Bank competition and stability: Reconciling con‡icting
empirical evidence
Thorsten Becky
Olivier De Jonghez
Glenn Schepensx
22 May 2011
Abstract
Theoretical models and empirical results o¤er con‡icting evidence on the relationship
between bank competition and bank stability. This paper aims to reconcile the seemingly
contrasting evidence on the bank competition-bank soundness relationship. We develop a
uni…ed framework to assess how regulation, supervision and other institutional factors may
make it more likely that the data favor one theory over the other (charter value paradigm
versus risk-shifting paradigm). Cross-country heterogeneity in these factors allows us to test
the assumptions and predictions of various theoretical models. We show that an increase
in competition will have a larger impact on banks’ risk taking incentives in countries with
stricter activity restrictions, more herding in revenue structure, unconcentrated banking
markets and more generous deposit insurance.
The authors would like to thank Fabio Castiglionesi, Hans Degryse, Claudia Girardone, Klaus Schaeck and
seminar participants at HEC Paris, Ghent University, Tilburg University, Cass Business School, Roma II Tor
Vergata, Université Libre de Bruxelles, Bangor Business School for interesting discussions and helpful comments.
y CentER,
European Banking Center, Tilburg University and CEPR.
z CentER,
European Banking Center, Tilburg University.
x Corresponding
author: [email protected]. Department of Financial Economics, Ghent University.
1
1
Introduction
The impact of bank competition on …nancial stability remains a widely debated and controversial
issue, both among policymakers and academics. The belief that …ercer competition among banks
would lead to a more e¤ective banking system initiated a deregulating spiral in the late 70s and
early 80s. While the deregulation of branching and activity restrictions may have resulted in
more intense competition among banks, with positive repercussions, it may as well have had
the unintended consequence of increasing banking sector instability.1 Similarly, the international
process of banking liberalization seemingly has gone hand in hand with an increased occurrence
of systemic banking crises in the last two decades of the twentieth century, culminating in the
global …nancial crisis of 2007-2009. However, there is no academic consensus on whether bank
competition leads to more or less stability in the banking system. On the one hand, an increase in
loan market competition leads to lower lending rates and hence lower interest margins. As banks’
franchise values erode, this may create incentives to gamble and may lead to a shift towards riskier
activities, because of the limited liability by bank shareholders that e¤ectively turns bank equity
into a put option on banks ’pro…ts. On the other hand, a similar argument but in the opposite
direction can be made for the bank’s borrowers. If entrepreneurs are confronted with lower loan
rates, they will choose safer projects and have fewer incentives for aggressive risk taking, i.e. the
adverse selection and moral hazard problems will be mitigated. These two opposite e¤ects may
help in explaining why empirical studies across di¤erent samples and time periods fail to …nd
a consensus on which e¤ect dominates. Moreover, comparing the results of di¤erent studies is
complicated by the use of di¤erent competition and risk measures.
A similar inconclusive debate as on the relationship between competition and stability has
been led on the e¤ect of the regulatory framework on banks ’risk-taking incentives and ultimately
bank stability. On the one hand, capital regulation and interest rate and activity restrictions
1 See
among other Keeley (1990) and Jayaratne and Strahan (1998)
2
are seen as fostering stability (Hellmann, Murdock, and Stiglitz (2000)); on the other hand, they
might lead to rent-seeking and might prevent banks from reaping necessary diversi…cation and
scale bene…ts. The role of deposit insurance schemes has been especially controversial. While
often introduced to protect small depositors’ lifetime savings and to prevent bank runs, they
also provide perverse incentives to banks to take aggressive and excessive risks. These perverse
incentives are held less in check in weak supervisory frameworks (Demirguc-Kunt and Detragiache
(2002)).
This paper combines the two literatures and assesses whether the relationship between competition and stability varies across markets with di¤erent regulatory frameworks, market structures
and levels of institutional development. Speci…cally, while holding the measure of bank competition and stability constant across samples, we document that support for either the competitionstability or competition-fragility view varies across countries and over time. Next, we identify and
test the possible channels that may create cross-country variation in the competition-stability
relationship. While we identify several country characteristics that explain the cross-country
variation in the competition-stability relationship, a large amount is still unexplained. Finally,
based on our results, we try to reconcile the seemingly con‡icting existing empirical results on
the competition-stability relationship.
<Insert Figure 1 around here>
As way of motivation, consider the cross-country variation in the relationship between competition and stability. In our sample of banks in 62 countries, the pairwise correlation2 between the
Lerner index and the Z-score, widely used proxies for market power and bank soundness, respectively, is 0:25.3 Figure 1, however, reveals that this full sample correlation masks a substantial
2 We
refer to simple pairwise correlation coe¢ cients in the introduction. A similar story can be made with
regression based conditional relationships. However, for ease of exposition, we postpone this to later sections.
3 The
Lerner index is the ratio of the di¤erene between price and marginal cost and the price, with higher
values indicating higher market power. The Z-score is an accounting measure of bank distress. It is measured
3
degree of country-level heterogeneity. Each bar in Figure 1 corresponds to the country-speci…c
pairwise correlation between market power and bank soundness. The average pairwise correlation over the 62 countries resembles the full sample correlation. However, there is a large
amount of heterogeneity in the competition-stability relationship, with correlations ranging from
below -0.2 to above 0.5. In some countries, the correlation is negative and signi…cant. In many
others it is not statistically di¤erent from zero. In most countries, the number is positive and
signi…cant. Rather than being interested in the sign of the relationship, we are interested in the
cross-sectional dispersion. Speci…cally, we are interested in which country-speci…c features make
it more likely that competition is less harmful or more bene…cial for bank soundness.
Exploring the variation in the competition-stability relationship is important for academics
and policy makers alike.
The academic debate on the e¤ect of competition on bank stability
has been inconclusive and by exploring factors that can explain variation in the relationship,
this paper contributes to the resolution of the puzzle. Policy makers have been concerned about
the e¤ect of deregulation and the consequent competition on bank stability but have also discussed di¤erent elements of the regulatory framework that have both an impact on competition
and directly on stability, including deposit insurance capital regulation and activity restrictions.
This paper shows a critical role for the regulatory framework in explaining the variation across
countries and over time in the relationship between competition and stability and has therefore
important policy repercussions.
Our paper builds on a rich theoretical and empirical literature exploring the relationship
between competition and stability in the banking system. On the one hand, the competitionfragility view posits that more competition among banks leads to more fragility. This “charter
value” view of banking, as theoretically modeled by Marcus (1984) and Keeley (1990), sees
as the sum of accounting pro…ts and the capital to asset ratio, divided by the volatility of pro…ts. As such, it
indicates with how many standard deviations pro…ts can fall before capital is depleted.
4
banks as choosing the risk of their asset portfolio. Bank owners, however, have incentives to
shift risks to depositors, as in a world of limited liability they only participate in the up-side
part of this risk taking. In a more competitive environment with more pressures on pro…ts,
banks have higher incentives to take more excessive risks, resulting in higher fragility. In systems
with restricted entry and therefore limited competition, on the other hand, banks have better
pro…t opportunities, capital cushions and therefore fewer incentives to take aggressive risks, with
positive repercussions for …nancial stability. In addition, in more competitive environment, banks
earn fewer informational rents from their relationship with borrowers, reducing their incentives to
properly screen borrowers, again increasing the risk of fragility (Boot and Thakor (1993), Allen
and Gale (2000), Allen and Gale (2004)). The competition-stability hypothesis, on the other
hand, argues that more competitive banking systems result in more rather than less stability.
Speci…cally, Boyd and De Nicolo (2005) show that lower lending rates reduce the entrepreneurs
cost of borrowing and increase the success rate of entrepreneurs’investments. In addition, these
…rms will refrain from excessive risk-taking to protect their increased franchise value. As a
consequence, banks will face lower credit risk on their loan portfolio in more competitive markets,
which should lead to increased banking sector stability. However, more recent extensions of the
Boyd and De Nicolo (2005) model that allow for imperfect correlation in loan defaults (MartinezMiera and Repullo (2010); Hakenes and Schnabel (2007)) show that the relationship between
competition and risk is U-shaped. Hence, the impact of an increase in competition can go either
way, depending on other factors. Wagner (2010) extends the Boyd and De Nicolo (2005) model
and allows for risk choices made by borrowers as well as banks. If lending rates decline due to
more competition, banks have less to lose in case a borrower defaults. Hence, a bank may …nd it
optimal to switch to …nancing riskier projects4 , which overturns the Boyd and De Nicolo (2005)
4 Other
authors have also shown that more intense competition may induce banks to (i) switch to more risky,
opaque borrowers (Dell’Ariccia and Marquez (2004)), and (ii) acquire less information on borrowers (Hauswald
and Marquez (2006)).
5
results.
The standard response to con‡icting theoretical predictions is to let the data speak. Numerous authors have used di¤erent samples, risk measures and competition proxies to discriminate
between the competition-fragility and competition-stability view.5 Empirical studies for speci…c
countries – many if not most for the U.S. – have not come to conclusive evidence for an either
stability-enhancing or stability-undermining role of competition. The cross-country literature has
found that more concentrated banking systems are less likely to su¤er a systemic banking crisis
as are more competitive banking systems (Beck, Demirguc-Kunt, and Levine (2006); Schaeck,
Cihak, and Wolfe (2009)). There seems also evidence that banks in more competitive banking
systems hold more capital, thus compensating for potentially higher risk they are taking (Schaeck
and Cihak (2010a), Berger, Klapper, and Turk Ariss (2009)). A consequence of the recent theoretical extensions is that the predicted impact of competition on bank stability moved from a
bipolar setting (good or bad per se) to a continuous approach (settings that are better or worse
in relative terms). These models lead to new testable implications that exceed a mere assessment
of the sign of the coe¢ cient of bank market power. For example, by allowing loan defaults to
be imperfectly correlated, the Martinez-Miera and Repullo (2010) model and the Hakenes and
Schnabel (2007) model imply that the impact of competition on risk is a¤ected by regulatory
constraints on asset diversi…cation, since the latter will a¤ect the correlation structure of loan
defaults.
Our results suggest that an increase in competition will have a larger impact on banks’risk
taking incentives in countries with stricter activity restrictions, more herding in revenue structure and unconcentrated banking markets. These …ndings are con…rmed both in cross-sectional
regressions as well as when we allow for additional time-series variation in the competitionstability relationship. However, we also …nd that a large part of the cross-country variation
5 For
an overview, see Beck (2008).
6
in the competition-stability relationship cannot be explained, which constitutes a challenge for
further research.
The remainder of the paper is structured as follows. Section 2 discusses di¤erent factors that
might explain the variation in the competition-stability relationship documented in Figure 1.
Section 3 introduces data and methodology, while section 4 presents the main results. Section 5
presents robustness, while section 6 concludes with policy implications.
2
Competition-stability relationship - theoretical considerations
We argue that country-speci…c features may a¤ect the existing empirical evidence on the relationship between competition and stability via three possible channels. First, a certain type of
regulation may limit the extent to which banks can or will engage in riskier activities if their
franchise values are eroded. For example, regulatory capital requirements should limit the extent to which banks can follow risk-taking incentives if banks’charter value is eroded. Second,
country-speci…c characteristics may also a¤ect the adverse selection problem that banks face if
they charge higher loan rates. For example, lending relationships or credit registries may reduce the likelihood that entrepreneurs will chose riskier project in response to higher loan rates.
Third, institutional characteristics may a¤ect the proportion of systematic and idiosyncratic risk
in loan defaults and may make it hence more likely that the empirical data favor one theory
over the other. For example, regulatory constraints on asset, revenue or geographical diversi…cation may make it more likely that loan defaults are highly correlated and hence lead to the
empirical outcome that competition is good for …nancial stability. More speci…cally, let
de-
note the estimated e¤ect of bank market power on stability. This point estimate is in‡uenced
by three factors:
CF
> 0;
CS
< 0; p(CF ) 2 [0; 1] where
7
CF
denotes the stability welfare
gains of a unit increase in market power (competition-fragility hypothesis),
denotes the
CS
stability loss as a result of a unit increase in market power (competition-stability hypothesis)
and p(CF ) indicates how likely it is that one theory dominates over the other. We conjecture
that
= p(CF )
CF
+ (1
p(CF ))
CS .
Our conjecture is that the regulatory environment,
strength of supervision and the institutional framework of a country a¤ect
CF ;
CS
and p(CF ).
More speci…cally, let x denote the speci…c feature under investigation. A change in x (or two
samples with di¤erent x) can lead to a di¤erent estimated impact of market power on stability
via three di¤erent channels:
@
@p(CF )
=
@x
@x
CF
@p(CF )
@x
CS
+ p(CF )
@
CF
@x
+ (1
p(CF ))
@
CS
@x
The relative strength of each of these three channels may explain why di¤erent studies obtain
di¤erent results in terms of sign and magnitude. That is, certain country-speci…c features may
make the assumption and prediction of a given theoretical model more realistic. In the remainder
of this section, we describe which country-speci…c features may play a role and why. We also
introduce speci…c measures that capture these di¤erent market-speci…c characteristics.
