What determines financial stability in EU: The effect of banking
competition and Eurozone membership on non-performing loans.
by
Panagiotis Asimakopoulos
In partial completion of the requirements for the
MPA in Public and Economic Policy
London School of Economics and Political Science
3
Table of Contents
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
2. Literature Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3. Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
3.1 Financial Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
3.2 Banking Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Eurozone Membership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18
3.4 Macroeconomic determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.5 Banking sector-specific determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.6 Regulatory and Institutional determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4. Data Analysis and Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.1 Data Source and Sample Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5. Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . .. . . . . . .. . . . . . 33
5.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
6. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50
Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51
5
Acknowledgements
I would like to express my gratitude to my family and my friend Nefeli for their support and
valuable advice during the implementation of this project.
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Abstract
Recent developments, the 2008-banking crisis, showed that when a country’s banking sector
faces a significant increase in the amount of non-performing loans, financial stability is
threatened. This study investigates the impact of banking competition and Eurozone membership
on non-performing loans. By using an up to date panel dataset for the period 1998-2011 and
focusing exclusively on the 28 European Union countries, this study facilitates the design of
macro-prudential policies in the context of the upcoming EU banking union. It adopts a macro-
oriented cross-country approach rather than bank-level or country specific approach and
combines macroeconomic, banking-sector specific, institutional and regulatory variables from a
large number of studies. A new World Bank series, the 2013 Global Financial Development
Database is used as the major source for the main measure of banking competition, the Lerner
index and also for alternative measures of competition and banking sector-specific determinants.
In order to assess the effect of Eurozone on financial stability, contrary to other studies, this study
adopts a difference-in-differences approach.
By using OLS fixed effects and Arellano-Bond GMM estimation methods, we find that a lower
level of banking competition as measured by the Lerner Index, is associated with a lower level of
non-performing loans in EU countries. The use of the Boone Indicator instead of the Lerner Index
appears to confirm these findings, whereas five-bank asset concentration is insignificant. Apart
from banking competition, macroeconomic and institutional determinants are found to be
associated with non-performing loans in EU. More specifically, higher levels of corruption and
unemployment are found to be associated with higher levels of NPLs in EU countries. On the
other hand, higher levels of economic growth, greater depth of credit information available to
financial institutions and a higher percentage of registered individuals in private credit bureaus
are associated with a lower level of NPLs in EU countries. The results of the difference-in-
differences approach show that Eurozone countries appear to have lower levels of non-
performing loans than non-Eurozone EU countries.
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1. Introduction
Currently, in Europe, the amount of non-performing loans1 is over 1 trillion euros (CNBC, 2013).
Recent developments, the 2008-banking crisis, showed that when a country’s banking sector
faces a significant increase in the amount of non-performing loans, financial stability is
threatened. More specifically, a large volume of non-performing loans in a bank’s balance sheet
may lead to lack of confidence from investors. Bank’s solvency is questioned and therefore,
access to funding becomes difficult. When the above holds for a large bank or a number of
banks in a country’s banking sector, financial stability is threatened.
The crisis stimulated changes in the European Union (EU) edifice, leading to further integration in
certain areas. More specifically, recent developments in EU suggest that the banking sectors of
EU countries move towards a union. Following the recent formal adoption of the Single
Supervisory Mechanism (SSM) in October 2013, the European Parliament and the Council have
agreed on Commission’s proposal regarding the adoption of the Single Resolution Mechanism
(SRM). (European Commission, 2014).
Extensive literature investigates the determinants of non-performing loans. However, most
studies focus on one country using bank-level data (Jimenez et al, 2007a; Louizis, 2012). There
are also studies, which consider non-performing loans as a proxy for financial stability and
investigate mostly the effect of banking competition on non-performing loans. However, these
studies focus on a small number of countries, more precisely in transition economies (Agoraki et
al, 2011; Klein, 2013). Studies focusing specifically on EU countries use other proxies for
financial stability such as z-scores and focus either on a small time period with bank-level data
(Andrier and Capraru, 2012) or a small time period with a backdated cross-country aggregated
bank-level dataset, using banking concentration as a proxy for competition (Uhde and Heimeshoff
,2009).
Since the Banking Union and more specifically, the integration of supervision and resolution
mechanisms appears to be inevitable, it becomes significant to investigate which factors affect
financial stability in EU. According to Anginer et al (2012), when a research aims to assess
systemic risk rather than individual bank risk, it is better to use country-level data instead of bank-
level data. Moreover, a cross-country analysis is the best approach to address macro-prudential
policy issues and facilitate in measuring the impact of institutional and regulatory environment. 1 A loan is characterized as non-performing when the borrower has not paid the scheduled amounts for a certain period of time and the loan is close to default or in default. http://lexicon.ft.com/Term?term=non_performing-loan--NPL
10
Considering all the above, this study adopts a macro-oriented cross-country approach rather than
bank-level or country specific approach.
The contribution of this study to the existing literature is multifaceted. First of all, this thesis
contributes to the existing literature that investigates the impact of banking competition on
financial stability by combining the Lerner Index and non-performing loans as measures of
banking competition and financial stability respectively. Secondly, it focuses specifically on the 28
European Union countries and uses an up to date panel dataset for the period 1998-2011,
facilitating in the development of macro-prudential policies in the context of the EU banking union.
Thirdly, it combines a large number of macroeconomic, banking sector-specific, institutional and
regulatory determinants from various studies. Fourthly, a new World Bank series, the 2013 Global
Financial Development Database is used as the major source for the main measure of banking
competition, the Lerner index and also for alternative measures of competition, such as the
Boone Indicator and five-bank asset concentration ratio, and banking sector-specific
determinants. Last but not least, contrary to other studies, which use a dummy variable to assess
the effect of Eurozone on financial stability, this study adopts a difference-in-differences approach
in order to capture any differences at the level of non-performing loans between Eurozone
countries and non-Eurozone countries.
This study is organized as follows. In section 2, existing literature is discussed. Section 3
presents the theoretical background. Section 4 includes the data analysis and describes empirical
strategies. Section 5 presents the main results and robustness checks. In section 6 we discuss
the results and present limitations. Section 7 concludes.
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2. Literature Review
Extensive theoretical and empirical literature exists investigating the impact of banking
concentration and banking competition on financial stability. Beck (2008) conducts an extensive
literature review on both theoretical and empirical studies around the topic. Regarding theoretical
literature, Beck (2008) makes a distinction between two central theories. On one hand, several
studies argue that banking systems with a high level of concentration and therefore, lower level of
competition may be more stable. On the other hand, others argue that less competitive and more
concentrated systems lead to a lower level of banking system stability. Both arguments are based
on several theories explaining possible links between banking competition and financial stability.
We elaborate further on these theories in the next section. Regarding empirical studies,
researchers mainly use z-scores as a proxy for financial stability, which measures banks’ default
risk. Another proxy is the non-performing loans (NPLs) ratio, which measures credit risk and bank
risk-taking. A major criticism is that neither of the abovementioned proxies considers actual
failure of banks. As for competition, several studies use measures of market structure such as
concentration ratios and Herfindahl-Hirschman indices (HHI). Other studies use measures of
competition and market power such as H-Statistics and Lerner indices. Finally, regulatory
measures such as entry requirements and barriers to entry are considered alternative proxies.
Beck (2008) concludes that bank-level empirical studies do not provide clear evidence for either a
negative or a positive relationship between competition, concentration and banking stability.
However, a conclusion from bank-level studies is that higher concentration is not necessarily
associated with a lower level of competition. A majority of cross-country studies find that
competition has a positive impact on stability, whereas the effect of concentration on stability is
ambiguous. Therefore, concentration ratios are less prominent measures of competition than
other proxies.
Anginer et al (2012) investigate the link between competition and risk-taking behavior of banks.
They obtain a sample of publicly traded banks from 63 countries for the period 1997-2009. They
focus on systemic risk rather than individual bank risk, in order to address macro-prudential policy
issues. Hence, they do not use bank-level data. Instead of z-scores, an alternative measure is
used to address potential spurious correlation between the Lerner Index and z-scores, since both
are calculated using profitability measures. They use R-squared which is found by “ regressing
the changes in bank default risks on changes in average default risk of all banks in a given
country”. Bank-level and macroeconomic determinants are used as controls. GDP per capita is
used to control for economic development, GDP growth for economic stability, population for
country size, and finally, stock market capitalization and private credit to control for financial
development and structure. The results presented show that higher competition leads banks to a
12
higher level of risk diversification and hence, to greater stability. Furthermore, systemic stability is
found to be negatively associated with weak supervision, government ownership of banks. For
robustness checks, they present the correlation of bank asset concentration and Lerner Index,
which is found high and positive.
Uhde and Heimeshoff (2009) aggregate balance sheet data from banks across EU-25 to
investigate the effect of banking concentration on financial stability for the period 1997-2005. It is
the first study investigating this relationship using panel data analysis for EU countries. Z-scores
are the proxy for financial stability. Random effects estimation is conducted as they argue that
Eastern countries differ in their historical transition periods. Macroeconomic determinants are
included such as GDP per capita, GDP growth and interest rates. As for bank-level control
variables, net interest margin is used to control for profitability, loan loss provisions for credit risk
and loan quality, deposit insurance system index for moral hazard and cost-to-income ratio for
efficiency. In addition, the study takes into the regulatory framework by including variables such
as capital regulatory index. Government ownership is considered, as government owned banks
might take up excessive risk because of moral hazard associated with bailouts. Finally, two
instrumental variables for competition are included to address potential endogeneity. The first is
based on the idea that if parties in a country support Keynesian demand-oriented policies, it
indicates preferences for less competitive markets and therefore, higher concentration. The
second is that countries with parties against EU integration are against competition and desire
higher concentration. Their results show a negative relationship between concentration and
stability. Banking sectors with more government owned banks are less stable. Additionally,
capital regulations, credit growth and GDP per capita are found positively correlated with stability,
while there is no evidence for moral hazard effects.
Jiménez et. al (2007a) examine the effect of banking competition on bank risk-taking in Spain for
the period 1988-2003. As a measure of bank risk-taking and financial stability, they use NPLs.
