 int_10 -.0539629 .1214517 -0.44 0.657 -.2923753 .1844494 int_11 -.2894271 .0941513 -3.07 0.002 -.4742481 -.104606 int_12 .3649735 .1214517 3.01 0.003 .1265611 .6033858 int_13 -.1225129 .0941513 -1.30 0.194 -.307334 .0623082 int_14 .0272274 .10533 0.26 0.796 -.1795376 .2339925 int_15 -.1755824 .1214517 -1.45 0.149 -.4139947 .06283 int_16 -.0090724 .0739613 -0.12 0.902 -.15426 .1361152 int_17 -.0142516 .0857337 -0.17 0.868 -.1825487 .1540455 int_18 .0684738 .10533 0.65 0.516 -.1382913 .2752388 int_19 .1378228 .1214517 1.13 0.257 -.1005896 .3762351 int_20 .02735 .0941513 0.29 0.772 -.1574711 .2121711 int_21 -.0222035 .10533 -0.21 0.833 -.2289685 .1845616 int_22 .0593239 .2085885 0.28 0.776 -.3501398 .4687877 int_23 .081704 .0941513 0.87 0.386 -.1031171 .266525 int_24 -.2011474 .0941513 -2.14 0.033 -.3859684 -.0163263 - 73 - int_25 -.0970979 .0941513 -1.03 0.303 -.281919 .0877232 int_26 -.2122392 .10533 -2.01 0.044 -.4190042 -.0054741 int_27 .0915684 .10533 0.87 0.385 -.1151967 .2983334 int_28 -.2138259 .1481937 -1.44 0.149 -.5047333 .0770816 int_29 -.1353626 .1481937 -0.91 0.361 -.42627 .1555449 int_30 -.0287441 .1214517 -0.24 0.813 -.2671565 .2096682 int_31 .2874686 .2085885 1.38 0.169 -.1219951 .6969324 int_32 .0720744 .10533 0.68 0.494 -.1346906 .2788395 int_33 -.0501318 .0941513 -0.53 0.595 -.2349529 .1346892 int_34 .0237104 .0941513 0.25 0.801 -.1611107 .2085315 int_35 -.338002 .10533 -3.21 0.001 -.544767 -.1312369 int_36 .0342576 .10533 0.33 0.745 -.1725074 .2410227 int_37 -.0544731 .0941513 -0.58 0.563 -.2392941 .130348 int_38 -.1044245 .10533 -0.99 0.322 -.3111896 .1023405 int_39 -.254121 .1481937 -1.71 0.087 -.5450285 .0367864 int_40 .1076269 .10533 1.02 0.307 -.0991381 .314392 int_41 -.0345154 .1214517 -0.28 0.776 -.2729278 .2038969 int_42 -.0684896 .0941513 -0.73 0.467 -.2533106 .1163315 int_43 -.0113872 .1214517 -0.09 0.925 -.2497996 .2270251 int_44 -.0766361 .1214517 -0.63 0.528 -.3150484 .1617763 int_45 -.3398125 .1214517 -2.80 0.005 -.5782248 -.1014001 int_46 -.0767403 .1214517 -0.63 0.528 -.3151527 .161672 int_47 -.1409019 .2085885 -0.68 0.500 -.5503657 .2685618 int_48 .1373685 .0941513 1.46 0.145 -.0474526 .3221896 int_49 -.0317143 .10533 -0.30 0.763 -.2384793 .1750508 int_50 -.0399943 .10533 -0.38 0.704 -.2467594 .1667707 int_51 .0325172 .2085885 0.16 0.876 -.3769466 .4419809 int_52 -.1133749 .1214517 -0.93 0.351 -.3517872 .1250375 int_53 -.1359935 .1214517 -1.12 0.263 -.3744058 .1024189 int_54 -.3479127 .1214517 -2.86 0.004 -.5863251 -.1095004 int_55 -.1399746 .10533 -1.33 0.184 -.3467396 .0667905 int_56 -.1115821 .0941513 -1.19 0.236 -.2964032 .073239 int_57 -.0555719 .10533 -0.53 0.598 -.2623369 .1511932 int_58 .1510083 .10533 1.43 0.152 -.0557567 .3577734 int_59 -.1016262 .2085885 -0.49 0.626 -.5110899 .3078376 int_60 .2499792 .2085885 1.20 0.231 -.1594846 .6594429 int_61 -.0306248 .2085885 -0.15 0.883 -.4400886 .3788389 int_62 -.1468397 .0941513 -1.56 0.119 -.3316608 .0379814 int_63 .1071062 .1214517 0.88 0.378 -.1313062 .3455185 int_64 -.0197965 .0941513 -0.21 0.834 -.2046176 .1650246 int_65 -.1135919 .0941513 -1.21 0.228 -.298413 .0712292 int_66 .0022568 .0941513 0.02 0.981 -.1825643 .1870778 int_67 .1751075 .0941513 1.86 0.063 -.0097135 .3599286 int_68 .3983401 .1481937 25235 0.007 .1074327 .6892476

