Description
This paper aims to examine the impact of charter type (national vs state), holding company
structure, and measures of bank fragility on the likelihood of bank failure during the late 2000s financial
crisis.
Journal of Financial Economic Policy
Bank structure and failure during the financial crisis
Wenling Lu David A. Whidbee
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To cite this document:
Wenling Lu David A. Whidbee, (2013),"Bank structure and failure during the financial crisis", J ournal of
Financial Economic Policy, Vol. 5 Iss 3 pp. 281 - 299
Permanent link to this document:http://dx.doi.org/10.1108/J FEP-02-2013-0006
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Eric Osei-Assibey, Baimba Augustine Bockarie, (2013),"Bank risks, capital and loan supply: evidence from
Sierra Leone", J ournal of Financial Economic Policy, Vol. 5 Iss 3 pp. 256-271http://dx.doi.org/10.1108/
J FEP-09-2012-0041
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Bank structure and failure
during the ?nancial crisis
Wenling Lu and David A. Whidbee
Department of Finance and Management Science,
Washington State University, Pullman, Washington, USA
Abstract
Purpose – This paper aims to examine the impact of charter type (national vs state), holding company
structure, and measures of bank fragility on the likelihood of bank failure during the late 2000s ?nancial
crisis.
Design/methodology/approach – The study estimates a series of logit regressions in an effort to
identify the causes of failure and assess the role of the bank-level characteristics while controlling for
the economic and regulatory environment.
Findings – The empirical results indicate that established institutions were more likely to fail,
dependent upon whether a bank received bailout funds or not, if they were relatively large, had relatively
low capital ratios, had relatively low liquidity, relied more heavily on brokered deposits, held a relatively
large portfolio of real estate loans, had a relatively large proportion of non performing loans, and had less
income diversity. Consistent with being ?nancially fragile, de novo banks and those banks that grew
substantially prior to the crisis faced an increased likelihood of failure relative to established banks.
However, capital levels were not signi?cantly related to the likelihood of failure in de novo institutions.
Originality/value – This paper provides a comprehensive analysis of the possible business models’
impact on the likelihood of failure during the recent ?nancial crisis. It contributes to the ongoing debate
regarding appropriate regulatory reform in the banking industry by shedding light on the extent to which
the business model decisions made bybankmanagers have animpact onthe stabilityof thebankingsystem.
Keywords Bank structure, Bank failure, Financial crisis, Banks, Banking, Business failures,
Financial risk
Paper type Research paper
1. Introduction
417 US commercial banks and thrifts failed between 2007 and 2011, accounting for
$671 billion in total assets. Although the incidence of bank failure has been examined in
earlier literature, the failures that occurred as a result of the 2007-2009 ?nancial crisis are
especially interesting. First, the most recent failures coincided with a dramatic and
relatively sudden change in the economic environment. Therefore, the empirical evidence
associated with the causes of earlier failures may differ from the causes of more recent
failures. The recent bank failures also occurred at approximately the same time many
banks were receivingbailout funds via the Capital Purchase Program(CPP). Furthermore,
it is not clear that Dodd-Frank Wall Street Reform and Consumer Protection Act
(Dodd-Frank Act) address the causes of failure and therefore will prevent future failures.
There are a variety of different banking models and some may have been better suited
to survive the recent ?nancial crisis. Although the goal of all banking business models is
to maximize shareholder wealth, the decisions bank managers make regarding holding
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
The authors thank James Barth, Gene Lai, Min-Teh Yu, and Sami Va¨ha¨maa for their helpful
comments and suggestions.
Journal of Financial Economic Policy
Vol. 5 No. 3, 2013
pp. 281-299
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-02-2013-0006
Bank structure
and failure
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company structure and charter type (national vs state), as examples, may make the bank
more vulnerable to failure during a ?nancial crisis. In addition, some banks are inherently
fragile. For example, de novo banks may be more inclined to fail despite enhanced
minimum capital standards during their ?rst seven years[1]. Although many of the bank
failures can be attributed to the 2007-2009 ?nancial crisis and related severe economic
conditions, the role of bank structure in determining the likelihood of failure has not been
fully explored. This paper examines the impact of different US bank structure (de novo
banks vs established banks, independent banks vs holding company banks, and national
banks vs state banks) on the likelihood of failure during year 2007-2011. Therefore, our
analysis contributes to the ongoingdebate regarding appropriate regulatoryreforminthe
banking industry by shedding light on the extent to which the business model decisions
made by bank managers have an impact on the stability of the banking system.
Overall, the results indicate that the likelihood of failure was affected by the decisions
made by bank managers and by the economic and regulatory environment. As expected,
perceived sources of strength, such as being part of a multibank holding company
structure, reduce the likelihood of failure. Those banks that are inherently fragile, such
as de novo banks and those that experienced rapid loan growth, were more likely to fail.
Finally, despite there being signi?cant differences in their assets and liabilities, there is
no signi?cant direct impact of charter type on the likelihood of failure.
The remainder of the paper is structured as follows. In Section 2, we brie?y review the
literature on bank failures. Section 3 discusses the data and the variables used in our
analysis. Section 4 provides and discusses the results. Section 5 provides conclusions.
2. Literature review
A broad body of research has been conducted to provide potential answers to this
particular question, “why do banks fail?” Demirgu¨c¸-Kunt (1989) emphasizes that
making a distinction between economic insolvency and failure is crucial in studying the
failure of ?nancial institutions[2]. More recently, some researchers have explored the
determinants of US bank failure during the recent ?nancial crisis. For example, brokered
deposits (Rossi, 2010), real-estate loans (Cole and White, 2011), liquidity funding
structure (Bologna, 2011), audit quality ( Jin et al., 2011), loan loss reserves (Ng and
Roychowdhury, 2012), nontraditional activities (e.g. investment banking and venture
capital, DeYoung and Torna (2012)), and bank ownership (Berger et al., 2012) played key
roles in US bank failures during the crisis. Furthermore, Aubuchon and Wheelock (2010)
?nd that 2007-2009 bank failures re?ect local economic conditions.
Although the extant literature helps us understand some of the causes of failure
during the ?nancial crisis, the role of several bank-level characteristics has not, to our
knowledge, been adequately explored. More speci?cally, no other study has examined
the role of banking organizational structures in explaining failure during the ?nancial
crisis. Unfortunately, there is no consensus in the theoretical and empirical literature on
the effect of different bank structures on bank failure. We contribute to the existing
literature by modeling the interaction of bank structure, bank fragility and bank failure
during the 2007-2009 ?nancial crisis.
2.1 The role of bank structure
Beginning with the Bank Holding Company Act of 1956, bank holding companies have
been expected by regulators to act as a source of strength to subsidiary banks.
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Gilbert (1991) examines the amount of capital injected into troubled banks and ?nds that
holding companies tendto inject more capital thanthe owners of independent banks when
the total assets of the holding company are substantially more than the assets of the
troubled bank. More recently, Ashcraft (2008) ?nds evidence that multibank holding
companies provide support for subsidiary banks, insofar as multi-bank holding company
subsidiary banks are less likely to fail and more likely to receive capital injections from
parent companies when faced with ?nancial distress. Ashcraft examines the 1984-2004
period, however, so it is unclear whether the holding companystructure acts as a source of
strength during a period of severe ?nancial crisis. If regulators applied rational criteria
whendecidingwhichbanks toallowtofail, we wouldexpect regulators to be more inclined
to allowto fail those banks that lack sources of strength. Therefore, we expect those banks
that are part of a holding company structure, especially a multi-bank holding company
structure, to be less likely to fail. Therefore, we categorize our sample into independent
banks, single-bank holding company banks, and multi-bank holding company banks.
2.2 The role of charter type
Surprisingly, little research has examined whether charter type affects the likelihood of
bank failure. Banks choose either a national charter or a state charter. Those banks
choosing a national charter are supervisedbythe Of?ce of the Comptroller of the Currency
(OCC). Those choosing a state charter are supervised jointly by their state regulator and
either the Federal Reserve or the Federal Deposit Insurance Corporation (FDIC)[3]. The
choice a bankmakes determines the activities the bankis allowed to engage in, the speci?c
regulations it is subject to, and the explicit or implicit supervisory costs it must pay. Rosen
(2003) discusses competitionbetweenstate andfederal regulators andexamines the choice
of regulator by banks. He ?nds evidence that banks switching from a state charter to a
federal charter, or vice versa, tend to perform better without a signi?cant change in risk
consistent with regulators competing with each other in a bene?cial manner. At the same
time, however, he ?nds evidence of a preference for the “quiet life” by examiners because
banks tend to switch charters prior to a change in their loan portfolio, in any direction.
More recently, Rauch (2010) ?nds evidence that national banks reduced their balance
sheet exposures in response to the crisis sooner than state banks. Whalen (2010, 2012)
suggests that the tendency by de novo banks to choose a state charter is due to higher
explicit national bank supervisory costs and local market conditions. In particularly,
it is less likely that a national bank would enter an intensely competition market.
Agarwal et al. (2012) examine jointly regulated state-chartered banks that are subject
to a rotation of state and federal examinations and ?nd that the federal authorities
are signi?cantly more likely to downgrade a bank’s capital adequacy, asset quality,
management experience and expertise, earnings quality, liquidity, and sensitivity to
market risk (CAMELS) rating. These results suggest that state banking authorities
may tend to be more lenient than federal authorities, but it is unclear whether this
leniency translates into an increased likelihood of failure during the recent ?nancial
crisis. We categorize banks based on their charter type to examine this possibility.
2.3 The role of de novo status
Between 2000 and 2006, the number of banks in the USA shrank from 8,317 to 7,407 as a
result of industry consolidation. At the same time, however, 627 of the banks in existence
as of 2006 were established during the 2000-2005 period. The motivations behind these
Bank structure
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new entries is beyond the scope of this study, but these de novo institutions represent
over 8 percent of US banking institutions. As pointed out by DeYoung (2003), these
institutions are “?nancially fragile” and face an increased likelihood of failure after an
initial period when start-up capital and restrictions on acquisitions shields them from
?nancial distress. In particularly, de novo bank entry tends to occur in high-growth
markets. Given that much of the growth in banking in the early 2000s was in mortgage
lending, we expect a greater incidence of de novo banks failingduring the ?nancial crisis,
but it is an empirical question whether they failed due to their loan portfolio composition
or their fragility. In an earlier study, Hunter et al. (1996) investigate the probability of
failure for de novo savings and loans, during 1980-1986, and ?nd strong evidence that
credit risk, low capital levels, and cost inef?ciencies contributed to the failure of de novo
savings and loans. We examine the role of a de novo business model in explaining bank
failures during the 2007-2009 ?nancial crisis. In our analysis, we de?ne de novo banks as
those commercial banks that were newly chartered during 2000-2005; and established
banks are those banks established before 2000 and still in existence at the end of 2006[4].
3. Data
3.1 Data and explanatory variables
The sample used in our empirical analysis includes all commercial banks in the 50 states
and Washington, DC that were in existence at the end of 2006, had call reports available
at the end of 2005 and 2006, had nonzero loan amounts and nonzero total income, and did
not merge or convert to a different type of institutionbetween2007 and 2010. We chose to
measure most of our variables at the end of 2006 because we are interested in the
managerial decisions that ultimately led to failure. Subsequent measures of our
variables may have been in?uenced by managers’ or regulators’ responses to the crisis.
The ?nancial data is compiled from the Reports of Condition and Income (call reports),
the data on bank failures is from the FDIC web site, the data on structure, economic and
regulatory environment is from multiple sources[5]. Our explanatory variables include
three categories: structure dummy variables, ?nancial variables, and economic and
regulatory environment variables.
As indicated earlier, we include structure dummy variables indicating whether a
bank is a de novo bank (De novos), is an established bank (Established), is an
independent bank (NBHC), is part of a single-bank holding company (SBHC), is part of a
multi-bank holding company (MBHC), has a national charter (NATIONAL), has total
assets smaller than $50 billion (Non-TBTF), received CPP funds (CPP), or is a
publicly-traded bank or subsidiary of a publicly-traded holding company (PUBLIC). The
full sample contains 6,236 banks. 324 of our full sample banks failed during 2007-2011.
See Figures 1 and 2 for full sample distributions and failure rate by type, respectively.
3.2 Firm-level ?nancial characteristics
De novo banks are not the only banks that are potentially “?nancially fragile”. Banks
with rapid loan growth, for example, may face an increased risk of ?nancial distress.
Therefore, we also consider the impact that a variety of balance sheet and income
statement variables have on the likelihood of failure during the ?nancial crisis. Drawing
on the research of DeYoung (2003), Rossi (2010), Jin et al. (2011) and Cole and White
(2011), we include the following ?nancial variables in our analysis: Tier 1 capital scaled
by risk-weighted assets (TIER1CAP); size as measured by total assets (ASSETS)
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or the natural log of total assets (SIZE); liquidity as measured by the sum of cash and
securities scaled by total assets (CSEC); brokered deposits scaled by total assets
(BROKDEP); yearly growth in total loans scaled by total assets (GLOANS); real estate
loans as a percent of total loans (RESTATE); nonperforming loans scaled by total assets
(NPL); and income diversity as measured by noninterest income to total income (NIITI).