Lerner index is estimated using interest rates. Macroeconomic variables are included and also
bank-level controls for profitability, bank size and market share of each bank. Results show that
measures of market concentration such as the HHI, do not affect NPLs. Contrary to previous
studies, Lerner index is negatively related to bank risk, implying that greater market power is
associated with lower level of NPLs. NPLs are also negatively linked with GDP growth.
Agoraki et al (2011) investigate whether the effect of regulations on bank risk-taking is direct or is
associated with market power. A panel dataset is used for 546 banks in 13 transition countries.
They use a panel of banks because some countries suffer from missing data. However, they also
conducted country-level analysis and results were similar. As a proxy for risk-taking they use both
13
NPLs and z-scores. As for competition they use bank-level Lerner index after estimating marginal
costs. Regulatory indices are constructed and include capital stringency, power of supervisory
agencies and restrictions in activities. The study controls for bank size and efficiency by including
total assets and cost to income ratio. Moreover, GDP growth and interest rates are included to
control for economic and monetary environment. Finally, foreign ownership and public ownership
are considered. GMM Instrumental variable approach is used as countries experienced increases
in regulations due to credit risk. Results show a negative significant relationship between market
power and NPLs. When capital requirements are combined with market power, risk-taking is
lower. Official supervisory power is the only mechanism to reduce directly risk. Activity restrictions
combined with greater market power may reduce credit risk and probability of default.
Regulations and restrictions alone cannot reduce credit risk.
Louzis et al (2012) investigate determinants of NPLs in Greece. Macroeconomic and banking-
specific determinants are examined using quarterly panel data set for the 9 largest Greek banks
for the period 2003-2009. A dynamic panel data method is applied to measure time persistence in
NPLs structure including lagged NPLs. GDP, unemployment, interest rates and public debt are
the macroeconomic factors considered. Return on equity is added to test bad management,
capital-to-assets to assess moral hazard effects and expenses-to-income to check for bad
management and skimping. To control for size and diversification, they include total assets of
each bank and non-interest income to total income. Results show that NPLs in Greece are
explained mainly by macroeconomic factors. Lower economic growth leads to higher NPLs ratio.
A higher level of unemployment is associated with a higher inability to repay debts and therefore,
higher level of NPLs. Lending rates have a positive effect on NPLs. Debt-to-GDP has a positive
effect on NPLs. Bank-specific variables such as performance and efficiency are found to explain
NPLs and confirming bad management hypothesis.
Klein (2013) investigates determinants of NPLs in Central-Eastern and South-Eastern European
countries from 1998 to 2011. A panel data of individual banks’ balance sheets of the ten largest
banks in 16 countries is used. As bank-specific variables the author includes equity-to-assets to
measure moral hazard, return on equity to control for profitability and better management, loan-
to-assets ratio and loans growth rate as measures of excessive risk-taking. Country-specific
variables are inflation, exchange rates and unemployment rates. Eurozone’s growth and global
risk aversion are also included as control variables. Dynamic panel data analysis is conducted,
including lagged NPLs as an explanatory variable. Results suggest that NPLs increase when
unemployment increases, exchange rate depreciates and inflation increases. As for bank-
specific, higher quality of management is associated with lower NPLs. Moreover, when equity-to-
assets ratio is higher, NPLs decrease. Klein (2013) checks the robustness of results and the
14
2008 financial crisis effect by splitting the dataset to pre and post crisis and finds that bank-
specific variables are significant in both periods. Inflation and unemployment effect is higher
before the crisis. This study does not include West, Central and southern European countries.
Goel and Hasan (2011) conduct an OLS estimation using country-level data for the year 2007 for
100 countries to assess the impact of economy-wide corruption on NPLs. In the banking sector,
corruption can serve as a proxy for exogenous effects such as institutional quality, riskiness and
economic uncertainty. Main measure of corruption is a corruption-perception index. Goel and
Hasan(2011) argue that financial performance can also be affected by economic growth and
lending rates. Banking sector specific institutions such as central bank autonomy, Eurozone
membership, bank-based economy, and underdevelopment of financial sector may also affect
NPLs. Results suggest that a higher corruption is associated with higher NPLs ratio. Furthermore,
higher growth and lending rates are associated with lower NPLs. Last but not least, results show
that NPLs are lower in countries members of the Eurozone, whereas Central bank autonomy and
banking sector-specific institutional variables do not affect NPLs.
15
3. Theoretical Background
3.1 Financial Stability
When a country’s banking sector faces a significant increase in the amount of non-performing
loans, financial stability is threatened. More specifically, a large volume of NPLs in a bank’s
balance sheet may lead to lack of confidence from investors. Bank’s solvency is questioned and
therefore, access to funding becomes difficult. When the above holds for a large bank or a
number of banks in a country’s banking sector, financial stability is threatened. Several studies
consider NPLs as a measure of risk-taking and financial stability (Jimenez et al, 2007a ; Agoraki
et al, 2011; Klein, 2013; Goel and Hasan, 2011).
The ratio of NPLs measures credit risk (Beck, 2008). It is basically the ratio of defaulting loans of
a country’s banking system to the total value of loan portfolio of a country’s banking system:
𝑁𝑃𝐿 =𝑁𝑃𝐿𝑠𝑇𝐺𝐿
(1)
Where NPLs is non-performing loans and TGL is total gross loans
There is extensive theoretical and empirical literature on the topic providing us with a variety of
possible variables affecting non-performing loans (BIS Papers, 2001). In the following
subsections we present the factors that are considered as determinants of NPLs and therefore,
financial stability and the theories supporting this link.
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3.2 Banking Competition
According to Beck (2008), the effect of banking competition on financial stability is ambiguous
and two main theories exist. The first one is that more banking competition and less market
concentration leads to a less stable banking system. This is based on several arguments. First of
all, higher profits “provide incentives against excessive risk-taking”. Moreover, under a
competitive environment, it is more likely that banks “earn fewer information rents from their
relationship with borrowers”, implying fewer incentives for bankers to screen borrowers correctly
and hence, NPLs may increase. Additionally, high levels of competition may prevent banks to
provide credit to another bank that suffers from temporary illiquidity. At the same time studies
show that a small number of banks may cooperate and facilitate such bank. Another argument is
that a more concentrated banking sector has larger banks and therefore, banks can better
diversify risk and take advantage economies of scale. Anginer et al (2012) present results that
higher competition leads banks to a higher level of risk diversification and hence, more systemic
stability. Last but not least, more concentrated banking sectors imply smaller number of banks,
which in turn leads to easier supervision by authorities and hence, financial stability. (Beck, 2008)
The second theory is that a less competitive and more concentrated banking system leads to
instability and this is based on the following. First of all, a more concentrated banking system
leads to greater market power, which may incentivize banks to charge firms higher interest rates.
As a result, firms take up more risk, increasing the likelihood that their loans become non-
performing. Therefore, financial stability is threatened (Boyd and De Nicolo, 2005). Secondly,
more concentration implies smaller number of banks and hence, regulators are more concerned
about bank bankruptcy. As a result, regulators provide larger subsidies to banks as they are
afraid that banks are “too-big to fail”, incentivizing them to take excess risks and therefore,
increase the probability of a financial distress. Finally, more concentrated banking sectors with
large banks may increase the risk of contagion. (Beck, 2008).
Lerner Index
The difficulty in measuring competition and especially, marginal costs, led to a variety of proxies.
Several studies use measurements of market structure such as concentration ratios, the number
of banks and HHI. However, these proxies measure market shares without considering the
competitive behavior of banks. The Lerner Index considers the competitive behavior of banks
(Beck, 2008). The Lerner Index is a measure of market power. Higher values of Lerner Index
17
indicate greater market power and hence, lower level of market competition. The Lerner Index is
given by the following formula (Lerner, 1935) :
𝐿𝑒𝑟𝑛𝑒𝑟 𝐼𝑛𝑑𝑒𝑥 =𝑃 −𝑀𝐶
𝑃 (2)
where P is the price and MC the marginal cost
In the banking sector prices are measured as the total bank revenue over assets. Marginal costs
are found by estimating the translog cost function with respect to output.
𝐿𝑒𝑟𝑛𝑒𝑟 𝐼𝑛𝑑𝑒𝑥 =𝑇𝐵𝑅𝑇𝐴 −𝑀𝐶𝑇𝐵𝑅𝑇𝐴
(3)
Where TBR is total bank revenue; TA is total assets and MC is marginal cost. Higher Lerner
Index implies greater market power and hence less competition in the banking system. The
database we use in this study, calculates Lerner Index from bank-level data and then it
aggregates on the country level to reflect the market power in a country’s banking sector. (The
World Bank, 2013).
Other proxies for banking competition
The Boone indicator measures the degree of competition in banking sectors. It is calculated by
estimating elasticity of profits and dividing it by marginal costs. Elasticity of profits is estimated by
regressing log of return-on-assets on the log of marginal costs. Boone Indicator is based on the
theory that more efficient banks enjoy higher profits. This implies that a more negative Boone
Indicator indicates a higher degree of competition because reallocation of profits effect is greater.
5- bank asset concentration is a measure of concentration in the banking system. It is defined as
the assets of five largest banks of a banking system divided by the total assets of a country’s
18
banking system. A higher asset concentration may be associated with a lower degree of
competition. (The World Bank, 2013)
3.3 Eurozone membership
One of the proxies, Goel and Hasan (2011) use for banking-sector institutions, financial
development and stability is a country’s membership in Eurozone. They use a dummy variable to
capture differences between Eurozone countries and other countries. Results suggest that NPLs
are lower in Eurozone countries. A concern that one can rise regarding these results is that the
comparison is conducted between Eurozone countries and significantly less developed countries,
without capturing the differences in NPLs before the introduction of Eurozone.
Back in 1999, it was expected that in countries of the euro area, credit risk would decrease and
financial stability would be strengthened, especially through the positive macroeconomic effects
of the Eurozone (European Central Bank, 1999). However, throughout the years, many
researchers argued that the prolonged period of a monetary policy of low interest rates might
have led banks to soften their lending standards and take up excessive credit risk. On the other
hand, it was also argued that at the same time, low interest rates might reduce the risk of
outstanding credit. (Jimenez et al, 2007b)
Considering all the above, it appears that Eurozone countries might have as well experienced an
increase in NPLs in the years after the introduction of Eurozone, compared to non-Eurozone
countries. Notwithstanding the aforementioned, with the introduction of the euro, interest rates
were significantly decreased, inter-bank lending became cheaper and easier, financial transaction
costs and exchange rate risks decreased. As a result, due to easier access to funding and
elimination of other risks, banks may have had an incentive to take up excessive risk in other
operations such as the loan market, by increasing provision of loans to less credible individuals
and companies. To sum up, Eurozone membership may have had either a positive effect or a
negative effect on NPLs. This study attempts to capture any differences in non-performing loans
between Eurozone countries and non-Eurozone EU countries.
