Banking Competition in UK Dissertion

41 Pages   |   10,112 Words

Abstract

A review of the literature shows that Panzar Rosse statistics can reliably gauge the competitive position of the banking industry by taking into account the price and bank-specific input variables. This research takes into account the top ten banks of UK and examines the financial data for the past ten years and comments on the competitiveness of the banking industry. Moreover, a special variable, the TIER 1 regulatory ratio is also taken into account to re-measure the competitive index. Panel least square analytical technique is used to formulate the regression equation while the redundant fixed effect likelihood ratio and Wald coefficient tests are used to test the fitness of the model and the proposed hypotheses respectively. The obtained results show that the banking sector of UK is not in the state of long run equilibrium. This implies that risk-adjusted returns tend to deviate from zero in the long run. Without taking into account TIER 1 as a bank-specific variable, the Wald test shows that the banking industry faces a monopolistically competitive revenue curve in relation to the input price variables. Asset, cost of capital and cost of funds, are all negatively related to the overall revenue. With the inclusion of TIER 1 variable, the competitive format of the industry remains the same at monopolistic competition. This shows that TIER 1 regulatory ratio has no effect on the market dynamics of the banking sector of UK. TIER 1 ratio tends to be negatively related to the revenue of the banks. These findings apply to the top ten banks of UK and the dynamics for the small sized banks may be different as in their case, competition and price taking capabilities shun the features of the monopolistically competitive environment.
 

 
Table of Contents
Abstract v
Chapter 1: Introduction. 1
1.1 Topic Area. 1
1.2 Research Importance. 1
1.3 Research Questions. 1
1.3 Aims and Objectives. 2
1.5 Plan of Dissertation. 2
Chapter 2: Literature Review.. 3
2.1 Introduction. 3
2.2 Regulatory Environment 3
2.2.1 Type of Regulations. 3
2.3 Competition Indexes. 4
2.3.1 Approaches for Measuring Competition. 4
2.3.1.1 Structural Approach. 4
2.3.1.2 Non Structural Models. 5
2.4 Conclusion. 6
Chapter 3: Empirical Framework & Data Description. 7
3.1 Introduction. 7
3.2Theoretical Basis for Methodology. 7
3.2.2 Rejection of Structural Approach. 7
3.2.2 Acceptance of Non-Structural Approach. 8
3.3Empirical Methodology. 8
3.4 Variables. 9
3.4.1 Breakdown of Input Price Variables. 9
3.4.2 Breakdown of Bank Specific Variables. 10
3.4.3 Derivation of Final Equation. 11
3.5 Hypothesis. 11
3.6 Sampling Technique. 11
3.6.1 Selected Sample. 12
3.7 Data Collection. 12
3.8 Data Analysis Techniques. 12
3.9 Limitations of the Research. 13
Chapter 4: Analysis and Discussion of Results. 14
4.1 Introduction. 14
4.2 Test for the Long Term Equilibrium Condition. 14
4.3 Test for the Market Competition. 16
4.4 Test for the Market Competition and TIER 1 Variable. 19
4.5 Discussion of Results. 22
Chapter 6: Conclusion. 24
6.1 Conclusion of Results. 24
6.2 Recommendations. 24
References. 26
Appendix A.. 29
Raw Financial Date for Model 29
Panel Data. 33
 
 

List of Tables

Table 1: Panzar Rosse Statistics Interpretation. 5
Table 2: Long Term Equilibrium Condition Results. 15
Table 3: Redundant Fixed Effects Likelihood Ratio Results. 15
Table 4: Wald Test Output 16
Table 5: PLS for Market Competition: Results. 17
Table 6: Redundant Fixed Effect Test for Market Competition. 17
Table 7: Wald Test for Market Competition. 18
Table 8: Wald Test for Market Competition (2) 19
Table 9: PLS Output for Market Competition with TIER 1 Variable. 20
Table 10: Redundant Fixed Effect Test for Market Competition with TIER 1 Variable. 20
Table 11: Wald Test for Market Competition with TIER 1 Variable. 21
Table 12: Wald Test for Market Competition with TIER 1 Variable (2) 21
 

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Chapter 1: Introduction

1.1 Topic Area

In the past decade or so, the corporate competition has risen to a great extent globally. Firms are trying to expand their revenue base and funding, financial entities are constantly their marketing and product strategies to gain market share (Boulding & Staelin, 1990). Whether it is about the total revenues, net profits or the total asset size, all banks compete with the other industry participants in order to establish their stature. In the financial arena, the banking sector has especially witnessed a boom. However, with a roar in the revenue, the competition rose significantly and so did the mismanagement of risk supervision practices. The financial crises of 2007-08 are credited to the lack of adequate regulations (Acharya & Richardson, 2009). Not only were the core regulatory frameworks evaded, but some instances of innovative or creative accounting were also discovered. At the current moment, it can be speculated that the fierce competition among banks forced them to bypass the regulations. This dissertation focuses on the two crucial aspects, the regulatory environment of banks and the competition in the banking industry. The research gives special attention to the variables from the context of UK.

1.2 Research Importance

A research on the link between the strictness of the regulatory environment and the level of competition is beneficial in two ways. Firstly, it will shed light on the market dynamics of the banking sector. The estimation of the competitive environment will also yield crucial insights on the mode in which the banks operate. Moreover, the banks themselves can benefit from the research as each market type has distinct features and in order to maximise the profits, the industry competition level plays a major role. Secondly, this research also sheds light on the impact of TIER 1 regulatory requirement on the banking industry. The relevant findings are beneficial for regulatory bodies.

1.3 Research Questions

This research focuses on the following questions.
1: What is the nature of competition in the banking industry of UK? 
2: Does the regulatory factor of TIER 1 ratio effects the overall competition in the banking industry?

1.3 Aims and Objectives

The excessive breach in the risk management practices before and during the financial crises can be credited to two factors, the lack of corporate governance structure or the strong industry wise compulsion (Erkens, et al., 2012). The primary aim of this reach is to find out the link between the level of regulatory variables and the competition. The objectives of this research are as follows:
1: To establish the value for the regulatory variables (TIER capital, etc.) and other price and bank related factors from the period of 2004 to 2013.
2: To critically examine the nature of competition, with and without TIER 1 criterion, and link the findings to the features of the banking industry.
3: To calculate the long run equilibrium condition for the banking sector of UK.

1.5 Plan of Dissertation

This dissertation is divided into five chapters. The introduction is followed by literature review. The literature review focuses on two distinct aspects of the research, the financial ratios that pertain to the regulatory framework and the model that is used to measure industry competition. Subsequently, chapter 3 examines the empirical framework and the data description. It contains information on data collection, sampling, methodology and limitations. Chapter 4 explains the data analysis, results and discussion of the results. Major portion of the chapter focuses on the descriptive statistics, the regression analysis and the application of the findings. This chapter is followed by chapter 5, which constitutes conclusion and recommendation.

 

Chapter 2: Literature Review

2.1 Introduction                            

This chapter takes a detailed look at the factors that are related to the core topics, aims and objectives. The literature review is divided into three parts. The first part lists and explains the regulatory variables or factors for UK banks. In this part, key ratios are explained and their minimum threshold is also established. The second part of the literature review takes into account the study of the variable that is used for the measurement of the competition, the structural and non-structural approaches to measuring banking competition. The third part relates to the conclusion and the identification of the relevant concepts for this research.

