What do we know about the impact of government interventions in the banking sector

What do we know about the impact of government interventions
in the banking sector? An assessment of various bailout programs on
bank behavior
Aneta Hryckiewicz ⇑
Accounting Department, Kozminski University, Jagiellonska Street 57/59, 03-301 Warsaw, Poland
a r t i c l e i n f o
Article history:
Received 12 February 2013
Accepted 11 May 2014
Available online 7 June 2014
JEL classification:
G21
G28
Keywords:
Government interventions
Crisis
Bailout
Moral hazard
Financial stability
a b s t r a c t
Systemic banking crises have placed enormous pressure on national governments to intervene. The
empirical literature, however, is inconclusive on what an optimal bailout program should look like to mit-
igate the negative consequences of government interventions in the banking sector. We find that, in gen-
eral government interventions have a negative impact on banking sector stability, significantly increasing
its risk. In particular, we find that among bailout measures, nationalization and asset management com-
panies (AMCs) contribute most to the risk effect and that among liquidity support mechanisms, public
guarantees are the largest contributor to the risk effect. However, we also find that by making an appro-
priate choice of intervention mechanisms, governments can mitigate the negative consequences stem-
ming from the above-mentioned effects.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
The ongoing mortgage crisis has witnessed the largest scale of
government interventions in financial sectors since the 1920s.
National authorities on nearly every continent have been com-
pelled to intervene, beginning in the United States and proceeding
through Europe and into Asia. The scale of government interven-
tion in the subprime crisis and the volume of government support
have been massive. The former government auditor for bailouts of
U.S. banks, Neil Barofsky, claimed in Bloomberg News on July 20th,
2009, that the cost of government intervention in the United States
aimed solely at rescuing the banking sector may reach nearly $20
trillion. This cost includes approximately $6.8 trillion of Federal
Reserve lending and credit lines to banks and more than $7.4 tril-
lion for the mortgage debt purchase program and other Treasury
Department stabilization programs. In addition, the Federal
Deposit Insurance Fund has offered $2.3 trillion for the banking
system in the form of bank guarantees. In the European Union, gov-
ernments have approved €311.4 billion in capital injections for dis-
tressed institutions, €2.92 trillion in liability guarantees, €33 billion
for relief of impaired banking assets and €505.6 billion for liquidity
support and bank funding. As a result, most of the largest banking
institutions in the world are either in government hands or have
implicit government protection.
The massive government interventions and regulatory mea-
sures undertaken during the systemic banking crises raise ques-
tions regarding the long-term effects of such actions on future
banking sector behavior. Specifically, we address the following
questions: What is the total impact of government interventions
on the banking sector? Does the coverage of the government inter-
ventions in the banking sector matter? Does the structure of gov-
ernment interventions matter? Which instruments among those
commonly employed by governments contribute to the estimated
effects? What is the optimal bailout package that helps to mitigate
the negative consequences stemming from regulatory actions?
To the best of our knowledge, this is the first study that
attempts to empirically assess the total impact of government res-
cue packages on bank behavior, capturing the entire set of policy
injections into banks in 23 countries. To date, existing studies have
examined the impact of government interventions on bank behav-
ior, using a single policy mechanism as an example. The results,
however, are mixed. For example, Gropp et al. (2011), Dam and
http://dx.doi.org/10.1016/j.jbankfin.2014.05.009
0378-4266/Ó 2014 Elsevier B.V. All rights reserved.
⇑ Tel.: +48 22 519 21 69.
E-mail address: ahryckiewicz@alk.edu.pl
Journal of Banking & Finance 46 (2014) 246–265
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Koetter (2012) and Fischer et al. (2012) investigate the role of gov-
ernment guarantees on bank behavior; Cordella and Yeyati (2003),
Corsetti et al. (2006) and Martin (2006) examine the impact of
liquidity provisions; Berger et al. (2011), Duchin and Sosyura
(2013) and Mehran and Thakor (2011) examine the effects of cap-
ital injections; and Black and Hazelwood (2012), Bayazitova and
Shivdasani (2012) and Harris et al. (2013) consider the effects of
the Troubled Asset Relief Program (TARP). Recently, several studies
have discussed the contributions of bailout programs to the cost of
resolving the banking crisis but without reaching a definitive con-
clusion (Bhattacharya and Nyborg, 2012; Landier and Ueda, 2009;
Veronesi and Zingales, 2010).
Consequently, several gaps and limitations regarding the reso-
lution of banking crises are evident in the existing literature. First,
to the best of our knowledge, there are no bank-level, cross-coun-
try empirical investigations of the total impact of bailout programs
on banking sector risk. The existing literature includes studies of
the impact of individual policy measures on bank behavior in spe-
cific countries, whereas some cross-country studies evaluate the
costs of these mechanisms. Due to the limitations of the methodol-
ogies employed, however, existing research does not provide any
definitive conclusions regarding the impact of government inter-
ventions on banking sector risk. As a result, policymakers have
been left without clear guidance regarding how to respond to prob-
lems in the banking sector during financial crises. The experience
of Japan shows that inappropriate regulatory policies can under-
mine incentives in the banking sector and lead to significantly
increased risk and severe recession (Hoshi and Kashyap, 2010).
For this reason, banking sector risk should be the central focus of
policymakers when formulating resolution strategies for finan-
cially distressed banks. Bearing this in mind, an important contri-
bution of the present study is to provide evidence regarding the
total impact of all bailout programs taken together on bank behav-
ior, using a large sample of assisted banks from 23 countries.
Importantly, our data enable us to evaluate the total impact of all
policy injections into banks rather than focus on a single policy
or set of policies. Systemic banking crises require the implementa-
tion of various measures consecutively. Additionally, regulators
often employ specific measures simultaneously. Thus, the esti-
mated effects of individual policy mechanisms on a bank’s behav-
ior do not reflect the true risk to the banking sector. This risk may
be lower or higher than that associated with single interventions.
A second important issue, which has not received significant
attention in the existing literature, is the risk associated with spe-
cific policy instruments relative to total banking sector risk. Regu-
lators have a wide range of policy measures that they can employ
in banking crises. However, these mechanisms affect bank risk in
different ways. Whereas public guarantees and liquidity provisions
may affect market discipline and through this channel affect bank
risk, restructuring and recapitalization mechanisms may affect
bank risk through balance sheet effects. In addition, effects may
differ within a given class of measures. Thus, by comparing the
effects of single policy measures with those of other intervention
mechanisms, we can determine which strategies have minimum
negative consequences for banking sector risk.
Finally, the magnitude of the effect of an individual instrument
will depend on the structure and effectiveness of the entire bailout.
Dam and Koetter (2012) demonstrate that certain regulatory
actions may limit or even reduce risk-taking in the banking sector.
The existing empirical literature is inconclusive regarding the form
of the optimal resolution program and the combination of mecha-
nisms that would allow countries to minimize the negative conse-
quences of government interventions. The lack of any empirical
evidence in this area indicates that policymakers cannot properly
assess banking sector risk. Although recent theoretical studies
provide important insights into optimal resolution strategies for
banking crises, they yield inconclusive results. For example,
Philippon and Schnabl (2013) demonstrate that the optimal bail-
out program should include equity instead of cash injections. Addi-
tionally, the optimal bailout program should not include asset
purchases and debt guarantees; otherwise, banks will have incen-
tives to engage in opportunistic behavior. Bhattacharya and
Nyborg (2012) argue that both equity injections and asset purchase
programs are optimal in resolving banking sector problems.
However, House and Masatlioglu (2010) find that debt guarantees
exhibit the best performance. The theoretical nature of these stud-
ies does not allow us to determine how these strategies might
operate in practice. Moreover, the main interest of the above-
mentioned studies lies in the determination of optimal strategies
from the perspectives of cost and banking sector recovery, with
risk a minor concern. The determination of the optimal bailout
scheme is especially important at present due to regulators’ recent
initiatives in implementing national directives regarding the reso-
lution of systemic banking crises (for example: Bank of England,
2009; European Commission, 2011 or World Bank, 2012).
In the present paper, we examine the effects of government
interventions on bank behavior at the aggregate and individual
levels. In addition, we examine the effect of single regulatory mea-
sures on risk behavior in the banking sector. For this purpose, we
have constructed a novel bank-level database comprising all
distressed and subsequently bailed-out institutions and the policy
measures applied to them during 23 systemic banking crises in 23
countries. In total, we could identify 92 banking institutions that
were either protected by governments that offered them blanket
guarantees or were bailed out through central banks’ actions
and/or government recapitalization and debt-restructuring pro-
grams. Our data enable us to match a specific government policy
measure to each bailed-out institution, which allows us to capture
the entire rescue program applied to a given institution. Then, by
comparing the behavior of assisted banks to that of non-assisted
competitors in the same country, we can assess the effects of
government intervention measures on bank behavior over several
subsequent years. Our approach allows us to identify the optimal
rescue package from a risk perspective.
We recognize several limitations of the present study. First, we
compare the behavior of institutions bailed out through specific
policy mechanisms to their non-bailed-out competitors. In several
countries in our sample, a bailout program was applied to most
existing banks. Therefore, in these countries, we observe limited
variation in bank risk, which is primarily driven by the bailed-
out banks. Furthermore, one may expect that banks in countries
more severely affected by a shock will generally exhibit poorer per-
formance than institutions in other countries. As a result, the mag-
nitude of a financial shock may affect risk-taking behavior and
could influence our risk measures. This may be especially true
when a banking crisis is accompanied by a currency crisis and
banks have been involved in foreign borrowing. Depreciation of
the domestic currency, if unhedged, might drastically decrease
the value of a bank’s capital and thus deteriorate a bank’s perfor-
mance indicators. In such cases, currency depreciations may place
the banks in weaker positions, which will be not a result of govern-
ment intervention programs. Second, our risk variables measure
bank behavior ex-post, which may cause a bank’s behavior prior
to an intervention to be correlated with its post-bailout behavior.
Finally, decisions to undertake bank bailouts may be influenced
by regulators’ long-run expectations regarding bank risk. We
attempt to control for these effects to the greatest extent possible,
given the available data, although we recognize that our approach
is far from perfect. To control for the magnitude of countries’ finan-
cial shocks, we include macroeconomic variables and a currency
crisis dummy in our regressions. In addition, we cluster our results
at the country level. At the bank level, we control for this effect by
A. Hryckiewicz / Journal of Banking & Finance 46 (2014) 246–265 247