2.1
Herding
A …rst important market characteristic that can in‡uence the relationship between competition
and stability is covariation of banks’behavior, also known as herding. Acharya and Yorulmazer
(2007) and ? show that the supervisory decision to intervene a failing bank is subject to an
implicit too-many-to-fail problem: when the number of bank failures is large, the regulator …nds
it ex-post optimal to bail out some or all failed banks. This gives banks incentives to herd and
increases the risk that many banks may fail together. Hence, herding behavior may also a¤ect
banks’incentive to increase risk-taking in response to an increase in competition. Bank activity
herding is measured by a heterogeneous banking system indicator that measures whether there
are substantial di¤erences among di¤erent …nancial institutions within a country. It is calculated
8
as the within country standard deviation of the non-interest income share. If all banks in a
country have a similar business model (either voluntarily or forced by regulation), the indicator
will be low. When bank activities are highly correlated across banks, a rise in competition will
do more damage to a banks franchise value since they do not have any other activities to fall
back on. Thus, we can hypothesize that competition will have a stronger impact on bank risk
CF
behavior in more homogeneous banking systems, i.e. @ @x
> 0. It is important to note in this
context that we do not relate the actual activity structure of banks to the relationship between
competition and stability, but rather the variation in activity structure within a market.
We
also look at herding in terms of risk taking behavior (systemic risk). If a majority of banks
has a high risk appetite, it is very well possible that other banks feel the pressure to take on
more risk due to the herding incentives described above. Therefore, we hypothesize that banks
operating in an environment with a high risk taking standard, will have a stronger incentive to
increase risk taking when competition changes.
2.2
Market structure
A second important market characteristics that might in‡uence the relationship between competition and stability is the structure of the market. Martinez-Miera and Repullo (2010) and
Hakenes and Schnabel (2007) show that a lower correlation of loan defaults makes it more likely
that …ercer competition harms stability. A bank’s potential to reduce the correlation of its loan
portfolio and other revenues is clearly a¤ected by restrictions on functional or geographical
diversi…cation. If x is a proxy for diversi…cation, we conjecture that
@p(CF )
@x
> 0.
Market concentration, measured by the Hirschmann-Her…ndahl index, may also play a
role in assessing the strength of the competition-stability relationship. While these measures
are not good proxies for competition6 , market concentration may play an important role in
6 See
Claessens and Laeven (2004).
9
determining the relationship between competition and stability. Fewer banks in the economy
(more concentrated banking markets) make supervision more e¤ective and accurate. If bank
supervisors have to monitor fewer banks, they may observe malpractices (risk-shifting, loan
portfolio concentration) in a more timely fashion. According to Allen and Gale (2000), countries
with a larger number of banks (such as the US) are, ceteris paribus, more likely to support the
competition-fragility view compared to banking sectors dominated by fewer larger banks (such
as Canada), i.e.
@
CF
@x
< 0 where x stands for the degree of concentration. Moreover, with
fewer banks in the system, entrepreneurs may behave more prudently as they have fewer outside
options when they default on their loans, which raises the franchise value of the bank. On the
other hand, the lower the number of banks in a country, the more they will be interconnected,
which may again encourage risk-taking behavior if banks perceive themselves as too-important-to
fail, i.e.
@
CF
@x
> 0. The e¤ect of market structure on the competition-stability relationship is
therefore a priori ambiguous.
2.3
Regulatory and supervisory framework
A third group of country traits that in‡uence the relationship between competition and stability consists of regulations designed to protect bank charter values and to prevent risk-seeking
behavior if charters are eroded. High capital levels reduce the moral hazard incentives to take
aggressive risks. More stringent (risk-based) capital regulation may therefore limit the
CF
< 0). Hellmann, Murdock,
negative in‡uence that competition may have on stability ( @ @x
and Stiglitz (2000), however, show that even with capital requirements, deposit interest rate
ceilings are still necessary to prevent banks from excessive risk-taking in competitive markets.
Furthermore, Allen, Carletti, and Marquez (2010) show that borrowers prefer well capitalized
banks, since these banks have a relatively higher incentive to monitor, which improves …rm performance. They …nd that franchise value and capital are substitute ways of providing banks with
10
monitoring incentives. Also, a recent study by Mehran and Thakor (2010) shows that there is
a positive relationship between bank capital holdings and total bank value. This rise in bank
value and the borrower preferences should induce a rise in bank charter value, thus lowering
the banks’ risk appetite. These e¤ects allow us to hypothesize that higher capital regulation
limits the negative e¤ect of competition on bank stability. In other words, the average impact
of an increase in competition on bank fragility is larger in countries with weak capital regulation
vis-à-vis strict capital requirement regimes.
Another popular regulatory measure to increase the stability of banking systems is deposit
insurance, as it reduces the risk of bank runs. On the other hand, generous deposit insurance
schemes might increase moral hazard and thus increase risk-taking incentives in more competitive
CF
> 0 (see, e.g., Demirguc-Kunt and Kane (2002)).
environments, i.e. @ @x
E¤ective banking supervision can be important for several reasons. First, monitoring banks
is both costly and di¢ cult for both depositors and shareholders, which can lead to suboptimal
bank risk behavior. Secondly, bank failures may be very costly, due to the crucial role banks
play within the economic system. Taking these points into account, more e¤ective supervision
should provide incentives to limit bank risk taking and thus could soften the e¤ect of competition
on risk taking. On the other hand, Boot and Thakor (1993) show that supervisors may pursue
self interest, which may lead to suboptimal actions. We integrate two supervisory variables into
our analysis, a dummy that indicates whether there is more than one supervisor and an external
governance indicator. The e¤ect of having multiple supervisors is not unambiguous. Kahn and
Santos (2005) argue that if a single institution is responsible for di¤erent regulatory functions, it
may not be able to su¢ ciently monitor all banks. Also, having multiple supervisors may lead to
di¤erent supervisory approaches, which can generate useful information which would otherwise
be neglected (Llewellyn (1999)). From this point of view, having multiple supervisors should
reduce banks risk taking incentives. On the other hand, a single supervisory institution may
11
be preferred because it reduces the chance of taking con‡icting policy measures. Furthermore,
Llewellyn (1999) argues that a single authority could prevent gaps in the regulation that could
arise when there are multiple supervisors and that having multiple supervisors could lead to
supervisory arbitrage, thus relaxing the overall supervision. The empirical evidence on this topic
is rather scarce. The only study that extensively focuses on the number of supervisors is Barth,
Dopico, Nolle, and Wilcox (2002) who …nd evidence for the supervisory arbitrage theory when
there are multiple supervisors. The external governance indicator measures the strength of
external auditors, …nancial statement transparency and accounting practices and the existence
of external ratings, thus the degree of potential market discipline. Having a wide range of private
control mechanisms such as external audit and external ratings should dampen the risk incentives
of a bank.
2.4
Institutional and …nancial development
A fourth set of country traits that can in‡uence the competition-stability relationship is the
institutional framework and …nancial system structure in which banks operate. First, we consider
the contractual framework. Loan defaults can arise if a borrower is unable or unwilling to repay
her loan. In the latter case, contract enforcement possibilities will be of great importance for
banks. If a borrower knows that a bank will have to go through numerous procedures, wait
for several weeks/months or simply has to pay large fees to enforce a contract, she will have
a greater incentive to evade the loan repayment. A part of the Boyd and De Nicolo (2005)
explanation of the competition-stability view relies on the fact that lower loan rates will reduce the
entrepreneurs’borrowing cost and thus will increase the success rate of his project, which lowers
the loan default probability. However, when operating in countries with protracted contract
enforcement procedures, the entrepreneur has a counteracting incentive to repay his loan,
independent of his success rate. Thus, we expect that a change in competition will be more
12
harmful to stability when operating in a country with low credit enforcement standards. In
other words, a rise in credit enforcement reduces the risk-shifting incentives of entrepreneurs,
CS
> 0.
i.e. @ @x
Take next the credit information sharing framework. Credit registry institutions are
public or private entities which collect information on the creditworthiness of borrowers. The
existence of these institutions facilitates the exchange of credit information among banks and
among investors. The existence of credit registers is expected to reduce both adverse selection
and moral hazard problems that are inherent on being in the lending business. Pagano and
Jappelli (1993) show that information sharing lowers adverse selection problems by lowering the
selection cost for lenders. Kallberg and Udell (2003) con…rm these …ndings when studying private
information exchanges in the U.S.. They …nd that private credit registry information is valuable
in assessing borrower quality, after controlling for information that would be available to a single
institution. Information sharing also tends to reduce moral hazard incentives through reputation
e¤ects (see, e.g. Diamond (1989)). As borrowers realize that it will be hard to get a loan at
another institution when they default on their current loan, they will have a stronger incentive
to repay and they will choose safer project (Padilla and Pagano (2000), Vercammen (1995)).
Furthermore, Houston, Lin, Lin, and Ma (2010) show for a sample of nearly 2400 banks in 69
countries that greater information sharing leads to higher bank pro…ts and lowers bank risk. This
leads us to hypothesize that countries with better information sharing systems will encounter
smaller e¤ects on stability when competition changes, since better information systems increases
a banks’franchise value and will lower the entrepreneurs’incentive to take more risk.
Finally, we consider …nancial structure and, more speci…cally, competition for banks coming
from …nancial markets. More developed stock markets make it easier for …rms to switch between
bank-based and market-based funding. This could lead to an additional e¤ect of a change in
competition on bank risk behavior. As mentioned above, Boyd and De Nicolo (2005) show that
13
a higher loan interest rate (due to lower competition) leads to a higher loan default probability.
Martinez-Miera and Repullo (2010) add that, when loans are not perfectly correlated, higher
interest rates also raise pro…ts on non-defaulting loans. In countries with strong developed
capital markets, however, …rms will have the possibility to substitute loans with market-based
funding, thus lowering the total amount of loans and bank pro…t. This leads us to hypothesize
that, ceteris paribus, it is more likely to …nd positive e¤ects of competition on bank stability in
countries with well developed …nancial markets.
3
Data and methodology
This section consists of two parts. First, we describe the sample composition and data sources.
Next, we explain how we allow for a country-level variation in the estimated impact of competition
on stability. In this section, we also describe how we compute the bank-speci…c measures of
soundness and market power.
3.1
Data sources
We combine several data sources. We obtain information on banks’ balance sheet and income
statement from Bankscope. Bankscope is a database compiled by Fitch/Bureau Van Dijck that
contains information on banks around the globe, based on publicly available data-sources. Moreover, the information in the database is harmonized and provided in a global format7 that facilitates the international comparison of banks’…nancial statements. Admittedly, in general this
comes at the cost of losing detailed information. However, this is not an issue for the information
we need in our analysis. The period of analysis is 1994-2006, and hence is not contaminated by
the exceptional events of the 2007-09 global …nancial crisis8 . If banks report information at the
7 As
of April 2009, the global format is replaced by the ’Fitch Universal Format on Bankscope’.
8 The
time period is mainly determined by the availability of the country-speci…c characteristics. In addition,
it spans the period before Basel II was implemented. As such, the change in capital regulation does not a¤ect our
14
consolidated level, we delete the unconsolidated entries of the group from the sample to avoid
double counting. We apply a number of selection criteria to arrive at our sample. First, we
exclude countries for which we have information on less than 50 bank-year observations. Second,
we limit our analysis to commercial, saving and cooperative banks. Third, we delete banks that
report information for less than three consecutive years, as our risk measure is computed over
rolling windows of three years. Fourth, we drop bank-year observations that do not have data
available on basic variables drop. Subsequently, we winsorize all variables at the 1 percent level
to mitigate the impact of outliers and to enhance robustness of the standard errors. While most
of the bank-speci…c variables are ratios, variables in levels (such as size) are expressed in 2007
US dollars using a GDP de‡ator.
The bank-speci…c data are linked to various country-level datasets that contain information on
the regulatory framework, strength of supervision and other institutional features. More speci…cally, we employ data from the three vintages (2000, 2003 and 2007) of the Bank regulation and
supervision dataset compiled by the World Bank (Barth, Caprio, and Levine (2008)). Additional
information is obtained from the Heritage Foundation,the World Development Indicators and
the Doing Business database. A detailed list of the variables used and the database from which
they are collected can be found in Table 13. Filtering the bank-speci…c database and matching it
with the country-level datasets yields a sample of banks from 62 countries. The sample consists
of a mix of developed and developing countries (see Table ??).
3.2
Empirical framework
In the literature, there are two main approaches to assessing the relationship between competition
and stability: a single country or multiple country setup. In a cross-country setup, proxies
measure of risk. Note also that by looking at a cross-country setting over the period 1994-2006, we do have other
crisis episodes in the sample.
15
of market power at the bank- or country-level are related to bank soundness (in a linear or
quadratic speci…cation). The sign of the coe¢ cient(s) then indicate whether competition helps
or harms stability (or whether there is a turning point at which there is a sign reversal). These
studies provide insight into the average relationship between competition and stability for the
set of countries under investigation (e.g.: developing countries as in Turk Ariss (2010), developed
countries as in Berger, Klapper, and Turk Ariss (2009), the European Union as in Schaeck
and Cihak (2010b)), while controlling for other country-speci…c factors such as macro-economic
conditions, regulation and supervision. However, single country studies (such as Keeley (1990),
Salas and Saurina (2003), Jimenez, Lopez, and Saurina Salas (2010), Boyd, De Nicolo, and
Jalal (2006)) document a large degree of variation in the competition-stability relationship. This
indicates that these other country-speci…c factors may not only have a level e¤ect but also a slope
e¤ect. Hence, it is not only important to control for the impact of these factors on risk but also
on how they shape the competition-stability relationship. For example, activity restrictions (i.e.
allowing banks to enter real estate, insurance or underwriting) may not only a¤ect the aggregate
level of risk, but may also in‡uence the extent to which loan market power a¤ect bank risk-taking
incentives. Put di¤erently, these variables may also determine whether it is more likely to …nd
support for the franchise value paradigm compared to the risk-shifting hypothesis or vice versa.
