Download How do insured deposits affect bank stability

How do insured deposits affect bank stability?
Evidence from the 2008 Emergency Economic Stabilization Act∗
Claudia Lambert†, Felix Noth and Ulrich Schüwer‡
preliminary version June 4, 2013
Abstract
This paper tests whether an increase in the amount of insured deposits causes a bank
to become more risky. We use variation introduced by the U.S. Emergency Economic
Stabilization Act in October 2008, which increased the deposit insurance coverage from
$100,000 to $250,000 per depositor. For some U.S. banks, this event significantly increased
the amount of insured deposits. For other U.S. banks, it had only a minor effect. Our
analysis shows that an increase in the amount of insured deposits induces the affected
banks to become more risky relative to the unaffected banks. In particular, the affected
banks increase their investments in risky loans. To our knowledge, this is the first study
that provides causal within-country evidence on the effect of insured deposits on bank
stability for a large economy.
Keywords: financial crisis, deposit insurance, bank regulation
JEL Classification: G21, G28
∗
We would like to thank Jan Pieter Krahnen, Karen K. Lewis, Isabel Schnabel, Falko Fecht, Simon Kwan,
the participants of the 2013 Financial Intermediation Research Society Conference in Dubrovnik, the 2012
Research Seminar in Riezlern and the 2013 Research Workshop in Financial Economies in Mainz for valuable
comments and suggestions. Any remaining errors are, of course, our own.
†
DIW Berlin, German Institute for Economic Research, Mohrenstr. 58, 10117 Berlin; Germany
‡
Goethe University Frankfurt, Department of Finance, Grüneburgplatz 1, 60323 Frankfurt am Main, Germany, E-mail: [email protected], [email protected], [email protected].
1
Introduction
The deposit insurance is a corner stone of many banking systems worldwide because it helps
to protect small savers and to prevent bank runs. However, it also gives banks incentives for
excessive risk-taking because, first, it weakens the market discipline carried out by creditors,
and second, mispricing of the deposit insurance premium, which come from the regulators’
limited ability to assess risks and to charge risk-adjusted premiums, make higher risk-takings
attractive for shareholders. Empirical evidence from cross-country studies indicates that
deposit insurance tends to increase the likelihood of banking crises (e.g., Demirgüç-Kunt
and Detragiache, 2002).1 Whether the deposit insurance also had an impact on banks’ risk
taking during the recent financial crisis has not been explored yet. An effect of the deposit
insurance may be complementary to other important aspects of the financial crisis that have
been explored by the literature, such as the effect of bank CEO incentives (Fahlenbrach and
Stulz, 2011) and government bailout policies on banks’ risk taking (Gropp et al., 2011; Dam
and Koetter, 2012; Black and Hazelwood, 2012).
This paper explores how a bank’s amount of insured deposits affects its stability and lending
decisions during the recent financial crisis. The main challenge for such an empirical test
is an identification problem. The banks with more insured deposits might be the ones that
take more risks, or, the more risky banks might be the ones that make more efforts to
attract deposits and thus have more insured deposits. To circumvent this problem, we use
variation introduced by the U.S. Emergency Economic Stabilization Act in October 2008,
which increased the deposit insurance coverage from $100,000 to $250,000 per depositor.
This change increased the total sum of insured deposits in the U.S. from roughly $4,800
billion to roughly $5,300 billion. Importantly for our identification strategy, banks were
affected differently. For some banks, this event significantly increased the amount of insured
deposits (“affected banks”). For other banks, it only had a minor effect (“unaffected banks”).
Using the affected banks as the treatment group and the unaffected banks as the control
group, we employ a difference-in-difference estimation technique conditional on propensity
1
On the contrary, Gropp and Vesala (2004) find in a cross-country study for 15 European countries that
explicit deposit insurance may increase bank stability because of market monitoring through non-deposit creditors. As depicted by Morrison and White (2011, p. 3400), “our understanding of the design and consequences
of deposit insurance schemes is in its infancy”.
2
score matching. Through the matching procedure we make sure that both groups of banks
are similar before the event and thereby that our results do not reflect systematic differences
in certain parameters between both groups.
Our empirical analysis shows that an increase in the amount of insured deposits causes the
affected banks to become more risky relative to the unaffected banks. This is reflected in
estimations for a variety of risk measures, including banks’ predicted probabilities of default,
banks’ z-scores2 , and loan loss provisions. Further, our analysis shows that affected banks’
increase their investments in risky commercial real estate loans and consumer loans, which
may explain lower asset quality and higher bank risk, relative to unaffected banks. Thereby,
the study provides empirical evidence on unintended long-term consequences of an important
and controversial part of bank regulation.
Figure 1 presents illustrative evidence. The upper left graph shows the number of failures for
affected banks and unaffected banks in the period following the introduction of the Emergency
Economic Stabilization Act.3 We observe that bank failures occur more often in the group of
affected banks than in the group of unaffected banks. The upper right graph shows predicted
probabilities of default that we estimate with a linear probability model. Here, affected banks
show on average higher predicted probabilities of default than unaffected banks following the
introduction of the act in the fourth quarter of 2008. The lower left graph shows banks’
z-scores, which are relatively lower for affected banks after the event (indicating lower bank
stability). Finally, the lower right graph shows banks’ loan loss provisions over assets, which
are relatively higher for affected banks after the event. In summary, the graphs of Figure 1
provide a first indication that affected banks become riskier relative to the unaffected banks
following the increase of insured deposits as induced by the Emergency Economic Stabilization
Act.
2
We use banks’ z-scores as a measure for bank risk following Laeven and Levine (2009). The z-score is
defined as the sum of a bank’s return on assets and its equity to assets ratio, standardized by the volatility of
the bank’s return on assets. It thus measures a bank’s ability to absorb losses by its equity. A lower z-score
indicates lower bank stability and accordingly higher bank risk.
3
As we explain later in more detail, the change in insured deposits that became effective in the fourth
quarter of 2008 only became visible in the banks’ balance sheets in the third quarter of 2009. Therefore, we
can only report bank failures for banks classified as affected or unaffected after the third quarter of 2009.
3
0
0
5
.002
10
.004
15
20
.006
25
.008
Figure 1: Illustrative evidence
2009
2010
2011
affected
2002q4
2012
2004q4
unaffected
2006q4
affected
2010q4
(b) Predicted probability of default
0
3.2
3.4
.005
3.6
3.8
.01
4
4.2
.015
(a) Number of bank failures
2008q4
unaffected
2002q4
2004q4
2006q4
affected
2008q4
2010q4
2002q4
unaffected
2004q4
2006q4
affected
(c) z-score
2008q4
2010q4
unaffected
(d) Loan loss provisions/ assets
The upper left graph shows the number of bank failures per year for the group of affected banks and the
group of unaffected banks (each group comprises a total of 1.103 banks). Note that the 2009 bank failures
only include the fourth quarter of 2009, and the bank failures in 2012 only include the first, second and third
quarter of 2012. The upper right graph shows banks’ predicted probabilities of defaults. The mean values
for affected banks and unaffected banks are represented by a solid and dotted line, respectively. The lower
left graph shows banks’ z-scores, where a high value represents a more stable bank. The lower right graph
shows banks’ loan loss provisions over assets. The three latter graphs cover the period from the second quarter
of 2001 to the second quarter of 2011. The two vertical lines surround the fourth quarter of 2008 in which
the U.S. Emergency Economic Stabilization Act was introduced. The dotted line depicts the previous quarter
when banks may have begun to anticipate the adoption of the law.