including bank’s profitability and liquidity indicators. Moreover,
we present results obtained through various alternative meth-
ods—for example, panel regressions with interactive time variables
and a difference-in-differences (DID) approach—in our robustness
analysis. We attempt to ensure that additional risk exhibited by
assisted banks in post-crisis periods is independent of the above-
mentioned effects and stems from specific government actions.
Our study contributes significantly to existing research by pro-
viding interesting results regarding the effect of government inter-
ventions and policy decisions on bank behavior. First, the evidence
in our study shows that government interventions are strongly
correlated with subsequent risk increase in the banking sector.
The magnitude of the effect is also shown to be strong. We argue
that this effect arises from the withdrawal of governance mecha-
nisms in the post-intervention period, inefficient bank manage-
ment and/or lack of appropriate restructuring process in the
bailed-out banks. We also find that the greater the risk is, the more
extensive bailout programs are. In particular, blanket guarantees
and subsequent government participation in the banking sector
exhibit the largest positive effects on bank’s risk measures. We find
that although public protection increases moral hazard, the
increased role of the government in the banking sector might
encourage politicians to act in self-interest, leading to inefficiency
and poor performance of affected institutions. Moreover, the lack
of necessary restructuring process at distressed banks might lead
to these banks’ poor performance, motivating these banks to risk
increase (Kane, 1989). In turn, intervention mechanisms relying
on market discipline are found to perform best with respect to risk
reduction.
More importantly, our results further document that negative
effects stemming from individual intervention policies might be
mitigated by the appropriate choice of instruments in an entire
bailout program. Consequently, our results show that the optimal
bailout program should possibly include policy measures aimed
at significant bank recovery and strengthening market discipline.
Our estimations are robust with regard to alternative risk mea-
sures, magnitude of financial crisis, cross-country institutional dif-
ferences such as rule of law, information disclosure rules, deposit
insurance schemes, and other features of countries captured by
unobserved country effects.
The remainder of the paper is organized as follows. In Section 2,
we discuss the policy instruments available to regulators to inter-
vene in the banking sector. Section 3 reviews the existing literature
on the mechanisms available to resolve banking crises. Section 4
describes our data and methodology. Section 5 presents our empir-
ical results, including a robustness analysis. Section 6 concludes
the paper.
2. Policy instruments in government hands
Beginning in July 2007, the subprime mortgage meltdown in
the United States triggered a systemic banking crisis in many
industrial countries, prompting the implementation of various
strategies to rescue distressed banks. Generally, these strategies
can be divided into two groups: systemic measures offered to all
financial institutions, independent of how affected these institu-
tions were by the crisis, and single-policy instruments aimed at
rescuing individual banks (Fahri and Tirole, 2012). The first group
of policy instruments is monetary in nature and generally involves
interest rate management (in practice, shifting the interest rate
toward zero). Its objective is to provide assistance to all financial
institutions to enable them to weather a financial shock. In con-
trast, the individual measures address the distress of single institu-
tions requiring significant public resources. Claessens et al. (2011)
summarize the range of bank-level regulatory mechanisms that are
necessary at various stages of a banking crisis. These instruments
include the following: (1) blanket guarantees and liquidity provi-
sions during the containment stage of a crisis; (2) capital injections
in the next phase; and (3) debt-restructuring mechanisms such as
‘‘Asset Management Companies’’ (AMCs) or ‘‘Bad Banks’’ in the
final stage of a crisis. Measures of the first type are used in the ini-
tial stage of a crisis when there is a loss of confidence in the finan-
cial system and substantial uncertainty. Distressed banks often
face runs on their deposits during this period, which rapidly reduce
the liquidity of affected banking institutions. More importantly, the
risk of contagion to other, healthy institutions increases. Without a
timely and effective intervention from the central bank, such a sit-
uation may lead to a further deterioration in the value of banks’
assets and ultimately bankrupt many institutions. Thus, central
banks tend to step in by offering blanket guarantees and injecting
liquidity, with the goal of increasing confidence in the banking sys-
tem. Interestingly, although these measures may target individual
institutions, in severe crises, regulators often extend these mea-
sures to the entire banking sector. This occurs when fear of runs
on healthy institutions is high or distress affects the entire banking
sector (Diamond and Dybvig, 1983; Jacklin and Bhattacharya,
1988; Freixas et al., 2000; Acharya and Yorulmazer, 2007).
When a liquidity crisis becomes a capital crisis, government
policies focus on rescuing insolvent institutions by recapitalizing
them. Instruments employed for this purpose include govern-
ment-assisted mergers and capital injections. In a government-
assisted merger, the government helps a troubled bank find a part-
ner willing to acquire the distressed institution. In practice, to
increase the probability of success of such an intervention, the gov-
ernment participates in restructuring the bank’s bad debt, often by
taking it over. In addition, the government may guarantee the
future losses of an acquired institution. In the crisis of 2007–2009,
the most spectacular examples of this type of intervention
were the acquisitions of Bear Sterns by JP Morgan and of Merrill
Lynch by Bank of America. Sheng (1996) argues that govern-
ment-assisted M&As are optimal when the government has limited
funds to cover the closure of an insolvent institution or the finan-
cial industry as a whole has sufficient resources to absorb a failing
bank (Acharya and Yorulmazer, 2007). Therefore, this type of inter-
vention is often used in the initial phase of a crisis. Another bailout
option available to policymakers is to inject capital into a dis-
tressed institution. The majority of bailed-out banks in our sample
received capital injections in exchange for ownership. The objec-
tive of nationalization is to save a bank from bankruptcy and thus
limit the negative consequences of its distress for the banking sec-
tor. In recent years, the types of institutions most frequently
nationalized have been systemically important banks. However,
this bailout instrument is very costly, requiring significant public
resources. Moreover, many studies argue that capital injections
should be accompanied by extensive restructuring of a bank’s debt.
Commonly employed restructuring mechanisms include writing
off a bank’s bad debt at a cost to taxpayers and creation of a
restructuring fund in the form of a ‘‘Bad Bank’’ or AMC. Under
the first strategy, the government takes over the bad debt of an
institution in the amount by which its assets have decreased. This
allows for the recapitalization of a bank, enabling the bank to sur-
vive. The strong assumption underlying this mechanism is that the
government does not participate in any of the bank’s operations;
thus, the strategy requires market-disciplining mechanisms to
function (Dell’ Ariccia and Ratnovski, 2012). However, the AMC
mechanism seeks to transfer non-performing loans from a dis-
tressed institution’s balance sheet to a newly created fund. The role
of the fund is to clean up the bank’s balance sheet and restore its
profitability. The fund then attempts to maximize the recovery of
the bad debt through active restructuring. Importantly, to ensure
the effectiveness of this mechanism, the AMC should be handled
248 A. Hryckiewicz / Journal of Banking & Finance 46 (2014) 246–265

in the private sector, and the state should not dispose of the man-
aged assets. In practice, however, the opposite is the case, with the
state controlling the fund, which makes this restructuring mecha-
nism less effective, creating bad incentives within the banking
sector (Klingebiel, 2000).
3. Impact of government intervention on bank behavior
This section reviews existing views regarding the effectiveness
of government interventions and specific policy measures used to
bail out distressed institutions.
3.1. What does the theory of government interventions tell us?
Theoretical work on government interventions presents mixed
results. The primary argument advanced by proponents of govern-
ment intervention is that regulatory actions are necessary to
restore confidence in the banking sector and credit system, thereby
preventing the economy from falling into prolonged recession.
More importantly, the proponents of regulatory intervention argue
that such actions do not negatively affect the banking sector. A
standard argument in this literature is that regulatory actions help
distressed banks recover, and banks’ restored charter values then
discipline the banks’ behavior. Formal support for this view is pre-
sented in Cordella and Yeyati (2003), Berger et al. (2011), Hackenes
and Schnabel (2010) and Mehran and Thakor (2011). In addition,
strong regulatory actions restricting the banking business may also
impose discipline on bank management (Dam and Koetter, 2012).
Government interventions are also likely to strengthen banks’
monitoring incentives (Dell’ Ariccia and Ratnovski, 2012; Mehran
and Thakor, 2011).
Opponents of government intervention, however, argue that
these actions cause the banking sector more harm than good. An
argument commonly advanced is that government interventions
increase moral hazard due to a decline in market discipline and
banks’ anticipations of bailouts (Flannery, 1998; Sironi, 2003;
Gropp and Vesela, 2004; Dam and Koetter, 2012). Furthermore,
Gropp et al. (2011) document that such actions undermine
competition in the banking sector, increasing the risk faced by
non-assisted banks. Other researchers argue that government
interventions are influenced by political interests and that politi-
cally connected institutions are more likely than others to receive
financial support (Tahoun and van Lent, 2010; Duchin and Sosyura,
2012). As a result, risk to the banking sector increases (Shleifer and
Vishny, 1994; Iannota and Sironi, 2007; Berger et al., 2011). Para-
doxically, there is substantial evidence documenting that regula-
tory interventions are not effective in restoring banks’ charter
values. Bonaccorsi di Patti and Kashyap (2009) argue that only
one-third of banks recover after receiving regulatory support. This
low figure may be attributed to the fact that rescue packages are
not sufficiently large to greatly improve distressed banks’ financial
conditions or that regulators do not have the proper incentives to
restructure a distressed banks’ balance sheet efficiently (Kane,
1989; Klingebiel, 2000; Hoshi and Kashyap, 2010; Igan et al.,
2011; Giannetti and Simonov, 2013). These impaired banks have
substantial incentives to provide funding for highly risky invest-
ments, as the Japanese experience has demonstrated (Hoshi and
Kashyap, 2010). In addition, Fahri and Tirole (2012) demonstrate
that systemic regulatory policies lead to a collective moral hazard
problem. This problem arises because such actions generally grant
banks access to cheaper capital, incentivizing them to increase
their borrowing and reduce their liquidity. In such cases, it is
‘‘unwise for some banks to play safely, when all other banks start
to gamble’’. In a similar vein, some studies document that larger
banks, which might be perceived as ‘‘too big to fail’’, are more
strongly incentivized than smaller banks to pursue risky strategies
(Boyd and Runkle, 1993; Schnabel, 2004, 2009). Some studies also
identify ownership structure as an important determinant of a
bank’s level of risk. Such studies show that state-owned banks
are more likely than private banks to pursue risky strategies
(Caprio and Martinez-Peria, 2000; Gropp et al., 2011; Berger
et al., 2011). These studies suggest that politicians tend to pursue
their self-interest, often granting loans to politically connected
corporations as a result of reduced exposure to governance mech-
anisms. Thus, increased state ownership in the banking sector
might lead to increased risk.
3.2. The role of specific mechanisms in banking sector risk
Dam and Koetter (2012) and Hoshi and Kashyap (2010) argue
that the effects of government interventions on bank behavior
depend on the mechanisms used to assist banks.
The existing literature broadly examines the relationship
between the individual policy measures applied to banks and
banks’ risk behavior. With respect to the mechanisms in the con-
tainment stage of the crisis, Demirgüc-Kunt and Detragiache
(2002) examine the effect of deposit insurance on bank behavior,
documenting that it is associated with moral hazard. However,
Gropp et al. (2011) and Fischer et al. (2012) find that this effect
only holds ex-post. Ex-ante government protection provides
cheaper access to capital for protected banks, improving their char-
ter values and discouraging such institutions from risk-taking.
Cordella and Yeyati (2003) obtain similar results for liquidity pro-
visions. However, Naqvi (2010) argues that the central bank’s role
as Lender of Last Resort (LoLR) spreads moral hazard. This effect
occurs because liquidity support goes not only to illiquid institu-
tions but also insolvent institutions, which then tend to gamble
(Goodhart and Huang, 1999; Rochet and Vives, 2004). However,
Dam and Koetter (2012) document that regulatory interventions
may also serve to discipline bank behavior because the regulatory
authority is authorized to impose restrictions on banking opera-
tions, often resulting in more careful monitoring of these banks.
Similarly, the LoLR’s provision of liquidity to banks signals to the
market which banks are distressed, which in turn may strengthen
market-monitoring mechanisms (Dell’ Ariccia and Ratnovski,
2012; Mehran and Thakor, 2011).
Ambiguous results are also obtained regarding the effects of
bank bailout mechanisms on bank risk. Berger et al. (2011) exam-
ine the effect of capital injections on bank behavior and find that
this instrument is effective in improving capital ratios for small
and large banks without increasing risk. However, Duchin and
Sosyura (2013) show that although capital injections are effective
in restoring bank capital, they are associated with increased risk
to the economy. This relation is observed because rescued banks
tend to engage in regulatory arbitrage. Similarly, Rose and
Wieladek (2012), using bank-level data on UK banks, examine
the effect of public capital injections and nationalizations. The
authors find that these measures were successful in restoring mar-
ket confidence during the mortgage crisis in the UK. However,
Philippon and Schnabl (2013) document that nationalization is a
more efficient approach than pure capital injections. The authors
argue that the distribution of a bank’s upside potential via the gov-
ernment should discourage opportunistic bank behavior. By con-
trast, empirical studies find that state-owned banks tend to be
less profitable and less efficient and therefore more willing to take
on additional risk (Shleifer and Vishny, 1994; Baumann and Nier,
2006; Iannota and Sironi, 2007). Similarly, Brei et al. (2013), exam-
ining rescue packages in Western economies during the period
1995–2010, find that recapitalization only helps banks recover
once injected capital exceeds a critical threshold and a bank’s bal-
ance sheet is sufficiently strong. Harris et al. (2013) examine the

