Does Competition in Banking explains Systemic Banking
Crises?
Abstract:
This paper examines the relation between competition in the banking sector and the financial stability on country level. Compared to previous research, it takes a different approach in that it uses realized systemic risk in the form of systemic banking crises instead of the total systemic risk. Theory provides us with two opposing theories regarding the role of competition on stability. Previous studies presented mixed results which leaves us with unresolved questions which this paper tries to answer. The results show that there is evidence for both views, but without giving an all comprehending answer.
Key words: financial stability, bank competition, systemic banking crisis Student number: S2020807
Name: Roy Hamstra Study Program: DDM UU IFM Supervisor: Martien Lamers Word count: 11,108*
1 1. Introduction
After the financial crisis of 2007/2008 the debate about the role of banks became very important for policymakers and society. The crisis showed the world how important banks are in modern day society and that distress in banks have broader consequences than for normal companies. The bankruptcy of Lehman in 2008 is the most well-known example in recent history and the consequences for the ‘real’ economy were significant across the world. Consequently policymakers wanted to prevent a next crisis from happening and so they asked regulators to develop a new set of rules. In most developed countries this new regulation would become Basel III which among others imposes higher capital requirements as a buffer against financial distress to achieve the main goal; the increase of financial stability.
To achieve this goal, one must understand which forces drive the financial stability of a country. One of these factors suggested by researchers is the effect of competition. Competition has the disadvantage that it increases the default risk of companies, which in the case of banks, has significant consequences for the economy.
The aim of this paper is to research the effects from competition on financial stability. This is an important issue because policy makers can have a direct influence on the competition with their power to stop mergers and acquisitions or on the contrary force Mergers & Acquisitions. In most markets, competition is considered as a positive characteristic because it lowers prices, increase product quality, and drives innovation. However in banking, competition could also cause instability. Two of the main views regarding this issue are the competition fragility and competition stability theory. While the first one argues that a lower degree of competition improves the stability, the latter states that less competition decreases stability. The competition fragility theory first suggested by Keeley (1990) states that more competition leads to lower margins and a higher risks preference. Higher risks combined with lower profits causes instability of the system. On the other hand, Boyd and De Nicoló (2005) point out that high margins in monopolistic markets create a new problem which they call the risk shifting problem. The high margins cause borrowers to compensate this margins with riskier projects which results in a higher risk for banks. In addition to these opposing views, the relationship is also influenced by other factors like the development of regulations, financial markets, and other institutions.
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measures the distance to default. The difference is that I take the realized excess risk on a national perspective instead of the theoretical risk on an individual bank level. Risk per se is not a bad thing and is part of the doing business, however excess risk is. The main input are the database of recent financial crises and the World Bank data on country level bank characteristics. The main questions this paper tries to answer is whether the level of competition has a significant effect on the financial stability measured by systemic banking crises. I also check the effect on the z-score to check whether this dataset is in line with the theories. In addition to the competition-financial stability relationship this paper also tries to give an explanation to the following question; did the level of institutional development have a significant effect on the likelihood of a systemic banking crisis? And what is the effect of competition on the severity of a crisis? The answers to these question could help policy makers improve their actions before and during crisis times.
The results show evidence for the competition fragility view where financial stability is measured in terms of systemic banking crises as well as in the z-score. Furthermore the results show that institutional development and non-interest income play a moderating role in the competition stability relation. Due to the quality of the available data there is little evidence for any relation between competition and the severity of a crisis which offers opportunities for future research.
The paper proceeds as follows: it starts with a review of the current literature including hypothesis. Section 2 provides an explanation of the methodology including the data used for the analysis, and how each variable is measured or calculated. Section 3 continues with the analysis of the regressions and present the results which will be linked back to the hypothesis formulated in the literature section. Section 4 provides an overview of the limitations and possible further research areas. Section 5 offers concluding remarks and implications.
2. Theory
This section elaborates on the current literature. It gives an overview of what is already known on the various subjects and links the different parts with each other to form hypothesis which will be tested later on.
2.1. Competition in banks and financial stability
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However the default risk increases when competition increases. The default risk or even the bankruptcy of a firm is in most cases not too harmful for society in general. So why do we care so much about the bankruptcy of banks? Banks are a special kind of company because they perform a crucial role in the economic system. Almost every firm or household depends on banks to perform various financial services. Bank failure could have enormous consequences when looking at for instance Lehman Brothers in 2008. Banks have a direct effect on the financial stability in a country on sometimes even in multiple countries. Financial stability is one of the key goals for organizations like the IMF and the European Central Bank. It is therefore important to understand which factors affect the financial stability and in what way. The relation between bank competition and financial stability has been studied over the years but is still incomplete. Over the years researchers have come up with several theories regarding this relation. The franchise value theory and the BDN model are the most established theories and the following section provides an analysis of both views.