Tier 1 capital ratios are included because previous literature routinely ?nds that
poorly capitalized banks are more likely to fail, hence the setting of minimum capital
levels required by bank regulators[6]. Including Tier 1 capital in our analysis adds to
the ongoing debate over the appropriate minimum levels and measures for bank
capital. Size is included to account for its potential role in bank failures, such as
too-big-to-fail. Measures of liquidity are included because liquidity concerns affected
many banks during the ?nancial crisis. Brokered deposits are included to account for
the extent to which a bank relied on noncore sources of funds. Loan growth is included
because it may indicate lax underwriting standards. Real estate lending as a percent of
total lending is included to account for the fact that those banks that focused on real
estate lending were more drastically impacted by the bursting of the housing bubble.
Nonperforming loans are included to account for the extent to which a bank’s loan
portfolio was already showing signs of weakness. Finally, noninterest income is
included to account for diversity in a bank’s revenue sources.
3.3 The economic and regulatory environment
Banks tend to be vulnerable to adverse local economic shocks and thus bank failure rates
typicallyare higher instates experiencingmore severe economic distress. Therefore, we also
consider the impact that a variety of economic and regulatory variables had on the
likelihood of failure during the ?nancial crisis. Drawing on DeYoung (2003), Rauch (2010),
Figure 1.
Bank distributions by
type: 6,236 sample banks
Figure 2.
Failure rate by type:
5.2 percent overall
Bank structure
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AubuchonandWheelock(2010) andothers, we include the followingvariables to control for
differences in the economic and regulatory environment for a bank’s home state: the
percentage change in the Federal Housing Finance Agency’s home price index between
the thirdquarter of 2003 andthe ?rst quarter of 2007 (HP0307); the percentage change inthe
FHFA home price index between the ?rst quarter of 2007 and the fourth quarter of 2009
(HP0709); growth in personal income between 2006 and 2009 (Pincome0609); a dummy
variable indicating whether the state prohibits de novo branching by out-of-state banks
(Limit); a dummy variable indicating whether a bank is located in a metropolitan statistical
area (METRO); the change in mortgage delinquency rates between the ?rst quarter of 2007
and the fourth quarter of 2009 (Delinquency); and the percentage change in subprime
mortgages betweenthe thirdquarter of 2003 andthe fourthquarter of 2006 (Subprime0306).
Our analysis includes various measures of state-level macro-economic conditions to
account for the extent to which a bank is sensitive to adverse environmental conditions.
Changes in pre-crisis home prices are included to account for the extent to which a bank’s
home state experienced larger increases in home prices during the boom. Home price
changes that occurred during or after the crisis may also have contributed to bank
failures. In other words, we include measures to account for both the housing boom and
the subsequent bust. Growth in personal income is included to account for the potential
impact on bank distress of state-level differences in how the ?nancial crisis impacted
household wealth. An indicator of a bank being located in a metropolitan statistical area
is included to further account for bank level economic conditions and the fact that those
banks in urban markets are likely to face more intense competition relative to other
banks. The change in mortgage delinquency rates subsequent to the bursting of the
housing bubble is included to account for a proximate cause of the ?nancial crisis.
Finally, the change in subprime mortgages during the housing boom (2003-2006) is
included to account for the extent to which a bank experienced rapid growth in subprime
mortgage loans and the fact that those banks that focused on subprime mortgage
lending were more impacted by the distress in mortgage markets.
In addition to economic variables, we include an indicator of the prohibition on
de novo interstate branching as a measure of the extent to which a state’s banks were
subject to competition from out-of-state banks. It is included to also account for the
extent to which branching facilitated geographic diversi?cation or scale economies.
Including de novo interstate branching restrictions in our analysis adds to the ongoing
debate over branching deregulation and relaxing restrictions on de novo interstate
banking. However, the relationship between branching restrictions or entry barriers and
bank failures is an empirical question that merits further study.
Table I displays de?nitions and summary statistics for the variables used in our
analysis. Unless indicated, all variables were observed at the end of 2006. Note that
continuous variables were winsorized at 1 and 99 percent in order to limit the impact of
outliers. Table II provides a correlation matrix for the variables included in our analysis.
Signi?cant correlation coef?cients at the 10 percent level are in italics. 21 of 23 bivariate
correlations between bank failure (FB) and other variables are statistically signi?cant.
4. Empirical results
4.1 Bank structure and fragility
We ?rst employ nonparametric tests to examine differences in ?nancial variables for the
various categories of banks. Table III reports the results for means and Kruskal-Wallis
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Variable De?nition Mean SD Maximum Minimum
FB ¼ 1 if the bank failed during
2007-2011
0.05 0.22 1.00 0.00
Structure dummy variables
De novos ¼ 1 if the bank established
during 2000-2005
0.10 0.30 1.00 0.00
NATIONAL ¼ 1 if the bank has federal
charter
0.23 0.42 1.00 0.00
NBHC ¼ 1 if the bank is an independent
bank
0.17 0.37 1.00 0.00
SBHC ¼ 1 if the bank is an af?liate in a
single-bank holding company
0.66 0.47 1.00 0.00
MBHC ¼ 1 if the bank is an af?liate in a
multibank holding company
0.17 0.37 1.00 0.00
TBTF ¼ 1 if the bank has total assets
larger than US$50 billion
0.00 0.06 1.00 0.00
CPP ¼ 1 if the bank or its bank
holding company received Capital
Purchase Program funds
0.10 0.30 1.00 0.00
PUBLIC ¼ 1 if the bank is a publicly-
traded bank or af?liated with a
publicly-traded holding company
0.04 0.19 1.00 0.00
Financial variables
TIER1CAP Tier 1 risk-based capital ratio:
Tier 1 capital, scaled by risk-
weighted assets
0.16 0.11 0.48 0.08
ASSETS Total assets (US$ thousands) 389,825 1,155,617 9,550,274 12,108
SIZE Natural log of total assets
(US$ thousands)
11.82 1.29 16.07 9.40
CSEC Sum of cash and securities, scaled
by beginning total assets
0.29 0.21 0.78 0.03
BROKDEP Brokered deposits, scaled by
beginning total assets
0.04 0.11 0.48 0.00
GLOANS Yearly growth in total loans,
scaled by beginning total assets
0.10 0.20 1.31 20.11
RESTATE Percent of real estate loans to total
loans
0.66 0.19 0.97 0.10
NPL Nonperforming loans (sum of
loans past due more than 90 days
and nonaccrual loans and leases),
scaled by beginning total assets
0.01 0.01 0.04 0.00
NIITI Percent of non-interest income to
total income
0.11 0.08 0.46 0.01
Economic and regulatory environment variables (measured at the state level except as indicated)
HP0709 Percentage change in the FHFA
House Price Index (2007Q1-
2009Q4)
25.75 9.83 6.79 241.00
HP0307 Percentage change in the FHFA
House Price Index (2003Q3-
2007Q1)
26.19 15.74 76.81 5.02
Pincome0609 Growth in personal income 9.95 4.23 27.86 0.40
(continued)
Table I.
Descriptive statistics
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test statistics for differences in samples (x
2
approximation). Overall, the results indicate
that there are substantial differences in ?nancial characteristics. Panel A of Table III
shows that capitalization, asset structure, funding strategy, loan growth, loan portfolio
composition, asset quality, and income structure differ depending on a bank’s
“?nancially fragile” status, holding company structure, and charter type[7]. Panels B
and C of Table III indicate that these differences hold even limiting the comparison to
failed and non-failed banks. This suggests that the structure variables have the potential
to impact the likelihood of failure directly and indirectly through their impact on the
variables shown in Table III.
4.2 Logit regressions of bank failure
To this point, our categorical analysis suggests a potential relation between a bank’s
?nancial fragility and the likelihood of failure. We next estimate a series of logit
regressions in an effort to identify the causes of bank failure during the ?nancial crisis.
We also run the bank failure logit regressions in each of the eight subsamples to assess
the impact of the bank-level characteristics for different bank organizational structures.
The dependent variable in the logit regressions is equal to one if a bank failed during the
2007-2011 period, zero otherwise. The explanatory variables are explained earlier.
Table IV shows the results of our logistic regression models.
The ?rst stepinthis analysis is toexamine the role of the ?nancial variables inaffecting
the likelihoodof failure. Table IV, Panel Ashows logit regressionresults for the full sample
and sub samples based on the various categories of banks identi?ed as being of interest.
As expected, for the whole sample, the likelihood of failure is inversely related to a bank’s
Tier 1 capital, liquidity, and level of noninterest income. The likelihood of failure is
positively related to size[8], growth in loans, real estate loans, nonperforming loans, and
the degree to which a bank relies on noncore sources of funds (brokered deposits).
Generally, the results for the whole sample hold for the various subsets. There are,
however, some notable differences. First, Tier 1 capital and loan growth are not
statistically signi?cant in explaining failure in de novo banks. This is surprising in light
of the fact that de novo banks are subject to enhanced capital requirements throughout
their ?rst seven years. In addition, de novo banks tend to have relatively high loan
growth rates (average 39 percent for failed and 45 percent for non-failed, respectively,
see Table III). Our ?ndings, consistent with DeYoung (2003), indicate that Tier 1 capital
ratios and loan growth continue playing an insigni?cant role in de novo bank failures
over the recent ?nancial crisis period. Furthermore, the results show the lack of
signi?cance for Tier 1 capital, size, and brokered deposits in the independent bank
Variable De?nition Mean SD Maximum Minimum
Limit ¼ 1 if the state is not allowing de
novo interstate branching
0.40 0.49 1.00 0.00
METRO ¼ 1 if the bank is located in a
metropolitan statistical area
0.50 0.50 1.00 0.00
Delinquency Change in mortgage delinquency
rate for all loans (2007Q1-2009Q4)
123.88 49.36 318.33 63.07
Subprime0306 Increase in percent of subprime
mortgages (2003Q3-2006Q4)
7.59 3.77 15.33 29.96
Table I.
JFEP
5,3
288
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
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V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
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a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
B
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s
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T
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6
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e
l
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y
0
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1
9
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1
7
S
u
b
p
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0
3
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0
1
(
c
o
n
t
i
n
u
e
d
)
Table II.
Correlation matrix
Bank structure
and failure
289
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
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V
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R
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T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
B
R
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P
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3
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2
9
Table II.
JFEP
5,3
290
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
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R
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t
2
1
:
4
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2
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6
(
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T
)
P
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,
8
0
3
%
o
f
a
l
l
s
a
m
p
l
e
1
0
0
.
0
1
0
.
1
8
9
.
9
1
6
.
6
6
6
.
5
1
6
.
9
2
3
.
0
7
7
.
0
P
a
n
e
l
B
:
f
a
i
l
e
d
b
a
n
k
s
s
a
m
p
l
e
b
y
b
a
n
k
i
n
g
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
V
a
r
i
a
b
l
e
A
l
l
f
a
i
l
e
d
F
a
i
l
e
d
d
e
n
o
v
o
s
F
a
i
l
e
d
e
s
t
a
b
l
i
s
h
e
d
F
a
i
l
e
d
d
e
n
o
v
o
s
v
s
f
a
i
l
e
d
e
s
t
a
b
l
i
s
h
e
d
F
a
i
l
e
d
N
B
H
C
F
a
i
l
e
d
S
B
H
C
F
a
i
l
e
d
M
B
H
C
F
a
i
l
e
d
N
B
H
C
,
f
a
i
l
e
d
S
B
H
C
v
s
f
a
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l
e
d
M
B
H
C
F
a
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l
e
d
n
a
t
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n
a
l
F
a
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d
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t
a
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e
F
a
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d
n
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T
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R
1
C
A
P
0
.
1
3
0
.
1
6
0
.
1
2
1
1
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1
*
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*
0
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1
8
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1
2
0
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1
2
2
3
5
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9
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0
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1
3
0
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1
3
0
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1
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S
5
7
2
,
9
0
9
2
0
2
,
0
4
6
7
1
7
,
7
5
2
2
3
2
.
9
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*
*
1
5
8
,
9
9
3
6
4
0
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1
1
1
7
4
7
,
9
7
7
2
4
2
.
5
*
*
*
1
,
0
2
2
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4
7
2
4
7
0
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7
3
5
0
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5
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C
0
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1
9
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1
8
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1
9
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1
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1
8
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1
9
2
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4
0
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1
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1
8
1
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B
R
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K
D
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P
0
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1
3
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5
0
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1
2
2
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1
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1
4
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1
0
6
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3
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0
9
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1
4
2
5
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1
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G
L
O
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0
.
2
5
0
.
4
5
0
.
1
7
7
2
.
3
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*
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0
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4
3
0
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2
0
0
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2
7
2
2
4
.
5
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0
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1
9
0
.
2
6
2
1
0
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5
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*
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R
E
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T
A
T
E
0
.
8
1
0
.
8
3
0
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8
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2
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1
0
.
8
4
0
.
8
0
0
.
8
4
2
8
.
7
*
*
0
.
7
9
0
.
8
1
2
0
.
9
N
P
L
0
.
0
0
9
0
.
0
0
8
0
.
0
1
0
2
1
2
.
5
*
*
*
0
.
0
0
9
0
.
0
0
9
0
.
0
0
8
8
.
5
*
*
0
.
0
0
9
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.
0
0
9
0
.
3
N
I
I
T
I
0
.
0
8
0
.
0
5
0
.
0
9
2
5
1
.
3
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*
*
0
.