effects of capital injections provided by the TARP on the opera-
tional efficiency of commercial banks. The authors find that such
restructuring methods decrease the operational efficiency of
funded banks while improving asset quality, thereby increasing
moral hazard. In addition, Black and Hazelwood (2010) document
that the risk levels associated with participation in the TARP pro-
gram differ for small and large banks. Whereas small banks
decreased their risk, large banks significantly increased it. The
authors thus conclude that the TARP induced moral hazard in large
banks. With respect to debt-restructuring programs, Kane (1989),
Klingebiel (2000) and Hoelscher (2006) criticize the AMC instru-
ment due to its political dominance, unregulated form and the
insufficient expertise of the politicians running such a firm. As a
result, banks’ debt recovery rates are low, and banks’ incentives
to transfer future toxic debt to such a vehicle are high. This situa-
tion increases risk in the banking sector and the costs of crises.
However, House and Masatlioglu (2010) argue that debt-restruc-
turing programs tend to perform better than capital injections in
providing liquidity to the banking sector. The authors argue that
transferring debt from a distressed bank tends to improve the
bank’s ability to raise capital in the interbank market on favorable
terms.
4. Empirical analysis
4.1. Empirical model
In the empirical analysis, we follow Gropp et al. (2011) and
explain banks’ risk-taking as a function of bank- and country-
specific characteristics. Our empirical specification is based on
the theoretical literature concerning the effects of various govern-
ment intervention measures on banks’ risk-taking presented in the
previous section. Because a bailout affects monitoring incentives,
risk premiums, operating performance and banks’ charter values,
risk-taking is expected to depend on the set of mechanisms used
in a bailout program.
We control for other important determinants of banks’ risk-
taking behavior suggested in the theoretical and empirical litera-
ture, such as a banks’ level of activity, efficiency, size, the intensity
of bank competition, the macroeconomic environment and the
institutional structure. Thus, we model the risk-taking of bank i
in country j as a function of the bailout measures applied to the
bank and control variables Xij.
Riskij ¼ a0 þ a1 Ã Xi;j þ eij ð1Þ
The construction of all variables is explained in detail below.
4.2. Data
Our main data source is Bureau van Dijk/IFCA’s Bankscope data-
base, which contains balance-sheet and other bank-specific infor-
mation on numerous banks from a broad set of countries. Our
analysis focuses on a cross section of banks from countries that
experienced systemic banking crises and is based on data from
Laeven and Valencia (2008). The authors provide guidance on the
timing of systemic banking crises in individual countries and the
government intervention measures implemented to address them.
We take five of the most important containment and resolution
policies from the database: blanket guarantees, liquidity provi-
sions, government-assisted mergers, nationalization and AMCs.
All of these policies were also widely employed by governments
during the mortgage crisis of 2007–2009. The data provided by
Laeven and Valencia (2008), however, are at the country level.
Therefore, we extend the dataset by identifying distressed institu-
tions during countries’ systemic crises and match the intervention
policies used by governments to rescue these institutions. Data
regarding bank names and the specific government policies
employed were derived from national banks’ reports and a survey
conducted among central banks. We were compelled to exclude
data from countries in which we could not identify either any dis-
tressed institutions or bailout strategies employed by govern-
ments. Moreover, we had to exclude the majority of countries in
which financial crises occurred prior to 1992 due to the unavail-
ability of bank data (for the years preceding 1992, Bankscope offers
information only on a very small number of banks). We were
therefore obliged to exclude 15 countries from the original sample
of Laeven and Valencia (2008). Specifically, emerging countries
that faced financial crises in the 1980s and the beginning of the
1990s were excluded. Table 1 presents the list of countries and
the number of banks subject to given intervention methods.
The countries differ with respect to developmental stage, the
nature and depth of their crises, the structure of their banking sec-
tors and government reactions to systemic banking crises.
Most of our sample countries are developing countries, with
only five countries out of twenty-five classified as developed econ-
omies. This distribution is not surprising because Kaminsky and
Reinhart (1999) documents that crises are much more prevalent
in emerging economies than in developed economies. The charac-
teristics of the crises in our sample countries differ. Whereas crises
in Russia in 1998 and Argentina in 2002 were precipitated by large
macroeconomic imbalances, the East Asian crises were more
closely associated with the maturity composition of debt and
foreign exchange risk exposure than with the level of public debt
and fiscal deficits (Laeven and Valencia, 2008). We can also observe
that many banking crises in our sample countries coincided with
currency crises. Indeed, currency crises characterize sixty percent
of the cases in our sample. Demirgüc-Kunt et al. (2000) document
that twin crises are much more severe in their consequences than
single crises. Regarding the extent of government involvement in
banking crises, such involvement was much more prevalent in
developing than in developed countries. In particular, in such
countries as Indonesia, Columbia and Malaysia, nearly all govern-
ment assistance went to the banking sector due to a high concen-
tration of domestic banks. With respect to types of government
support granted, we do not observe significant differences between
developing and developed countries. Although the blanket guaran-
tees were generally more extensive than liquidity provisions in the
containment stage of a crisis, the latter were employed more often.
Sixty percent of countries offered blanket guarantees, whereas
liquidity provisions were used by more than eighty percent of
our sample countries. This finding suggests that liquidity provi-
sions may be the first line of defense against the consequences of
systemic banking crises. With respect to the bailout mechanisms
employed, we observe that large-scale government interventions
occurred mostly through government-assisted mergers. In our sam-
ple, forty-six financial institutions were involved in merger transac-
tions. Nationalizations were also prevalent among our sample
banks; however, the scale of nationalizations was smaller than that
of mergers because the former type of intervention is very costly for
governments, especially in developing countries. Special bank restruc-
turing agencies were set up to restructure distressed banks (in ninety
percent of the crises examined). Asset management companies tend
to be centralized or decentralized. In most of our countries, these com-
panies assumed a centralized form. In only a handful of episodes,
banking systems survived without experiencing at least some bank
closures. In some countries, these closures—in terms of banks’ assets
dissolved—were significant. For example, in Nicaragua or in the Czech
Republic, dissolved assets amounted to almost thirty or forty percent
of banking assets, respectively, whereas in such countries as Uruguay
or Venezuela, these closures were limited to small banks.
The construction of an appropriate control sample, which would
allow us to make reliable comparisons between the behavior of