4
The contribution of our study to the literature is twofold. First, we add to the large literature
that examines the effect of insured deposits on bank stability.4 Important theoretical contributions include Merton (1977) and Diamond and Dybvig (1986).5 The existing empirical
literature typically conducts cross-country studies to examine how the existence of explicit
deposit insurance affects the stability of a country’s banking system (e.g., Demirgüç-Kunt and
Detragiache, 2002; Gropp and Vesala, 2004). To our knowledge, our study is the first empirical
assessment of the relation between deposit insurance and bank stability that provides withincountry evidence for a large economy using bank-level data.6 We are able to restrict our study
to U.S. banks because the legislative event did not affect all banks equally in practice. This
allows us to examine how affected banks change their risk-taking and lending decisions within
a homogeneous macroeconomic area. Both perspectives, the cross-country perspective from
the existing literature and the within-country perspective in our study, provide important
insights and complement each other.
Second, we demonstrate that the increase in insured deposits during the 2008 financial crisis,
which may have had beneficial short-term effects, has long-term effects on banks’ risk-taking
incentives and can amplify financial system distortions. This finding adds to the long-standing
as well as the more recent assessments of several researchers that the U.S. deposit insurance
needs to be changed (e.g., Berlin et al., 1991; Pennacchi, 2006, 2009).
The paper proceeds as follows. Section 3 describes the U.S. Emergency Economic Stabilization
Act of October 2008 as regards its consequences for banks’ insured deposits. Section 4 presents
our identification strategy, the data and summary statistics. Section 5 explains the empirical
model and shows the estimation results. Section 6 analyzes whether risk taking of banks
differs for banks with high or low capital levels subject to state guarantees or not after the
change in the deposit insurance scheme. Finally, Section 7 concludes. All tables appear in
the appendix.
4
For an overview, see, for example, Demirgüç-Kunt et al. (2008).
More broadly related to the effects of deposit insurance are the seminal papers by Diamond (1984) and
Gorton and Pennacchi (1990) that study the function of financial institutions to create liquid assets for uninformed customers. In a recent paper, Morrison and White (2011) argue that socially too few deposits are
made in equilibrium because of market failures, and they find in their set-up that deposit insurance should be
funded out of general taxation to improve welfare.
6
Related to our study is Wheelock (1992) who examines the voluntary membership in the Kansas state
insurance system and its consequences for bank failures in the 1920s.
5
5
2
The Emergency Economic Stabilization Act of October 2008
3
The Emergency Economic Stabilization Act of October 2008
The Emergency Economic Stabilization Act was a reaction to the financial crisis of 2007 and
2008, which is considered the worst such crisis since the Great Depression. It was signed into
law on October 3, 2008. The rationale of the act was to restore consumer confidence in the
banking system and financial markets.
The central item of the act is the Troubled Asset Relief Program (TARP) that allowed the
Treasury Department to spend $700 billion to support troubled institutions (Shah, 2009).7
The program then enacted the Capital Purchase Program (CPP), which enabled the government to purchase equity directly from troubled institutions. The act also includes further
stabilizing actions, e.g., allowing the FED to pay higher interest rates on banks’ deposits held
as reserve requirements, or foreclosure avoidance and homeowner assistance with regard to
mortgage payments (see, e.g., Nothwehr, 2008).
Importantly for our study, Section 136 of the Emergency Economic Stabilization Act provided
a piece of reform that affected deposit insurance: It raised insured deposits per account from
$100,000 to $250,000, effective as of October 3, 2008. Though initially temporarily until
December 31, 2009, the increase of insured deposits was prolonged through January 1, 2014
on May 20, 2009 by the Helping Families Save Their Homes Act. Finally, the introduction
of the Dodd-Frank Act, signed into law on July 21, 2010, made the temporary increase in
insured deposits permanent.
4
Identification strategy and empirical model
To assess how an increase in insured deposits affects banks’ risk takings, we have to consider
potential parallel macroeconomic and industry-wide factors that affect all banks, independent
of the regulatory change. It would be misleading to simply test how (or whether) banks adapt
7
The amount was reduced to $475 billion with the Dodd-Franck Act of 2010.
6
their risk takings after the regulatory change. Rather, we need to explore how affected banks
adapt their risk taking relative to the risk taking of a counter-factual that is similar to the
affected banks. We therefore construct a group that includes the affected banks, i.e., the
treatment group, and a group that includes banks representing the counter-factual behavior,
i.e., the control group. The identification of the treatment group and the control group is
the main challenge of our study. In particular, we need to make sure that banks in both
groups are similar before the event. We proceed in using a difference-in-difference estimation
technique with time and bank fixed effects.
Our identification strategy is based on variation introduced by the regulatory change and as
such the intensity of banks being effectively affected by the change in the deposit insurance
scheme initiated through the Emergency Economic Stabilization Act of October 2008. In
particular, we observe that some banks reported relatively high increases of their insured
deposits following the regulatory change, while other banks reported relatively low increases.
We classify the former banks as affected banks, which form our preliminary treatment group,
and we classify the latter banks as unaffected banks, which form our preliminary control
group.
Important for our identification strategy, we have to consider that the external variation which
determines the selection of banks into both groups is not purely random. The affected banks
are those with relatively many deposit accounts above $100,000, while the unaffected banks
are those with relatively few such accounts. We need to make sure that this difference between
banks in both groups does not create a selection bias for our estimation results. In principle,
the following could be relevant for our estimations: First, the difference between banks may be
uncorrelated to the dependent variables we are interested in. In this case, there is no problem
and we get unbiased estimation results. Second, the difference between banks may have a
static level effect on the dependent variables we are interested in. For example, banks in the
treatment group and control group have different business models, such that banks in the
treatment group are systematically more likely to fail by certain percentage points relative
to banks in the control group. In this case, a difference-in-difference estimation technique
also provides unbiased estimation results because it explores how differences between both
groups change post an event, not the different levels itself. Furthermore, bank fixed effects in
7
our estimation account for potential static differences between banks. Third, the difference
between banks may have a dynamic effect on the dependent variables we are interested in. For
example, banks in the treatment group are systematically more risk loving during recessions
and more risk averse during booms relative to banks in the control group. In this case, we
are limited in controlling for this bias in our estimation model. Therefore, we need to rule
out that this problem exists by restricting our sample to banks in the treatment group and
banks in the control group that are similar over the business cycle. Accordingly, we reduce
our treatment group and control group to a matched sample of affected banks and unaffected
banks that show similar characteristics over many variables and over time.
In the following of this section, we first describe the data used in this study (Subsection 4.1).
We then describe in more detail our definition of affected and unaffected banks (4.2). Next,
we explain how we construct a matched sample of banks (4.3). The subsequent subsections
provide the summary statistics (4.4) and further evidence on the similarity of treatment and
control group over the business cycle (4.5).
4.1
Data
Our data comes from different sources of the FDIC as well as the U.S. Department of the
Treasury. First, we use quarterly data on U.S. commercial banks from the FDIC Statistics on Depository Institutions, which includes detailed balance sheet, income statement and
deposits data for all FDIC-insured banks in the United States since 1993. Note that the
data refer to individual FDIC-insured institutions, which may be part of larger bank holding
companies. Second, we use information on bank failures in the period 1993 to 2012 from the
FDIC Failed Bank List to estimate probabilities of default. Finally, we use data about which
banks received TARP from the TARP Investment Program Transaction Reports, which are
available on the website of the U.S. Department of the Treasury.
Our main analyses cover the period up to ±10 quarters around the introduction of the Emergency Economic Stabilization Act in Q4 2008, i.e., the period from Q2 2006 to Q2 2011. In
Q4 2008 our preliminary sample consists of 8,463 commercial banks. Previous studies that
also used the FDIC data found that some of the data is erroneous or includes banks that
8
are not “viable”. Therefore, we follow Berger and Bouwman (2009) and exclude banks that
(1) have no commercial real estate or commercial and industry loans outstanding, (2) have
zero or negative equity capital, (3) hold assets below $25 million or (4) hold consumer loans
exceeding 50% of gross total assets.8 Accordingly, we are left with 5,696 banks. We additionally exclude banks for which we find missing values for our variables of interest, i.e.,
TO BE ADDED. This leaves us with 5,307 banks. Furthermore, we want to exclude biases from
newly founded banks and therefore require banks’ existence two years before the Emergency
Economic Stabilization Act was enacted, which further reduces the sample to 5,305 banks.
