The Impact of Territoriality of Court Bailiffs and Its Quantification in the Czech Republic LUBOŠ SMRČKA and JAN PLAČEK Department of Strategy, Faculty of Business Administration University of Economics, Prague W. Churchill Square 4, Prague 3 CZECH REPUBLIC th International Conference on Social Sciences and Society (ICSSS 2015) May 20-21, 2015, Paris, France

The Czech Ministry of Justice takes into account an idea about higher regulation of court bailiffs. There are discussed several concepts as territoriality and mandatory deposits from creditors. These concepts would influence the Czech market, its structure and the level of its competition among the different bailiffs. This paper is focused on the territoriality and its possible impacts on the market, participants and efficiency of a process. The paper tries to quantify recovery rates of receivables after an introduction of the territoriality. The paper's results enable to compare two different states of the world – before introducing the territoriality and after introducing the territoriality. The aim of this paper is to quantify impacts of the territoriality. The paper does not want to discuss the changes from the legal point of view but from the economical perspective.

Figure 4 – Distribution of foreclosures among the executor offices (2014) Source: Ministry of Justice and own computations

Distribution foreclosures among bailiffs Source: own computations

Annual number of cases Number of bailiff offices Total number of cases Total number of new cases + / - Change in number of employees 70 – 30 thous – 10 thous – 5 thous – 2,5 thous ,5 – 0 thous Changes in number of employees of bailliffs in the case of territoriality introduction (2014) Source: own computations

Territorial differences in the recovery of claims in CR Source: Anonymised bailiff office, own computations

Territorial differences in the recovery of claims in Prague Source: Anonymised bailiff office, own computations

Experience with the success of bailiff offices Provider of hire purchases 1 Source: Anonymised creditor Provider of hire purchases 1 Bailiff comparison – months from the start of enforcement

Experience with the success of bailiff offices Provider of hire purchases 2 Source: Anonymised creditor Provider of hire purchases 2 Bailiff comparison – months from the start of enforcement

Source: Anonymised creditor Experience with the success of bailiff offices Bank 1 Bailiff comparison – months from the start of enforcement

Source: Anonymised creditor Experience with the success of bailiff offices Bank 2 Bailiff comparison – months from the start of enforcement

Source: Anonymised creditor Experience with the success of bailiff offices Mobile operator 1 Mobile operator Bailiff comparison – months from the start of enforcement

Methods and data sample Our prediction is based on the current observed data and our assumptions introduced in the following subchapter. The availability of data is very low. Authors cooperated with several creditors (providers of hire purchases, banks and mobile operator) whose value of problematic receivables is high regularly. Methods and data sample Our prediction is based on the current observed data and our assumptions introduced in the following subchapter. The availability of data is very low. Authors cooperated with several creditors (providers of hire purchases, banks and mobile operator) whose value of problematic receivables is high regularly.

% / type of creditor Provider of hire purchases 1 (2 years) Provider of hire purchases 2 (2 years) Bank 1 (3 years) Bank 2 (3 years) Mobile operator (3 years) Mobile operator (7 years) Highest recovery rate Average recovery rate Lowest recovery rate Ratio of the lowest to average recovery rate Ratio of the lowest to highest recovery rate Table 1. Differences among the premium bailiffs in the case of several types of creditors. Source: own computations

Assumptions Authors' predictions of the future state of the world are based on several assumptions. The first assumption is connected with the cases redistribution among the agents. There are approximately 20 agents who can be called premium and they solve most of the cases. Their market share would decrease to 10% (precisely 12.58%) in the case of an equal coverage. In total there are 159 registered agents. The second assumption is that the creditors will not change their preferences and they will still use this type of enforcement. The third and fourth assumptions are crucial for authors' data modeling or simulation. The third assumption is that the worse premium agent is so successful as the best “other” agent. The last assumption is the most important. It says that there is the ratio the highest to the lowest recovery rate is same for premium as well as other agent group. The assumption is reasonable because the real differences are probably higher. Assumptions Authors' predictions of the future state of the world are based on several assumptions. The first assumption is connected with the cases redistribution among the agents. There are approximately 20 agents who can be called premium and they solve most of the cases. Their market share would decrease to 10% (precisely 12.58%) in the case of an equal coverage. In total there are 159 registered agents. The second assumption is that the creditors will not change their preferences and they will still use this type of enforcement. The third and fourth assumptions are crucial for authors' data modeling or simulation. The third assumption is that the worse premium agent is so successful as the best “other” agent. The last assumption is the most important. It says that there is the ratio the highest to the lowest recovery rate is same for premium as well as other agent group. The assumption is reasonable because the real differences are probably higher.