0
6
0
.
0
8
0
.
0
9
2
1
1
.
7
*
*
*
0
.
0
9
0
.
0
7
9
.
7
*
*
*
S
I
Z
E
1
2
.
4
3
1
1
.
8
5
1
2
.
6
6
2
3
2
.
9
*
*
*
1
1
.
5
2
1
2
.
5
8
1
2
.
7
9
2
4
2
.
5
*
*
*
1
2
.
6
5
1
2
.
3
8
0
.
5
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
3
2
4
9
1
2
3
3
5
4
2
3
1
3
9
6
0
2
6
4
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
1
4
.
5
4
.
2
5
.
2
5
.
6
3
.
7
4
.
2
5
.
5
(
c
o
n
t
i
n
u
e
d
)
Table III.
Nonparametric tests for
differences in the average
?nancial characteristics
Bank structure
and failure
291
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
a
n
e
l
C
:
n
o
n
-
f
a
i
l
e
d
b
a
n
k
s
s
a
m
p
l
e
b
y
b
a
n
k
i
n
g
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
V
a
r
i
a
b
l
e
A
l
l
n
o
n
-
f
a
i
l
e
d
N
o
n
-
f
a
i
l
e
d
d
e
n
o
v
o
s
N
o
n
-
f
a
i
l
e
d
e
s
t
a
b
l
i
s
h
e
d
N
o
n
-
f
a
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l
e
d
d
e
n
o
v
o
s
v
s
n
o
n
-
f
a
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l
e
d
e
s
t
a
b
l
i
s
h
e
d
N
o
n
-
f
a
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l
e
d
N
B
H
C
N
o
n
-
f
a
i
l
e
d
S
B
H
C
N
o
n
-
f
a
i
l
e
d
M
B
H
C
N
o
n
-
f
a
i
l
e
d
N
B
H
C
,
n
o
n
-
f
a
i
l
e
d
S
B
H
C
v
s
n
o
n
-
f
a
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l
e
d
M
B
H
C
N
o
n
-
f
a
i
l
e
d
n
a
t
i
o
n
a
l
N
o
n
-
f
a
i
l
e
d
s
t
a
t
e
N
o
n
-
f
a
i
l
e
d
n
a
t
i
o
n
a
l
v
s
n
o
n
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f
a
i
l
e
d
s
t
a
t
e
T
I
E
R
1
C
A
P
0
.
1
6
0
.
1
7
0
.
1
6
2
.
7
*
0
.
2
1
0
.
1
5
0
.
1
4
5
0
9
.
4
*
*
*
0
.
1
6
0
.
1
6
1
3
.
7
*
*
*
A
S
S
E
T
S
3
7
9
,
7
9
1
2
2
5
,
8
5
7
3
9
5
,
1
3
9
2
6
.
8
*
*
*
1
4
4
,
2
1
0
3
3
8
,
2
8
8
7
6
7
,
1
8
2
2
2
7
0
.
0
*
*
*
6
0
4
,
1
7
9
3
1
1
,
9
1
6
8
8
.
1
*
*
*
C
S
E
C
0
.
2
9
0
.
2
4
0
.
2
9
2
8
5
.
7
*
*
*
0
.
3
3
0
.
2
8
0
.
2
7
6
8
.
0
*
*
*
0
.
3
1
0
.
2
8
5
1
.
1
*
*
*
B
R
O
K
D
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P
0
.
0
3
0
.
1
0
0
.
0
3
1
7
6
.
8
*
*
*
0
.
0
3
0
.
0
3
0
.
0
3
1
9
.
7
*
*
*
0
.
0
2
0
.
0
4
2
1
6
.
7
*
*
*
G
L
O
A
N
S
0
.
0
9
0
.
3
9
0
.
0
6
7
3
0
.
9
*
*
*
0
.
1
5
0
.
0
8
0
.
0
8
1
5
.
4
*
*
*
0
.
0
7
0
.
1
0
2
2
4
.
3
*
*
*
R
E
S
T
A
T
E
0
.
6
6
0
.
7
2
0
.
6
5
9
1
.
2
*
*
*
0
.
6
4
0
.
6
6
0
.
6
5
1
.
9
0
.
6
5
0
.
6
6
2
1
.
6
N
P
L
0
.
0
0
6
0
.
0
0
4
0
.
0
0
6
2
2
3
8
.
4
*
*
*
0
.
0
0
6
0
.
0
0
6
0
.
0
0
5
4
6
.
1
*
*
*
0
.
0
0
6
0
.
0
0
6
2
1
.
0
N
I
I
T
I
0
.
1
1
0
.
0
7
0
.
1
1
2
3
7
3
.
5
*
*
*
0
.
0
9
0
.
1
1
0
.
1
2
2
1
8
3
.
3
*
*
*
0
.
1
2
0
.
1
0
7
5
.
2
*
*
*
S
I
Z
E
1
1
.
7
8
1
1
.
6
3
1
1
.
8
0
2
6
.
8
*
*
*
1
1
.
2
2
1
1
.
8
4
1
2
.
1
0
2
2
7
1
.
0
*
*
*
1
2
.
0
7
1
1
.
6
9
8
8
.
1
*
*
*
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
5
,
9
1
2
5
3
6
5
,
3
7
6
9
8
1
3
,
9
1
5
1
,
0
1
6
1
,
3
7
3
4
,
5
3
9
S
u
r
v
i
v
e
r
a
t
e
(
%
)
9
4
.
8
8
5
.
5
9
5
.
8
9
4
.
8
9
4
.
4
9
6
.
3
9
5
.
8
9
4
.
5
N
o
t
e
s
:
M
e
a
n
s
a
r
e
s
i
g
n
i
?
c
a
n
t
l
y
d
i
f
f
e
r
e
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
m
i
n
u
s
s
i
g
n
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
m
e
a
n
o
f
t
h
e
s
e
c
o
n
d
c
a
t
e
g
o
r
y
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
m
e
a
n
o
f
t
h
e
?
r
s
t
c
a
t
e
g
o
r
y
,
e
x
c
e
p
t
f
o
r
N
B
H
C
,
S
B
H
C
v
s
M
B
H
C
;
t
h
e
m
i
n
u
s
s
i
g
n
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
m
e
a
n
o
f
M
B
H
C
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
m
e
a
n
o
f
S
B
H
C
;
n
o
t
e
t
h
a
t
t
h
e
s
e
c
o
m
p
a
r
i
s
o
n
s
d
o
n
o
t
c
o
n
t
r
o
l
f
o
r
d
i
f
f
e
r
e
n
c
e
s
i
n
o
t
h
e
r
f
a
c
t
o
r
s
Table III.
JFEP
5,3
292
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
a
n
e
l
A
:
s
a
m
p
l
e
b
y
b
a
n
k
i
n
g
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
V
a
r
i
a
b
l
e
A
l
l
s
a
m
p
l
e
D
e
n
o
v
o
s
E
s
t
a
b
l
i
s
h
e
d
N
B
H
C
S
B
H
C
M
B
H
C
N
a
t
i
o
n
a
l
S
t
a
t
e
N
o
n
-
T
B
T
F
I
n
t
e
r
c
e
p
t
2
8
.
7
1
*
*
*
2
1
0
.
1
9
*
*
*
2
7
.
5
5
*
*
*
2
6
.
6
3
*
*
2
7
.
6
4
*
*
*
2
1
2
.
8
0
*
*
*
2
6
.
2
3
*
*
*
2
9
.
6
3
*
*
*
2
8
.
7
7
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
T
I
E
R
1
C
A
P
2
4
.
0
3
*
*
*
1
.
5
4
2
1
0
.
9
3
*
*
*
2
1
.
7
9
2
9
.
9
7
*
*
*
2
3
.
5
3
2
6
.
6
8
*
2
3
.
3
3
*
*
2
4
.
0
0
*
*
*
(
0
.
0
1
)
(
0
.
4
1
)
(
0
.
0
0
)
(
0
.
4
0
)
(
0
.
0
0
)
(
0
.
3
8
)
(
0
.
0
7
)
(
0
.
0
5
)
(
0
.
0
1
)
S
I
Z
E
0
.
2
7
*
*
*
0
.
3
3
*
*
0
.
2
3
*
*
*
2
0
.
0
1
0
.
3
0
*
*
*
0
.
2
4
*
0
.
1
5
0
.
3
3
*
*
*
0
.
2
8
*
*
*
(
0
.
0
0
)
(
0
.
0
4
)
(
0
.
0
0
)
(
0
.
9
6
)
(
0
.
0
0
)
(
0
.
0
8
)
(
0
.
2
0
)
(
0
.
0
0
)
(
0
.
0
0
)
C
S
E
C
2
3
.
2
3
*
*
*
2
2
.
8
2
*
*
*
2
2
.
6
9
*
*
*
2
3
.
0
8
*
*
*
2
3
.
1
4
*
*
*
2
2
.
0
0
2
2
.
9
4
*
*
2
3
.
2
8
*
*
*
2
3
.
2
5
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
1
9
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
0
0
)
B
R
O
K
D
E
P
3
.
2
9
*
*
*
1
.
3
9
*
4
.
4
4
*
*
*
0
.
6
1
4
.
0
4
*
*
*
3
.
5
0
*
*
2
.
9
9
*
*
3
.
3
3
*
*
*
3
.
2
9
*
*
*
(
0
.
0
0
)
(
0
.
1
0
)
(
0
.
0
0
)
(
0
.
6
2
)
(
0
.
0
0
)
(
0
.
0
2
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
G
L
O
A
N
S
1
.
4
8
*
*
*
0
.
4
7
1
.
7
1
*
*
*
2
.
0
0
*
*
*
1
.
0
9
*
*
*
1
.
6
2
*
*
*
1
.
1
4
*
1
.
5
8
*
*
*
1
.
4
9
*
*
*
(
0
.
0
0
)
(
0
.
2
2
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
7
)
(
0
.
0
0
)
(
0
.
0
0
)
R
E
S
T
A
T
E
4
.
7
6
*
*
*
5
.
9
0
*
*
*
4
.
5
9
*
*
*
5
.
7
6
*
*
*
4
.
1
2
*
*
*
9
.
0
8
*
*
*
4
.
0
6
*
*
*
4
.
9
6
*
*
*
4
.
7
2
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
N
P
L
3
9
.
9
9
*
*
*
5
2
.
1
4
*
*
*
3
6
.
8
2
*
*
*
4
9
.
6
4
*
*
*
3
7
.
1
2
*
*
*
3
3
.
9
9
3
1
.
0
0
*
*
4
3
.
9
3
*
*
*
4
0
.
0
2
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
1
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
N
I
I
T
I
2
5
.
6
3
*
*
*
2
6
.
8
4
*
*
2
4
.
1
8
*
*
*
2
5
.
2
6
*
2
6
.
4
1
*
*
*
2
2
.
1
2
2
5
.
3
4
*
*
2
5
.
8
4
*
*
*
2
5
.
5
4
*
*
*
(
0
.
0
0
)
(
0
.
0
2
)
(
0
.
0
0
)
(
0
.
0
7
)
(
0
.
0
0
)
(
0
.
4
6
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
7
0
2
2
1
4
2
7
3
2
2
1
6
0
2
6
6
9
2
1
2
1
2
2
0
2
2
7
6
6
2
9
7
0
P
s
e
u
d
o
-
R
2
0
.
2
4
0
.
1
7
0
.
2
5
0
.
2
4
0
.
2
5
0
.
2
7
0
.
1
9
0
.
2
5
0
.
2
4
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
5
.
4
7
9
.
8
8
6
.
2
8
7
.
1
8
5
.
7
8
6
.
9
8
2
.
3
8
6
.
1
8
5
.
4
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
2
7
5
,
6
0
9
1
,
0
3
5
4
,
1
4
6
1
,
0
5
5
1
,
4
3
3
4
,
8
0
3
6
,
2
1
4
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
1
4
.
5
4
.
2
5
.
2
5
.
6
3
.
7
4
.
2
5
.
5
5
.
2
P
a
n
e
l
B
:
f
u
l
l
s
a
m
p
l
e
i
n
t
e
r
a
c
t
i
n
g
w
i
t
h
b
a
n
k
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
d
u
m
m
y
v
a
r
i
a
b
l
e
s
V
a
r
i
a
b
l
e
1
2
3
4
5
6
7
8
9
D
e
n
o
v
o
s
0
.
4
6
*
*
0
.
5
*
*
*
0
.
4
6
*
*
(
0
.
0
1
)
(
0
.
0
1
)
(
0
.
0
1
)
N
B
H
C
0
.
0
6
(
0
.
7
5
)
S
B
H
C
0
.
2
4
*
0
.
2
8
*
*
(
0
.
0
9
)
(
0
.
0
5
)
M
B
H
C
2
0
.
4
5
*
*
2
0
.
4
5
*
*
(
0
.
0
2
)
(
0
.
0
2
)
(
c
o
n
t
i
n
u
e
d
)
Table IV.
Logit regression results
Bank structure
and failure
293
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
N
A
T
I
O
N
A
L
0
.
0
0
(
0
.
9
9
)
T
B
T
F
2
1
2
.
0
2
(
0
.
9
8
)
P
U
B
L
I
C
0
.
0
5
(
0
.