19
3.4 Macroeconomic Determinants
Existing literature provides us with macroeconomic variables that may affect NPLs. First of all,
GDP growth is used to control for economic environment and stability (Agoraki et al, 2011;
Anginer et al, 2012). GDP growth controls for business cycles, as NPLs tend to change along
cycles (Jimenez et al, 2008). In times of economic growth, debtors are able to pay back their
loans and at the same time, countries with higher growth may have better mechanisms to screen
loan applications and recover loan payments (Goel and Hasan, 2011). Moreover, under higher
growth rates, banks may increase their capital precautionary against upcoming recessions. (Uhde
and Heimeshoff, 2009).
In many studies interest-rates are included to control for monetary environment (Agoraki et al,
2011). A higher interest rate increases deposit rates, which may increase banks’ costs. An
increase in lending rates may increase profitability, but at the same time makes it difficult for
debtors to repay loans and therefore, NPLs may increase (Uhde and Heimeshoff, 2009). On the
other hand, higher interest-rates discourage potential defaulters to request credit. Moreover,
already existing debtors in long-term loans with lower interest rates may be incentivized to pay
their debts on time (Goel and Hasan, 2011).
Other macro-determinants are inflation, unemployment and GDP per capita. Regarding inflation,
banks usually consider current and expected inflation in several decisions. Moreover, interest-
rates rise when inflation is higher and hence, profitability may be realized as higher by banks
(Uhde and Heimeshoff, 2009). As for unemployment, a higher level is associated with higher
inability to repay debts and therefore, higher level of NPLs (Louizis et al, 2012). Finally, GDP per
capita is used to control for economic development, more developed countries should present
greater stability (Anginer et al, 2012).
NPLs may be also affected by government debt. A sovereign debt crisis may lead to a banking
crisis and the reverse may hold. A sovereign debt crisis might lead to a banking crisis for two
reasons. Firstly, in a debt crisis, markets tend to be extremely conservative when evaluating the
credibility of banks, putting banks under pressure for liquidity. As a result, banks cut lending and
consequently, debtors may not be able to refinance their debts. Secondly, high debt levels might
lead to cuts in government spending and more specifically, in social expenditure and wages.
Hence, people might not be able to repay their loans. To sum up, higher public debt may be
associated with a higher level of NPLs. (Louzis et al, 2012)
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3.5 Banking sector-specific Determinants
Most studies include measures to control for profitability, loan quality, moral hazard and efficiency
of the banking sector. Studies either include net interest margin (Uhde and Heimeshoff, 2009) or
return on equity (Louzis et al, 2012; Klein, 2013) as measures of profitability. In both studies
higher profitability is associated with better management, whereas lower profitability may imply
bad management and hence, higher level of NPLs. In other words, higher quality of management
is associated with lower NPLs. (Louzis et al, 2012; Klein, 2013).
To control for efficiency, cost-to-income ratio is used. Higher efficiency should lead to lower level
of NPLs and hence, more stability (Uhde and Heimeshoff, 2009). More specifically, lower
efficiency is associated with bad management, which implies “poor skills in credit scoring,
appraisal of pledged collaterals and monitoring borrowers” (Louzis et al, 2012). On the other
hand, higher levels of efficiency i.e. lower cost-to-income ratio may increase NPLs. The intuition
is that banks that allocate fewer resources in issuing and monitoring loans will present lower
costs and therefore, will be more efficient. However, at the same time the level of NPLs may be
higher (Louzis et al, 2012).
Capital-to-assets is included in many studies as a proxy for moral hazard (Louizis et al, 2012;
Klein, 2013). When capital-to-assets ratio is lower, NPLs increase (Klein, 2013). The theory
underlying this is that the managers of banks with lower capital may have moral hazard
incentives “to increase the riskiness of their loan portfolio” (Louzis et al, 2012). Louzis et al (2012)
include noninterest income to total income to control for diversification opportunities. This
measure shows the sources of banks’ income other than lending rates. Therefore, it reflects the
level of banks’ diversification; the extent to which a bank takes up risks in order to make profits in
other areas other than loans. Researchers have also included stock market capitalisation and
private credit as a percentage of GDP in order to control for financial development and structure
(Anginer et al, 2012).
21
3.6 Institutional and Regulatory Determinants
There is limited literature for the effect of corruption in the banking sector. As a result, Goel and
Hasan (2011) base their model on general theory of the effect of corruption on institutions. They
argue that lending practices in banking sectors are affected by institutions. A corrupt banking
sector may accept bribes to approve loans with excessive risk. Moreover, if high levels of
corruption characterize a country, law enforcement agents are more likely to be corrupt,
encouraging corruption in the banking sector. Additionally, in countries with greater corruption,
borrowers closer to default might offer bribes to reduce punishments and therefore, probability of
loan default is increased.
As for regulations, Beck et al (2008) concludes from theoretical studies that a minimum capital
requirement for banks reduces incentives for excessive risk taking. Capital regulatory index is
used in Uhde and Heimeshoff (2009) to control for the regulatory framework. In the same
manner, Agoraki et al (2011) construct regulatory indices, which include measures of capital
stringency and power of supervisory agencies. It is argued that capital requirements may reduce
risk directly and they also find that supervisory power directly reduces credit risk. Indeed, it is also
a basic argument in other studies (Uhde and Heimeshoff, 2009) that strength and quality of
institutions have great impact on financial stability. Finally, Uhde and Heimeshoff (2009) control
for origins of judicial systems and argue that different judicial systems have different level of
protection for creditors and this impacts financial development.
In line with existing literature, this study considers the effect of institutions and regulations. First of
all, we include a measure of corruption and a measure of control of corruption, which reflects both
“the extent to which public power is exercised for private gain” and “the capture of the state by
elites and private interests.” Secondly, a measure of regulatory quality is included to control for
quality of institutions. Thirdly, a measure of bank regulatory capital is included. As mentioned
above, a higher level of regulatory capital is expected to reduce risk-taking. Fourthly, two
measures are included to control for the level of availability and depth of credit information used
by financial institutions to make lending decisions. We expect that greater availability and depth
of information may lead to a lower level of NPLs. Finally, a measurement of strength of legal
rights of a country’s creditors and debtors is included to control for the level of protection of
creditors and borrowers. In countries with greater strength of legal rights, access to credit
becomes easier (World Bank, 2014). Therefore, we expect that greater strength of legal rights
may increase NPLs.
22
22
legal rights, access to credit becomes easier (World Bank). Therefore, we expect that greater
strength of legal rights may increase NPLs.
Table 1: Expected effect of main determinants
Determinants
Expected effect
Banking competition
(+), (-)
Eurozone membership
(+), (-)
Economic growth
(-)
Unemployment
(+)
Interest rates
(+), (-)
Inflation rate
(+), (-)
GDP per capita (Economic Development)
(-)
Government Debt
(+)
Return on Equity (Profitability-Better Management)
(-)
Cost Efficiency
(-), (+)
Capital-to-assets (Moral Hazard)
(-)
Stock market capitalisation (Financial Development) (-), (+)
Private credit as % of GDP (Financial structure)
(+)
Corruption
(+)
Control of Corruption
(-)
Regulatory Capital
(-)
Regulatory quality
(-)
Availability and depth of credit information
(-)
Level of protection of Legal Rights of debtors and creditors
(+)
4. Data Analysis and Empirical Strategy
23
4. Data Analysis and Empirical Strategy
In this section we first present the sources of data and sample statistics followed by the empirical
strategy.
4.1 Data Sources and Sample statistics
We use a panel dataset for the 28 EU countries for the period 1998-2011. Our main source is a
new World Bank 2013 series, the Global Financial Development Database2. Other sources are
the OECD3, European Commission’s AMECO database4, World Bank5, World Bank’s Worldwide
Governance Indicators (WGI)6 and Transparency International7
Our dependent variable, NPLs, is measured as the ratio of non-performing loans to total assets
and data are available at the GFDD for EU countries for the period 1998-2011. Regarding
measures of competition, most studies conduct their estimations using a variety of proxies. We
construct our dataset using GFDD. There are 5 indicators available associated with banking
competition. The estimated Lerner index is used as the main proxy for each country’s banking
sector competition for the period 1998-2011. Five-bank asset concentration and the Boone
indicator are used as alternative proxies for banking competition. In GFDD, five-bank asset
concentration is defined as the assets of five largest banks of a banking system divided by total
assets of a country’s banking system.8
In line with existing literature on the topic, we include measures of unemployment, inflation, GDP
growth, GDP per capita and short-term interest rates. Unemployment rates, short-term interest
rates and GDP per capita are found in the European Commission AMECO database. Inflation-
rates are calculated using the GDP deflator available at the AMECO Database. We use real GDP
growth and real short-term interest-rates. Since real short-term interest rates for Luxembourg are
not available in the AMECO database, we calculate them using nominal short-term interest-rates
from the OECD database and the calculated inflation rates. Finally, real GDP growth rate is
calculated using a measure of real GDP available at the World Bank Database9
2Available at: http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTGLOBALFINREPORT/0,,contentMDK:23269602~pagePK:64168182~piPK:64168060~theSitePK:8816097,00.html 3 http://www.oecd.org/statistics/ 4 http://ec.europa.eu/economy_finance/ameco/user/serie/SelectSerie.cfm 5 http://data.worldbank.org 6 http://info.worldbank.org/governance/wgi/index.aspx#home 7 http://www.transparency.org/research/cpi/overview 8 Please be referred to GFDD for details regarding calculation methodology for each variable 9 GDP constant at 2005 prices ($) available at: http://data.worldbank.org/indicator/NY.GDP.MKTP.KD/countries/1W?display=default
24
As for banking sector-specific variables, we include return-on-equity as a proxy for profitability,
stock market capitalization for financial development, private credit as a percentage of GDP for
financial structure, capital-to-assets ratio for moral hazard, non-interest income to total income for
diversification, cost-to-income for efficiency and provisions to non-performing loans. All the above
are available at the GFDD. In GFDD, all variables are aggregated to the country level to reflect
the characteristics of a country’s banking sector. We use the before tax return on equity, which is
measured as banks’ income divided by yearly averaged equity. Cost-to-income ratio is the
operating expenses of a bank, as a share of the sum of net-interest revenue and other operating
income. Non-interest income is banks’ income from noninterest related activities such as trading,
derivatives, other securities, fees and commissions divided by banks’ total income, which is the
sum of net interest income and non-interest income. Capital-to-assets ratio is the shares,
common stocks, other reserves and regulatory capital of a banking sector divided by total
financial and non-financial assets. Finally, stock market capitalization is the total value of all listed
shares in a stock market, as a percentage of GDP, whereas, private credit is the credit provided
by banks to individuals, governments and companies as a percentage of GDP.