- 74 - int_69 -.0709094 .10533 -0.67 0.501 -.2776744 .1358557 int_70 -.0447763 .0941513 -0.48 0.635 -.2295974 .1400447 int_71 .1592167 .0941513 1.69 0.091 -.0256043 .3440378 int_72 -.2519832 .1214517 -2.07 0.038 -.4903955 -.0135708 int_73 .3600414 .0857337 4.20 0.000 .1917443 .5283386 int_74 -.0485376 .10533 -0.46 0.645 -.2553026 .1582275 int_75 -.0929794 .1214517 -0.77 0.444 -.3313917 .145433 int_76 .0663772 .0941513 0.71 0.481 -.1184439 .2511982 int_77 -.0431289 .1481937 -0.29 0.771 -.3340364 .2477785 int_78 -.129405 .0941513 -1.37 0.170 -.3142261 .0554161 int_79 .0290927 .0941513 0.31 0.757 -.1557284 .2139137 int_80 -.040346 .2085885 -0.19 0.847 -.4498097 .3691178 int_81 -.0120569 .0941513 -0.13 0.898 -.196878 .1727642 int_82 .2673065 .1214517 2.20 0.028 .0288942 .5057189 int_83 -.0806277 .0941513 -0.86 0.392 -.2654488 .1041934 int_84 .1300254 .1214517 1.07 0.285 -.1083869 .3684378 int_85 -.0133047 .0941513 -0.14 0.888 -.1981258 .1715164 int_86 .0733564 .10533 0.70 0.486 -.1334086 .2801215 int_87 -.0889953 .2085885 -0.43 0.670 -.498459 .3204685 int_88 .2248515 .1481937 1.52 0.130 -.066056 .5157589 int_89 -.154628 .0941513 -1.64 0.101 -.3394491 .0301931 int_90 -.0311378 .1481937 -0.21 0.834 -.3220453 .2597696 int_91 .4236457 .0941513 4.50 0.000 .2388246 .6084667 int_92 .1394003 .0941513 1.48 0.139 -.0454207 .3242214 int_93 .194312 .0941513 2.06 0.039 .0094909 .379133 int_94 .0753876 .10533 0.72 0.474 -.1313775 .2821526 int_95 .0080938 .10533 0.08 0.939 -.1986713 .2148588 int_96 .0433445 .0941513 0.46 0.645 -.1414766 .2281656 int_97 -.0795568 .0941513 -0.84 0.398 -.2643779 .1052643 int_98 -.1054661 .10533 -1.00 0.317 -.3122312 .101299 int_99 -.0462653 .10533 -0.44 0.661 -.2530303 .1604998 int_100 -.2609563 .0941513 -2.77 0.006 -.4457774 -.0761353 int_101 -.225569 .2085885 -1.08 0.280 -.6350327 .1838948 int_102 -.0344499 .0941513 -0.37 0.715 -.219271 .1503711 int_103 -.2157894 .1481937 -1.46 0.146 -.5066968 .0751181 int_104 .2358869 .1214517 1.94 0.052 -.0025255 .4742992 int_105 -.1312011 .10533 -1.25 0.213 -.3379661 .075564 int_106 .1487526 .10533 1.41 0.158 -.0580125 .3555176 int_107 .3940098 .1481937 2.66 0.008 .1031023 .6849172 int_108 -.1531421 .2085885 -0.73 0.463 -.5626058 .2563217 int_109 .0510369 .0941513 0.54 0.588 -.1337842 .235858 int_110 -.0684522 .10533 -0.65 0.516 -.2752173 .1383128 int_111 .2367144 .1214517 1.95 0.052 -.0016979 .4751268 int_112 -.101021 .1214517 -0.83 0.406 -.3394334 .1373913