8
3
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
6
7
2
9
7
0
2
9
6
9
2
9
6
7
2
9
7
0
2
9
7
0
2
9
7
0
2
9
6
5
2
9
6
4
P
s
e
u
d
o
-
R
2
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
5
.
5
8
5
.
4
8
5
.
5
8
5
.
6
8
5
.
4
8
5
.
4
8
5
.
4
8
5
.
6
8
5
.
6
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
P
a
n
e
l
C
:
f
u
l
l
s
a
m
p
l
e
i
n
t
e
r
a
c
t
i
n
g
w
i
t
h
e
c
o
n
o
m
i
c
a
n
d
r
e
g
u
l
a
t
o
r
y
e
n
v
i
r
o
n
m
e
n
t
v
a
r
i
a
b
l
e
s
V
a
r
i
a
b
l
e
1
2
3
4
5
6
7
8
9
H
P
0
3
0
7
0
.
0
2
*
*
*
(
0
.
0
0
)
H
P
0
7
0
9
2
0
.
0
5
*
*
*
(
0
.
0
0
)
P
i
n
c
o
m
e
0
6
0
9
2
0
.
1
1
*
*
*
2
0
.
1
1
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
L
i
m
i
t
2
0
.
2
7
*
*
2
0
.
3
2
*
*
(
0
.
0
4
)
(
0
.
0
2
)
M
E
T
R
O
0
.
7
2
*
*
*
0
.
6
6
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
D
e
l
i
n
q
u
e
n
c
y
0
.
0
1
*
*
*
(
0
.
0
0
)
S
u
b
p
r
i
m
e
0
3
0
6
0
.
2
3
*
*
*
(
0
.
0
0
)
C
P
P
2
2
.
0
2
*
*
*
(
0
.
0
0
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
5
0
2
9
3
7
2
9
5
0
2
9
6
8
2
9
6
0
2
9
4
1
2
9
4
7
2
9
4
1
2
9
3
9
P
s
e
u
d
o
-
R
2
0
.
2
5
0
.
2
6
0
.
2
5
0
.
2
4
0
.
2
5
0
.
2
6
0
.
2
6
0
.
2
6
0
.
2
6
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
6
.
4
8
7
.
2
8
6
.
5
8
5
.
4
8
5
.
9
8
6
.
9
8
6
.
6
8
6
.
3
8
6
.
9
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
P
a
n
e
l
D
:
s
a
m
p
l
e
b
y
b
a
n
k
i
n
g
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
i
n
t
e
r
a
c
t
i
n
g
w
i
t
h
e
c
o
n
o
m
i
c
a
n
d
r
e
g
u
l
a
t
o
r
y
e
n
v
i
r
o
n
m
e
n
t
v
a
r
i
a
b
l
e
s
V
a
r
i
a
b
l
e
A
l
l
s
a
m
p
l
e
D
e
n
o
v
o
s
E
s
t
a
b
l
i
s
h
e
d
N
B
H
C
S
B
H
C
M
B
H
C
N
a
t
i
o
n
a
l
S
t
a
t
e
N
o
n
-
T
B
T
F
I
n
t
e
r
c
e
p
t
2
9
.
6
9
*
*
*
2
1
3
.
3
5
*
*
*
2
7
.
8
5
*
*
*
2
6
.
5
4
*
*
2
8
.
1
9
*
*
*
2
1
6
.
0
7
*
*
*
2
8
.
1
6
*
*
*
2
1
0
.
5
*
*
*
2
9
.
7
8
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
2
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
T
I
E
R
1
C
A
P
2
5
.
3
4
*
*
*
1
.
7
0
2
1
3
.
2
9
*
*
*
2
1
.
6
1
2
1
1
.
8
4
*
*
*
2
2
.
0
4
2
7
.
6
4
*
2
4
.
7
2
*
*
*
2
5
.
3
0
*
*
*
(
0
.
0
0
)
(
0
.
3
9
)
(
0
.
0
0
)
(
0
.
4
7
)
(
0
.
0
0
)
(
0
.
6
3
)
(
0
.
0
6
)
(
0
.
0
1
)
(
0
.
0
0
)
(
c
o
n
t
i
n
u
e
d
)
Table IV.
JFEP
5,3
294
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
S
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Table IV.
Bank structure
and failure
295
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1
:
4
7
2
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2
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6
(
P
T
)
regressions; the lack of signi?cance for Tier 1 capital, liquidity measure, nonperforming
loan, and income diversity measure in the multibank holding company regression; and
the lack of signi?cance for size in the national bank regressions. Importantly, this
indicates that the determinants of bank failure are not the same for the subsamples
based on the organizational structure categories. For example, size is not an important
determinant of failure for those banks that have relatively little variation in size within
the subsample. Speci?cally, independent banks tend to be relatively small and national
banks tend to be relatively large. Furthermore, a bank as an af?liate in a multibank
holding company may be able to draw on the resources of the holding company to meet
its liquidity needs and has other potential sources of diversi?cation.
Furthermore, Panel B of Table IV incorporates structure dummy variables that
capture the impact of de novo status, holding company structure, and charter type on
the likelihood of failure while controlling for the ?nancial variables in Panel A[9].
As expected, de novo banks are more likely to fail than established banks, consistent
with these banks being ?nancially fragile (Model 1). Interestingly, banks that are
part of a single-bank holding company are more likely to fail (Model 3) than other
banks, while banks that are part of a multibank holding company are less likely to fail
(Model 4)[10]. This result holds even after controlling for de novo status (Models 8
and 9). Charter type (Model 5) and banks being publicly traded (Model 7), however,
seem to have little direct impact on the likelihood of bank failure.
Panel C of Table IV includes economic and regulatory environment variables[11]. Our
analysis accounting for economic variables ?nds results with the expected. For example,
consistent with the housing bubble playing a major role in bank failures, changes in home
prices between 2003 and 2007 and growth insubprime lending between 2003 and 2006 ina
given state are positively related to the incidence of failure for banks in that state.
Subsequent changes in home prices between 2007 and 2009 are negatively related to the
likelihood of failure and increases in delinquency rates between 2007 and 2009 in a bank’s
state are positivelyrelated to the likelihoodof failure. Changes ina state’s personal income
levels between 2006 and 2009 are also negatively related to the likelihood of failure. Being
located in a metropolitan area is positively associated with the likelihood of failure.
Interestingly, limits on de novo branching by out-of-state banks are associated with
a reduced likelihood of failure. This suggests that limits on out-of-state competition
enhance the ?nancial stability of a state’s banks[12]. Ironically, the Dodd-Frank Act
removes the restrictions on de novo interstate banking. Combined with the ?ndings of
DeYoung (2003) and Hunter et al. (1996) that de novo bank failure is more sensitive to
adverse environment conditions, this deregulation legislation may contribute to a
higher likelihood of de novo bank failure. As expected, governmental capital injection
via the CPP has a signi?cantly negative in?uence on failure[13].
Finally, Panel D of Table IV shows logit regression results for the whole sample and
eight subset samples and includes the economic and regulatory environment variables that
pre-date the ?nancial crisis (HP0307 andSubprime0306), Limit, METRO, andCPP. Overall,
most of these results are consistent with those shown in Panel Aof Table IV. Nevertheless,
after controlling for other economic variables, the coef?cients associated with brokered
deposits no longer exhibit statistical signi?cance for de novobanks andindependent banks.
Interestingly, a majority of economic and regulatory environment variables have not
shown statistical signi?cance in explaining bank failure among sub sample independent
banks and multibank holding company banks, suggesting local economic conditions may
JFEP
5,3
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(
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be subsumed in the balance sheet among those banks included in our sample. Note that the
coef?cients on ?nancial variables and economic and regulatory environment variables
remain robust when we exclude the largest banks (Non-TBTF).
5. Summary and conclusions
This paper examines the impact of different banking organizational structures and
measures of institutional fragility on the likelihood of a bank failing during the late-2000s
?nancial crisis. As expected, the results indicate that the likelihood of failure was affected
by bank structure. Such factors as capitalization, asset structure, funding strategy, loan
portfolio composition, asset quality, and income structure affected the likelihood of failure
and these underlying bank characteristics differed by holding company structure, charter
type, and degree of fragility (de novo banks and high loan-growth banks).
De novo banks and banks that are part of a single-bank holding company are more
likely to fail while banks that are part of a multibank holding company are less likely
to fail. This is consistent with the argument that the multibank holding company
structure can act as a source for strength for subsidiary banks. However, charter type
and being publicly traded seem to have had little direct impact on the likelihood of
bank failure, suggesting that their impact on the likelihood of failure, if any, is due to
their in?uence over a bank’s operations.
Consistent with being ?nancially fragile, our results suggest that de novo banks and
those banks that grew substantially prior to the crisis faced an increased likelihood of
failure relative to established banks. Overall, established institutions were more likely
to fail if they had relatively low capital ratios, were relatively large, had relatively low
liquidity, relied on brokered deposits, held a large portfolio of real estate loans, had a
relatively large proportion of nonperforming loans, and less income diversity.
Interestingly, capital levels do not play a key on the likelihood of failure in de novo
banks, independent banks and banks that are part of a multibank holding company.
This suggests that the economic shock of the ?nancial crisis combined with being
inherently ?nancially fragile caused many de novo banks to fail in spite of them having
capital requirements that far exceed the requirements for established banks.
From a banking regulation perspective, our ?ndings shed light on those factors that
may enhance the stability of the banking systemand those that may weaken the system.
Speci?cally, our results provide support for the notion that the multibank holding
company structure does reduce the likelihood of failure. The Dodd-Frank Act’s removal
of restrictions on de novo interstate branching, on the other hand, may increase the
likelihood of future bank failures by encouraging more intense competition.
Notes
1. In response to the large number of de novo bank failures, the FDIC extended the enhanced
capital standards for de novo banks from three years to seven years in 2008.
Newly chartered institutions are subject to higher capital requirements, more frequent
examinations, and prior approval of business plan changes during their ?rst seven years of
operation.
2. Demirgu¨c¸-Kunt (1989) points out that failure is a regulatory decision and should be modeled
formally as the outcome of a regulatory decision-making process.
3. See Rezende (2011) for a detailed study on the determinants as to whether federal and state
supervisors examine state banks independently or together.
Bank structure
and failure
297
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1
6
(
P
T
)
4. On the Federal Reserve Bank of Chicago web site, a de novo bank is de?ned as banks in
operation for ?ve years or less. In our sample, all of the established banks had been in
operation for at least six years as of the end of 2006.
5. The PUBLIC variable data is from Center for Research in Security Prices Sift Enterprises and
the National Information Center. The economic environment variable data is from Federal
Housing Finance Agency, Bureau of Economic Analysis, FDIC Summary of Deposits, and
Mortgage Bankers Association. The LIMIT variable data is from Rice and Strahan (2010).
6. We consider alternative measures of bank capital, but do not ?nd support for using other
measures of capital over Tier 1 capital in our empirical analysis.
7. We also implement nonparametric tests showing that all of the ?nancial variables are
signi?cantly different in means at the 1 percent level between failed and non-failed banks for
the whole sample and by organizational structure, except for differences in nonperforming
loan between failed and non-failed independent banks and banks with af?liates with
multibank holding companies. These results are available from the authors upon request.
8. When we estimate separate regressions by bank size, the overall results tend to be driven by
larger banks. In fact, the failure rate increases monotonically with size and, consequently, many
of the variables that are signi?cant inthe overall regressionare not signi?cant whenthe analysis
is restricted to the smallest banks. Interestingly, the SIZE variable is only signi?cant in the
75th-90th percent size quintile. These results are available from the authors upon request.
9. Note that we omit the coef?cients on ?nancial variables in Panels B and C of Table IV.
All ?nancial variables show statistical signi?cance and exhibit the same signs as in Panel A.
10. This suggests that holding companies act as a source of strength only when there are
multiple banks within the holding company structure, consistent with the ?ndings of both
Ashcraft (2008) and DeYoung and Torna (2012).
11. There is an ongoing debate regarding whether economic conditions that are subsumed in the
balance sheet play a signi?cant role in bank failures. DeYoung and Torna (2012) ?nd that
GDP and home price growth play a signi?cant role in recent bank failures. However,
Berger et al. (2012) suggest that subprime mortgage risks, housing price in?ation, and GDP
do not play any signi?cant role in predicting bank defaults.
12. The Riegle-Neal Interstate Banking and Branching Ef?ciency Act of 1994 removed federal
restrictions on interstate branching. This deregulation encourages banks to diversify
geographically and reduces their vulnerability to local economic shocks.
13. The CPPdummyvariable is includedhere to allowfor the impact of governmental interventionon
the reduced likelihood of failure for a bank. Given the endogenous nature of the bailout and failure
decisions by regulators, we also estimate the likelihood of bank failure and the likelihood of a
bank receiving CPP funds in a multivariate setting using a bivariate probit model. The coef?cient
estimates in the failure portion of the model are consistent with the results shown in Table IV.
References
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Ashcraft, A.B. (2008), “Are bank holding companies a source of strength to the banking
subsidiaries?”, Journal of Money, Credit and Banking, Vol. 40 Nos 2/3, pp. 273-294.
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US bank failures, 2007-2010: do bank failures still re?ect local economic conditions?”,
Federal Reserve Bank of St Louis Review, Vol. 92 No. 5, pp. 395-415.