As a measure of regulatory capital we consider the bank total regulatory capital as a percentage
of risk-weighted assets, available at GFDD. It measures capital adequacy of banks. It is the total
regulatory capital divided by assets held weighted according to their risk. Regulatory quality is
measured using a perception index from WB’s Worldwide Governance Indicators (WGI), which “
reflects the capacity of governments to formulate and implement sound policies and regulations
that permit and promote private sector development”. It takes values between -2.5 and 2.5 with
lower values indicating weak governance and low regulatory quality.
World Bank database is the source for institutional variables related to availability and depth of
credit information and legal rights of creditors and borrowers. These measures are the private
credit bureau coverage, credit depth of information and strength of legal rights. Credit depth of
information is a measure of rules that determine the scope, accessibility and quality of credit
information available through public or private credit registries. It measures the depth of
information available from public or private bureaus to financial institutions in order to facilitate
lending decisions. It takes values between 0 and 6. Higher values imply that more credit
information is available to facilitate lending decisions (World bank,2014) As for strength of legal
rights, it measures the degree to which rights of debtors and creditors are protected by collateral
and bankruptcy laws. A higher degree of legal protection facilitates lending. The index takes
values from 0 to 10 and higher values reflect that laws are “better designed to expand access to
credit” Finally, private bureau coverage measures the number of individual and firms as a
25
percentage of total population, for which information on repayment history, unpaid debts, credit
outstanding is registered in a private credit bureau. Higher percentage of registered individuals
and companies implies greater availability of credit information.
In order to test the impact of corruption on banking stability, we use two variables. For levels of
corruption in each country, we construct a dataset for EU countries for the period 1998-2011
using the Transparency International Corruption Perception Index. As an alternative measure we
included an indicator described as “control of corruption” available at Worldwide Governance
Indicators. It reflects both “the extent to which public power is exercised for private gain” and “the
capture of the state by elites and private interests.” It takes values from -2.5 to 2.5 with higher
values implying strong governance performance and lower values weak governance
performance.
Table 2 presents the descriptive statistics of our dataset. It shows the number of observations
(obs), the mean, the standard deviation (Std.Dev.) and finally, the minimum (min) and maximum
(max) values for each variable. Furthermore, Figure 2 and 3 in Appendix A present the evolution
of nonperforming loans and Lerner index in EU-28 banking sectors.
26
26
Table 2: Descriptive Statistics EU-28 (1998-2011)
Variable Obs Mean Std. Dev. Min Max
Nonperforming loans 357 4.882353 4.978617 .1 31.6
to total gross loans (%)
Credit (%GDP ) 383 106.3765 58.92467 12.98579 327.9785
Stock Market Capitalisation 392 52.72189 44.4748 1.818865 246.0504
Cost to income ratio 388 59.04336 13.2045 1.530612 113.0802
Capital to assets 363 7.204132 2.685085 2 18.3
Non-interest income 389 38.62244 11.65302 3.33 83.73
to total income
Regulatory capital 368 13.83234 3.847638 6.6 41.8
Provisions to NPLs 293 67.12765 36.95095 11.5 322.1
5-bank asset concentration 366 82.12367 13.85307 37.56137 100
Boone Indicator 359 -.0345934 .3484278 -2.081715 5.968345 Lerner Index 376 .1884571 .128051 -1.60869 .503105
Real short-term interest rate 363 1.321006 3.286742 -18.3634 20.14596
Unemployment rate 392 8.558673 3.9398 1.9 21.7
Inflation rate 392 3.191883 4.485956 -15.82509 25.47408 Government debt (%GDP) 304 62.50101 41.37504 3.610249 261.7287
GDP per capita 390 24632.29 16914.47 2484.949 87716.73
Control of corruption 336 1.044018 .8396315 -.72 2.59
Regulatory quality 336 1.195298 .440482 -.12 2.08
Bureau coverage 211 36.4346 36.94953 0 100 Credit depth of information 211 4.43128 1.410426 0 6
Strength of legal rights 211 6.78673 2.015893 3 10
Return on equity 362 11.5883 15.21468 -102.9232 43.0201
Growth rate 392 2.717635 3.633329 -17.69907 10.98815
Euro 392 .4387755 .4968716 0 1
Member 392 .6071429 .4890096 0 1
Euro*Member 392 .4387755 .4968716 0 1
Corruption 375 6.2772 2.00256 2.6 10
Sources: World Bank GFDD, European Commission AMECO, World Bank, World Bank Worldwide Governance
Indicators, Transparency International, OECD, own calculations
27
4.2 Empirical Strategy
Effect of banking competition
The basic model used to conduct empirical analysis in order to investigate the effect of
competition on NPLs is the following:
ln (NPLit) = α + γi+ θt + β1 ln (NPLit-1) + β2 ln (lernerit) + β3 capitalization it +
β4 creditit + β5 roeit + β6 cost_to_incomeit + β7 capital_to_assetsit + β8
regulatory_capitalit + β9 non_interest_incomeit + β10 gov_debtit + β11
interest_ratesit + β12 unemplit + β13 growth_rateit + β14 inflationit+ β15
lngdpcapitait + β16 qualityit + β17 corruptionit + β18 bureau_coverageit + β19
legalit + β20 depthit + uit (4)
Where ln(NPLit) is the natural logarithm of non-performing loans to total assets for the banking
sector of country i at time t; NPLit-1 is the lag of NPLs ratio; ln (lerner) is the natural logarithm of
the Lerner Index; capitalization is the stock market capitalization of country i at time t as a
percentage of GDP; credit is the credit provided by country’s i banking sector as a percentage of
GDP; roe is the return on equity of a country’s banking sector at time t; cost_to_income is the
ratio of costs to total income for banking-sector of country i at time t; ; capital_to_assets is the
ratio of a country’s banking sector equity divided by the banking sector’s total assets;
regulatory_capital is the total regulatory capital to risk-weighted; non_interest_income is the non-
interest income for the banking sector of country i at time t, divided by the total income of the
country’s banking sector; gov_debt is the government debt as a percentage of GDP;
interest_rates is the real short-term interest-rates for country i at time t; unempl the
unemployment rate in country i at time t; growth_rate is the growth rate of real GDP; inflation is
the inflation rate; lngdpcapita is the natural logarithm of the real GDP per capita for country i at
time t; quality is the level of regulatory quality in country i at time t; corruption is the level of
corruption; legal is the strength of legal rights of creditors and debtors in country i at time t;
bureau_coverage is the percentage of individuals and companies for which credit information are
available and registered in a private bureau; depth is the credit depth of information.
28
We consider two approaches to estimate the above model. To begin with, after conducting a
Hausman test on whether to choose fixed effects or random effects estimation, we find that the
most appropriate approach is the fixed effects model. The fixed effects model allows controlling
for unobserved time invariant country characteristics and therefore, addresses potential omitted
variable bias. As a result, we first estimate our model with an OLS panel data approach including
country and time fixed effects to control for both time and country invariant unobserved
characteristics. After estimating the model, we conduct a Wooldridge test for autocorrelation and
we find that autocorrelation is present. In order to address autocorrelation and heteroskedasticity
we estimate the model with robust standard errors.
Since previous levels of NPLs may affect current levels of NPLs, we include a lagged dependent
variable as an additional explanatory variable (Louzis et al, 2012; Klein, 2013). However,
estimating the model with OLS fixed effects may result to biased estimations due to possible
endogeneity of the lagged dependent variable and the fixed effects in the error term (Klein, 2013).
More specifically, the lagged dependent variable is correlated with the error terms. Another issue
we identify in our model is reverse causality. More specifically, NPLs may affect several
explanatory variables and more specifically, Lerner Index and banking-sector specific
determinants. Moreover, several explanatory variables may be correlated with the error term and
also with fixed effects. Due to the abovementioned reasons, estimations may suffer from
endogeneity and therefore, an OLS fixed effects approach may lead to inconsistent and biased
estimates.
Given the abovementioned, we apply a dynamic panel data approach using the Arellano-Bond
difference GMM estimator in order to address the abovementioned problems (Arellano and Bond,
1991). The Arellano-Bond difference GMM estimator takes the differences of the variables in the
model and as a result, eliminates any unobserved country heterogeneity (Arellano and Bond,
1991; Roodman, 2009a). Furthermore, it allows addressing endogeneity of variables by using
lagged levels of explanatory variables as instruments (Roodman, 2009a). In order to give
consistent estimates the Arellano-Bond GMM approach requires the errors not to be second
order autocorrelated (Louzis et al, 2012). It is important to note that we instrument
macroeconomic and institutional determinants in IV-style and banking competition and banking
sector-specific measures in GMM-style10.
10 For more information please be referred to Roodman (2009a) and Arellano and Bond (1991).
29
Effect of Eurozone Membership
In order to assess the effect of Eurozone on NPLs, we apply a difference-in-differences
approach. Table 2 shows which countries are allocated to the treatment and control group. The
control group is EU countries non-members of the Eurozone and the treatment group is Eurozone
members. Due to the fact that some countries entered the Eurozone in different time periods, we
have different treatment periods for each country. Table 3 lists Eurozone countries and the year
of entry in Eurozone. As post treatment periods we consider the years after each country of the
treatment group entered the Eurozone. For example, for countries that entered the Eurozone in
1999 such as Germany, France, Netherlands e.t.c the post-treatment period starts from the year
1999 and the pre-treatment period is the year 1998. For countries that entered the Eurozone in
later years such as Greece, Cyprus, Malta, Slovenia e.t.c, the post-treatment period is considered
the year of entry and the years that follow until 2011. A key assumption of the difference-in-
differences estimation is that in the absence of the treatment, treatment and control group would
follow the same path. Table 4 presents the countries of the treatment group and the years in
which the treatment was introduced in each country.