- 75 -

APPENDIX F

Table 1: Effects of judicial system efficiency on GDP (using GDP 1)

Pool FE RE (1) (2) (3) GDP1 GDP1 GDP1 VRS_TE_hat 2.18240e+09 -209969480.2 -129660853.5** (417007700.7) (54330025.1) (55539527.2)

VRS_TE_EU 8.49528e+09** 2.07059e+09** 2.24056e+09*** (324426230.1) (73101732.2) (83705920.2)

_cons -312911186.1* -398937228.0*** (41685182.5) (99997018.1)

Region FE Yes Yes Yes TimeFE Yes Yes Yes N 896 896 896 Standard errors in parentheses t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

Table 2: Effects of judicial system efficiency on GDP (using GDP 2)

Pool FE RE (1) (2) (3) GDP2 GDP2 GDP2 VRS_TE_hat 257.0862** -48.37297 -7.693385 (9.436976) (16.48878) (14.9392)

VRS_TE_EU 291.6508 -7.59598 49.02365* (68.24838) (27.83891) (29.54899)

_cons 462.9606** 434.3041*** (12.15486) (14.70484)

Region FE Yes Yes Yes TimeFE Yes Yes Yes N 896 896 896 Standard errors in parentheses t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

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APPENDIX G

Table 1: Effects of judicial system efficiency on GDP (GDP 1)

Pool FE RE (1) (2) (3) GDP1 GDP1 GDP1 VRS_TE_L1 4.70e+09** -2.20e+09** -2.11e+09*** (1.74e+08) (6.60e+07) (6.31e+07)

VRS_TE_EU_lag -6336023 2.42e+09** 2.39e+09*** (2.11e+08) (1.02e+08) (29.54899)

_cons 1.32e+09*** 5.78e+08*** (68e+07) (4.28e+07) Region FE Yes Yes Yes TimeFE Yes Yes Yes N 784 784 784 Standard errors in parentheses t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

Table 2: Effects of judicial system efficiency on GDP (GDP 2)

Pool FE RE (1) (2) (3) GDP2 GDP2 GDP2 VRS_TE_L1 241.6379** -161.4486** -103.3891*** (17.60186) (11.11956) (12.56059)

VRS_TE_EU_lag -3.226181 19.79262 12.54103 (14.33675) (25.83252) (22.7129)

_cons 493.6748*** 509.049*** (5.632398) (19.27172) Region FE Yes Yes Yes TimeFE Yes Yes Yes N 784 784 784 Standard errors in parentheses t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

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8. BIBLIOGRAPHY

Aigner, D., Lovell C., & Schmidt P. 1977, Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics vol. 6(1), pp. 21.37.

Meeusen, W. & van den Broeck J. 1977, Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error, International Economic Review vol. 18 (2), pp.435-444

Annual Reports for District Courts (2006, 2012)

Banker, R.D., Charnes, A. & Cooper, W.W. 1984, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, vol.30(9), pp. 293-306.

Barro, R. 1991, Economic Growth in a Cross Section of Countries, Quarterly Journal of Economics, vol. 106, pp. 407-43

Bhat R., B.V. Bharat, E. Reuben, 2001. Hospital Efficiency and Data Envelopment Analysis (DEA). Indian Institute of Management, pp 1-40

Botero, J.C., La Porta, R., Lopez-de-Silanes F., Shleifer A., Volokh A. 2003, Judicial Reform, The World Bank Research Observer. Vol. 18(1), pp. 61-88.