institutions targeted by interventions and their non-assisted compet-
itors, proved challenging. Decisions to intervene might be deter-
mined by political factors, the systemic importance of an
institution, a bank’s specialization or other bank characteristics.
Therefore, comparing the behavior of rescued institutions to that of
all non-rescued institutions would be inappropriate because it might
involve an identification problem. Therefore, we restrict our control
sample of non-assisted institutions to banks with the same special-
izations and similar asset sizes as the institutions in our sample that
received support.
4.3. Risk measures
As dependent variables, we use z-scores. A z-score is defined as
the ratio of the sum of a bank’s average return on assets and cap-
italization (equity/total assets) to the standard deviation of the
return on assets. Z-scores are estimated as four-year moving aver-
ages. Intuitively, the measure represents the number of standard
deviations below the mean by which profits would have to fall to
deplete equity capital (Boyd and De Nicolo, 2005; Boyd et al.,
2006). As a measure of a bank’s distance from insolvency (Roy,
1952), z-scores have been widely used in recent literature (e.g.,
Laeven and Levine, 2009). A higher z-score indicates greater bank
stability.
In addition, to gain a sense of which components of the z-score
are principally driving the relationship between the independent
variables (e.g., government bailout decisions, intervention mecha-
nisms) and z-scores, we employ the two components of a z-score
(i.e., capital ratio and standard deviation of ROA) as separate
dependent variables.1
Decomposing the z-score measure, we expect
that, all else equal, higher bank capital ratios translate into higher
z-scores, whereas a larger decline in a bank’s ROA translates into a
lower z-score. Therefore, in our case, government interventions do
not necessarily have to indicate a bank’s risk increase; a decline in
a z-score may be attributed to a drop in bank profitability. It is
important to keep this notion in mind when interpreting our results.
In the robustness analysis, we also verify the validity of our regres-
sion results for the ratio of loan loss reserves to total loans estimated
at t + 3 as an additional proxy for banks’ risk levels.2
4.4. Control variables
Our primary interest lies in the effect of various intervention
mechanisms on bank’s risk-taking behavior. To this end, we
include five intervention mechanisms in our regressions as well
as a general intervention dummy. The latter exclusively captures
the effect of any type of support granted for a distressed bank.
We define the intervention variable as a dummy variable equal
to one if any type of intervention, including blanket guarantees,
liquidity injections, nationalization, government-assisted merger
or use of an AMC, has been used to restore a distressed bank’s
financial position and zero for the non-assisted banks. Moreover,
in further analysis, we examine the effect of individual policy mea-
sures on intervened bank’s behavior. Therefore, we include a
dummy variable that is equal to one if an assisted bank has been
offered government protection and zero otherwise. Similarly, we
include a dummy variable that is equal to one if an assisted bank
has either received liquidity provisions or been nationalized or
has been restructured with the help of government and then
Table 1
Descriptive statistics at the country level.
Country Year of
systemic
crisis
Currency Crisis
(Yes = 1, No = 0)
Number of
banks’
bankruptcies
Number of
non-bailed
banks
Number of all
bailed banks
Number of banks
intervened by
public guarantee
Number of banks
intervened by
liquidity support
Number
of nation.
banks
Number of banks
intervened by
assisted merger
Number of
banks
intervened
by AMC
Argentina 2001 1 1 6 8 0 7 2 1 3
Bulgaria 1996 1 0 7 2 0 1 2 0 2
Colombia 1998 0 2 4 9 0 5 2 5 2
Croatia 1998 0 1 8 6 0 0 4 3 4
Czech Republic 1996 0 1 1 1 0 0 0 1 0
Ecuador 1998 1 0 8 2 2 1 0 0 2
Estonia 1992 1 0 2 4 0 2 1 3 3
Finland 1991 0 0 3 1 1 1 1 0 1
Indonesia 1997 1 2 1 12 11 5 10 1 8
Jamaica 1996 1 0 4 3 3 3 3 2 2
Japan 1997 0 2 4 13 11 0 2 8 9
Korea 1997 1 0 7 6 3 1 2 4 2
Lithuania 1995 0 0 1 2 2 0 2 1 2
Malaysia 1997 1 5 8 7 3 2 1 4 2
Mexico 1994 1 1 3 5 4 3 1 3 2
Nicaragua 2000 0 3 4 1 1 1 0 0 1
Norway 1991 0 0 5 7 7 6 2 0 4
Paraguay 1995 0 0 6 1 0 1 0 0 0
Russia 1998 1 0 6 2 0 1 0 1 1
Sweden 1991 1 0 4 3 2 1 0 2 1
Thailand 1997 0 2 5 5 5 2 3 1 3
Turkey 2000 1 0 5 8 3 4 1 6 4
Ukraine 1998 1 0 6 2 0 2 0 0 2
Uruguay 2002 1 4 6 2 0 2 2 0 1
Venezuela 1994 1 1 4 2 0 1 1 0 1
Total – – 25 118 114 58 52 42 46 62
The data present statistics for countries for which we could identify institutions subject to government intervention actions.
Sources: Data on systemic banking crises in individual countries and implemented intervention policies on a country level come from Laeven and Valencia (2008); data on
intervened banks in individual countries and the injected policy instruments to these banks come from the central banks’ reports and surveys conducted among the central
banks.
1
All variables are calculated based on balance-sheet data from Bankscope.
2
We regress loan loss reserves on other explanatory variables at t + 3, due to
greater data availability compared with the t + 4 time framework.

merged with another institution or has been restructured through
the use of an AMC. For all banks for which none of these policies
apply, we assign an intervention variable a value of zero.
In addition, to ensure that the effect of intervention is not dis-
turbed by other bank or country characteristics, we include a large
set of control variables.
Total assets (in logarithmic form) is used to measure a bank’s mar-
ket power, returns to scale and diversification benefits. The inclusion
of this variable is especially important because it allows us to distin-
guish between risk effects stemming from diversification and those
of an associated government bailout (Gropp et al., 2011).
Additionally, we use the ratio of loans-to-total assets to control
for a bank’s activity level. On the one hand, Hannan and Rhoades
(1987) show that a high loan rate indicates aggressive behavior
by a bank. Thus, a higher loan ratio might indicate higher risk-
taking by banks. On the other hand, De Jonghe (2010) documents
that banks more heavily involved in traditional banking activities
tend to take on less risk than those more heavily involved in
non-traditional activities. In addition, the inclusion of this ratio is
important because it allows us to control for the different attitudes
of various banking groups toward lending. Although rescued banks
could be influenced by governments to continue lending in a risky
crisis environment to counteract a credit crunch, non-rescued
institutions might prefer to wait until markets stabilize. Thus, by
controlling for a bank’s credit activity, we ensure that our risk
effect is not driven by the lending volume of the intervened bank
but rather by the riskiness of this bank’s portfolio.
Several studies suggest that less efficient banks may be tempted
to take on additional risk to improve their financial performance.
Indeed, Kwan and Eisenbeis (1997) and Williams (2004) document
that inefficiency positively affects banks’ risk-taking. Following
these studies, we include a cost-to-income ratio to control for
operating efficiency.
We also control for concentration in the banking sector at the
country level, measured as the percentage of banking system
assets held by the three largest banks. We expect a negative
relationship between the risk level and this variable because more
concentrated banking sectors are easier for regulators to monitor
and thus more carefully scrutinize (Beck et al., 2006).
In the robustness analysis, we also include additional bank
characteristics that may influence our risk measures. Notably,
these characteristics include a bank’s profitability ratio, measured
as a bank’s average return on assets (ROA), and its liquidity ratio,
defined as liquid assets divided by short-term liabilities. These
variables allow us to partially control for the differential magni-
tude of financial shocks affecting different banking systems. Con-
sistent with the charter value theory, we expect that financially
stronger banks are less prone to additional risk-taking due to the
threat of losing future rents (Keeley, 1990).
We also control for a country’s macroeconomic environment by
including the GDP growth rate and the inflation rate (in logarithms).
Additionally, we include a currency dummy that is equal to one if a
systemic banking crisis was accompanied by a currency crisis and
zero otherwise. In countries where a banking crisis was accompa-
nied by a currency crisis, banks are likely to be more affected due
to a greater decline in asset values; hence, these banks will tend to
have lower financial ratios (Demirgüc-Kunt et al., 2000). Thus, the
currency variable allows us to ensure that the risk effect is not
driven by characteristics of a group of banks most affected by a
financial crisis.
Numerous studies suggest that banks in developing countries
are more exposed to moral hazard than banks in developed coun-
tries due to less effective market mechanisms (Baumann and Nier,
2006; Laeven and Levine, 2009). We control for this factor by
including a country variable that is equal to one for developing
countries and zero for developed countries.
The behavior of rescued institutions might differ under different
institutional structures. Shifting risk should be more difficult if
regulations and information disclosure requirements are stricter.
Therefore, in the robustness analysis below, we also control for
rule of law and disclosure requirements. Risk-taking might also
be strengthened by additional explicit government guarantees.
Demirgüc-Kunt and Detragiache (2002) find that deposit insurance
increases the likelihood of banking crises due to a reduction of
market discipline. Therefore, we include a dummy variable that
is equal to one if an explicit insurance deposit network exists. In
the robustness check, we also include country fixed effects to
ensure that our results are not driven by any other unobserved
and uncontrolled country characteristics.
Table A1 in the Appendix presents a detailed description of all
variables used in our study.
4.5. Descriptive statistics
Figs. 1 and 2 allow us to compare the financial performance of
two groups of banks: intervened banks and their non-intervened
competitors at the time of intervention and four years following
intervention, respectively. By contrast, Fig. 3 presents assisted
banks’ performance four years following intervention, segregated
by intervention type. To facilitate interpretation of our analysis,
we also include numerical statistics in the Appendix (Table A2).
As Fig. 1 indicates, the profitability and capital ratios of assisted
banks were lower than those of their competitors at the time of
intervention. This result may suggest that intervention was
restricted to institutions facing financial distress. Interestingly,
the lending ratios of assisted banks were higher than those of
non-assisted institutions at the time of intervention. In addition,
the risk levels of assisted banks, measured by z-scores and the vol-
atility of ROA, were considerably higher than those of non-assisted
banks. This result may suggest that the assisted institutions had
riskier portfolios than their non-assisted counterparts already at
the intervention period, which is consistent with the recent
observations of Igan et al. (2011) based on the mortgage crisis of
2007–2009.
Fig. 2 depicts the situation four years after intervention. It can
be observed that, although the performance indicators of assisted
banks increased significantly compared with their values in the
pre-crisis period, assisted banks still underperformed their
non-assisted national competitors. The data show that four years
following the intervention period, assisted banks were still less
profitable and less capitalized than their non-assisted competitors.
This result may suggest the ineffectiveness of intervention mecha-
nisms in restoring banking sector stability. Interestingly, we also
observe that assisted banks had higher risk levels compared with
those observed in the initial period as well as those of their
non-assisted counterparts four years after intervention, although
their lending activities had decreased. This result could suggest that
such banks continued risky lending, possibly as a result of a reduc-
tion in market discipline, a conclusion that is consistent with the
recent IMF findings of Igan et al. (2011). The authors document that
the most aggressive lenders in the financial industry between 2000
and 2007 received the largest bailouts during the mortgage crisis
and continued to increase their risk levels in the post-crisis period.
This finding clearly indicates that government bailouts additionally
increased the market’s perception of these banks’ importance and
undermined the effectiveness of governance mechanisms.
However, Fig. 3 provides us with a clearer picture of which
mechanisms perform best in restoring bank performance while
limiting risk. We find that among liquidity mechanisms, central
bank interventions in the interbank market are more effective than
public protection offered to distressed banks. All performance
indicators are weaker for publicly protected banks than for

institutions assisted by liquidity injections. The situation looks even
worse when we compare these banks to non-assisted competitors.
The results appear to support other studies by suggesting that public
guarantees incentivize moral hazard behavior (Gropp and Vesela,
2004). Interestingly, the data show that banks equipped with the
liquidity provisions tended to have much lower risk levels than
banks given public guarantees. This finding may suggest that these
banks exhibit greater discipline. In addition, we find that although
banks that are nationalized have higher profitability ratios than
other assisted and non-assisted banks, they also exhibit a higher risk
level. Interestingly, the lending activity of such banks has generally
weakened. This result may suggest increased risk-taking by these
institutions as a result of reduced governance mechanisms, the asso-
ciated behavior of politicians aimed at realization of self-interest
and/or neglected restructuring process at distressed institutions.
5. Estimation results
5.1. Bank-level estimations
Tables 2–4 present the bank-level regression results, with
z-scores, earnings volatility and the equity ratio as risk measures,
respectively.
The estimation results are consistent with our expectations and
with the existing literature. They unambiguously demonstrate that
government interventions in the banking sector negatively affect
banking sector stability in the long run. The economic significance
of these effects is also large. According to Table 2, government
intervention is found to decrease a bank’s z-score by almost four,
where the mean z-score for sample banks amounts to 10.39 with
a standard deviation of 11.2. This result is consistent with the view
that government interventions tend to increase risk in the banking
sector as a result of reduced market discipline and inefficient bank-
ing structure (Flannery, 1998; Caprio and Martinez-Peria, 2000;
Sironi, 2003; Gropp and Vesela, 2004).
The estimation results with respect to specific intervention
mechanisms provide important insights into intervention theory.
They show that blanket guarantees, nationalizations and AMCs
are associated with greater subsequent risk-taking by assisted
institutions. This result is confirmed by the regressions that use
z-scores and volatility of earnings as dependent variables (see
Tables 2 and 3). With respect to the equity ratio, we observe a neg-
ative effect only in the case of public guarantees (see Table 4). The
lack of significant effect for the other intervention measures on the
capital ratio is consistent with existing studies documenting that
capital injection mechanisms successfully improve bank’s capital
Fig. 1. Banks’ financial performance at the time of intervention. The graph presents the banks’ performance indicators defined as: ROA is return on asset, volatility of ROA
(rROA) is constructed as four-year moving average, capital ratio (CAR) is measured as bank’s equity to its total asset, z-score equals (ROA + CAR)/(rROA), and credit activity is
the ratio of bank’s loans in its total asset.
Fig. 2. Banks’ financial performance four years after the government interventions. The graph presents the banks’ performance indicators defined as: ROA is return on asset,
volatility of ROA (rROA) is constructed as four-year moving average, capital ratio (CAR) is measured as bank’s equity to its total asset, z-score equals (ROA + CAR)/(rROA),
and credit activity is the ratio of bank’s loans in its total asset.