2.1.1. Competition fragility
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banks want to protect their franchise value and therefore have a low risk preference (Berger, Klapper, Turk-Ariss, 2009). This combination results in less risky, high quality loans with high margins. When competition increases, the pricing power of banks decrease and they need to increase their risk preference in order to sustain revenue. This results in lower margins and riskier loans. The combination of riskier loans and low margins increases the systemic risk of banks and consequently reduce the franchise value of a bank (Jimenez, Lopez, and Saurina, 2012). These authors also show that more competition leads to lower capital ratios which also leads to higher systemic risk.
The literature provides us with several studies which support the franchise value theory. Repullo (2004) presents a model of imperfect competition where banks can invest in a risky or less risky asset. He shows that banks have an incentive to invest in the risky asset when margins are small and the franchise value is low. To overcome this problem he suggests to imply capital requirements to counter this risk taking incentive. Beck, Schepens, and De Jonghe (2013) provide empirical evidence where competition measured by the Lerner index has a negative effect on the financial stability measured by the z-score. However this relation is altered by the development of institutions. I will analyze the effects of institutional development later on. Considering the previous the following hypothesis can be derived:
Hypothesis 1: Competition has a negative effect on financial stability measured in terms of the occurrence of financial crisis
2.1.2. Competition stability
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portfolio will increase. More competition leads to lower interest rates which reduces the moral hazard problem and thereby increases the financial stability.
Martinez-Miera and Repullo (2010) extended the BDN model and the most significant contribution is the introduction of the margin effects. The MMR model is based on two opposing forces namely the margin effect and the risk shifting effect. The margin effect works as follows: when competition is low, margins are high, and this causes banks to build up a buffer from the profits derived by the high margins. The risk shifting problem refers to the risk preference of borrowers based on the interest rates. MMR show that there is a trade-off between both forces and that the margin effect is more dominate in competitive markets while the risk shifting problem dominates in monopolistic markets. As a consequence the financial stability is low in markets with high or low degrees of competition. In markets with very low competition, the risk shifting problem is too large to be compensated for with the buffers from the high margins, while high competitive markets suffer very little from the risk shifting problem but the margins are too low for banks to be profitable. The most stable situation is a situation where the risk shifting problem and the margin effect are relatively equal. There will be some risk shifting problems but the buffers are sufficient to cope with it. This results in a U-shaped relation between competition and risk taking and an inverted U-shape between competition and financial stability. In practice this means that competition increase stability when competition is low till a certain point where after more competition leads to less stability. Schaek, Cihak, and Wolfe (2009) provided empirical evidence in favor for the competition stability theory. They document that markets with higher levels of competition measured by the Panzar and Rosse (1987) H-statistic are less likely to experience a systemic banking crisis. Similar to the competition fragility theory there can be an alternative hypothesis derived according to the BDN and MMR theory.
Hypothesis 2: Competition has an inverted U-shaped relation with financial stability measured by the likelihood of a financial crisis
2.2. Concentration
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concentration does not exclude a high degree of competitiveness. Research from Asia shows that a higher concentration is bad for the financial stability (Fu, Lin, Molyneux, 2014). Another paper examining the Turkish banking sector found similar results where a higher degree of concentration has a positive effect on the non-performing loan (NPL) ratio which as a consequence increases the default risk and make the financial system unstable (Kasman and Kasman, 2015). Their conclusion is therefore that policymakers could stimulate mergers between small banks to reduce the concentration and give these smaller banks more chance of survival. Beck, Demirgüç-Kunt, and Levine (2005) found similar results in which lower levels of bank concentration result in more financial stability. This is in line with the competition stability view. They used the likelihood of a systemic banking as a proxy for financial stability. However they did not investigate the mechanisms driving this relationship.
An explanation of the negative effects in case of high concentration can be found in the moral hazard problem. These negative effects are caused by the fact that the banks are too big or too important to fail and are therefore ‘insured’ of subsidies by their governments (Fu, Lin, Molyneux, 2014). This could provide incentives for managers to take more risk because they know they will be bailed out in case things turn out negative. It can be said that these too big to fail banks have a put option on their bank where there is only upside potential. When the situations turns out bad they know that they will be bailed out by the government. However one should bear in mind that these bail-outs also improves the financial stability because it reduces the change of bank runs and default. Concluding this leads to the following hypothesis: Hypothesis 3: Bank concentration has a negative relation with financial stability
2.3. Development of financial markets, institutions, and regulators
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institutions, and regulatory supervisors to achieve additional competition benefits while maintaining financial stability.