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during the recent ?nancial crisis”, SSRN Working Paper, December, available at: http://
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Bologna, P. (2011), “Is there a role for funding in explaining recent US banks’ failures?”,
IMF Working Paper No. 180, International Monetary Fund, Washington, DC.
Cole, R.A. and White, L.J. (2011), “De´ja` Vu all over again: the causes of US commercial bank
failures this time around”, Journal of Financial Services Research, Vol. 42 No. 1, pp. 5-29.
Demirgu¨c¸-Kunt, A. (1989), “Deposit-institution failures: a review of empirical literature”, Federal
Reserve Bank of Cleveland Economic Review, Vol. 25 No. 4, pp. 2-18.
DeYoung, R. (2003), “De novo bank exit”, Journal of Money, Credit and Banking, Vol. 35 No. 5,
pp. 711-728.
DeYoung, R. and Torna, G. (2012), “Nontraditional banking activities and bank failures during
the ?nancial crisis”, SSRN Working Paper, March, available at:http://ssrn.com/abstra
ct¼2032246 (accessed February 4, 2013).
Gilbert, R.A. (1991), “Do bank holding companies act as ‘sources of strength’ for the bank
subsidiaries?”, Federal Reserve Bank of St Louis Economic Review, Vol. 73 No. 1, pp. 3-18.
Hunter, W.C., Verbrugge, J.A. and Whidbee, D.A. (1996), “Risk taking and failure in de novo savings
and loans in the 1980s”, Journal of Financial Services Research, Vol. 10 No. 3, pp. 235-271.
Jin, Y.J., Kanagaretnam, K. and Lobo, G.J. (2011), “Ability of accounting and audit quality
variables to predict bank failure during the ?nancial crisis”, Journal of Banking &Finance,
Vol. 35 No. 11, pp. 2811-2819.
Ng, J. and Roychowdhury, S. (2012), “Do loan loss reserves behave like capital: evidence from
recent bank failures”, SSRN Working Paper, December, available at:http://ssrn.com/a
bstract¼1646928 (accessed February 4, 2013).
Rauch, C. (2010), “Bank fragility and the ?nancial crisis: evidence from the US dual banking
system”, International Finance Review, Vol. 11, pp. 33-86.
Rezende, M. (2011), “Howdo joint supervisors examine ?nancial institutions? The case of state banks”,
Finance and Economics Discussion Series No. 43, Federal Reserve Board, Washington, DC.
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Finance, Vol. 65 No. 3, pp. 861-889.
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Money, Credit and Banking, Vol. 35 No. 6, pp. 967-998.
Rossi, C.V. (2010), “Decomposing the impact of brokered deposits on bank failure: theory and
practice”, working paper, Robert H. Smith School of Business, University of Maryland,
College Park, MD, September 9.
Whalen, G.W. (2010), “Why do de novo banks choose a national charter?”, OCC Economic
Working Paper No. 2010-2012, Of?ce of the Comptroller of the Currency, Washington, DC.
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About the authors
Wenling Lu is a PhD candidate in Finance at Washington State University.
Dr David A. Whidbee is the Omer L. Carey Chair in Financial Education and Associate Dean
for Faculty Affairs and Research in the College of Business at Washington State University.
David A. Whidbee is the corresponding author and can be contacted at: [email protected]
Bank structure
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This article has been cited by:
1. Paul Simshauser. 2014. The cost of capital for power generation in atypical capital market conditions.
Economic Analysis and Policy 44, 184-201. [CrossRef]
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doc_738924419.pdf
This paper aims to examine the impact of charter type (national vs state), holding company
structure, and measures of bank fragility on the likelihood of bank failure during the late 2000s financial
crisis.
Journal of Financial Economic Policy
Bank structure and failure during the financial crisis
Wenling Lu David A. Whidbee
Article information:
To cite this document:
Wenling Lu David A. Whidbee, (2013),"Bank structure and failure during the financial crisis", J ournal of
Financial Economic Policy, Vol. 5 Iss 3 pp. 281 - 299
Permanent link to this document:http://dx.doi.org/10.1108/J FEP-02-2013-0006
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Eric Osei-Assibey, Baimba Augustine Bockarie, (2013),"Bank risks, capital and loan supply: evidence from
Sierra Leone", J ournal of Financial Economic Policy, Vol. 5 Iss 3 pp. 256-271http://dx.doi.org/10.1108/
J FEP-09-2012-0041
Puspa Amri, Apanard P. Angkinand, Clas Wihlborg, (2011),"International comparisons of bank regulation,
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Bank structure and failure
during the ?nancial crisis
Wenling Lu and David A. Whidbee
Department of Finance and Management Science,
Washington State University, Pullman, Washington, USA
Abstract
Purpose – This paper aims to examine the impact of charter type (national vs state), holding company
structure, and measures of bank fragility on the likelihood of bank failure during the late 2000s ?nancial
crisis.
Design/methodology/approach – The study estimates a series of logit regressions in an effort to
identify the causes of failure and assess the role of the bank-level characteristics while controlling for
the economic and regulatory environment.
Findings – The empirical results indicate that established institutions were more likely to fail,
dependent upon whether a bank received bailout funds or not, if they were relatively large, had relatively
low capital ratios, had relatively low liquidity, relied more heavily on brokered deposits, held a relatively
large portfolio of real estate loans, had a relatively large proportion of non performing loans, and had less
income diversity. Consistent with being ?nancially fragile, de novo banks and those banks that grew
substantially prior to the crisis faced an increased likelihood of failure relative to established banks.
However, capital levels were not signi?cantly related to the likelihood of failure in de novo institutions.
Originality/value – This paper provides a comprehensive analysis of the possible business models’
impact on the likelihood of failure during the recent ?nancial crisis. It contributes to the ongoing debate
regarding appropriate regulatory reform in the banking industry by shedding light on the extent to which
the business model decisions made bybankmanagers have animpact onthe stabilityof thebankingsystem.
Keywords Bank structure, Bank failure, Financial crisis, Banks, Banking, Business failures,
Financial risk
Paper type Research paper
1. Introduction
417 US commercial banks and thrifts failed between 2007 and 2011, accounting for
$671 billion in total assets. Although the incidence of bank failure has been examined in
earlier literature, the failures that occurred as a result of the 2007-2009 ?nancial crisis are
especially interesting. First, the most recent failures coincided with a dramatic and
relatively sudden change in the economic environment. Therefore, the empirical evidence
associated with the causes of earlier failures may differ from the causes of more recent
failures. The recent bank failures also occurred at approximately the same time many
banks were receivingbailout funds via the Capital Purchase Program(CPP). Furthermore,
it is not clear that Dodd-Frank Wall Street Reform and Consumer Protection Act
(Dodd-Frank Act) address the causes of failure and therefore will prevent future failures.
There are a variety of different banking models and some may have been better suited
to survive the recent ?nancial crisis. Although the goal of all banking business models is
to maximize shareholder wealth, the decisions bank managers make regarding holding
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
The authors thank James Barth, Gene Lai, Min-Teh Yu, and Sami Va¨ha¨maa for their helpful
comments and suggestions.
Journal of Financial Economic Policy
Vol. 5 No. 3, 2013
pp. 281-299
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-02-2013-0006
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company structure and charter type (national vs state), as examples, may make the bank
more vulnerable to failure during a ?nancial crisis. In addition, some banks are inherently
fragile. For example, de novo banks may be more inclined to fail despite enhanced
minimum capital standards during their ?rst seven years[1]. Although many of the bank
failures can be attributed to the 2007-2009 ?nancial crisis and related severe economic
conditions, the role of bank structure in determining the likelihood of failure has not been
fully explored. This paper examines the impact of different US bank structure (de novo
banks vs established banks, independent banks vs holding company banks, and national
banks vs state banks) on the likelihood of failure during year 2007-2011. Therefore, our
analysis contributes to the ongoingdebate regarding appropriate regulatoryreforminthe
banking industry by shedding light on the extent to which the business model decisions
made by bank managers have an impact on the stability of the banking system.
Overall, the results indicate that the likelihood of failure was affected by the decisions
made by bank managers and by the economic and regulatory environment. As expected,
perceived sources of strength, such as being part of a multibank holding company
structure, reduce the likelihood of failure. Those banks that are inherently fragile, such
as de novo banks and those that experienced rapid loan growth, were more likely to fail.
Finally, despite there being signi?cant differences in their assets and liabilities, there is
no signi?cant direct impact of charter type on the likelihood of failure.
The remainder of the paper is structured as follows. In Section 2, we brie?y review the
literature on bank failures. Section 3 discusses the data and the variables used in our
analysis. Section 4 provides and discusses the results. Section 5 provides conclusions.
2. Literature review
A broad body of research has been conducted to provide potential answers to this
particular question, “why do banks fail?” Demirgu¨c¸-Kunt (1989) emphasizes that
making a distinction between economic insolvency and failure is crucial in studying the
failure of ?nancial institutions[2]. More recently, some researchers have explored the
determinants of US bank failure during the recent ?nancial crisis. For example, brokered
deposits (Rossi, 2010), real-estate loans (Cole and White, 2011), liquidity funding
structure (Bologna, 2011), audit quality ( Jin et al., 2011), loan loss reserves (Ng and
Roychowdhury, 2012), nontraditional activities (e.g. investment banking and venture
capital, DeYoung and Torna (2012)), and bank ownership (Berger et al., 2012) played key
roles in US bank failures during the crisis. Furthermore, Aubuchon and Wheelock (2010)
?nd that 2007-2009 bank failures re?ect local economic conditions.
Although the extant literature helps us understand some of the causes of failure
during the ?nancial crisis, the role of several bank-level characteristics has not, to our
knowledge, been adequately explored. More speci?cally, no other study has examined
the role of banking organizational structures in explaining failure during the ?nancial
crisis. Unfortunately, there is no consensus in the theoretical and empirical literature on
the effect of different bank structures on bank failure. We contribute to the existing
literature by modeling the interaction of bank structure, bank fragility and bank failure
during the 2007-2009 ?nancial crisis.
2.1 The role of bank structure
Beginning with the Bank Holding Company Act of 1956, bank holding companies have
been expected by regulators to act as a source of strength to subsidiary banks.
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Gilbert (1991) examines the amount of capital injected into troubled banks and ?nds that
holding companies tendto inject more capital thanthe owners of independent banks when
the total assets of the holding company are substantially more than the assets of the
troubled bank. More recently, Ashcraft (2008) ?nds evidence that multibank holding
companies provide support for subsidiary banks, insofar as multi-bank holding company
subsidiary banks are less likely to fail and more likely to receive capital injections from
parent companies when faced with ?nancial distress. Ashcraft examines the 1984-2004
period, however, so it is unclear whether the holding companystructure acts as a source of
strength during a period of severe ?nancial crisis. If regulators applied rational criteria
whendecidingwhichbanks toallowtofail, we wouldexpect regulators to be more inclined
to allowto fail those banks that lack sources of strength. Therefore, we expect those banks
that are part of a holding company structure, especially a multi-bank holding company
structure, to be less likely to fail. Therefore, we categorize our sample into independent
banks, single-bank holding company banks, and multi-bank holding company banks.
2.2 The role of charter type
Surprisingly, little research has examined whether charter type affects the likelihood of
bank failure. Banks choose either a national charter or a state charter. Those banks
choosing a national charter are supervisedbythe Of?ce of the Comptroller of the Currency
(OCC). Those choosing a state charter are supervised jointly by their state regulator and
either the Federal Reserve or the Federal Deposit Insurance Corporation (FDIC)[3]. The
choice a bankmakes determines the activities the bankis allowed to engage in, the speci?c
regulations it is subject to, and the explicit or implicit supervisory costs it must pay. Rosen
(2003) discusses competitionbetweenstate andfederal regulators andexamines the choice
of regulator by banks. He ?nds evidence that banks switching from a state charter to a
federal charter, or vice versa, tend to perform better without a signi?cant change in risk
consistent with regulators competing with each other in a bene?cial manner. At the same
time, however, he ?nds evidence of a preference for the “quiet life” by examiners because
banks tend to switch charters prior to a change in their loan portfolio, in any direction.
More recently, Rauch (2010) ?nds evidence that national banks reduced their balance
sheet exposures in response to the crisis sooner than state banks. Whalen (2010, 2012)
suggests that the tendency by de novo banks to choose a state charter is due to higher
explicit national bank supervisory costs and local market conditions. In particularly,
it is less likely that a national bank would enter an intensely competition market.
Agarwal et al. (2012) examine jointly regulated state-chartered banks that are subject
to a rotation of state and federal examinations and ?nd that the federal authorities
are signi?cantly more likely to downgrade a bank’s capital adequacy, asset quality,
management experience and expertise, earnings quality, liquidity, and sensitivity to
market risk (CAMELS) rating. These results suggest that state banking authorities
may tend to be more lenient than federal authorities, but it is unclear whether this
leniency translates into an increased likelihood of failure during the recent ?nancial
crisis. We categorize banks based on their charter type to examine this possibility.