As described in the following subsection in more detail, our identification strategy is based on
the reported change in insured deposits per customer for each bank from the second to the
third quarter of 2009. Our sample therefore only includes banks that did not fail before the
third quarter of 2009, i.e., a total of XXX banks. Further, our identification strategy
only considers banks in the top quartile (treatment group) or lowest quartile (control group)
of reported changes. This cuts our sample in half, i.e., we get a total of XXX banks.
Finally, as described in Subsection 4.3 in more detail, we apply propensity score matching
and require that banks in the treatment group and control group have very similar characteristics before the regulatory change (1% caliper). After our matching procedure, the final
data set includes 2,208 banks, of which 1,104 are classified as affected and 1,104 are classified
as unaffected.
While our main difference-in-difference estimations cover the period from Q2 2006 to Q2 2011,
we also use data of the period Q1 1993 to Q2 2012 to estimate probabilities of default for
each bank and quarter. For this extended period, our estimation includes on average 10,000
banks per quarter and a total of 767,649 bank-quarter observations.
4.2
Definition of affected and unaffected banks
We define our treatment and control group based on the change in insured deposits initiated
through the Emergency Economic Stabilization Act of 2008. The increase in insured deposits
is reflected in the reporting of FDIC insured banks, which includes the total amount of
8
Some further exclusion criteria used by Berger and Bouwman (2009) are not relevant for our sample.
9
deposits and the amount of insured deposits per bank and quarter. Note that although the
act was effective as of the fourth quarter of 2008, banks were required to continue reporting
their amount of insured deposits based on the old $100,000 amount per customer for several
quarters, and only starting with the Q3 2009 reporting period adapted the new $250,000
amount per customer for the calculation and reporting of insured deposits. Thus, we use the
reported amounts of insured and total deposits of the third quarter of 2009 to differentiate
between treatment and control group.
We construct the sample of affected and unaffected banks following two steps. First, we
calculate the change in the ratio of insured deposits over assets from Q2 to Q3 2009. Second,
we assign banks to the treatment group (“affected”) if this change is in the top quartile of the
sample. Banks that exhibit a change in the ratio of insured deposits over assets in the lowest
quartile belong to the control group (“unaffected”). Intuitively, affected banks have relatively
more customers in their portfolio with deposits exceeding $100,000 before the introduction of
the act.
.55
.7
.6
.75
.65
.8
.7
.85
.9
.75
Figure 2: Change of insured deposit ratios
2002q4
2004q4
2006q4
affected
2008q4
2010q4
2002q4
unaffected
2004q4
2006q4
affected
(a) Insured deposits/assets
2008q4
2010q4
unaffected
(b) Insured deposits/deposits
The figure shows the development of the mean values of the ratio of insured deposits to assets (a) and the ratio
of insured deposits to deposits (b) from the second quarter of 2001 to the second quarter of 2011. The two
vertical lines surround the fourth quarter of 2008 in which the Emergency Economic Stabilization Act took
place. The mean value for affected banks is represented by a solid line. The mean values for unaffected banks
is represented by a dotted line.
Fig. 2 shows the ratio of insured deposits to total assets (a), and the ratio of insured deposits to
total deposits (b) before and after the Emergency Economic Stabilization Act was introduced.
10
In each sub-figure we present separate average values for affected banks and unaffected banks.
In both sub-figures we observe that the ratio of insured deposits to assets (deposits) between
affected and unaffected banks converges as a consequence of the change in regulation. Both
sub-figures also show that our identification does not hinge on the denominator of this ratio.
By construction we see a sharp change for the affected banks relative to the group that we
consider unaffected by the event. Using total assets or deposits as the denominator only
changes the level, but not the relative impact of the event on the control and treatment
group. This also holds for our later results.
4.3
Matching estimation
To disentangle a potential selection bias from the treatment effect, we first estimate a logit
model that explains the probability that a bank is materially affected by the legislative change.
The propensity score matching technique uses bank-specific characteristics and constructs
similar samples before the event. In particular, we match banks based on key characteristics
which determine bank stability closely following Wheelock and Wilson (2000), which reflect
banks’ capital adequacy, asset quality, management, earnings and liquidity.
We estimate the model in averaging the data for the period from Q4 2006 to Q3 2008, i.e., the
two and a half years before the act was introduced.9 In particular, we estimate the following
matching equation:
Affectedi = β0 + β1 Equi + β2 Loansi + β3 RELoansi + β4 CILoansi +
β5 OREi + β6 Inci + β7 Liqui + β8 N P Li + β9 T Ai + β10 RoAi + ϵi
(1)
The explanatory variables in this equation are: total equity to total assets (Equ), total loans
to total assets (Loans), real estate loans to total loans (RELoans), commercial and industry
loans to total loans (CILoans), other real estate owned to total assets (ORE ), income earned,
not collected on loans to total assets (Inc), liquidity measured as the difference between federal
9
Note that subprime loan problems surfaced for the first time before the 2001 recession. These problems
quickly subsided during the period from 2002 to 2005, which was accompanied by a recovery of the economy.
DiMartino and Duca (2007) shows that this pattern gradually deteriorated thereafter. Given this development
we conduct the matching two years before the law was introduced.
11
funds purchased minus federal funds sold standardized by total assets (Liqu), non-performing
loans to total assets (NPL), the size represented by total assets measured in logarithmic
form (TA) and the net interest income (plus total non-interest income (RoA). We restrict the
sample to commercial banks.
For our main specification, we use the nearest neighbor matching procedure (on a one-toone basis), which selects the banks closest in terms of their propensity scores. We conduct
the analysis without replacement, which means that a neighbor can only be used once. We
require common support and impose a tolerance level of 1%, the so-called caliper. The caliper
is equivalent to choosing an individual from the comparison group as a matching partner for
a treated individual that lies within the caliper (propensity range) and is closest in terms of
propensity score.
4.4
Summary statistics
We present mean values and standard deviation for our main variables and further bank
characteristics in Table 2 for the period from Q2 2005 to Q4 2008.10 The table differentiates
between affected and unaffected banks in order to compare characteristics of both samples
before the event. Since our difference-in-difference estimation is based on a matched sample,
which uses affected banks as the treatment group and unaffected banks as the control group,
we technically insure that banks in both groups have similar characteristics.
Additionally, as suggested by Imbens and Wooldridge (2009), Table 2 also reports normalized
differences to compare both groups with respect to important bank characteristics. Normalized differences are a scale-free measure of the difference in distributions, and calculated as
“the difference in averages by treatment status, scaled by the square root of the sum of the
variances” (Imbens and Wooldridge, 2009, p. 24):
ND = √
X̄1 − X̄0
,
V AR1 + V AR0
(2)
where X̄1 and X̄0 represent the mean values of the group of affected banks and unaffected
10
See Table 10 for a description of the FDIC data that we use in our study.
12
banks, respectively, and V AR1 and V AR0 represent the respective variances. As a rule of
thumb, groups are regarded as sufficiently equal and adequate for linear regression methods
if normalized differences are basically in the range of ±0.25.11
Overall we find that both groups resemble quite similar characteristics before the event.12
Characteristics regarding size (e.g., total assets), structure of the asset side (e.g., real estate
loans, C&I loans) in both groups are all quite comparable between affected and unaffected
banks referring to normalized differences well below ±0.25. With respect to risk taking (e.g.,
probability of default, z-score, total risk-based capital, provisions and charge-offs), we observe
that both groups behave quite similar before the introduction of the Emergency Economic
Stabilization Act that changed the amount of insured deposits in Q4 2008. Normalized differences well above ±0.25 are only evident for the share of insured to deposits and assets, which
we use for identifying treatment and control group.