This results in the following setup:
Riski;j;t = c +
j Competitioni;j;t 1
+
j Xi;j;t 1
+ Zj;t + "i;j;t
(1)
In this setup, the indices i; j ; t stand respectively for bank, country and time. The impact of
competition (as well as any other bank-speci…c variable, Xi;j;t ) on risk is allowed to vary at the
country level. This is denoted by giving the corresponding (vector of) coe¢ cient(s) a j subscript.
The vector of bank-speci…c variables, Xi;t
1,
characterizes a bank’s business model. In particular,
we include proxies for the funding structure (share of wholesale funding in total funding), asset
16
(loans to assets ratio) and revenue mix (share of non-interest income in total income) as well
bank size (natural logarithm of total assets), credit risk (loan loss provisions to interest income)
and asset growth. In addition, we include specialization dummies to allow for di¤erent intercepts for commercial banks, bank holding companies, saving banks and cooperatives. Summary
statistics on the control variables that determine bank soundness are in the upper part of Table
1. In addition, time-varying country-speci…c characteristics may also a¤ect bank soundness are
included in the vector Zj;t . We hypothesize that
j
can be modelled as a function of (a subset
of) these country-speci…c factors.
To gain insight in the potential drivers of heterogeneity in , we take a two-step approach.
In a …rst step, we relate bank market power to a measure of bank soundness. This relationship
is assessed at the country level. More speci…cally, for each country in our sample, we estimate
the following equation:
Riski;t = c +
Competitioni;t
1
+ Xi;t
1
+ vt + "i;t
(2)
Including time …xed e¤ects and estimating this equation country by country creates many advantages. First, we allow for the maximum extent of heterogeneity in the competition-stability
trade-o¤ across countries. Second, the time dummies di¤er in each country regression and hence
indirectly capture the level e¤ect of country-speci…c regulation or the business cycle on bank risk.
In the second step, we will explore which country-speci…c variables explain the heterogeneity in
the estimated
3.3
j s.
Indicators of market power and bank soundness
In this subsection, we describe how we measure competition, discuss the correlation with other
measures of competition and introduce our indicator of bank soundness.
17
3.3.1
The Lerner index: measure of pricing power
For our analysis, we need a measure of market power that varies at the bank level rather than a
competition or concentration proxy at the country level. The Lerner index is an obvious candidate
as it captures the extent to which banks can increase the marginal price beyond the marginal
cost. Conditional on having an estimate of the marginal price and cost, we can construct the
Lerner index for each bank and each year, as follows:
Lerneri;t =
Pi;t
M Ci;t
Pi;t
(3)
where Pi;t is proxied by the ratio of total operating income to total assets. As banks have the
opportunity to expand their activities into non-interest generating activities, we include both
interest and non-interest revenue. The marginal cost, M Ci;t , is derived from a translog cost
function. As Berger, Klapper, and Turk Ariss (2009), we model the total operating cost of
running the bank as a function of a single, aggregate output proxy, Qi;t , and three input prices,
j
wi;t
, with j 2 f1; 2; 3g. More speci…cally, we estimate:
ln Ci;t =
0+
1 ln Qi;t +
3
X
2
(ln
Q
)
+
2
i;t
j=1
3
3 X
X
j
ln
w
+
j
i;t
j=1 k=1
3
X
j
k
ln
w
ln
w
+
j;k
i;t
i;t
j
j
ln wi;t
ln Qi;t +vt +"i;t
j=1
(4)
We also include time dummies to capture technological progress as well as varying business
cycle conditions, as well as a bank specialization dummy. Homogeneity of degree one in input
3
3
X
X
prices is obtained by imposing the restrictions:
=
1;
j
j = 0 and 8 k 2 f1; 2; 3g :
j=1
3
X
j;k
j=1
= 0. Marginal cost is then obtained as follows:
j=1
M Ci;t
0
1
j
2
X
wi;t
Ci;t @
@Ci;t
=
b 1 + 2b 2 ln Qi;t +
bj ln 3 A
=
@Qi;t
Qi;t
wi;t
j=1
18
(5)
in which Ci;t measures total operating costs (interest expenses, personnel and other administrative or operating costs), Qi;t represents a proxy for bank output or total assets for bank i at
time t. The three input prices capture the price of …xed assets, the price of labor and the price
of borrowed funds. They are constructed as respectively the share of other operating and administrative expenses to total assets, the ratio of personnel expenses to total assets and the ratio of
interest expenses to total deposits and money market funding. Following Berger, Klapper, and
Turk Ariss (2009), Equation (4) is estimated separately for each country in the sample to re‡ect
potentially di¤erent technologies.
<Insert Table 1 around here>
Table 1 presents summary statistics on the variables needed to construct the Lerner index as
well as the estimated Lerner index. The average Lerner index at the country level is 10%, but
varies across countries, from
3.3.2
3:8% in Uruguay to 22:0% in Denmark (see Table 14).
The Z-Score: measure of bank soundness
In our analysis, bank risk is measured using the Z-score. The Z-score measures the distance from
insolvency (Roy (1952)) and is calculated as
Zi;t =
ROAi;t + (E=A)i;t
(ROA)i;t
where ROA is return on assets, E=A denotes the equity to asset ratio and
(6)
(ROA) is the
standard deviation of return on assets. While in large parts of the literature the volatility of
pro…ts is computed over the full sample period, we use a three-year rolling time window for
the standard deviation of ROA to allow for variation in the denominator of the Z-score. This
approach avoids that the Z-scores are exclusively driven by variation in the levels of capital and
pro…tability (Schaeck and Cihak (2010b)). Moreover, given the unbalanced nature of our panel
dataset, it avoids that the denominator is computed over di¤erent window lengths for di¤erent
19
banks. The Z-score can be interpreted as the number of standard deviations by which returns
would have to fall from the mean to wipe out all equity in the bank (Boyd and Runkle (1993)).
A higher Z-score implies a lower probability of insolvency, providing a more direct measure of
soundness than, for example, simple leverage measures. Because the Z-score is highly skewed,
we use the natural logarithm of Z-score to smooth out higher values9 . Table 1 shows that the
average value of ln(Z-score) slightly exceeds four with a standard deviation of 1:15.
Our indicator of market power is signi…cantly correlated with the Z-score and other indicators
of competition, but also with indicators of market concentration. Table 2 presents correlations on
the country-year level between the Z-score, the Lerner index if market power as well as bank and
country-level indicators of competition and market structure. The Lerner index is positively and
signi…cantly correlated with the Z-score, consistent with Figure 1. The Lerner index is negatively
and signi…cantly correlated with the average market share of banks in a country and year, while
there is no signi…cant correlation with the number of banks. The Lerner index is higher for more
concentrated banking markets, as measured by the Her…ndahl index. Interestingly, there is no
signi…cant correlation between the Lerner index of market power and two other industry-level
behavioral indicators of bank competition, the H-Statistics and the Boone index.
4
The competition-stability relationship: explaining cross9 Others
have used the transformation ln(1+Z-score) to avoid truncating the dependent variable at zero. We
take the natural logarithm after winsorizing the data at the 1% level. As none of the Z-scores is lower than zero
after winsorizing, this approach is similar, save for a rescaling, to the former approach and winsorizing after the
transformation.
20
country variation
4.1
The competition-stability relationship: Cross-country heterogeneity
In the Introduction, we mentioned that the full sample unconditional correlation between the
Lerner index and the Z-score is positive (0:31), but that this number hides a substantial amount
of cross-country variation (see Figure 1). Using a regression framework, we show that this
relationship also holds when conditioning on other variables . Following common practice in
the literature, we regress the bank soundness measure on the Lerner index and a wide range of
control variables using our total sample. The results are presented in table 3.
<Insert Table 3 around here>
The …rst column shows OLS estimates, whereas the second column are IV (2SLS) regression
results. In the second column, we take into account that market power may be endogenous.
The instruments are loan growth and lagged values of the Lerner index. We employ the panel
structure of the database and control for …xed heterogeneity at the country and time level by
including country and time …xed e¤ects. The standard errors are robust and clustered at the
bank level. Moreover, to avoid that our results are driven by countries that are overrepresented
in our sample, we weigh each variable with the inverse of the number of banks in the country.
Doing so, we give equal weight to each country. We again …nd a positive and signi…cant e¤ect
of a change in market power on bank stability. This result is in line with existing literature that
also uses the Lerner index as a market power proxy (see, e.g. Berger, Klapper, and Turk Ariss
(2009)). However, as already mentioned in the introduction, our interest is less on arguing that
competition is good or bad, but rather on uncovering which country characteristics make the
impact better of worse. Therefore, it is crucial to show that the regression-based methods also
indicate that there is a substantial degree of heterogeneity.
21
<Insert Figure 2 around here>
Figure 2 is very similar to Figure 1 of the introduction. The correlation between the conditional and unconditional correlation is 0:69 and highly signi…cant. Both bar charts show that the
unconditional and conditional correlations are positive in most countries. In Figure 2, the height
of the bars shows the magnitude of the coe¢ cient of the Lerner index when estimating Equation
(2) for each country separately. The bars are sorted from low to high and the country labels
are mentioned on the X-axis. The coe¢ cients that are signi…cantly di¤erent from zero have a
lighter shade. The average of the 62 estimated coe¢ cients equals 0.982, which resembles the full
sample coe¢ cient. Hence, on average, it seems that the franchise value paradigm dominates the
risk-shifting hypothesis. In response to an increase in market power, banks will behave more
prudently to protect their monopoly rents that create a larger franchise value. Or vice versa,
an increase in competition increases banks’ appetite for risk-taking. However, there is a large
amount of heterogeneity in the competition-stability relationship. The standard deviation of the
coe¢ cient across the 62 countries is 1.464. A quick look at the country labels on the X-axis also
reveals that it is not just a developed versus developing countries story or that regions exhibit
similar behavior. In the remainder of this section, we will empirically explore what drives this
high cross-country variation in the competition-stability relationship.
4.2
A binary classi…cation approach
Our goal is to shed light on the underlying factors that drive this heterogeneous impact. Uncovering which institutional features drive the cross-sectional variation in b j will allow us to identify
which banking sectors will not be harmed (or harmed less) by more intense banking competi-
tion. To assess the impact of regulatory, supervision and institutional features on the estimated
competition-stability trade-o¤, we …rst classify the sample countries into two distinct groups ac-
22
cording to the median value of each such speci…c feature. We perform two sorts of tests10 , which
di¤er in their treatment of potential cross-country di¤erences in the impact of bank-speci…c characteristics on bank risk. More speci…cally, we employ a SEPARATE and POOLED approach.
Both approaches di¤er in the amount of heterogeneity they allow in the relationship between
control variables and bank risk. In the SEPARATE test methodology, we employ t-tests to compare the di¤erence in magnitude of the competition-stability relationship across the two groups.
More speci…cally, we perform t-tests on two measures. On the one hand, we look at variation in
the (unconditional) correlation between the Z-score and the lagged Lerner index. On the other
hand, we also estimate Equation (2) separately for each country, and then average the estimated
values within each group formed based on a particular institutional characteristic. The POOLED
methodology estimates Equation (2) across all banks from countries with a speci…c institutional
feature. We thereby impose common slopes (both on the Lerner index and the control variables) within each group. For example, we estimate equation (2) across all countries with weak
activity restrictions, and then across all banks in the strong activity restriction countries. We
thus estimate a single coe¢ cient for each group of countries11 , and test whether the coe¢ cients
di¤er between the two groups of countries. To compare the impact of the Lerner index on bank
stability across the two groups, we employ Chow tests. The POOLED approach is implemented
using OLS as well as IV(2SLS). According to Oztekin and Flannery (2008), the separate and
pooled methodologies each have its own merits. Averaging individual country regressions in the
1 0 Oztekin
and Flannery (2008) perform a similar kind of analysis to examine how country-speci…c features a¤ect
the adjustment speed at which …rms converge to their target leverage.
1 1 Larger
countries with more banks may be overrepresented in a particular group. For example, the majority
of banks within the sample operate in the US. On the one hand, could this lead to US-biased results, on the other
hand does it make a fair comparison with the separate approach invalid. Therefore, we give equal weight to each
country in the pooled approach. The weight that each individual bank observation gets is proportional to 1=ni ,
where ni equals the number of observations for country i. Moreover, we include time and country …xed e¤ects
and cluster the standard errors at the bank level.
23
SEPARATE method allows for full heterogeneity in parameter estimates and error variances.
However, estimating Equation (2) for countries with few banks might also yield noisy coe¢ cient
estimates. The POOLED method assumes slope and error variance homogeneity across countries, raising the possibility that the gains from pooling would outweigh any costs imposed by
ignoring the inherent heterogeneity in the slope estimates. Since we believe that neither method
is superior, we document our results using both approaches.