BTA news agency, Sofia, Available at: www.bta.bg/en [27 March 2014]

Bulgarian Ministry of Justice, 2007, Strategia za Prodaljavane na Reformata, pp 1-40

Bulgarian News Agency BTA 2014, Document BTA0000020140410ea4a0018h, 10 April 2014 Bulgarian News Agency, Sofia

Buscaglia E. & Dakolias M. 1996, Judicial Reform in Latin American Courts: The Experience in Argentina and Ecuador, World Bank technical paper no. WTP 350, Available at: http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=47806 0&piPK=64165421&menuPK=64166093&entityID=000009265_3970128132105 [12 July 2014]

Buscaglia, E. & Dakolias M. 1999, Comparative International Study of Court Performance Indicators: A Descriptive and Analytical Account, The World Bank: The International Bank for Reconstruction and Development.

Buscaglia, E. & Ulen, T. S. 1997, A Quantitative Assessment of the Efficiency of the Judicial Sector in Latin America, International Review of Law and Economics, vol. 17, No. 2, Available from SSRN: http://ssrn.com/abstract=1079845 [12 August]

Buscaglia, E., An Analysis of the Causes of Corruption in the Judiciary (Buscaglia and Dakolias, 1999) (September 2006). Available from SSRN: http://ssrn.com/abstract=931386 [14 June 2014]

Center for the Study of Democracy. 2013, Corruption and Anti-corruption in Bulgaria (2012- 2013), Policy Brief No. 43, Available at : Charnes, A., Cooper, W.W. & Rhodes, E. 1978, Measuring the efficiency of decision making units, European Journal of Operational Research, vol. 2(6), pp.429-444 - 78 -

Chemin, Mattheiu, 2012. Does Court Speed Shape Economic Activity? Evidence from a Court Reform in India. Journal of Law, Economics, and Organization 28(3) pp 460-485

Chemin, Matthieu , 2009. The Impact of the Judiciary on Entrepreneurship: Evaluation of Pakistan’s “Access to Justice Programme”. Journal of Public Economics, pp 93:114-125

Chemin, Matthieu, 2009. Do judiciaries matter for development? Evidence from India. Journal of Comparative Economics 37, pp 230-250

COMMISSION DECISION of 13/XII/2006 establishing a mechanism for cooperation and verification of progress in Bulgaria to address specific benchmarks in the areas of judicial reform and the fight against corruption and organised crime, Accessible at: http://ec.europa.eu/enlargement/archives/bulgaria/key_documents_en.htm [14 June 2014]

Commission of the European Communities 2006, Commission Decision of 13/XII/2006 establishing a mechanism for cooperation and verification of progress in Bulgaria to address specific benchmarks in the area of judicial reform and the fight against corruption and organised crime, Available from: http://ec.europa.eu/enlargement/pdf/bulgaria/bg_accompanying_measures_1206_en.pdf . [6 August, 2014]

COMMUNICATION FROM THE COMMISSION Monitoring report on the state of preparedness for EU membership of Bulgaria and Romania, Brussels, 26.9.2006, COM(2006)54final, Available from: http://ec.europa.eu/enlargement/archives/bulgaria/key_documents_en.htm [29 June 2014]

Constitution of Bulgaria Atr. 125(1)), Available at http://www.parliament.bg/bg/const [01 August 2014]

Creation of the SJC, Available from < http://www.vss.justice.bg/en/start.htm> [01 August 2014]

Dakolias, M. 1999, Court Performance Around the World – A Comparative Perspective. World Bank Technical Paper No. 430. Washington, D.C.