2.2 Regulatory Environment

2.2.1 Type of Regulations

In the context of UK, there are two types of regulations that are in place for banks. Rosenbluth & Schaap (2003) stated that these two regulations are the prudential regulations and the profit padding regulation. The profit padding regulations ensure that the competition among the banks is fair and that the banks do not compete with their profits. As depicted by their name, prudential regulations are in place as a cautionary or preventive measure (Hellmann, et al., 2000). These regulations are put in place in order to stop an event (loss, collapse, risky behaviour, etc.) from happening in the first place. These regulations do so by maintaining some minimum requirements on numerous benchmark ratios. The prudential regulatory authority of UK or PRA has the sole responsibility to measure, examine and regulate the safety and soundness of the bank in relation to its claims by its debt holders and depositors (Bank of England, 2014). For this purpose, PRA has put into place some stringent capital requirements, or capital adequacy ratios for the banks. There are two key ratios that are put in place, Tier 1 ratio and the capital adequacy ratio (Bank of England, 2014). Tier 1 capital is the most liquid form of capital for a bank. It consists of shareholder’s equity, accumulated earnings and preferred shares (Jones, 2000). On the other hand, Tier 1 ratio can be measured as:

The regulatory authorities rank all asset classes of the banks into different categories of risk. Each class has its own risk constant. The equivalent ‘risk weighted asset amount’ for any class of asset is calculated by multiplying the risk constant with the amount of assets (Hancock & Wilcox, 1994). On the other hand, the capital adequacy ratio can be calculated as:

Tier 2 Capital includes different classes that do not come under to format of permanency as the revaluation reserves, subordinated debt and some hybrid instruments (Benston, et al., 2003). The prudential regulation authority has set the Tier 1 ratio and the capital adequacy ratios at a minimum of 4.5% and 7% respectively (Bank of England, 2014). Even though these ratios have not witnessed a huge increase or decrease after 2004, it is vital the overall movement in these ratios is taken into account. Any movement in these two key ratios will imply that the regulatory environment of the bank has strengthened or deteriorated. This research applies TIER 1 ratio to measure the change in the regulatory framework of the banks.

2.3 Competition Indexes

The competition in the banking industry can be measured by employing any traditional index. However, the peculiar working and business model of the banks demands special approaches to be used in order to gauge the competitive environment of the industry.

2.3.1 Approaches for Measuring Competition

Researchers have pointed out two types of methodological approaches in order to estimate banking competition.

2.3.1.1 Structural Approach

 The structural approach links the performance of the banks to the market structure. The most commonly used structural approaches, the efficiency hypothesis and the structure conduct performance correlate competition to the market concentration. The structural conduct performance approach hypothesises that concentrated industry tends to be less competitive and more collusive because of the underlying market structure (Hannan, 1991).  On the contrary, the efficiency hypothesis proposes that an efficient bank can increase its market share by decreasing the price of its products and services (Berger, 1995).  In this theory, competitive ratios are perceived to influence the market share by virtue of the market structure.

2.3.1.2 Non-structural Models

Contrary to the structural approach, the non-structural approach to measuring competition maintain the fact that apart from the market forces and dynamics, firm or bank specific factors can also influence the level of competition in the banking industry. The most widely used model for measuring the non-structural competition is the Panzar Rosse Model. This model is named after its founders JC Panzar and JN Rosse. They developed this model in 1987 and published their findings in the Journal of Industrial Economics (Panzar & Rosse, 1987). The basic motivation to develop this model was derived from the Cournot Oligopoly model in which the profits of both the individual banks and the industry need to be maximized (Al-Nowaihi & Levine, 1985).
In order to determine the level of competition (to term the market as competitive, monopolistic, oligopolistic or monopolistically competitive), Panzar and Rosse developed a statistic named as the H statistic. To compute the H statistics, Panzar and Rosse theorized that the elasticity of the revenue of the banks in relation to the input prices can be used to measure the overall competition. The following equation explains the concept:

Where,
Ri = Revenue function of the bank in Equilibrium
Wi = Input prices of the banks
The following table shows the interpretation of the H statistic as explained by Panzar and Rosse (Kashi & Beynabadi, 2013):
Table 1: Panzar Rosse Statistics Interpretation
Competitive Environment
Values of H Market Interpretation
H≤0       Monopoly or perfectly collusive oligopoly
0 Monopolistic Competition
H=1 Perfectly Competitive Industry
Equilibrium Environment
0 Disequilibrium
H=0 Equilibrium
 
The value of the H statistic can vary from -∞ to 1. However, Panzar and Rosse proved that the industry is competitive when the H statistic is close to 1. If the H statistic equals unity, then the market or the industry is perfectly competitive.
Panzar and Rosse Model had been used by many researchers in order to compute the competition among the banking industries. Weill (2004) used a sample of European banks from a period of 1994-1999 and applied the H statistic methodology in order to compute market dynamics. The results showed that the monopolistic competition in the banking industry of EU is gradually decreasing. Staikouras et al. (2006) established a detailed research to include different European countries in the analysis (25 countries) and covered a period of 1998-2002. The results show that even though the market is monopolistically competitive, the larger banks tend to depict a highly competitive behaviour.

2.4 Conclusion

The literature review has pointed out two prime findings. Firstly, TIER 1 ratio and the capital adequacy ratio can be used to measure the level of regulatory compliance by the banks. Secondly, the H statistic can be used to measure the competitive landscape of the banking industry. Additionally, it is also found that it would be better to include the leading market players of the banking industry in the computation of H statistic. The inclusion of small market players will complicate the computations in regards to the restricted scope of this research.

 

Chapter 3: Empirical Framework & Data Description

3.1 Introduction

Academic researches always adopt a core methodology on which the proposed hypotheses are tested. Hence, it is vital that all the perspectives of the methodological framework are laid forward. This chapter focuses on the three distinct features, the theoretical basis for the methodology, the empirical framework and the data description. The theoretical basis forms an opinion as to why the proposed method is most suitable in formulating a research design. The empirical framework portion lists the underlying theory (PanzarRosse model) and its constituent factors including the independent, dependent variables and the hypotheses. The data description part contains the sampling technique, selected sample and the mathematical models of the research. Limitations of the research are also discussed in a concise manner.

3.2Theoretical Basis for Methodology

3.2.2 Rejection of Structural Approach

In the past, numerous studies have linked the competition of the banking industry to the market structure (based on the structure conduct performance theory) in which they presumed market structure to be an exogenous factor in the model (Matthews, et al., 2007). As a result of this assumption, these studies carried out regression analysis for the profitability and the concentration ratios (mostly measured by the HHI index). Additionally, the bulk of control variables were also added in the regression analysis. Any positive relationship between the profitability and concentration ratio was cited to be an adequate reason of the market power exercised by the banks. At the same time, other studies adopted efficient structure hypothesis. This structural approach also linked the performance and market power to the efficiency of the banks. Various studies show that these two structural approaches (SCH and ESH) are not pertinent in measuring the market power of the banks. Berger (1965) argued that linking the market power of the banks to their profitability and the overall industry structure is not adequate because it leaves behind some crucial non-structural determinants of market power. Berger and Humphrey (1997) also put forward that the structural approaches to measure market power are not adequate as they do not offer explanations for the differences and the causes of the deviation in market power. Paul (1999) also argued that a suitable empirical methodology for measuring market power should constitute the marginal cost and revenue structure of the bank.