Table 5 consists of two panels. The …rst panel corresponds to the separate approach, whereas
the second panel contains the results of the pooled approach. Summary statistics on the countryspeci…c variables are reported in Table 4. Variable de…nitions and sources are reported where
they occur for the …rst time in the text. The country-speci…c variables are obtained from various
sources and may vary at a di¤erent level. Some variables are (almost) constant over time, other
vary by country and over time. Moreover, not all variables are available for all countries or all
time periods.
<Insert Table 4 around here>
In Table 4, the summary statistics of the country-speci…c variables are categorized in four
groups. The results on the SEPARATE and POOLED tests will be described in a similar order.
4.2.1
Herding
As discussed above, we use two proxies of herding. The heterogeneous banking system indicator
measures whether there are substantial revenue di¤erences among di¤erent …nancial institutions
within a country. It is calculated as the within country standard deviation of the non-interest
income share. If all banks in a country have a similar business model (either voluntarily or forced
by regulation), the indicator will be low. A higher value indicates that the banking system is
more diverse. In heterogeneous banking systems, an increase in competition is less detrimental
for bank risk compared to homogeneous banking systems. A second indicator of the too-many-
24
to-fail problem is the aggregate Z-score. This variable is a proxy of systemic risk. Lower values
of the aggregate Z-score points to an overall reduction in banking sector soundness and larger
scope for herding as the likelihood of joint failures is larger in unstable banking sectors.
The results in Table 5 indicate that competition will do more harm when banks’activities are
highly correlated as they mimic their rivals’business model12 . We also …nd signi…cant and robust
evidence that banks operating in less stable banking systems will gamble more in response to an
increase in competition compared to stable banking sectors, in which it is more likely that default
will be an idiosyncratic event. Herding thus exacerbates the negative impact of competition on
stability.
4.2.2
Market structure
Market structure consists of several di¤erent dimensions. First, we look at whether overall activity
restrictions limit the types of banks in the country. Therefore we include an activity restriction
index, taken from the World Bank’s “Bank regulation and supervision”database (Barth, Caprio,
and Levine (2008)), which measures the degree to which banks are permitted to engage in feebased activities related to securities, insurance and real estate rather than more traditional
interest spread-based activities. Lower values of the index indicate that no restrictions are placed
on this type of diversi…cation by banks and higher values indicate that such diversi…cation is
prohibited. Next, we look at the Hirschmann-Her…ndahl index (based on total assets) as a proxy
of bank market concentration13 . Larger values denote a more disperse distribution of market
shares and hence higher concentration. Finally, we also check whether the existence of entry
barriers, as a proxy of the contestability of the market, a¤ect the strength of the competition1 2 We
also tried a similar measure of herding on the funding side, which, however, does not seem to matter for
the competition-stability relationship.
1 3 We
also looked at a Hirschmann-Her…ndahl index based on total loans and a CR3 ratio as concentration
proxies. Both indicators lead to similar results as for the HHI based on total assets.
25
stability relationship. Entry barriers is an index measuring the degree to which applications to
receive a banking licence have been denied over the past …ve years. Higher values of the index
indicate greater stringency, hence less competition.
The results in Table 5 indicate that the impact of competition on bank risk is larger in
banking sectors that face higher restrictions. The di¤erences are signi…cant in all test set-ups
(unconditional versus conditional, separate versus pooled). An increase in competition (lower
Lerner index) will lead, ceteris paribus, to a much larger reduction in Z-scores for banks operating
in countries with stricter restrictions on di¤erent …nancial activities. If we classify the countries
in our sample in a low and high concentrated group, we …nd that the positive impact of market
power on stability is larger in concentrated banking systems. Put di¤erently, from a …nancial
stability perspective, an increase in loan market competition is more detrimental in countries
with more disperse bank market structures. This is in line with Allen and Gale (2000). In the
separate and pooled approaches, the mean of the high group is lower than the mean of the low
group. The means are signi…cantly di¤erent in the two groups for the classi…cation based on the
Her…ndahl index. Finally, using the fraction of entry applications denied to classify countries in
a low and high entry barriers group yields less stable results in terms of signi…cance. The pooled
and separate approach yield opposite conclusions.
4.2.3
Regulatory and supervisory structure
To test the hypothesis that capital regulation reduces the negative impact of more competition
on stability, we divide the countries in the sample in two groups based on a capital stringency
index. The capital stringency index checks whether there are explicit requirements regarding
the amount of capital that a bank should have. A higher index indicates greater stringency. In
addition to capital regulation, deposit insurance schemes have been designed to protect depositors
from excessive risk-taking by banks. Deposit insurance coverage is proxied by deposit insurance
26
coverage relative to GDP per capita. This variable taken from the Deposit Insurance Around the
World database of the World Bank (Demirguc-Kunt, Karacaovali, and Laeven (2005)). Banking
supervision is captured by an external governance index and a dummy that equals one if there are
multiple supervisors in a country. The external governance index allows us to check the in‡uence
of private monitoring mechanism, while the supervisory dummy gives more info on the structure
of public supervision.
The results in Table 5 show that the average unconditional correlation between the Lerner
index and the Z-score is larger in the set of countries with a capital stringency index below the
mean. Hence, an increase in competition is more harmful for bank stability in countries with
weak capital regulation. However, the averages are not signi…cantly di¤erent across both groups.
Hence, we …nd no support for the theories that predict that stricter capital requirements are a
substitute for franchise values and will prevent to take on excessive risks when they are faced with
more intense competition. Looking at deposit insurance coverage relative to GDP per capita, it
is clear that countries with more generous deposit insurance systems face a worse competitionstability trade-o¤. As a more generous deposit insurance system reduces the e¤ectiveness of
outside monitors, banks have a larger incentive to gamble in response to a shrinking Lerner
index. When we divide the countries in our sample in a high and low group based on the external
governance index, we see that countries with weak external monitoring mechanisms react stronger
to changes in competition. Thus, an increase in loan market competition will be more harmful
for bank stability in countries with a low focus on private monitoring. On the other hand, having
multiple bank supervisors leads to a stronger impact of loan market competition on bank risk
taking behavior, while the impact of competition is far milder when there is only one supervisor.
Multiple supervisors may lead to coordination problems if they have supervisory responsibilities
over di¤erent activities. For both measures, the four approaches yield di¤erences of consistent
magnitude, though the statistical signi…cance at the conventional levels is not achieved across all
27
methods.
4.2.4
Institutional and …nancial development
We hypothesized that variables capturing the extent to which information on borrowers (and
defaults) are shared and the cost of enforcing contracts may a¤ect the risk-shifting incentives
of entrepreneurs. We use a dummy to capture whether there is a public or a private registry
present in a country, as well as an indicator of credit information depth. This variable captures
the di¤erence in information content between the registers in di¤erent countries, since some of
them only collect limited information on large borrowers, while others have extensive information
on a whole range of borrowers and their characteristics (Miller (2003)). The index ranges between
0 and 6, with a higher value indicating that there is more information available.. Both variables
are based on the Doing Business database from the World Bank. From the same database we also
use the a proxy measuring the contract enforcement cost in a country, thus a negative indicator
of the e¢ ciency of the contractual framework. Furthermore, we use stock market turnover , i.e.
the ratio of stocks traded to stocks listed, as indicator of …nancial market development. Finally,
we consider a sample split according to GDP per capita, the most general indicator of economic
and institutional development.
Table 5 shows that the split up based on the credit registry dummy does not lead to signi…cant
di¤erences in the competition-stability trade-o¤. Also, the depth of credit information and the
cost of contract enforcement do not play a decisive role in the competition-stability relationship.
The stock market turnover variable, on the other hand, leads to signi…cant and consistent …ndings
over the four methods. Our results show that banks in countries with a high stock market turnover
tend to have higher risk taking incentives when there is a rise in competition. Finally, we …nd
no signi…cant di¤erence in the competition-stability relationship across countries with di¤erent
levels of economic development, as measured by GDP per capita.
28
4.3
Competition-stability relationship: a continuous approach
The binary classi…cation approach provides evidence that many country-speci…c characteristics
may have an e¤ect on the competition-stability trade-o¤. One could still wonder (i) to what
extent these variables capture di¤erent information and do they still have an impact when they
are simultaneously controlled for, and (ii) whether the relationship is con…rmed using all the
variability in the proxy (rather than creating high and low groups) . In this section, we provide
further insight into these issues. Based on the results reported in Table 5, we select a number
of variables to include in a continuous rather than binary analysis. Country-speci…c variables
are only included if they satisfy the following criteria: (i) they have to be signi…cant in the
IV(2SLS) case, (ii) they have to be signi…cant in more than half of the test setups in Table 5.
This leaves us with Herding - Revenues, Activity Restrictions, entry applications denied, HHI,
Multiple Supervisors, and Stock Market Turnover ratio.
Table 6 provides information on the correlation matrix of these six variables as well as the
(un)conditional correlation coe¢ cients at the country level. The binary approach showed that
these characteristics potentially play an important role in the competition-stability relationship.
The correlation table con…rms these …ndings and also provides information on how these countryspeci…c variables are correlated with each other. Table 7 provides information on estimation
results of regressions of the following form:
Riski;j;t = c + (
0
+
j Zj;t )
Competitioni;j;t
1
+
j Xi;j;t 1
+ vt + vj + "i;j;t
(7)
where Zj;t is either just one of the above-mentioned country-speci…c characteristics or a vector
containing all of them. The …rst column shows the outcome for the baseline regression at the
bank level, i.e. when regressing our stability measure (Z-score) on the Lerner index, a group
of bank-speci…c control variables and GDP per capita. In each subsequent column, we add an
interaction term of the Lerner index with a country-speci…c characteristic. In the last column, we
29
show the result when we add all interaction terms simultaneously. For ease of comparability of the
economic signi…cance, all country-speci…c variables have been normalized to have zero mean and
unit variance. In the characteristic-by-characteristic regressions, each of the interaction terms is
signi…cant (except the systemic risk proxy) with the expected sign. As we selected the variables
based on their signi…cance in the binary approach, this is not surprising. When exploiting all
of the heterogeneity rather than only classifying them into a high or low group, we reinforce
the previous …ndings. We …nd strong and convincing multivariate evidence that competition is
more harmful for stability in countries where (i) banks herd more in terms of revenue structure,
(ii) there are more restrictions on the permissible range of activities, (iii) they operate in less
concentrated markets, (iv) deposit insurance is more generous and (v) stock markets are more
liquid. The last columns show that almost all results hold when they are simultaneously controlled
for. The high correlation of deposit insurance with Activity Restrictions and HHI also in‡ates
its standard errors. Interestingly, the absolute value of the coe¢ cients of the signi…cant variables
varies in magnitude (between 0:14 and 0:27). As we normalized the variables, there seems to
be an important e¤ect in economic terms, and the importance varies with the variable under
consideration. The coe¢ cient on the Lerner index without interaction is 1:04. A one standard
deviation increase in one of these variables hence leads to a 13% to 25% change in the impact of
competition on stability.
5
5.1
Additional results
Time variation in the competition-stability relationship
So far, our analysis has focused on the cross-country di¤erences in the relationship between
competition and bank stability. In this part, we allow for additional variation over time in
these country-speci…c relationships. More speci…cally, for each country we regress our bank
30
stability measure (Z-score) on the Lerner index, a group of bank speci…c control variables and
GDP per capita, while using …ve year rolling windows. In this way, we retrieve time-varying
(conditional) correlations between the Lerner index and bank stability. Since our sample period
is 1994-2006, and taking into account that we lag our independent variables with one period, we
get a maximum of eight country-speci…c conditional correlations. The retrieved correlations are
subsequently regressed on country-speci…c variables measured at the …rst year of the …ve year
windows. Table 8 displays the results for three di¤erent regression speci…cations using this setup.
In the …rst column, we show the results coming from a regression of the conditional, time-varying
correlations on all seven country-speci…c characteristics, while clustering the standard errors over
time. In the second column, we only include those variables that are individually signi…cant in
the continuous approach. Standard errors are again clustered over time. The third regression
mimics the speci…cation in the last column of the previous table.
The results in column 1 of Table 8 show that country characteristics explain only 24 percent of
the variation in the conditional correlation coe¢ cients. Hence, after taking into account countryspeci…c characteristics (and having considered a wide range of potential drivers), there is still a
lot of unexplained variation in the competition-stability relationship. If this heterogeneity is all
random, this is worrying as it implies that it is di¢ cult to design an optimal regulatory setting
to minimize the negative e¤ect of competition on stability.
The results of this time-varying analysis largely con…rm our previous …ndings. First of all,
the impact of revenue herding on stability is negative and signi…cant for two out of three speci…cations. Thus, banks in countries where the majority of the banks collect their revenues from
the same type of business will respond stronger – in terms of risk behavior - to a change in
competition than banks in dispersed markets. In other words, heterogeneous banking markets
are more likely to support the competition-stability theorem. Furthermore, the results show that
more activity restrictions have a positive and signi…cant impact on the correlation coe¢ cient.
31
Financial institutions that are allowed to become universal banks will face imperfectly correlated revenue streams, which in‡uences their reaction to a change in competition. This result is
consistent over the two regression speci…cations and in line with our previous …ndings It again
con…rms that the impact of competition on bank risk behavior will be larger in markets with a
more concentrated bank focus. The fact that more diversi…ed banks tend to have higher charter
values seems to dominate the higher risk taking incentive for diversi…ed banks due to the lower
correlation in loan defaults. The third and fourth country characteristic that have a consistent
signi…cant impact on bank stability is deposit insurance and stock market turnover. They are
positive and signi…cant in all three speci…cations. Given that the variables are normalized, the
economic impact of deposit insurance is twice as large as that of stock market turnover..