The development of financial markets can have an enhancing effect on the financial stability. Higher developed markets are usually larger and more liquid than less developed markets and provide entrepreneurs more options to obtain funds (Beck, Schepens, and De Jonghe, 2013). Therefore entrepreneurs can easily switch between bank- and market-based funding which reduce the dependability on banks and therefore increase the financial stability. Furthermore the authors state that higher developed markets are associated with more transparency and disclosure obligations which in turn could reduce the bank risk behavior.
The effects of competition are also moderated by the development of institutions and regulatory supervision like customer protection (Anginer, 2014). Better rules and supervision can decrease the negative side-effects from too much competition like risk taking behavior and allows more competition without the decrease of financial stability. One of the most popular methods of supervisors is the deposit insurance scheme where deposits are insured up to a certain amount. This measure is intended to prevent bank runs and thereby increasing financial stability (Beck, Schepens, and De Jonghe, 2013). However as already mentioned, this measure could by itself also reduce the stability because it offers incentives for bank managers to increase their risk taking behavior. Because it costly as well as difficult for individual investors to control bank behavior, a proper working supervisor with sufficient power could help reduce risk taking behavior and again increasing stability while maintaining the same level of competition. Likewise Beck, Schepens, and De Jonghe (2013) state that better institutions increase the information about borrowers which forces borrowers to behave less risky in order to obtain future loans. This can be combined with the MMR theory where the stability depends on the risk taking behavior of the borrowers. When borrowers take less risk this increases the stability and if everything else remains equal, countries with better institutions are more stable than countries with less developed institutions.
Based on these different theories a number of hypotheses can be derived;
Hypothesis 4: The effect of competition on stability is negatively affected by the development of a country’s financial market.
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Hypothesis 6: The effect of competition on stability is negatively affected by the development of the regulatory institutions of a country.
In the remaining of this paper when is spoken about institutional factors, it refers to all the factors; institutions, financial market, and regulators unless stated otherwise or is self-evident due to the context.
2.4. Systemic banking crises and competition
This paper looks at realized systemic risk as an inverted proxy for financial stability. This realized excess risk is measured by using real life systemic banking crises during the sample period similar to the approach of Schaek, Cihak, and Wolfe (2009). The use of systemic banking crises is justified due to the mixed results from previous research which used the theoretical systemic risk. As a consequence it could be beneficial to find out why some risks materialize and why others do not. Both the competition fragility and stability views can and will be used in regard to systemic banking crises.
The difference between the z-score used in other research and systemic banking crises used in this paper is that we can test the competition fragility and stability theories in practice. However one must bear in mind that realized risk does not take the unrealized risk into account which means that some countries could have faced risk which did not materialized in a systemic banking crisis. For further implications of this limitation I refer to the limitation section. The use of systemic banking crises instead of any other banking crises is necessary because competition has an effect on the entire market and standalone bank failures can be caused by many other reasons and therefore cannot be solely attributed to competition. In the remaining of the paper the broad definition of systemic banking crisis from Laeven and Valencia (2008) is used. In their definition there is a systemic banking crisis when a country’s corporate and financial sectors experience a large number of defaults and financial institutions and corporation face great difficulties repaying contracts on time.
9 3. Methodology
3.1.1. Data
The main data source of this paper is the Global Financial Development Database (GFDD) (2015) from the Worldbank. This database consists of characteristics of countries’ financial systems for 203 countries. Even though the time span of the database runs from 1960 till 2013 there is a lot of missing data especially in the period until 1998. Therefore I examine the period from 1999-2013. Furthermore some countries provided none or very little information which therefore have been excluded. A full list of countries can be found in appendix A. The remaining sample provides sufficient data for each variable, country, and year for a proper analysis and results can be generalized due to the variation in countries over multiple years. An overview of all variables, how they are measured, and which sources are used can be found in table 1.
3.1.2. Database systemic banking crises
We combined the previous mentioned database with a recent database regarding systemic banking crises over the years. Laeven and Valencia (2012) created a database on the timing and resolution of all important banking crises from 1970-2012. This database contains 147 systemic banking crises as well as 218 currency crises and 66 sovereign crisis from 162 countries. While the aim was to examine policy implication during crisis times, the data provides a broad scope of information about the different aspects of each crisis which in combination with other data can be used for examining other factors than initially intended like competition. The information most important for the purpose of this research is when each crisis occurred. Furthermore it would be interesting to see whether competition has an effect on the severity of a crisis but this is not possible with the information available.