3.2.2 Acceptance of Non-Structural Approach

Keeping in view the shortcomings of the structural approach, this research employs non-structural approach in its empirical methodology which overcomes many of the inadequacies of the structural approaches. More specifically, the Panzar and Rosse (1987) reduced form revenue model is being used to arrive at the conclusion. Researches point out that in a competitive market; the participants’ banks follow the marginal cost pricing. Hence, the reduced form of the nonlinear revenue is the best way to measure the dynamics of market power (Nathan & Neave, 1989) & (Perrakis, 1991).

3.3Empirical Methodology

In the adopted methodology, the test for the competitive conditions is based on the log-linear revenue equation. The generalized format of the equation is shown below (Matthews, et al., 2007):

Where:
Rit = Net Interest Revenue of the bank I at time t
αo = Intercept of the Regression line
W(jit) = input prices (Wj)for the bank I at time t
X(kit) = Bank Specific Variables (Xk) for the bank I at time t
ε = Stochastic disturbance term
From the above mentioned reduced form revenue equation, the PanzarRosse H statistic can be computed by the following formula:

Hence, the H statistic is equal to the sum of the constants of all the input price variables. Panzar and Rosse labelled this statistic as the sum of the elasticity’s of the revenue in relation to the bank’s input prices (Panzar & Rosse, 1987).However, before measuring for the market competition; one has to test the model for one of its prerequisite or compulsive condition. Panzar and Rosse (1987) showed that the reduced form revenue model is only applicable when the market is in long term equilibrium. In the long term equilibrium, the risk-adjusted rate of return should be uncorrelated with the given input price factors. In this testing equation, the Rit (Net interest revenue of the bank I at time t) is replaced with πit (pretax return on assets)(Matthews, et al., 2007). For the long term equilibrium condition to hold, the sum of the αj should be equal to zero. Therefore, the testing equation for long term equilibrium can be written as:

Where:
 

3.4Variables

3.4.1 Breakdown of Input Price Variables

The generalized form of the PanzarRosse competition index lists the input price factors as the foremost and the most crucial independent variables. Borrowing the idea of input price variables from Matthews, et al. (2007), three distinct price factors are added to the list.
1: The first factor is the unit price of the labour. This factor is calculated by dividing the personal expenses of the bank to the total number of employees. No distinction is made among the levels of the hierarchy of employees. For the banks that are also operating outside UK, only the UK employees were considered as a relevant input to the model.
2: The second factor is the unit price of the capital. This value is generated by dividing the other operating expenses by the total assets. All types of fixed assets are included in the computation including the property, plant and equipment. Only the total assets pertaining to UK were inputted in the equation.
3: The third price factor, the unit cost of funds is computed by dividing total interest expenses to the loan-able funds (deposit and short-term funding). All banks face interest expenses at different levels (like different customers are paid different interest rates depending upon the account type); hence, the unit cost of funds should further be divided into relevant values. However, the inclusion of such a concept will complicate the already lengthy model. Therefore, only one variable is used in relation to the unit price of funds.
From the above discussion, the generalized part of the cost variables of the equation can be replaced with the following part.

Where:
PL = Unit Price of Labour
PK = Unit price of capital
PF = Unit price of funds/deposits

3.4.2 Breakdown of Bank Specific Variables

The following bank-specific variables are included in the generalized equation.
1: The first bank-specific variable is the equity to asset ratio of the banks. This variable is depicted by EQUITY. This variable represents the general level of the risk of any bank.
2: The second bank-specific variable is the operating size of the bank as measured by the total assets. This variable is defined as ASSET.
3: The third variable is the loan to asset ratio which depicts the general level of credit risk of the bank. This variable is shown as LOAN.
4: The fourth and the most crucial variable in the study is the TIER 1 ratio. This is the testing variable that will be inducted into the equation after the computation of H statistic. Initially, the PanzarRosse equation is formulated without the TIER 1 control variable and the value of H statistics is computed. Afterwards, the H statistics will be re-measured in order to calculate the effects of TIER 1 variable by inducting it into the equation. In this way, one can measure the effect of the TIER 1 regulation on the competitive environment of the bank. However, this fact should be considered that it is the TIER 1 ratio and not the TIER 1 capital that is utilized as the bank specific control variable.
The bank-specific part of the generalized equation can be broken down in the following format.
 

3.4.3 Derivation of Final Equation

The final equation of PanzarRosse Model comes out to be:
 

3.5 Hypothesis

This study poses the following three null hypotheses:
H1: The banking sector of UK is in the state of long term equilibrium. Hence, mathematically,

H2: The banking sector of UK faces a monopolistically competitive environment. Therefore, H statistics lies between 0 and 1.
 or
{}
H3: The TIER 1 capital ratio influences the market dynamics of the Banking Industry of UK by making it more competitive, i.e. by pushing the H value close to 1. 

3.6 Sampling Technique

Any scientific research can sample the given data in any format. The most common forms of data sampling are random sampling, representative sampling and forced sampling (Petersen, et al., 2005) (Kadilar & Cingi, 2005). The research shortlists the banks on the criteria of size (total assets). Hence, forced sampling is used to select the sample of banks. The use of forced sampling technique is totally justified as the banks with  small asset sizes do not tend to capture a lot of market share (in terms of loans). Hence, it is better to exclude them from the research. In the case of random sampling, there is quite a probability that the banks with high loan and asset base can be neglected. Representative sampling is very complicated to be applied in the current research. Hence, both the random and representative sampling techniques are not suitable in the current scenario.

3.6.1 Selected Sample

This research is limited to the selection of the top ten banks of UK (in terms of asset size). The 2013 year-end asset base is used to shortlist the banks. The selected banks are depicted on the following table:
Bank Name Total Assets in Mill US Dollar Ranking
HSBC Holdings Plc  $                     2,671,318.00 1
Barclays Bank Plc  $                     2,162,121.00 2
Barclays Plc  $                     2,161,178.00 3
Royal Bank of Scotland Group Plc  $                     1,692,816.00 4
Royal Bank of Scotland Plc  $                     1,679,733.00 5
Lloyds Bank Plc  $                     1,419,638.00 6
Lloyds Banking Group Plc  $                     1,394,977.00 7
MSBC Bank plc  $                     1,336,784.00 8
Bank of Scotland Plc  $                        936,120.00 9
HSBC Plc  $                        918,470.00 10
 
Source: Bankscope Database

3.7 Data Collection

The secondary data for the current research is collected from the Bankscope database. This database offers the data for all public limited banks of UK. However, only top ten banks were selected for analysis. Bankscope database offers authentic data for all banks. Hence, the use of bank scope saves time and effort of collecting the individual values from the annual financial statements. Some readymade ratios (like the TIER 1 ratio) are also collected directly from the database. The data from the last ten financial years is employed to carry out the analysis. This implies that the time period from 2004 to 2013 is covered in the study.