% / type of creditor Provider of hire purchases 1 (2 years) Provider of hire purchases 2 ( 2 years) Bank 1 (3 years) Bank 2 (3 years) Mobile operator (3 years) Mobile operator (7 years) Highest recovery rate Average recovery rate Lowest recovery rate Ratio of the lowest to highest recovery rate Source: own computations

Quantification and results Aforementioned assumptions and the original data sample allow quantify the future state of the world. The future state of the world is the territoriality which would influence the market structure and as well as the efficiency expressed by the recovery rates. The territoriality leads to the cases redistribution. The market share of the premium agents would significantly drop. In the case of evenly distribution each agent has 0.6% market share. Probably the market share of the premium agent would exceed 0.6% because his/her place of residence is connected with more cases because more people as well as corporations have there their place of residence. Due to this reason authors make three different predictions. The first is the strictest and it forecasts that 20 current premium agents will operate with the market share 10%. The middle prediction is based on the market share 15% and the most optimistic one on 20%. The recovery rates decrease in all three predictions (comparison with table 1). The results are included in table 3, 4 and 5. These tables display highest, lowest and average recovery rates for the different types of creditors.

Table 3. Decrease of recovery rates (market share of the premium bailiffs 10%). Source: own computations. % / type of creditor Provider of hire purchases 1 (2 years) Provider of hire purchases 2 (2 years) Bank 1 (3 years) Bank 2 (3 years) Mobile operator (3 years) Mobile operator (7 years) Highest recovery rate Average recovery rate Lowest recovery rate Source: own computations

Table 4. Decrease of recovery rates (market share of the premium bailiffs 15%) % / type of creditor Provider of hire purchases 1 (2 years) Provider of hire purchases 2 (2 years) Bank 1 (3 years) Bank 2 (3 years) Mobile operator (3 years) Mobile operator (7 years) Highest recovery rate Average recovery rate Lowest recovery rate Source: own computations

Table 5. Decrease of recovery rates (market share of premium bailiffs 20%) % / type of creditor Provider of hire purchases 1 (2 years) Provider of hire purchases 2 (2 years) Bank 1 (3 years) Bank 2 (3 years) Mobile operator (3 years) Mobile operator (7 years) Highest recovery rate Average recovery rate Lowest recovery rate Source: own computations

Table 5 should display the best results in the case of authors' predictions but in the comparison with table 1 the recovery rates are half. The authors' simulation of territoriality leads to a serious deterioration. The creditors lose money and their capital always because the recovery rates in the case of risk clients are never 100%. Before the territoriality the average recovery rate for provider of hire purchases 1 is 19.2%, for provider of hire purchases 2 is 8.3%, for bank %, for bank and mobile operator in time frame 3 years 20% or 46% after 7 years. After the introduction of the territoriality the average recovery rates drop to 50%. This is the extreme deterioration which would affect not only the creditors but all their customers. These customers should pay higher prices to cover these expected losses. The impact is then predictable on the whole country economy.

The territoriality would lead to the redistribution of the cases. Only several agents would solve the same amount of the cases as they do right now. The territoriality makes it difficult for the creditors choose their agent. The enforcement agent is on one hand regulated by the law but on the other hand he/she is an entrepreneur whose incentives are strictly economical. According to the redistribution the best agents would lose thousands of the cases annually. It does not matter if the efficiency of the agent is high or low because of territoriality he/she has a certain number of the cases. This could decrease their effort (in another words productivity, quality of the enforcement process, recovery rates etc.).

Source: Ministry of Justice, own computation Development of debt relief in the years