3.2. Variables 3.2.1. Z-score
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The equation is as follows:
𝑍𝑖𝑇 =
𝑅𝑂𝐴𝑖𝑇+𝐸𝑄𝑖𝑇 𝑇𝐴𝑖𝑇 ⁄
𝜎𝑖𝑇𝑅𝑂𝐴 (1)
i stands for bank i while the T stand for the time. ROA is return on assets for a certain year and EQ/TA is the percentage of equity on total assets whereas 𝜎 is the volatility of the return on assets for bank i at time T. The score used from the GFDD represent the median.
3.2.2. Lerner Index
The Lerner index is a measure of competition based on the pricing power of a bank. It is a proxy for the current and future profits stemming from this pricing power (Beck, Schepens, and De Jonghe, 2013). It is measured by subtracting the marginal cost (𝑀𝐶𝑖,𝑡) from the ratio of total operating income to total assets (𝑃𝑖,𝑡) before dividing it by again the total operating income to total assets and where i stand for a specific bank at time t. This results in a score between 0 and 1. Where 0 is perfect competition and 1 a monopoly. In perfect competition the price is similar to the marginal costs because banks compete on prices which drives the margin down and therefore the score will be 0. Similar to the z-score we use the median at a country level. The Lerner index can be written as:
𝐿𝑒𝑟𝑛𝑒𝑟
𝑖,𝑡=
𝑃𝑖,𝑡 − 𝑀𝐶𝑖,𝑡𝑃𝑖,𝑡 (2)
To examine whether the relation is non-linear, as proposed by the MMR model, the Lerner index needs to be adjusted. For this purpose the squared Lerner index is used in combination with the normal Lerner index. When both coefficients have opposing signs this is evidence of a non-linear relationship.
3.2.3. H-statistic and Boone indicator
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scores are greater than 1 which is the case in some oligopolistic markets (GFDD, 2015). A country’s score is again the median of all scores in the same country.
The Boone indicator provides the elasticity between marginal costs and profits (GFDD, 2015). In the GFDD this indicator is the coefficient of the log of profits regressed over the log of marginal costs. A country’s score is the median of all scores in a country. The model is based on the efficiency of firms (Boone, 2004). Firms who are more efficient measured by lower marginal costs, gain more market share or profit. An advantage of this approach is that it is possible to examine market segments instead of the whole firm (Leuvensteijn, Bikker, Adrian, van Rixtel, and Sorensen, 2007).
3.2.4. Asset concentration
Concentration shows a lot of similarities with competition but it refers to the market power of the largest banks. In situations with a few large players and a lot of small players the competition could be relative high but the market power of those large banks is too large to speak of fair competition. The asset concentration used in this research is the percentage of assets for the three largest banks compared to total assets. This is like the previous variables the median of each country.
3.2.5. Systemic banking crisis
Systemic banking crises is a dummy variable which results in a zero when there is no systemic banking crisis and in a one for the years there is a systemic banking crisis. Once again we use the definition of Laeven and Valencia (2012) to identify a systemic banking crisis when a country’s corporate and financial sector experience a large number of defaults and face great difficulties in repaying contracts on time. This data is provided out of the systemic banking crises database (2012).
3.3. Institutional, market, and regulatory development
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For the institutional framework we use the depth of information sharing which provides an index for the amount of information credit agencies have. A higher value translates to more information. Credit agencies play an important part in determine whether a borrower receives a loan and the information sharing between both provides a good proxy of institutional development (Beck, Schepens, and De Jonghe, 2013). The development of the financial markets will be measured by using the stock market turnover. This is the ratio of the total value of shares traded to average market capitalization. A higher ratio means a more liquid market which in turn refers to a higher financial market development. Capital stringency refers to an index which is between 0-8 depending on how strict the rules regarding capital requirements are. The higher the score the stricter the rules. Stricter rules are intended to decrease bank risk taking and should therefore limit the effects of competition. Deposit insurance is measured by dividing the insured deposits by GDP. A higher value relates to a more generous deposit insurance. In addition this paper examines whether multiple supervisors play a role. Multiple supervisors can have an advantage over a single supervisor because of different approaches. This is a dummy variable which takes the value of 0 if case of a single supervisor and 1 in case of multiple. The last factor is activity restrictions which is an index between 4 and 16 where a higher score is the results of more restrictions. This index consists out of the four areas with a score of 1 till 4 where a higher score means more restrictions. The areas are insurance, securities, real estate, and voting shares in non-financial firms.
To see whether these variables influence the effect of competition on stability, they are multiplied with the Lerner index. This provides a coefficient which enhances the effect of competition when it is positive and reduces the effect when negative.