3.8 Data Analysis Techniques

The given data can be examined in a number of softwares including the Eviews, Excel and Stata. However, Eviews is best suited for the job of carrying out a regression analysis and other mathematical approaches (including the Hypothesis testing, redundant fixed effects likelihood ratio and Wald coefficient restriction for hypothesis testing). Moreover, Eviews is also chosen because of its diverse features. Additionally, the results of the analysis will be the same regardless of the choice of the computational software.

3.9 Limitations of the Research

In spite of the fact that the methodology is formulated by taking into account diverse factors, yet it possesses some minor limitations. Firstly, only a limited number of top banks are used in the analysis. Even though small banks do not tend to manipulate the dynamics of market power, it could have been better if they were included in the research. Secondly, although the bank specific control factors are numerous, only four of them are included in the equation. The inclusion of other control variables might have influenced the H statistics. However, the scope of the research does not allow so many variables to be a part of the equation.

 

Chapter 4: Analysis and Discussion of Results

4.1 Introduction

After the formulation of solid research methodology, it is the time to examine the collected data, analyse it and draw meaningful conclusions. Before moving forward to the analysis and discussion itself, this is important to mention that panel least square regression analysis is used as the primary mathematical tool for the analysis. Alongside, the fixed effect likelihood ratio and Wald coefficient restriction are used to test the model and the hypothesis respectively. This chapter deals not only with the core mathematical calculations but also their interpretation and analysis. The three proposed hypothesis are tested one by one.

4.2 Test for the Long Term Equilibrium Condition

As pointed out by Matthews et al. (2007), one of the most basic propositions of the Panzar Rosse model is that the banking industry should be in the state of long term equilibrium. For this purpose, pre-tax profit is used as the pertinent dependent variable. For the long term equilibrium to hold, the sum of the coefficients of the input price variables should be zero. By using the ‘fixed effect specification’ for both the cross-section and period level factors, the values can be inputted into the model to compute the forecasted equation. By applying the above mentioned specification on the variables, the following output table is achieved.
 
Table 2: Long Term Equilibrium Condition Results

The sum of the three price coefficients is almost equal to 1. However, at this point it is also vital to test the model and hypothesis through alternative channels. The fitness of the data for this model can be tested by employing redundant fixed effect likelihood ratio. The results of the test are depicted in the following table:
Table 3: Redundant Fixed Effects Likelihood Ratio Results

In order for the data to fit properly in the model, the null hypothesis of the test (that the period fixed effects are together zero) should be rejected. The highly significant values of the cross square chi test, period Chi-square and Cross-Section/Period Chi-square prove that the data fits the model. The output of the Wald coefficient is also shown in the following table:
Table 4: Wald Test Output

The value of the Chi-square is comparatively significant; however, the Chi-Square value needs to be insignificant in comparison with the significance level in order for the null hypothesis to be accepted. In this case, the Chi-square value of 0.36 is quite large than the significance level of 0.01. Hence, it can safely be inferred that the null hypothesis is rejected. Moreover, the sum of the three coefficients of the price variable inputs also shows that their sum is equal to 1. These findings are in favour of the rejection of the first null hypothesis.

4.3 Test for the Market Competition

The second hypothesis is the central argument of research and it tends to measure the competitive environment of the banking industry without the inclusion of the TIER 1 variable. Two null hypothesis need to be tested in the current scenario. The first null hypothesis demands the sum of the coefficients to be equal to zero and the second assumes it to be 1. Hence, both the hypotheses need to be tested (in case the first hypothesis gets rejected). The output of the panel least square regression equation is as follows:
Table 5: PLS for Market Competition: Results


The examination of the coefficients of the three relevant variables shows that the sum of the three variables is not equal to zero. However, the model needs to be tested for fitness and hypothesis testing. The following table shows that the output of the results obtained by running the redundant fixed effect likelihood ratio:
Table 6: Redundant Fixed Effect Test for Market Competition

The highly significant values of Chi-square prove that the period fixed effects are not greater than zero. It is an indication that the data has properly fitted the model. Now, it is time to move forward to test the first hypotheses; if the first hypothesis is true, than the following equation must hold.

Where, C is the coefficient of the 2nd, 3rd and 4th output variables. The result of the Wald coefficient restrictions is depicted below:
Table 7: Wald Test for Market Competition

The value of Chi-square is highly significant; therefore, the proposed null hypothesis cannot be accepted in the current scenario. This implies that the banking sector is not in the state of perfect competition. For the second null hypothesis to be true, the following equation must hold.


The output of the Wald test is described in the following table:
Table 8: Wald Test for Market Competition (2)

Again, the Chi-square value of the Wald test is significant in relation to the significance level. Hence, the second null hypothesis that the banking sector is in the state of monopoly is also rejected. Hence, the proposed second hypothesis of the research is accepted because none of these two propositions was accepted by the data. Hence, it is concluded that without the inclusion of TIER 1 variable, the banking sector faces a monopolistically competitive environment.

4.4 Test for the Market Competition and TIER 1 Variable

TIER 1 is a ratio that depicts the percentage of TIER 1 capital in relation to its total risk-weighted assets. TIER 1 ratio should influence the competitive environment of the banking industry. Ideally, a minimum TIER 1 capital should increase the competitive environment of the banking industry. Hence, the value of the Panzar Rosse index should get close to 1 after the introduction of TIER 1 as an additional variable.
Relying on the same panel date specifications, the panel least square regression output of the model is depicted below:
Table 9: PLS Output for Market Competition with TIER 1 Variable

The regression equation shows that the sum of the three price variables is less than zero. The result of the likelihood test for the fitness of the model is shown below:
Table 10: Redundant Fixed Effect Test for Market Competition with TIER 1 Variable

The data completely fits the model as the values of Chi-square, cross section Chi Square and cross section/period chi-square are all positive and significant. After the validation of data is complete, this is the time to test the two propositions one by one. The first proposition states that the sum of the coefficients of the price variables is equal to 1. The Wald test outputs the following table:
Table 11: Wald Test for Market Competition with TIER 1 Variable

The highly significant value of Chi-square shows that the null hypothesis is rejected. Now, as the first hypothesis is rejected, let’s test the second hypothesis. The results of the Wald test are shown below:
Table 12: Wald Test for Market Competition with TIER 1 Variable (2)

Again, in spite of the fact that the value of the coefficient is -0.22, yet the positive and substantial value of Chi-square infer that the null hypothesis is again rejected. This proves that the competitive environment of the banking industry remains monopolistically competitive both with and without the inclusion of TIER 1 as an additional variable.