2.3 The role of de novo status
Between 2000 and 2006, the number of banks in the USA shrank from 8,317 to 7,407 as a
result of industry consolidation. At the same time, however, 627 of the banks in existence
as of 2006 were established during the 2000-2005 period. The motivations behind these
Bank structure
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new entries is beyond the scope of this study, but these de novo institutions represent
over 8 percent of US banking institutions. As pointed out by DeYoung (2003), these
institutions are “?nancially fragile” and face an increased likelihood of failure after an
initial period when start-up capital and restrictions on acquisitions shields them from
?nancial distress. In particularly, de novo bank entry tends to occur in high-growth
markets. Given that much of the growth in banking in the early 2000s was in mortgage
lending, we expect a greater incidence of de novo banks failingduring the ?nancial crisis,
but it is an empirical question whether they failed due to their loan portfolio composition
or their fragility. In an earlier study, Hunter et al. (1996) investigate the probability of
failure for de novo savings and loans, during 1980-1986, and ?nd strong evidence that
credit risk, low capital levels, and cost inef?ciencies contributed to the failure of de novo
savings and loans. We examine the role of a de novo business model in explaining bank
failures during the 2007-2009 ?nancial crisis. In our analysis, we de?ne de novo banks as
those commercial banks that were newly chartered during 2000-2005; and established
banks are those banks established before 2000 and still in existence at the end of 2006[4].
3. Data
3.1 Data and explanatory variables
The sample used in our empirical analysis includes all commercial banks in the 50 states
and Washington, DC that were in existence at the end of 2006, had call reports available
at the end of 2005 and 2006, had nonzero loan amounts and nonzero total income, and did
not merge or convert to a different type of institutionbetween2007 and 2010. We chose to
measure most of our variables at the end of 2006 because we are interested in the
managerial decisions that ultimately led to failure. Subsequent measures of our
variables may have been in?uenced by managers’ or regulators’ responses to the crisis.
The ?nancial data is compiled from the Reports of Condition and Income (call reports),
the data on bank failures is from the FDIC web site, the data on structure, economic and
regulatory environment is from multiple sources[5]. Our explanatory variables include
three categories: structure dummy variables, ?nancial variables, and economic and
regulatory environment variables.
As indicated earlier, we include structure dummy variables indicating whether a
bank is a de novo bank (De novos), is an established bank (Established), is an
independent bank (NBHC), is part of a single-bank holding company (SBHC), is part of a
multi-bank holding company (MBHC), has a national charter (NATIONAL), has total
assets smaller than $50 billion (Non-TBTF), received CPP funds (CPP), or is a
publicly-traded bank or subsidiary of a publicly-traded holding company (PUBLIC). The
full sample contains 6,236 banks. 324 of our full sample banks failed during 2007-2011.
See Figures 1 and 2 for full sample distributions and failure rate by type, respectively.
3.2 Firm-level ?nancial characteristics
De novo banks are not the only banks that are potentially “?nancially fragile”. Banks
with rapid loan growth, for example, may face an increased risk of ?nancial distress.
Therefore, we also consider the impact that a variety of balance sheet and income
statement variables have on the likelihood of failure during the ?nancial crisis. Drawing
on the research of DeYoung (2003), Rossi (2010), Jin et al. (2011) and Cole and White
(2011), we include the following ?nancial variables in our analysis: Tier 1 capital scaled
by risk-weighted assets (TIER1CAP); size as measured by total assets (ASSETS)
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or the natural log of total assets (SIZE); liquidity as measured by the sum of cash and
securities scaled by total assets (CSEC); brokered deposits scaled by total assets
(BROKDEP); yearly growth in total loans scaled by total assets (GLOANS); real estate
loans as a percent of total loans (RESTATE); nonperforming loans scaled by total assets
(NPL); and income diversity as measured by noninterest income to total income (NIITI).
Tier 1 capital ratios are included because previous literature routinely ?nds that
poorly capitalized banks are more likely to fail, hence the setting of minimum capital
levels required by bank regulators[6]. Including Tier 1 capital in our analysis adds to
the ongoing debate over the appropriate minimum levels and measures for bank
capital. Size is included to account for its potential role in bank failures, such as
too-big-to-fail. Measures of liquidity are included because liquidity concerns affected
many banks during the ?nancial crisis. Brokered deposits are included to account for
the extent to which a bank relied on noncore sources of funds. Loan growth is included
because it may indicate lax underwriting standards. Real estate lending as a percent of
total lending is included to account for the fact that those banks that focused on real
estate lending were more drastically impacted by the bursting of the housing bubble.
Nonperforming loans are included to account for the extent to which a bank’s loan
portfolio was already showing signs of weakness. Finally, noninterest income is
included to account for diversity in a bank’s revenue sources.
3.3 The economic and regulatory environment
Banks tend to be vulnerable to adverse local economic shocks and thus bank failure rates
typicallyare higher instates experiencingmore severe economic distress. Therefore, we also
consider the impact that a variety of economic and regulatory variables had on the
likelihood of failure during the ?nancial crisis. Drawing on DeYoung (2003), Rauch (2010),
Figure 1.
Bank distributions by
type: 6,236 sample banks
Figure 2.
Failure rate by type:
5.2 percent overall
Bank structure
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AubuchonandWheelock(2010) andothers, we include the followingvariables to control for
differences in the economic and regulatory environment for a bank’s home state: the
percentage change in the Federal Housing Finance Agency’s home price index between
the thirdquarter of 2003 andthe ?rst quarter of 2007 (HP0307); the percentage change inthe
FHFA home price index between the ?rst quarter of 2007 and the fourth quarter of 2009
(HP0709); growth in personal income between 2006 and 2009 (Pincome0609); a dummy
variable indicating whether the state prohibits de novo branching by out-of-state banks
(Limit); a dummy variable indicating whether a bank is located in a metropolitan statistical
area (METRO); the change in mortgage delinquency rates between the ?rst quarter of 2007
and the fourth quarter of 2009 (Delinquency); and the percentage change in subprime
mortgages betweenthe thirdquarter of 2003 andthe fourthquarter of 2006 (Subprime0306).
Our analysis includes various measures of state-level macro-economic conditions to
account for the extent to which a bank is sensitive to adverse environmental conditions.
Changes in pre-crisis home prices are included to account for the extent to which a bank’s
home state experienced larger increases in home prices during the boom. Home price
changes that occurred during or after the crisis may also have contributed to bank
failures. In other words, we include measures to account for both the housing boom and
the subsequent bust. Growth in personal income is included to account for the potential
impact on bank distress of state-level differences in how the ?nancial crisis impacted
household wealth. An indicator of a bank being located in a metropolitan statistical area
is included to further account for bank level economic conditions and the fact that those
banks in urban markets are likely to face more intense competition relative to other
banks. The change in mortgage delinquency rates subsequent to the bursting of the
housing bubble is included to account for a proximate cause of the ?nancial crisis.
Finally, the change in subprime mortgages during the housing boom (2003-2006) is
included to account for the extent to which a bank experienced rapid growth in subprime
mortgage loans and the fact that those banks that focused on subprime mortgage
lending were more impacted by the distress in mortgage markets.
In addition to economic variables, we include an indicator of the prohibition on
de novo interstate branching as a measure of the extent to which a state’s banks were
subject to competition from out-of-state banks. It is included to also account for the
extent to which branching facilitated geographic diversi?cation or scale economies.
Including de novo interstate branching restrictions in our analysis adds to the ongoing
debate over branching deregulation and relaxing restrictions on de novo interstate
banking. However, the relationship between branching restrictions or entry barriers and
bank failures is an empirical question that merits further study.
Table I displays de?nitions and summary statistics for the variables used in our
analysis. Unless indicated, all variables were observed at the end of 2006. Note that
continuous variables were winsorized at 1 and 99 percent in order to limit the impact of
outliers. Table II provides a correlation matrix for the variables included in our analysis.
Signi?cant correlation coef?cients at the 10 percent level are in italics. 21 of 23 bivariate
correlations between bank failure (FB) and other variables are statistically signi?cant.
4. Empirical results
4.1 Bank structure and fragility
We ?rst employ nonparametric tests to examine differences in ?nancial variables for the
various categories of banks. Table III reports the results for means and Kruskal-Wallis
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Variable De?nition Mean SD Maximum Minimum
FB ¼ 1 if the bank failed during
2007-2011
0.05 0.22 1.00 0.00
Structure dummy variables
De novos ¼ 1 if the bank established
during 2000-2005
0.10 0.30 1.00 0.00
NATIONAL ¼ 1 if the bank has federal
charter
0.23 0.42 1.00 0.00
NBHC ¼ 1 if the bank is an independent
bank
0.17 0.37 1.00 0.00
SBHC ¼ 1 if the bank is an af?liate in a
single-bank holding company
0.66 0.47 1.00 0.00
MBHC ¼ 1 if the bank is an af?liate in a
multibank holding company
0.17 0.37 1.00 0.00
TBTF ¼ 1 if the bank has total assets
larger than US$50 billion
0.00 0.06 1.00 0.00
CPP ¼ 1 if the bank or its bank
holding company received Capital
Purchase Program funds
0.10 0.30 1.00 0.00
PUBLIC ¼ 1 if the bank is a publicly-
traded bank or af?liated with a
publicly-traded holding company
0.04 0.19 1.00 0.00
Financial variables
TIER1CAP Tier 1 risk-based capital ratio:
Tier 1 capital, scaled by risk-
weighted assets
0.16 0.11 0.48 0.08
ASSETS Total assets (US$ thousands) 389,825 1,155,617 9,550,274 12,108
SIZE Natural log of total assets
(US$ thousands)
11.82 1.29 16.07 9.40
CSEC Sum of cash and securities, scaled
by beginning total assets
0.29 0.21 0.78 0.03
BROKDEP Brokered deposits, scaled by
beginning total assets
0.04 0.11 0.48 0.00
GLOANS Yearly growth in total loans,
scaled by beginning total assets
0.10 0.20 1.31 20.11
RESTATE Percent of real estate loans to total
loans
0.66 0.19 0.97 0.10
NPL Nonperforming loans (sum of
loans past due more than 90 days
and nonaccrual loans and leases),
scaled by beginning total assets
0.01 0.01 0.04 0.00
NIITI Percent of non-interest income to
total income
0.11 0.08 0.46 0.01
Economic and regulatory environment variables (measured at the state level except as indicated)
HP0709 Percentage change in the FHFA
House Price Index (2007Q1-
2009Q4)
25.75 9.83 6.79 241.00
HP0307 Percentage change in the FHFA
House Price Index (2003Q3-
2007Q1)
26.19 15.74 76.81 5.02
Pincome0609 Growth in personal income 9.95 4.23 27.86 0.40
(continued)
Table I.
Descriptive statistics
Bank structure
and failure
287
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test statistics for differences in samples (x
2
approximation). Overall, the results indicate
that there are substantial differences in ?nancial characteristics. Panel A of Table III
shows that capitalization, asset structure, funding strategy, loan growth, loan portfolio
composition, asset quality, and income structure differ depending on a bank’s
“?nancially fragile” status, holding company structure, and charter type[7]. Panels B
and C of Table III indicate that these differences hold even limiting the comparison to
failed and non-failed banks. This suggests that the structure variables have the potential
to impact the likelihood of failure directly and indirectly through their impact on the
variables shown in Table III.
4.2 Logit regressions of bank failure
To this point, our categorical analysis suggests a potential relation between a bank’s
?nancial fragility and the likelihood of failure. We next estimate a series of logit
regressions in an effort to identify the causes of bank failure during the ?nancial crisis.
We also run the bank failure logit regressions in each of the eight subsamples to assess
the impact of the bank-level characteristics for different bank organizational structures.
The dependent variable in the logit regressions is equal to one if a bank failed during the
2007-2011 period, zero otherwise. The explanatory variables are explained earlier.
Table IV shows the results of our logistic regression models.
The ?rst stepinthis analysis is toexamine the role of the ?nancial variables inaffecting
the likelihoodof failure. Table IV, Panel Ashows logit regressionresults for the full sample
and sub samples based on the various categories of banks identi?ed as being of interest.
As expected, for the whole sample, the likelihood of failure is inversely related to a bank’s
Tier 1 capital, liquidity, and level of noninterest income. The likelihood of failure is
positively related to size[8], growth in loans, real estate loans, nonperforming loans, and
the degree to which a bank relies on noncore sources of funds (brokered deposits).
Generally, the results for the whole sample hold for the various subsets. There are,
however, some notable differences. First, Tier 1 capital and loan growth are not
statistically signi?cant in explaining failure in de novo banks. This is surprising in light
of the fact that de novo banks are subject to enhanced capital requirements throughout
their ?rst seven years. In addition, de novo banks tend to have relatively high loan
growth rates (average 39 percent for failed and 45 percent for non-failed, respectively,
see Table III). Our ?ndings, consistent with DeYoung (2003), indicate that Tier 1 capital
ratios and loan growth continue playing an insigni?cant role in de novo bank failures
over the recent ?nancial crisis period. Furthermore, the results show the lack of
signi?cance for Tier 1 capital, size, and brokered deposits in the independent bank
Variable De?nition Mean SD Maximum Minimum
Limit ¼ 1 if the state is not allowing de
novo interstate branching
0.40 0.49 1.00 0.00
METRO ¼ 1 if the bank is located in a
metropolitan statistical area
0.50 0.50 1.00 0.00
Delinquency Change in mortgage delinquency
rate for all loans (2007Q1-2009Q4)
123.88 49.36 318.33 63.07
Subprime0306 Increase in percent of subprime
mortgages (2003Q3-2006Q4)
7.59 3.77 15.33 29.96
Table I.
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Table II.