5.2
Alternative risk measures
Until now, we have used the Z-score as our preferred bank risk measure. The Z-score combines
bank equity over total assets, return on assets, and the volatility of the returns to come up with
a measure of bank stability. It thus combines three di¤erent risk aspects. In this part, we will
look at the reaction of these three subcomponents when the level of competition changes. We
are particularly interested whether the subcomponents all move in the same direction or whether
there is a component overruling the other two subcomponents. Furthermore, we also look at the
amount of non-performing loans as a potential alternative bank risk measure.
The Table 9 con…rms our …ndings while using the alternative risk measures. The …rst column
shows the standard results when using the Z-score as stability measure and regressing it on the
Lerner index and a group of bank-speci…c control variables. For regression (2) to (5) we use four
alternative stability measures, being non-performing loans and the three subcomponents of the Zscore. For each stability measure we perform the basic regression with country-year …xed e¤ects.
When using non-performing loans as stability measure, we leave out the loan loss provisions
32
over interest income ratio as a control variable, since both variables are heavily related to each
other. The results show that all subcomponents of the Z-score react in a similar way to a change
in competition. A rise in market power leads to a higher equity ratio, more return on assets
and lower return volatility. Furthermore, a rise in competition also leads to less non-performing
loans. The results for the alternative risk measures are thus in line with the Z-score results; we
again …nd consistent and signi…cant evidence in favour of the competition-fragility theorem. A
rise in market power (measured by the Lerner index) leads to more stability, independent of the
stability proxy we use.
5.3
Bank-variation in the competition-stability relationship
So far, we have exploited cross-country and time-series variation in the competition-stability
relationship. However, banks’risk-taking incentives might be also in‡uenced by their own relative
position in the market. Speci…cally, we posit that failing banks have a greater incentive to exploit
competition towards more aggressive risk-taking. Further, banks with a larger market-share and
that therefore consider themselves too-big-to-fail might also exploit increasing competition to
take more aggressive risks. This subsection assesses whether such bank-level variation exists.
Table 10 shows the impact of competition on bank stability while controlling for the potential
impact of failing banks. The …rst column shows our baseline competition-stability regression. In
the second and the third column, we interact the Lerner index with an exit dummy. In the second
regression, the exit dummy equals one in the two years before the bank leaves the sample. These
banks seem to react less intense when competition changes. However, notice that this dummy
does not discriminate between defaults and distressed mergers at the one hand and ’normal’
mergers or acquisitions at the other hand. Therefore, in the third regression, the exit dummy
only equals one in the two years before a bank leaves the sample when the bank had a negative
return on assets in that period. In this way, we only capture the banks that actually where
33
in distress before they leave the sample. The signi…cant and positive interaction term between
competition and the exit dummy indicates that these banks that are in trouble before leaving the
sample react more strongly to a change in competition. Thus, banks that are in distress gamble
even more than others when competition rises, probably because there is not much left to loose
for them. In the fourth regression, we only look at banks that did not exit the sample (Distressed
Exit Dummy =0), while adding interaction terms between the Lerner index and country-speci…c
characteristics that potentially in‡uence the competition-stability relationship. The results show
that market power still has a positive impact on bank stability for these banks. Furthermore, as
shown in our previous analysis, banks operating in a country with high activity restrictions or
with a highly liquid stock market tend to react stronger to a change in competition.
Table 11 shows the results for the baseline competition-stability regression while controlling
for the impact of bank market share. The …rst column retakes our baseline results, while we
interact the Lerner index with a bank’s market share (measured in terms of total assets) in the
second regression. This allows us to check whether banks with a higher market share have an
incentive to take more risk, because they can potentially see themselves as too-big-to-fail. The
results indicate that there is no direct too-big-to-fail e¤ect in‡uencing the competition-stability
relationship. In the third column, we do a similar exercise, but now using a market share dummy
that equals one for bank with a market share that is larger than 10 percent. Again, we do not
…nd a signi…cant direct e¤ect of a banks’market share on the competition-stability relationship.
6
Conclusion
This paper aims to reconcile the seemingly contrasting evidence on the bank competition-bank
soundness relationship. Theoretical models and empirical results o¤er con‡icting evidence. A
…rst look at a worldwide sample of banks tells us that the relationship between market power
and bank soundness is positive. Hence, on average, it seems that the franchise value paradigm
34
dominates the risk-shifting hypothesis. In response to an increase in market power, banks will
behave more prudently to protect their monopoly rents that create a larger franchise value. Or
vice versa, an increase in competition increases banks’ appetite for risk-taking. However, this
full sample relationship hides a substantial amount of cross-sectional heterogeneity, with estimates ranging from negative to positive, with many countries showing insigni…cant relationships
between competition and stability.
We develop a framework to assess how regulation, supervision and other institutional factors
may make it more likely that the data favor one theory over the other, i.e. the charter value
paradigm over the risk-shifting paradigm. We show that an increase in competition will have a
larger impact on banks’risk taking incentives in countries with stricter activity restrictions, more
herding in revenue structure and unconcentrated banking markets. Our …ndings help in understanding the seemingly con‡icting empirical evidence. Most studies tend to …nd results in favour
of the competition-fragility view. However, if one would sample banks from countries/regions
with concentrated banking markets and homogeneous operations (either homogeneous because of
regulatory restrictions or due to herding), obtaining the opposite …nding need not be inconsistent.
Our …ndings have important policy repercussions. They suggest that activity restrictions
and herding trends can exacerbate the negative impact of competition on bank stability so that
regulatory reforms have to take this into account. We show that the too-many-to-fail phenomenon
is worse in more competitive environments. On the other hand, capital regulations seem to have
less of an in‡uence on the relationship between competition and stability, which puts the current
debate on capital bu¤ers somewhat in perspective.
References
Acharya, Viral V., and Tanju Yorulmazer, 2007, Too many to fail–an analysis of timeinconsistency in bank closure policies, Journal of Financial Intermediation 16, 1–31.
35
Allen, Franklin, Elena Carletti, and Robert Marquez, 2010, Credit market competition and
capital regulation, Review of Financial Studies , forthcoming.
Allen, F., and D. Gale, 2000, Comparing …nancial systems, Cambridge, MA: MIT Press.
, 2004, Competition and …nancial stability, Journal of Money Credit and Banking 36,
453–480.
Barth, James, Gerard Caprio, and Ross Levine, 2008, Bank regulation and supervision database
(updated june 2008), World Bank.
Barth, James, R., G. Dopico, Luis, E. Nolle, Daniel, and A. Wilcox, James, 2002, Bank safety
and soundness and the structure of bank supervision: A cross-country analysis, International
Review of Finance 3, 163–188.
Beck, Thorsten, 2008, Bank competition and …nancial stability : friends or foes ?, Policy Research
Working Paper Series 4656 Policy Research Working Paper Series.
, Asli Demirguc-Kunt, and Ross Levine, 2006, Bank concentration, competition, and
crises: First results, Journal of Banking Finance 30, 1581–1603.
Berger, A. N., L. F. Klapper, and Rima Turk Ariss, 2009, Bank competition and …nancial
stability, Journal of Financial Services Research 35, 99–118.
Boot, Arnoud W. A., and Anjan V. Thakor, 1993, Self-interested bank regulation, The American
Economic Review 83, 206–212.
Boyd, John H., and Gianni De Nicolo, 2005, The theory of bank risk taking and competition
revisited, Journal of Finance 60, 1329–1343.
, and Abu M. Jalal, 2006, Bank risk-taking and competition revisited: New theory and
new evidence, IMF Working Paper No. 06/297.
36
Boyd, J. H., and D. E. Runkle, 1993, Size and performance of banking …rms - testing the
predictions of theory, Journal of Monetary Economics 31, 47–67.
Claessens, Stijn, and Luc Laeven, 2004, What drives bank competition? some international
evidence, Journal of Money, Credit and Banking 36, 563–83.
Dell’Ariccia, G., and R. Marquez, 2004, Information and bank credit allocation, Journal of
Financial Economics 72, 185–214.
Demirguc-Kunt, Asli, and Enrica Detragiache, 2002, Does deposit insurance increase banking
system stability? an empirical investigation, Journal of Monetary Economics 49, 1373–1406.
Demirguc-Kunt, Asli, and Edward Kane, 2002, Deposit insurance around the globe: Where does
it work?, Journal of Economic Perspectives 16, 175–195.
Demirguc-Kunt, Asli, Baybars Karacaovali, and Luc Laeven, 2005, Deposit insurance around the
world: A comprehensive database, Policy Research Paper , World Bank.
Diamond, Douglas W., 1989, Reputation acquisition in debt markets, The Journal of Political
Economy 97, 828–862.
Hakenes, Hendrik, and Isabel Schnabel, 2007, Bank competition and capital regulation,
http://ssrn.com/paper=965430.
Hauswald, R., and R. Marquez, 2006, Competition and strategic information acquisition in credit
markets, Review of Financial Studies 19, 967–1000.
Hellmann, T. F., K. C. Murdock, and J. E. Stiglitz, 2000, Liberalization, moral hazard in banking,
and prudential regulation: Are capital requirements enough?, American Economic Review 90,
147–165.
Houston, Joel F., Chen Lin, Ping Lin, and Yue Ma, 2010, Creditor rights, information sharing
and bank risk taking, Journal of Finacial Economics 96, 485–512.
37
Jayaratne, Jith, and Philip Strahan, 1998, Entry restrictions, industry evolution, and dynamic
e¢ ciency: Evidence from commercial banking, Journal of Law and Economics 41, 239–273.
Jimenez, Gabriel, Jose A. Lopez, and Jesus Saurina Salas, 2010, How does competition impact bank risk-taking?, Banco de Espana Working Paper No. 1005. Available at SSRN:
http://ssrn.com/abstract=1582331.
Kahn, Charles M., and Joao A. C. Santos, 2005, Allocating bank regulatory powers: Lender of
last resort, deposit insurance and supervision, European Economic Review 49, 2107–2136.
Kallberg, Jarl G., and Gregory F. Udell, 2003, The value of private sector business credit information sharing: The us case, Journal of Banking Finance 27, 449–469.
Keeley, M. C., 1990, Deposit insurance, risk, and market power in banking, American Economic
Review 80, 1183–1200.
Llewellyn, D.T., 1999, Introduction: The institutional structure of regulatory agencies, in
N.Courtis (ed.), How countries supervise their banks,insurers and securities markets.
Marcus, Alan J., 1984, Deregulation and bank …nancial policy, Journal of Banking Finance 8,
557–565.
Martinez-Miera, David, and Rafael Repullo, 2010, Does competition reduce the risk of bank
failure?, Review of Financial Studies , forthcoming.
Mehran, Hamid, and Anjan Thakor, 2010, Bank capital and value in the cross-section, Review
of Financial Studies , forthcoming.
Miller, Margaret J., 2003, Credit reporting systems and the international economy, The MIT
Press.
Oztekin, Ozde, and Mark J. Flannery, 2008, Institutional determinants of capital structure adjustment speeds, http://ssrn.com/paper=1364273.
38
Padilla, A. Jorge, and Marco Pagano, 2000, Sharing default information as a borrower discipline
device, European Economic Review 44, 1951–1980.
Pagano, Marco, and Tullio Jappelli, 1993, Information sharing in credit markets, The Journal of
Finance 48, 1693–1718.
Roy, A. D., 1952, Safety …rst and the holding of assets, Econometrica 20, 431–449.
Salas, V., and J. Saurina, 2003, Deregulation, market power and risk behaviour in spanish banks,
European Economic Review 47, 1061–1075.
Schaeck, Klaus, and Martin Cihak, 2010a, Banking competition and capital ratios, European
Financial Management , Forthcoming.
,
2010b,
Competition,
e¢ ciency,
and soundness in banking:
An industrial
organization perspective, European Banking Center Discussion Paper No. 2010-20S,
http://ssrn.com/paper=1635245.
, and Simon Wolfe, 2009, Are competitive banking systems more stable?, Journal of
Money, Credit and Banking 41, 711–734.
Turk Ariss, Rima, 2010, On the implications of market power in banking: Evidence from developing countries, Journal of Banking and Finance 34, 765–775.
Vercammen, James A., 1995, Credit bureau policy and sustainable reputation e¤ects in credit
markets, Economica 62, 461–478.
Wagner, W., 2010, Loan market competition and bank risk-taking, Journal of Financial Services
Research 37, 71–81.
39
Table 1: Bank-speci…c variables: Summary Statistics
This table shows the total sample summary statistics for the bank speci…c variables used throughout the paper.