4.5 Discussion of Results

In order to interpret the results in the correct fashion, the definitions and forms of market structure need to be discussed. The findings of both regression models; with and without the TIER 1 show that the banking sector of UK is in the state of monopolistic competition. A monopolistic competition is also defined as imperfect competition. In this type of market, each firm faces numerous competitors but it sells is same products to the market that are same, but not identical (Feenstra, 2003). From a generic perspective, this statement is quite true for banks as their products such as different types of demand accounts (savings, current, etc.), lease schemes, loan and mortgage financing differs a bit from each other with every bank offering different rates to different customers and different products. Moreover, in a monopolistically competitive environment, every firm makes the decisions of cost and pricing individually (Carson, 2006). Another feature of this market type is that there are no barriers to entry and that any new or existing firm is the ‘price maker’ instead of ‘price taker’ (Pecorino, 2009). Moreover, all firms in a monopolistically competitive environment are profit maximizes. Moreover, as the product differentiation is a prominent feature of this type of market, hence, consumers enjoy a well-diversified pool of products and services.
The data for this research pertained to only the top ten banks of UK. From the point of view, it will not be entirely wrong to state the market dynamics for the small size banks may be different. Large banks tend to enjoy economy of scale and scope and rely on their experience and asset size to develop core growth strategies (Park, 2000). In this sense, they possess a fair discretion in designing differentiated products in order to serve the customers. Therefore, it should be true that large banks experience a monopolistically competitive environment. This is because of the fact that these banks do possess the assets and resources to choose a differentiation strategy that allows them to be more profitable. However, Bankscope listed 731 listed banks in UK. For small banks, the scarcity of enough tangible resources, lack of geographical distribution and low market share do not allow them to be price takers. In this situation, the banking industry might be facing more than one structure. The small banks tend to compete and survive and for this reason, their product base is almost similar to the product base of other banks. Hence, there might be a possibility that this monopolistically competitive environment is only applicable for the large sized banks.
These findings also point out that the inclusion of a regulatory variable does not change the perceived market dynamics. The Prudential Regulatory Authority has put into place a floor in the TIER 1 ratio. However, the absolute value of the floor is 4.5% which is not strong enough to push the banks into changing their differentiation, marketing and growth strategy. Therefore, the revenue of the banks and the price variables tend to remain unaffected by this regulatory capital ratio. In contrast to this, the analysis of the TIER 1 ratio also reveals surprising finding. The average of TIER 1 ratio for these banks stand at 10.026% (The raw data used in this research is listed in Appendix A). The average value of the ratio proves that the compliance to the regulatory requirements is certainly up to the desired mark. However, some interesting findings are also obtained from the regression equation. Both models show that an asset, cost of capital and cost of funds are negatively related to the revenue. Ideally, the size of the assets should positively be related to the revenue. This is because the asset base of any bank allows it to diversify its operations and maximize its profits. However, there is also the possibility that banks care more about the net profits rather than the net interest revenues.
The findings also prove that there is no collusive environment in the top ten banks. Various industries include top market players that have the capability to capture the majority of consumer base and collude in such a way that small firms tend to get excluded from the industry or they are forced to merge and diversify (Al-Karasneh & Fatheldin, 2005). The situation with the top ten banks of UK is quite different than this. The level of competition and the market dynamics have allowed these banks to compete in such a format that any growth can only be credited to the differentiation strategy of the banks and nothing else. This is also a healthy sign for the UK banking industry because the macro and micro economic factors not only favour the banking sector but also the end consumers. Had the market been completely competitive, the banks would have suffered because of their inability to set the price levels in the market. On the other hand, complete monopoly would have been devastating for the economy, individuals and institutions because the price was totally out of their control. A monopolistically competitive environment stands in between these two extreme situations. Not only do the banks tend to get benefited from the price setting capability but the consumers also enjoy the benefit of choosing between a well-diversified pool of products and services.
This research also proves that TIER 1 requirement is not a suitable technique to alter the market dynamics. If the central banks and other regulatory authorities induce these ratios to alter the market structure, then the situation will not be beneficial at all because TIER 1 does not change the market structure at all. Hence, it is recommended that if the regulatory bodies want to control the market and their ‘price setting’ capability, then they have to divert their attention towards other relevant factors. However, it is true that the inclusion of TIER 1 ratio in the regulations is beneficial for consumers and banks.

Chapter 6: Conclusion

6.1 Conclusion of Results

This research was designed to not only measure the competitive environment of the banking sector of UK, but also to gauge the effect of the conclusion of TIER 1 ratio on the industry dynamics. The findings of the study showed that the banking sector of UK is not in the state of long run equilibrium. The risk-adjusted rate of return should be uncorrelated with the input price factors in order for the long run equilibrium to exist. The results of the Wald test showed that the case of UK banking sector was totally opposite. The pre-tax profit was highly correlated with the input price factors. Additionally, the findings also propose that the relevant regulatory variable (TIER 1) does not alter the dynamics of the market. Regardless of the inclusion of the TIER 1 capital, the market structure of the banking industry remains monopolistically competitive.
The findings of the research can be backed by the real life environment of the banks. The overall competitive environment of the industry has forced the banks to establish a distinguishing strategy by developing products that are specific to the bank. Moreover, with the prevalent revenues, it is evident that banks in UK are price setters to a small extent. These observations correlate with the core finding of the research. Moreover, the fact that most of the banks offer well diversified pool of same and different products and services also infer that the market dynamics of the banking industry should be monopolistically competitive. The conceptions of collusion and high-level market competition are also proved as wrong. There is another facet of the achieved result. The notion of monopolistic competition renders that the price war and price competition is UK is quite low.

6.2 Recommendations

The findings of the research also heave some recommendations for not only the banks but also the core regulatory bodies and any further research on a similar topic.
1: If the banks want to enhance their profit, then they should focus on their product strategy and marketing. These steps will allow them to become prominent in the market and consequently, they will tend to gather more market share. Additionally, the findings also show that in order to attain maximum profitability, the banks have to take into consideration the profit as the sole criterion for optimization.
2: The findings also show that this environment is only applicable for the top banks of UK. The small banks may still face a competitive environment. Therefore, it is recommended that small sized banks should care more about their price and costs structure rather than a suitable product mix.
3: The results also imply that TIER 1 capital requirement is not a suitable tool to alter the market dynamics for large banks. If regulatory bodies want to enhance or de-motivate the competitive environment in the banking industry, it should take steps directly to influence the factors that affect the costs of the banks. Moreover, the monetary policy can also be used as an appropriate tool for altering the dynamics of the market. If the prevalent yields on the government bonds changes, it will largely affect the product structure of the banks because, in that scenario, the banks will reorient their price and interest rates for consumers.
4: Any future research should include a more detailed model of the banking industry by taking into account more than 50 banks over a period of at least 15 years. Future studies should also compare the competitive behaviour in large and small sized banks and comment on any possible disparity among the results.