30
29
countries are allocated to the treatment and control group. The control group is EU countries non-
members of the Eurozone and the treatment group is Eurozone members. Due to the fact that
some countries entered the Eurozone in different time periods, we have different treatment
periods for each country. Table 3 lists Eurozone countries and the year of entry in Eurozone. As
post treatment periods we consider the years after each country of the treatment group entered
the Eurozone. For example, for countries that entered the Eurozone in 1999 such as Germany,
France, Netherlands e.t.c the post-treatment period starts from the year 1999 and the pre-
treatment period is the year 1998. For countries that entered the Eurozone in later years such as
Greece, Cyprus, Malta, Slovenia e.t.c , the post-treatment period is considered the year of entry
and the years that follow until 2011. A key assumption of the difference-in-differences estimation
is that in the absence of the treatment, the treatment and the control group would follow the same
path.
Table 3: Treatment and Control Group
Treatment Group: Eurozone Countries
(1998-2011)
Control Group: Non Eurozone Countries
(1998-2011)
30
Austria
Belgium
Cyprus*
Estonia
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Malta
The Netherlands
Portugal
Slovenia
Slovakia
Spain
Bulgaria
Croatia
Czech Republic
Denmark
Hungary
Latvia
Lithuania
Poland
Romania
Sweden
United Kingdom
Source: http://www.ecb.europa.eu/euro/intro/html/map.en.html
* excluded from the dataset as data for non-performing loans before Cyprus’ entry in Eurozone are not available.
Table 4: Eurozone Countries and year of entry in Eurozone
Countries Year of Entry (Treatment)
31
Equation (5) specifies a simplification of the difference-in-differences model. We create a dummy
variable denoted as “euro” that is equal to 1 when an observation is from the period after the
introduction of the euro in country i and 0 otherwise. Another dummy variable is created, denoted
as “member” which takes the value of 1 when a country is a Eurozone member and zero
otherwise. In order to apply the difference in differences estimator, we construct the interaction of
these two dummy variables, which is the variable we use to capture the treatment effect and we
denote it as “euromember”.
30
Austria
Belgium
Cyprus*
Estonia
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Malta
The Netherlands
Portugal
Slovenia
Slovakia
Spain
Bulgaria
Croatia
Czech Republic
Denmark
Hungary
Latvia
Lithuania
Poland
Romania
Sweden
United Kingdom
Source: http://www.ecb.europa.eu/euro/intro/html/map.en.html
* excluded from the dataset as data for non-performing loans before Cyprus’ entry in Eurozone are not available.
Table 4: Eurozone Countries and year of entry in Eurozone
Countries Year of Entry (Treatment)
31
Austria
Belgium
Cyprus
Estonia
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Malta
The Netherlands
Portugal
Slovenia
Slovakia
Spain
1999
1999
2008
2011
1999
1999
1999
2001
1999
1999
1999
2008
1999
1999
2007
2009
1999
Source: http://www.ecb.europa.eu/euro/intro/html/map.en.html
Equation (6) specifies a simplification of the difference-in-differences model. We create a dummy
variable denoted as “euro” that is equal to 1 when an observation is from the period after the
introduction of the euro in country i and 0 otherwise. Another dummy variable is created, denoted
as member which takes the value of 1 when a country is a Eurozone member and zero
otherwise. In order to apply the difference in differences estimator, we construct the interaction of
these two dummy variables, which is the variable we use to capture the treatment effect and we
denote it as euromember.
ln (NPLit) = α+ eurot + memberi + βDD euromemberit+ uit (5)
Where ln (NPLit): natural logarithm of nonperforming loans in country i at time t.
eurot: dummy variable = 1 if post-treatment period, 0 otherwise
memberi: dummy variable = 1 if country i is member of Eurozone, 0 otherwise
In order to assess the impact of Eurozone membership on non-performing loans, we estimate
equation (6) with two methods; the OLS fixed effects and the Arellano-Bond GMM estimation. We
choose these two methods for the same reasons described in 4.2.1. Equation (6) includes
several additional explanatory variables. We include the Lerner Index and we control for certain
32
ln (NPLit) = α+ eurot + memberi + βDD euromemberit+ uit (5)
Where ln (NPLit): natural logarithm of nonperforming loans in country i at time t.
eurot: dummy variable = 1 if post-treatment period, 0 otherwise
memberi: dummy variable = 1 if country i is member of Eurozone, 0 otherwise
In order to assess the impact of Eurozone on non-performing loans, we estimate equation (6)
with two methods; OLS fixed effects and Arellano-Bond GMM. We choose these two methods for
the same reasons described above. Equation (6) includes several additional explanatory
variables in order to capture any other differences between observations that may affect NPLs in
treatment and control group, supporting the parallel trends assumption and making our estimates
more precise. We control for macroeconomic, regulatory, institutional and banking-specific
determinants. 11 As we also control for time-invariant and country invariant unobserved
characteristics, we only add the treatment variable “euromember” and not the dummies “member”
and “euro” to avoid collinearity with θt and γi.
ln (NPLit) = α + γi + θt + β1 NPLit-1 + β2 euromemberit + β3 ln (lernerit) +β4
capitalizationit + β5 creditit + β6 roeit + β7 cost_to_incomeit + β8
capital_to_assetsit + β9 regulatory_capitalit + β10 non_interest_incomeit + β11
unemplit + β12 growth_rateit + β13 lngdpcapitait + β14 qualityit + β15 corruptionit
+ uit (6)
11 For full description of the variables in Equation (6), please be referred to the previous section
33
5. Empirical Results
5.1. Main results
Effect of banking competition
Table 5 presents the main results after estimating equation (5). In all estimations we include year
dummy variables. Due to space constraints we do not include the coefficients of the year
dummies in Table 5. The complete table can be viewed in Appendix B.
Column [1] presents the OLS fixed effects estimations and does not include banking sector-
specific determinants. The Lerner index is negative and statistically significant with a p-value
smaller than 0.01. Therefore, higher market power is associated with a lower level of NPLs.
Credit provided by banks is statistically significant at the 10% level. As for macroeconomic
determinants, interest rates are statistically significant at the 10% level, indicating that a higher
interest rate is linked with a higher NPLs ratio. The coefficient for unemployment is statistically
significant and presents a positive sign. It confirms the theoretical expectation, that a higher
unemployment rate is associated with a higher level of NPLs. The coefficient for growth rate is
negative as expected but it is insignificant. GDP per capita presents a positive significant
coefficient, which contradicts theoretical expectations. Moreover, the coefficient for corruption is
statistically significant with a p-value smaller than 0.1 implying that a higher level of corruption is
associated with a higher level of nonperforming loans. Last but not least, the coefficient for credit
depth of information is statistically significant indicating that when there is greater depth of
information available a lower level of NPLs ratio is observed.
When we include banking-specific determinants (Column [2]), the levels of significance and the
signs of the coefficients discussed above do not change. The changes we observe are at the
coefficient for interest rates, which becomes insignificant, and at the coefficient for domestic
credit, which becomes significant at the 5% level. The coefficient for Lerner Index increases while
it remains highly significant. Furthermore, it appears that neither of the banking-sector specific
determinants is significant. Only regulatory capital is significant with a p-value smaller than 0.1,
implying that a higher regulatory capital to risk-weighted assets is associated with a higher level
of NPLs. This is inconsistent with the expected effect discussed in section 3. In the estimation
presented in column [3], we include lag NPLs ratio, which enters with a positive sign and the
34
coefficient is highly significant. Coefficient for unemployment becomes insignificant, while growth
rate becomes significant and is negatively related with NPLs, as expected. Moreover, corruption
and credit depth of information become insignificant. At the same time, bureau coverage
becomes significant with a p-value smaller than 0.05, implying that a higher percentage of
individuals and companies for which credit information are registered in private credit bureaus is
associated with a lower NPLs ratio.
As mentioned in the previous section, in order to address endogeneity, we apply the Arelano
Bond difference GMM estimation method. The results are presented in column [4]. It is important
to note that the tests we run, reported no second order autocorrelation and the probability of the
Hansen test was sufficiently high (p-value of 1.0) to accept the null hypothesis that the
instruments used are exogenous. The results presented in column [4] are the outcome of using
up to four lags as instruments. We attempt to keep the number of instruments as low as possible
by using the collapsed instruments technique suggested by Roodman (2009b), as a large number
of instruments may lead to biased estimates. 12 As we can observe the coefficient for Lerner
Index remains strongly significant. The sign is negative, which implies that higher market power
and therefore, lower level of market competition, is associated with a lower NPLs ratio. Credit
provided by banks remains strongly significant. Interestingly, our measure for profitability, return
on equity, becomes significant with a p-value smaller than 0.1. The coefficient is weak but
positive, contradicting theoretical expectations that better management i.e higher profitability of
the banking sector, leads to lower NPLs ratio. The coefficient for growth rate remains significant
with a p-value smaller than 0.1. It also remains negative, which is in line with our expectations
and implies that a prosperous economic environment is related with a lower level of NPLs.
Furthermore, GDP per capita remains significant and indicates that the more developed the
economy is, the higher is the level of NPLs. Last but not least, the coefficients of corruption,
private bureau coverage and credit depth of information are significant and maintain the signs
observed in the previously discussed estimations, which are in line with the theory presented in
section 3.