Bank speci…c data is retrieved from the Bureau Van Dijck Bankscope database. The full sample contains 99963
observations. The table consists of three parts. Panel A contains information on the mean and standard deviation
of the variables that are used as control variables in the competition - stability regressions. The impact of banks’
business model on bank soundness is proxied via its funding structure (share of wholesale funding equals the share
of money market funding in money market funding and total deposits), asset mix (loans to total assets) and revenue
composition (non-interest income in total income). We also control for bank size, credit risk (loan loss provisions
to total interest income) and bank strategy (annual growth in total assets). We have three types of banks in our
sample: Commercial Banks, Cooperative Banks and Savings Banks. Panel B summarizes the variables that are
needed to construct the Lerner index. The Lerner index is the relative markup of price over marginal cost. The
average price of bank activities equals the ratio of total revenues over total assets. Marginal costs are obtained
after estimating a translog cost function. Using a translog speci…cation, we relate banks’ total operating cost to
three input prices (price of …xed assets, price of labor and price of funding). They are constructed as respectively
the share of other operating and administrative expenses to total assets, the ratio of personnel expenses to total
assets and the ratio of interest expenses to total deposits and money market funding. Panel C contains info on the
main variables of interest: market power and bank riskiness. Market power is measured through the Lerner index,
whereas our bank stability indicator is the natural logarithm of the Z-score. The Z-score is calculated as the sum
of equity over total assets and return on assets divided by the three year rolling standard deviation of return on
assets.
Variable
Mean
Standard Deviation
Determinants of Bank Soundness
Share of Wholesale Funding
0.0417
0.0933
Loans to Total Assets
0.596
0.1795
Non-Interest Revenue Share
0.2256
0.1637
ln(Total Assets)
5.8664
1.8034
Loan Loss Provisions to Interest Income
0.1135
0.1742
Annual Growth in Total Assets
0.0658
0.1804
Commercial Bank dummy
0.6785
0.4671
Cooperative Bank dummy
0.1665
0.3725
Savings Bank dummy
0.155
0.3619
Translog Cost Function
Total Operating Cost
166.8183
613.047
Price of Fixed Assets
1.545
2.9279
Price of Labor
0.016
0.0093
Price of Funding
0.0365
0.0335
Average Price of bank activities
0.0752
0.0445
Marginal Cost
0.0639
0.0394
Market Power and Bank Soundness
ln(Z-score)
4.0842
1.1529
Lerner
0.1484
0.1419
40
41
Boone
-H-statistic
HHI(TA)
- nbanks
Market Share
Variables
Lerner
Bank Soundness
0.409
(0.000)
-0.362
(0.000)
-0.158
(0.000)
0.029
(0.467)
0.049
(0.226)
0.079
(0.049)
Lerner
-0.076
(0.038)
0.025
(0.539)
0.110
(0.003)
0.037
(0.313)
0.018
(0.619)
1.000
Market Share
0.401
(0.000)
0.466
(0.000)
0.048
(0.191)
-0.023
(0.537)
1.000
- nbanks
0.273
(0.000)
-0.053
(0.193)
-0.070
(0.087)
1.000
0.112
(0.002)
0.011
(0.770)
1.000
HHI(TA)
This table provides information on the correlation between bank soundness and various proxies of bank market power,
market structure and competition. Correlation measures are obtained at the time-varying country level. If a variable
varies at a more detailed level (e.g. bank soundness varies at the bank level) it is …rst averaged at the time-country level.
Bank soundness is the natural logarithm of the Z-score. The Lerner index is a bank-speci…c, time–varying measure of
market power. Market Share is the average market share of a bank in a country in a given year. Nbanks is the number
of banks in a country. In this table, we use the inverse of the number of banks, such that a higher value indicates an
increase in market power. HHI(TA) is the Hirschmann-Her…ndahl index of concentration of total assets. The more disperse
the market structure, the lower this value will be. The last two measures are estimated structural competition measures.
The estimations are done at the country level over …ve year rolling windows. We take the opposite of the Panzar-Rosse
H-statistics, such that a higher value also indicates an increase in market power. Finally, the Boone indicator is a new
measure of competition following Boone (2008). All competition or market structure measures are thus constructed that
an increase indicates more market power or concentration. p-values are in parentheses.
Table 2: Bank Soundness and Competition measures: Correlations
0.152
(0.000)
1.000
-H-statistic
Table 3: The market power-bank soundness relationship: Full sample regressions
This table contains information on the relationship between competition and stability in the total sample. The total
sample consists of 61 countries and spans the time period 1994-2006. Bank soundness (ln Z-score) is the dependent
variable and is regressed on a competition proxy (Lerner index), a group of bank speci…c control variables (including
specialization dummies) and GDP per capita. We employ the panel structure of the database and control for …xed
heterogeneity at the country and time level by including country and time …xed e¤ects. The standard errors are
robust and clustered at the bank level. To mitigate the impact of reverse causality, we use one period lagged values
of the independent variables. The …rst two columns show OLS estimates, whereas the third column are IV (2SLS)
regression results. In the …rst column, we include year and country …xed e¤ects. In the second column, we interact
them to account for time-varying country speci…c heterogeneity. In the third column, we take into account that
market power may be endogenous. The instruments are loan growth and lagged values of the Lerner index. The
Stock-Yogo weak ID test critical values at the 15 per cent level is 11.59 and at the 10 per cent level is 19.93. To
avoid that our results are driven by countries that are overrepresented in our sample, we weigh each observation
with the inverse of the number of banks in the country. Doing so, we give equal weight to each country.
VARIABLES
Lerner index
Share of Wholesale Funding
Loans to Total Assets
Non-Interest Revenue Share
ln(Total Assets)
Loan Loss Provisions to Interest Income
Annual Growth in Total Assets
GDP per Capita
Constant
Observations
R-squared
Type dummies
Year dummies
Country dummies
Time x Country dummies
Nr Countries
Instruments
F-stat_IV
J-stat
p-value
OLS
ln(Z-score)
OLS
ln(Z-score)
IV
ln(Z-score)
0.957***
(0.0909)
-0.0442
(0.111)
0.241***
(0.0845)
-0.620***
(0.0893)
0.0230***
(0.00891)
-0.595***
(0.0608)
-0.287***
(0.0514)
-2.23e-05*
(1.25e-05)
3.339***
(0.155)
0.931***
(0.0942)
-0.0672
(0.117)
0.143*
(0.0837)
-0.608***
(0.0918)
0.0198**
(0.00920)
-0.513***
(0.0618)
-0.363***
(0.0578)
2.141***
(0.168)
0.0534
(0.121)
0.181**
(0.0890)
-0.680***
(0.0962)
0.000349
(0.00966)
-0.271***
(0.0731)
-0.305***
(0.0540)
-2.93e-05**
(1.28e-05)
3.419***
(0.163)
99963
0.285
YES
YES
YES
NO
61
99963
0.352
YES
2.891***
(0.203)
99115
0.264
YES
YES
YES
YES
NO
61
61
lagged Lerner and Loan Growth
630.5
1.128
0.288
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
42
Table 4: Determinants of heterogeneity in the Competition-Stability relationship: summary
statistics
This table shows the summary statistics for the country-speci…c variables used in this paper. We categorize them
in four groups. First, we construct two herding measures: revenue heterogeneity (the within country dispersion of
non-interest income share) and systemic risk (measured by the country-level Z-score). The second group is a set of
market structure variables: an activity restrictions indicator (higher values indicate more restrictions on activities
related to insurance, real estate, underwriting, brokerage), the Hirschmann Her…ndahl index of concentration in
total assets and a measure of restrictions on bank entry. The third set of variables is related to regulation and
supervision with proxies for the strength of capital regulation, deposit insurance, the number of supervisors and
the strength of external governance. The last group of variables captures the …nancial structure of a country and
are related to the availability of credit history information, the strength of contract enforcement, GDP per capita
and the deepness of the stock market (turnover ratio). Not all variables are available for all countries or for the full
sample period (1994-2006). This explains why the number of observations ranges between 524 and 740. Detailed
information on the construction and data source of these country-speci…c variables are provided in table13.
Observations
Mean
St. Dev.
Herding
Heterogeneity - Revenues
740
0.187
0.059
Systemic Stability
616
4.219
0.953
Market Structure
Activitity Restrictions
638
9.318
2.339
HHI(Total Assets)
740
1304.967
732.734
Entry Applications Denied
524
0.106
0.201
Regulation and Supervision
Strength of Capital Regulation
638
5.904
1.822
ln(DI coverage - GDP per capita)
580
0.799
0.999
Multiple Supervisors
628
0.178
0.383
External Governance
628
12.980
2.020
Institutional and Financial development
Credit Registry
740
0.404
0.491
Credit Bureau - Depth
671
4.083
1.818
Contract enforcement - Cost
688
26.972
17.541
GDP per Capita
737
11715.333 12009.704
Stock Market Turnover Ratio
702
0.555
0.623
43
Min
Max
0.000
1.178
0.342
6.688
4.000
114.774
0.000
15.000
6903.013
1.000
2.000
-1.347
0.000
6.000
10.000
2.533
1.000
16.800
0.000
0.000
0.000
281.620
0.003
1.000
6.000
122.700
54629.023
6.224
Table 5: Determinants of heterogeneity in the Competition-Stability relationship: binary classi…cation results
The …rst column of the table reports the country-speci…c characteristics that are hypothesized to in‡uence
the competition-stability relationship. For each of these characteristics, we divide our sample countries
in a high and a low group, indicating whether the value of this characteristic for a speci…c country is
above (high) or below (low) the median over the sample of countries. The …rst group consists of two
herding measures: revenue heterogeneity (within-country dispersion of banks’ non-interest income share)
and systemic stability. The second group is a set of market structure variables: an activity restrictions
indicator (higher values indicate more restrictions on activities related to insurance, real estate, underwriting,
brokerage), the Hirschmann Her…ndahl index of concentration in total assets and a measure of restrictions on
bank entry. The third set of variables is related to regulation and supervision with proxies for the strength
of capital regulation, deposit insurance, the number of supervisors and the strength of external governance.
The last group of variables captures the …nancial structure of a country and are related to the availability
of credit history information, the strength of credit enforcement, GDP per capita and the deepness of the
stock market (turnover ratio).
The table consists of two panels and each panel consists of two sets of columns. The …rst panel contains the
results for the separate approach. In the separate approach, we …rst compute a measure of the strength of the
market power-stability relationship for each country. Subsequently, we test whether the average is di¤erent
in the high or low group. The left hand side part contains info on the test of di¤erences in the pairwise
correlation coe¢ cient (unconditional correlation), whereas the right hand side part tests for di¤erences in the
average of the estimated coe¢ cients on the Lerner index (in a country-speci…c regression of bank stability
on competition and a group of control variables). For each country-speci…c characteristic, we report the
number of countries in each group, the average value in each group as well as the p-value of the t-test of
equal means (allowing for unequal variances). The second panel contains the results for the pooled approach.
In the pooled approach, we regress our stability measure (Z-score) on the Lerner index and a group of control
variables for the total sample. We interact each explanatory variable with a dummy indicating whether the
bank belongs to a country from the high or to the low group (based on the country-speci…c characteristic
under investigation). The left hand side panel shows the OLS results, whereas the right hand side panel shows
IV (2SLS) results, where we use loan growth and lags of the Lerner index to instrument our competition
measure in these regressions. To avoid that the results in the pooled approach are driven by countries that
are overrepresented in our sample, we weigh each observation with the inverse of the number of banks in the
country. Doing so, we give equal weight to each country. For each country-speci…c characteristic, we report
the estimated beta coe¢ cient of the Lerner index in the low and high group as well as the p-value of the
Chow test, which indicates whether the average conditional correlation coe¢ cient between competition and
stability is di¤erent across both groups. All pooled regressions include time varying country …xed e¤ects
and specialization dummies. The separate regressions include year and specialization dummies.