 

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Appendix A

Raw Financial Date for Model

  Bank Id Year Rit πit PL(it) PK(it) PF(it) EQUITY(it) ASSET(it) LOAN(it) TIER1(it)
HSBC Holdings Plc 1 2013 35861 0.00845 0.07299 0.00539 0.00928 181920 2671318 0.058 14.300
1 2012 37893 0.00767 0.07589 0.00515 0.01240 174550 2692538 0.058 13.400
1 2011 40811 0.00856 0.07103 0.00883 0.01519 157530 2555579 0.063 11.540
1 2010 39553 0.00776 0.06461 0.00833 0.01309 150056 2454689 0.066 12.100
1 2009 40856 0.00299 0.06115 0.01152 0.01517 130831 2364452 0.066 10.800
1 2008 42835 0.00368 0.06273 0.01247 0.03544 95685 2527465 0.073 8.300
1 2007 38119 0.01028 0.06620 0.00767 0.03913 132935 2354266 0.093 9.300
1 2006 34826 0.01187 0.06167 0.00685 0.04049 112378 1860758 0.174 9.400
1 2005 31489 0.01396 0.05418 0.00701 0.03470 96050 1501970 0.164 9.000
1 2004 31721 0.01480 0.05069 0.00998 0.02439 88479 1279974 0.186 8.900
                       
Barclays Bank Plc 2 2013 11667 0.00220 0.08664 0.00671 0.00915 63160 1312840 0.047 15.700
2 2012 11692 0.00044 0.07980 0.00744 0.01020 59863 1488761 0.056 13.200
2 2011 12335 0.00382 0.07620 0.00401 0.01136 65110 1563402 0.052 12.900
2 2010 12647 0.00408 0.07876 0.00455 0.00999 62581 1490038 0.056 13.500
2 2009 11675 0.00331 0.06588 0.00531 0.01601 58639 1379148 0.180 13.000
2 2008 11611 0.00294 0.04977 0.00412 0.02616 43514 2053029 0.127 8.600
2 2007 9627 0.00579 0.06231 0.00229 0.02817 31775 1227583 0.173 7.300
2 2006 9158 0.00722 0.06663 0.00273 0.02663 27072 996503 0.180 7.500
2 2005 8097 0.00575 0.05576 0.00301 0.02094 24213 924170 0.161 6.900
2 2004 6833 0.00641 0.05075 0.00320 0.01910 17150 715719 0.129 7.600
                       
Barclays  Plc 3 2013 11614 0.00219 0.08707 0.00494 0.00916 63949 1312267 0.046 15.700
3 2012 11696 0.00054 0.08238 0.00262 0.01011 59986 1488335 0.056 13.300
3 2011 12330 0.00369 0.08755 0.00369 0.01094 65196 1563527 0.092 12.900
3 2010 12639 0.00407 0.08079 0.00482 0.00957 62262 1489645 0.102 13.500
3 2009 11924 0.00333 0.06899 0.00506 0.01555 58478 1378929 0.179 13.000
3 2008 11665 0.00250 0.04609 0.00533 0.02608 47411 2052980 0.125 8.600
3 2007 9636 0.00577 0.06231 0.00286 0.02820 32476 1227361 0.173 7.800
3 2006 9158 0.00716 0.06888 0.00279 0.02663 27390 996787 0.179 7.700
3 2005 8097 0.00571 0.06808 0.00305 0.02094 24430 924357 0.161 7.000
3 2004 6833 0.00851 0.06320 0.00427 0.01817 22913 538181 0.059 7.100
                       
Royal Bank of Scotland Gro 4 2013 11068 -0.00802 0.06040 0.01748 0.01044 59215 1027878 0.073 13.100
4 2012 11461 -0.00394 0.05886 0.01044 0.01109 70448 1312295 0.074 12.400
4 2011 12357 -0.00079 0.05692 0.01003 0.01283 76053 1506867 0.076 13.000
4 2010 14209 -0.00027 0.06512 0.01059 0.01218 76851 1453576 0.101 12.900
4 2009 13388 -0.00156 0.05440 0.01277 0.01447 94631 1696486 0.096 14.100
4 2008 15482 -0.01070 0.04453 0.01051 0.02491 80498 2401652 0.070 10.000
4 2007 12668 0.00521 0.03233 0.00146 0.01803 91426 1900519 0.079 7.300
4 2006 10596 0.01054 0.04741 0.00162 0.02524 45490 871432 0.077 7.500
4 2005 9918 0.01022 0.04374 0.00255 0.02518 37544 776827 0.133 7.600
4 2004 9071 0.01046 0.03798 0.00257 0.01808 30973 696530 0.133 7.000
                       
Royal Bank of Scotland Plc 5 2013 10677 -0.00663 0.06603 0.01710 0.01052 48865 1019934 0.077 11.400
5 2012 10866 -0.00266 0.06881 0.01112 0.01089 59425 1284274 0.080 11.000
5 2011 11514 -0.00060 0.06607 0.00864 0.01343 61854 1432781 0.121 11.000
5 2010 12287 -0.00013 0.06937 0.00953 0.01310 57607 1307330 0.150 10.100
5 2009 11543 0.00000 0.05041 0.00866 0.01689 56197 1332981 0.155 10.500
5 2008 13509 -0.00491 0.05392 0.01210 0.02819 47250 1877930 0.117 8.500
5 2007 11116 0.00821 0.05005 0.00176 0.02515 47835 1115668 0.062 8.230
5 2006 10392 0.00985 0.05122 0.00240 0.02494 38332 848227 0.081 7.300
5 2005 9711 0.00963 0.04446 0.00319 0.02502 34614 757322 0.151 7.600
5 2004 9053 0.01215 0.04017 0.00354 0.01964 34999 570890 0.145 6.800
                       
Lloyds Bank Plc 6 2013 6990 0.00104 0.05855 0.00694 0.02817 44086 862004 0.102 17.000
6 2012 7398 -0.00054 0.04642 0.00807 0.03138 46983 952463 0.116 15.800
6 2011 12856 -0.00155 0.05119 0.01459 0.02706 51273 988366 0.170 14.250
6 2010 12686 0.00072 0.04447 0.01141 0.03133 47732 1008732 0.174 12.150
6 2009 5299 -0.00764 0.05113 0.01689 0.01571 13713 572980 0.126 10.500
6 2008 7772 0.00189 0.04409 0.01593 0.03594 9572 436191 0.147 8.000
6 2007 6133 0.01157 0.04175 0.00764 0.05079 13439 353543 0.142 6.500
6 2006 5539 0.01239 0.03602 0.00364 0.04321 12476 345664 0.120 6.300
6 2005 5672 0.01237 0.03587 0.00598 0.03728 11620 311621 0.096 7.800
6 2004 5094 0.01223 0.03258 0.00549 0.03164 12783 286363 0.084 6.000
                       
Lloyds Banking Group Plc 7 2013 7338 0.00049 0.05968 0.00457 0.02872 39336 847030 0.144 14.500
7 2012 7718 -0.00065 0.04833 0.00508 0.03148 42581 934221 0.181 13.800
7 2011 12698 -0.00383 0.06189 0.01352 0.02535 46180 980248 0.191 12.500
7 2010 13495 0.00028 0.05394 0.01287 0.03031 46902 991574 0.208 11.600
7 2009 9026 0.00101 0.06230 0.01937 0.03218 44107 1027255 0.201 9.600
7 2008 7718 0.00185 0.04409 0.01585 0.04140 9699 436033 0.246 8.000
7 2007 6099 0.01132 0.04175 0.00780 0.05489 12425 353346 0.231 9.500
7 2006 5329 0.01236 0.03602 0.00315 0.04351 11507 343598 0.123 8.200
7 2005 5671 0.01233 0.04274 0.00563 0.04238 10630 309754 0.167 7.900
7 2004 5721 0.01246 0.04402 0.00779 0.03917 9570 292854 0.136 8.200
                       