Correlation matrix
Bank structure
and failure
289
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Table II.
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n
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t
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B
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C
M
B
H
C
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B
H
C
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v
s
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4
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9
3
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8
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1
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3
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8
8
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2
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0
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o
.
o
f
o
b
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r
v
a
t
i
o
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s
6
,
2
3
6
6
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7
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,
6
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9
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4
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1
4
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3
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8
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3
%
o
f
a
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l
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m
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1
0
0
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0
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0
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1
8
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5
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3
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0
7
7
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0
P
a
n
e
l
B
:
f
a
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d
b
a
n
k
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s
a
m
p
l
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b
y
b
a
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g
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r
g
a
n
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z
a
t
i
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n
a
l
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r
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c
t
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e
V
a
r
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a
b
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e
A
l
l
f
a
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d
F
a
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d
d
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d
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F
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d
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d
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a
b
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d
F
a
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d
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B
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C
F
a
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e
d
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B
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C
F
a
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d
M
B
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F
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d
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C
,
f
a
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d
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B
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7
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0
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7
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0
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8
1
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9
N
P
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0
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8
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1
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3
N
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T
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0
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3
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2
1
2
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5
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1
2
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7
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4
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1
2
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6
5
1
2
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3
8
0
.
5
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
3
2
4
9
1
2
3
3
5
4
2
3
1
3
9
6
0
2
6
4
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
1
4
.
5
4
.
2
5
.
2
5
.
6
3
.
7
4
.
2
5
.
5
(
c
o
n
t
i
n
u
e
d
)
Table III.
Nonparametric tests for
differences in the average
?nancial characteristics
Bank structure
and failure
291
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
a
n
e
l
C
:
n
o
n
-
f
a
i
l
e
d
b
a
n
k
s
s
a
m
p
l
e
b
y
b
a
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k
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g
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r
g
a
n
i
z
a
t
i
o
n
a
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s
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r
u
c
t
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V
a
r
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a
b
l
e
A
l
l
n
o
n
-
f
a
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l
e
d
N
o
n
-
f
a
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e
d
d
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n
o
v
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s
N
o
n
-
f
a
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d
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t
a
b
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h
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d
N
o
n
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f
a
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d
d
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n
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f
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t
a
b
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d
N
o
n
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f
a
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d
N
B
H
C
N
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n
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f
a
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d
S
B
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C
N
o
n
-
f
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d
M
B
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C
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n
-
f
a
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d
N
B
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C
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n
o
n
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f
a
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d
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B
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C
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B
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N
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f
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d
n
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t
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n
a
l
N
o
n
-
f
a
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d
s
t
a
t
e
N
o
n
-
f
a
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d
n
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t
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s
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o
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1
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P
0
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1
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2
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4
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4
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1
6
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1
6
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3
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7
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A
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T
S
3
7
9
,
7
9
1
2
2
5
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8
5
7
3
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5
,
1
3
9
2
6
.
8
*
*
*
1
4
4
,
2
1
0
3
3
8
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2
8
8
7
6
7
,
1
8
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2
2
7
0
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0
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6
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4
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1
7
9
3
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1
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C
0
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2
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7
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3
3
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0
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1
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6
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3
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7
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4
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6
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7
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G
L
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S
0
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0
9
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3
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6
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3
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1
5
0
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0
8
0
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0
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1
5
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4
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0
7
0
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1
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2
2
4
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3
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R
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0
.
6
6
0
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7
2
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6
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1
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2
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*
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0
.
6
4
0
.
6
6
0
.
6
5
1
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9
0
.
6
5
0
.
6
6
2
1
.
6
N
P
L
0
.
0
0
6
0
.
0
0
4
0
.
0
0
6
2
2
3
8
.
4
*
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0
.
0
0
6
0
.
0
0
6
0
.
0
0
5
4
6
.
1
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*
*
0
.
0
0
6
0
.
0
0
6
2
1
.
0
N
I
I
T
I
0
.
1
1
0
.
0
7
0
.
1
1
2
3
7
3
.
5
*
*
*
0
.
0
9
0
.
1
1
0
.
1
2
2
1
8
3
.
3
*
*
*
0
.
1
2
0
.
1
0
7
5
.
2
*
*
*
S
I
Z
E
1
1
.
7
8
1
1
.
6
3
1
1
.
8
0
2
6
.
8
*
*
*
1
1
.
2
2
1
1
.
8
4
1
2
.
1
0
2
2
7
1
.
0
*
*
*
1
2
.
0
7
1
1
.
6
9
8
8
.
1
*
*
*
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
5
,
9
1
2
5
3
6
5
,
3
7
6
9
8
1
3
,
9
1
5
1
,
0
1
6
1
,
3
7
3
4
,
5
3
9
S
u
r
v
i
v
e
r
a
t
e
(
%
)
9
4
.
8
8
5
.
5
9
5
.
8
9
4
.
8
9
4
.
4
9
6
.
3
9
5
.
8
9
4
.
5
N
o
t
e
s
:
M
e
a
n
s
a
r
e
s
i
g
n
i
?
c
a
n
t
l
y
d
i
f
f
e
r
e
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
m
i
n
u
s
s
i
g
n
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
m
e
a
n
o
f
t
h
e
s
e
c
o
n
d
c
a
t
e
g
o
r
y
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
m
e
a
n
o
f
t
h
e
?
r
s
t
c
a
t
e
g
o
r
y
,
e
x
c
e
p
t
f
o
r
N
B
H
C
,
S
B
H
C
v
s
M
B
H
C
;
t
h
e
m
i
n
u
s
s
i
g
n
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
m
e
a
n
o
f
M
B
H
C
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
m
e
a
n
o
f
S
B
H
C
;
n
o
t
e
t
h
a
t
t
h
e
s
e
c
o
m
p
a
r
i
s
o
n
s
d
o
n
o
t
c
o
n
t
r
o
l
f
o
r
d
i
f
f
e
r
e
n
c
e
s
i
n
o
t
h
e
r
f
a
c
t
o
r
s
Table III.
JFEP
5,3
292
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
a
n
e
l
A
:
s
a
m
p
l
e
b
y
b
a
n
k
i
n
g
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
V
a
r
i
a
b
l
e
A
l
l
s
a
m
p
l
e
D
e
n
o
v
o
s
E
s
t
a
b
l
i
s
h
e
d
N
B
H
C
S
B
H
C
M
B
H
C
N
a
t
i
o
n
a
l
S
t
a
t
e
N
o
n
-
T
B
T
F
I
n
t
e
r
c
e
p
t
2
8
.
7
1
*
*
*
2
1
0
.
1
9
*
*
*
2
7
.
5
5
*
*
*
2
6
.
6
3
*
*
2
7
.
6
4
*
*
*
2
1
2
.
8
0
*
*
*
2
6
.
2
3
*
*
*
2
9
.
6
3
*
*
*
2
8
.
7
7
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
T
I
E
R
1
C
A
P
2
4
.
0
3
*
*
*
1
.
5
4
2
1
0
.
9
3
*
*
*
2
1
.
7
9
2
9
.
9
7
*
*
*
2
3
.
5
3
2
6
.
6
8
*
2
3
.
3
3
*
*
2
4
.
0
0
*
*
*
(
0
.
0
1
)
(
0
.
4
1
)
(
0
.
0
0
)
(
0
.
4
0
)
(
0
.
0
0
)
(
0
.
3
8
)
(
0
.
0
7
)
(
0
.
0
5
)
(
0
.
0
1
)
S
I
Z
E
0
.
2
7
*
*
*
0
.
3
3
*
*
0
.
2
3
*
*
*
2
0
.
0
1
0
.
3
0
*
*
*
0
.
2
4
*
0
.
1
5
0
.
3
3
*
*
*
0
.
2
8
*
*
*
(
0
.
0
0
)
(
0
.
0
4
)
(
0
.
0
0
)
(
0
.
9
6
)
(
0
.
0
0
)
(
0
.
0
8
)
(
0
.
2
0
)
(
0
.
0
0
)
(
0
.
0
0
)
C
S
E
C
2
3
.
2
3
*
*
*
2
2
.
8
2
*
*
*
2
2
.
6
9
*
*
*
2
3
.
0
8
*
*
*
2
3
.
1
4
*
*
*
2
2
.
0
0
2
2
.
9
4
*
*
2
3
.
2
8
*
*
*
2
3
.
2
5
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
1
9
)
(
0
.
0
1
)
(
0
.
0
0
)
(
0
.
0
0
)
B
R
O
K
D
E
P
3
.
2
9
*
*
*
1
.
3
9
*
4
.
4
4
*
*
*
0
.
6
1
4
.
0
4
*
*
*
3
.
5
0
*
*
2
.
9
9
*
*
3
.
3
3
*
*
*
3
.
2
9
*
*
*
(
0
.
0
0
)
(
0
.
1
0
)
(
0
.
0
0
)
(
0
.
6
2
)
(
0
.
0
0
)
(
0
.
0
2
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
G
L
O
A
N
S
1
.
4
8
*
*
*
0
.
4
7
1
.
7
1
*
*
*
2
.
0
0
*
*
*
1
.
0
9
*
*
*
1
.
6
2
*
*
*
1
.
1
4
*
1
.
5
8
*
*
*
1
.
4
9
*
*
*
(
0
.
0
0
)
(
0
.
2
2
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
1
)
(
0
.
0
7
)
(
0
.
0
0
)
(
0
.
0
0
)
R
E
S
T
A
T
E
4
.
7
6
*
*
*
5
.
9
0
*
*
*
4
.
5
9
*
*
*
5
.
7
6
*
*
*
4
.
1
2
*
*
*
9
.
0
8
*
*
*
4
.
0
6
*
*
*
4
.
9
6
*
*
*
4
.
7
2
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
N
P
L
3
9
.
9
9
*
*
*
5
2
.
1
4
*
*
*
3
6
.
8
2
*
*
*
4
9
.
6
4
*
*
*
3
7
.
1
2
*
*
*
3
3
.
9
9
3
1
.
0
0
*
*
4
3
.
9
3
*
*
*
4
0
.
0
2
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
0
0
)
(
0
.
1
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
N
I
I
T
I
2
5
.
6
3
*
*
*
2
6
.
8
4
*
*
2
4
.
1
8
*
*
*
2
5
.
2
6
*
2
6
.
4
1
*
*
*
2
2
.
1
2
2
5
.
3
4
*
*
2
5
.
8
4
*
*
*
2
5
.
5
4
*
*
*
(
0
.
0
0
)
(
0
.
0
2
)
(
0
.
0
0
)
(
0
.
0
7
)
(
0
.
0
0
)
(
0
.
4
6
)
(
0
.
0
3
)
(
0
.
0
0
)
(
0
.
0
0
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
7
0
2
2
1
4
2
7
3
2
2
1
6
0
2
6
6
9
2
1
2
1
2
2
0
2
2
7
6
6
2
9
7
0
P
s
e
u
d
o
-
R
2
0
.
2
4
0
.
1
7
0
.
2
5
0
.
2
4
0
.
2
5
0
.
2
7
0
.
1
9
0
.
2
5
0
.
2
4
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
5
.
4
7
9
.
8
8
6
.
2
8
7
.
1
8
5
.
7
8
6
.
9
8
2
.
3
8
6
.
1
8
5
.
4
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
2
7
5
,
6
0
9
1
,
0
3
5
4
,
1
4
6
1
,
0
5
5
1
,
4
3
3
4
,
8
0
3
6
,
2
1
4
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
1
4
.
5
4
.
2
5
.
2
5
.
6
3
.
7
4
.
2
5
.
5
5
.
2
P
a
n
e
l
B
:
f
u
l
l
s
a
m
p
l
e
i
n
t
e
r
a
c
t
i
n
g
w
i
t
h
b
a
n
k
o
r
g
a
n
i
z
a
t
i
o
n
a
l
s
t
r
u
c
t
u
r
e
d
u
m
m
y
v
a
r
i
a
b
l
e
s
V
a
r
i
a
b
l
e
1
2
3
4
5
6
7
8
9
D
e
n
o
v
o
s
0
.
4
6
*
*
0
.
5
*
*
*
0
.
4
6
*
*
(
0
.
0
1
)
(
0
.
0
1
)
(
0
.
0
1
)
N
B
H
C
0
.
0
6
(
0
.
7
5
)
S
B
H
C
0
.
2
4
*
0
.
2
8
*
*
(
0
.
0
9
)
(
0
.
0
5
)
M
B
H
C
2
0
.
4
5
*
*
2
0
.
4
5
*
*
(
0
.
0
2
)
(
0
.
0
2
)
(
c
o
n
t
i
n
u
e
d
)
Table IV.
Logit regression results
Bank structure
and failure
293
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
N
A
T
I
O
N
A
L
0
.
0
0
(
0
.
9
9
)
T
B
T
F
2
1
2
.
0
2
(
0
.
9
8
)
P
U
B
L
I
C
0
.
0
5
(
0
.
8
3
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
6
7
2
9
7
0
2
9
6
9
2
9
6
7
2
9
7
0
2
9
7
0
2
9
7
0
2
9
6
5
2
9
6
4
P
s
e
u
d
o
-
R
2
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
0
.
2
4
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
5
.
5
8
5
.
4
8
5
.
5
8
5
.
6
8
5
.
4
8
5
.
4
8
5
.
