Bank concentration, competition and
financial stability
Maurice Doll
Tilburg University
April - July 2010
Abstract
Using data on 76 countries from 1990 – 2007 this paper provides evidence that concentrated
banking systems are less likely to experience episodes of systemic banking crises. Besides
that, the results also support the competition-fragility hypothesis. Especially banking sectors
that are (almost) perfectly competitive are prone to financial crises. Although competition
negatively affects financial stability, policy makers should not curtail competition. Instead
they should adopt an incentive compatible financial safety net and monitor banks more
closely.
JEL Classification: D4, G01, G21, G28
Key words: Bank competition, Bank concentration, Financial crises, Regulation
Master thesis International Economics and Finance (390.308)
Bank concentration, competition and financial stability
Master thesis International Economics and Finance
Maurice Doll
ANR: 98.78.52
[email protected]
Tilburg University
Tilburg School of Economics and Management
Department Economics
Examination committee
prof. dr. T.H.L. Beck
dr. B.V.G. Goderis
Number of words: 19.117
2
‘Never allow a crisis to go to waste. They are opportunities to do big things’
RAHM EMANUEL, WHITE HOUSE CHIEF OF STAFF TO PRESIDENT BARACK OBAMA
‘What we know about the global financial crisis is that we don't know very much’
PAUL SAMUELSON, AMERICAN ECONOMIST, WINNER OF THE NOBEL PRIZE IN ECONOMICS IN 1970
3
Summary
In the 1990’s economists started to examine the determinants of banking crises. The earliest
research focused on macroeconomic factors that influenced the environment in which banks
operate. Recent economic research focused more on factors that might induce banks to take
more risk. Among these factors is bank competition.
Economic theory provides an ambiguous answer to the question how bank competition, bank
concentration and financial stability are related. On the one hand competition reduces the
market power of banks, which reduces the discounted value of the profits that banks are
expected to earn in the future. This enhances the incentives for banks to take more risk,
because the opportunity costs of going bankrupt are lower. On the other hand, market power
in the loan market allows banks to charge higher interest rates. This increases moral hazard
and adverse selection, which increases the probability of failure of banks.
Using a multivariate logit model and data on 76 countries from 1990 - 2007 this paper
examines the relation between bank concentration, competition and financial stability. This
paper provides evidence that concentrated bank systems tend to be more stable. This result is
robust to the inclusion of several macroeconomic variables as well as variables that capture
the regulatory and institutional framework. Besides that, the results support the competitionfragility hypothesis. Especially banking systems that are close to perfect competition are
prone to episodes of systemic banking distress.
However competition in the banking sector, as in other sectors, has its benefits as well.
Therefore, when bank competition induces bank managers to take more risk the right
regulatory response is not to curtail competition. Instead, because the results also stress the
importance of the institutional environment, regulators and supervisory authorities should
monitor banks more closely or require banks to bear the costs of their own risk-taking.
4
Samenvatting
Sinds de jaren ’90 hebben economen geprobeerd de oorzaken van bankencrisissen te
achterhalen. Het oorspronkelijke onderzoek was vooral gericht op welke macro-economische
factoren een rol spelen bij het ontstaan van bankencrisissen. Het latere onderzoek was meer
gericht op factoren die een rol spelen bij de mate waarin het bankwezen risico’s neemt. Eén
van die factoren die daarbij een rol speelt is de hoeveelheid concurrentie in het bankwezen.
Economen hebben geen eenduidig antwoord op de vraag hoe concurrentie en concentratie in
het bankwezen gerelateerd zijn aan financiële stabiliteit. Enerzijds vermindert concurrentie in
het bankwezen de marktmacht van banken. Omdat hierdoor de toekomstige winstgevendheid
van banken vermindert zijn banken geneigd meer risico te nemen. Aan de andere kant leidt
marktmacht er toe dat banken hogere rentevoeten hanteren bij het verstrekken van leningen.
Dit leidt tot moreel wangedrag en averechtse selectie van hypotheekafnemers. Omdat zij
wellicht niet in staat zijn hun financiële verplichtingen na te komen neemt de kans dat een
bank zelf in financiële problemen komt toe.
In deze scriptie wordt gebruik gemaakt van een multivariate logit model en een panel data set
van 76 landen in de periode 1990 – 2007 om empirisch de relatie tussen bank concentratie,
concurrentie en financiële stabiliteit te onderzoeken. Eén van de conclusies in deze scriptie is
dat geconcentreerde banksystemen stabieler zijn. Daarnaast wordt aangetoond dat bank
systemen gekenmerkt door volledige mededinging het meest kwetsbaar zijn voor financiële
crisissen. Deze resultaten blijken robuust te zijn als de macro-economische omgeving en het
institutionele en regulerende kader in beschouwing worden genomen.
Toch zal betoogd worden dat deze resultaten niet direct impliceren dat overheden de
concurrentie in het bankwezen dienen te beperken. Meer concurrentie in het bankwezen heeft
namelijk ook voordelen. De maatschappij kan deze voordelen van concurrentie benutten mits
de overheid beter toezicht houdt op het bankwezen en banken de kosten van hun eigen risico’s
laat dragen.
5
Preface
This master thesis is the final project for receiving the Master of Science degree in
International Economics and Finance from Tilburg University. In the summer of 2007, just
before I decided to study economics, the subprime mortgage market in the United States
crashed. The problems originated in the United States started to spread over the world. I
became intrigued by the financial crisis and therefore attended courses that were related to
banking and financial markets.
In the master International Economics and Finance I attended the seminar Competitiveness of
the European Economy from prof. dr. van Damme. In this seminar we discussed the
measurement of competition and examined the effects competition has on welfare. Although
there was no relation of the material covered in this seminar with financial markets and/or the
financial crisis I liked the various topics discussed in this seminar. I became aware of a
possible relation between bank competition and financial stability during the seminar
Financial Markets and Institutions from prof. dr. Beck. I decided to combine the material
covered in these two seminars and started to write my master thesis on the relationship
between bank competition, bank concentration and financial stability.
Writing this thesis was quite a challenge. Without support writing this thesis would be much
harder and therefore I would like to take the opportunity to thank some people.
First of all, I would like to thank my supervisor at Tilburg University, prof. dr. Beck, for his
valuable comments and discussions on earlier drafts.
I am also grateful to my fellow students of Tilburg University. I want to thank them for the
valuable discussions and further tips for research, but even more for the talks during the
breaks about everything except our theses. Koen Andriessen deserves a word of special
thanks. He commented on the last draft of this thesis.
Last but not least, I would like to thank my parents, my brother and friends for support during
my thesis and my studies.
Maurice Doll
‘s-Hertogenbosch, 29 July 2010
6
Table of contents
1
Introduction ........................................................................................................................................................... 9
2
Theory................................................................................................................................................................... 12
2.1
2.1.1
The charter value hypothesis ............................................................................................................... 12
2.1.2
Interbank contagion and relationship banking ..................................................................................... 14
2.1.3
Effectiveness of bank regulation ......................................................................................................... 15
2.2
3
Adverse selection and moral hazard in the loan market ...................................................................... 15
2.2.2
Credit rationing, monitoring and market structure .............................................................................. 16
2.2.3
Systemic risk and effectiveness of regulation ..................................................................................... 16
Measuring bank competition and financial stability ........................................................................................ 17
6
7
8
9
Measuring financial stability and banks’ risk taking .................................................................................... 17
3.1.1
Micro-indicators of financial stability ................................................................................................. 17
3.1.2
Crisis definitions and crisis dating ...................................................................................................... 18
3.2
5
The competition-stability hypothesis ............................................................................................................ 15
2.2.1
3.1
4
The competition-fragility hypothesis ............................................................................................................ 12
Measuring banking competition ................................................................................................................... 20
3.2.1
Bank concentration .............................................................................................................................. 20
3.2.2
More direct measures of bank competition ......................................................................................... 22
3.2.3
Regulatory policies as indicator of bank competition ......................................................................... 24
Empirics ............................................................................................................................................................... 25
4.1
Bank level evidence ...................................................................................................................................... 25
4.2
Banking sector evidence ............................................................................................................................... 26
Data and summary statistics ............................................................................................................................... 28
5.1
Crisis, competition and concentration variables ........................................................................................... 29
5.2
Bank regulation and deposit insurance ......................................................................................................... 30
5.3
Macroeconomic and other control variables ................................................................................................. 32
Research methodology......................................................................................................................................... 34
6.1
Shortcomings of the signals approach and the linear probability model ...................................................... 34
6.2
The logit probability model .......................................................................................................................... 34
Results................................................................................................................................................................... 36
7.1
Bank concentration and financial stability.................................................................................................... 36
7.2
Bank competition and financial stability ...................................................................................................... 37
7.3
Entry restrictions and financial stability ....................................................................................................... 40
Robustness and sensitivity analysis .................................................................................................................... 42
8.1
Bank concentration, regulatory policies and financial stability .................................................................... 42
8.2
Bank competition, regulatory policies and financial stability ....................................................................... 45
8.3
National characteristics ................................................................................................................................ 47
8.4
Nonlinearity of the results ............................................................................................................................ 48
8.5
Dependent variable definition ...................................................................................................................... 50
Conclusion and policy implications .................................................................................................................... 52
References...................................................................................................................................................................... 55
Appendix........................................................................................................................................................................ 60
7
List of tables
Table
1
2
3
4
5
6
7
8
9A
9B
10
Summary statistics
Systemic banking crises and bank concentration
Systemic banking crises and bank competition
Systemic banking crises and entry restrictions
Systemic banking crisis, bank concentration and regulatory policies
Systemic banking crisis and other bank concentration measures
Systemic banking crisis, bank competition and regulatory policies
Systemic banking crisis, bank concentration, bank competition and legal origin
Nonlinear relationship between bank concentration and systemic banking crises
Nonlinear relationship between bank competition and systemic banking crises
Bank competition, bank concentration and various banking crisis definitions
31
37
39
40
44
45
47
48
49
50
51
Appendix
A1 Systemic banking crisis, bank concentration and bank competition by country
A2 Correlation between competition, concentration and bank regulation
A3 Capital stringency index
A4 Data sources
60
62
63
64
8
1
Introduction
Both policy makers and academics recognize the importance of a well functioning financial
system for an economy. Well functioning financial systems allow for reducing asymmetric
information by producing information and monitoring investment, facilitate the trading of
risk, mobilize and pool savings and ease the exchange of goods and services (Levine, 2004;
Bodie and Merton, 2005). Moreover, well functioning financial systems allow for a more
efficient allocation of productive resources, which fosters economic growth (Levine, 2004).
During the recent financial crisis large parts of the banking system were not able to perform
their intermediation function effectively. The resulting dry up in liquidity negatively affected
the world economy. Economic growth declined rapidly in the Asian tigers and most
industrialized countries entered in their most severe post-war recession. These recent
experiences illustrated again that financial crises have a prolonged impact on the real
economy. Reinhart and Rogoff (2009b) show that on average unemployment increases with
seven percentage points after a financial crisis. GDP per capita declines with 9.3 percentage
points and downturns lasts on average 1.9 years. Most remarkable is the increase in real
public debt with on average 86 percent.
The prolonged effect on the real economy and large fiscal costs of financial crises are one
reason why policy makers are concerned about the functioning of the financial system 1.
Governments therefore adopted supervision, prudential regulation and introduced a financial
safety net to deal with bank insolvencies. Economic research in the field of banking focused
therefore on the effectiveness and design of these regulatory policies as well as on the
determinants of banking crises. The earlier research on the determinants of banking crises
focused mostly on the macroeconomic environment banks operate in. For instance, adverse
shocks to the economy often result in increasing unemployment. Higher unemployment rates
often force banks to take losses on their loan portfolio. Recent economic research focused
more on factors that might induce banks to take more risks. Among these factors is bank
competition.
Several economists stress the relationship between bank competition and the occurrence of
banking crises. Although risk transfer mechanisms are widely attributed as one of the causes
of the current crisis2, Eichengreen (2008) points out that the banks’ shift to the originate-todistribute business model has its roots in key policy decisions made by the government of the
United States (U.S.) decades ago. He argues that the elimination of the Glass-Steagall act in
the 1990’s led to greater competition in the financial sector. Therefore financial intermediaries
entered in riskier and more leveraged activities. Although Eichengreen mentions the
deregulation of commissions for stock trading along with the elimination of the Glass-Steagall
act as key policy decisions, he forgets to include the Depository Institutions Deregulation and
Monetary Control Act (DIDMCA) enacted in 1980. The deregulation of interest rates
enhanced competition between financial intermediaries. The resulting increase in competition
in the financial sector is widely attributed as one of the determinants of the savings and loan
crisis in the 1980’s (Keeley, (1990); Edwards and Mishkin, as cited by Carletti (2008)).
1
Notice that in this paper the words banking crisis, financial crisis and episode of systemic (banking) distress are
used interchangeably, unless otherwise indicated
2
See for instance Blommestein, Hoogduin and Peeters (2009); Van Ewijk and Teulings (2009)
9
However, not only in the U.S. bank competition is attributed as one of the causes of financial
crises. Based on experience from Latin America and the East-Asian countries, Kaminsky and
Reinhart (1999) argue that the occurrence of banking crises is often preceded by financial
liberalization. Englund (1999) argued that deregulation of credit markets in Sweden resulted
in fiercer competition between financial institutions. This contributed to an asset price boom
in Sweden, which subsequently resulted in a financial crisis3.
Although these studies stress the positive link between banking competition and banking
crises in individual countries, other economists (e.g. Schaeck, Čihák and Wolfe (2006)) use
cross-country data to show that competition in the banking sector reduces the likelihood of
financial crises. Also Berger, Klapper and Turk-Ariss (2008) argue that there is theoretical
and empirical support for this competition-stability view. These contradicting findings in the
economic literature make it difficult to derive precise policy implications.
Nevertheless in wake of the current financial crisis many governments are rethinking the
design of their financial system and its supervision. Therefore the question how bank
concentration and competition affect financial stability is more relevant than ever before.
According to Buiter (2009) countries that have banks that ‘are not just too-big-to-fail, but also
too-big-to-save’ should seriously consider redesigning their financial system. He points out
that all countries with a small open economy, a large internationally exposed sector, limited
fiscal spare capacity and its own not-widely accepted reserve currency are vulnerable for
Icelandic scenarios. According to Buiter (2009) Belgium, Ireland and The Netherlands have
three of the four vulnerable characteristics. He stresses the point that aggressive competition
policy in the banking system is needed to reduce the size of banks. This contradicts the
findings of among others Eichengreen (2008), Kaminsky and Reinhart (1999) and Keeley
(1990) who argued that financial crises are the result of too much competition.
In order to gain insight in how much competition and concentration in the banking system is
desirable from a financial stability point of view this paper examines the following question:
What is the relation between bank concentration, bank competition and financial stability?
The first part of this paper discusses the relevant theoretical background. However, theory
provides an ambiguous answer to the question how bank concentration, competition and
financial stability are related. Therefore the second part of this paper provides empirical
evidence. This paper uses a multivariate logit model and data on 76 countries from 1990 –
2007 to test the relation between financial stability, bank concentration and competition. The
approach used is the same as in Beck, Demirgüç-Kunt and Levine (2006). However this paper
should not be interpreted just as a replication of their results. Besides the use of additional and
other countries the runner up period of the current financial crisis is included. Furthermore,
some additional variables are added related to ownership structure of financial institutions.
This paper contributes to the empirical literature in the sense that more direct measures of
competition in the financial sector are used to proxy bank competition. Besides that, this
paper examines whether policy recommendations by e.g. the Maas committee, the Turner
review and the Geithner plan for restructuring U.S. regulation are in line with economic
3
However also the government’s expansive macroeconomic policy contributed to the asset boom.
10
theory and the empirical evidence on the relation between bank concentration, competition
and financial stability.
This paper proceeds as follows. Section 2 summarizes the main theories about the relationship
between financial stability and bank competition and concentration. This section addresses
both the competition-fragility hypothesis and the competition-stability hypothesis. Because
the measurement of financial stability and bank competition is crucial in this paper section 3
will elaborate on this. The first part of this section distinguishes between micro and macro
indicators of financial stability. The second part discusses the strengths and weaknesses of
various indicators of bank competition. Although there is a large body of literature on the
measurement of competition, only the most frequently used competition indicators will be
discussed. After the discussion of the theoretical studies and the measures of competition and
financial fragility empirical evidence will be discussed in section 4. In this section both microand more macro-level studies will be discussed. Section 5 describes the dataset and elaborates
on the data used in this paper. The research methodology will be discussed in section 6. This
section also describes briefly other approaches used to investigate the determinants of banking
crisis, such as the signals approach. Section 7 presents the main results of this paper. The
robustness of these results to other specifications and other measures of bank concentration
will be discussed in section 8. Section 9 concludes and derives policy implications for the way
the financial sector should be designed and regulated.
11
2
Theory
The standard industrial organization literature stresses that competition is good and more
competition is better. From a static point of view, competition increases welfare because
competition reduces prices and increases the quantity supplied by the market. Although highly
debated (see for instance Aghion et al, 2002) also from a dynamic perspective it can be
argued that competition increases the standard of living if competition increases the incentives
for firms to innovate. In contrast to this, both economists and policy makers are convinced
that competition in the banking sector is different. The presence of market failures explains
why the standard competition paradigm is not directly applicable to the banking sector.