12 For more information please be referred to Roodman (2009)
35
35
Table 5: Main results EU-28 Countries (1998-2011)
[1] [2] [3] [4] Explanatory Variables Fixed Effects Fixed Effects Fixed Effects GMM L.lnnpl 0.616*** 0.330* (0.0984) (0.162) lnlerner -0.378*** -0.434*** -0.319** -0.471** (0.109) (0.136) (0.136) (0.192) capitalization -0.00127 -0.00219 -0.000389 -0.00107 (0.00415) (0.00379) (0.00300) (0.00479) credit 0.00732* 0.0107** 0.0108*** 0.0172*** (0.00375) (0.00380) (0.00336) (0.00583) roe -0.000946 -0.000713 0.00678* (0.00291) (0.00185) (0.00377) cost_to_income -0.00466 -0.00303 -0.00492 (0.00399) (0.00374) (0.00549) capital_to_assets 0.0248 0.0351 0.0653 (0.0575) (0.0599) (0.0684) regulatory_capital 0.0620* 0.0328* -0.0136 (0.0299) (0.0158) (0.0314) non_interest_income 0.00577 0.00426 0.00551 (0.00442) (0.00358) (0.00430) gov_debt 0.00400 0.00340 0.00407 0.00866 (0.00503) (0.00626) (0.00410) (0.00742) interest_rates 0.0272* 0.00957 0.00520 0.0175 (0.0146) (0.0201) (0.0133) (0.0223) unempl 0.142*** 0.133*** 0.0212 0.0629 (0.0394) (0.0290) (0.0264) (0.0467) growthrate -0.0290 -0.0296 -0.0464** -0.0502* (0.0251) (0.0230) (0.0167) (0.0248) inflation -0.000141 0.00220 -0.0117 -0.00680 (0.00762) (0.00902) (0.00729) (0.0106) lngdpcapita 5.415** 5.059** 4.824*** 4.638** (1.998) (1.875) (1.647) (1.768) quality 0.414 0.395 0.303 0.549 (0.417) (0.397) (0.344) (0.482) corruption 0.276* 0.257* 0.00800 0.306* (0.147) (0.139) (0.117) (0.164) bureau_coverage -0.00500 -0.00322 -0.00765** -0.00780* (0.00462) (0.00498) (0.00331) (0.00400) legal 0.0797 0.0831 0.149 0.145 (0.725) (0.562) (0.394) (0.557) depth -0.868** -0.865** -0.711 -0.927** (0.411) (0.411) (0.417) (0.430) Constant -57.17*** -55.11*** -50.42*** (19.28) (17.93) (15.95) Observations 141 137 134 112 R-squared 0.840 0.860 0.904
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
36
Effect of Eurozone membership
Figure 1 presents the trend of NPLs in treatment and control group. In Table 6 the main results
are presented for the difference-in-differences approach. Cyprus is excluded from the dataset, as
it is the only country in the treatment group for which data on NPLs for the period before the entry
in Eurozone are not available. Again we include year dummies, but due to constraints in space,
they are removed from Table 6. Appendix B presents the complete table. It is important to note
that we excluded controls for the monetary environment; inflation rates and short-term interest
rates; as a central authority, the European Central Bank, conducts monetary policy for Eurozone
countries while supervision is the responsibility of national authorities (Maddaloni and Peydro,
2013).
Column [1] presents the OLS fixed effects estimations. As we can observe the difference-in-
differences coefficient is marginally insignificant. The coefficient for unemployment is positive and
significant with a p-value smaller than 0.01. This indicates that higher levels of unemployment are
associated with higher NPL ratios. As mentioned in section 3, a higher level of unemployment
may imply higher inability to repay debts and therefore, higher level of NPLs. Moreover, the
coefficient for growth rate is statistically significant at the 1% level. Consistent with theory and
previous results, there is a negative relationship between growth rate and NPLs. As for banking
sector-specific determinants, we observe a negative statistically significant coefficient for capital-
to-assets ratio implying that lower capital, which is theoretically linked with the presence of moral
hazard and higher risk-taking, is associated with a higher level of NPLs.
36
Effect of Eurozone membership
In Table 6 the main results are presented for the difference-in-differences approach. We exclude
Cyprus from our estimations, as it is the only country in the treatment group for which data on
non-performing loans for the period before the entry in Eurozone are not available. Again we
include year dummies, but due to constraints in space, they are removed from Table 6. Appendix
B presents full results. It is important to note that we excluded controls for the monetary
environment; inflation rates and short-term interest rates; as a central authority, the European
Central Bank, conducts monetary policy for Eurozone countries. Figure 1 presents the evolution
of non-performing loans for the treatment and control group during the time period 1998-2011.
Figure 1: Non-performing loans (%) in the Treatment and Control Group
Column [1] presents the OLS fixed effects estimations. As we can observe the difference-in-
differences coefficient is marginally insignificant. The coefficient of unemployment rate is positive
and significant with a p-value smaller than 0.01. This indicates that higher levels of
unemployment are associated with higher NPL ratios. Moreover, the coefficient of growth rate is
statistically significant at the 1% level. Consistent with theory and previous results, there is a
negative relationship between growth rate and NPLs. As for banking sector-specific determinants
we observe a significant negative coefficient for the capital to assets ratio implying that lower
capital, which is theoretically associated with the presence of moral hazard and higher risk-taking,
is associated with a higher level of NPLs.
0
2
4
6
8
10
12
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Treatment Group
control group
37
In the estimation presented in Column [2], the lag of NPLs ratio is included and enters the
regression with a strongly significant positive coefficient. The difference-in-differences coefficient
becomes significant with a p-value smaller than 0.01 indicating that the level of NPLs is lower in
Eurozone countries. Another coefficient that becomes significant is that of credit provided by
banks, which presents a positive sign. Last but not least; with the inclusion of the lag NPLs ratio,
GDP per capita becomes strongly significant and positive implying that when levels of economic
development are higher, NPLs are higher. As far as unemployment is concerned, the coefficient
is reduced and the p-value is now between 0.01 and 0.05. The value of the coefficient of capital
to assets ratio is significantly reduced and the significance level approximates 10%.
Column [3] presents the Arellano-Bond difference GMM estimations. We use up to two lags as
instruments for endogenous variables. The total number of instruments is 33. There is no second
order autocorrelation since the probability of the test for AR(2) is 0.518. Moreover, the probability
of the Hansen Test is 0.985, which permits us to accept the null hypothesis that the instruments
used are exogenous. Results show that the magnitude of the difference-in-differences coefficient
significantly increased and it is significant at the 10%. This indicates that the NPLs ratio is 35.3%
lower in Eurozone countries than the countries in the control group. If the parallel trends
assumption holds this suggests that the effect of the introduction of the Eurozone on NPLs is
negative. The coefficients for unemployment rate, growth rate and capital-to-assets ratio are
significantly increased and they are statistically significant with p-values smaller than 0.05. The
signs of these coefficients remain the same with the estimations presented in columns [1] and [2]
and are in line with expectations in section 3.
38
38
Table 6: Main results, Eurozone effect (difference-in-differences)
Explanatory Variables
[1] Fixed Effects
[2] Fixed Effects
[3] GMM
L.lnnpl 0.589*** -0.0859 (0.0764) (0.195) euromember -0.141 -0.181*** -0.353* (0.144) (0.0623) (0.190) lnlerner -0.116 -0.124 -0.276 (0.0909) (0.0751) (0.173) credit 0.00182 0.00344** 0.00192 (0.00270) (0.00158) (0.00704) capitalization 0.000604 -0.00139 0.000917 (0.00153) (0.00110) (0.00250) unempl 0.106*** 0.0411** 0.137** (0.0238) (0.0189) (0.0500) growthrate -0.0596*** -0.0457*** -0.0664** (0.0138) (0.0101) (0.0289) lngdpcapita 0.999 1.325*** -0.0314 (0.829) (0.451) (1.475) roe -0.00339 -0.00249 0.00627 (0.00282) (0.00169) (0.00940) cost_to_income -0.000573 -0.000676 -0.00426 (0.00365) (0.00248) (0.0119) capital_to_assets -0.0822** -0.0494* -0.256** (0.0395) (0.0280) (0.119) regulatory_capital 0.0310 0.0203 -0.0235 (0.0281) (0.0161) (0.0493) non_interest_income 0.000474 0.000689 0.00678 (0.00267) (0.00213) (0.00491) corruption 0.0310 -0.00582 0.152 (0.126) (0.0639) (0.195) quality -0.151 0.197 -0.270 (0.297) (0.196) (0.509) Constant -9.938 -13.44*** (7.778) (4.524) Observations 254 245 218 R-squared 0.760 0.844
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
39
5.2. Robustness Checks
In order to check for robustness of our results regarding the effect of banking competition on
NPLs, we estimate the mode (equation (4)) with alternative measures of banking competition. As
mentioned in previous sections, we replace the Lerner Index first with the Boone Indicator and
then with the five-bank asset concentration ratio. In all estimations presented in Table 7, we
included year dummies to control for time variant unobserved characteristics. The complete table
can be viewed in Appendix B. Results show that the coefficient for the Boone Indicator is
negative and statistically significant in both OLS fixed effects and GMM estimations (Columns [1]
and [3]), whereas the 5-bank asset concentration is insignificant (Columns [2] and [4]).
The coefficient for the Boone Indicator is positive, which implies that higher values of the Boone
indicator are associated with higher levels of NPLs. Considering the fact that the more negative
the Boone indicator is, the lower is the level of banking competition, the results confirm our
findings in the previous section that a lower level of banking competition is associated with a
lower level of NPLs. Moreover, the coefficient of growth rate is negative and statistically
significant at the 1% level, when using either of the two alternative proxies for competition. Last
but not least, credit provided by banks as a percentage of GDP is positive and statistically
significant in all estimations of Table 7.