3.4. Control variables
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volatile. According to Köhler (2014) more non-interest income increases systemic risk and to exclude this effect from the regression, non-interest income is included as a control variable. Furthermore the analysis makes use of time- as well as country fixed effects. These are used to exclude any variation which is caused by either time or country of origin. These fixed effects can be seen as a dummy where they are 1 in a specific year or country and zero in all the others. This procedure is done for every year and country so in the end there is a dummy for every time-country pair.
3.5. Method
This paper uses two regression methods to analyze the relationship between the variables explained in the previous parts of this study. The main methods are the ordinary least squares (OLS) regression and a binary logit model for the relationship between the different variables and the systemic banking crisis dummy. Whereas the OLS regression results in a coefficient
Table 1 Summary of sources
Variable Source Explanation
Competition measures
Lerner index GFDD Aggregated median at country level H-statistic GFDD Aggregated median at country level Boone indicator GFDD Aggregated median at country level Bank concentration (%) GFDD Aggregated median at country level
Institutional development
Depth of information sharing Doingbusiness.org Getting credit database score Stock market turnover ratio (%) GFDD Aggregated median at country level Capital stringency (0-8) Bank regulation and
supervision database
Index of capital requirements strictness
Coverage limit / GDP per Capita (in %)
Bank regulation and supervision database
Coverage limit divided by GDP per capita Single bank supervisory = 0
Bank regulation and supervision database
Dummy variable of 0 when single supervisor and 1 if multiple Activity restrictions
Bank regulation and supervision database
index of activity restrictions imposed by regulator
Depend variables
Bank Z-score GFDD Aggregated median at country level Systemic banking crisis
Systemic banking crisis database
Systemic banking crisis dummy based on general definition by Laeven and Valencia (2008)
Control variables
GDP per Capita
GFDD Weighted average of country level GDP in constant 2005 US Dollars
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which gives the marginal increase of the dependent variable when the independent variable increases with one unit, the logit model gives a probability instead of the marginal change. Furthermore the R-square from the logit regression is a pseudo R-square which differs from the R² of an OLS regression. The interpretation is similar to the normal R² but the value is in most cases significantly lower. The equation in the first part of the analysis is as follows:
𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 = 𝑐 + 𝛽 ∗ 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖,𝑡+ 𝛾 ∗ 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡+ 𝜇𝑖+ 𝜏 + 𝜀𝑖,𝑡
(3)
Where β and γ are the coefficients for the competition and control variables, 𝜇 refers to the country fixed-effects, 𝜏 to the time fixed effects, and ϵ is the error term. Financial stability can either be the z-score or the systemic banking crisis dummy and competition is one of the competition variables. As always refers i and t to a specific country and time period. For the second part some extra variables are included namely the institutional development variables. Therefore the equation will be extended with an extra variable z and looks as follows:
𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 = 𝑐 + 𝛽 ∗ 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖,𝑡+ 𝛾 ∗ 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡+ 𝑧 ∗
𝐿𝑒𝑟𝑛𝑒𝑟 𝑖𝑛𝑑𝑒𝑥𝑖,𝑡∗ 𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡𝑖,𝑡+ 𝜇𝑖 + 𝜏 + 𝜀𝑖,𝑡 (4)
3.6. Correlations Table 2
Variables Lerner H-statistic Boone indicator Concentration
Lerner 1 -H-statistic -0.190 1 (0.000) -Boone indicator 0.167 -0.067 1 (0.002) (0.210) -Concentration 0.206 -0.026 0.109 1 (0.000) (0.635) (0.043)
-Table 2 presents the correlations between the different measures of competition. The first value is the correlation between the variables while the p-values are below between parentheses. The results are cross-country over the period 1999-2013. The Lerner index is a measure of competition defined as the pricing power of a bank. The H-statistic measures the elasticity between revenue and input prices while the Boone indicator measures the elasticity between marginal costs and profits. Concentration is the percentage of market share of the three largest banks measured in terms of assets.
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The correlations between the various competition measures are shown in table 2. It can be seen that the Lerner index is significant correlated with all the other measures which is positive because in further analysis the Lerner index can be used instead of all. The highest correlation is between concentration and the Lerner index and has a value of .206 which does not provide any signs for multicollinearity and therefore can be ignored.
Table 3 presents the correlations of the various institutional development variables. While some variables are significantly correlated, others are not. More important is there are no signs of multicollinearity with a highest significant correlation of .224 between depth of information sharing and stock market turnover. Therefore we can ignore the correlation between the independent variables.
3.7. Summary statistics
The summary statistics can be found in table 4 and the results will be discussed here. Each variable is categorized into a type. The four types are; competition measures, institutional factors, control variables, and the dependent financial stability measures. For each variable the table shows the mean, standard deviation, minimum, maximum, and number of observations. The data also shows that there are no abnormal values, the dummy variables are all between 0 and 1 and also the index variable show no signs of errors.