4
8
5
.
6
8
5
.
6
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
6
,
2
3
6
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
5
.
2
P
a
n
e
l
C
:
f
u
l
l
s
a
m
p
l
e
i
n
t
e
r
a
c
t
i
n
g
w
i
t
h
e
c
o
n
o
m
i
c
a
n
d
r
e
g
u
l
a
t
o
r
y
e
n
v
i
r
o
n
m
e
n
t
v
a
r
i
a
b
l
e
s
V
a
r
i
a
b
l
e
1
2
3
4
5
6
7
8
9
H
P
0
3
0
7
0
.
0
2
*
*
*
(
0
.
0
0
)
H
P
0
7
0
9
2
0
.
0
5
*
*
*
(
0
.
0
0
)
P
i
n
c
o
m
e
0
6
0
9
2
0
.
1
1
*
*
*
2
0
.
1
1
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
L
i
m
i
t
2
0
.
2
7
*
*
2
0
.
3
2
*
*
(
0
.
0
4
)
(
0
.
0
2
)
M
E
T
R
O
0
.
7
2
*
*
*
0
.
6
6
*
*
*
(
0
.
0
0
)
(
0
.
0
0
)
D
e
l
i
n
q
u
e
n
c
y
0
.
0
1
*
*
*
(
0
.
0
0
)
S
u
b
p
r
i
m
e
0
3
0
6
0
.
2
3
*
*
*
(
0
.
0
0
)
C
P
P
2
2
.
0
2
*
*
*
(
0
.
0
0
)
L
o
g
-
l
i
k
e
l
i
h
o
o
d
2
9
5
0
2
9
3
7
2
9
5
0
2
9
6
8
2
9
6
0
2
9
4
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2
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9
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v
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t
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s
6
,
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3
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2
3
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2
3
6
6
,
2
3
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6
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2
3
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,
2
3
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6
,
2
3
6
F
a
i
l
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r
e
r
a
t
e
(
%
)
5
.
2
5
.
2
5
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5
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2
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:
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k
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V
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A
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p
l
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D
e
n
o
v
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E
s
t
a
b
l
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h
e
d
N
B
H
C
S
B
H
C
M
B
H
C
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a
t
i
o
n
a
l
S
t
a
t
e
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T
B
T
F
I
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e
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t
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1
)
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0
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0
0
)
(
c
o
n
t
i
n
u
e
d
)
Table IV.
JFEP
5,3
294
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
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N
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V
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R
S
I
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Y
A
t
2
1
:
4
7
2
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J
a
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r
y
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(
P
T
)
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i
m
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t
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)
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4
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0
0
)
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u
b
p
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m
e
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C
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0
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0
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0
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0
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0
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9
8
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9
5
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0
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0
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L
o
g
-
l
i
k
e
l
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h
o
o
d
2
9
0
4
2
1
9
2
2
6
8
1
2
1
5
2
2
6
1
7
2
1
0
5
2
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8
5
2
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3
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9
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3
P
s
e
u
d
o
-
R
2
0
.
2
9
0
.
2
6
0
.
3
0
0
.
2
8
0
.
3
1
0
.
3
7
0
.
2
6
0
.
3
0
0
.
2
9
P
e
r
c
e
n
t
c
o
n
c
o
r
d
a
n
t
8
8
8
5
.
1
8
8
.
4
8
8
.
7
8
8
.
6
9
1
.
3
8
4
.
5
8
8
.
6
8
8
N
o
.
o
f
o
b
s
e
r
v
a
t
i
o
n
s
6
,
2
3
6
6
2
7
5
,
6
0
9
1
,
0
3
5
4
,
1
4
6
1
,
0
5
5
1
,
4
3
3
4
,
8
0
3
6
,
2
1
4
F
a
i
l
u
r
e
r
a
t
e
(
%
)
5
.
2
1
4
.
5
4
.
2
5
.
2
5
.
6
3
.
7
4
.
2
5
.
5
5
.
2
N
o
t
e
s
:
S
i
g
n
i
?
c
a
n
t
a
t
:
*
1
0
,
*
*
5
a
n
d
*
*
*
1
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
p
-
v
a
l
u
e
s
a
r
e
e
n
c
l
o
s
e
d
i
n
p
a
r
e
n
t
h
e
s
e
s
a
n
d
b
e
l
o
w
t
h
e
c
o
e
f
?
c
i
e
n
t
s
Table IV.
Bank structure
and failure
295
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
7
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
regressions; the lack of signi?cance for Tier 1 capital, liquidity measure, nonperforming
loan, and income diversity measure in the multibank holding company regression; and
the lack of signi?cance for size in the national bank regressions. Importantly, this
indicates that the determinants of bank failure are not the same for the subsamples
based on the organizational structure categories. For example, size is not an important
determinant of failure for those banks that have relatively little variation in size within
the subsample. Speci?cally, independent banks tend to be relatively small and national
banks tend to be relatively large. Furthermore, a bank as an af?liate in a multibank
holding company may be able to draw on the resources of the holding company to meet
its liquidity needs and has other potential sources of diversi?cation.
Furthermore, Panel B of Table IV incorporates structure dummy variables that
capture the impact of de novo status, holding company structure, and charter type on
the likelihood of failure while controlling for the ?nancial variables in Panel A[9].
As expected, de novo banks are more likely to fail than established banks, consistent
with these banks being ?nancially fragile (Model 1). Interestingly, banks that are
part of a single-bank holding company are more likely to fail (Model 3) than other
banks, while banks that are part of a multibank holding company are less likely to fail
(Model 4)[10]. This result holds even after controlling for de novo status (Models 8
and 9). Charter type (Model 5) and banks being publicly traded (Model 7), however,
seem to have little direct impact on the likelihood of bank failure.
Panel C of Table IV includes economic and regulatory environment variables[11]. Our
analysis accounting for economic variables ?nds results with the expected. For example,
consistent with the housing bubble playing a major role in bank failures, changes in home
prices between 2003 and 2007 and growth insubprime lending between 2003 and 2006 ina
given state are positively related to the incidence of failure for banks in that state.
Subsequent changes in home prices between 2007 and 2009 are negatively related to the
likelihood of failure and increases in delinquency rates between 2007 and 2009 in a bank’s
state are positivelyrelated to the likelihoodof failure. Changes ina state’s personal income
levels between 2006 and 2009 are also negatively related to the likelihood of failure. Being
located in a metropolitan area is positively associated with the likelihood of failure.
Interestingly, limits on de novo branching by out-of-state banks are associated with
a reduced likelihood of failure. This suggests that limits on out-of-state competition
enhance the ?nancial stability of a state’s banks[12]. Ironically, the Dodd-Frank Act
removes the restrictions on de novo interstate banking. Combined with the ?ndings of
DeYoung (2003) and Hunter et al. (1996) that de novo bank failure is more sensitive to
adverse environment conditions, this deregulation legislation may contribute to a
higher likelihood of de novo bank failure. As expected, governmental capital injection
via the CPP has a signi?cantly negative in?uence on failure[13].
Finally, Panel D of Table IV shows logit regression results for the whole sample and
eight subset samples and includes the economic and regulatory environment variables that
pre-date the ?nancial crisis (HP0307 andSubprime0306), Limit, METRO, andCPP. Overall,
most of these results are consistent with those shown in Panel Aof Table IV. Nevertheless,
after controlling for other economic variables, the coef?cients associated with brokered
deposits no longer exhibit statistical signi?cance for de novobanks andindependent banks.
Interestingly, a majority of economic and regulatory environment variables have not
shown statistical signi?cance in explaining bank failure among sub sample independent
banks and multibank holding company banks, suggesting local economic conditions may
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be subsumed in the balance sheet among those banks included in our sample. Note that the
coef?cients on ?nancial variables and economic and regulatory environment variables
remain robust when we exclude the largest banks (Non-TBTF).
5. Summary and conclusions
This paper examines the impact of different banking organizational structures and
measures of institutional fragility on the likelihood of a bank failing during the late-2000s
?nancial crisis. As expected, the results indicate that the likelihood of failure was affected
by bank structure. Such factors as capitalization, asset structure, funding strategy, loan
portfolio composition, asset quality, and income structure affected the likelihood of failure
and these underlying bank characteristics differed by holding company structure, charter
type, and degree of fragility (de novo banks and high loan-growth banks).
De novo banks and banks that are part of a single-bank holding company are more
likely to fail while banks that are part of a multibank holding company are less likely
to fail. This is consistent with the argument that the multibank holding company
structure can act as a source for strength for subsidiary banks. However, charter type
and being publicly traded seem to have had little direct impact on the likelihood of
bank failure, suggesting that their impact on the likelihood of failure, if any, is due to
their in?uence over a bank’s operations.
Consistent with being ?nancially fragile, our results suggest that de novo banks and
those banks that grew substantially prior to the crisis faced an increased likelihood of
failure relative to established banks. Overall, established institutions were more likely
to fail if they had relatively low capital ratios, were relatively large, had relatively low
liquidity, relied on brokered deposits, held a large portfolio of real estate loans, had a
relatively large proportion of nonperforming loans, and less income diversity.
Interestingly, capital levels do not play a key on the likelihood of failure in de novo
banks, independent banks and banks that are part of a multibank holding company.
This suggests that the economic shock of the ?nancial crisis combined with being
inherently ?nancially fragile caused many de novo banks to fail in spite of them having
capital requirements that far exceed the requirements for established banks.
From a banking regulation perspective, our ?ndings shed light on those factors that
may enhance the stability of the banking systemand those that may weaken the system.
Speci?cally, our results provide support for the notion that the multibank holding
company structure does reduce the likelihood of failure. The Dodd-Frank Act’s removal
of restrictions on de novo interstate branching, on the other hand, may increase the
likelihood of future bank failures by encouraging more intense competition.
Notes
1. In response to the large number of de novo bank failures, the FDIC extended the enhanced
capital standards for de novo banks from three years to seven years in 2008.
Newly chartered institutions are subject to higher capital requirements, more frequent
examinations, and prior approval of business plan changes during their ?rst seven years of
operation.
2. Demirgu¨c¸-Kunt (1989) points out that failure is a regulatory decision and should be modeled
formally as the outcome of a regulatory decision-making process.
3. See Rezende (2011) for a detailed study on the determinants as to whether federal and state
supervisors examine state banks independently or together.
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4. On the Federal Reserve Bank of Chicago web site, a de novo bank is de?ned as banks in
operation for ?ve years or less. In our sample, all of the established banks had been in
operation for at least six years as of the end of 2006.
5. The PUBLIC variable data is from Center for Research in Security Prices Sift Enterprises and
the National Information Center. The economic environment variable data is from Federal
Housing Finance Agency, Bureau of Economic Analysis, FDIC Summary of Deposits, and
Mortgage Bankers Association. The LIMIT variable data is from Rice and Strahan (2010).
6. We consider alternative measures of bank capital, but do not ?nd support for using other
measures of capital over Tier 1 capital in our empirical analysis.
7. We also implement nonparametric tests showing that all of the ?nancial variables are
signi?cantly different in means at the 1 percent level between failed and non-failed banks for
the whole sample and by organizational structure, except for differences in nonperforming
loan between failed and non-failed independent banks and banks with af?liates with
multibank holding companies. These results are available from the authors upon request.
8. When we estimate separate regressions by bank size, the overall results tend to be driven by
larger banks. In fact, the failure rate increases monotonically with size and, consequently, many
of the variables that are signi?cant inthe overall regressionare not signi?cant whenthe analysis
is restricted to the smallest banks. Interestingly, the SIZE variable is only signi?cant in the
75th-90th percent size quintile. These results are available from the authors upon request.
9. Note that we omit the coef?cients on ?nancial variables in Panels B and C of Table IV.
All ?nancial variables show statistical signi?cance and exhibit the same signs as in Panel A.
10. This suggests that holding companies act as a source of strength only when there are
multiple banks within the holding company structure, consistent with the ?ndings of both
Ashcraft (2008) and DeYoung and Torna (2012).
11. There is an ongoing debate regarding whether economic conditions that are subsumed in the
balance sheet play a signi?cant role in bank failures. DeYoung and Torna (2012) ?nd that
GDP and home price growth play a signi?cant role in recent bank failures. However,
Berger et al. (2012) suggest that subprime mortgage risks, housing price in?ation, and GDP
do not play any signi?cant role in predicting bank defaults.
12. The Riegle-Neal Interstate Banking and Branching Ef?ciency Act of 1994 removed federal
restrictions on interstate branching. This deregulation encourages banks to diversify
geographically and reduces their vulnerability to local economic shocks.
13. The CPPdummyvariable is includedhere to allowfor the impact of governmental interventionon
the reduced likelihood of failure for a bank. Given the endogenous nature of the bailout and failure
decisions by regulators, we also estimate the likelihood of bank failure and the likelihood of a
bank receiving CPP funds in a multivariate setting using a bivariate probit model. The coef?cient
estimates in the failure portion of the model are consistent with the results shown in Table IV.
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About the authors
Wenling Lu is a PhD candidate in Finance at Washington State University.
Dr David A. Whidbee is the Omer L. Carey Chair in Financial Education and Associate Dean
for Faculty Affairs and Research in the College of Business at Washington State University.
David A. Whidbee is the corresponding author and can be contacted at: [email protected]
Bank structure
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This article has been cited by:
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