Relationships in financial markets are characterized by asymmetric information, the presence
of network externalities and (implicit) switching costs (Carletti, 2008). These market frictions
and entry barriers explain why the market mechanism in financial markets might not function
as well as it functions in other markets. Theoretical models make contradicting predications
about the effect bank competition has on financial stability. This section deals with the
theoretical ambiguity of the relation between concentration, competition and financial
stability. The first part of this section deals with the negative effect competition has on
financial stability, the so-called: competition-fragility hypothesis. The second part of this
section covers the competition-stability hypothesis and introduces theories that argue that
competition increases financial stability.
2.1
The competition-fragility hypothesis
2.1.1 The charter value hypothesis
One of the first to address the relationship between competition in the banking sector and
financial stability was Keeley (1990). He argues that competition in the banking sector
reduces financial stability. The channel which explains this negative relationship between
competition and financial stability is often described as the ‘charter value channel’. Guttentag
and Herring (1983) define charter value as: “the present value of the net income the bank
would be expected to earn on new business if it were to retain only its office, employees and
customers.4” A bank’s charter value can thus be interpreted as the discounted value of the
future profits the bank is expected to earn based on its reputation, its markets and its superior
information about its customers. Alternatively, a bank’s charter value is an intangible asset
from which the bank derives profits, but the value of this intangible asset would be foregone
once the bank goes bankrupt or is closed by the supervisory authority. Keeley (1990) argues
that increased competition reduces banks’ charter values. This is because increased
competition reduces banks’ market power (Berger, Klapper and Turk-Ariss, 2008). Banks
with higher charter values face higher opportunity costs of going bankrupt. Therefore these
banks have little incentive to engage in excessive risk taking and behave more prudently and
hold more capital, hold less risky portfolios or originate a smaller loan portfolio (Berger,
Klapper and Turk-Ariss, 2008) 5. In the 1980’s for example, the U.S. government started to
4
Alternatively, economists often use a bank’s franchise values as a synonym for a bank’s charter value.
This argument does however not hold in a stakeholder model in which shareholders have disproportional
power. Maas et al (2009) argue that the increased power of shareholders increased the attention that was paid to
5
12
reduce restrictions on entry and competition in financial markets which reduced the market
power of banks and therefore banks’ charters became less valuable. Deregulation of the
financial system followed by the erosion of profits explains the increase in bank failures
during the 1980’s6 (Edwards and Mishkin, as cited by Carletti (2008); Keeley (1990)).
Hellman, Murdock and Stiglitz (2000) studied the role of a bank’s charter value in an
environment with capital requirements. Capital requirements force banks to have more own
capital at risk if they invest in risky activities. Hellman, Murdock and Stiglitz (2000) call this
effect the ‘capital-at-risk’ effect. However, they argue that there is a second effect that capital
requirements have on banks’ incentives to engage in risk taking: the so-called ‘future
franchise value effect’. Because capital requirements act as an implicit tax on the banking
system, capital requirements erode a bank’s profitability. This second effect decreases a
bank’s charter value and therefore increases, rather than decreases, its incentives to take risk.
However, Behr, Schmidt and Xie (2010) argue that capital regulation might not be effective in
highly concentrated banking systems. They find a negative relation between the capital
stringency index and bank risk taking, measured as non-performing loans ratio, for low
concentrated banking systems. However, in markets were charter values are expected to be
higher, i.e. the concentrated banking systems, they do not find such relationship.
Bank competition in the deposit market tends to increase the interest paid to depositors. This
is due to the market stealing effect. Banks try to attract depositors by offering slightly higher
interest rates than their competitors. This kind of competition leads, ceteris paribus, to an
erosion of profitability and thus a decline in a bank’s charter value. This provides an incentive
to engage in risk taking. This might further be enhanced by competition in the loan market.
Demirgüç-Kunt and Detragiache (2000) examined how interest rate ceilings affect financial
stability in an environment with deposit insurance. They found that explicit deposit insurance
tends to be more detrimental to financial stability in countries where interest rates are
deregulated. Interest rate ceilings might thus contribute to financial stability7. The
introduction of (binding) interest rate ceiling might however also increase the level of
monitoring of banks. A formal model, based on Carletti (2008), to illustrate this is covered in
the appendix.
Allen and Gale (2000a; 2004) derived similar results8. Due to increased competition, assumed
as an increase in the number of banks that compete à la Cournot, each bank attracts a smaller
amount of deposits and therefore the effect an individual bank has on the interest paid to
depositors becomes negligible. As the number of banks increases the market becomes more
similar to a situation of perfect competition in which firms have an incentive to increase their
business as long as their expected profits are positive. These competitive forces ensure that
short-term gains, which therefore weakened long-term risk management. Stated differently, bank managers
(driven by shareholders and remuneration policies) did not face the opportunity costs of going bankrupt
themselves.
6
Another reason often suggested in the economic literature is the moral hazard created by the deposit insurance
schedule in the US. See for instance Saunders and Cornett (2008)
7
Also Hellman, Murdock and Stiglitz (2000) confirm this. They argued that capital requirements should be
accompanied with the introduction of deposit interest ceilings in order to obtain a Pareto efficient outcome.
Deposit rate ceilings allow the government to relax the capital requirement constraints and therefore increase the
charter value of banks, which leads to less risk taking.
8
The theoretical model of Allen and Gale (2000; 2004) is included in the appendix
13
the profit of each bank approaches zero and therefore banks have extreme incentives to take
risk.
2.1.2 Interbank contagion and relationship banking
The theoretical models discussed so far are all more or less related to the charter value
channel. There are, however, also other theories in the economic literature that support the
competition-fragility hypothesis. Allen and Gale (2000b) emphasize the importance of the
interbank market as a channel of financial contagion. The interbank market, with its
overlapping claims, links different regions and sectors of the banking sector. Therefore the
interbank market can spread financial shocks to other regions of the banking system. Allen
and Gale (2000b) show that the extent of contagion depends on the market structure of the
interbank market. Complete and fully connected markets can attenuate the impact of a
financial crisis, because the initial financial shock is transmitted to all banks and therefore the
impact on any one bank decreases. This reduces the potential for contagion. Incomplete
market structures, however, are more susceptible to contagion, because the initial shock is
borne by only a few banks. Therefore the probability that an individual bank is not able to
absorb its losses is higher. This increases contagion, because the shock continues to spread.
Northcott (2004) argues that a more consolidated banking system with fewer banks is more
likely to maintain complete and fully connected linkages than a perfectly competitive market 9.
This implies that perfectly competitive banking systems are more prone to wide spread
banking failures. Allen and Gale (2004) extend their initial model to allow for imperfect
competition in the banking sector. In oligopolistic markets actions of individual banks have an
effect on the price of liquidity. Therefore banks have an incentive to provide liquidity to the
troubled bank to avoid their own bankruptcy. Also Saez and Shi (2004) argue that a more
consolidated banking system is better able to coordinate its actions. In these circumstances
banks are more likely to provide liquidity to the troubled bank. This prevents financial
contagion and makes the liquidity providing banks better off.
Besides the effects competition in the banking sector has on banks’ charter values and the
interaction of banks in the interbank market, competition might also affect the duration of the
relation between a bank and its customers. Boot (2000) defines relationship banking as ‘the
provision of financial services by a financial intermediary that (i) invests in obtaining
customer-specific information, often proprietary in nature and (ii) evaluates the profitability
of these investments across multiple interactions with the same customer over time and/or
across products.’ Increased competition in the banking sector reduces the importance and
duration of relationship banking (Besanko and Thakor, 1993; Smith as cited by Beck, 2008).
A shorter duration of the relation between a bank and its customers might reduce the private
information obtained by the bank about the creditworthiness of a borrower. Relationship
banking could make the financial system more stable because it might reduce the number of
defaulting loans. Jímez and Saurina (2004) empirically tested the role of relationship banking
on the probability of default of individual loans in Spain. They found that financial
9
In essence, Northcott (2004) stresses the argument that consolidated, i.e. concentrated, banking systems tend to
be more stable. This argument is part of the concentration-stability hypothesis. Although some economists argue
that concentrated banking systems lack competition, it will be argued in section 3 that there is no one-to-one
relationship between competition and concentration.
14
institutions are willing to finance riskier loans when they have a closer relationship with
borrowers. As possible explanation for this result they suggest the existence of possible
informational rents.
Although relationship banking concerns the relation between the bank and its borrowers,
competition in the deposit market might also reduce the duration of a relation between the
bank and its depositors. Fierce competition and market stealing might therefore increase
liquidity risk. Iyer and Puri (2008) empirically examined the behavior of depositors during
bank runs. They found that depositors that have a longer relationship with a bank are less
likely to panic. Therefore if competition in the banking sector reduces the duration of the
relation between a bank and its depositors this might increase the likelihood that a bank faces
liquidity problems.
2.1.3 Effectiveness of bank regulation
Next to the direct effects competition in the banking system has on banks’ risk taking,
competition and the market structure might have an effect on the effectiveness of prudential
supervision. If a more concentrated banking system implies a smaller number of banks, then
Beck (2008) suggests that the burden for the supervisory authority is lower. This would
enhance the stability of the banking system. Allen and Gale (2000a) suggest therefore that the
traditional policy of the U.S. to create a banking system with a large number of banks and
other financial intermediaries is misplaced. Furthermore, Beck (2008) states that proponents
of the competition-fragility hypothesis argue that larger banks are better able to diversify their
portfolio and thus lowering their exposure to idiosyncratic risk.
2.2
The competition-stability hypothesis
2.2.1 Adverse selection and moral hazard in the loan market
During the last decades economists challenged the competition-fragility hypothesis and
developed models and theories illustrating that competition in the banking system increases,
rather than decreases, financial stability. The development of these theories and models gave
birth to the so-called competition-stability hypothesis. Boyd and De Nicoló (2005) argue that
the assumption of the portfolio problem that banks face, i.e. banks determine their asset
allocation based on given returns and prices, does not take into account the information
asymmetries that are common in financial markets. They argue that the optimal contracting
problem, i.e. the actions of borrowers are unobservable or observable at a costs, is a better
description of the environment in which banks operate. Their model takes into account that
banks compete with other banks in both deposit and loan markets. Less competition in the
loan market implies that banks can use their market power to charge higher interest rates. First
of all, higher interest rates on loans to the private sector themselves increase the bankruptcy
risk for these firms, due to the stochastic element of the entrepreneur’s return. Furthermore,
higher interest rates give rise to adverse selection and moral hazard. These both effects
contribute to an increase in the probability of default. Thus allowing for competition in the
loan market can reverse the above described relation between banking sector competition and
financial stability.
15
2.2.2 Credit rationing, monitoring and market structure
Caminal and Matutes (2002) also show that competition in the banking sector not necessarily
decreases financial stability. They argue that banks in more monopolistic markets are more
likely to monitor their borrowers than banks in a competitive market. These monopolistic
banks have market power and are therefore able to appropriate a larger part of the rents
created by monitoring. The market structure therefore explains why banks in a more
monopolistic banking sector are more willing to solve the agency problem in banking.
Monopolistic banks therefore rely less on credit rationing compared to banks in a more
competitive environment. This lack of credit rationing increases their exposure to aggregate
portfolio risk or non-diversifiable risk, which increases the probability of a banking failure.
Furthermore Petersen and Rajan (1995) argue that banks in a more concentrated banking
system are more willing to finance smaller credit-constrained firms because relationship
banking allows them to internalize the benefits of these firms later on in their relationship. It
is well-known that these small firms are riskier than larger firms; therefore banks in
concentrated banking systems that engage in relationship banking might be more risky than
banks in a more competitive environment. Jímez and Saurina (2004) provide some empirical
evidence for this.
2.2.3 Systemic risk and effectiveness of regulation
One of the arguments in favor of the competition-fragility hypothesis is that a more
concentrated banking system, with a smaller number of banks, creates a lower supervisory
burden. This might however not be the case. Beck (2008) points out that the recent trend of
consolidation in the financial sector created financial conglomerates which offer a broad array
of services. This makes the work for supervisors harder. Besides that, although larger
financial institutions are better able to diversify their loan portfolio, Wagner (2009) shows
that this might increase systemic risk. He argues that diversification entails a cost because
diversification makes financial institutions more similar. This increases the probability of
having a financial crisis. Hence, Wagner (2009) argues that full diversification is no longer
desirable from a society’s point of view.
Related to this, Mishkin (1999) argues that the presence of large financial institutions is
dangerous for the soundness of the financial system because failures of large financial
institutions expose the financial system to systemic risk. Especially, large bank failures have
negative externalities and can therefore hasten the failure of other banks. Furthermore,
Mishkin (1999) argues that financial consolidation increases the pressure for governments to
adopt a too-big-to-fail policy. The adoption of this policy increases moral hazard because it
reduces the incentives for depositors and other debt-holders to monitor the bank. This
increases the scope for the bank to engage in excessive risk taking. This is confirmed
empirically by Boyd and Gertler (1994). They argued that the main stress on the financial
system has not been the ‘raw number of bank failures; rather, it has been the poor
performance of large banks’.
16
3
Measuring bank competition and financial stability
Since the 1990’s economists started to examine empirically the impact bank concentration
and competition has on the likelihood of financial crises. To test this relationship, economists
had to overcome a number of practical issues related to the measurement of bank competition
and financial stability. The nature of the banking system makes the standard competition
measures not directly applicable for the banking sector. This is further enhanced by the fact
that most banks compete on both sides of their balance sheet. They try to attract stable and
cheap deposits and therefore compete with other banks in the deposit market. Besides that,
banks offer loans and therefore are also direct competitors in the loan market. Moreover the
elimination of capital controls and the developments in information and computer technology
resulted in an increase in direct competition from foreign financial institutions (Huizinga and
Nicodème, 2006). The latter is especially the case in Europe since the introduction of the euro
and the European Union’s (E.U.) policies directed at the integration of the banking market.
Due to increased globalization defining the relevant market in which banks compete with each
other becomes harder. Besides practical issues in measuring concentration and competition in
the banking system, measuring financial stability might also be subjective. Therefore before
introducing the empirical evidence on the relation between bank concentration, competition
and financial stability the following section addresses the conceptual problems of measuring
competition and financial stability. This section provides an overview of the most used
indicators of competition and financial stability.
3.1
Measuring financial stability and banks’ risk taking
3.1.1 Micro-indicators of financial stability
The resurgence of banking crises in Latin America, East-Asia and Scandinavia contributed to
a rapid growth in literature on banking crises. One of the main difficulties that economist had
to overcome was the measurement of financial stability. One can distinguish between microand macro-indicators of financial stability. Micro-indicators of financial stability are for
instance related to the solvency, liquidity or mismatch risks of individual institutions (Van
den Eerd and Tabbae, 2005). Keeley (1990) was one of the first who empirically tested the
relationship between the market power of banks and the risk banks take. As a measure of the
banks’ risk he uses the interest banks pay on large checkable deposits (CDs). To use this risk
measure he implicitly assumed that the interest rate paid on large deposits contained a risk
premium that is related to the bank’s risk of default. There are, however, reasons why the
interest rate paid to large uninsured depositors might not fully reflect the inherent risk
depositors face. The first reason is related to the too-big-to-fail policy followed by politicians
and regulators in many countries. Ellis and Flanery (1992) argue that if regulators and
supervisors of the financial system are believed to insure the full value of large CDs and other
liabilities, markets will not accurately price a bank’s default risk. Secondly, the complexity of
a bank’s business makes monitoring banks for depositors a costly activity. Although this is
especially the case for the small depositors, acquiring information about the solvency and
liquidity of a bank and the quality of its underlying assets is still a complex and costly activity
which is subjective to free-riding (Demirgüç-Kunt and Detragiache, 2002). This might even
17
be the case for larger depositors10. Nevertheless, Ellis and Flanery (1992) found that the
market requires a risk premium on large CDs that depends on the available information about
the banks creditworthiness. Their results are consistent with Hannan and Hanweck (1988).
Another widely used indicator of financial stability is the non-performing loans to assets ratio.
The main conceptual difficulty with the use of this indicator is that there are no clear and
commonly accepted criteria to decide what should be classified as a non-performing loan
(Bloem and Gorter (2001)). Another disadvantage of the non-performing loans to assets ratio,
but also for the risk premium on large CDs, is that it not considers actual failure of banks.
Boyd, De Nicoló and Jalal (2006) use an alternative indicator to measure a bank’s risk: the Zscore. The Z-score is defined as:
(3.1)
In which ROA is the return on assets, EA is the ratio of equity to assets and σ(ROA) is an
estimate, also based on accounting data, of the standard deviation of the return on assets. The
Z-score represents ‘the numbers of standard deviations below the mean by which profits have
to fall so as to just deplete equity capital’ (Boyd, De Nicoló and Jalal, 2006). A bank’s Zscore declines if the variability of earnings increases. Higher profitability and capitalization
levels increase a bank’s Z-score. Among others, Elton (1999) questions whether realized
returns are a good indicator for future performance and returns. Like the alternative measures
discussed so far, the Z-score does not consider actual failure of banks (Beck, 2008).