Table 3
Depth of information sharing Stock market turnover Capital stringency Coverage Limit Single bank supervisor Activity restriction 1
-Stock market turnover 0.224 1
(0.018)
--0.174 0.025 1
(0.068) (0.793)
-0.093 0.000 -0.045 1
(0.332) (0.997) (0.639)
-Single bank supervisor -0.029 -0.033 0.003 0.102 1
(0.763) (0.734) (0.974) (0.288)
--0.138 -0.322 0.185 0.555 -0.179 1
(0.150) (0.001) (0.052) (0.563) (0.061)
-Correlation between institutional development factors
Table 3 presents the correlations between the institutional development factors. These factors can be divided into institutional factors; depth of information sharing, financial market development; stock market turnover, and supervisory development; capital stringency, coverage limit, single bank supervisor, and activity restrictions. These factors could influence the effect of competition on financial stability. The correlations are given with underneath their corresponding p-value between brackets. The values are cross-country over the period between 1999-2013
Activity restriction Capital stringency Coverage Limit
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4. Analysis
In the following section there will be an overview of the results. It starts with the analysis of the different competition measures on both the z-score as well as systemic banking crisis. After that the model will be expanded to include the institutional development factors and at the end there will be an analysis of every factor.
4.1. Competition and stability
The results of the regression analysis of the competition measurements and financial stability measured through the z-score and systemic banking crisis can be found in table 5. In this regression I used the different competition measures in combination with the control variables.
Table 4 Summary statistics
Variable Mean Standard dev. Minimum Maximum Observations
Competition measures Lerner index 0.276 0.130 0.001 0.939 1883 H-statistic 0.607 0.271 -0.562 2.028 354 Boone indicator -0.052 0.185 -2.082 5.968 2346 Bank concentration (%) 74.414 21.519 7.248 100 2186 Institutional development
Depth of information sharing 4.366 3.169 0 8 2790
Stock market turnover ratio (%) 47.210 62.926 0.009 511.672 1402
Capital stringency (0-8) 4.796 1.773 1 8 2055
Coverage limit / GDP per Capita
(in %) 734 2017 20 8799 289
Single bank supervisory = 0 0.254 0.435 0.000 1 2009
Activity restrictions 10.597 2.195 4 16 1935
Depend variables
Bank Z-score 15.294 10.639 -21.224 74.129 2600
Systemic banking crisis 0.054 0.226 0 1 2639
Control variables
GDP per Capita 11078 18007 133 158803 2856
Non-interest income 39.014 16.104 1.425 95.742 2628
Stock market return (%) 12.810 37.725 -63.163 402.463 1145
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This shows that the Lerner index and the H-statistic have a significant influence on the z-score, also known as the distance to default. The first has a positive effect while the latter has a negative effect. This means that a higher Lerner index increases the distance to default and a lower risk of default is similar to a higher financial stability. A higher Lerner score means more pricing power for a firm. This result is in compliance with the competition fragility theory, this means that more competition leads to less stability. Secondly the H-statistic is significant and negative, this result is similar to the Lerner index because the H-statistic has a reversed scale where 1 is perfect competition and 0 a monopoly. A lower H-statistic, less competition, increases the z-score and therefore the financial stability. The Boone-indicator and the concentration ratio both provide no significant results. The squared Lerner index gives no significant relation. There are no signs of non-linearity in the relation between the Lerner index and the z-score.
When the competition measures are regressed against the occurrence of a systemic banking crisis all measures except the Boone-indicator provide significant results. The Lerner index and the H-statistic have their signs reversed which is consistent with the competition z-score relation. A higher Lerner index or a lower H-statistic reduces the occurrence of a systemic banking crisis which is a dummy of 1 when a crisis occurs and 0 in absence. Furthermore we see that concentration is negatively related to systemic banking crises, this implies that more concentration decrease the occurrence of a banking crisis. Again, this is consistent with the competition stability view where more concentration or market power leads to more stability. The squared Lerner index, in combination with the normal index, is significant and negative which is similar to the standard Lerner index. This result shows that an increase in the Lerner index, which is a decrease in competition, lowers the chance on a systemic crisis.
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financial world, banks which were involved in these non-interest income activities suffered the most and thereby explain why the percentage of non-interest income is positive related to the occurrence of a systemic banking crisis.
Concluding can be said that there is evidence for the competition fragility theory and no evidence for the competition stability. Also there is no evidence for hypothesis 3 which states that concentration has a negative relation to financial stability while the regression shows a positive relation.