44
Panel A: Separate Approach
Heterogeneity - Revenues
Systemic Stability
Activitity Restrictions
HHI(Total Assets)
Entry Applications Denied
Strength of Capital Regulation
DI coverage - GDP per capita
Multiple Supervisors
External Governance
Credit Registry
Credit Bureau - Depth
Contract enforcement - Cost
GDP per Capita
Stock Market Turnover Ratio
Unconditional
Number of Countries Average p-value
Herding
Low
31
0.269
0.003
High
31
0.152
Low
31
0.219
0.358
High
31
0.203
Market Structure
Low
30
0.164
0.006
High
28
0.275
Low
31
0.251
0.032
High
31
0.170
Low
27
0.195
0.104
High
26
0.254
Regulation and Supervision
Low
29
0.236
0.216
High
29
0.200
Low
26
0.143
0.008
High
26
0.257
Low
39
0.194
0.048
High
19
0.267
Low
29
0.225
0.377
High
29
0.211
Institutional and Financial Development
Low
36
0.202
0.327
High
26
0.222
Low
28
0.218
0.307
High
27
0.197
Low
29
0.218
0.277
High
28
0.193
Low
31
0.212
0.359
High
30
0.197
Low
31
0.151
0.003
High
30
0.271
45
Conditional
Average p-value
1.271
0.872
1.088
1.055
0.104
0.872
1.487
1.218
0.925
0.909
1.519
0.021
1.249
1.088
0.629
1.453
1.081
1.348
1.400
0.938
0.302
1.005
1.164
1.202
1.061
1.318
0.919
1.026
1.146
0.648
1.535
0.305
0.458
0.178
0.031
0.011
0.196
0.067
0.335
0.112
0.355
0.002
Panel B: Pooled Approach
OLS
Number of Countries Beta p-value
Herding
Heterogeneity - Revenues
Low
31
1.226
0.001
High
31
0.622
Systemic Stability
Low
31
0.816
0.168
High
31
1.081
Market Structure
Activitity Restrictions
Low
30
0.754
0.020
High
28
1.221
HHI(Total Assets)
Low
31
1.138
0.023
High
31
0.710
Entry Applications Denied
Low
27
0.963
0.261
High
26
1.209
Regulation and Supervision
Strength of Capital Regulation Low
29
0.992
0.848
High
29
1.030
DI coverage - GDP per capita
Low
26
0.722
0.036
High
26
1.144
Multiple Supervisors
Low
39
1.033
0.737
High
19
0.964
External Governance
Low
29
1.127
0.208
High
29
0.877
Institutional and Financial Development
Credit Registry
Low
36
0.931
0.861
High
26
0.964
Credit Bureau - Depth
Low
28
0.934
0.987
High
27
0.931
Contract enforcement - Cost
Low
29
1.023
0.362
High
28
0.845
GDP per Capita
Low
31
0.911
0.364
High
30
1.093
Stock Market Turnover Ratio
Low
31
0.616
0.000
High
30
1.442
46
IV(2SLS)
Beta (IV) p-value
2.570
1.770
2.241
2.060
0.021
1.918
2.652
2.874
1.540
2.187
2.945
0.042
2.404
2.231
1.514
2.595
1.949
3.300
2.322
2.340
0.634
2.470
1.857
2.546
1.906
2.693
1.692
2.054
2.545
1.498
3.204
0.071
0.594
0.000
0.050
0.003
0.001
0.961
0.064
0.004
0.165
0.000
47
Stock Market Turnover Ratio
Multiple Supervisors
DI coverage - GDP per capita
Entry Applications Denied
HHI(Total Assets)
Activitity Restrictions
Heterogeneity - Revenues
Lerner
Variables
Conditional Correlation
Unconditional correlations
0.719
(0.000)
-0.183
(0.154)
-0.337
(0.007)
0.298
(0.023)
-0.275
(0.031)
0.239
(0.071)
0.361
(0.009)
0.243
(0.066)
0.290
(0.023)
Conditional Correlation
0.003
(0.982)
-0.218
(0.088)
0.293
(0.025)
-0.191
(0.136)
0.327
(0.012)
0.314
(0.023)
0.108
(0.419)
0.163
(0.210)
1.000
Lerner
-0.155
(0.229)
-0.271
(0.039)
0.245
(0.054)
-0.128
(0.338)
-0.310
(0.026)
-0.020
(0.879)
0.012
(0.924)
1.000
Heterogeneity - Revenues
-0.264
(0.045)
0.223
(0.082)
-0.130
(0.331)
-0.189
(0.180)
-0.040
(0.765)
0.055
(0.676)
1.000
Activitity Restrictions
-0.062
(0.645)
0.253
(0.056)
0.534
(0.000)
0.014
(0.916)
-0.050
(0.707)
1.000
HHI(Total Assets)
-0.010
(0.943)
-0.050
(0.726)
-0.204
(0.124)
-0.132
(0.312)
1.000
Entry Applications Denied
0.440
(0.002)
0.010
(0.942)
0.136
(0.310)
1.000
0.129
(0.382)
0.145
(0.309)
1.000
DI coverage - GDP per capita
This table provides information on the correlation between the market power-stability relationship and a selection of country-speci…c variables.
The table contains pairwise correlation coe¢ cients as well as p-values (in brackets) that indicate the signi…cance of the correlation. The market
power-stability relationship is proxied by both the unconditional (pairwise) correlation and the conditional (regression-based) correlation. The
country-speci…c variables are a subset of the full set of country-speci…c characteristics. The selection of the variables is based on signi…cance in
the binary (low/high groups) classi…cation approach (either separate or pooled). This binary approach shows that these characteristics potentially
play an important role in the competition-stability relationship. The correlation table con…rms these …ndings and also provides information on
how these country-speci…c variables are correlated with each other.
Table 6: Determinants of heterogeneity in the Competition-Stability relationship: correlation table
0.374
(0.004)
1.000
Multiple Supervisors
48
Observations
R-squared
Control Variables
Type dummies
Year x Country dummies
Nr Countries
97593
0.341
YES
YES
YES
51
2.851***
(0.202)
0.964***
(0.109)
-0.268***
(0.0940)
Lerner index
Heterogeneity - Revenues
x Lerner
Activity Restrictions
x Lerner
HHI of Total Assets
x Lerner
Entry Applications denied
x Lerner
DI coverage - GDP per capita
x Lerner
Multiple Supervisors
x Lerner
Stock Market Turnover Ratio
x Lerner
Constant
ln(Zscore)
VARIABLES
96543
0.353
YES
YES
YES
47
2.789***
(0.203)
0.205**
(0.0878)
1.035***
(0.116)
ln(Zscore)
2.817***
(0.210)
-0.0613
(0.127)
1.031***
(0.129)
ln(Zscore)
2.797***
(0.202)
0.195**
(0.0829)
0.964***
(0.110)
ln(Zscore)
96434
0.350
YES
YES
YES
47
2.840***
(0.203)
0.121
(0.0857)
1.051***
(0.116)
ln(Zscore)
97593
94695
97593
0.341
0.362
0.340
YES
YES
YES
YES
YES
YES
YES
YES
YES
51
45
51
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2.767***
(0.202)
-0.316***
(0.122)
0.958***
(0.116)
ln(Zscore)
97593
0.340
YES
YES
YES
51
0.152**
(0.0732)
2.816***
(0.202)
1.012***
(0.109)
ln(Zscore)
96543
0.357
YES
YES
YES
47
0.171**
(0.0763)
2.839***
(0.203)
0.115
(0.122)
1.026***
(0.125)
-0.446***
(0.0944)
0.106
(0.111)
-0.241*
(0.143)
ln(Zscore)
96543
0.363
YES
YES
YES
47
0.157*
(0.0863)
2.841***
(0.206)
0.974***
(0.128)
-0.408***
(0.0968)
0.137
(0.0957)
-0.321**
(0.136)
ln(Zscore)
97593
0.343
YES
YES
YES
51
0.155**
(0.0741)
2.838***
(0.202)
0.137*
(0.0817)
-0.219*
(0.121)
0.931***
(0.115)
-0.269***
(0.0941)
ln(Zscore)
This table contains information on the drivers of the relationship between competition and stability in the total sample. The regressions are based on the baseline regression at
the bank level (see table 3), i.e. when regressing our stability measure (Z-score) on the Lerner index and a group of bank-speci…c control variables.We employ the panel structure
of the database and control for …xed heterogeneity at the year-country level by interacting country and time …xed e¤ects. We also add banktype dummies to the regression.
Furthermore, to mitigate the impact of reverse causality, we use one period lagged values of the independent variables. To avoid that our results are driven by countries that
are overrepresented in our sample, we weigh each variable with the inverse of the number of banks in the country. Doing so, we give equal weight to each country. The standard
errors are robust and clustered at the bank level. In the …rst seven columns, we add an interaction term of the Lerner index with a country-speci…c characteristic to the basic
regression. Doing so, we present a continuous extension to the binary approaches in the separate and pooled analysis. The choice of interaction variables is guided by signi…cance
in the previous part of the analysis. The continuous approach exploits all of the variation in the country-speci…c variables. In the last three columns, we show the result when
we add multiple interaction terms simultaneously (that are individually signi…cant). For ease of comparability (in terms of economic signi…cance), all country-speci…c variables
have been normalized to have zero mean and unit variance.
Table 7: Determinants of heterogeneity in the Competition-Stability relationship: regression results
Table 8: Drivers of Time-varying Conditional Correlation between Lerner and Z-score
This table explains the variation in the conditional (regression-based), time-varying correlation between bank stability and competition by regressing these correlation coe¢ cients on country-speci…c characteristics. The time-varying
correlation is retrieved by running country by country regressions over …ve year rolling windows. More speci…cally,
for each country we regress our bank stability measure (Z-score) on the Lerner index, a group of bank-speci…c control
variables and GDP per capita, while using …ve year rolling windows. Since our sample period is 1994-2006, and
taking into account that we lag our independent variables with one period, we get a maximum of eight countryspeci…c conditional correlations. The retrieved conditional correlation is subsequently matched to country-speci…c
variables measured at the …rst year of the …ve year window. As our dependent variable is an estimated dependent
variable, we require at least 30 observations over the …ve year estimation period. All three regressions also include
year-speci…c e¤ects.
VARIABLES
Heterogeneity - Revenues
HHI of Total Assets
DI coverage - GDP per capita
Stock Market Turnover Ratio
Activity Restrictions
Multiple Supervisors
Entry Applications Denied
Constant
-0.0524
(0.0863)
-0.0379
(0.115)
0.453***
(0.116)
0.210*
(0.0969)
0.298***
(0.0452)
0.0729
(0.0784)
0.0544
(0.0350)
1.358***
(0.0441)
-0.276**
(0.104)
-0.114
(0.102)
0.332**
(0.120)
0.175*
(0.0879)
0.182**
(0.0642)
-0.316**
(0.101)
-0.135
(0.0840)
0.378***
(0.0795)
0.162*
(0.0785)
0.939***
(0.0294)
0.918***
(0.0209)
Observations
257
303
R-squared
0.244
0.163
Year dummies
YES
YES
clustered standard errors
TIME
TIME
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
49
326
0.143
YES
TIME
50
Observations
R-squared
Type dummies
Time x Country dummies
Constant
Annual Growth in Total Assets
0.931***
(0.0942)
-0.0672
(0.117)
0.143*
(0.0837)
-0.608***
(0.0918)
0.0198**
(0.00920)
-0.513***
(0.0618)
-0.363***
(0.0578)
2.891***
(0.203)
(1)
ln(Z-score)
0.0433***
(0.00218)
0.00169
(0.00264)
-0.00245
(0.00156)
-7.14e-05
(0.00196)
-0.000670***
(0.000166)
0.00319**
(0.00150)
0.00358***
(0.00135)
0.0182***
(0.00507)
(2)
ROA
99963
0.379
YES
YES
0.0618***
(0.0109)
0.0315**
(0.0133)
-0.0622***
(0.00964)
-0.0148
(0.0109)
-0.0238***
(0.00102)
0.00239
(0.00481)
-0.0482***
(0.00593)
0.341***
(0.0180)
(3)
Equity/TA
99963
99963
0.352
0.306
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Loan Loss Provisions to Interest Income
ln(Total Assets)
Non-Interest Revenue Share
Loans to Total Assets
Share of Wholesale Funding
Lerner index
VARIABLES
99963
0.406
YES
YES
-0.483***
(0.0670)
0.185*
(0.0946)
-0.241***
(0.0532)
0.265***
(0.0583)
-0.0957***
(0.00586)
0.302***
(0.0405)
0.0760*
(0.0418)
1.803***
(0.204)
(4)
(ROA)
99286
0.272
YES
YES
0.00995
(0.0140)
-0.196***
(0.0432)
-0.241***
(0.0198)
-0.0314
(0.0286)
0.204***
(0.0205)
0.120***
(0.0263)
0.00847***
(0.00183)
(5)
NPL
This table shows the results for the total sample competition-stability regressions. The …rst column shows the
standard results when using the Z-score as stability measure and regressing it on the Lerner index and a group
of bank-speci…c control variables. For regression (2) to (5) we use four alternative stability measures, being nonperforming loans and the three subcomponents of the Z-score (equity over total assets, return on assets and the three
year volatility of the return on assets). For each stability measure we perform the basic regression with country-year
…xed e¤ects. When using non-performing loans as stability measure, we leave out the loan loss provisions over
interest income ratio as a control variable, since both variables are heavily related to each other.
Table 9: Alternative risk measures
Table 10: Failing Banks
This table shows regression results for the competition-stability trade-o¤ while checking for the impact of failing
banks. The …rst column shows our baseline regression for the full sample. We regress the Z-score on the Lerner
index and a group of bank-speci…c control variables, country-year dummies and bank type dummies. In the second
and the third regression, we interact the Lerner index with an exit dummy. For the second regression, the exit
dummy equals one in the two years before the bank leaves the sample. Notice that this dummy does not discrimate
between defaults and distressed mergers on the one hand and ’normal’ mergers or acquisitions on the other hand.
Therefore, in the third regression, the exit dummy only equals one in the two years before a bank leaves the sample
and the bank had a negative ROA in that period. In this way, we only capture the banks that actually where in
distress before they leave the sample. The signi…cant and positive interaction term between competition and the
distressed exit dummy indicates that these banks that are in trouble before leaving the sample react more strongly
to a change in competition. Thus, banks that are in distress gamble even more than others when competition rises,
probably because there is not much left to loose for them. In the fourth regression, we only look at banks that did
not exit the sample Exit Dummy =0). We use these banks to redo our continuous analysis, where we regress the
Z-score on the Lerner index, a group of control variables and on interaction terms between the Lerner index and
country-speci…c characteristics. For each regression, error terms are clustered at the bank level.