3.1.2 Crisis definitions and crisis dating
Another stream of the economic literature has focused on more macro based indicators of
financial stability. Rather than focusing on the creditworthiness of an individual financial
institution research focused on episodes of systemic banking crises. One could of course
aggregate non-performing loan ratios, take a weighted average of banks’ risk premiums paid
to large uninsured depositors or even aggregate Z-scores, but these measures do not consider
actual bank failures. Recently, economists focused therefore on episodes of systemic banking
crises. According to Boyd, de Nicoló and Loukoianova (2009) the different classifications of
episodes of systemic banking crises are all modifications or expansions of the classification of
banking crises by Caprio and Klingebiel (1996). The classification of Caprio and Klingebiel
(1996; 2003) relies on the assessments of financial experts and supervisory sources. Like
other authors, Caprio and Klingebiel distinguish between systemic and non-systemic crises or
border-line crises as they call them. According to Caprio and Klingebiel (1996) a crisis is
systemic if ‘much or all of bank capital has been exhausted.’ However, they don’t use a
quantitative definition of how much bank capital should be exhausted in order to qualify for a
systemic crisis. Furthermore, as argued by Boyd, de Nicoló and Loukoianova (2009) the
extent of bank failures across the banking system is not spelled out in their definition. Caprio
and Klingebiel’s definition is therefore subjective.
Demirgüç-Kunt and Detragiache (2002) define a systemic crisis as ‘a situation in which
significant segments of the banking sector become insolvent or illiquid, and cannot continue
to operate without special assistance from the monetary or supervisory authorities.’ Their
10
Assumed that these larger depositors are especially those who occupy a prominent position in society and
therefore earn higher wages the opportunity costs they face when they engage in monitoring of banks might also
be higher than for small depositors.
18
definition is more or less similar to the definition provided by Beck (2008), who defines
systemic banking crises as ‘periods where the banking system is not capable of fulfilling its
intermediation function for the economy effectively anymore.’ Contrary to Caprio and
Klingebiel (1996), Demirgüç-Kunt and Detragiache (1998a) define explicit criteria to
determine whether a crisis is systemic. An episode of distress is classified as a fully-fledged
crisis if at least one of the following four criteria holds:
1. The ratio of nonperforming assets to total assets in the banking system exceeds 10
percent;
2. The cost of the rescue operation is at least 2 percent of GDP;
3. Banking sector problems result in a large scale nationalization of banks;
4. Extensive bank runs take place or emergency measures such as deposit freezes,
prolonged bank holidays, or generalized deposit guarantees are enacted by the
government in response to the crisis.
Demirgüç-Kunt and Detragiache (1998a) admit that the criteria used are chosen somewhat
arbitrarily. Besides that, especially the third criterion is subjective because the minimum scale
of nationalizations in order to qualify for an episode of systemic crisis is not quantitatively
defined. The main critique however on the criteria of Demirgüç-Kunt and Detragiache, but
also on the definition of Caprio and Klingebiel is provided by Boyd, de Nicoló and
Loukoianova (2009). They argue that criteria 2, 3 and 4 of Demirgüç-Kunt and Detragiache
characterize a systemic banking crisis by dates of ‘government responses to a systemic
banking shock.’ These government responses however lag the start of the crisis because of the
uncertainty about the scale of the problems in the banking sector (Boyd, de Nicoló and
Loukoianova, 2009). Therefore crisis dating based on criteria that are related to government
intervention date crises on average too late. Besides uncertainty, due to lags in recognition,
decision making and implementation of policies government responses to crises often occur
after the initial crisis starts. These problems with crisis dating not only influence the crisis
dating of Demirgüç-Kunt and Detragiache but also the dating of Caprio and Klingebiel,
because they also gathered data from government offices and supervisory authorities.
Reinhart and Rogoff (2009a) argue that if the beginning of banking crises is marked by a
sudden unexpected increase in deposit withdrawals, than the dating of episodes of systemic
banking crises should be based upon these bank runs. However, not all banking problems are
related to the liability side of banks’ balance sheets. Therefore, they argue, to give an
appropriate criterion to date banking crises one also has to take into account problems
originated on the asset side. An unexpected increase in non-performing assets could therefore
qualify as a criterion to date financial crises. However, Reinhart and Rogoff (2009a) point out
that there are not enough data available to use such a criterion and therefore mark a banking
crisis by the following two events:
1. Bank runs that lead to the closure, merging or takeover by the public sector of one or
more financial institutions; or
2. If there are no runs, the closure, merging, takeover or large-scale government
assistance of an important financial institution (or group of institutions) that marks the
start of a string of similar outcomes for other institutions.
The first criterion is related to criterion 3 provided by Demirgüç-Kunt and Detragiache and
focuses on the liability side of a bank’s balance sheet and its vulnerability to liquidity
19
problems. The main shortcoming of the definition of Reinhart and Rogoff is that they are also
not able to provide the appropriate starting date for a banking crisis. They recognize however
another problem with dating banking crises. It is extremely difficult to accurately pinpoint the
year in which a crisis ended. Caprio and Klingebiel (1996) stress the same point and argue
that in case of multiple episodes within one country it can be the case that later events are a
continuation of events that occurred earlier.
Laeven and Valencia (2008) give a much broader definition of systemic banking crisis. They
include sharp increases in real interest rates and depressed asset prices in their definition.
Unlike earlier crisis dating studies, Laeven and Valencia (2008) explicitly exclude banking
system distress episodes that affected one financial institution in isolation. As a cross-check
they examine whether during the crisis bank runs occurred, defined as a monthly percentage
decline in deposits in excess of 5 percent, or if the government introduced unlimited deposit
insurance. An episode is also classified as systemic if most of banks’ capital is exhausted or
the banking system has a large proportion of non-performing loans. Also episodes in which
the regulator or monetary authority provides liquidity support or there is government
intervention are classified as systemic banking crises. Laeven and Valencia (2008) do
however not provide estimates of the duration of the crisis.
In sum, the analysis above shows that there is no single definition of systemic banking crises.
Therefore the criteria to classify episodes of banking crises as systemic differ between various
studies. Due to criteria based on regulatory policies and reliance on data provided by
supervisory authorities and government officials, studies date systemic banking crises on
average too late. Besides that, it’s difficult to pinpoint the year in which a crises ended. One
should be aware of these shortcomings when one uses these data.
3.2
Measuring banking competition
The measurement of competition in the banking sector is perhaps more difficult than
measuring financial stability. An increase in competition increases the probability of failure
for the least-efficient firms. Besides this selection effect, there is also a replacement effect.
More competition in itself shifts output from less-efficient firms to firms that are more
efficient. It is generally accepted that these two forces also shape the banking sector.
Profitability and the interest rate spread, i.e. the difference between the lending rate and the
costs of fund, are not very good indicators of the competiveness of the banking system. The
interest rate spread reflects also several other factors such as the probability of default of the
borrower. Claessens and Laeven (2004) argue that also a country’s macroeconomic stability,
economic performance, the taxing system and the informational and judicial framework
influence the profitability of the banking system. Besides that, bank specific factor have a
direct impact on profitability, e.g. banks might differ in their risk appetite or the possibility to
exploit economies of scale and scope.
3.2.1 Bank concentration
Because of the shortcomings of profit as an indicator of competition the traditional industrial
organization literature offers a broad array of indicators that measure competition. The first
group of these measures is related to the market structure and concentration. Sutton (2008)
relates market structure to the number and size distribution of firms in a market. However, it
20
is especially the interaction of suppliers and buyers that is important, because this interaction
determines the price and quantity sold in a certain market. The most simple and therefore one
of the most frequently used measures is the k bank concentration ratio. This measure is the
cumulative market share of the k largest banks in the sector, or mathematically:
(3.2)
In which
is the concentration ratio of the k largest banks and is the market share of
bank i. According to Marfels (1971) various concentration measures differ in their weighting
schemes of market structures. The concentration ratio weights the market shares of the k
largest banks with a weight equal to unity, while a weight of zero is attached to the other
banks in the market. The latter implies that the concentration ratio has a very limited data
requirement. Bikker and Haaf (2002) state that there is no standard rule for the determination
of the number of banks that should be included in the concentration measure.
Another widely used measure of concentration is the Herfindahl-Hirschman Index (HHI). The
HHI is widely used as a screening device for mergers. According to Motta (2004) the ability
of merging firms to extract market power depends on the concentration in the market.
Therefore not only the level of the HHI is important but also its change due to a merger. The
HHI is defined as the sum of the squared market shares of all banks; mathematically:
(3.3)
The main difference between the concentration ratio and the HHI is that the HHI takes into
account the market shares of all banks rather than the arbitrary chosen market shares of the k
largest banks. Besides that, in terms of Marfels (1971), banks’ market shares are used as their
own weights. This implies that a larger weight is attached to banks with a larger market share.
Marfels (1971) and Bikker and Haaf (2002) distinguishes other weighting schemes that are
used to measure competition. A discussion of these weighting schemes and other sparingly
applied competition measures is beyond the scope of this paper.
Although above described market structure and concentration measures are widely used in the
empirical literature, these measures have a number of shortcomings. According to Beck
(2008) these kind of indicators measure market shares, but do not allow inferences on the
competitive behavior of banks. The well-known Bertrand paradox illustrates that even a
market with only two firms can reach the Nash-equilibrium in which both firms set their price
equal to marginal costs, i.e. they reach the same outcome as a perfect competitive market.
Besides that, many banks offer a broad array of products and services or concentrate on a
particular segment of the market. Therefore not all banks compete in the same line of
business. Beck (2008) stresses also the importance of ownership on the degree of competition
that is ignored by the concentration measures.
Besides the ambiguous relationship between competition and concentration, there is also a
possible endogeneity problem with the concentration measures. It is not clear what the
direction of the causality is between bank behavior and the market structure. Economic theory
provides two opposing views that explain the relationship between market structure and firm
performance. The traditional structure-conduct-performance paradigm states that the market
structure determines the way businesses conduct their activities. Individual firm behavior
affects the performance and success of the industry. Berger et al (2004) argue that the
structure-conduct-performance paradigm implies that bank concentration and other frictions
that impede competition create an environment in which banks do not operate efficiently from
21
a social point of view. Besides the view that the market structure affects firm behavior there is
an opposing view that states that the market structure is the result of performance, the socalled efficient structure hypothesis. According to Bikker and Haaf (2002) the efficient
structure hypothesis claims that if banks become more efficient relative to competitors, profit
maximization induces banks to lower its prices and attract a larger market share. Therefore
market share is shaped endogenously which implies that concentration is a natural
consequence of the existence of efficient banks. This ambiguity of the direction of the
causation of market structure and bank behavior impedes the use of concentration measures to
measure competition directly.
3.2.2 More direct measures of bank competition
As described above, concentration measures have a number of shortcomings. Therefore,
according to Claessens and Laeven (2004), competition in the banking sector should be
measured with respect to the actual behavior of banks. One of the more competition oriented
measures is the so-called price-cost margin. The price-cost margin is defined as the output
price minus the marginal costs divided by the output price. Boone, Griffith and Harrison
(2005) prefer to use a price-cost margin that is weighted by market share. They argue that the
unweighted price-cost margin would give the impression that competition goes down after
entry of a firm that charges a higher price than the incumbent firms. However the entrant
would only obtain a small market share because he charges a high price and therefore the
competitiveness of the industry hardly declines. The market share weighted price-cost margin
is defined as follows:
(3.4)
In which
is the market share of firm i,
is the price charged by firm i and
are the
marginal costs of firm i. According to Van Leuvenstein et al (2007) the price-cost margin is
frequently used as an empirical approximation of the Lerner index. The Lerner index is based
on the monopolistic profit maximization condition and is equal to price minus marginal costs
divided by price. When prices are equal to marginal costs a market is said to be perfectly
competitive. The main difficulty with applying the price-cost margin or Lerner index in the
banking sector is that prices and marginal costs are often not directly observable. Besides that,
the lending rate should be properly adjusted for default risk (Beck, 2008).
Panzar and Rosse (1987) develop another approach to measure competition. Their so-called
H-statistic is defined as the sum of the elasticities of the total revenue of a bank with respect
to the bank’s input prices. The main disadvantage of this competition indicator is the
assumption one has to make in order to derive the H-statistic. Panzar and Rosse’s H-statistic
yields only plausible results if banks operate in their long-run equilibrium (Bikker and Haaf,
2002). In order to obtain the equilibrium number of banks and having each bank sell the
equilibrium output profits need to be maximized both at the firm as well as at the industry
level; mathematically11:
(3.5)
The profit of bank i depends on its output , the number of banks in the industry which is
defined as n and a vector of m factor input price . and represent a vector of exogenous
11
This part of section III is partly based on Bikker and Haaf (2002)
22
variable that shift the banks’ revenue respectively cost function. Profit maximization of bank i
implies that marginal revenues are equal to marginal costs:
(3.6)
If the market is in equilibrium the number of firms is constant and each firm produces its
profit maximizing quantity the zero profit constraint holds at the market level:
(3.7)
Variables that are marked with * represent the equilibrium values of these respective
variables. Panzar and Rosse (1987) measure market power as the extent that firms are able to
reflect a change in factor input prices, denoted as
, in their equilibrium revenues, denoted
as
.
The H-statistic is then defined as the sum of the elasticities of the bank’s total revenues with
respect to the bank’s input prices:
(3.8)
If the H-statistic is equal to one this implies that the market is characterized by perfect
competition, because total revenues and marginal costs of a firm move together. In a perfect
competitive market increases in input prices are shifted towards consumers. In monopolistic
markets firms do not have an incentive to shift the full price increase to consumers, because
this negatively affects their profit. Therefore an H-statistic smaller than 0 indicate
monopolistic markets, while 0 < H < 1 indicates monopolistic competition. The main
advantage of the H-statistic is according to Panzar and Rosse (1987) that their technique can
be employed if one knows the factor prices but one is not able to estimate cost functions.
Recently, Leuvenstein et al (2007) applied the Boone-indicator to measure competition in the
loan market. The Boone-indicator is based on the intuitive premise that more efficient firms,
i.e. firms with lower marginal costs, gain market share and that this effect tends to be stronger
the more competitive the market is. Put differently, ‘in a more competitive industry firms are
punished more harshly in terms of profit for cost inefficiency’ (Boone, Griffith and Harrison,
2005). Boone, Griffith and Harrison (2005) prove that a decline in fixed entry costs and an
increase in the substitutability of products produced by different firms reallocates market
share from less efficient firms to more efficient firms. Therefore the Boone-indicator takes
into account both the selection and reallocation effect. The HHI and concentration ratio are
not able to take these effects into account, because they suggest that concentration increases
once market shares are reallocated to larger, more efficient, firms12.
The Boone-indicator is estimated with the following model:
(3.9)
In equation (3.9) is the market share of firm i which is defined as follows:
,
are the marginal costs of firm i and
is a vector of control variables. Market shares of
banks with lower marginal costs are expected to be higher than market shares of less efficient
firms and therefore the Boone-indicator, denoted as β, should be negative. The larger the
value of β in absolute value the more competition there is in the market.
12
Assume for instance that there are three firms in an industry with market shares equal to 50%, 30% and 20%.
The HHI in this industry is equal to 0.38. Suppose competition reallocates market shares towards the more
efficient firms; the market shares become equal to 60%, 30% and 10%. The HHI increased to 0.46, which
indicates that there is more concentration in the industry.
23
According to Leuvenstein et al (2007) the Boone indicator is better suited to measure
competition than the traditional price-cost margin. They argue that more efficient firms are
able to charge a price that is higher than the marginal costs. Therefore a reallocation of market
shares towards more efficient firms, resulting from an increase in competition, increases the
industries price-cost margin. However, the Boone indicator also has its shortcomings.
Leuvenstein et al (2007) stress that the Boone indicator assumes that all banks have a similar
pass-through behavior of efficiency gains to consumers. If this is not the case, because for
example more efficient firms choose to translate lower prices relatively more in higher profit
margins than less efficient firms, the Boone indicator will not measure competition correctly.
Furthermore, the Boone-indicator, as well as the H-statistic, is only an estimate of the degree
of competition in the market. Therefore these indicators introduce some uncertainty
surrounding the measurement of competition. A more practical problem for estimating the
Boone-indicator is that marginal costs are not directly observed. They have to be estimated
which might introduce some additional biases in the estimate of the Boone-indicator.
3.2.3 Regulatory policies as indicator of bank competition
Besides traditional concentration measures and the recently developed estimates of
competition such as the Boone indicator and the H-statistic, Beck (2008) argues that
indicators of the regulatory and supervisory framework can indicate the contestability of a
country’s banking system. First of all, the regulatory framework provides restriction on the
activities banks can undertake. Banks that offer a broad array of services become financial
conglomerates and Barth, Caprio and Levine (2004) hypothesize that this might reduce
competition and efficiency. Besides these activity restrictions, regulators might impose entry
restrictions for domestic and foreign banks. As a result, this might reduce competition because
it becomes harder for entrants to enter the market. Activity restriction and the presence of
entry restrictions might therefore give an indication on the degree of contestability of the
market. However, one has to be careful with interpreting the presence of restrictions and
requirements for entry, because it is the actual policy made by the regulators and supervisors
that matters for the degree of competition.
24
4
Empirics
Because economic theory provides an ambiguous answer to the question how competition and
concentration in the banking system affect financial stability economists started to empirically
examine this relationship. Up until recently economic research focused on case studies in
individual countries and comparisons between two countries. The occurrence of banking
crises on a global scale in the 1990’s and the availability of large cross-country time-series
allowed economists to examine the relation between concentration, competition and financial
stability in cross-country studies. These studies are based on the banking sector as whole
instead of individual banks. This section discusses briefly both the bank level and banking
sector evidence on the relation between bank concentration, competition and financial
stability.
4.1
Bank level evidence
One of the first who empirically examined the relation between the market structure of the
financial system and a bank’s risk-taking behavior was Keeley (1990). He attributed the
increase in bank failures in the 1980’s in the U.S. to increased competition in the banking
sector. Jimenez, Lopez and Saurina (2007) use Spanish data to examine the relationship
between concentration, competition and the level of non-performing loans. They find that
bank concentration does not affect the degree of non-performing loans. However, they find
that the Lerner index is negatively related to a bank’s non-performing loan ratio. This
supports Keeley’s charter value hypothesis.