In summary, Table 2 shows that dividing the sample in affected and unaffected banks by the
change in insured deposits leaves us with two sub-samples that resemble similar characteristics before the event. The descriptive statistics show that our identification procedure is
appropriate and that our results are not likely to be disturbed by differences in groups.
[Table 2]
4.5
Similarity of treatment and control group over the business cycle
In this section we provide further evidence that our results are not driven by dynamics resulting from differences between the treatment group and the control group over the business
cycle. As such, we inspect whether the trend of the z-score and the probability of default is
parallel for both groups in Section 4.5.1. Additionally, in Section 4.5.2 we observe whether
the normalized differences are within the required range for different risk proxies comparing
both groups of banks.
11
Note the difference with the t-statistics, which is sensitive to the sample size, and which tests whether
there is a significant difference in means, not, as normalized differences, whether linear regression methods
tend to be sensitive the the specification.
12
This is not surprising because we use propensity score matching to construct our sample.
13
4.5.1
Parallel trend assumption
To adequately employ a difference-in-difference estimation technique we have to guarantee
that the parallel trend assumption is satisfied. As such, we have to guarantee that the
developments of the probability of default and the z-score for the treatment and control
group follow similar trends before the event. Analogous to previous studies we graphically
inspect the trend of mean probability of default and z-scores for both groups in Fig. 1, and
we confirm that the parallel-trend assumption holds.
Further, in Subsection 4.4 and shown in Table 2, we observe that the groups of affected and
unaffected banks do not differ regarding most characteristics.
4.5.2
Relevance of the business cycle to bank risk
Using a propensity matching technique as in section 4.3, we alleviated concerns that banks
differ between the group of banks that benefited from the change in the law and the group
that did not. Furthermore, we would like to show that banks are similar across the business
cycle with regard to different risk proxies. Doing so, we would like to rule out that banks
that benefited from the change in the deposit insurance scheme were more risky ex-ante.
In Fig. 3 and 4 we graphically compare the development of our main variables of interest and
different risk proxies for separately for the treatment and the control group. Additionally,
we also show the evolution of the normalized differences. In Figure 3 4 we observe that the
values of the normalized differences are between ±0.25 for the period 2000 to 2007, which
is equivalent to non-significant differences between groups (Imbens and Wooldridge, 2009).
Thereby, concerns are alleviated that banks’ risk taking differed for banks in the treatment
and control group before the event. For all variables we observe that groups are similar before
the event with trends slightly more diverging thereafter.
[Fig. 3]
14
5
5.1
Empirical model and results
Baseline estimation
By applying a conditional difference-in-difference estimation technique – which is eqivalent
to a matching of groups procedure beforte estimating the treatment effect –, we estimate
whether higher risk taking by banks is systematic and can be attributed to the change in
regulation. Formally, we estimate the following equation:
Riskit = β0 + β1 Eventt + β2 (Eventt ∗ Affectedi ) + τγ + νi + ϵit
(3)
The short-hand Riskit is a proxy for bank risk of bank i at time t. The different measures
that we use for Riskit are common in the literature and explained in more detail in the
following subsection. Our event window is the fourth quarter of 2008. Accordingly, the
variable Eventt is a time dummy with a value of zero for all quarters before the Emergency
Economic Stabilization Act was introduced (t < Q4 2008) and a value of one for all quarters
after the event (t ≥ Q4 2008). The variable Affected i is a dummy variable of bank i that is one
if the bank experienced a sharp increase in insured deposits per customer (belongs to the top
25% quantile) and thus belongs to the treatment group, and zero otherwise (belongs to the
bottom 25% quantile). Hence, the interaction term Event t ∗Affected i is one if both the variable
Eventt and the variable Affected i amount to one, and zero otherwise. The corresponding
coefficient β2 is the main interest. It captures the average effect of the introduction of the
act on the bank stability of affected banks. The variable τγ represents yearly time fixed
effects (TE). Further, we are concerned that unobserved differences between banks might
influence our results. Thus, we include fixed effects (FE) νi for each bank i. Finally, ϵit is
the idiosyncratic error term. To account for heterogeneity among banks, we use clustered
standard errors at the bank level. For robustness, we re-estimate our baseline estimation
without bank fixed effects. The variable Affected i that otherwise interferes with bank fixed
effects then enters the equation.
15
5.2
Results for different risk proxies
5.2.1
Predicted probabilities of default
First, we use banks’ predicted probabilities of default as a proxy for bank risk, Riskit . We
thereby provide evidence whether the predicted probabilities of default of affected banks
increase significantly relative to unaffected banks after the regulatory increase of insured
deposits in the fourth quarter of 2008. Because banks’ predicted probabilities of default are
not directly observable, we follow three steps: First, we run a probability model with historical
bank failures to explore how different bank characteristics determine a bank’s probability of
default. Second, we use these regression results to calculate predicted probabilities of default
for all banks in our sample. Finally, we use the predicted probability of default as dependent
variable in Eq. (3).
Our probability model is based on a sample of all U.S. banks registered at the FDIC in the
period of 1993-2012, i.e., a total of about 10,000 banks on average per quarter, of which 574
banks failed during this period.13 In particular, we estimate the following linear probability
model:
Failureit =β0 + β1 Equit + β2 Loansit + β3 RELoansit + β4 CILoansit + β5 OREit +
β6 Incit + β7 Liquit + β8 N P Lit + β9 T Ait + β10 RoAit + τγ + νi + ϵit
(4)
The explanatory variables are the same as the ones we use for the propensity score matching
in the previous section and based on Wheelock and Wilson (2000). The variables τγ and νi
represent yearly time fixed effects and bank fixed effects, respectively.
We use a linear probability model instead of a nonlinear probability model because we want
to include bank and time fixed effects in our model. Thus, we can correctly address that bank
failures may be explained by bank- and time-specific factors that are not captured by our
explanatory variables. With a nonlinear regression model, the introduction of many dummy
variables leads to i) practical problems because the presence of many dummy variables makes
13
The total number of banks for the year 1993 was over 13,000, and then declined subsequently to about
7,000 for the year 2012. The total number of observations for our estimation is 767,649.
16
the estimation much more difficult,14 and ii) the so-called incidental parameters problem
(see, e.g., Greene et al., 2002; Fernández-Val, 2009).15 We are aware that using a linear
model to predict bank failures comes with the problem that we do not capture the shape of
the distribution of bank failures correctly, and we may predict probabilities of default of less
than 0% and above 100%. However, we weight this bias less severe than the potential problem
from neglecting fixed bank and time effects or the potential incidental parameters problem.
Furthermore we are not directly interested in the level of the coefficients of the explanatory
variables, but in their relative weights.16
From the (unreported) regression we observe that most of the variables determine bank failures
in an intuitive way: banks with a larger capital buffer, with more loans relative to total assets,
larger banks, banks with higher liquidity and banks that are more profitable are significantly
less likely to fail whereas banks with higher non-performing loan ratios and risky real estate
investments have a significant higher probability of failure.
Next, we calculate the predicted probability of default for each bank and each quarter. See
Figure 1 for the development of the mean predicted probability of default for the groups of
affected and unaffected banks, and Table 2 for related summary statistics.
Finally, we estimate Equation (3) for the sample of affected banks (treatment group) and
unaffected banks (control group), using the predicted probability of default of each bank and
quarter as dependent variable. Table 3 shows results for five different specifications of Eq.