HSBC Bank plc 8 2013 6979 0.00406 0.04232 0.00325 0.00645 32919 811695 0.052 13.000
8 2012 6920 0.00123 0.06455 0.00427 0.00939 32200 815481 0.052 12.400
8 2011 7239 0.00376 0.05725 0.00533 0.01051 31604 827970 0.092 10.000
8 2010 7712 0.00502 0.06366 0.00448 0.00859 32357 798494 0.094 11.400
8 2009 8120 0.00534 0.05410 0.00601 0.01149 28428 751928 0.075 11.200
8 2008 5782 0.00472 0.05141 0.00326 0.03071 20661 924231 0.081 6.800
8 2007 3897 0.00653 0.05069 -0.00027 0.03945 24999 622280 0.115 7.530
8 2006 4246 0.00861 0.04965 0.00102 0.03408 20938 440760 0.120 7.530
8 2005 4070 0.00966 0.04751 0.00088 0.02899 19634 386422 0.103 6.910
8 2004 4798 0.01010 0.04410 0.00631 0.02691 20316 292481 0.107 7.400
                       
Bank of Scotland Plc 9 2013 6784 0.00255 0.04066 0.00929 0.01476 18374 568412 0.068 15.800
9 2012 6732 -0.00221 0.03682 0.01420 0.01935 18134 561433 0.097 10.200
9 2011 7857 -0.00620 0.04172 0.02028 0.02194 18413 558143 0.162 8.750
9 2010 8825 -0.00675 0.04434 0.02213 0.02403 20043 573794 0.202 8.570
9 2009 6195 -0.02311 0.04375 0.03233 0.02800 22353 661344 0.176 7.900
9 2008 8401 -0.01613 0.04094 0.02775 0.07094 11925 659176 0.219 5.300
9 2007 7917 0.00820 0.03664 0.00506 0.09143 21844 603632 0.372 7.000
9 2006 4148 0.00664 0.01774 0.00239 0.07498 12405 455179 0.344 6.600
9 2005 3799 0.00686 0.01653 0.00351 0.07595 9476 371310 0.366 6.300
9 2004 3800   0.01519 0.00317 0.07370 8977 361879 0.357 5.000
                       
HBOS Plc 10 2013 6529 0.00537 0.04129 0.00625 0.01533 21884 557695 0.065 20.800
10 2012 6706 0.00044 0.03227 0.01106 0.02006 24930 583479 0.090 14.800
10 2011 8398 -0.00686 0.04265 0.01858 0.02225 24170 567999 0.157 12.300
10 2010 8370 -0.00320 0.00412 0.01624 0.02695 26410 641752 0.183 11.400
10 2009 5503 -0.01721 0.00597 0.02383 0.03106 26156 719183 0.192 9.600
10 2008 8171 -0.01569 0.00574 0.02623 0.09155 13499 689917 0.317 6.000
10 2007 7304 0.00821 0.00560 0.00287 0.09731 22234 666947 0.346 7.400
10 2006 7400 0.00964 0.00514 0.00319 0.07979 21171 591813 0.344 8.100
10 2005 6829 0.00889 0.00468 0.00382 0.07427 22269 540873 0.322 8.100
10 2004 5885 0.01028 0.00440 0.00289 0.06740 20174 448165 0.269 7.900
 

 