However, not all empirical evidence supports the competition-fragility hypothesis. Among
others Jayaratne and Strahan (1998) use the fact that geographic restrictions on the banking
industry have been lifted gradually between 1970 and 1990 in the U.S..
They find that loan losses decreased sharply. However, Dick (2006) also examined the effect
of lifting interstate branching restrictions on the performance of banks. She finds that the
riskiness of the banks’ loan portfolios increases after branch deregulation. The latter is in line
with the charter-value hypothesis.
De Nicoló (2000) was one of the first to test the relationship between a bank’s charter value
and financial fragility for other countries than the U.S.. His sample included publically traded
banks for 21 industrialized countries. Charter values are measured as estimates of Tobin’s Q;
financial fragility is measured with Z-scores for the individual banks13. He finds that bank
consolidation is likely to result in an increase in banks’ insolvency risk. Recently also Berger,
Klapper and Turk-Ariss (2008) examined the relation between bank competition and financial
stability. They used data for 8,235 banks in 23 different industrial countries. They showed that
banks with a higher degree of market power, as measured by the Lerner index, have a lower
overall risk exposure as measured by a bank’s Z-score. Although this supports the
competition-fragility hypothesis, they also find some support for the competition-stability
hypothesis. Banks with more market power have a higher ratio of non-performing loans and
therefore tend to have riskier portfolios. Berger, Klapper and Turk-Ariss (2008) do however
not interpret this as a contradiction of their other finding. They argue that banks with more
13
Tobin’s Q is defined as the market value of a bank divided by its replacement costs
25
market power might for instance use other risk management techniques to lower their overall
risk exposure.
Boyd, De Nicoló and Jalal (2009) use two different samples to test the relationship between
competition and financial stability. The first sample is based on 2,500 U.S. banks; they also
employ a second panel data set on 2600 banks in 134 countries. They find, in both samples,
that banks in countries that have a lower Herfindahl-Hirschman Index, which they interpret as
a more competitive banking system, have a lower probability of failure.
4.2
Banking sector evidence
From the late 1990’s onwards economists started to examine the relationship between bank
concentration, competition and financial stability on the banking system level. These crosscountry studies have the advantage that the regulatory framework can be included as a control
variable. Financial liberalization has been widely attributed as one of the determinants of
banking crises (Reinhart and Rogoff, 2009a). According to Demirgüç-Kunt and Detragiache
(2005) financial liberalization allows banks to take more risk. Kaminsky and Reinhart (1999)
support this hypothesis with a sample that includes 26 banking crises. They find that the
probability of having a banking crisis conditional on financial liberalization having taken
place is higher than the unconditional probability of having a banking crisis. Demirgüç-Kunt
and Detragiache (1998b) confirm this. Using data for the period 1980-1995 for 53 countries
they show that financial liberalization increases the likelihood of financial crises. However,
this effect tends to be weak in countries with a stronger institutional environment.
Beck, Demirgüç-Kunt and Levine (2006) examined the effect of bank concentration on
financial stability using data on 69 countries for the period 1980 – 1997. They found that bank
concentration reduces the probability that a country experiences a systemic banking crisis,
even after controlling for a wide array of institutional and regulatory factors. Furthermore,
they provided evidence that entry and activity restrictions make countries more prone to
banking crises. These two contradicting findings lead them to conclude that concentration is
not a good indicator for competition. This is confirmed by Schaeck, Čihák and Wolfe (2006)
and Claessens and Laeven (2004).
Schaeck, Čihák and Wolfe (2006) are the first who employ the H-statistic in an aggregate
cross-country study to determine the effect of competition on financial stability. Using data on
45 countries for the period 1980 – 2005 they found that competition in the banking sector
increases financial stability, even when controlled for institutional and regulatory factors.
Moreover, they confirmed the findings of Beck, Demirgüç-Kunt and Levine (2006) that more
concentrated banking systems tend to be more stable. This is in contrast to the findings of De
Nicoló, Bartholomew, Zaman and Zephirin (2003). They used a sample of 104 countries for
the period 1993 – 2000 and found that more concentrated banking systems have lower Zscores.
Overall the picture emerges that the empirical literature is inconclusive on the relation
between concentration, competition and financial stability. However, some important
conclusions can be drawn. Concentration indicators are usually not good indicators for the
degree of competition in the banking sector. Beck (2008) argues that this might imply that
concentration might have a positive effect on financial stability through other channels than
the lack of competition, such as increased diversification possibilities. Furthermore, cross26
country studies and the theoretical studies discussed in section 2 clearly indicate the
importance of the institutional and regulatory framework in explaining a country’s
vulnerability to systemic banking crises.
27
5
Data and summary statistics
One of the first cross-country analyses in the field of systemic banking distress is provided by
Beck, Demirgüç-Kunt and Levine (2006). They covered 69 countries in the period 1980 to
1997. This paper updates their results to 2007 and tests whether their results are robust to the
inclusion of other countries. In line with Beck, Demirgüç-Kunt and Levine (2006) and
Demirgüç-Kunt and Detragiache (2005) economies in transition are excluded, because the
problems in these countries are of a special nature and almost all countries experience these
problems during their development process. To classify whether economies are in transition
the IMF definition of 2000 is used. Countries with insufficient data on banking crises,
concentration, competition and the main macroeconomic control variables are also excluded
from the sample.
Also the following four Latin American countries are excluded: Brazil, Argentina, Nicaragua
and Peru. The macroeconomic environment experienced by these four countries in the
beginning of the 1990’s was so exceptional that these outliers (in terms of inflation and
depreciation) influence the estimates too much. Furthermore, Rwanda and Sierra Leone are
excluded because these countries experienced a war during the measurement period.
Observations from Lebanon start from 1992 onwards, because the Lebanese civil war ended
in 1990. For Kuwait the first observation dates from 1994 because the First Golf War in the
beginning of 1990’s might lead to some biases. Hong-Kong is also excluded because Great
Britain governed until 1997, while in 1997 China regained sovereignty. Although the
economic regime was stable in that period, Hong-Kong became a special administrative
region of China. The main results are however robust to the inclusion of Hong-Kong.
Furthermore, Zimbabwe is also excluded from the sample. In the 1990’s The Economist
(1997) noticed Zimbabwe’s economic progress, because ‘Africa is a bit short of economic
stars’. In the late 2000’s the Zimbabwean economy experienced excessive inflation and
massive political corruption. Because these problems are of a special nature which might not
be appropriately controlled for in a cross-country analysis, Zimbabwe is not included in the
sample. Therefore the data set used for this paper contains 76 countries and covers the period
1990 - 200714.
Table 1 presents descriptive statistics for the sample. Table A1 in the appendix provides
detailed information about banking crises, competition and concentration for all countries in
the sample. Table A2 provides the correlation between the various competition, concentration
and regulation measures. In table A4 detailed information about the sources of the data is
provided. The remaining part of this section describes the main variables included in the
dataset.
14
As one can see from table 1 the number of countries for which the H-statistics from Claessens and Laeven
(2004) and/or the fraction of entrants denied are available is substantially less than for the countries for which
concentration ratios are available. Therefore the number of countries included varies per regression. However,
excluding all countries for which the two above mentioned variables are not available would reduce the size of
the sample for the other regressions too much. Additionally not for all countries all control variables for the
whole time span are available. Except for regression (2) in table 3 the number of observations is much lower
than 300. The minimum number of countries included per regression is always 20 or more.
28
5.1
Crisis, competition and concentration variables
Crisis is a dummy variable that takes the value one if a country experiences an episode of
systemic banking distress and zero if not. To classify an episode of banking distress as system
the definition of Demirgüç-Kunt and Detragiache is used. Demirgüç-Kunt and Detragiache
(2005) updated their crisis data set until 2005. To include the years 2006 and 2007 in the
sample the data from Laeven and Valencia (2008) is used. Unfortunately, there are no data
available for the period after 2007 and therefore the ongoing financial crisis is not included in
this sample. Nevertheless, Laeven and Valencia (2008) classify 2007 as the starting year of an
episode of systemic banking distress for United Kingdom and the United States. In total there
are 48 systemic banking crises included in the sample; 181 years are classified as banking
crisis. For robustness of the results also other crisis definitions will be used. These will be
discussed in section 8.
Concentration equals the cumulative share of assets of the three largest banks in a country.
This three bank concentration ratio is available for a large number of years and countries.
Besides that concentration is an insufficient indicator of the degree of competition in the
banking sector it might have some additional shortcomings. First of all, a country is used as
the relevant market. Especially in Europe, banks might be in fierce competition with foreign
banks, while in the U.S. the relevant market might be sub-national. The main problem with
the use of concentration as explanatory variable is that bank concentration might be
influenced by the occurrence of a financial crisis. To overcome this possible reverse causality
problem concentration ratios are averaged over the period 1990 – 2007. Beck, Demirgüç-Kunt
and Levine (2006) stress another problem with the concentration indicator. The data obtained
to construct the concentration ratios are from Bankscope. The sample of banks covered by
Bankscope increased over time, which might cause some biases in the concentration ratios.
Although this might be the case, this problem is less severe in the period 1990-2007 than in
the period considered by Beck, Demirgüç-Kunt and Levine. Averaging over the period 1990 –
2007 reduces the possible biases caused by this coverage problem.
To get some insight in the relation between competition and financial stability H-statistics are
used. The estimates of the H-statistic for several countries in the period 1994 – 2001 are
provided by Claessens and Laeven (2004). The results, however, have to be interpreted with
care because estimates of H-statistics vary widely (Beck, 2008). Therefore also the Hstatistics of Bikker and Spierdijk (2008) will be used to test the relationship between banking
competition and financial stability.
As discussed in section 3, besides the use of H-statistics also indicators of the regulatory
framework might be used to indicate the degree of contestability of the banking system. The
indicators of the regulatory framework are constructed based on the revisited data from Barth,
Caprio and Levine (2007). As a measure of the degree of entry restriction in a banking system
the fraction of entrants denied is used. This is the number of applications for a commercial
banking license denied as a fraction of the total number of applications. These applications are
received from both domestic and foreign entities. Because there might be differences between
domestic and foreign entities that want to apply for a commercial banking license also the
fraction of entrants denied for domestic and foreign entities separately will be used.
Theoretically, a low fraction of entrants denied makes banks vulnerable for competition from
entrants and erodes banks’ future profits. Therefore, based on Keeley’s charter-value
29
hypothesis, a positive relation between the fraction of entrants denied and financial stability is
expected.
5.2
Bank regulation and deposit insurance
Also capital requirements are widely used to regulate the banking system. Many countries
adopted the Basel II accord in which pillar I covers the regulatory minimum capital
requirements for credit, market and operational risk. Behr, Schmidt and Xie (2010) argue that
country-specific capital ratios are not a good indicator for the stringency of capital regulation.
Definitions of capital differ across countries and therefore capital ratios are not comparable
between countries. Behr, Schmidt and Xie (2010) use data from Barth et al. (2008) to
construct an overall measure of the stringency of capital regulation. This capital stringency
index takes into account, among other things, whether the capital requirements are in line with
the Basel guidelines and whether the minimum ratio varies with market and credit risk. Behr,
Schmidt and Xie (2010) use principal component analysis to create country specific indices.
The principal component analysis does not assign equal weights to the twelve questions from
Barth et al. (2008), but assigns a larger weight to the variable with the largest variation. To
avoid negative indices Behr, Schmidt and Xie (2010) standardized the country-specific
indices with a mean of 10 and a standard deviation of 1. More information about the capital
stringency index is provided in table A3.
One of the shortcomings of the Basel II guidelines was that these guidelines did not properly
take liquidity risk into account. Liquidity risk was at the heart of the current financial crisis
(Pedersen, 2008)15. A bank’s exposure to liquidity risk has an influence on a banking system’s
stability. Therefore a dummy variable, called liquidity requirements, is included, which takes
the value one if banks are required to hold reserves or deposits at the central bank and a value
of zero if not. Liquidity requirements on the one hand increase stability because banks are less
vulnerable to a dry-up of liquidity. However on the other hand, liquidity requirements, like
capital requirements, are an implicit tax on the banking system. Therefore also liquidity
requirements might have a future franchise value effect.
Several bank regulators and supervisors rely on activity restrictions to prevent banks from
entering into risky activities. However, on the other hand, these restrictions reduce the
diversification possibilities for banks and therefore might be detrimental to financial stability.
To measure the degree of activity restrictions in a banking system, in line with Beck,
Demirgüç-Kunt and Levine (2006), a composite index is created that measures restrictions on
securities, insurance and real estate activities and whether banks are allowed to own voting
shares in non-financial firms. These activities can be unrestricted (0), permitted (1), restricted
(2) or prohibited (3)16. Although recognizing that there is no objective way to construct a
composite indicator of the degree of activity restrictions, the activity restrictions indicator is
15
According to Allen and Carletti (2008) there are two reasons for the liquidity hoarding. First of all, banks
wanted to protect themselves from unexpected withdrawals. Furthermore, there was uncertainty about
counterparty’s exposure to subprime related securities. These two reasons explain why the LIBOR rose
significantly.
16
The difference between unrestricted and permitted is the following: unrestricted implies that a full range of
activities in the given category can be conducted directly in the bank, permitted implies that all or some of these
activities should be conducted in subsidiaries. See also Guide to the 2003 World Bank Survey.
30
simply the sum of the scores on these four activities. The total score varies between zero and
twelve, in which a higher score indicates that there are more restrictions.
Table 1:
Summary statistics
Variable
Obs.
Mean
St. Dev.
Systemic banking crisis
Min.
Max.
1194
0.040
0.197
0
1
Concentration
76
0.665
0.169
0.256
0.985
H-statistic (Claessens and Laeven)
39
0.665
0.116
0.410
0.920
H-statistic (Bikker and Spierdijk)
63
0.551
0.476
-1.840
1.410
Fraction of entrants denied
46
0.129
0.241
0
1.000
Activity restrictions
69
6.855
2.556
0
12
Capital stringency index
45
10.051
0.929
8.060
11.250
Liquidity requirements
69
0.928
0.259
0
1
Explicit deposit insurance
1368
0.549
0.498
0
1
Interbank deposits
709
0.219
0.414
0
1
Coinsurance
705
0.194
0.396
0
1
Risk-based premiums
687
0.178
0.382
0
1
GDP growth
1327
3.799
3.332
-13.127
18.665
GDP per capita
1188
9565.059
8578.575
488.742
40248.707
Inflation
1365
9.305
15.344
-23.479
165.534
Real interest rate
1099
7.978
9.538
-41.790
86.980
M2/Reserves
1106
7.280
13.951
0.000
145.367
Terms of trade change
1248
0.516
9.563
-52.357
68.254
Depreciation
1368
7.190
22.526
-28.233
321.905
Private credit to GDP
1334
56.915
47.740
3.657
231.082
Credit growth
1314
7.814
20.457
-79.024
310.857
French legal origin
76
0.526
0.499
0
1
German legal origin
76
0.066
0.248
0
1
British legal origin
76
0.316
0.465
0
1
Government ownership
55
0.173
0.227
0
0.900
Foreign ownership
57
0.299
0.247
0
0.946
KKZ index
76
0.217
0.930
-1.477
1.861
Economic freedom
76
63.156
7.996
49.036
87.500
Financial freedom
76
56.491
14.928
28.571
90.000
See appendix tables A1 and A4 for sources and measurement of these statistics. Systemic banking distress is a dummy
variable that equals one if a particular country at a particular time experiences an episode of systemic banking distress. Years
that classify as crises after the initial crisis year are excluded (see section 6). The H-statistic of Bikker and Spierdijk is a
weighted average over the period 1989 – 2004. ‘Liquidity requirements’ is a dummy variable that equals one if a country
installed liquidity requirements. All variables are discussed in section 5.
Demirgüç-Kunt and Detragiache (2002) empirically examined the role explicit deposit
insurance has on financial stability. Their empirical work illustrated the importance of deposit
insurance on financial stability and therefore a dummy variable that indicates whether a
country has explicit deposit insurance is included. Besides the externalities and contagious
effects associated with bank runs and bank failures, one of the rationales to establish deposit
31
insurance is to prevent the small and uninformed depositors. To prevent moral hazard and
exert market discipline it is important to exclude interbank deposits in the deposit insurance
schedule. Therefore a dummy variable, conditional on having explicit deposit insurance,
which equals one if interbank deposits are included in the deposit insurance schedule is
included in the sample. Another way to reduce moral hazard introduced by deposit insurance
is the introduction of coinsurance. A dummy variable that is equal to one is introduced to
indicate that a deposit insurance schedule has some form of coinsurance. However, the
introduction of coinsurance makes deposit insurance schedules less effective in preventing
bank runs as the bank run on Northern Rock in September 2007 illustrated. A better way to
reduce moral hazard, without having these negative side effects, is to charge risk-based
premiums. Therefore also a dummy variable that equals one when a country introduced risk
based premiums is included in the data set.