Dakolias, M. and Said, J. 1999, Judicial Reform: A Process of Change Through Pilot Courts World Bank Legal and Judicial Reform Series, The World Bank, Washington, D.C

De Shazo, P. & Vargas, J. E. 2006, Judicial Reform in Latin America – an Assessment, Center for Strategic and International Studies: Washington, D.C. Policy Papers on the Americas, vol. XVII, Study 2

De Sousa, M.C.S. & Schwengber S.B. 2005, Efficiency Estimates for Judicial Services in Brazil: Nonparametric FDH (Free Disposal Hull) and the Expected Order-M Efficiency Scores for Rio Grande Do Sul Courts. Available from: http://www.anpec.org.br/encontro2005/artigos/A05A053.pdf [18 July, 2014]

- 79 -

Dimitrova-Grajzl, V., Grajzl P., Sustersic J. & Zajc K. 2012, Court Output, Judicial Staffing, and the Demand for Court Services: Evidence from Slovenian Courts of First Instance. International Review of Law and Economics vol.32(1): pp. 9-29.

Djankov, S., La Porta R., De-Silanes L. & Shleifer A. 2003, Courts, The Quarterly Journal of Economics vol. 118(2) pp. 453-517.

Elbialy, N. & García-Rubio M.A. 2011, Assessing Judicial Efficiency of Egyptian First Instance Courts A DEA Analysis, MAGKS Joint Discussion Paper Series in Economics. No. 19- 2011. Available from: https://www.unimarburg.de/fb02/makro/forschung/magkspapers/19-2011_elbialy.pdf [05 June 2014]

García-Rubio, M.A. & Rosales-López V. 2010, Justice & Economy: Evaluating Judicial Efficiency in Andalusia, InDret 4. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1738227 [12 June 2014]

Garoupa, N. & Ginsburg T. (forthcoming), Reputation and the Organization of the Judiciary: A Study in Comparative Law, Forthcoming with the University of Chicago Press

Gray, Ch.W. 1991, Legal Process and Economic Development: A Case Study of Indonesia, World Development, vol. 19(7), pp 763-78

Gray, Ch.W. 1997, Reforming Legal Systems in Developing and Transition Economies, Finance and Development, vol. 93(3)

Gyaurova B., Delcheva V., Kovacheva D., Ivanova Iv., Todorova M., Smedovska- Toneva R. & Dochev T. 2008, Evaluation of the strategy for a judicial reform in Bulgaria, Report issued by the Open Society Institute Sofia http://www.osi.bg/?cy=100&lang=2 [28 June 2014]

Jacobs, R. 2001, Alternative Methods to Examine Hospital Efficiency: Data Envelopment Analysis and Stochastic Frontier Analysis, Health Care Management Science vol.4, pp.103-115. Ji, Y. & Lee Ch. 2010, Data Envelopment Analysis . The Stata Journal 10, Number 2, pp. 267- 280

Judicial systems in Member States – Bulgaria, Available from < https://e justice.europa.eu/content_judicial_systems_in_member_states-16-bg-en.do?member=1> [10 March 2014]

Kakalik, J.S.1997, Just, Speedy, and Inexpensive? An Evaluation of Judicial Case Management under the Civil Justice Reform Act

Kittelsen, S. & Forsund, F. 1992, Efficiency analysis of Norwegian district courts, Journal of Productivity Analysis, vol.3, pp. 277-306

Lewin A.Y., Morey R.C. & Cook T.J. 1982, Evaluating the Administrative Efficiency of Courts, Omega vol. 10(4) pp. 401-411

Lopez, V. 2008, Economics of Court Performance: An Empirical Analysis, European Journal of Law and Economics, vol. 25 (3), pp. 231-251

- 80 -

Mahoney, B., Sipes, L., Ito L., & Jeanne A. 1985, Implementing Delay Reduction and Delay Prevention Programs in Urban Trial Courts: Preliminary Findings From Current Research, Williamsburg, VA: National Center for State Courts

Messick, R. 1999, Judicial Reform and Economic Development: A survey of the Issues,World Bank Research Observer vol.14(1): 117-136

Messick, R. 2002, Judicial Reform: The Why, the What, and the How, Paper prepared for Delivery at a conference on Strategies for Modernizing the Judicial Sector in the Arab World. March, 2002. Marrakech, Morocco. Retrieved from:http://www.pogar.org/publica- tions/judiciary/messick/reform.pdf. [17 June 2014]