40
40
Table 7: Robustness Checks for the effect of banking competition
Explanatory [1] [2] [3] [4] Variables Fixed effects Fixed Effects GMM GMM L.lnnpl 0.726*** 0.832*** (0.147) (0.136) asset_concentration -0.00253 0.0143 (0.0102) (0.0124) boone 2.611** 3.123** (1.236) (1.355) credit 0.0104* 0.0104* 0.0103* 0.0114** (0.00577) (0.00602) (0.00516) (0.00481) capitalization 0.000559 0.00163 -0.00656* -0.00589 (0.00669) (0.00643) (0.00362) (0.00455) capital_to_assets 0.00547 0.0110 0.129 0.0732 (0.0672) (0.0671) (0.103) (0.0904) non_interest_income -0.000820 0.00170 0.00232 0.00652 (0.00294) (0.00419) (0.00331) (0.00726) gov_debt 0.00410 0.00250 0.00781** 0.00219 (0.00593) (0.00602) (0.00340) (0.00322) regulatory_capital 0.0850* 0.0855* 0.00421 0.00804 (0.0414) (0.0435) (0.0246) (0.0351) cost_to_income -0.00902 -0.00394 -0.00630 0.00210 (0.00640) (0.00606) (0.00688) (0.00712) provisions -0.000615 -0.000227 -0.00232 2.62e-05 (0.00116) (0.00103) (0.00142) (0.00191) interest_rates 0.0221 0.0196 -0.0117 -0.000203 (0.0165) (0.0157) (0.0152) (0.0162) unempl 0.145*** 0.146*** -0.00294 0.00899 (0.0448) (0.0443) (0.0506) (0.0522) bureau_coverage -0.00242 -0.00183 -0.00447 -0.00851** (0.00612) (0.00638) (0.00396) (0.00397) corruption 0.187 0.209 -0.0737 (0.165) (0.180) (0.152) quality 0.380 0.340 0.409 0.515 (0.603) (0.590) (0.447) (0.640) roe 0.00134 4.93e-05 0.00462 0.00273 (0.00274) (0.00261) (0.00309) (0.00381) growthrate -0.0196 -0.0294 -0.0752*** -0.0811*** (0.0246) (0.0308) (0.0190) (0.0188) lngdpcapita 3.905* 4.198* 0.761 3.356 (2.042) (2.216) (2.486) (3.417) inflation 0.000537 -0.00407 (0.00998) (0.00963) control_of_corruption 0.188 (0.280)
(Table 7 continues on the next page)
41
41
Table 7: Robustness Checks for the effect of banking competition (Continued)
Explanatory [1] [2] [3] [4] Variables Fixed effects Fixed Effects GMM GMM
legal 0.749 0.746 0.589 0.206 (1.218) (1.200) (0.957) (0.859) depth -0.662 -0.572 -0.515 -0.288 (0.574) (0.615) (0.528) (0.535) Constant -43.42** -47.23** (19.70) (21.49) Observations 124 121 100 98 R-squared 0.868 0.852
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
6. Discussion
This study uses a large panel dataset for the period 1998-2011. It combines a large number of
explanatory variables from various studies and focuses on European Union countries. By
including a large number of relevant explanatory variables and controlling for time invariant and
country invariant unobserved characteristics, it manages to reduce omitted variable bias and
hence, potential endogeneity problems. Furthermore, by using clustered standard errors this
study aims to address autocorrelation and heteroskedasticity. To address reverse causality and
endogeneity of explanatory variables, it applies a dynamic panel data Arellano-Bond difference
GMM estimation method using as instruments, lag values of explanatory variables.
Regarding the impact of banking competition on financial stability, we combine non-performing
loans, which we use as a proxy for financial stability with the Lerner Index and the Boone
Indicator, which we consider as proxies for banking-sector competition. Compared to existing
literature we focus specifically in EU countries. We find that greater market power i.e. lower level
of competition in the banking sector is associated with a higher non-performing loans ratio.
Moreover, when growth rate, which is linked with the ability of debtors to pay back their loans and
the availability of mechanisms to screen loan applications and recover loan payments, is higher,
the level of NPLs is lower. We also find evidence that higher levels of corruption are associated
with higher NPLs ratio. Economic development as measured by GDP per capita is also linked
with higher levels of NPLs, which contradicts our expectations. Furthermore, we provide evidence
that the greater the depth of credit information available to financial institutions is, the lower is the
42
6. Discussion
This study uses a large panel dataset for the period 1998-2011 and focuses exclusively on
European Union countries. It combines a large number of explanatory variables from various
studies. By including a large number of relevant explanatory variables and controlling for time
invariant and country invariant unobserved characteristics, it manages to reduce omitted variable
bias and hence, potential endogeneity problems. Furthermore, by using clustered standard errors
this study aims to address autocorrelation and heteroskedasticity. To address reverse causality
and endogeneity of explanatory variables, it applies a dynamic panel data Arellano-Bond
difference GMM method using as instruments, lag values of explanatory variables. Our
estimations pass the test for no second order autocorrelation required for consistency of the
GMM estimator and the Hansen tests required for exogeneity of our instruments.
Regarding the impact of banking competition on financial stability, we combine non-performing
loans, which we use as a proxy for financial stability, with the Lerner Index, which we consider as
the main proxy for banking-sector competition. Compared to existing literature we focus
specifically in EU-28 countries. We find that greater market power i.e. lower level of competition
in the banking sector is associated with a higher non-performing loans ratio. Moreover, higher
growth rate, which is theoretically linked with the ability of debtors to pay back their loans and the
availability of mechanisms to screen loan applications and recover loan payments, is associated
with a lower level of NPLs is lower. This confirms theoretical expectations and findings of
previous studies. Moreover, we present evidence that higher levels of corruption are associated
with higher NPLs ratio, which confirms the theory presented in section 3. Economic development
as measured by GDP per capita is also linked with higher levels of NPLs, which contradicts the
theoretical background that more developed countries should present lower levels of NPLs.
Moreover, higher unemployment, which reflects higher inability to repay debts, is linked with a
higher NPLs ratio, in line with expectations in section 3. Furthermore, we provide evidence that
the a greater depth of credit information available to financial institutions is associated with lower
levels of NPLs. Last but not least, a higher percentage of registered individuals and firms in
private credit bureaus is associated with a lower NPLs ratio. Overall, our findings confirm the
results of previous studies on the topic of competition and financial stability, which find a negative
relationship between competition and financial stability i.e less competition is associated with
higher stability (Jimenez et al, 2007).
43
As for investigating the effect of Eurozone on financial stability, we find that NPLs tend to be
lower in Eurozone countries than in non-Eurozone EU countries. As for banking sector-specific
determinants, we observe a negative statistically significant coefficient for capital-to-assets ratio
implying that lower capital is associated with higher level of NPLs. This finding is consistent with
the theory suggesting that the managers of banks with lower capital may have moral hazard
incentives “to increase the riskiness of their loan portfolio”. Moreover, in line with theory, higher
growth rate is associated with lower level of NPLs, while higher unemployment is linked with a
higher NPLs ratio. In contrast with the theory that more developed countries should experience
lower level of NPLs ratio, GDP per capita is associated with a higher level of NPLs. It is important
to note here, that the Lerner index is marginally insignificant in all regressions when we apply
difference-in-differences approach to investigate the effect of Eurozone. However, this may be
due to the fact that part of the effect of banking competition may be captured by the Eurozone
coefficient as the introduction of the Eurozone led to an increase in banking competition.
Limitations
This study is subject to certain limitations. First of all, despite the fact that there is no missing data
for most countries and variables, data for a significantly small number of years are not available.
It is important to note that the only variables that suffer from a large number of missing data are
the following: credit depth of information, private bureau coverage and strength of legal rights. In
all three, the number of observations is limited to 211. Furthermore, as we mentioned in previous
sections, data for non-performing loans for Cyprus are available only from 2008 to 2011.
Secondly, as noted in the GFDD and highlighted in Klein (2013), the classification of non-
performing loans in each country may be different due to differences in national accounting,
taxation and supervisory regimes and therefore, data may not be strictly comparable across
countries. However, the use of fixed effects may address this problem up to a certain degree.
Regarding the Arellano-Bond GMM estimation method, as noted by Roodman(2009b), as the
number of instruments approaches the number of observations, results reported using the
Arellano-Bond GMM estimation may be biased towards OLS. We attempted to keep the number
of instruments as low as possible with various techniques proposed by Roodman(2009b)
However, it would be difficult to argue with certainty that we managed to address the problem of
endogenous regressors completely.
Fourthly, in the context of the difference-in-differences approach, a strong limitation that this
study faces is that for countries that entered the Eurozone in 1999, the pre-treatment period is
44
only one year, which poses constrains in any attempts to support the parallel trends assumptions
by increasing the pre-treatment period. Unfortunately, data for NPLs for all EU countries are
available only from 1998 and onwards. We were also unable to construct another control group,
which could potentially be valid, with the data available. The limited capacity to support the
parallel trends assumption threatens the internal validity of the results presented in Table 6.
Therefore, results presented in Table 6 should be treated with caution in regards to any causal
interpretations.
7. Conclusion
Recent developments, the 2008-banking crisis, showed that when a country’s banking sector
faces a significant increase in the amount of non-performing loans, financial stability is
threatened. This study investigates the impact of banking competition on financial stability in the
28 European Union countries, using non-performing loans as a proxy for financial stability.
Moreover, it applies a difference-in-differences approach to assess the impact of Eurozone
membership on non-performing loans. By using an up to date panel dataset for the period 1998-
2011 and focusing exclusively on the 28 European Union countries, this study facilitates the
design of macro-prudential policies in the context of the upcoming EU banking union. This study
finds that a lower level of banking competition as measured by the Lerner Index, is associated
with a lower level of non-performing loans in EU countries. The use of the Boone Indicator
instead of the Lerner Index appears to confirm these findings, whereas five-bank asset
concentration is insignificant. Apart from banking competition, also macroeconomic and
institutional determinants are found to be associated with non-performing loans in EU. More
specifically, higher levels of corruption, unemployment are found to be associated with higher
levels of NPLs in EU countries. On the other hand, higher levels of economic growth, greater
depth of credit information available to financial institutions and a higher percentage of registered
individuals in private credit bureaus are associated with a lower level of NPLs in EU countries.
The results of the difference-in-differences approach should be treated with caution. Results
show that Eurozone countries appear to have lower levels of non-performing loans than non-
Eurozone EU countries. Moreover, higher levels of unemployment are found to be associated
with higher levels of NPL, while higher capital-to-assets ratio and higher levels of economic
growth, are associated with a lower level of NPLs.