(3). The first column presents results for an OLS regression without bank fixed effects using
± 8 quarters around the event. The four remaining columns provide results for regressions
using bank fixed effects to control for unobserved bank specific effects for periods of ± 4, 6,
8, 10 quarters around the event. Each column of Table 3 includes yearly time fixed effects
and standard errors (in parentheses) that are clustered on the bank level. The interaction
term Affectedi *Eventt measures the average difference in banks’ default probabilities between
14
When we try to fit a fixed effects logit model, the estimations do not converge or drop huge amounts of
observations. Only roughly 25,000 from the original 767,649 observations stay in the estimation.
15
According to Fernández-Val (2009), the incidental parameters problem “arises because the unobserved
individual characteristics are replaced by inconsistent sample estimates, which, in turn, bias estimates of
model parameters.”
16
Note that despite the practical problems and concerns mentioned above, a recent literature review on the
determinants of bank failures by Mayes and Stremmel (2012) shows that logit and probit models are most
common in this field of research.
17
affected and unaffected banks after the event relative to the period before the event. In
particular the interaction term in Table 3 indicates whether affected banks become riskier
(higher default probability) or less risky (lower default probability) after the event relative to
the group of unaffected banks.
The first column shows a positive and significant interaction effect that indicates that banks
more affected by the raise in insured deposits have higher probabilities of default after the
event relative to the control group. This results is corroborated by the remaining four columns
that include bank fixed effects. Those columns also indicate the economic significance of the
effect increases with the window size around the event. While for the shortest period of ± 4
quarters, the increase in default probabilities for affected banks relative to the control group
is 15 basis points after the event, this effect increases to 22 basis point for the window of ±
10 quarters. The economic significance is roughly 50%, calculated as the average increase of
the probability of default of the treatment group relative to the control group after the event.
Note that in calculating this measure we omit the level effect prior to the event.
The results in Table 3 support results from the literature that emphasize the detrimental
effects of deposit insurance schemes on bank stability (e.g., Demirgüç-Kunt and Detragiache,
2002). With regard to this body of literature that investigates the effects of deposit insurance
schemes on bank stability on a cross-country base, our study complements this research by
conducting the first within-country analysis for the United States. Furthermore, we are able
to identify the direction of this effect exploiting the Emergency Economic Stabilization Act
as at least quasi exogenous shock and are thus able to evaluate recent policy changes in the
U.S.
[Table 3]
18
5.2.2
Z-scores
5.2.3
Z-scores
As a second risk measure we use the bank z-score, which measures a bank’s ability to absorb
losses by its equity. Following Laeven and Levine (2009), the bank z-score is defined as the
natural logarithm of the sum of a bank’s return on assets, ROA, and its equity to assets ratio,
AR, standardized by the volatility of bank’s return on assets, ST D(ROA), i.e.,
z-scoreit = log
ROAit + ARit
.
ST D(ROA)it
(5)
ROAit and ARit reflect book values as reported by the banks. Technically, we calculate
ST D(ROA)it as the 12 quarter rolling standard deviation of return on assets for each bank
and quarter. Note that the definition comprises the natural logarithm because the measure is
otherwise highly skewed. A larger value of the z-score is associated with a more stable bank.
Table 4 shows the regression results for Eq. (3) using the banks’ z-scores as the dependent
variable. The set-up of Table 4 follows that of Table 3. In column (1) we observe a negative
and significant coefficient of the interaction term, which indicates that affected banks become
riskier after the event relative to the control group. In other words, the raise in insured
deposits from $100,000 to $250,000 in Q4 2008 has a destabilizing effect on banks that “benefited” from this act. The point estimate of 0.17 shows that this effect is also economically
relevant. As we are estimating a log-linear model (the left-hand side represents the logarithm
of banks’ z-score) the interaction term is equivalent to a 8% difference in stability between
treatment and control group after Q4 2008 (i.e., banks in the treatment group become 8%
less stable).
Columns (2) to (5) of Table 4 confirm the results from column (1) using bank fixed affects
to control for bank characteristics that might disturb the initial results in column (1). We
find that the interaction term remains significant and negative, indicating that affected banks
become riskier after the event. Results further indicate that this effect is very persistent.
Even for the period of ±10 quarters around the event, we find a significant effect of the
interaction term that is statistically and economically similar. Economic significance of these
19
effects amounts to roughly 7%.
[Table 4]
5.2.4
Z-score components
To determine the drivers for the z-score results, Table 5 shows results for the volatility of
banks’ returns on assets, SD(RoA), equity-to-assets ratios, and returns on assets, RoA. Column (1) shows that affected banks have a higher volatility after the event in comparison to
their unaffected peers, as reflected in the positive and significant coefficient of the interaction
term. Column (2), which uses the equity-to-assets ratio as dependent variable, shows a negative and significant coefficient for the interaction term. In Column (3) we report that the
profitability of affected banks is significantly lower after the event relative to the unaffected
banks. Given that these banks take more risks after the introduction of the law, one is likely
to expect higher profits for these banks. In Section 5.2.5 we will show that affected banks
classify a larger amount of loans as non-performing, which could explain a decline in profits.
Furthermore, looking at interest income (total and interest income separately) we observe no
significant disparities for affected and unaffected banks17 . Overall, Table 5 shows that the
lower z-score values for affected banks’ relative to unaffected banks after the event stem from
all three components of the z-score.
[Table 5]
5.2.5
Asset quality
We further approximate bank risk with five other variables that capture banks’ asset quality.
Here we rely on the ratio of loan loss provisions to total assets, charge-offs to total assets, the
ratio of banks’ non-accrual assets, the amount of other real estate owned to banks’ total assets
and non-performing loans18 to banks’ total assets. Higher values for one of these measures
17
18
Note that we standardize interest income with fixed values of assets one period before the event.
Classified as assets past due 90 days still accruing interest.
20
indicate higher asset risk for banks.
Column (1) of Table 6 shows results for banks’ loan loss provisions over assets as the dependent
variable. We observe higher provisions for affected banks after the event relative to their
unaffected peers. Column (2) shows similar results for banks’ charge-offs over assets. This
is not surprising given the results for loan loss provisions over assets, because both measures
are highly correlated. Next, we evaluate the effect of the change on non-accrual assets over
assets. As shown in column (3), we observe that affected bank have a higher volume of nonaccrual assets after the event relative to the control group, which is qualitatively similar to
the results above. Column (4) shows results for “other real estate owned” over assets, which
indicates foreclosure activity. We find that this measure significantly increases after the event
for the group of affected banks relative to unaffected banks. Finally, column (5) shows that
non-performing loans increase for affected banks relative to the control group after the event.
In summary, we get a clear indication that the volume of problem loans increased for affected
banks after the event relative to unaffected banks. Note that magnitude, sign and level of
significance of the coefficients remains within range if the dependent variable is standardized
by asset values one period before event (q3 2008).
[Table 6]
Note further that the trend for loan loss provisions over assets is illustrated in Figure 1, and
the trends for the other measures of asset quality are illustrated in Figure 3 in the appendix.
5.2.6
Asset composition
Finally, we use measures as dependent variables that reflect asset composition, which is a more
indirect measure of bank risk, but may provide interesting results as regards the investment
decision of banks towards assets considered more or less risky. In particular, we estimate total
loans over assets, real estate loans over assets, consumer loans over assets, commercial and
industrial loans, and commercial real estate loans.
21
Panel A of Table 7 shows the results for the impact of the event on the composition of banks’
assets.
First, we find that affected banks do not change their total loan share after the event relative
to unaffected banks, as shown in column (1). Next, taking a closer look at the different
loan categories, we find that the composition of loans changed after the introduction of the
Emergency Economic Stabilization Act. We see that affected banks have higher shares of
consumer loans (column (3)) and real estate commercial loans (column (5)) after the event
relative to the unaffected banks. These loan types are considered relatively risky. In particular, consumer loans typically include credit card loans. This may explain an overall lower
asset quality and higher bank risk of affected banks relative to unaffected banks.