5.3
Macroeconomic and other control variables
In line with Demirgüç-Kunt and Detragiache (2002; 2005) and Beck, Demirgüç-Kunt and
Levine (2006) several macroeconomic control variables are included in the data set. The real
interest rate, measured as the lending interest rate minus the inflation as measured by the GDP
deflator, is included to measure the real costs of borrowing17. Inflation itself, as measured by
the GDP deflator, is also included as control variable. Boyd, Levine and Smith (2001) showed
that inflation and financial sector performance are strongly negatively related. Furthermore
additional controls for the growth rate of real GDP, the change in the terms of trade and
exchange rate depreciation vis-à-vis the US dollar are included. Also the money and quasimoney (M2) to international reserves ratio is included, because according to Demirgüç-Kunt
and Detragiache (2005) this indicator measures a country’s vulnerability to a run on its
currency. Banks’ exposure to foreign exchange rate risk, due to currency mismatches, can
result in banking crises after unexpected exchange rate movements.
The recent financial turmoil illustrated that rapid credit growth might also have a detrimental
effect on financial stability. For instance, Mishkin (1996) argued experiences in the U.S. and
Mexico illustrated that rapid credit growth can be disastrous if bank managers and their
supervisors do not have enough expertise to manage and measure risk properly18. To control
for credit growth, the rate of growth of real domestic credit provided to the private sector is
included. Besides that, also the private credit to GDP ratio is included as financial sector
control variable. As a raw control variable for (institutional) development real GDP per capita
is included.
In line with Beck, Demirgüç-Kunt and Levine (2006) additional variables are added that
capture the economic freedom and institutional quality. The Kaufman-Kraay-Zoido index
(KKZ-index) is used as an indicator for various dimensions of governance. Their index
17
More specifically, the lending interest rate is the interest rate charged by banks to their prime customers.
Strictly speaking the real interest rate is defined as the nominal interest rate minus the expected inflation. The
definition used assumes that economic agents are perfect forecasters, i.e. inflation equals expected inflation.
18
Also during the current financial crisis credit growth played a key role. Morris (2008), for instance, argued that
due to recent advances in risk transfer instruments and other risk management techniques lending money seemed
to be riskless and therefore ‘when money is free, and lending is costless and riskless the rational lender will keep
on lending until there is no one else to lend.’ The lending was, however, far from riskless as we observed from
mid-2007 onwards.
32
consists of six basic governance concepts: voice and accountability, political instability and
violence, government effectiveness, regulatory burden, rule of law and graft (i.e. absence of
corruption). Kaufman, Kraay, and Zoido–Lobáton (1999) show that, ceteris paribus, countries
that score better on these governance criteria and the overall KKZ-index have a higher level
of development. The KKZ-index is available since 1996; the data used in this paper are based
on the average score in the period 1996 – 2008. Expected is that countries with a lower KKZscore are more prone to financial crises.
According to the Heritage foundation (2010) economic freedom is defined as ‘the
fundamental right of every human to control his or her own labor and property.’ The Heritage
foundation provides an international economic freedom indicator which consists of ten
components. It ranks countries in terms of trade policies, the ease to start businesses, taxation
structure, government spending, inflation and price controls, investment freedom, financial
freedom (see below), the ability of individuals to accumulate private property, freedom from
corruption and the labor markets’ regulatory framework. These ten components are weighted
equally and a higher score on this index indicates a higher level of economic freedom.
Besides the aggregated economic freedom index also the financial freedom index is included
as an additional control variable. The financial freedom index measures the degree of
government involvement in the financial sector. The stringency of financial regulation,
government ownership of banks, the ease of obtaining a banking license and the government
influence on the allocation of credit are included in this indicator. Like the economic freedom
index, all countries are assigned an overall score between 0 and 100. A score of 100 indicates
negligible government influence. Data on both freedom indicators are averaged over the
period 1995 – 2008.
Foreign or government ownership of banks might have an impact on the degree of
competition in the banking system and/or an effect on financial stability (Beck, 2008). It is
therefore important to control for both the degree of foreign ownership and government
ownership of the banking system. Claessens, Demirgüç-Kunt and Huizinga (2001) found that
in the long run foreign bank entry improves the performance of the domestic banking system.
This is in line with the ‘collateral effects’ that accrue to a country’s governance and
(financial) institutions once it opens up to cross-border capital flows (Prasad and Rajan,
2008). However, foreign ownership might make a country more prone to financial contagion
as argued by Demirgüç-Kunt and Detragiache (2005). Concerning government ownership, it
is generally accepted that government ownership of banks reduces competition and makes
banks less productive. Caprio and Martínez Pería (2000) found that government ownership of
banks increases both the likelihood and the fiscal costs of having a financial crisis, although
empirical research by Barth, Caprio and Levine (2004) does not confirm this.
Finally, the economic literature suggests that country-specific characteristics might play a role
in financial development. La Porte et al (1998) argue that the readiness of investors to finance
firms depends on the legal rules in a certain country and the way these legal rules are
enforced. As controls for these country-specific characteristics dummy variables are added to
indicate the legal origin of a country. Distinguished, in line with La Porte et al (1998), are
British, French, German and Scandinavian legal origin. To prevent perfect multicollinearity
Scandinavian legal origin is the base group.
33
6
Research methodology
6.1
Shortcomings of the signals approach and the linear probability model
For decades, economists have been trying to predict banking crises. One of the empirical
methods to predict this kind of crises is the so-called signals approach which was originally
developed to indentify turning points in business cycles. Kaminsky and Reinhart (1999) are
one of the first to apply this approach to investigate the determinants of banking crises. Rather
than focusing on banking crises, they focused on twin-crises, the simultaneous occurrence of
banking and currency crises. Although a complete review of their method is beyond the scope
of this paper, the idea of their econometric method is to compare macroeconomic variables
just before the start of a crisis with the growth rates and/or levels of these macroeconomic
variables in tranquil times. If macroeconomic variables surpass a certain threshold level this
variable signals a crisis. The threshold level is chosen to minimize the noise-to-signal ratio.
The main shortcoming of this empirical method is that it does not provide an aggregate crisis
indicator. For instance, some variables might signal a crisis while other variables do not.
Furthermore, the signal approach ignores a lot of information, since it does not take into
account how much a variable surpasses its threshold. Because of these shortcomings
economist applied and developed other econometric methods to consider the determinants of
banking crises.
One of the easiest ways to deal with the binominal nature of the crisis dummy variable is to
apply ordinary least squares to generate a linear probability model. Wooldridge (2009)
stresses however that this widely used technique has some shortcomings. Due to its linearity
the response probability is linear in the parameters of the model, which is not always
appropriate. Furthermore, estimated probabilities can be smaller than zero or larger than one,
which is of course nonsense. Due to the Bernoulli nature of the dependent variable there is
heteroskedasticity and therefore one of the Gauss-Markov assumptions is not satisfied19.
6.2
The logit probability model
Econometricians developed other models to overcome the problems associated with the linear
probability model. Demirgüç-Kunt and Detragiache (1998a) applied a logit probability
model20 to study the determinants of banking crises. Their technique is widely used in crosscountry analysis of determinants of banking crises21. This approach estimates the probability
that a crisis occurs in particular country at a particular point in time. This probability is
assumed to be a function of a vector of n explanatory variables
.
denotes the
dummy variable that takes the value one if country i at time t experiences an episode of
systemic banking distress and a value of zero otherwise.
is a vector of n unknown
coefficients and
is the cumulative probability distribution evaluated at
.
19
The linear probability model states that
. The variance of a random variable that
takes only two values, one of these two with probability , is
. Stated differently,
the variance of the dependent variable is a function of the explanatory variables. Therefore there must be
heteroskedasticity in this case and this implies the standard errors are biased.
20
Also called a multivariate logit model, or simply logit model.
21
For a review of the empirical literature on the determinants of banking crises see Demirgüç-Kunt and
Detragiache (2005).
34
Following the approach of Demirgüç-Kunt and Detragiache (1998a) the log-likelihood
function of the model is:
(4.1)
Although the sign of an estimated coefficient indicates the direction of change, due to the use
of the logistic functional form the numerical value of the estimated coefficient does not
indicate the effect a marginal change in the explanatory variable has on the probability of a
banking crisis. Instead the estimated coefficients reflect the effect of a change in an
explanatory variable on
. This implies that the increase in
probability depends upon the initial values of all independent variables and their coefficients.
Alternatively, the impact on the probability of a crisis depends on the slope of the cumulative
distribution at
.
Although the logit probability model uses another model specification than the linear
probability model, this does not automatically imply that the error terms in the logit model do
not exhibit heteroskedasticity. It is very likely that the opposite is true and that error terms
exhibit heteroskedasticity. To allow and correct for heteroskedasticity White’s
heteroskedasticity consistent standard errors are used for statistical inference.
To avoid the problem that some of the explanatory variables - such as bank concentration or
one of the macroeconomic control variables - are affected by financial crises, years classified
as crisis after the initial year of a crisis are excluded. For example, it is likely that the real
interest rate falls due to a loosening of monetary policy that accompanies other banking
rescue operations. Besides that, credit-to-GDP might fall due to a reduction in the availability
of credit. The resulting credit crunch might lower GDP growth. These examples illustrate that
it is difficult to estimate the relationship that has to be identified. Two approaches will be used
to prevent this reverse causality problem. The first is the one adopted by Demirgüç-Kunt and
Detragiache (1998a). They eliminate all observations following a banking crisis, but this
severely reduces the total number of observations. Demirgüç-Kunt and Detragiache (1998a;
2002) and Beck, Demirgüç-Kunt and Levine (2006) provide also an alternative approach. In
this approach they exclude years classified as crisis after the initial crisis year. The panel used
in this approach is larger than in the first approach but (see also section 3) its validity depends
crucially on the determination of the ending date of banking crises.
It is usual to include country fixed effects when one uses panel data, because country specific
effects that cannot be controlled for might be related to bank competition and financial
stability. However, as argued by Greene (2008) the inclusion of country fixed effects in a logit
model drops all countries in which no banking crises occurred. Because a large number of
countries included in the sample, e.g. many of the western European countries, did not
experience banking crises in the period 1990 – 2007 the observations for these countries will
be dropped. This is problematic because these countries act as a control group for the crisis
countries. Furthermore, a data set with only crisis countries would produce a biased sample.
Therefore, in line with Demirgüç-Kunt and Detragiache (1998a), estimating the model
without country fixed effects is a preferable approach.
35
7
Results
7.1
Bank concentration and financial stability
Table 2 presents the results of the relation between bank concentration and systemic banking
crises. Several macroeconomic and financial sector control variables are used as well as GDP
per capita as a proxy for the level of development of a country. Table 2 includes 5 different
specifications of the model outlined in section 6. The first specification includes all standard
macroeconomic and financial sector control variables suggested in the literature. Real GDP
growth is strongly negatively related to financial instability, i.e. countries with lower real
GDP growth are more prone to financial crises. Furthermore, the real interest rate – which
reflects the costs of borrowing – is positively related to financial instability. Also the
M2/reserve ratio enters positively and significantly. This indicates that countries with a higher
M2/reserve ratio are more susceptible to a run on their currency which might result in a
balance of payment crisis. These findings are in line with Demirgüç-Kunt and Detragiache
(1998a; 2002; 2005) and Beck, Demirgüç-Kunt and Levine (2006). Specifications (3), (4) and
(5) show that the private credit to GDP ratio is positively related to financial instability.
Although Beck, Demirgüç-Kunt and Levine (2006) didn’t include the private credit to GDP
ratio as a control variable it turns out to be significantly related with the likelihood of banking
crises.
Specification (2) adds bank concentration. Bank concentration enters negatively and
significantly at the 5%-level. This indicates that more concentrated banking systems tend to
be more stable. Moreover this estimate implies that, ceteris paribus, a one standard deviation
decrease in bank concentration increases the probability of having a systemic crisis with more
than 1%. Specification (3) controls for the level of overall development of a country
approximated by the level of GDP per capita. Less developed countries turn out to be more
prone to episodes of systemic banking distress. In this specification the coefficient of bank
concentration remains negative, although it is no longer significantly different from zero22.
In specification (4) all observations after the first crisis year are dropped. This reduces the
number of observations, but is a way to control for reversed causality. The results for the main
macroeconomic control variables are robust to this other specification. This indicates that the
findings for the main macroeconomic control variables are not driven by reverse causality.
However, bank concentration does not enter significantly. Specification (5) includes all
observations, including years classified as systemic banking crisis. Bank concentration enters
negatively and significantly at the 1%-level23.
The fit of the models presented is satisfactory. The predictions of the models are correct in
65% up to 77% depending on the specification used. Due to the fact that financial crises are
rare events, on average only 4% of the observations is classified as crisis year, this fraction,
rather than a cut-off value of 0.5 is used as threshold24. This approach is suggested by
22
More specifically, the p-value is equal to 0.228. This result does not change if only the first observation of
bank concentration is used or the yearly bank concentration ratio instead of the average over the period 19902007.
23
Specification (5) suggests that, ceteris paribus, the impact of one standard deviation decrease in bank
concentration increases the likelihood of experiencing a financial crisis with 5%.
24
For specifications that use all observations, i.e. including all crisis years, the sample average of 0.13 will be
used as cut-off value.
36
Wooldridge (2009). Moreover, the model specifications in table 2 are able to classify more
than 60% of the crisis observations correctly. The pseudo-R2, also called Mc Fadden R2,
ranges from 0.11 to 0.19.
Table 2:
Systemic banking crises and bank concentration
Real GDP growth
Inflation
Real interest rate
Terms of trade change
Depreciation
M2/Reserves
Private credit / GDP
Credit growth t-2
(1)
-0.0055***
(0.0016)
0.0002
(0.0003)
0.0018***
(0.0006)
0.0007
(0.0006)
0.0001
(0.0002)
0.0006*
(0.0003)
0.0001
(0.0002)
0.0001
(0.0002)
(2)
-0.0049***
(0.0016)
0.0000
(0.0003)
0.0014**
(0.0006)
0.0005
(0.0006)
0.0001
(0.0002)
0.0005*
(0.0003)
7.06e-06
(0.0002)
0.0001
(0.0002)
-0.0777**
(0.03626)
(3)
-0,0041***
(0.0016)
0.0002
(0.002)
0.0007
(0.005)
0.0001
(0.0005)
0.0001
(0.0001)
0.0006**
(0.0003)
0.0005***
(0.0002)
0.0001
(0.0001)
-0.0462
(0.0382)
-3.97e-06***
(0.0000)
(4)
-0.0052**
(0.0024)
-0.0009*
(0.0005)
0.0006
(0.0008)
0.0003
(0.0009)
0.0009**
(0.0004)
0.0006
(0.0004)
0.0004**
(0.0002)
0.0002
(0.0002)
-0.0417
(0.0573)
-4.77e-06***
(0.0000)
684
34
73
68
0.1066
657
31
74
61
0.1166
530
25
77
72
0.1689
327
21
65
81
0.1936
Concentration
Real GDP per capita
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(5)
-0.0144***
(0.0037)
0.0009
(0.0010)
0.0015
(0.0014)
-0.0009
(0.0015)
0.0005
(0.0006)
0.0020***
(0.0007)
0.0019***
(0.0004)
-0.0017**
(0.0008)
-0.2941***
(0.0826)
-1.52e-04***
(0.0000)
620
115
70
63
0.1547
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results .Variables are defined in section 5. In specification (1), (2) and (3) years classified as
crisis after the initial crisis year are excluded. Specification (4) excludes all observations after the first crisis year for each
country. Specification (5) includes all observations. For the ease of interpretation, this table presents the marginal effects of
the logit regressions. White’s heteroskedasticity consistent standard errors are given in parentheses. *** indicates statistical
significance at 1%, ** indicates statistical significance at 5%, * indicates statistical significance at 10%
7.2
Bank competition and financial stability
In table 3 the effect of competition on financial stability is considered25. As discussed in
section 5, to measure competition H-statistics are used. In specification (1) the H-statistics of
Claessens and Laeven enter negatively and significantly along with the main macroeconomic
control variables. As discussed in section 3, the closer the H-statistic to one the more
competitive the market is. This indicates that more competition increases financial stability.
25
The H-statistics of Claessens and Laeven are estimated for the period 1994 – 2001, therefore an additional
sample is created that covers only the period 1994 – 2001. Because there are only H-statistics available for 39
countries this reduces the sample size too much to allow for thorough statistical inference. The H-statistics of
Claessens and Laeven are used for the entire period 1990 – 2007, because Bikker and Spierdijk (2008) argue that
competition does not change rapidly in the banking sector.
37
This result is robust to other treatments of crisis observations. However as specification (2)
shows, the H-statistic loses its significance if overall development and the financial sector
control variables are added to the specification. This result does not change if all years after
the first crisis are dropped. The findings for the macroeconomic control variables are in line
with table 2. Additionally, in specification (1) and (2) inflation enters positively and
significantly. This implies that ceteris paribus high inflationary countries are more prone to
financial crises. This finding is in line with Boyd, Levine and Smith (2001).
Specification (3) – (6) include all observations, also those classified as crisis year after the
initial crisis year. This substantially increases the number of observations and the years
classified as crisis year. The main disadvantage is that the results might be driven by reverse
causality. Specification (3) suggests that competition is negatively related to financial
instability, but the direction of this causality is not clear.
Because estimates of H-statistics vary widely between different studies, for robustness of the
results also H-statistics of Bikker and Spierdijk are used to test the relationship between
competition and financial stability. Their estimates are indeed different from the estimated Hstatistics estimated by Claessens and Laeven. The correlation between the two H-statistics is
even below 0.39 (see appendix). In specification (4) the H-statistics of Bikker and Spierdijk
are used instead of the H-statistics provided by Claessens and Laeven. To rule out reserve
causality the H-statistics for the year 1989 are used and therefore it is by design not possible
that crises during the measurement period have any effect on the level of competition in the
banking sector26. Besides that, Bikker and Spierdijk (2008) argue that changes in the degree
of competition in the banking system over time are small. In specification (4) the H-statistic
enters positively and significantly, indicating that increased competition increases financial
instability. This result is inconsistent with the results obtained with Claessens and Laeven’s
H-statistics.