Fig. 3. Banks’ financial performance four years after the government interventions. The graph presents the banks’ performance indicators defined as: ROA is return on asset,
volatility of ROA (rROA) is constructed as four-year moving average, capital ratio (CAR) is measured as bank’s equity to its total asset, z-score equals (ROA + CAR)/(rROA),
and credit activity is the ratio of bank’s loans in its total asset.
Table 2
Government intervention and banks’ risk-taking using z-scores.
Intervention dummy (1,7)
Guarantee dummy (2)
Liquidity dummy (3)
National. dummy (4)
Merger dummy (5)
AMC dummy (6)
(1) (2) (3) (4) (5) (6) (7)
Resolution policy À3.921***
À6.989***
À1.499 À4.745***
2.191 À3.393**
À3.882**
(1.455) (1.528) (1.705) (1.687) (1.603) (1.546) (1.853)
Change in lending À0.013
(0.010)
Change in lending⁄intervention dummy À0.006
(0.016)
Credit activity 0.126***
0.124***
0.128***
0.109**
0.127***
0.114**
0.102**
(0.044) (0.042) (0.044) (0.043) (0.044) (0.045) (0.048)
Cost-to-income ratio À0.019*
À0.012 À0.028**
À0.022*
À0.028**
À0.025**
À0.025*
(0.011) (0.012) (0.012) (0.013) (0.012) (0.013) (0.015)
Asset (log) 0.160 0.155 À0.233 À0.083 À0.506 À0.078 0.506
(0.484) (0.484) (0.487) (0.486) (0.494) (0.491) (0.574)
Concentration ratio 0.074**
0.096***
0.069**
0.085***
0.064**
0.078**
0.069**
(0.031) (0.033) (0.032) (0.033) (0.033) (0.032) (0.038)
gdp growth À0.320 À0.507**
À0.412**
À0.432**
À0.461 À0.423**
À0.039
(0.211) (0.206) (0.202) (0.214) (0.201) (0.211) (0.189)
inflation (log) À1.395 À1.747*
À1.312 À1.307 À1.394 À1.552 À1.304
(1.000) (0.994) (1.085) (1.011) (1.022) (1.012) (1.212)
Dummy for developing country = 1 7.815***
6.672***
6.889***
7.036***
6.246**
7.116***
8.344**
(2.566) (2.455) (2.520) (2.466) (2.455) (2.569) (3.144)
Dummy for currency crisis = 1 1.488 2.589 2.041 1.919 2.386 1.990 À0.159
(2.560) (2.544) (2.623) (2.589) (2.627) (2.586) (2.688)
Constant À1.502 À1.752 1.360 0.500 3.676 1.100 À3.545
(6.647) (6.686) (6.664) (6.702) (6.586) (6.759) (8.374)
R2 0.117 0.144 0.096 0.117 0.098 0.136 0.163
Number of countries 23 23 23 23 23 23 23
Number of observations 183 183 183 183 183 183 183
Bank-level estimations: The dependent variable is the z-score = (ROA + CAR)/(rROA), where ROA is return on assets and CAR is the capital-asset ratio, both estimated four
years after a specific policy intervention has been implemented. The r(ROA) is constructed as a four-year moving average. A higher z-score implies greater stability. The
change in lending measures the average growth in the lending activity over four year-period after the government intervention has been injected. The data present bank-level
estimations based on OLS regressions. Standard errors that control for clustering at the country-level are reported in brackets.
*
Statistical significance at the 10%, respectively.
**
***
Statistical significance at the 1% levels, respectively.

ratios (Berger et al., 2011; Duchin and Sosyura, 2013). However,
this result does not indicate that banks assess their risk carefully.
Duchin and Sosyura (2013) document that bailed-out banks tend
to shift their risk within the same asset class, significantly increas-
ing their credit risk without altering banks’ closely monitored cap-
ital levels. This finding is clear evidence that banks engage in
capital arbitrage.
The negative effects of our intervention measures on bank risk
levels are also economically significant. The coefficient for the pub-
lic guarantee dummy indicates the largest effect. According to
Table 2, the z-score declines by seven as a result of the introduction
of banks’ public protection, where the mean of the z-score is 10.39
and the standard deviation is approximately 11.2. The magnitude
of the effect when using earnings volatility is similar (see Table 3).
Among other mechanisms, the coefficients for nationalization
and for the AMC dummies exhibit statistical and economic signif-
icance. According to Table 2, the effects suggest that nationaliza-
tions and AMCs decrease the banks’ z-score by five and three,
respectively. This result is consistent with the existing literature,
which finds that the use of these instruments is inefficient and
tends to increase risk in the banking sector. There are several
explanations for these effects. Existing research shows that if pol-
iticians have control over institutions, they tend to use this power
to pursue their own interests. As such, Claessens et al. (2007) and
Khawaj and Mian (2005) document that state-owned banks tend to
pursue less stringent credit policies than privately held banks. The
authors, for example, find that state-owned banks tend to grant
larger amounts of credit on more favorable terms to corporations
that have never received such credit. However, Sapienza (2004)
shows that such loans go to politically connected corporations.
Second, nationalized institutions are more likely to undermine
market discipline, further encouraging banks to increase their risk
levels. This behavior occurs because depositors perceive state-
owned banks as publicly protected and thus have limited incen-
tives to conduct careful monitoring (Baumann and Nier, 2006).
As an example, nationalized banks contributed to a severe crisis
in Sweden. Relaxed credit policies in government-owned institu-
tions, accompanied by reduced market incentives, led to severe
distress on the part of these banking institutions, generating huge
bailout costs for the Swedish government (Sandal, 2004). Similarly,
recent studies on the mortgage crisis document that the political
influence exercised in the financial industry has led to the accumu-
lation of risk, contributing to the financial crisis (Igan et al., 2011).
Finally, as previously mentioned, Kane (1989) shows that politi-
cians do not have an interest to run a deep bank’s asset restructur-
ing due to their short sight and a fear of the negative undertone
with which such actions might be associated. Therefore, the dis-
tressed banks may tend to stay weak and, according to the charter
value hypothesis, might be more willing to take additional risk.
Interestingly, our results suggest that LoLR actions and govern-
ment-assisted mergers do not exert any negative effects on bank
behavior, a finding that holds for all risk measures. Consistent with
views recently expressed in the literature, the dummies that proxy
for these types of assistance programs are statistically insignificant
in our regressions. Regulatory actions are likely to attract market
scrutiny, especially at banks not covered by ‘‘government para-
chutes’’, which seems to discipline banks’ management (Dam and
Koetter, 2012; Dell’ Ariccia and Ratnovski, 2012; Mehran and
Thakor, 2011).
To ensure that the additional risk taken by government-assisted
institutions does not result from higher volumes of credit granted
by such institutions, we include additional control variables. In
theory, the effect of interventions could stem from the fact that
government bailout decisions were restricted to banks on the
Table 3
Government intervention and banks’ risk-taking using the volatility of ROA.
Intervention dummy (1)
Guarantee dummy (2)
Liquidity dummy (3)
National. dummy (4)
Merger dummy (5)
AMC dummy (6)
(1) (2) (3) (4) (5) (6)
Resolution policy 3.541***
7.833***
1.578 7.644***
À1.906 3.111**
(1.168) (1.934) (1.454) (2.019) (1.622) (1.454)
Credit activity À0.100***
À0.098***
À0.102***
À0.074**
À0.101***
À0.084***
(0.030) (0.028) (0.032) (0.028) (0.032) (0.030)
Cost-to-income ratio À0.011 À0.021**
À0.004 À0.013 À0.003 À0.006**
(0.010) (0.011) (0.009) (0.009) (0.009) (0.009)
Asset (log) À0.095 À0.190 0.242 À0.057 0.495 0.118
(0.277) (0.210) (0.267) (0.200) (0.375) (0.259)
Concentration ratio À0.003 À0.029 0.001 À0.025 0.006 À0.007
(0.024) (0.024) (0.024) (0.024) (0.026) (0.024)
gdp growth À0.227 À0.044**
À0.144 À0.118**
À0.096 À0.134
(0.172) (0.129) (0.163) (0.174) (0.145) (0.167)
Inflation (log) 1.338***
1.677***
1.252**
1.169**
1.367**
1.471***
(0.498) (0.563) (0.521) (0.509) (0.552) (0.534)
Dummy for developing country = 1 À1.089 À0.016***
À0.278 À0.590***
0.295**
À0.457
(1.231) (1.241) (1.077) (1.130) (1.378) (1.071)
Dummy for currency crisis = 1 0.886 À0.181 0.398 0.654 0.067 0.440
(1.275) (1.317) (1.410) (1.477) (1.318) (1.418)
Constant 7.953 9.090**
5.531 7.520*
3.319 5.621
(4.964) (4.600) (4.783) (4.477) (5.560) (4.708)
R2 0.305 0.145 0.147 0.295 0.145 0.108
Number of countries 23 23 23 23 23 23
Number of observations 182 182 182 182 182 182
Bank-level estimations: The dependent variable is the standard deviation of ROA constructed as a four-year moving average, starting from t + 1, where t is the year of
government intervention. Higher volatility indicates higher risk in a given banking sector. The data present bank-level estimations based on OLS regressions. Standard errors
that control for clustering at the country-level are reported in brackets.
*
**
Statistical significance at the 5%, levels, respectively.
***