[Table 7]
If we standardize the dependent variables using the value of total assets one period before
event (Q3 2008), results slightly change. In Panel B of Table 7 we observe that total loans,
real estate loans and real estate commercial loans increase after the change in the law relative
to the control group. In an additional analysis we standardize total assets with asset values
one period before the event. We thereby find that the interacted affect Event ∗ Af f ected is
positive and significant. This suggests that banks affected by the raise in deposit insurance
became larger after Q4 2008 than banks that did not benefit from the raise. This is in line
with results in Table 7 which shows an increase in the volume of most loan categories but not
in relation to banks total assets.
5.3
5.3.1
Robustness tests
Cross-section results
To guarantee that the results are not driven by serial correlation, a typical problem with
difference-in-difference estimation Bertrand et al. (2004) suggest to ignore the time structure
of the data. Therefore, we average the data before and after the event and rerun the estimation
22
for this collapsed sample. In unreported results, we find that our findings are not driven by
serial correlation and observe coefficient of similar magnitude and significance.
5.3.2
Time-placebo estimation
We also want to be sure that our results are not driven by time trends unrelated to the change
in insured deposits. Therefore, we conduct a placebo estimation in which we shift the event
to Q4 2005. We then rerun our estimation for observations ±8 quarters before and after this
“2005 pseudo event”. In unreported results we do not find an effect for the 2005 pseudo event
in any of our baseline specifications. This finding supports our assumption that our results
are not driven by factors unrelated to increase in deposit insurance per customer.
6
Bank bailouts, bank risk taking and bank capitalization
In this section we address two further issues that are important for our analysis. First, we
have to deal with the fact that right after the raise in deposit insurance banks were able to
apply for TARP under which banks receive capital support from the U.S. government. This
may affect our results because both events occur at the same time which makes it difficult to
identify the effect from the change in insured deposits alone. Furthermore, the government
intervention may distort our results in other ways. For example, banks subject to TARP may
also have profited from the raise in insured deposits, i.e., gained more insured deposits. Due
to the fact that equity plays a central role for several of our risk measures, a possible outcome
of our analysis may be that a raise in insured deposits results in (higher capitalized) saver
banks which may only stem from the fact that some banks received government support at
the same time. To eliminate these concerns we split our sample in banks that receive TARP
and banks that did not receive capital support from the government.
Second, we are interested whether the effect from the raise in insured deposits is different for
banks that were already riskier in the run-up to the financial crisis. We therefore define a
bank to be highly capitalized if its total risk-based capital ratio is above the average median
of 14.96% for the period 8 quarters before the event and lowly capitalized, if a bank’s capital
23
ratio is below the median. In our analysis we are interested how results differ for TARP and
non-TARP banks that were either lowly or highly capitalized before the event.
In Table 8 we observe higher risk taking – in terms of default probabilities – due to the raise
of insured deposits only for banks that benefited from the raise in insured deposits but were
not subject to TARP. We find an insignificant effect for banks that received TARP right after
Q4 2008, our event window. Thus, the results show that our previous findings are not driven
by banks that receive TARP. We observe that the high and low capitalized banks that were
not subject to TARP exhibit higher risk taking (measured as the probability of default) after
the event relative to a control group. Further, with regard to the economic impact, we find
that this effect is even stronger for banks, that hold a lower cushion against insolvency before
the event.
[Table 8]
In Table 9 we provide further corroborating results with regard to loan loss provisions. We
observe that only the non-TARP banks that had low capital ratios before the event become
riskier after the increase in insured deposits relative to the control group. Once more we do not
detect a significant effect for the TARP banks. We conclude that TARP banks are subject to
implicit guarantees for their entire debt capital which potentially outweighs the change in the
deposit insurance scheme. Or put differently: an increase in the amount of insured deposits
does not additionally affect risk taking of banks (as implicit guarantees triggered risk taking
of banks prior to the change in insured deposits)19 .
[Table 9]
Results are also robust for the z-score, zscore components, charge-offs, nonaccrual assets.
Regarding other real estate owned we observe a significant increase for the non-TARP banks
and TARP banks that were lowly capitalized ex ante. Non-performing loans increase after
the change in regulation for the non-TARP banks that were lowly capitalized before the
19
Note that results could of course be driven by the low sample size, in particular for the ex-ante highly
capitalized banks subject to TARP.
24
event. For illustrative purposes we only report results for probabilities of default and loan
loss provisions.
7
Conclusion
Deposit insurance has long been regarded as a source of moral hazard that potentially increases banks’ risk takings. Despite these findings, the U.S. regulator decided to increase the
deposit insurance from $100,000 to $250,000 during the peak of the financial crisis, which
increased the total amount of insured deposits in the U.S. by roughly $500 billion. This
paper analyzes the consequences of this regulatory change, and thereby provides the first
empirical assessment of the impact of insured deposits on banks’ risk taking within one large
economy. Our results add to the existing literature that provides empirical evidence based
on cross-country studies.
We find that banks materially affected by the regulatory change, measured as the change in
the ratio of insured deposits to total deposits, become more risky after the regulatory change
relative to the more unaffected banks. This result is robust for a large variety of different risk
proxies and alternative model specifications. Further, we observe that after the regulatory
change, the asset quality of affected banks deteriorates, and affected banks invest relatively
more in risky loans such as consumer loans and commercial reals estate loans. Thereby, the
study provides empirical evidence on unintended long-term consequences of an important and
controversial part of bank regulation.
25
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28
A
Tables
29
Table 1: Descriptive statistics and group comparison
This table reports descriptive statistics for all variables used in later analyses for the period two years before the economic
stabilization act took place in Q4 2008. We provide mean values and standard deviations for unaffected and affected
banks. We assign banks to the treatment group (affected) if the change of insured deposits over deposits in Q3 2009 is
in the top quartile of the sample. Banks that exhibit a change in the ratio of insured deposits over deposits in the lowest
quartile belong to the control group (unaffected). The sample consists of 1104 affected and 1104 unaffected banks. The
last column shows the normalized differences (ND) according to Imbens and Wooldridge (2009) and compare differences
between unaffected and affected banks. As a rule of thumb, values between ±0.25 are equivalent to nonsignificant
differences between groups.
Variables are defined as follows: Insured Deposits/Deposits is the ratio of insured deposits over deposits; Insured
Deposits/Assets is the ratio of insured deposits over total assets; Deposits/Assets is the ratio of total deposits over
total assets; Probability of default is the quarterly default probability per bank estimated via a linear probability model
explaining bank failures with variables according to Wheelock and Wilson (2000); z-score is the ratio of the sum of the
equity-to-asset ratio and return on assets to the 12 quarter rolling standard deviation of return on assets and serves as
a risk proxies according to Laeven and Levine (2009); SD(RoA) is the 12 quarter rolling standard deviation of return
on assets; RoA is banks’ return on assets20 ; Equity/Assets is the total equity-to-asset ratio, Size is measured as the
log of total assets; Real estate loans/Loans is the ratio of banks’ mortgage loans to total assets; C&I loans/Assets is
the ratio of banks’ corporate and industrial loans to total loans; Other real estated owned is standardized over loans.
Income earned not collected on loans is standardized over total assets, Provision for loans and leases/Assets is the ratio
of banks’ loan loss provisions to their assets, Net fed funds/Assets is the difference between fed funds purchases and fed
funds sold to total assets, Net Charge offs on loans and leases/Assets is the ratio of banks’ net charge-offs to their assets,
Total risk-based capital ratio represents banks’ total risk-based capital to its risk-weighted assets; PD is the probability
of default calculated as in Section 4. The U.S. $ values are denoted in millions. Furthermore, a very detailed description
of all variables with FDIC codes is given in Table 10.