This contradicting result could of course be due to the fact that both indicators use a different
time span. Bikker and Spierdijk measure competition during the period 1989 – 2004.
Claessens and Laeven use the period 1994 – 2001. However, Bikker and Spierdijk (2008)
argue that the degree of competition doesn’t change much over time. Therefore the two
contradicting results are unlikely to be due to the two different measurement periods. Bikker
(2006) provides a more convincing explanation. He points out that there is a serious
misspecification in the empirical translation of the Panzar-Rosse models estimated by e.g.
Claessens and Laeven. As dependent variable Claessens and Laeven (2004) use the natural
logarithm of the ratio of gross interest revenue to total assets27. According to Bikker (2006)
this scaling leads to a serious overestimation of the degree of competition in the banking
sector. By taking the natural logarithm of the ratio of interest income to assets the estimated
H-statistic is based on a price equation rather than on a revenue equation. This biases the Hstatistic towards one, which indicates perfect competition. The positive relationship between
competition and financial instability in specification (4) is however not robust to other
treatments of the crisis observations.
26
27
Furthermore, the H-statistics provided by Bikker and Spierdijk are available for a larger set of countries.
Bikker and Spierdijk (2008) use the natural logarithm of interest income as dependent variable.
38
Table 3:
Systemic banking crises and bank competition
Real GDP growth
Inflation
Real interest rate
Terms of trade change
Depreciation
(1)
-0.0024
(0.0022)
0.0022***
(0.0007)
0.0025***
(0.0009)
9.23e-06
(0.0007)
0.0001
(0.0002)
M2/Reserves
Private credit / GDP
Credit growth t-2
H-statistic CL1997-2004
-0.1608**
(0.0690)
(2)
-0.0029
(0.0027)
0.0024**
(0.0011)
0.0021*
(0.0012)
-0.0001
(0.0009)
0.0001
(0.0002)
0.0008
(0.0007)
0.0006**
(0.0002)
0.0003
(0.0002)
-0.0099
(0.1142)
(3)
-0.0213***
(0.0071)
0.0096***
(0.0026)
0.0059**
(0.0029)
-0.0026
(0.0021)
0.0004
(0.0007)
0.0006
(0.0018)
0.0031***
(0.0005)
-0.0009
(0.0012)
-0.4536*
(0.2756)
H-statistic BS1989
(4)
-0.0154***
(0.0047)
0.0013
(0.0012)
0.0016
(0.0014)
-0.0019
(0.0018)
0.0008
(0.0007)
0.0007
(0.0012)
0.0026***
(0.0000)
-0.0018**
0.0009
(6)
-0.0163***
(0.0045)
0.0007
(0.0011)
0.0008
(0.0015)
-0.0017
(0.0015)
0.0006
(0.0007)
-0.0003
(0.0010)
0.0021***
(0.0004)
-0.0020**
(0.0009)
0.0644**
(0.0294)
Real GDP per capita
448
22
72
72
0.1217
0.0543*
(0.0293)
-4.42e-06***
(0.0000)
-2.02e-05***
(0.0000)
-1.65e-05***
(0.000)
-2.12e-05***
(0.0000)
0.3175
(0.2054)
-1.53e-05***
(0.0000)
-0.3540***
(0.0996)
250
15
65
67
0.1854
306
71
71
65
0.2082
521
103
61
68
0.1458
299
69
72
68
0.2258
513
101
68
68
0.1731
Concentration
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(5)
-0.0183**
(0.0076)
0.0116***
(0.0031)
0.0075**
(0.0037)
-0.0031
(0.0023)
0.0008
(0.0008)
0.0014
(0.0020)
0.0033***
(0.0005)
-0.0015
(0.0015)
-0.6648
(0.3143)
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. Variables are defined in section 5. The H-statistics of Bikker
and Spierdijk are the estimated for the year 1989. In specification (1) and (2) years classified as crisis after the initial crisis
year are excluded. Specifications (3) - (6) include all observations. For the ease of interpretation, this table presents the
marginal effects of the logit regressions. White’s heteroskedasticity consistent standard errors are given in parentheses. ***
indicates statistical significance at 1%, ** indicates statistical significance at 5%, * indicates statistical significance at 10%.
Specification (5) and (6) include bank concentration as explanatory variable. The H-statistic
now measures the effect of a change in competition on financial stability holding the market
share of the three largest banks constant. In specification (5) the coefficient of Claessens and
Laeven’s H-statistic is no longer significantly different from zero. However, the H-statistic
provided by Bikker and Spierdijk turns out to be positively and significantly related to
financial instability once the degree of bank concentration is taken into account. The results
are, however, not robust to other treatments of crisis years.
39
7.3
Entry restrictions and financial stability
As discussed in section 3 regulatory policies can proxy the degree of contestability of the
financial system. More specifically entry restrictions, measured as the fraction of entrant
denied, have a direct effect on the level of competition in the financial sector. Therefore table
4 presents the results when the fraction of entrants denied is used as explanatory variable. The
main disadvantage of the data from Barth, Caprio and Levine (2008) is that there are no
observations for a number of crises countries28. Therefore the panel is less balanced than in
other specifications. In specification (1) the fraction of total entrants denied (in the period
2000 – 2005) enters negatively. This would indicate that entry restrictions boost financial
stability, but this result is not significantly different from zero. This result does not change
when the fraction of total entrants denied for the period 1998 – 2003 is used. Moreover, also
the fraction of foreign entrants denied as well as the fraction of domestic entrants denied does
not enter significantly.
Table 4
Systemic banking crises and entry restrictions
Fraction of entrants denied 2000-2005
(1)
-0.0283
(0.0393)
(2)
-0.2631***
(0.0831)
(3)
-0.1986**
(0.0854)
Fraction of entrants denied 1998-2003
Concentration
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
341
12
80
67
0.2342
384
55
72
69
0.2243
-0.2203***
(0.0726)
376
55
70
72
0.2427
(4)
-0.1519**
(0.0635)
-0.2128**
(0.0859)
336
47
77
69
0.2437
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. For brevity the coefficients on the macroeconomic variables are not
reported. Several lagged versions of credit growth are used, credit growth lagged two periods provided the best results and is
therefore included. Variables are defined in section 5. In specification (1) years classified as crisis after the initial crisis year
are excluded. Specifications (2) - (4) include all observations. For the ease of interpretation, this table presents the marginal
effects of the logit regressions. White’s heteroskedasticity consistent standard errors are given in parentheses. *** indicates
statistical significance at 1%, ** indicates statistical significance at 5%, * indicates statistical significance at 10%.
To include more crisis observations in the sample, specification (2) includes all crisis
observations. The fraction of entrants denied enters negatively and significantly, which
implies that financial systems with more entry restrictions are less prone to financial crises.
This result does not change when the fraction of entrants denied over the period 1998 – 2003
is used. Specification (3) adds bank concentration as explanatory variable. Bank concentration
enters negatively and significantly different from zero at the 5%-level. The fraction of
entrants denied remains negative and significant. Specification (4) confirms the results from
specification (3). In specification (4) the fraction of entrants denied for the period 1998 –
28
Another disadvantage is that the data of Barth, Caprio and Levine are gathered from surveys.
40
2003 is used. The coefficient on bank concentration is similar and the coefficient of the
fraction of entrants denied is negative and significantly different from zero.
These findings above support each other, because a high fraction of entrants denied allows
banking systems to be more concentrated over time. The presence of entrants in the banking
sector would generally imply that banking systems become less concentrated, because market
share of incumbent banks is reallocated towards entrants. These findings provide support for
Keeley’s charter value hypothesis. A lack of new entrants favors the incumbent banks in the
industry and this increases their charter values. These banks adopt less risky activities because
their opportunity costs are higher; therefore the banking system as whole becomes more
stable.
41
8
Robustness and sensitivity analysis
The results of the preceding section might be due to the fact that the regulatory framework
and national characteristics are not controlled for. Therefore this section investigates the
robustness and sensitivity of these results to the use of other crises definitions, the
introduction of indicators of regulatory quality and national characteristics. It might for
instance be the case that the negative relationship between concentration and the likelihood of
financial crises is driven by regulations that impede competition. If this is the case, controlling
for bank regulation (e.g. activity restrictions) will drive out the significance of bank
concentration. Then banking regulation itself explains why we find a negative relation
between concentration and the probability of having a financial crisis. Besides this, also other
concentration measures, e.g. the C5-ratio and the number of banks per capita, will be used to
investigate the robustness of the results.
8.1
Bank concentration, regulatory policies and financial stability
Table 5 confirms the findings of table 2 that more concentrated banking systems tend to be
more stable. In all specifications, except the one that controls for deposit insurance, bank
concentration enters negatively and significant29. Specification (1) shows that countries with
lower scores on the KKZ-index are more prone to financial crises. This result is even larger in
absolute terms if the level of GDP per capita is not controlled for. In specification (2) the level
of financial freedom enters negatively and significantly, indicating that countries with less
government influence in the financial system are less likely to experience episodes of
systemic distress.
Although not shown in table 5, the percentage of government ownership in the banking
system enters negatively and significantly in specification (2)30. This does not support the
thesis that government ownership might be detrimental to financial stability. A possible
explanation for the negative coefficient on government ownership might be the fact that
government owned banks that caused the crisis are privatized afterwards. For instance,
Carletti, Hakenes and Schnabel (2005) found that in Italy government ownership decreased
sharply after the banking reforms that were taken after the Italian banking crisis in the
beginning of the 1990’s31. Privatization of government owned banks after financial crises can
explain the negative coefficient on government ownership.
The degree of foreign ownership enters positively and significantly in specification (2). This
indicates that banking systems with more foreign ownership tend to be more prone to
systemic banking crises. This might be due to the fact that these banking systems are more
vulnerable for contagion. Indeed, Kaminsky and Reinhart (1999) argue that cross-border
financial sector linkages play a key role in how shocks are propagated. Also Cull and
29
The regressions in table 5 include GDP per capita as one of the control variables. However, it can be argued
that GDP per capita is a crude measure for the overall institutional environment. Very similar results for all
reported coefficients are obtained when GDP per capita is excluded. Moreover, bank concentration does enter
significantly into regression (4) if GDP per capita is not included.
30
Moreover, the level of financial freedom is robust the inclusion of the percentage of government ownership in
the banking system.
31
Government ownership of banks decreased from 68% in 1992 to 12% in 1999. The ownership shares are
measured as the share of assets held by foundations with a majority interest in Italian banks.
42
Martínez Pería (2007) find that countries that experience a banking crisis have higher levels
of foreign bank ownership. However, they show that foreign ownership increases as a result
of crises. This effect could also explain the positive relation between financial instability and
foreign ownership.
Specification (3) shows that the economic freedom index is negatively related to financial
instability, i.e. the more humans are able to control their own labor and property the less likely
a financial crisis. Besanko and Thakor (1993) argued that the presence of deposit insurance
might affect the impact bank competition has on financial stability. Demirgüç-Kunt and
Detragiache (2002) proved that deposit insurance increases the likelihood of having a
financial crisis. This paper confirms their results. The coefficient and its level of significance
of the deposit insurance dummy are robust to other treatments of the crises observations (not
shown). Specification (4) indicates that the moral hazard effect associated with deposit
insurance outweighs the preventive function of deposit insurance. Deposit insurance thus
decreases financial stability. Specification (5) controls for several design features of the
deposit insurance schedule conditional on having deposit insurance. It turns out that the
presence of coinsurance positively affects financial stability. This might be due to the fact that
the introduction of coinsurance increases market discipline32. In specification (5) bank
concentration is positively related to financial stability at a significance level of 5%.
In specifications (6) and (7) activity restrictions and liquidity requirements are entered along
with bank concentration and the macroeconomic control variables. These regulatory policies
do not enter significantly. Bank concentration remains significantly negative in these
specifications. Different treatments of crisis observations and use of the 2003 regulatory
policies instead of the regulatory policies of 2007 do not change these results. Furthermore,
Beck, Demirgüç-Kunt and Levine (2006) argue that regulation does not change much over
time. They argue that after financial crises regulation and restrictions are often relaxed.
In specification (8) the capital stringency index enters positively and significantly. This
implies that countries with more stringent capital regulation are more prone to financial crises.
At a first glace this seems to contradict the findings of Barth, Caprio and Levine (2004).
However, they argue that there is not a strong relationship between the stringency of capital
regulation and the likelihood of a crisis once they controlled for other regulatory control
variables. Furthermore, the negative correlation between the capital stringency index and
GDP per capita indicates that more developed countries rely less on stringent capital
regulation. This could indicate that in developed countries the private sector is better able to
exert control over the financial sector and therefore stringent capital requirements are less
needed. Hence, these countries rely less on stringent capital requirements, but because of
market discipline these countries are less prone to financial instability33.
32
Regression (8) is also run for each of the three design features separately. Although the coefficients in these
regressions are larger in absolute terms which indicates that there is multicollinearity, only the coefficient on
coinsurance is significantly different from zero.
33
In unreported regressions bank concentration is interacted with the variables that capture the institutional and
regulatory environment. Only the interaction terms of bank concentration and activity restrictions and the capital
stringency index enter significantly.
43
Table 5:
Systemic banking crises, bank concentration and regulatory policies
Concentration
KKZ-index
(1)
-0.1526*
(0.0833)
(2)
-0.2687***
(0.0900)
(3)
-0.2767***
(0.0843)
(4)
-0.1214
(0.0885)
(5)
-0.3021**
(0.1478)
(6)
-0.2785***
(0.0918)
(8)
-0.2905**
(0.1222)
-0.0886***
(0.0261)
Financial freedom
-0.0046***
(0.0014)
Economic freedom
-0.0051*
(0.0029)
Deposit insurance
0.1439***
(0.0319)
Interbank deposits
-0.0476
(0.0421)
-0.1638***
(0.0344)
0.1360
(0.1178)
Coinsurance
Risk-based premiums
Activity restrictions
-0.0037
(0.0083)
Liquidity
requirements
Capital stringency
index
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(7)
-0.2196***
(0.0746)
0.0689
(0.0457)
0.0571**
(0.0285)
620
115
68
71
0.1721
620
115
63
73
0.1739
620
155
60
77
0.1592
620
155
61
80
0.1888
327
76
69
64
0.2273
593
112
69
65
0.1558
593
112
68
66
0.1605
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. For brevity only the results for bank concentration and regulatory
policies are shown. Variables are defined in section 5. In all specifications years classified as crisis after the initial crisis year
are included. For the ease of interpretation, this table presents the marginal effects of the logit regressions. White’s
heteroskedasticity consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates
statistical significance at 5%, * indicates statistical significance at 10%
In table 6, the sensitivity of the results to the use of other definitions of bank concentration is
considered. Specification (1), (2) and (3) use the C5-ratio, which is the ratio of total assets
hold by the 5 largest banks divided by the total assets hold by the banking system as whole.
The C5-ratio enters negatively and significantly in all three specifications. Specification (2)
suggest that a one standard deviation decrease in the C5-ratio increases the probability of
having a financial crisis with more than 1%, which is similar to the results obtained in table 2.
Notice that the absolute value of the concentration measure increases strongly when all crisis
observations are included. This is similar to the results in table 2. The crude number of banks
per capita turns out to be not significantly related to financial stability. Besides these
additional indicators of concentration also the first observation for bank concentration is used
as explanatory variable (not shown in table 6). This bank concentration indicator does not
enter significantly into one of the specifications. Nevertheless, bank concentration and the C544
398
82
71
68
0.2122
ratio have a coefficient that is consistently negative in all specifications. This supports the
concentration-stability hypothesis.
Table 6
Systemic banking crises and other bank concentration measures
(1)
-0.0773***
(0.0265)
C5-ratio
(2)
-0.0496**
(0.0214)
(3)
-0.1900***
(0.0555)
Number of banks per capita
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(4)
-0.0011
(0.0010)
312
19
77
63
0.2336
493
21
66
66
0.1675
571
99
68
71
0.1454
493
21
73
67
0.1550
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. For brevity only the results for the bank concentration
measures are shown. Variables are defined in section 5. Specification (1) excludes all observations after the initial crisis.
Specifications (2) and (4) exclude all crisis years after the initial crisis year. Specification (3) includes all observations. For
the ease of interpretation, this table presents the marginal effects of the logit regressions. White’s heteroskedasticity
consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates statistical
significance at 5%, * indicates statistical significance at 10%
8.2
Bank competition, regulatory policies and financial stability
Section 7 elaborated on a serious misspecification in the H-statistics of Claessens and Laeven.
Although not shown, it turns out that their H-statistics are no longer significantly different
from zero if (the quality of) regulatory policies are added to the model specifications. The Hstatistics of Claessens and Laeven enters only significant, at the 10%-level, along with activity
restrictions and deposit insurance. Besides the misspecification described above, Claessens
and Laeven’s H-statistics might be influenced by crises during the measurement period.
Furthermore, Bikker and Spierdijk’s H-statistic is available for a larger set of countries. For
these reasons the use of the H-statistics from Bikker and Spierdijk (2008) is preferred.