condition that these banks not limit their lending activities during
a financial crisis to prevent a country from a credit crunch. There-
fore, the increased lending of this banking group could translate
into higher risk measures. In our regressions, we control for this
factor and ensure that the effect of interventions is associated with
the risky behavior of assisted banks relative to non-assisted banks
by holding the volume of bank’s lending activity constant. There-
fore, in specification (7) of Table 2, we include an interaction term
that interacts the average growth in lending activity over the four-
year period after the government support has been injected with
the intervention dummy. A positive and statistically significant
coefficient for this variable would indicate that a portion of the
increased risk incurred by intervened banks four years after the
intervention period stems from their increased lending activity rel-
ative to their non-intervened peers. This result would thus indicate
that the additional risk of the intervened banks is not necessarily
derived from these banks’ riskier behavior.
The regression results suggest that neither the interaction vari-
able nor changes in lending are statistically significant, which
implies that the increase in risk observed at the intervened banks
is not a result of the differences in the lending between the inter-
vened and non-intervened banks. This result thus indicates that,
holding the volume of lending of the two groups constant, assisted
banks tended to grant more risky loans than their non-assisted
competitors, supporting our previous argument that assisted banks
engage in riskier behavior.
The remaining coefficients are largely as expected. The coeffi-
cient for banks’ credit activities is negatively correlated with risk,
which implies that institutions with higher proportions of loans
in their portfolio face a lower risk level. Furthermore, banks that
are less efficient tend to engage in riskier activities, a result that
is consistent with the existing literature (Eisenbeis and Kwan,
1997; Williams, 2004). Concentration is negatively correlated with
risk levels, which implies that greater concentration has a positive
effect on the long-run stability of a banking sector; this finding is
consistent with the results of Beck et al. (2006), who show that
greater concentration is associated with reduced frequency of
financial crises due to more careful monitoring. The negative coef-
ficient for GDP growth shows that countries with higher recovery
rates are more exposed to increased risk in the banking sector. This
finding may support the view that, although government interven-
tions may help countries recover from crises more rapidly, they
induce additional banking sector risk. Finally, the coefficient on
the dummy controlling for developing countries is statistically sig-
nificant and negatively correlated with increased risk-taking. This
result demonstrates that banks in developing economies assess
their risk more carefully than banks in developed economies, pos-
sibly due to a greater scrutiny of the international organizations
that supported the developing countries during their financial
crises.
5.2. Endogeneity problem
As noted in the Introduction, one of the disadvantages of our
methodology is that we model bank’s behavior using ex-post mea-
sures. This means that our risk proxies include significant portions
of risk inherited from pre-crisis and initial crisis periods. In addi-
tion, a significant amount of time may be required for a bank to
experience distress, which the regulatory authority takes into
account. In turn, non-assisted banks tend to be stronger and are
likely to remain so for a certain period. Thus, differences in the risk
levels of assisted and non-assisted banks might arise from the
Table 4
Government resolution policy and banks’ risk-taking using equity to total assets.
Guarantee dummy (2)
Liquidity dummy (3)
National. dummy (4)
Merger Dummy (5)
AMC dummy (6)
(1) (2) (3) (4) (5) (6)
Resolution policy À0.892 À2.220**
À0.386 À0.433 1.330 À0.456
(1.312) (0.879) (1.083) (1.138) (1.743) (0.996)
Credit activity À0.020 À0.020 À0.019 À0.021 À0.019 À0.021
(0.044) (0.044) (0.044) (0.045) (0.043) (0.044)
Cost-to-income ratio À0.018 À0.015 À0.020 À0.019 À0.020 À0.020
(0.014) (0.013) (0.013) (0.013) (0.013) (0.013)
Asset size (log) À1.181**
À1.139**
À1.267**
À1.270**
À1.400**
À1.258**
(0.566) (0.503) (0.508) (0.500) (0.573) (0.508)
Concentration ratio À0.005 0.002 À0.007 À0.006 À0.009 À0.006
(0.033) (0.032) (0.032) (0.032) (0.032) (0.032)
gdp growth À0.251 À0.301 À0.272 À0.276 À0.298 À0.275
(0.174) (0.189) (0.184) (0.186) (0.181) (0.186)
Inflation (log) 0.199 0.097 0.224 0.195 0.235 0.170
(0.787) (0.792) (0.830) (0.801) (0.803) (0.789)
Dummy for developing country = 1 1.915 1.647 1.709 1.704 1.358 1.726
(1.717) (1.523) (1.523) (1.513) (1.783) (1.518)
Dummy for currency crisis = 1 0.698 0.986 0.819 0.830 1.984 0.829
(2.255) (2.317) (2.311) (2.308) (2.231) (2.310)
Constant 21.315***
20.880***
21.934***
22.035***
22.963***
22.031***
(7.641) (6.897) (6.909) (6.791) (7.389) (6.810)
R2 0.187 0.193 0.184 0.184 0.187 0.184
Bank-level estimations: The dependent variable is the ratio of equity to total assets estimated four years after a specific policy intervention has been implemented. A higher
capital ratio implies greater stability. The data present bank-level estimations based on OLS regressions. Standard errors that control for clustering at the country-level are
reported in brackets.
⁄
**
***

natural characteristics of these two groups of banks and not from
government intervention. However, we are interested in the addi-
tional risk of government-assisted banks, which goes beyond
‘‘inherited’’ risk, relative to that of non-assisted banks. Because
our methodology does not allow us to directly control for this
problem, we present two additional analyses that aim at solving
it. The new regression estimates, however, confirm the results of
the previous analysis.
First, we include in our regression an interaction term that
interacts the intervention mechanism and the specific time period
(Table 5). We argue that if intervention contributes to additional
risk-taking by assisted banks relative to their non-assisted compet-
itors, we should observe increasing differences in the risk levels
between these two groups over time and beyond the intervention
period.
The results confirm our previous estimates, demonstrating that
differences in the risk levels of assisted and non-assisted institu-
tions increase as the time period expands beyond the intervention
period. This difference becomes significant in the third year for
blanket guarantees, nationalization and AMCs. These results sug-
gest that our standard estimations are not driven by endogeneity
and that the differences in risk levels between the assisted and
non-assisted institutions are not a result of differences in the bank
characteristics of the two groups.
Second, we use difference-in-differences (DID) estimation,
which allows us to compare the behavior of non-assisted banks
and assisted banks between two time periods: at the time of gov-
ernment intervention and four years afterwards. A significant dif-
ference in the risk levels after government actions relative to the
intervention period would suggest that assisted banks take on
additional risk due to such intervention policies, controlling for
all other effects. The estimates are presented in Table 6.
The results show that the differences between the two groups of
banks in our sample with respect to the risk levels they take on fol-
lowing government interventions are statistically significant,
which confirms our hypothesis that government interventions lead
to greater risk-taking by assisted banks. Interestingly, we do not
find such a statistically significant difference between these two
groups of banks at the year of the intervention.
5.3. Do other characteristics affect our results?
We perform several regressions to check the robustness of our
results. As argued, the magnitude and type of financial shock might
influence our results. Currency collapses negatively affect the bal-
ance sheet of banks when banks are involved in the foreign cur-
rency borrowing and their positions are not hedged, by for
example foreign currency lending. Following this argument, we
expect that banks more affected by the crisis may be more willing
to engage in excessive risk-taking, consistent with the charter
value hypothesis (Keeley, 1990). Although we control for the mag-
nitude of financial shocks by including macroeconomic variables
Table 5
Government intervention and banks’ risk-taking using z-score measures.
Guarantee dummy (1)
Liquidity dummy (2)
National. dummy (3)
Merger dummy (4)
AMC dummy (5)
(1) (2) (3) (4) (5)
Resolution policy⁄t1 À5.311*
À3.055 À4.605 5.166*
À1.287
(3.074) (2.756) (2.779) (2.591) (2.192)
Resolution policy⁄t2 À8.447***
À4.755*
À1.895 1.442 À3.883
(2.618) (2.601) (2.837) (2.267) (2.925)
À3.191 À4.499**
1.750 À5.008***
(2.159) (2.097) (1.978) (2.366) (1.703)
À2.595 À4.494**
1.462 À3.895**
(2.348) (1.941) (2.157) (2.037) (1.876)
0.137***
0.120***
0.133***
0.124***
(0.034) (1.941) (0.033) (0.035) (0.035)
Cost-to-income ratio À0.036**
À0.050**
À0.047**
À0.050**
À0.048**
(0.017) (0.016) (0.017) (0.016) (0.018)
Asset (log) À0.162 À0.466 À0.453 À0.817**
À0.398
(0.391) (0.366) (0.377) (0.382) (0.375)
concentration ratio 0.102*
0.057 0.056 0.050 0.065
(0.054) (0.047) (0.047) (0.048) (0.049)
gdp growth À0.020 0.004 0.015 0.042 0.034
(0.106) (0.084) (0.094) (0.094) (0.094)
Inflation (log) À0.153 0.180 À0.048 À0.102 À0.014
(0.939) (0.981) (0.944) (0.973) (0.937)
Dummy for developing country = 1 4.942 4.996 5.163 4.559 5.399*
(3.068) (3.304) (3.119) (3.071) (3.144)
Dummy for currency crisis = 1 À2.168 À2.190 À2.335 À1.868 À2.330
(2.721) (2.674) (2.744) (2.841) (2.767)
Constant 1.524 6.036 6.733 8.561 5.604
(6.063) (5.421) (5.380) (5.337) (5.374)
R2 0.129 0.103 0.102 0.097 0.104
Number of countries 24 24 24 24 24
Number of observations 755 755 755 755 755
Panel data estimations: The dependent variable is the z-score = (ROA + CAR)/r(ROA), where ROA is return on assets and CAR is the capital-asset ratio. r(ROA) is constructed
as one-, two-, three-, and four-year moving averages. A higher z-score implies greater stability. The regressions include the interaction variables consisting of policy dummies
and a time dummy indicating the number of years after a specific policy intervention has occurred. The data present bank-level estimations based on OLS regressions.
Standard errors that control for clustering at the country-level are reported in brackets.
*
**
***