Assets
Log(assets)
Equity/assets
total risk-based capital/assets
ROA
log(zscore)
SD(ROA)
Real estate loans/loans
C&I loans/loans
Other RE owned/loans
Income earned, not collected on loans/assets
Net Fed funds/assets
Total loans/assets
Loan loss provisions/assets
Non-accrual assets/assets
Charge-offs/assets
Consumer loans/assets
Commerical RE loans/asset
Non-performing loans/assets
PD
Insured deposits/deposits
Insured deposits/asset
Deposits/assets
# of banks
Unaffected
Mean
SD
5859417.5 69014217
12.2896
1.5905
11.1187
4.1641
16.8767
7.5721
0.0106
0.0064
4.1668
0.7874
0.0024
0.0027
0.6753
0.1629
0.1389
0.0849
0.0018
0.0035
0.0082
0.0038
-0.0057
0.0631
0.6501
0.134
0.0012
0.003
0.0061
0.0096
0.0009
0.0026
0.0511
0.0436
0.1368
0.0932
0.017
0.0161
-0.0003
0.0049
0.7896
0.144
0.6394
0.1444
0.8031
0.0949
1104
30
Affected
Mean
SD
887470.39 13667001
12.2492
1.0565
10.3441
3.5903
15.4576
9.337
0.0101
0.0079
3.971
0.7356
0.0028
0.0034
0.7126
0.1646
0.1489
0.0767
0.0022
0.0042
0.0073
0.0034
-0.0153
0.0491
0.6788
0.1491
0.0012
0.0024
0.0057
0.009
0.0007
0.002
0.0405
0.0376
0.1895
0.1174
0.0154
0.0157
-0.0004
0.0046
0.6948
0.1256
0.5764
0.1188
0.8281
0.0729
1104
ND
-0.0707
-0.0212
-0.1409
-0.118
-0.0486
-0.1817
0.0808
0.1614
0.0871
0.0675
-0.1871
-0.1203
0.1436
-0.0077
-0.0349
-0.0607
-0.1834
0.3516
-0.0703
-0.0169
-0.4963
-0.3367
0.2092
Table 2: Descriptive statistics and group comparison continued
This table reports descriptive statistics for all variables used in later analyses for the period two years after the economic
stabilization act took place in Q4 2008. We provide mean values and standard deviations for unaffected and affected
banks. We assign banks to the treatment group (affected) if the change of insured deposits over deposits in Q3 2009 is
in the top quartile of the sample. Banks that exhibit a change in the ratio of insured deposits over deposits in the lowest
quartile belong to the control group (unaffected). The sample consists of 1104 affected and 1104 unaffected banks. The
last column shows the normalized differences (ND) according to Imbens and Wooldridge (2009) and compare differences
between unaffected and affected banks. As a rule of thumb, values between ±0.25 are equivalent to nonsignificant
differences between groups.
Variables are defined as follows: Insured Deposits/Deposits is the ratio of insured deposits over deposits; Insured
Deposits/Assets is the ratio of insured deposits over total assets; Deposits/Assets is the ratio of total deposits over
total assets; Probability of default is the quarterly default probability per bank estimated via a linear probability model
explaining bank failures with variables according to Wheelock and Wilson (2000); z-score is the ratio of the sum of the
equity-to-asset ratio and return on assets to the 12 quarter rolling standard deviation of return on assets and serves as
a risk proxies according to Laeven and Levine (2009); SD(RoA) is the 12 quarter rolling standard deviation of return
on assets; RoA is banks’ return on assets21 ; Equity/Assets is the total equity-to-asset ratio, Size is measured as the
log of total assets; Real estate loans/Loans is the ratio of banks’ mortgage loans to total assets; C&I loans/Assets is
the ratio of banks’ corporate and industrial loans to total loans; Other real estated owned is standardized over loans.
Income earned not collected on loans is standardized over total assets, Provision for loans and leases/Assets is the ratio
of banks’ loan loss provisions to their assets, Net fed funds/Assets is the difference between fed funds purchases and fed
funds sold to total assets, Net Charge offs on loans and leases/Assets is the ratio of banks’ net charge-offs to their assets,
Total risk-based capital ratio represents banks’ total risk-based capital to its risk-weighted assets; PD is the probability
of default calculated as in Section 4. The U.S. $ values are denoted in millions. Furthermore, a very detailed description
of all variables with FDIC codes is given in Table 10.
Assets
Log(assets)
Equity/assets
total risk-based capital/assets
ROA
log(zscore)
SD(ROA)
Real estate loans/loans
C&I loans/loans
Other RE owned/loans
Income earned, not collected on loans/assets
Net Fed funds/assets
Total loans/assets
Loan loss provisions/assets
Non-accrual assets/assets
Charge-offs/assets
Consumer loans/assets
Commerical RE loans/asset
Non-performing loans/assets
PD
Insured deposits/deposits
Insured deposits/asset
Deposits/assets
# of banks
Unaffected
Mean
SD
6400990.5 77830850
12.2772
1.5737
11.1338
4.2424
0.1681
0.07
0.0105
0.0066
3.6476
1.0146
0.0046
0.0061
0.6752
0.1622
0.1389
0.0841
0.0018
0.0035
0.0082
0.0038
-0.0061
0.0631
0.6496
0.134
0.0039
0.0069
0.0148
0.0205
0.0031
0.006
0.0431
0.0398
0.1443
0.0984
0.0268
0.0263
0.0026
0.0091
0.841
0.1378
0.6874
0.1374
0.8125
0.0818
1104
31
Affected
Mean
SD
1398808.1 26710052
12.2268
1.0509
10.593
4.9455
0.1504
0.0734
0.0098
0.0094
3.2342
1.1322
0.0064
0.0079
0.7107
0.1641
0.1497
0.0766
0.0021
0.0037
0.0073
0.0034
-0.0163
0.0499
0.6753
0.1504
0.0058
0.0099
0.0209
0.0264
0.0046
0.0085
0.0342
0.0325
0.2029
0.1201
0.0338
0.0342
0.0057
0.0121
0.8042
0.1225
0.6793
0.118
0.8435
0.0578
1104
ND
-0.0608
-0.0266
-0.083
-0.1742
-0.0618
-0.2719
0.1797
0.1536
0.0954
0.0525
-0.1877
-0.1265
0.1277
0.1563
0.1832
0.148
-0.1744
0.3772
0.1621
0.2034
-0.1995
-0.0447
0.3093
Table 3: Baseline results: Probability of default
This table shows results for regressions of Eq. (3) with banks’ quarterly probability of default as the dependent variable.
Event is a dummy variable that is zero for the pre-event period and one for the post-event period, where the event is the
signing into law of the Emergency Economic Stabilization Act in Q4 2008. Affected is a dummy variable that is one for
banks affected by the event (the change of insured deposits following the event is in the top quartile) and zero for banks
unaffected by the event (the change is in the lowest quartile). Event*Affected is the respective interaction term. The
first column shows results for a regression without bank fixed effects (FE) and a period of ±8 quarters around the event.
The following four columns show results for regressions with FE and periods of ±4, 6, 8 and 10 quarter around the
event. All regressions include time fixed effects (TE). We show clustered standard errors on bank level in parentheses.
The ***, ** and * stand for significant coefficients at the 1%, 5%, and 10% levels, respectively.