Murell, P. 2001, Demand and Supply in Romanian Commercial Courts: Generating Information for Institutional Reform, SSNR Working Paper No. 280428

Pedraja, F., & Salinas, J. 1995, La Eficiencia en la Administracio´n de Justicia. Las Salas de lo Contencioso de los Tribunales Superiores de Justicia, Revista de Economı´a Aplicada, vol. 3(8), pp.163–195. Cited in Garcia- Rubio and Rosales-Lopez (2010)

Pedraja-Chaparro F and J. Salinas-Jiménez. 1996. An assessment of the efficiency of Spanish Courts using DEA. Applied Economics 28 (11),pp 1391-1403

Pereira M. C & Sara Moreira, 2007. A Stochastic Frontier Analysis of Secondary Education Output in Portugal, Working Papers w200706, Banco de Portugal, Economics and Research Department

Posner, R. 1998, Creating a Legal Framework for Economic Development. World Bank Research Observer, vol. 13(1), pp. 1-11.

Posner, R. A. 1993, What Do Judges and Justices Maximize--(The Same Thing Everybody Else Does), Supreme Court Economic Review vol.3, pp.1-41

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL On Progress in Bulgaria under the Co-operation and Verification Mechanism , Brussels 23.7.2008, COM (2008) 495 final http://ec.europa.eu/cvm/progress_reports_en.htm [29 June 2014]

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL On Progress in Bulgaria under the Co-operation and Verification Mechanism Brussels,22.1.2014 COM(2014) 36 final http://ec.europa.eu/cvm/progress_reports_en.htm [29 June 2014]

Cameron, C. & Trivedi, P. 2009, Microeconometrics using Stata, College Station, Tex. : Stata Press, c2009

Rosales-López,V. (2008). Economics of court performance: An empirical analysis. European journal of law and economics, vol.25(3), pp. 231-251

Schumpeter, J.A.1934. The Theory of Economic Development. Cambridge: Harvard University Press

- 81 -

Staiger, D. and J. H. Stock. 1997, Instrumental Variables Regression with Weak Instruments, Econometrica, 65:3 pp 557-586.

SUPPORTING DOCUMENT Accompanying the REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL On Progress in Bulgaria under the Co-operation and Verification Mechanism Brussels, 22.07.2009 SEC(2009) 1074 http://ec.europa.eu/cvm/progress_reports_en.htm [29 June 2014]

Tone, K., & Miki Tsutsui.2013, An epsilon-based measure of efficiency in DEA. Discussion Paper 09-13 GRIPS Policy Information Center

Tsekov, N. 2012, Cheap but Not Easily Accessible Justice, DW, 03 August , Available from: http://www.dw.de/ [ 03 August 2012]

Tulkens, H. 1993, On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts and Urban Transit, The Journal of Productivity Analysis vol. 4, pp.183- 210

Varela D. & Veena M. 2001. The Domenican Republic: A First Statistical Review of the Justice Sector, Report 232013- DR. World Bank, Latin America and the Caribbean Region. Poverty Reduction and Economic Management Unit, Washington, D.C

Voigt, S. and N. El Bialy,2013. Identifying the Determinants of Judicial Performance: Taxpayers’ Money Well Spent? Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2241224. [30 March 2013]

Voigt, Stefan. 2014, Determinants of Judicial Efficiency: A Survey pp. 1-31 Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2390704 [04 February 2014]

Weder, B. 1995, Legal Systems and Economic Performance: The Empirical Evidence. World Bank, Judicial Reform in Latin America and the Caribbean: The International Bank for Reconstruction and Development, Washington DC., Technical Number 280, pp. 21-26

Wooldridge J.M., 2002. Introductory Econometrics: A Modern Approach, MIT Press Books, edition 2E

World Bank Report .2008, Bulgaria Resourcing the Judiciary for Performance and Accountability, Report No. 42159-BG, pp 1-95

- 82 -