and a currency dummy in our regressions, here we explore the het-
erogeneity between banks within a country. To this end, we
include bank performance indicators, such as profitability and
liquidity ratios, which allow us to control for the extent to which
financial shocks affect individual institutions.
Moreover, the risk-taking of banks might also be an effect of the
institutional and regulatory environment of a country. One may
expect that in countries where the regulations and disclosure rules
are weaker, banks tend to hold more risk in their balance sheet. To
ensure that our results do not depend on country’s features, we
include the country fixed effect. In addition, we also check the
robustness of our results for the individual institutional variables
as rule of law, information disclosure rules and deposit insurance
schemes (Barth et al., 2004; Beck et al., 2013). However, because
the results do not change from our estimations with the fixed
effect and the main effects of interest remain the same, we do
not report them here.
Table 7 presents the regression results obtained after including
additional controls for bank performance (specifications (1)–(6));
however, the specifications (7) and (12) present the regression
results using the fixed-effect model.
First, we observe that the inclusion of additional bank charac-
teristics, such as profitability and liquidity ratios, only slightly
change our results. We observe that the significance level of the
nationalization variable decreases; however, the AMC variable
loses its statistical significance at all. Nevertheless, we argue that
this result is probably an effect of a significant decrease in the
number of observations after the inclusion of additional bank’s
variables. In particular, this is an effect of the inclusion of the
liquidity ratio, which is missing for a great number of banks. Fur-
thermore, additional variables in the regressions are mostly statis-
tically insignificant, which indicates that the effect of financial
shock on bank behavior has already been captured by our other
variables. Our other effects of interest remain unchanged.
Moreover, regression results from specifications (7) through
(12) show that all of the effects of interest remain unchanged after
controlling for individual country characteristics. These findings
support our main results, suggesting that public guarantees and
capital injections significantly contribute to banking sector insta-
bility in the long run. We thus conclude that banks’ risk-taking
behavior results in part from government interventions.
Additionally, we check the robustness of our results, using an
alternative risk measure, one widely used in the existing litera-
ture—loan loss reserves. Again, we observe that the main results
are unchanged, supporting our previous conclusions.3
5.4. How do other government decisions during the financial crisis
affect the risk-taking of banks?
Fahri and Tirole (2012) document that systemic intervention
measures result in a collective moral hazard problem, especially
when banks have access to the same instruments. This problem
arises because if some banks gamble, others will tend to follow,
leading to correlated risk across the banking sector. As such, the
more banks are assisted, the greater the risk in the banking sector.
We test this theory using a bank-level specification. In particular,
the largest effects may occur in the cases of mechanisms aimed
at resolution of the containment phase of a crisis because such
efforts tend to target a significant portion of the banking sector.
Moreover, consistent with the existing literature, higher levels
of state ownership may also have this effect. To examine this
question, we consider the share of bank assets affected by a specific
regulatory measure to total banking sector assets to control for the
scale of intervention. We expect that risk will rise with the number
of institutions subject to government intervention. Table 8 reports
the regression results.
The results indicate that broader government protection,
nationalization and use of AMCs increase banking sector risk,
Table 6
Government intervention and banks’ risk-taking using logarithmic z-score measures.
Period Non-intervened All intervened Difference
Intervention
period
1.336 1.222 À0.114
(0.870) (1.180) (0.622)
Four years after 0.424 À2.212 À0.636**
(0.928) (0.887) (0.305)
R2 = 0.301
N = 240
Period Non-intervened Intervened by guarantee Difference
Intervention
period
0.523 1.487 0.964*
(0.719) (0.802) (0.473)
Four years after À0.081 À1.709 À1.628**
(0.663) (0.886) (0.599)
R2 = 0.412
N = 240
Period Non-intervened Intervened by liquidity Difference
Intervention
period
1.895 2.336 0.441
(0.763) (1.183) (0.420)
Four years after 0.816 0.593 À0.222
(1.136) (1.192) (0.056)
R2 = 0.295
N = 240
Period Non-intervened Intervened by Nationalization Difference
Intervention
period
1.454 0.843 À0.611
(0.923) (1.525) (1.014)
Four years after 0.457 À0.824 À1.281**
(0.902) (0.968) (0.595)
R2 = 0.383
N = 240
Period Non-intervened Intervened by Merger Difference
Intervention
period
2.412 2.882 0.471
(1.269) (1.460) (0.521)
Four years after 1.253 1.730 0.478*
(1.900) (1.298) (0.266)
R2 = 0.301
N = 240
Period Non-intervened Intervened by AMC Difference
Intervention
period
1.616 1.836 0.220
(1.836) (1.367) (0.652)
Four years after 0.692 0.054 À0.639*
(1.006) (0.985) (0.348)
R2 = 0.282
N = 240
Bank-level estimations: The dependent variable is the logarithmic z-score, where
z-score = (ROA + CAR)/r(ROA), ROA is return on assets and CAR is the capital-asset
ratio. The r (ROA) is constructed as a four-year moving average. A higher z-score
implies greater stability. The data present bank-level estimations based on a dif-
ference-in-difference approach. Difference has been calculated as the difference of
the log z-score measures of the assisted banks and their non-assisted counterparts
at the time of intervention and four years after a specific government intervention.
The regressions include a standard set of bank and country variables, as in the
previous regression, although the coefficients are not reported here. Standard errors
that control for clustering at the country-level are reported in brackets.
*
Statistical significance at the 10% levels.
**
Statistical significance 5% levels.
⁄⁄⁄
Statistical significance at the 1% levels.
3
Again, results are available upon request.

holding all other factors constant. Interestingly, the magnitude of
these effects is much greater than banks’ individual effects, as
shown in the regression analysis in Section 5.1. This result appears
to support the hypothesis that government intervention instru-
ments increase in collective risk, as a result of a greater number
of institutions exposed to the intervention actions during the
financial crises. The coefficient for nationalization suggests that
nationalization exhibits the greatest economic effect.
The effect of blanket guarantees is consistent with the market
discipline view. The more institutions are protected, the lower
depositors’ incentives to monitor the banking sector become and
thus the greater the willingness of banks to take on additional risk
becomes (Baumann and Nier, 2006). However, the effect of nation-
alization appears to be consistent with the alternative view sug-
gesting that the more state-owned banks there are in a banking
sector, the lower the efficiency of the banking sector and the higher
banks’ motivation to take on additional risk become (Khwaj and
Mian, 2005; Iannota and Sironi, 2007; Claessens et al., 2007). The
result for AMC also has an interesting implication. The result sug-
gests that the more banks participate in the AMC restructuring
process, the higher the banking sector risk becomes due to the lim-
ited effectiveness of this mechanism, as demonstrated in the exist-
ing literature. As a result, the continuing weak positions of affected
banks incentivize such banks to engage in more risky activities.
From the regulators’ perspective, the results suggest that national-
ization and debt-restructuring instruments such as AMCs are not
only costly but ineffective in restoring long-term stability to the
banking sector.
Although interventions appear to reduce market discipline and
lead to inefficient banking structures, governments may, through
appropriate actions, initiate greater public scrutiny of banks during
a financial crisis. The typical government mechanism aimed at rein-
forcing this mechanism is to allow some banks to collapse during the
crisis to increase uncertainty regarding future government decisions,
which is suggested to have a disciplining effect on bank manage-
ment. To empirically test this notion, we run the regressions in
which we include variables that allow us to control for the number
of bankruptcies in a country. We therefore construct a variable that
measures the amount of dissolved banking assets relative to the size
of the overall banking sector and interact it with the individual inter-
vention mechanisms. Intuitively, we expect that a larger number of
bank failures should impose greater discipline on banks and thus
limit risky behavior induced by government interventions. Thus, a
positive coefficient for this interaction term would suggest that the
number of bankruptcies in a country has a disciplinary effect on
the behavior of assisted banks. Table 9 presents the empirical results.
Table 7
Government intervention and banks’ risk-taking after controlling for additional variables.
Intervention dummy (1,7)
Guarantee dummy (2,8)
Liquidity dummy (3,9)
National. dummy (4,10)
Merger dummy (5,11)
AMC dummy (6,12)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Resolution policy À4.908**
À7.246**
À0.929 À5.961*
3.769*
À2.369 À3.027**
À9.067***
À1.778 À3.952**
2.276 À2.749*
(2.212) (2.732) (3.026) (3.087) (1.954) (2.013) (1.501) (2.047) (1.662) (1.896) (1.608) (1.573)
Credit activity 0.122 0.100 0.135 0.079 0.132 0.118 0.089*
0.083*
0.096*
0.085*
0.101**
0.087*
(0.092) (0.086) (0.096) (0.094) (0.089) (0.099) (0.052) (0.048) (0.052) (0.051) (0.052) (0.052)
Cost-to-income ratio À0.008 À0.005 À0.017 À0.011 À0.017 À0.015 À0.012 0.002 À0.016 À0.013 À0.018*
À0.014
(0.013) (0.014) (0.015) (0.016) (0.015) (0.015) (0.011) (0.012) (0.011) (0.011) (0.011) (0.011)
Asset (log) À0.031 À0.282 À0.675 À0.441 À1.123 À0.530 0.456 0.456 0.153 0.275 À0.166 0.283
(0.618) (0.752) (0.759) (0.794) (0.799) (0.758) (0.553) (0.513) (0.528) (0.545) (0.552) (0.560)
Profitability ratio 0.437 0.460 0.608*
0.638*
0.647*
0.576*
(0.283) (0.309) (0.326) (0.364) (0.331) (0.317)
Liquidity ratio À0.014 À0.034 À0.007 À0.025 À0.010 À0.009
(0.110) (0.107) (0.120) (0.113) (0.113) (0.115)
Concentration 0.047 0.084 0.046 0.070 0.044 3.706
(0.052) (0.070) (0.055) (0.064) (0.057) (3.050)
gdp growth À0.227 À0.464*
À0.381*
À0.403**
À0.459**
À0.403**
(0.181) (0.247) (0.168) (0.191) (0.180) (0.191)
Inflation (log) À1.713 À2.292*
À1.894 À1.883 À1.993 À1.883*
(1.175) (1.265) (1.442) (1.168) (1.207) (1.168)
Dummy for currency
crisis = 1
3.130 4.638 4.036 3.706 4.475 3.888 0.168 11.156 7.240 8.377 À5.596 À3.376
(2.988) (3.221) (3.243) (3.050) (3.311) (3.101) (3.976) (9.842) (10.116) (10.054) (7.201) (7.984)
Dummy for developing
country = 1
6.248**
4.774 5.241*
5.143*
4.346 5.324*
(2.940) (3.552) (2.728) (2.724) (2.844) (2.825)
Constant 1.941 5.018 5.381 6.557 8.988 5.489 8.290 8.959 11.970 11.256 13.035 11.259
(12.182) (13.817) (13.008) (14.030) (13.144) (13.676) (6.335) (9.149) (9.225) (9.268) (8.708) (9.282)
R2 0.124 0.147 0.096 0.126 0.110 0.102 0.296 0.334 0.285 0.293 0.286 0.289
Number of countries 22 22 22 22 22 22 24 24 24 24 24 24
Number of observations 136 136 136 136 136 136 189 189 189 189 189 189
Bank-level estimations: The dependent variable is the z-score = (ROA + CAR)/r(ROA), where ROA is return on assets and CAR is the capital-asset ratio, both estimated four
years after a specific policy intervention has been implemented. r(ROA) is constructed as a four-year moving average. A higher z-score implies greater stability. In
specifications (1)–(6), we include additional bank characteristics that might affect risk levels. In specifications (7)–(12), we include fixed effects to control for countries’
unobserved and uncontrolled characteristics. The data present bank-level estimations based on OLS regressions. Standard errors that control for clustering at the country-
level are reported in brackets.
*
**
***