4.2. Institutional development
The results of the regressions between the institutional development factors and financial stability can be found in table 6. First are all factors individually tested in combination with the control variables in order to provide an answer for hypotheses 4, 5, and 6 and secondly they are all combined into one regression. They are similar to the previous regression two part. In the first part, the z-score is taken as the dependent variable whereas in the second part, the banking crisis dummy is used. As already mentioned, this part leans heavily on Beck, De Jonghe, and Schepens (2013).
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Both in the depth of information sharing as the activity restriction the Lerner index is significant. However this is in both cases the opposite sign compared to the institutional factor. In the case of depth of information sharing, the Lerner index is positive and information sharing is negative while activity restriction is positive and the Lerner index is negative. When taking to the extreme, when there is no information sharing, less competition is necessary to increase stability because the negative value of information sharing does not need to be taken into account due to the value of zero. However it becomes interesting when there is a lot of information sharing because this lowers the financial stability in both high and low competition markets. This leads to the consequences that information sharing has a negative effect on the financial stability but this effect is larger when competition is less severe. In the second case the Lerner index is negative which evidence for the competition stability hypothesis is, but more activity restrictions have a positive effect on, in this case, the z-score. So when taken to the extreme, when there is no activity restrictions more competition is preferred because only the (negative) value of the initial Lerner coefficient has an effect. However when there are a lot of restrictions less competition is preferred.
When we look at the economic implications of the result we see the following. In the case of low competition we do not need information sharing, one explanation could be that there is no need for sharing because the large banks have enough information about their customers and there is no need for sharing. Why more information sharing has a negative effect on the financial stability remains unclear and needs to be further investigated. When there is high competition the impact of information sharing is much smaller than in low competition and eventually diminishes. The case of activity restrictions is much clearer and as expected. More restrictions are preferred when competition is low and less restrictions when competition is high. This makes sense when looking at diversification theory, less restrictions provides banks the opportunity to diversify and thereby reducing their risk while restrictions decrease the diversify possibilities and increasing the risk. Especially in high competition markets this forms a problem because banks have less options to compensate for this risk due to their small market and pricing power compared to banks in low competition markets.
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plausible, one more theoretical and one more practical. As described in the literature section there are opposing views on the consequences of depositor insurance. One of the views is that it decreases bank runs and the other states that it increases moral hazard problems in banks. These opposing effects could lead to an insignificant relation. A second explanation could be the sample. The first limitation of the sample is that it only gives the coverage limits during three years and thereby decreasing the sample, secondly systemic risk materialized into a banking crisis in the most developed countries which were the ones who also had a deposit insurance system and which causes almost all deposit insurances to experience a systemic banking crisis which in turn leads to an insignificant relation. Single bank supervisory is also insignificant, this means that there is no evidence that multiple supervisors have a positive effect on the financial stability as proposed by the literature. One explanation is that not the number of supervisory agencies is important but rather the quality of these agencies. Capital stringency is the final insignificant variable which means that more stricter rules regarding capital requirements does not have any significant effect on the competition financial stability relation. Beck, Schepens, and De Jonghe (2013) had the similar result with regards to capital stringency but do not give any possible explanations.
4.3. Control variables
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crises but this could also be due to the fact that more high GDP countries suffered from the 2008 crisis.
4.4. Further analysis
The following section provides some insights with regard to the effects of competition on the severity of each crisis. The available data gives only limited information about the severity which makes it hard to provide hard evidence for any relation. However it is possible to give some characteristics regarding this topic. Graph 1 and 2 show a scatterplot of the relation between output loss and competition and between fiscal costs and competition. We can see that there are a few upside outliers which make it hard to draw conclusions regarding a coefficient from it. In both graph 1 and 2 the outliers are mostly on the left side of the graph which could imply that the costs of a crisis are higher in more competitive markets. This graph does not provide any significant evidence but it could give some indication that when a crisis has a high impact, this is more likely to happen in a more competitive market. There does not seem to be any relation between the output loss and the degree of competition when we exclude the outliers. For the fiscal costs we can see a slightly upwards sloping line. Further research in combination with more detailed data is necessary in order to give more meaningful results. The results stay the same when other measures of competition are used.
00 20 40 60 80 100 120 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 Output l oss Lerner index
Graph 1
Output loss in % GDP
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4.5. Hypothesis
There is evidence for hypothesis 1 with regards to the competition fragility theory. Especially the Lerner index is in most cases significant and its coefficient is in line with the franchise value theory. There is no evidence for hypothesis 2 so we have to reject it. Hypothesis 3 with regards to the concentration cannot be rejected but the results are mixed. There is evidence for hypothesis 4 with regards to the development of the financial markets measured by the stock market turnover. Hypothesis 5 should be rejected based on the data which provide evidence that the depth of information sharing has a negative effect on the financial stability. Hypothesis 6 should not be rejected because of the significance of activity restrictions. The results provide evidence that more restrictions could have a positive effect on the financial stability.