Panel Data

Bank Id Year LnR lnpat lnpl lnpk lnpf lnequity lnasset lnloan lnTier1
1 2013 10.487 -4.774 -2.617 -5.224 -4.680 12.111 14.798 -2.851 2.660
1 2012 10.543 -4.871 -2.578 -5.269 -4.390 12.070 14.806 -2.842 2.595
1 2011 10.617 -4.761 -2.645 -4.730 -4.187 11.967 14.754 -2.763 2.446
1 2010 10.585 -4.859 -2.739 -4.788 -4.336 11.919 14.714 -2.714 2.493
1 2009 10.618 -5.811 -2.794 -4.463 -4.188 11.782 14.676 -2.716 2.380
1 2008 10.665 -5.604 -2.769 -4.385 -3.340 11.469 14.743 -2.623 2.116
1 2007 10.548 -4.577 -2.715 -4.871 -3.241 11.798 14.672 -2.370 2.230
1 2006 10.458 -4.434 -2.786 -4.984 -3.207 11.630 14.436 -1.750 2.241
1 2005 10.357 -4.272 -2.915 -4.961 -3.361 11.473 14.222 -1.808 2.197
1 2004 10.365 -4.213 -2.982 -4.607 -3.714 11.391 14.062 -1.681 2.186
2 2013 9.365 -6.120 -2.446 -5.004 -4.694 11.053 14.088 -3.056 2.754
2 2012 9.367 -7.736 -2.528 -4.901 -4.586 11.000 14.213 -2.878 2.580
2 2011 9.420 -5.567 -2.574 -5.519 -4.477 11.084 14.262 -2.955 2.557
2 2010 9.445 -5.502 -2.541 -5.393 -4.606 11.044 14.214 -2.885 2.603
2 2009 9.365 -5.712 -2.720 -5.237 -4.135 10.979 14.137 -1.716 2.565
2 2008 9.360 -5.829 -3.000 -5.492 -3.644 10.681 14.535 -2.066 2.152
2 2007 9.172 -5.152 -2.776 -6.078 -3.570 10.366 14.021 -1.752 1.988
2 2006 9.122 -4.931 -2.709 -5.905 -3.626 10.206 13.812 -1.717 2.015
2 2005 8.999 -5.159 -2.887 -5.804 -3.866 10.095 13.737 -1.824 1.932
2 2004 8.830 -5.050 -2.981 -5.745 -3.958 9.750 13.481 -2.051 2.028
3 2013 9.360 -6.126 -2.441 -5.311 -4.693 11.066 14.087 -3.073 2.754
3 2012 9.367 -7.532 -2.496 -5.944 -4.594 11.002 14.213 -2.889 2.588
3 2011 9.420 -5.602 -2.436 -5.601 -4.516 11.085 14.262 -2.383 2.557
3 2010 9.445 -5.504 -2.516 -5.336 -4.649 11.039 14.214 -2.283 2.603
3 2009 9.386 -5.706 -2.674 -5.286 -4.164 10.976 14.137 -1.719 2.565
3 2008 9.364 -5.991 -3.077 -5.234 -3.647 10.767 14.535 -2.078 2.152
3 2007 9.173 -5.156 -2.776 -5.856 -3.569 10.388 14.020 -1.754 2.054
3 2006 9.122 -4.939 -2.675 -5.883 -3.626 10.218 13.812 -1.718 2.041
3 2005 8.999 -5.165 -2.687 -5.793 -3.866 10.104 13.737 -1.824 1.946
3 2004 8.830 -4.766 -2.761 -5.456 -4.008 10.039 13.196 -2.828 1.960
4 2013 9.312 0.000 -2.807 -4.047 -4.562 10.989 13.843 -2.611 2.573
4 2012 9.347 0.000 -2.833 -4.562 -4.502 11.163 14.087 -2.606 2.518
4 2011 9.422 0.000 -2.866 -4.602 -4.356 11.239 14.226 -2.571 2.565
4 2010 9.562 0.000 -2.731 -4.547 -4.408 11.250 14.190 -2.294 2.557
4 2009 9.502 0.000 -2.911 -4.361 -4.236 11.458 14.344 -2.340 2.646
4 2008 9.647 0.000 -3.111 -4.556 -3.692 11.296 14.692 -2.662 2.303
4 2007 9.447 -5.257 -3.432 -6.532 -4.016 11.423 14.458 -2.534 1.988
4 2006 9.268 -4.552 -3.049 -6.427 -3.679 10.725 13.678 -2.560 2.015
4 2005 9.202 -4.584 -3.130 -5.971 -3.682 10.533 13.563 -2.017 2.028
4 2004 9.113 -4.560 -3.271 -5.966 -4.013 10.341 13.454 -2.019 1.946
5 2013 9.276 0.000 -2.718 -4.069 -4.555 10.797 13.835 -2.569 2.434
5 2012 9.293 0.000 -2.676 -4.499 -4.520 10.992 14.066 -2.527 2.398
5 2011 9.351 0.000 -2.717 -4.751 -4.310 11.033 14.175 -2.113 2.398
5 2010 9.416 0.000 -2.668 -4.653 -4.335 10.961 14.083 -1.894 2.313
5 2009 9.354 0.000 -2.988 -4.749 -4.081 10.937 14.103 -1.862 2.351
5 2008 9.511 0.000 -2.920 -4.414 -3.569 10.763 14.446 -2.145 2.140
5 2007 9.316 -4.803 -2.995 -6.344 -3.683 10.776 13.925 -2.787 2.108
5 2006 9.249 -4.620 -2.972 -6.031 -3.691 10.554 13.651 -2.517 1.988
5 2005 9.181 -4.643 -3.113 -5.747 -3.688 10.452 13.538 -1.888 2.028
5 2004 9.111 -4.410 -3.215 -5.644 -3.930 10.463 13.255 -1.933 1.917
6 2013 8.852 -6.871 -2.838 -4.970 -3.569 10.694 13.667 -2.281 2.833
6 2012 8.909 0.000 -3.070 -4.819 -3.462 10.758 13.767 -2.154 2.760
6 2011 9.462 0.000 -2.972 -4.228 -3.610 10.845 13.804 -1.770 2.657
6 2010 9.448 -7.238 -3.113 -4.473 -3.463 10.773 13.824 -1.746 2.497
6 2009 8.575 0.000 -2.973 -4.081 -4.154 9.526 13.259 -2.068 2.351
6 2008 8.958 -6.270 -3.121 -4.140 -3.326 9.167 12.986 -1.919 2.079
6 2007 8.721 -4.460 -3.176 -4.874 -2.980 9.506 12.776 -1.948 1.872
6 2006 8.620 -4.391 -3.324 -5.617 -3.142 9.432 12.753 -2.119 1.841
6 2005 8.643 -4.393 -3.328 -5.120 -3.289 9.360 12.650 -2.345 2.054
6 2004 8.536 -4.404 -3.424 -5.205 -3.453 9.456 12.565 -2.472 1.792
7 2013 8.901 -7.621 -2.819 -5.388 -3.550 10.580 13.649 -1.940 2.674
7 2012 8.951 0.000 -3.030 -5.282 -3.458 10.659 13.747 -1.709 2.625
7 2011 9.449 0.000 -2.782 -4.304 -3.675 10.740 13.796 -1.656 2.526
7 2010 9.510 -8.169 -2.920 -4.353 -3.496 10.756 13.807 -1.569 2.451
7 2009 9.108 -6.894 -2.776 -3.944 -3.436 10.694 13.842 -1.603 2.262
7 2008 8.951 -6.292 -3.121 -4.145 -3.185 9.180 12.985 -1.403 2.079
7 2007 8.716 -4.481 -3.176 -4.854 -2.902 9.427 12.775 -1.464 2.251
7 2006 8.581 -4.393 -3.324 -5.762 -3.135 9.351 12.747 -2.099 2.104
7 2005 8.643 -4.396 -3.153 -5.179 -3.161 9.271 12.644 -1.789 2.067
7 2004 8.652 -4.385 -3.123 -4.855 -3.240 9.166 12.587 -1.992 2.104
8 2013 8.851 -5.507 -3.162 -5.730 -5.043 10.402 13.607 -2.964 2.565
8 2012 8.842 -6.700 -2.740 -5.456 -4.668 10.380 13.612 -2.949 2.518
8 2011 8.887 -5.584 -2.860 -5.234 -4.556 10.361 13.627 -2.384 2.303
8 2010 8.951 -5.294 -2.754 -5.408 -4.758 10.385 13.590 -2.368 2.434
8 2009 9.002 -5.233 -2.917 -5.114 -4.466 10.255 13.530 -2.588 2.416
8 2008 8.663 -5.355 -2.968 -5.725 -3.483 9.936 13.737 -2.510 1.917
8 2007 8.268 -5.031 -2.982 0.000 -3.233 10.127 13.341 -2.160 2.019
8 2006 8.354 -4.755 -3.003 -6.887 -3.379 9.949 12.996 -2.120 2.019
8 2005 8.311 -4.640 -3.047 -7.039 -3.541 9.885 12.865 -2.274 1.933
8 2004 8.476 -4.596 -3.121 -5.066 -3.615 9.919 12.586 -2.233 2.001
9 2013 8.822 -5.971 -3.202 -4.679 -4.216 9.819 13.251 -2.692 2.760
9 2012 8.815 0.000 -3.302 -4.255 -3.945 9.806 13.238 -2.336 2.322
9 2011 8.969 0.000 -3.177 -3.898 -3.820 9.821 13.232 -1.818 2.169
9 2010 9.085 0.000 -3.116 -3.811 -3.729 9.906 13.260 -1.599 2.148
9 2009 8.731 0.000 -3.129 -3.432 -3.576 10.015 13.402 -1.735 2.067
9 2008 9.036 0.000 -3.196 -3.584 -2.646 9.386 13.399 -1.520 1.668
9 2007 8.977 -4.803 -3.307 -5.287 -2.392 9.992 13.311 -0.990 1.946
9 2006 8.330 -5.015 -4.032 -6.038 -2.590 9.426 13.028 -1.067 1.887
9 2005 8.242 -4.983 -4.103 -5.652 -2.578 9.157 12.825 -1.006 1.841
9 2004 8.243 0.000 -4.187 -5.755 -2.608 9.102 12.799 -1.030 1.609
10 2013 8.784 -5.227 -3.187 -5.076 -4.178 9.994 13.232 -2.736 3.035
10 2012 8.811 -7.739 -3.434 -4.505 -3.909 10.124 13.277 -2.410 2.695
10 2011 9.036 0.000 -3.155 -3.986 -3.806 10.093 13.250 -1.853 2.510
10 2010 9.032 0.000 -5.491 -4.120 -3.614 10.181 13.372 -1.698 2.434
10 2009 8.613 0.000 -5.120 -3.737 -3.472 10.172 13.486 -1.649 2.262
10 2008 9.008 0.000 -5.161 -3.641 -2.391 9.510 13.444 -1.149 1.792
10 2007 8.896 -4.803 -5.185 -5.852 -2.330 10.009 13.410 -1.061 2.001
10 2006 8.909 -4.642 -5.270 -5.747 -2.528 9.960 13.291 -1.068 2.092
10 2005 8.829 -4.723 -5.365 -5.567 -2.600 10.011 13.201 -1.132 2.092
10 2004 8.680 -4.577 -5.426 -5.848 -2.697 9.912 13.013 -1.313 2.067

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