45
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The World Bank Group. 2013. Worldwide Governance Indicators. [Online] Available at:
http://info.worldbank.org/governance/wgi/index.aspx#home
[Accessed 10/04/2014]
Uhde, A. and Heimeshoff, U. 2009. Consolidation in banking and financial stability in Europe:
Empirical evidence. Journal of banking and finance 33, pp. 1299-1311
49
Appendix A
Figure 2: Non-performing loans in EU-28 (1998-2007)
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Austria Belgium Bulgaria Croatia Cyprus Czech Republic
Denmark Estonia Finland France Germany Greece
Hungary Ireland Italy Latvia Lithuania Luxembourg
Malta Netherlands Poland Portugal Romania Slovak Republic
Slovenia Spain Sweden United Kingdom
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YearGraphs by Country
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Figure 3: Market power in EU-28 (1998-2011)
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2000 2005 2010 2000 2005 2010
2000 2005 2010 2000 2005 2010 2000 2005 2010 2000 2005 2010
Austria Belgium Bulgaria Croatia Cyprus Czech Republic
Denmark Estonia Finland France Germany Greece
Hungary Ireland Italy Latvia Lithuania Luxembourg
Malta Netherlands Poland Portugal Romania Slovak Republic
Slovenia Spain Sweden United Kingdom
Lern
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YearGraphs by Country
51
Appendix B
Main Results: Effect of banking competition (Complete Table)
Explanatory (1) (2) (3) (4) Variables Fixed Effects Fixed Effects Fixed Effects GMM L.lnnpl 0.616*** 0.330* (0.0984) (0.162) lnlerner -0.378*** -0.434*** -0.319** -0.471** (0.109) (0.136) (0.136) (0.192) capitalization -0.00127 -0.00219 -0.000389 -0.00107 (0.00415) (0.00379) (0.00300) (0.00479) credit 0.00732* 0.0107** 0.0108*** 0.0172*** (0.00375) (0.00380) (0.00336) (0.00583) roe -0.000946 -0.000713 0.00678* (0.00291) (0.00185) (0.00377) cost_to_income -0.00466 -0.00303 -0.00492 (0.00399) (0.00374) (0.00549) capital_to_assets 0.0248 0.0351 0.0653 (0.0575) (0.0599) (0.0684) regulatory_capital 0.0620* 0.0328* -0.0136 (0.0299) (0.0158) (0.0314) non_interest_income 0.00577 0.00426 0.00551 (0.00442) (0.00358) (0.00430) gov_debt 0.00400 0.00340 0.00407 0.00866 (0.00503) (0.00626) (0.00410) (0.00742) interest_rates 0.0272* 0.00957 0.00520 0.0175 (0.0146) (0.0201) (0.0133) (0.0223) unempl 0.142*** 0.133*** 0.0212 0.0629 (0.0394) (0.0290) (0.0264) (0.0467) growthrate -0.0290 -0.0296 -0.0464** -0.0502* (0.0251) (0.0230) (0.0167) (0.0248) inflation -0.000141 0.00220 -0.0117 -0.00680 (0.00762) (0.00902) (0.00729) (0.0106) lngdpcapita 5.415** 5.059** 4.824*** 4.638** (1.998) (1.875) (1.647) (1.768) quality 0.414 0.395 0.303 0.549 (0.417) (0.397) (0.344) (0.482) corruption 0.276* 0.257* 0.00800 0.306* (0.147) (0.139) (0.117) (0.164) bureau_coverage -0.00500 -0.00322 -0.00765** -0.00780* (0.00462) (0.00498) (0.00331) (0.00400) legal 0.0797 0.0831 0.149 0.145 (0.725) (0.562) (0.394) (0.557) depth -0.868** -0.865** -0.711 -0.927** (0.411) (0.411) (0.417) (0.430) yr7 -0.0769 0.284 0.412* 0.281 (0.288) (0.274) (0.212) (0.315)
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(1) (2) (3) (4) VARIABLES Fixed Effects Fixed Effects GMM GMM yr8 -0.386 -0.0269 0.181 -0.0515 (0.293) (0.281) (0.191) (0.306) yr9 -0.517* -0.161 0.115 -0.222 (0.274) (0.269) (0.200) (0.305) yr10 -0.679** -0.343 -0.131 -0.498 (0.283) (0.285) (0.172) (0.302) yr11 -0.477** -0.162 0.0448 -0.209 (0.189) (0.237) (0.136) (0.237) yr12 -0.141 -0.0533 0.103 -0.144 (0.165) (0.176) (0.112) (0.210) yr13 0.110 0.168 0.178* 0.125 (0.123) (0.100) (0.0898) (0.132) Constant -57.17*** -55.11*** -50.42*** (19.28) (17.93) (15.95) Observations 141 137 134 112 R-squared 0.840 0.860 0.904
53
Main results: Eurozone effect, difference-in-differences (Complete Table)
(1) (2) (3) Explanatory Variables Fixed Effects Fixed Effects GMM L.lnnpl 0.589*** -0.0859 (0.0764) (0.195) euromember -0.141 -0.181*** -0.353* (0.144) (0.0623) (0.190) lnlerner -0.116 -0.124 -0.276 (0.0909) (0.0751) (0.173) credit 0.00182 0.00344** 0.00192 (0.00270) (0.00158) (0.00704) capitalization 0.000604 -0.00139 0.000917 (0.00153) (0.00110) (0.00250) unempl 0.106*** 0.0411** 0.137** (0.0238) (0.0189) (0.0500) growthrate -0.0596*** -0.0457*** -0.0664** (0.0138) (0.0101) (0.0289) lngdpcapita 0.999 1.325*** -0.0314 (0.829) (0.451) (1.475) roe -0.00339 -0.00249 0.00627 (0.00282) (0.00169) (0.00940) cost_to_income -0.000573 -0.000676 -0.00426 (0.00365) (0.00248) (0.0119) capital_to_assets -0.0822** -0.0494* -0.256** (0.0395) (0.0280) (0.119) regulatory_capital 0.0310 0.0203 -0.0235 (0.0281) (0.0161) (0.0493) non_interest_income 0.000474 0.000689 0.00678 (0.00267) (0.00213) (0.00491) corruption 0.0310 -0.00582 0.152 (0.126) (0.0639) (0.195) quality -0.151 0.197 -0.270 (0.297) (0.196) (0.509) yr3 0.765*** 0.505*** 0.462 (0.234) (0.178) (0.445) yr5 0.419** 0.224* 0.331 (0.159) (0.129) (0.346) yr6 0.262 0.0628 0.0543 (0.160) (0.130) (0.346) yr7 0.0627 -0.0382 -0.154 (0.161) (0.121) (0.358) yr8 -0.250 -0.264* -0.543 (0.182) (0.152) (0.345) yr9 -0.162 -0.132 -0.457 (0.180) (0.147) (0.296) yr10 -0.0373 -0.0379 -0.270 (0.170) (0.148) (0.256)
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(1) (2) (3) Explanatory Variables Fixed Effects Fixed Effects GMM yr11 0.0366 0.00835 -0.0286 (0.168) (0.149) (0.260) yr13 0.422*** 0.0398 0.572** (0.150) (0.101) (0.236) yr14 0.460** 0.0577 0.640** (0.167) (0.118) (0.242) Constant -9.938 -13.44*** (7.778) (4.524) Observations 254 245 218 R-squared 0.760 0.844
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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Robustness checks: effect of banking competition, Boone Indicator and 5-bank asset concentration (Complete Table)
Explanatory Variables (1) (2) (3) (4) OLS fixed
effects OLS fixed
effects GMM GMM
L.lnnpl 0.726*** 0.832*** (0.147) (0.136) asset_concentration -0.00253 0.0143 (0.0102) (0.0124) boone 2.611** 3.123** (1.236) (1.355) credit 0.0104* 0.0104* 0.0103* 0.0114** (0.00577) (0.00602) (0.00516) (0.00481) capitalization 0.000559 0.00163 -0.00656* -0.00589 (0.00669) (0.00643) (0.00362) (0.00455) capital_to_assets 0.00547 0.0110 0.129 0.0732 (0.0672) (0.0671) (0.103) (0.0904) non_interest_income -0.000820 0.00170 0.00232 0.00652 (0.00294) (0.00419) (0.00331) (0.00726) gov_debt 0.00410 0.00250 0.00781** 0.00219 (0.00593) (0.00602) (0.00340) (0.00322) regulatory_capital 0.0850* 0.0855* 0.00421 0.00804 (0.0414) (0.0435) (0.0246) (0.0351) cost_to_income -0.00902 -0.00394 -0.00630 0.00210 (0.00640) (0.00606) (0.00688) (0.00712) provisions -0.000615 -0.000227 -0.00232 2.62e-05 (0.00116) (0.00103) (0.00142) (0.00191) interest_rates 0.0221 0.0196 -0.0117 -0.000203 (0.0165) (0.0157) (0.0152) (0.0162) unempl 0.145*** 0.146*** -0.00294 0.00899 (0.0448) (0.0443) (0.0506) (0.0522) bureau_coverage -0.00242 -0.00183 -0.00447 -0.00851** (0.00612) (0.00638) (0.00396) (0.00397) corruption 0.187 0.209 -0.0737 (0.165) (0.180) (0.152) quality 0.380 0.340 0.409 0.515 (0.603) (0.590) (0.447) (0.640) roe 0.00134 4.93e-05 0.00462 0.00273 (0.00274) (0.00261) (0.00309) (0.00381) growthrate -0.0196 -0.0294 -0.0752*** -0.0811*** (0.0246) (0.0308) (0.0190) (0.0188) lngdpcapita 3.905* 4.198* 0.761 3.356 (2.042) (2.216) (2.486) (3.417) inflation 0.000537 -0.00407 (0.00998) (0.00963) control_of_corruption 0.188 (0.280) legal 0.749 0.746 0.589 0.206 (1.218) (1.200) (0.957) (0.859)
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Explanatory Variables (1) (2) (3) (4) OLS fixed
effects OLS fixed
effects GMM GMM
depth -0.662 -0.572 -0.515 -0.288 (0.574) (0.615) (0.528) (0.535) yr7 0.343 0.665* 0.109 0.231 (0.292) (0.348) (0.339) (0.350) yr8 -0.0820 0.219 -0.0970 0.0906 (0.307) (0.204) (0.203) (0.189) yr9 -0.135 0.128 0.148 0.277 (0.300) (0.136) (0.170) (0.177) yr10 -0.231 (0.351) yr11 0.0226 0.283 0.0979 0.0778 (0.242) (0.189) (0.122) (0.139) yr13 0.0167 0.396 -0.263 -0.0515 (0.136) (0.405) (0.250) (0.246) yr14 0.0960 0.439 -0.254 -0.0638 (0.167) (0.470) (0.260) (0.222) yr12 0.299 -0.385 -0.165 (0.403) (0.233) (0.185) Constant -43.42** -47.23** (19.70) (21.49) Observations 124 121 100 98 R-squared 0.868 0.852
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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