Lerner index
Baseline
Exit
Distressed Exit
Not Distressed
0.931***
(0.0942)
0.977***
(0.0996)
-0.254
(0.156)
0.878***
(0.0985)
0.914***
(0.117)
Lerner x Exit
Lerner x Distressed Exit
Heterogeneity - Revenues
x Lerner
HHI of Total Assets
x Lerner
DI coverage - GDP per capita
x Lerner
Stock Market Turnover Ratio
x Lerner
Constant
0.617**
(0.271)
2.891***
(0.203)
2.885***
(0.203)
2.908***
(0.203)
Observations
99,963
99,963
99,963
R-squared
0.352
0.352
0.352
Control Variables
YES
YES
YES
Type dummies
YES
YES
YES
Year x Country dummies
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
51
-0.292***
(0.0857)
-0.202
(0.126)
0.183**
(0.0845)
0.117
(0.0772)
2.810***
(0.203)
96836
0.337
YES
YES
YES
52
Observations
R-squared
Control Variables
Type dummies
Year x Country dummies
Heterogeneity - Revenues
x Lerner
HHI of Total Assets
x Lerner
DI coverage - GDP per capita
x Lerner
Stock Market Turnover Ratio
x Lerner
Constant
Lerner x Lerner
TBTF(25%) x Lerner
TBTF x Lerner
Large Market Share x Lerner
Market Share x Lerner
Lerner index
99963
0.352
YES
YES
YES
2.891***
(0.203)
0.931***
(0.0942)
Baseline
99963
0.352
YES
YES
YES
2.914***
(0.205)
0.912***
(0.0952)
0.278
(0.219)
Market Share
2.929***
(0.206)
0.682
(0.560)
0.917***
(0.0941)
Too Big to Fail
99963
99963
0.352
0.352
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2.886***
(0.205)
-0.384
(1.312)
0.941***
(0.0967)
Large Market Share
99963
0.352
YES
YES
YES
2.930***
(0.204)
0.574*
(0.313)
0.923***
(0.0941)
Too Big to Fail 25%
99963
0.352
YES
YES
YES
2.872***
(0.204)
0.0459
(0.0493)
0.953***
(0.0987)
Lerner-squared
97593
0.343
YES
YES
YES
0.0251
(0.0575)
-0.329***
(0.0804)
-0.220*
(0.119)
0.156*
(0.0863)
0.145**
(0.0734)
2.820***
(0.203)
0.970***
(0.115)
Lerner-squared
This table shows the results for the basic stability-competition regression, while controlling for the impact of bank size in various ways. The question here is whether banks with a larger
market share have a stronger incentive to take more risk, because they can potentially see themselves as too-big-to-fail. In the …rst column the results for the basic regression are shown,
i.e a regression of the Z-score on the Lerner index and a group of bank-speci…c control variables, while also controlling for country-year and bank type e¤ects. In the second column, we
add an interaction term between the Lerner index and a bank’s market share (total assets market share). In the third column we introduce a dummy that equals one for banks having
a market share larger than 10 percent. We interact this dummy with the Lerner index to see whether these banks react di¤erently to a change in competition. In the fourth and …fth
column, we proxy Too-big-to-fail by the ration of bank size to a country’s GDP. We interact the Lerner index with this share (column 4) or a dummy variable that is one if this share
exceeds 25Column 6 shows an updated version of the basic competition-stability regression, including a squared Lerner index. Column 7 also adds the interaction terms between the
Lerner index and a group of country-speci…c characteristics. By including the squared term, we control for potential non-linear competition e¤ects and reduce the possibility that the
other interaction terms are picking up a competition e¤ect. For each regression, error terms are clustered at the bank level.
Table 11: Robustness test - Market Share
53
3.300272
3.644814
3.75954
3.568
SLOVAK REPUBLIC
INDIA
NORWAY
Average
0.071
0.121
0.184
0.126
0.136
0.098
0.118
0.117
Lerner
3.047
3.260
3.732
3.346
3.840
3.721
3.936
3.832
ln(Z-score)
Rev. Heterogeneity
Lowest Vigintile
0.168
0.228
0.238
0.211
Highest vigintile
0.187
0.151
0.133
0.157
1587.153
836.9371
1647.784
1357.291
2671.202
356.2214
1783.815
1603.746
HHI(TA)
0.973
1.526
1.925
1.475
1.463
-0.908
-0.349
0.069
DI coverage
This table summarizes country-speci…c characteristics over the full sample period for the 5 percent of countries with the highest and lowest conditional correlation between bank competition and stability. We focus
on the characteristics that have a signi…cant impact on this relationship, being bank market concentration
(HHI), revenue heterogeneity, deposit insurance and the stock market turnover ratio. This table allows us
to check whether our results hold for the countries at both extremes of the competition-stability spectrum.
The results in these table con…rm our previous …ndings. Countries where bank market power has a strong
positive impact on bank stability tend to have less concentrated banking markets, a lower degree of revenue
heterogeneity, more generous deposit insurance and deeper stock markets.
-2.6494
-2.00572
-1.5465
-2.067
Cond.corr.
VIETNAM
LUXEMBOURG
IRELAND
Average
Country
Table 12: Economic assessement
0.550
1.600
0.822
0.991
0.194
0.018
0.462
0.225
stock market
10
11
11
10.667
9
7
8
Act. Res.
25
80
130
78.333
31
144
41
72
banks
54
Entry applications denied
Doing Business database
Doing Business database
Financial structure database, Beck and Demirguc-Kunt (2009)
World Development Indicators, World Bank
GDP per capita
Institutional and …nancial development
Doing Business database
Credit Bureau - Depth
Contract Enforcement - cost
Stock market turnover ratio
Credit Registry
External governance
Bank regulation and supervision database , Barth et al. (2000,2003,2008)
Bank regulation and supervision database , Barth et al. (2000,2003,2008)
Regulation and Supervision
Bank regulation and supervision database , Barth et al. (2000,2003,2008)
Deposit insurance around the world database , Demirguc-Kunt et al. (2005)
Bank regulation and supervision database , Barth et al. (2000,2003,2008)
Bank concentration
Capital regulatory index
Deposit Insurance coverage
Multiple supervisors
Bankscope, own calculations
Activity restrictions
Herding
Bankscope, own calculations
Source
Bankscope, own calculations
Market structure
Bank regulation and supervision database , Barth et al. (2000,2003,2008)
Systemic Stability
Revenue Heterogeneity
Variable
Table 13: Country-speci…c characteristics
Dummy capturing whether there is a private
and/or public credit registry
Strength of the information content of the credit bureaus
This variable measures the cost of enforcing a contract
Ratio of the value of total shares traded
to average real market capitalization
Gross Domestic Product per capita
The strength of capital regulation in a country
Deposit insurance coverage relative to GDP per capita
Dummy equal to one when there are
multiple bank supervisors
The strength of external auditors, …nancial statement
transparancy, and the existence of an external rating
Degree to which banks can participate
in various non-interest income activities.
Measured as the Hirschmann-Her…ndahl index (HHI)
of total assets
% of entry applications that were denied
Within country standard deviation of
non-interest income share
Z-score at the country level
Description
55
Country
ARGENTINA
AUSTRIA
AUSTRALIA
BANGLADESH
BELGIUM
BRAZIL
CANADA
SWITZERLAND
CHILE
COLOMBIA
COSTA RICA
CYPRUS
CZECH REPUBLIC
GERMANY
DENMARK
DOMINICAN REPUBLIC
ECUADOR
SPAIN
FRANCE
UNITED KINGDOM
GREECE
HONG KONG, CHINA
CROATIA
HUNGARY
INDONESIA
IRELAND
INDIA
ITALY
JAPAN
KENYA
ln(Z-score)
2.755
3.963
3.997
3.235
3.723
2.852
3.791
4.596
3.551
2.715
3.851
2.462
3.014
4.543
3.752
3.267
3.273
4.287
3.910
3.839
3.044
3.924
3.357
3.267
3.138
3.936
3.260
4.064
3.429
3.619
Lerner
0.050
0.108
0.146
0.164
0.082
0.128
0.108
0.167
0.152
0.015
0.093
0.073
0.096
0.083
0.220
-0.004
0.105
0.168
0.099
0.128
0.140
0.164
0.097
0.110
0.078
0.118
0.121
0.155
0.003
0.172
GDP per cap.
7568.866
23998.51
20717.88
364.1659
21897.4
3714.598
22823.11
34157.36
4879.885
2451.793
4251.311
12410.36
5657.648
22530.21
29218.36
2175.146
1428.235
14133.81
21842.6
24521.81
11793.78
25376.1
4261.193
4904.271
852.8801
25111.98
480.6477
19067.38
37199.11
410.1668
banks
130
278
41
33
86
188
72
524
36
41
57
20
37
2535
110
35
37
245
462
195
29
48
61
38
114
41
80
866
852
42
Country
KOREA, REP.
KAZAKHSTAN
LEBANON
LUXEMBOURG
LATVIA
MEXICO
MALAYSIA
NIGERIA
NETHERLANDS
NORWAY
PANAMA
PERU
PHILIPPINES
PAKISTAN
POLAND
PORTUGAL
PARAGUAY
ROMANIA
RUSSIAN FEDERATION
SWEDEN
SINGAPORE
SLOVENIA
SLOVAK REPUBLIC
THAILAND
TURKEY
UKRAINE
UNITED STATES
URUGUAY
VENEZUELA, RB
VIETNAM
SOUTH AFRICA
Table 14: Country List
ln(Z-score)
2.612
3.201
3.503
3.721
2.639
2.662
3.309
2.923
3.934
3.732
3.559
3.232
3.906
2.956
3.072
3.611
2.860
2.861
3.213
3.840
4.094
3.509
3.047
2.430
2.449
3.131
4.172
2.270
2.780
3.840
3.359
Lerner
0.090
0.137
0.101
0.098
0.136
0.039
0.173
0.190
0.123
0.184
0.157
-0.036
0.104
0.128
0.093
0.089
0.080
0.085
0.107
0.196
0.213
0.118
0.071
0.043
0.128
0.139
0.185
-0.038
0.146
0.136
0.092
GDP per cap.
12095.13
1696.844
4600.541
43511.89
3527.425
5396.252
4013.81
377.1743
23164.49
37589.07
3978.826
2091.894
1001.83
543.7955
4417.057
10412.33
1380.28
2032.468
2208.739
29016.04
23597.53
10923.43
3975.83
2167.972
4013.615
850.3606
35132.82
6031.189
4728.954
466.2205
3110.659
banks
29
26
64
144
30
48
50
75
64
130
95
29
61
29
68
37
26
29
1003
107
21
23
25
26
57
62
10088
51
63
31
39
LURUBDVNUAMXCHIE DKSGDENLPETRSI CRATPABEPYARSEUYBRECLBGBKZVEPLPTRODOAUHUGRIT CAKEZALVCLCYCOFRID IN ESHRPKNGUSKRSKPHHKMYCZJPTHNOTW
Figure 1: Pairwise Correlation of Bank Market Power and Stability
The graph contains information on the relationship between bank market power and bank soundness. Bank market
power is proxied by the Lerner index. Bank soundness is captured by the Z-score, which equals the number of
standard deviations bank pro…ts have to fall before the equity cushion s depleted. The full sample correlation is
depicted by the black line and equals 0.252. The full sample consists of banks from 62 countries. The set of countries
is heterogeneous and they have di¤erent regulatory frameworks and institutional settings. We conjecture that this
heterogeneity across countries countries may a¤ect the competition-stability relationship at the country level. This
requires that the competition-soundness relationship also exhibits variation at the country level. The height of
the bars shows the pairwise correlation between market power and bank soundness per country. The bars are
sorted from low to high and the country labels are mentioned on the X-axis. The correlations that are signi…cantly
di¤erent from zero have a lighter shade. The average pairwise correlation over the 62 countries ressembles the full
sample correlation. However, there is a large amount of heterogeneity in the competition-stability relationship, with
correlations ranging from below -0.2 to above 0.5. The standard deviation of the correlation across the 62 countries
is 0.173.
Pairwise correlation of Bank Market Power and Stability
.4
.2
0
-.2
Pairwise Correlation Coefficient
.6
Heterogeneity across 62 Countries
insignificant
significant
Red line = Full sample pairwise correlation (=0.252)
Country Average=.211, Country Standard Deviation=.173
56
VNLUIE NLMXUAPERUSI PADELBBDDKHUCHTWDOARKZSESGLVBEID ROECTRPYZABRATKRGBPLTHMYHKCRHRPKKEIT VEUYPHCLUSESGRCAJPFRNGCYPTAUCOCZSKIN NO
Figure 2: Conditional Correlation of Bank Market Power and Stability
The graph contains information on the relationship between bank market power and bank soundness. Bank market
power is proxied by the Lerner index. Bank soundness is captured by the Z-score, which equals the number of
standard deviations bank pro…ts have to fall before the equity cushion is depleted. The full black line crosses the Yaxis at the value of the estimated coe¢ cient of the Lerner index retrieved by regressing stability on bank soundness
and a set of control variables (see Equation (2) ). The dotted lines indicate a 95 percent con…dence interval. We
conjecture that this heterogeneity across countries countries may a¤ect the competition-stability relationship at
the country level. This requires that the competition-soundness relationship also exhibits variation at the country
level. The height of the bars shows the magnitude of the coe¢ cient of the Lerner index when estimating Equation
X for each country separately. The bars are sorted from low to high and the country labels are mentioned on
the X-axis. The coe¢ cients that are signi…cantly di¤erent from zero have a lighter shade. The average of the 62
estimated coe¢ cients equals 1.07, which ressembles the full sample coe¢ cient. However, there is a large amount
of heterogeneity in the competition-stability relationship. The standard deviation of the coe¢ cient across the 62
countries is 1.24.
Conditional correlation of Bank Market Power and Stability
2
0
-2
-4
Regression coefficient Lerner
4
Heterogeneity across 62 Countries
insignificant
significant
Red line: full sample beta (=1.011) and 95% confidence bounds
Country Average=0.982, Country Standard Deviation=1.464
1
57