The empirical results have very interesting implications. Sur-
prisingly, in general, they show that bank failures do not have a
disciplining effect on bank behavior. Moreover, the estimates also
show that the interaction of the bankruptcy ratio with the inter-
vention dummy is statistically insignificant. This result may indi-
cate that, because bankruptcies generally affect smaller banks,
they do not have any disciplinary effect on larger banks, which
tend to be bailed out. However, assuming that a government
allows a larger institution to collapse, the subsequent conse-
quences of the bank’s failure on the banking sector are so severe
that the government will never repeat such a decision. The recent
experience with Lehman Brothers also confirms this observation.
The consequences of the decision about the bankruptcy of Lehman
Brothers were so severe for the financial system that it was com-
mon sense that the government would not make the same decision
again. As a result, surviving banks could feel even more secure,
especially if they have implicit or explicit government protection.
These findings are also in accord with recent empirical research
by Hett and Schmidt (2013). The authors empirically document
that the collapse of Lehman Brothers resulted in a significant dete-
rioration in market discipline after September 2008. Moreover, we
also find that in all specifications in which a bank is protected by a
government either explicitly, as with blanket guarantees, or
implicitly, as under state ownership, we observe that the number
of bankruptcies has no disciplining effect on assisted bank’s behav-
ior. Interestingly, however, we observe that bank failures discipline
the behavior of assisted banks that do not have ‘‘government para-
chutes’’. We find that for liquidity provisions, the interaction vari-
able is statistically significant and positive, whereas the
intervention mechanism itself is statistically significant and nega-
tive. This result suggests that governments can mitigate the nega-
tive consequences derived from their intervention actions by
reinforcing the market mechanisms to work; however, this mea-
sure only appears to work at ‘‘non-protected’’ banks and when
the number of banks’ failures is sufficiently large to induce bank’s
discipline. For other intervention mechanisms, we observe no sta-
tistically significant effect of the bankruptcy ratio on banks’ risk
behavior but a negative statistical significance of these interven-
tion mechanisms. This result suggests that market discipline does
not work at publicly protected banks, in line with studies such as
Gropp and Vesela (2004) or Demirgüc-Kunt and Detragiache
(2002).
5.5. Does the structure of a government bailout program matter?
Thus far, we have examined how various individual policy
mechanisms affect banks’ behavior. However, as already sug-
gested, our estimates do not indicate the true effect of risk in the
banking sector. On the one hand, the individual dummies do not
explicitly control for the size of government support injected into
a distressed bank. However, as Giannetti and Simonov (2013)
argue, the amount of financial assistance influences banks’ risk-
taking behavior. On the other hand, Dam and Koetter (2012) argue
that the total risk of banks also depends on other set of policies
injected into a bank. Although we cannot directly control for the
size of financial assistance due to a lack of such information, we
argue that a type of individual policy instrument might be a good
proxy for a scale of such support. Both the existing literature and
Table 8
Government intervention and banks’ risk-taking using z-score measures and coverage of government intervention policies.
Guarantee coverage (1)
Liquidity coverage (2)
National. coverage (3)
Merger coverage (4)
AMC coverage (5)
(1) (2) (3) (4) (5)
Resolution policy À4.239**
À0.082 À9.511***
1.147 À6.203**
(2.011) (3.267) (2.349) (3.599) (2.649)
0.126***
0.102**
0.126***
0.114**
(0.044) (0.044) (0.044) (0.044) (0.045)
Cost-to-income ratio À0.025**
À0.029**
À0.029**
À0.028**
À0.028**
(0.11) (0.012) (0.011) (0.012) (0.011)
Asset (log) À0.109 À0.326 À0.366 À0.396 À0.245
(0.517) (0.497) (0.468) (0.521) (0.488)
0.067**
0.124***
0.066**
0.079**
(0.034) (0.032) (0.468) (0.032) (0.033)
gdp growth À0.520***
À0.427**
À0.330*
À0.445**
À0.287
(0.201) (0.195) (0.190) (0.198) (0.210)
Inflation (log) À1.587 À1.481 À1.396 À1.387 À1.722*
(1.012) (1.046) (0.969) (1.017) (1.024)
Dummy for developing country = 1 6.623***
6.783***
6.508**
6.444**
7.143***
(2.528) (2.572) (2.583) (2.625) (2.692)
Dummy for currency crisis = 1 2.560 2.186 1.385 2.296 2.030
(2.563) (2.664) (2.437) (2.650) (2.539)
Constant 0.909 2.412 3.340 2.722 3.417
(6.860) (6.890) (6.593) (6.821) (6.690)
R2 0.108 0.093 0.150 0.094 0.114
Number of countries 23 23 23 23 23
Number of observations 183 183 183 183 183
Bank-level estimations: The dependent variable is the z-score = (ROA + CAR)/r(ROA), where ROA is return on assets and CAR is the capital-asset ratio, both estimated four
years after a specific policy intervention has been implemented. r(ROA) is constructed as a four-year moving average. A higher z-score implies greater stability. The policy
coverage is calculated as the share of the banks’ assets covered by a specific policy to the total assets in the banking sector. The data present bank-level estimations based on
OLS regressions. Standard errors that control for clustering at the country-level are reported in brackets.
*
**
***

countries’ experiences document that debt purchase programs and
capital injections are the largest intervention mechanisms aimed at
improving bank performance (Cordella and Yeyati, 2003; Berger
et al., 2011; Honohan and Klingebiel, 2003; Veronesi and
Zingales, 2010). Thus, we assume that any combination of these
policy measures should improve bank performance and, according
to the charter value argument, should decrease banks’ appetite for
risk. However, to investigate the second effect, we control for most
popular bailout programs covering the set of individual instru-
ments injected into the same bank. We argue that the charter value
effect might be mitigated if the market discipline does not operate
efficiently. Therefore, we expect that bailout programs involving
government protection mechanisms, as in the case of nationaliza-
tions or restructuring vehicles such as AMCs, should be comple-
mented by instruments that strengthen monitoring mechanisms,
which might include either liquidity provisions or regulations
requiring greater transparency of institutions subject to interven-
tion. To examine the effect of various government bailout pro-
grams, we include in the regressions combinations of the most
commonly used policy measures as proxies for the full intervention
program used to rescue distressed banking institutions. Specifi-
cally, we analyze (1) guarantees with nationalization and the
AMC dummy; (2) liquidity provision with nationalization and the
AMC dummy; 3) guarantees with government-assisted mergers;
(4) guarantees with liquidity provisions, nationalization and the
AMC dummy; and (5) guarantees with liquidity provisions and
mergers. Importantly, because we are interested in the total effect
of the individual government bailout programs on bank behavior
and not their marginal contribution to the risk effect, compared
with that of individual policy mechanisms, we do not include indi-
vidual policy dummies as control variables in these regressions. By
following this approach, we restrict the effect of the intervention
program to banks that were directly covered by this program,
and we compare it with the banks’ risk effect derived from other
bailout packages. Thus, by examining an intervention program in
its entirety, we can assess which combination of policy measures
is the least effective from a risk perspective. Moreover, because
similarly distressed banks in a country are often intervened by
the same set of policy measures, comparing the behavior of banks
covered by the entire intervention program and individual policy
measures would allow, to a large extent, for the same group of
banks to be captured, leading to a multicollinearity problem in
our regressions. Table 10 presents the results.
In line with our expectations, the results show that bailout pro-
grams, including blanket guarantees, nationalization and AMCs,
result in the largest increases in risk in the banking sector. The eco-
nomic effect is also strong. We observe similar effects for specifica-
tion (4), which additionally includes liquidity provisions.
Interestingly, eliminating blanket guarantees and retaining liquid-
ity provisions in the above-mentioned set of bailout measures
eliminates the negative effects of the intervention program on
banking sector stability (see specification (2)). This finding
Table 9
Government intervention, bankruptcies, and banks’ risk-taking using z-score measures.
Guarantee dummy (2)
Liquidity dummy (3)
National. dummy (4)
Merger dummy (5)
AMC dummy (6)
(1) (2) (3) (4) (5) (6)
Intervention dummy À4.132***
À5.304***
À4.667***
À3.277*
1. 240 À2.671*
(1.550) (1.796) (1.591) (1.950) (1.586) (1.619)
Bankruptcy ratio 0.159 0.211 0.148 0.172 0.148 0.172
(0.161) (0.131) (0.128) (0.129) (0.150) (0.130)
Bankruptcy ratio * intervention dummy 0.053 À0.230 0.891**
À0.231 0.120 À0.061
(0.159) (0.162) (0.377) (0.197) (0.141) (0.169)
0.135***
0.128***
0.117***
0.131***
0.123***
(0.042) (0.041) (0.040) (0.042) (0.042) (0.044)
Cost-to-income ratio À0.018*
À0.010 À0.022**
À0.021*
À0.027**
À0.024**
(0.010) (0.011) (0.011) (0.012) (0.011) (0.012)
Asset (log) À0.046 0.003 À0.500 À0.269 À0.691 À0.260
(0.489) (0.484) (0.529) (0.493) (0.504) (0.501)
0.087***
0.075**
0.079**
0.064**
0.074**
(0.030) (0.033) (0.031) (0.031) (0.031) (0.031)
gdp growth À0.147 À0.377 À0.138 À0.314 À0.274 À0.296
(0.243) (0.253) (0.247) (0.260) (0.240) (0.258)
Inflation (log) À0.372 À1.274 À0.265 À0.512 À0.304 À0.799
(1.219) (1.378) (1.265) (1.250) (1.228) (1.324)
Dummy for developing country = 1 6.147**
5.756**
4.120 5.684*
4.661*
5.751**
(2.734) (2.737) (2.573) (2.706) (2.677) (2.777)
Dummy for currency crisis = 1 0.914 2.492 0.708 1.694 1.788 1.710
(2.567) (2.643) (2.663) (2.666) (2.654) (2.651)
Constant À2.057 À2.523 1.858 0.064 2.872 0.445
(6.646) (6.605) (6.797) (6.706) (6.650) (6.797)
R2 0.132 1.667 0.138 0.130 0.116 0.121
BankÀlevel estimations: The dependent variable is the z-score = (ROA + CAR)/r(ROA), where ROA is return on assets and CAR is the capital-asset ratio, both estimated four
years after a specific policy intervention has been implemented. r(ROA) is constructed as a four-year moving average. A higher z-score implies greater stability. The
bankruptcy ratio is defined as the share of assets of banks dissolved divided by total assets in the banking sector. The data present bank-level estimations based on OLS
regressions. Standard errors that control for clustering at the country-level are reported in brackets.
*
**
***

What do we know about the impact of government interventions in the banking sector

What do we know about the impact of government interventions in the banking sector

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