00 10 20 30 40 50 60 0 0,1 0,2 0,3 0,4 0,5 0,6 F isca l costs in % G DP Lerner index
Graph 2
Fiscal costs in % GDP
25 5. Limitations and further research
This sections provides an overview of the limitations of this research and presents a few further research areas which can be addressed by fellow researchers. One of the limitations of this research is the use of materialized systemic risk in the form of systemic banking crises. However, systemic risk does not have to materialize in order to be a risk and little risk can also materialize. One of the consequences is that countries who did not face a systemic banking crisis were also very risky but were rated as stabile because the risk did not materialized. In order to check for this limitation every regression is done with both the systemic banking crisis independent variable and the theoretical z-score. The latter includes all the systemic risk and is used to check whether the results from the realized systemic risk regressions hold. Both regression showed similar results which strengthens the conclusions drawn from them. Despite this limitation the results still provide useful insights because it gives an overview of real life events but one should be aware of this limitation which could also lie at the basis of some differences between the theory and practice.
The second limitation is the estimation of certain values in the sample. Some values have been assumed to be constant over the years because surveys were not conducted every year and there was no information for the other years. This refers in particular to the institutional development factors which have been assumed constant between surveys. This is doubtful to be true in practice because these values probably changed over time. However due to the nature of the factors it is plausible that values changed gradually between survey moments. For example, the values changed from 4 to 6 between surveys, the expected value in the intermediate period will be gradually increasing to 6.
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5.2 Further research
This paper gives an explanatory view into the relation between systemic banking crises and competition. While it gives an overview of the most recent years it could be interesting to extent the time period also to earlier periods. This could help our understanding because the data used in this research is biased due to the large 2008 financial crisis. In addition one could study whether the severity of a crisis is influenced by the degree of competition. This paper tries to address the question but the results have little scientific value. Researchers with more resources are probably able to gather the required information which was missing for this paper.
Moreover the effect of non-interest income should be taken into account. This factor was highly significant in this research but have been left out in most others. Whether this relation is biased due to the nature of the recent crisis or that non-interest income is more important could be an interesting question.
6. Conclusion
This paper uses a new approach to provide new insights between the relation of competition in the banking sector and financial stability. The importance of this in aftermath of the most recent financial crisis is relevant for both policymakers as well as the general public who’s money have been spent on saving banks and other financial institutions. While previous research mainly focused on the systemic risk measured by the so called z-score this paper uses a different approach using systemic banking crises while checking the results with the z-score for robustness. The limitations of this approach have been addressed multiple times and should be taken into account.
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This variable was highly significant in combination with the occurrence of a systemic banking crisis but not in combination with the z-score. Whether this is due to the nature of the most recent financial crisis or a factor which should be given more attention in literature should be answered in further research.
28 7. References
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Appendix A provides an overview of all the countries included in this research
Appendix A Countries included
Countries
Afghanistan Cote d'Ivoire Lebanon Rwanda
Albania Croatia Luxembourg Saudi Arabia
Algeria Curacao Macao SAR, China Senegal
Angola Cyprus Macedonia, FYR Serbia
Argentina Czech Republic Madagascar Sierra Leone
Armenia Denmark Malaysia Singapore
Austria Dominican Republic Mali Slovak Republic
Azerbaijan Ecuador Mauritania Slovenia
Bahamas, The Egypt, Arab Rep. Mauritius South Africa
Bahrain El Salvador Moldova Spain
Bangladesh Ethiopia Mongolia Sudan
Belarus France Montenegro Sweden
Belgium Gambia, The Morocco Switzerland
Belize Georgia Nepal Syrian Arab Republic
Benin Germany Netherlands Tanzania
Bolivia Ghana New Zealand Thailand
Bosnia and Herzegovina Haiti Niger Togo
Brazil Honduras Nigeria Tunisia
Bulgaria Hungary Oman Turkey
Burkina Faso India Pakistan Uganda
Burundi Indonesia Panama Ukraine
Cambodia Italy Paraguay United Arab Emirates
Cameroon Japan Peru United Kingdom
Canada Jordan Philippines United States
Cayman Islands Kazakhstan Poland Uruguay
China Kenya Portugal Venezuela, RB
Colombia Kuwait Qatar Vietnam
Congo, Dem. Rep. Kyrgyz Republic Romania Zambia