Table 7 shows that the H-statistics (for the year 1989) provided by Bikker and Spierdijk turns
out to be positively and significantly related to financial stability, except in the first
specification that uses the KKZ-index as explanatory variable. These results are robust to the
inclusion of concentration as explanatory variable34. The estimates of the coefficient of the Hstatistics vary between 0.04 and 0.12, which indicates that a one standard deviation increase in
the level of competition in the banking industry increases the probability of having a financial
crisis between 2 and 6%. The financial freedom index, economic freedom index and the
KKZ-index all have their expected signs. Deposit insurance enters positively indicating that
deposit insurance decreases financial stability. However, deposit insurance schedules with
34
More specifically, in all specifications, except in specification (1) with the KKZ-index along the control
variables, both bank concentration and bank competition enter significantly. The coefficient on bank
concentration is always negative, the coefficient on bank competition is positive.
45
explicit coinsurance are less prone to financial crises. Contrary to table 5, activity restrictions
enter positive and significant into specification (6). This result is similar to the findings of
Beck, Demirgüç-Kunt and Levine (2006). Activity restrictions reduce the diversification
possibilities for banks and this decreases financial stability. Liquidity requirements seem to be
strongly related to financial instability. The capital stringency index enters with a positive
coefficient3536.
In section 7 the fraction of entrants denied was used as an indicator for the degree of
competition in the financial sector. Table 4 shows that this fraction is negatively related to the
likelihood of financial crises, i.e. a higher fraction of entrants denied increases financial
stability. Although this tends to support Keeley’s charter value hypothesis, other empirical
studies which used the fraction of entrants denied as explanatory variable seem to contradict
this (see for instance Beck, Demirgüç-Kunt and Levine (2006); Barth, Caprio and Levine
(2004)). Nevertheless when controlling for other regulatory policies and the overall
development, the fraction of entrants denied enters negatively and significant in all
specifications, except in the specification that takes into account the degree of activity
restrictions. However, when one takes into account the degree of competition in the banking
system, as measured with H-statistics provided by Bikker and Spierdijk (2008), the fraction of
entrants denied loses its significance in all specifications. This indicates that the actual degree
of competition matters. Though, this fraction can be used as a crude proxy for the degree of
competition in the banking system, because both the H-statistics from Bikker and Spierdijk
and the fraction of entrants denied indicate that competition is detrimental to financial
stability.
35
Very similar results are obtained when GDP per capita is excluded as control variable. Activity restrictions and
the capital stringency index enter positively and significantly when GDP per capita is not included.
36
Although not shown, interaction terms between the bank competition and the regulatory environment do not
enter significantly into one of the specifications.
46
Table 7:
Systemic banking crises, bank competition and regulatory policies
H-statistic
(Bikker and Spierdijk)
KKZ-index
(1)
0.0392
(0.0300)
(2)
0.0560*
(0.0300)
(3)
0.0697**
(0.0293)
(4)
0.0495*
(0.0272)
(5)
0.1172***
(0.0363)
(6)
0.0836**
(0.0358)
(7)
0.0595**
(0.0288)
(8)
0.0642*
(0.0348)
-0.1002***
(0.0260)
Financial freedom
-0.0064***
(0.0014)
Economic freedom
-0.0120***
(0.0032)
Deposit insurance
0.1431***
(0.0302)
Interbank deposits
0.0362
(0.0452)
-0.1285***
(0.0412)
0.1703
(0.1289)
Coinsurance
Risk-based premiums
Activity restrictions
0.0127
(0.0085)
Liquidity requirements
0.1119***
(0.0379)
Capital stringency
index
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
0.0408
(0.0258)
521
103
70
70
0.1747
521
103
72
66
0.1830
521
103
70
70
0.1694
521
103
66
72
0.1799
306
73
71
74
0.2529
497
98
68
65
0.1644
497
98
70
69
0.1703
399
82
69
66
0.2029
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. Variables are defined in section 5. In all specifications years
classified as crisis after the initial crisis year are included. For the ease of interpretation, this table presents the marginal
effects of the logit regressions. For brevity coefficients on all macroeconomic control variables are not shown. White’s
heteroskedasticity consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates
statistical significance at 5%, * indicates statistical significance at 10%
8.3
National characteristics
It is widely accepted that national characteristics influence institutional development. Beck
and Levine (2005) stress the importance of legal origin for the financial development of a
country. To control for the possibility that national characteristics influence the relationship
between competition, concentration and financial stability legal origin is added as explanatory
variable. Table 8 shows the results.
The relationship between concentration and financial instability remains negative and
significant even if national factors are controlled for. Moreover, the coefficient hardly
changes. Also bank competition remains positive and significant once legal origin is used as
additional control variable. Specification (2) furthermore indicates that countries with French
and British legal origins are less prone to systemic banking crises as countries that have a
Scandinavian legal origin. Although this might be related to differences in legal origin, one
has to take into account that Scandinavian legal origin is not spread over the world. Only four
47
countries in the sample have a Scandinavian legal origin and three of these countries
experienced a systemic banking crisis in the beginning of the 1990’s. The results are robust to
the exclusion of the four countries with a Scandinavian legal origin. In this model it appears
that countries with a British legal origin are less prone to systemic crises than countries that
have a French legal origin.
Table 8
Systemic banking crisis, bank concentration, bank competition and legal origin
Concentration
(1)
-0.2947***
(0.0736)
H-statistic (Bikker and Spierdijk)
(2)
0.0524*
(0.0303)
Fraction of entries denied
French legal origin
British legal origin
German legal origin
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(3)
0.0390
(0.0750)
-0.0323
(0.0721)
0.1354
(0.1420)
-0.1795**
(0.0907)
-0.2198***
(0.0824)
-0.0784
(0.0609)
-0.2629***
(0.0850)
-0.0163
(0.0478)
-0.0613
(0.0416)
-0.0579
(0.0330)
620
115
70
65
0.1721
521
103
68
69
0.1658
384
55
71
71
0.2345
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. For brevity the results for the macroeconomic control
variables are not shown. Variables are defined in section 5. The legal origin dummy variables take the value one if a country
has a certain legal origin; Scandinavian legal origin is the base group. Specifications (1)-(3) include all crisis years after the
initial crisis year. For the ease of interpretation, this table presents the marginal effects of the logit regressions. White’s
heteroskedasticity consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates
statistical significance at 5%, * indicates statistical significance at 10%
8.4
Nonlinearity of the results
The relationship between bank concentration, competition and financial stability might be
nonlinear. To allow for a nonlinear relation between bank concentration and financial stability
a model that includes concentration and concentration squared is estimated. Table 9A
presents the results. The signs of concentration and concentration squared indicate that there
is a level of concentration that maximizes the likelihood of experiencing a financial crisis.
However, these coefficients are not significantly different from zero. In line with Beck,
Demirgüç-Kunt and Levine (2006) the model is re-estimated with interaction terms of five
quintile dummies and bank concentration. The quintile dummies take the value one if the
level of bank concentration in a certain country falls within that quintile. Therefore this
specification has five concentration variables. One of these concentration variables equals the
level of concentration, four variables equal zero. In each quintile bank concentration enters
48
negatively. However, only for concentration levels above 83.72% the coefficient is
significantly different from zero.
Also the relationship between bank competition and financial stability might be nonlinear.
Because some of the estimates of the H-statistic of Bikker and Spierdijk (2008) are negative
including a squared H-statistic into the model specification is nonsense. Therefore five
quintiles are created to check for nonlinearity of the results. Table 9B shows that there might
be a nonlinear relationship between competition and financial stability, although this result
appears to be not significant at convenient levels of significance. The H-statistic enters
significantly for levels above 0.96. This indicates that only highly competitive banking
systems are detrimental to financial stability. This result is robust to the inclusion of deposit
insurance and other regulatory policies such as activity restrictions. Although not shown,
similar results are obtained if the H-statistics (provided by Bikker and Spierdijk) averaged
over the period 1989 – 2004 are used.
Table 9
Panel A: Nonlinear relationship between bank concentration and systemic banking crises
(A1)
Concentration
Concentration squared
0.2637
(0.5767)
-0.4475
(0.4648)
Concentration * Quintile 1
-0.4486
(0.3968)
-0.3970
(0.2913)
-0.3382
(0.2434)
-0.2659
(0.2069)
-0.4432**
(0.2014)
Concentration * Quintile 2
Concentration * Quintile 3
Concentration *Quintile 4
Concentration *Quintile 5
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
(A2)
620
115
70
64
0.1558
620
115
73
68
0.1664
For information about the estimated logit model see below.
49
Panel B: Nonlinear relationship between bank competition and systemic banking crises
(B1)
H-statistic BS * Quintile 1
H-statistic BS * Quintile 2
H-statistic BS * Quintile 3
H-statistic BS *Quintile 4
H-statistic BS *Quintile 5
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
0.0053
(0.0247)
-0.2121
(0.1342)
-0.1101
(0.1213)
0.0477
(0.0759)
0.0643**
(0.0327)
475
103
71
63
0.1671
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. For brevity the results for the macroeconomic control
variables are not shown. Variables are defined in section 5. In all specifications crisis observations after the initial crisis year
are included. For the ease of interpretation, this table presents the marginal effects of the logit regressions. White’s
heteroskedasticity consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates
statistical significance at 5%, * indicates statistical significance at 10%
8.5
Dependent variable definition
Bank competition, as measured with H-statistics from Bikker and Spierdijk (2008), tends to
be insensitive to the use of other definitions of systemic banking crisis. In specification (3) the
crisis definition of Caprio and Klingebiel (2003) is used as dependent variable. Caprio and
Klingebiel’s definition is updated until 2002 and also borderline crises are classified as
systemic crisis. Bank competition enters with a positive sign that is significantly different
from zero at the 1%-level. Specification (4) uses the definition provided by Reinhart and
Rogoff (2009a). Although, the coefficient on bank competition is positive and significant, it is
much smaller in size than in specification (3). This might be due to the fact that Reinhart and
Rogoff exclude crisis observations after the first crisis year, while Caprio and Klingebiel
include all observations. Specification (1) and (2) test the sensitivity of bank concentration to
other crisis definitions. It turns out to be the case that bank concentration only enters with a
negative coefficient that is significantly different from zero if all observations are included.
The latter is the case in specification (1) that uses Caprio and Klingebiel’s definition.
50
Table 10
Bank competition, bank concentration and various banking crisis definitions
Concentration
(1)
-0.3429***
(0.1134)
(2)
-0.0153
(0.0306)
H-statistic (Bikker and
Spierdijk)
Observations
Number of crisis
% total correct
% crises correct
Pseudo R2
444
101
70
68
0.1812
580
19
72
68
0.1787
(3)
(4)
0.1277***
(0.0411)
0.0179*
(0.0094)
380
83
73
73
0.2145
491
17
87
79
0.2162
The following logit probability model is estimated:
in which j denotes the country and t denotes the year. Several lagged versions of credit growth are used, credit growth lagged
two periods provided the best results and is therefore included. For brevity the results for the macroeconomic control
variables are not shown. Variables are defined in section 5. Specification (1) and (3) use the crisis definition of Caprio and
Klingebiel (2003) as dependent variable. Specification (2) and (4) use the systemic banking crisis definition of Reinhart and
Rogoff (2009a) as banking crisis variable. The Laeven and Valencia (2009) definition is not used in this section because it is
already partly included in the banking crisis dummy introduced in section 3.
For the ease of interpretation, this table presents the marginal effects of the logit regressions. White’s heteroskedasticity
consistent standard errors are given in parentheses. *** indicates statistical significance at 1%, ** indicates statistical
significance at 5%, * indicates statistical significance at 10%
51
9
Conclusion and policy implications
Economic theory provides an ambiguous answer to the question what the impact of bank
competition and concentration on financial stability is. Keeley (1990) stresses that in more
competitive banking systems bank managers are inclined to take more risk due to lower
charter values. On the other hand there are economists who argue that bank competition
increases financial stability. Because of these contradicting theories on the relation between
bank concentration, competition and financial stability economists started to empirically
examine this relationship.
Among the first to empirically examine the relation between the banking system’s market
structure and risk-taking was Keeley (1990). He attributed the increase in bank failures in the
1980’s to increased competition in the banking and financial services industry. However,
recently economists challenged his findings and provided evidence for the competitionstability hypothesis. These mixed findings in the empirical literature can be attributed to the
wide array of competition, concentration and financial fragility measures in the literature.
Bank concentration is argued to be an insufficient indicator of competition and also the
measurement of competition is surrounded by a great deal of uncertainty. Nevertheless,
empirical studies stress the importance of the interaction of the market structure of the
banking systems with the institutional environment and regulatory policies.
Using a logit probability model and data on 76 countries from 1990 – 2007, this paper
provides evidence that more concentrated banking systems tend to be more stable. The
coefficient on concentration is consistently negative in all specifications that control for
macroeconomic, institutional and regulatory factors. Moreover, bank concentration enters
negatively and significantly when other crisis definitions are used. This finding is also robust
to the use of the C5-ratio instead of the C3-ratio. This finding is in line with Beck, DemirgüçKunt and Levine (2006) and Schaeck, Čihák and Wolfe (2006). However this finding
contradicts, among others, Mishkin (1999) and Buiter (2009) who argued that concentrated
banking systems are more prone to banking crises. It might however very well be the case that
financial crises tend to be more severe in countries with a concentrated banking system.
Further research regarding this is necessary.
In contrast to the findings of Schaeck, Čihák and Wolfe (2006) this paper shows that
competition is detrimental to financial stability. Bank competition, as measured with Hstatistics, enters positively and significantly in almost all specifications. This result is robust
to the inclusion of several macroeconomic variables and variables that capture the
institutional and regulatory framework. Furthermore, bank competition enters positively and
significantly once a country’s legal origin is taken into account. Important to recognize is that
there might be a nonlinear relation between bank competition and financial stability.
Recognizing that further research is necessary, this paper provides evidence that banking
systems that are close to perfect competition are more prone to financial crises.
Although this paper provides support for both the competition-fragility hypothesis and the
concentration-stability hypothesis these findings should not be interpreted as an outright
recommendation to support mergers and acquisitions in the banking sector to foster bank
concentration. Nor should restrictions be introduced that limit activities banks can undertake
to reduce competition in the banking sector. Instead there are still good arguments to not
52
curtail competition directly and prevent banks from becoming too-big-to-fail. Important is
that policy makers recognize the importance of the interaction between the market structure of
the banking system, the institutional environment and the regulatory and supervisory
framework. Well designed financial regulation and strong institutional environments limit the
likelihood that countries experience episodes of systemic banking crises. The latter manifests
itself in the strongly negative coefficient on the KKZ-index.
Policy makers should recognize that competition in the banking system, as in other sectors,
has its benefits as well. Therefore when competition in the banking sector induces bank to
take more risks the regulatory response should not be to curtail competition in the banking
sector. Instead, regulators and supervisory authorities should monitor banks more closely or
require banks to bear the costs of their own risk-taking.
Several advisory committees on the future of banking and financial supervision and regulation
pointed in the same direction. Maas et al (2009) recommend that if banks push against the
boundaries of Basel’s Pillar 1, regulators should have and use the means under Pillar 2 to
prevent banks from doing so. The Financial Service Authority (2009) argued in the Turner
Review that capital requirements for systemically important banks should entirely be
expressed in terms of high quality capital, i.e. Core-Tier 1. In line with the Turner Review,
Timothy Geithner (U.S. Treasury secretary) proposes that large systemically important banks
should pay to a special fund to deal with failures of these large banks (Reuters, 2009). De
Larosière et al (2009) argue that fierce competition in the banking sector induced bank
managers to search for higher returns. One of the recommendations from the high level group
on financial supervision is therefore that off-balance sheet vehicles should also be subject to
minimum capital requirements. They also stress the importance of risk-adjusted funding
arrangements for deposit insurance.
Most of these advisory committees agree that the financial system’s safety net should be
incentive compatible. In this respect, this paper provides evidence that this isn’t yet the case
with deposit insurance. Explicit deposit insurance increases, due to the moral hazard effects
associated with deposit insurance, the likelihood of systemic banking crises. Therefore it is
important that banks are forced to assume the consequences of their own risk-taking. Policy
makers should recognize the effects the market structure of the banking sector has on risktaking. Deposit insurance for instance can have very different welfare effects in different
market structures. Besides recognizing the malfunctioning of some parts of the financial
safety net this paper provides evidence that enhanced market discipline might contribute to
financial stability. For instance, the presence of coinsurance reduces the probability of having
a financial crisis significantly. Unfortunately policy makers did not adopt the right policies.
Instead of moving towards an incentive compatible financial safety net and increasing market
discipline, deposit insurance became more generous in terms of both coverage levels and the
existence of coinsurance.
Although this paper provides evidence for the competition-fragility hypothesis and the
concentration-stability hypothesis a lot need to be done to explore the mechanisms how
competition and concentration in the banking sector affect financial stability. Furthermore,
due to the rapid increase in financial interlinkages, measuring competition becomes harder
because a single country might not be the relevant banking market. Hence, competition in
banking industries can have serious effects for financial stability in other countries due to
53
contagion of financial shocks. Besides that, as this study illustrated, there is a serious problem
that the results might be driven by reversed causality. Anyhow, the current financial crisis
provides economists with new data to explore the mechanisms how bank competition and
concentration affect financial stability. Hopefully, these new insights give policy makers an
incentive to take the right policies to increase the stability of our financial system.
54
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59
Appendix
Table A1
Systemic banking crisis, bank concentration and bank competition by country
Country
Crisis episodes
1990 - 2007
Bank
concentration
Algeria
Australia
Austria
Bangladesh
Belgium
Benin
Bolivia
1990 – 1992
0.806
0.595
0.854
0.837
0.834
0.765
0.878
Botswana
Burkina Faso
Cameroon
Canada
Chile
Colombia
Costa Rica
Côte d'Ivoire
Denmark
Dominican
Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Ethiopia
Finland
France
Gabon
Germany
Ghana
Greece
Guatemala
Honduras
India
Indonesia
Ireland
Israel
Italy
Japan
Jordan
Kenya
Korea, Rep.
Kuwait
Lebanon
Luxembourg
Madagascar
1990
1994 – 1997
2001 – 2002
1990 – 1994
1990 – 1993
1995 – 1998
1999 – 2000
1994 – 1997
1990 – 1991
H-statistic
(Claessens
and Laeven)
0.80
0.66
0.69
0.73
0.826
0.695
0.645
0.762
0.755
0.698
0.628
0.742
0.714
0.701
1995 – 2002
H-statistic
(Bikker and
Spierdijk)
0.37
0.26
-0.06
0.92
0.86
Fraction of
entries denied
1.00
0
0.67
0.66
0.66
0.92
0.50
0.72
0.44
0.78
0.51
0.96
-0.08
0.25
0.58
0.68
0.72
0.54
1997 – 2005
1991 – 1994
1992 – 1995
1997 – 2002
1990 – 1995
1992 – 2005
1990
1994 - 1997
1997 – 2002
1990
0.676
0.657
0.616
0.689
0.613
0.744
0.557
0.630
0.711
0.656
0.705
0.563
0.656
0.640
0.701
0.648
0.644
0.688
0.625
0.673
0
0.14
0.543
1991 – 1993
0
0
0.69
0.58
-0.07
1.33
0.76
0.9
0.70
0.40
0.81
0.53
0.62
0.48
-0.08
0.60
0.47
0.58
0.69
0.82
1.33
0.14
0.08
0.42
0.39
0.35
1.33
0.58
0.34
0.36
0
0.14
0
0
0.33
0
0
0
0
0
0.27
0.05
0
0.33
0.65
0
0
0.20
0.10
0
0.90
0.71
0
0
1990 -1991
60
Malawi
Malaysia
Mali
Mauritius
Mexico
Morocco
Mozambique
Nepal
Netherlands
Nigeria
Norway
Pakistan
Panama
Paraguay
Philippines
Portugal
Saudi Arabia
Senegal
Singapore
South Africa
Spain
Sri Lanka
Sweden
Switzerland
Thailand
Trinidad and
Tobago
Tunisia
Turkey
Uganda
United Kingdom
United States
Uruguay
Venezuela, RB
Zambia
0.3
1997 – 2001
1994 -1997
0.653
0.630
0.645
0.619
0.691
0.68
0.640
0.751
0.626
0.685
0.578
0.657
0.647
0.645
0.739
0.629
0.561
0.650
0.697
0.614
0.657
0.589
0.675
0.86
0.67
0.57
0.48
0.74
0.60
0.66
0.67
0.78
1990 – 1991
1991 – 1995
1990 – 1993
1995 – 1999
1997 – 2005
1990 – 1993
1990 – 1993
1997 – 2005
0.85
0.53
0.67
0.75
0.68
1.41
0.38
0.57
0.89
0.97
0.68
0.51
0.55
0.57
0.67
0.61
-0.02
0.57
1.23
0.26
1.34
0.57
0.75
-1.84
0.85
0.66
0.24
1991 – 1995
1991
1994
2000 – 2005
1994 – 1997
2007
1990 – 1992
2007
2002 -2005
0.643
0.617
0.46
0.24
0.632
0.685
0.642
0.74
0.41
0.895
0.445
1993 – 1997
0.617
0.686
0
0.33
0
0
0
0
0
0
0
0
0
0.02
0.23
1
0.25
0.712
0.74
0.57
0
1.02
0.61
0.11
61
Table A2
Correlation between competition, concentration and bank regulation
Bank
concentration
H-statistic
(Claessens and
Laeven)
H-statistic
(Bikker and
Spierdijk)
C5-ratio
Banks per
capita
Fraction of
entry denied
Liquidity
requirements
Activity
restrictions
Bank
concentration
1
H-statistic
(Claeassens
and Laeven)
H-statistic
(Bikker and
Spierdijk)
C5-ratio
0.192***
(0.000)
1
-0.229***
(0.000)
0.386***
(0.000)
1
0.485***
(0.000)
0.242**
(0.000)
-0.120***
(0.000)
1
Banks per
capita
-0.255***
(0.000)
0.175***
(0.000)
-0.078**
(0.014)
-0.312***
(0.000)
1
Fraction of
entry denied
0.045
(0.199)
-0.092**
(0.047)
0.019
(0.603)
0.088**
(0.011)
-0.132***
(0.000)
1
Liquidity
requirements
-0.020
(0.492)
-0.061
(0.121)
-0.004
(0.903)
-0.104***
(0.001)
-0.019
(0.538)
0.182***
(0.000)
1
Activity
restrictions
-0.174***
(0.000)
-0.115***
(0.003)
-0.161***
(0.000)
0.001
(0.972)
-0.193***
(0.000)
0.139***
(0.000)
0.137***
(0.000)
1
Capital
stringency
0.033
(0.344)
-0.92**
(0.026)
0.175***
(0.000)
-0.067*
(0.055)
0.062*
(0.081)
-0.039
(0.335)
0.033
(0.347)
-0.153***
(0.000)
P-values are given in parentheses. *** Correlation is significant at the 1%-level; ** Correlation is significant at the 5%-level; * Correlation is significant at the 10%-level.
Table A3
Capital stringency index
Relevant questions for the construction of the capital stringency index:
1. Is it legally required that applicants submit information on the source of funds to be
used as capital?
2. Are the sources of funds to be used as capital verified by the regulator/supervisory
authorities?
3. Can the initial disbursement or subsequent injections of capital be done with assets
other than cash or government securities?
4. Can the initial disbursement of capital be done with borrowed funds?
5. Is the minimum capital asset ratio risk weighted in line with the Basel guidelines?
6. Does the minimum ratio vary as a function of an individual bank’s credit risk?
7. Does the minimum ratio vary as a function of market risk?
8. Is subordinated debt allowable as part of capital?
Before minimum capital adequacy is determined, which of the following are deducted from the
book value of capital?
9. Market value of loan losses not realized in accounting books
10. Unrealized losses in security portfolios?
11. Unrealized foreign exchange losses?
12. Are banks required to hold either liquidity reserves or any deposits at the central bank?
Direction
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
This table from Behr, Schmidt and Xie (2010) contains all questions used in the surveys from Barth et al (2008) to construct
the capital stringency index. The second column denotes the answer that indicates more stringent regulation. Answers are
specified as dummy variables that take the value one when the answer indicates that regulation is stringent. Behr, Schmidt
and Xie (2010) use principal component analysis to construct an overall measure of capital stringency. Note that question 12
about the liquidity reserves banks are required to hold is also used separately as a dummy variable to indicate whether a
country installed liquidity requirements.
Table A4
Data sources
Name:
Real interest rate
GDP growth
GDP per capita
Inflation
M2/reserves
Change in terms of trade
Private credit to GDP
Depreciation
H-statistic (Claeassens and
Laeven)
H-statistic (Bikker and Spierdijk)
Credit growth
Concentration
Crisis dummy
Crisis dummy CK
Crisis dummy RR
French legal origin
British legal origin
German legal origin
Explanation:
Real interest rate (%)
Real GDP growth; measured in constant local currency units (annual %)
GDP per capita (in 1990 US$; converted at Geary Khamis PPPs)
Inflation, GDP deflator (annual %)
Money and quasi money (M2) to total reserves ratio
Yearly change in the bet barter terms of trade index (2000 = 100)
Domestic credit to private sector (% of GDP)
Yearly depreciation local currency unit vis-à-vis the US$
The sum of the elasticities of the total revenue of a bank with respect to
the bank’s input prices
The sum of the elasticities of the total revenue of a bank with respect to
the bank’s input prices
Rate of growth of real domestic credit provided to the private sector
Cumulative market shares (measured in assets) of the three largest banks
to total banking system assets
Dummy variable that takes the value one if a period is classified as a
systemic crisis based on the Demirgüç-Kunt and Detragiache definition,
updated for 2006 and 2007 based on data from Laeven and
Valencia(2008)
Dummy variable that takes the value one if a period is classified as a
systemic crisis based on the Caprio and Klingebiel definition
Dummy variable that takes the value one if a period is classified as a
systemic crisis based on the Reinhart and Rogoff definition
Dummy variable that takes the value one if a country has a French legal
origin
Dummy variable that takes the value one if a country has a British legal
origin
Dummy variable that takes the value one if a country has a German legal
origin
Coverage:
1990 - 2007
1990 - 2007
1990 - 2007
1990 - 2007
1990 - 2007
1990 - 2007
1990 - 2007
1990 - 2007
1994 -2001
Source:
WDI
WDI
The Conference Board
WDI
WDI
WDI
WDI
WDI
Claessens and Laeven (2004)
1989 - 2004
Bikker and Spierdijk (2008)
1990 - 2007
1990 - 2007
IFS, WDI
Beck, Demirgüç-Kunt and Levine
(2010)
Demirgüç-Kunt & Detragiache
(2005); Laeven and Valencia
(2008)
1990 - 2005
1990 - 2002
Caprio and Klingebiel (2003)
1990 - 2007
Reinhart and Rogoff (2010)
CIA factbook
CIA factbook
CIA factbook
Name:
Scandinavian legal origin
KKZ index
Deposit insurance
Interbank deposits
Coinsurance
Risk based premiums
Fraction of entry denied (total)
Fraction of entry denied
(Domestic)
Fraction of entry denied
(Foreign)
Capital stringency index
Activity restrictions
Liquidity requirements
State ownership
Explanation:
Dummy variable that takes the value one if a country has a Scandinavian
legal origin
Weighted average of the KKZ-index in the period 1996 - 2008
Coverage:
Source:
CIA factbook
1996 - 2008
Dummy variable that takes the value one if a country has an explicit
deposit insurance schedule
Dummy variable that takes the value one if a country’s deposit insurance
schedule covers interbank deposits (conditional on having explicit deposit
insurance)
Dummy variable that takes the value one if a country’s deposit insurance
schedule includes coinsurance (conditional on having explicit deposit
insurance)
Dummy variable that takes the value one if a country’s deposit insurance
schedule charges risk-adjusted premiums (conditional on having explicit
deposit insurance)
Total number of applications (from both domestic and foreign entities)
for commercial banking licenses denied divided by the total number of
applications
Total number of applications (from domestic entities) for commercial
banking licenses denied divided by the total number of applications from
domestic entities
Total number of applications (from foreign entities) for commercial
banking licenses denied divided by the total number of applications from
foreign entities
Overall index based on the stringency of capital regulation
Composite index that measures the degree of activity restrictions present
in the banking system, based on security, insurance and real estate
activities and whether banks can own voting shares in non-financial
companies. A higher score indicates more restrictions
Dummy variable that takes the value one if banks are required to hold
either liquidity reserves or any deposits at the central bank
Percentage of governments owned banks in 2005
1990 - 2007
Worldbank; Kaufman, Kraay and
Zoido-Lobaton (1999; etc.)
Demirgüç-Kunt, Karacaovali,
Laeven (2005)
Demirgüç-Kunt, Karacaovali,
Laeven (2005)
2003
1990 -2003
Demirgüç-Kunt, Karacaovali,
Laeven (2005)
2003
Demirgüç-Kunt, Karacaovali,
Laeven (2005)
2000 - 2005
Barth, Caprio and Levine (2008;
2005)
2000 - 2005
Barth, Caprio and Levine (2008;
2005)
2000 - 2005
Barth, Caprio and Levine (2008;
2005)
2006
2005
Behr, Schmidt and Xie (2010)
Barth, Caprio and Levine (2008;
2005)
2008
Barth, Caprio and Levine (2008;
2005)
Barth, Caprio and Levine (2008;
2005)
2005
65
Name:
Foreign ownership
Explanation:
Percentage of foreign owned banks in 2005
Coverage:
2005
Economic freedom
Weighted average of the Economic freedom index over the period 1995 2008
Weighted average of the Financial freedom index over the period 1995 2008
Total assets hold by the 5-largest banks at year end 2005
1995 - 2008
Source:
Barth, Caprio and Levine (2008;
2005)
Heritage foundation
1995 -2008
Heritage foundation
2005
The number of commercial banks at year end 2005 divided by the total
population of a country at year end 2005
2005
Barth, Caprio and Levine (2008;
2005)
Barth, Caprio and Levine (2008;
2005); Population WDI
Financial freedom
C5-ratio
Banks per 1.000.000 residents
66
Model A1
The effect of deposit interest rates on monitoring effort
The following stylized model, adapted from Carletti (2008), illustrates the effect the level of
interest rates paid to depositors has on the monitoring effort of banks. Assume a two period
economy in which a bank at date
decides to invest in a risky project and sets the deposit
rate paid to depositors. This risky project has a return equal to if the projects turns out to be
successful and a return of if not. The probability that the projects turns out to be a success
depends on the monitoring effort
of the bank. The probability equals
if the bank
monitors, but is equal to if the bank doesn’t monitor, with
. Monitoring is, however,
costly for the bank. It is assumed that the monitoring costs function is convex and defined as:
, with
. For simplicity assume that the bank is fully financed with debt.
The debt holders earn a promised return of
. Furthermore assume that
.
The profit of the bank is therefore equal to:
The bank is assumed to maximize its profit with respect to the level of monitoring. This
implies that the optimal level of monitoring is equal to:
In which
larger than 0.
. Given the assumptions above, the level of monitoring is always
To determine the effect an increase in the level of interest rates paid to debt holders has on the
monitoring effort one takes the first order derivative of the optimal monitoring effort with
respect to the promised return paid to debt holders:
This expression shows that an increase in the interest rate paid to debt holders decreases the
monitoring effort of a bank. Intuitively, because the bank shares more of its profits if
depositors are paid a higher interest rate the bank has an incentive to reduce monitoring. A
higher monitoring effort increases the probability that depositors are paid back. The
introduction of deposit interest ceiling, with
, might therefore increase the monitoring
effort of banks.
Model A2
Bank competition and risk taking (Allen and Gale (2000a; 2004)37)
The following Nash-Cournot game between banks, Allen and Gale (2000a; 2004) and Carletti
(2008), demonstrates that competition creates incentives for banks to adopt riskier strategies.
Suppose there are banks identified with
. All these banks choose a portfolio, i.e.
a specific risk-return tradeoff that can be characterized by its size and its risk-return tradeoff.
For simplicity the portfolio has only two possible returns. The return of the portfolio equals
with a probability equal to
; the return is zero with a probability equal to
.
The function
,
has the following characteristics:
and
,
,
To make investments in portfolios banks need to attract deposits. Without bank capital, one
unit of investment in the portfolio requires one unit of deposits. Deposits from bank , denoted
as , are costly. The interest paid to depositors depends on the total demand for deposits
from all banks, denoted as
. The upward-sloping supply of funds curve,
, has
the following characteristics:
,
and
,
For simplicity, assume that all deposits are insured, which implies that the function of
is
independent of . The bank is assumed to have no other costs than the costs of funds and
pays no deposit insurance premium. This situation is similar to the deposit insurance schedule
in the Netherlands where banks do not pay premiums ex ante.
The profit of bank ,
In which
is therefore equal to:
and
In the Nash-Cournot equilibrium, each bank chooses the size and riskiness of its portfolio that
is the best-response to the size and riskiness of the portfolios of the other banks. This implies
that:
and
.
(1)
(2)
For simplicity assume that the resulting equilibrium is symmetric, i.e.
such that (1) and (2) can be rewritten as:
(3)
37
and
This model is based on Allen and Gale (2000a; 2004)
68
(4)
From (4) it follows that:
(5) –
Using (3):
(6) –
Given the assumptions above, –
is decreasing in . Allen and Gale (2000a; 2004) show
that there is only one symmetric equilibrium under the maintained assumptions.38
Allen and Gale (2000a; 2004) assume that the degree of competitiveness of the banking
system can be described by the number of banks. This assumption is not valid if banks would
compete à la Bertrand. In such setting of price competition, two competing banks would be
enough to support the outcome generated under perfect competition. Therefore this
assumption is only valid if banks compete à la Cournot, i.e. each bank chooses its quantity to
maximize its profits rather than its price. Nevertheless, it is interesting to see what happens to
the risk-taking behavior of the banks if increases.
The assumptions about the supply of funds imply that
if
, because otherwise
, which could not generate an equilibrium. This would imply that
and from (3) it follows that
as well. Hence,
, which is the same as
and
converge to the maximum risk a bank can take .
Intuitively, an increase in the number of banks implies that each bank attracts fewer deposits.
This implies that the effect an individual bank has on the price of deposits become negligible.
In this setting banks behave almost like perfect competitors and have an incentive to increase
their business as long as the profits they could earn are positive. The resulting equilibrium is
the one in which all profits converge to zero which implies that banks have an incentive to
engage in excessive risk taking, because the opportunity costs of taking excessive risk, i.e. the
profits that could be earned without excessive risk taking, are small.
38
Suppose that there would be two symmetric equilibriums denoted as
and
. If
this implies
that
(*). This would imply from the assumptions made on
that
and hence
because of symmetry
. In this case
, which contradicts the equation
denoted with (*). This implies that there at most one symmetric denoted with equation (6).
69