Event
Affected
Event*Affected
Constant
FE
TE
Number of Banks
Number of Observations
adj. R2
Dependent variable: Probability of default
±8 OLS
±4
±6
±8
±10
0.0022***
0.0030*** 0.0038***
0.0038*** 0.0032***
(0.0002)
(0.0002)
(0.0002)
(0.0003)
(0.0002)
0.0000
(0.0002)
0.0031***
0.0024*** 0.0030***
0.0033*** 0.0034***
(0.0004)
(0.0004)
(0.0004)
(0.0004)
(0.0004)
-0.0001 -0.0005***
-0.0001 -0.0006***
-0.0001
(0.0001)
(0.0001)
(0.0001)
(0.0001)
(0.0001)
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
2208
2208
2208
2208
2208
34906
17624
26317
34906
43390
0.0837
0.6524
0.5918
0.5527
0.5242
32
Table 4: Baseline results: Log of z-score
This table shows results for regressions of Eq. (3) with banks’ z-scores as the dependent variable. Event is a dummy
variable that is zero for the pre-event period and one for the post-event period, where the event is the signing into law of
the Emergency Economic Stabilization Act in Q4 2008. Affected is a dummy variable that is one for banks affected by
the event (the change of insured deposits following the event is in the top quartile) and zero for banks unaffected by the
event (the change is in the lowest quartile). Event*Affected is the respective interaction term. The first column shows
results for a regression without bank fixed effects (FE) and a period of ±8 quarters around the event. The following four
columns show results for regressions with FE and periods of ±4, 6, 8 and 10 quarter around the event. All regressions
include time fixed effects (TE). We show clustered standard errors on bank level in parentheses. The ***, ** and *
stand for significant coefficients at the 1%, 5%, and 10% levels, respectively.
Event
Affected
Event*Affected
Constant
FE
TE
Number of Banks
Number of Observations
adj. R2
±8 OLS
-0.6426***
(0.0290)
-0.2340***
(0.0293)
-0.1797***
(0.0382)
4.2603***
(0.0227)
No
Yes
2205
34308
0.1237
Dependent variable: Log of z-score
±4
±6
±8
-0.5795*** -0.4607*** -0.4336***
(0.0277)
(0.0266)
(0.0269)
±10
-0.6090***
(0.0298)
-0.1709***
(0.0370)
4.1323***
(0.0148)
Yes
Yes
2196
17309
0.7032
-0.1978***
(0.0406)
4.1106***
(0.0140)
Yes
Yes
2208
42696
0.6020
-0.1945***
(0.0394)
3.9319***
(0.0086)
Yes
Yes
2200
25855
0.6565
-0.2023***
(0.0405)
3.9335***
(0.0093)
Yes
Yes
2205
34308
0.6244
Table 5: Z-score components
This table shows results for regressions of Eq. (3) with standard deviation of return on assets (SD(ROA)), equity-to-asset
ratio (Equity/Assets) and return on assets (RoA) as dependent variables. Event is a dummy variable that is zero for the
pre-event period and one for the post-event period, where the event is the signing into law of the Emergency Economic
Stabilization Act in Q4 2008. Affected is a dummy variable that is one for banks affected by the event (the change of
insured deposits following the event is in the top quartile) and zero for banks unaffected by the event (the change is in
the lowest quartile). Event*Affected is the respective interaction term. All regression are with bank fixed effects (FE),
time fixed effects (TE) and a period of ±8 quarters around the event. We show clustered standard errors on bank level
in parentheses. The ***, ** and * stand for significant coefficients at the 1%, 5%, and 10% levels, respectively.
SD(RoA)
±8
0.0020***
(0.0002)
0.0014***
(0.0003)
0.0030***
(0.0001)
Yes
Yes
2205
34542
0.5424
Event
Event*Affected
Constant
FE
TE
Number of Banks
Number of Observations
adj. R2
33
Equity/Assets
±8
-0.0059***
(0.0010)
-0.0080***
(0.0014)
0.1191***
(0.0006)
Yes
Yes
2208
34920
0.7014
RoA
±8
-0.5258***
(0.0350)
-0.3344***
(0.0516)
1.1875***
(0.0186)
Yes
Yes
2208
34920
0.4760
Table 6: Asset quality
This table shows results for regressions of Eq. (3) with the ratios of loan loss provisions to assets, charge-offs to assets,
other real estate owned to assets and non-performing loans to assets as dependent variables. In Panel A we standardize
the dependent variable over total assets, in Panel B over assets one period before the change of the law. Event is a
dummy variable that is zero for the pre-event period and one for the post-event period, where the event is the signing
into law of the Emergency Economic Stabilization Act in Q4 2008. Affected is a dummy variable that is one for banks
affected by the event (the change of insured deposits following the event is in the top quartile) and zero for banks
unaffected by the event (the change is in the lowest quartile). Event*Affected is the respective interaction term. All
regression are with bank fixed effects (FE), time fixed effects (TE) and a period of ±8 quarters around the event. We
show clustered standard errors on bank level in parentheses. The ***, ** and * stand for significant coefficients at the
1%, 5%, and 10% levels, respectively.
Event
Event*Affected
Constant
FE
TE
Number of Banks
Number of Observations
adj. R2
Event
Event*Affected
Constant
FE
TE
Number of Banks
Number of Observations
adj. R2
Panel A
Provisions Charge-offs
±8
±8
0.0019***
0.0019***
(0.0002)
(0.0001)
0.0018***
0.0017***
(0.0002)
(0.0002)
0.0015***
0.0010***
(0.0001)
(0.0001)
Yes
Yes
Yes
Yes
2208
2208
34920
34920
0.3059
0.2984
Panel B
Provisions Charge-offs
±8
±8
0.0026***
0.0024***
(0.0003)
(0.0002)
0.0018***
0.0016***
(0.0003)
(0.0003)
0.0014***
0.0010***
(0.0001)
(0.0001)
Yes
Yes
Yes
Yes
2208
2208
34920
34920
0.2906
0.2711
34
Oth. RE owned
±8
0.0063***
(0.0003)
0.0034***
(0.0005)
0.0012***
(0.0002)
Yes
Yes
2208
34920
0.5343
Non-perf. loans
±8
0.0136***
(0.0007)
0.0081***
(0.0010)
0.0131***
(0.0004)
Yes
Yes
2208
34920
0.5824
Oth. RE owned
±8
0.0067***
(0.0003)
0.0035***
(0.0005)
0.0011***
(0.0002)
Yes
Yes
2208
34920
0.5004
Non-perf. loans
±8
0.0173***
(0.0009)
0.0085***
(0.0013)
0.0120***
(0.0004)
Yes
Yes
2208
34920
0.5308
Table 7: Asset composition
This table shows results for regressions of Eq. (3) with the ratios of total loans to asset, real estate (RE) loans to assets,
consumer loans to assets, commercial and industrial (C&I) loans to assets and real estate commercial loans to assets
as dependent variables. In Panel A we standardize the dependent variable over total assets, in Panel B over assets one
period before the change of the law. Event is a dummy variable that is zero for the pre-event period and one for the postevent period, where the event is the signing into law of the Emergency Economic Stabilization Act in Q4 2008. Affected
is a dummy variable that is one for banks affected by the event (the change of insured deposits following the event is
in the top quartile) and zero for banks unaffected by the event (the change is in the lowest quartile). Event*Affected
is the respective interaction term. All regression are with bank fixed effects (FE), time fixed effects (TE) and a period
of ±8 quarters around the event. We show clustered standard errors on bank level in parentheses. The ***, ** and *
stand for significant coefficients at the 1%, 5%, and 10% levels, respectively.
Event
Event*Affected
Constant
FE
TE
No. of Banks
No. of Obs.
adj. R2
Event
Event*Affected
Constant
FE
TE
No. of Banks
No. of Obs.
adj. R2
Total loans
±8
-0.0230***
(0.0024)
0.0017
(0.0029)
0.6559***
(0.0013)
Yes
Yes
2208
34920
0.8913
Total loans
±8
0.1166***
(0.0070)
0.0221**
(0.0086)
0.5854***
(0.0029)
Yes
Yes
2208
34920
0.6315
Panel
RE loans
±8
-0.0003
(0.0021)
0.0032
(0.0025)
0.4583***
(0.0012)
Yes
Yes
2208
34920
0.9380
Panel
RE loans
±8
0.0975***
(0.0056)
0.0204***
(0.0069)
0.4082***
(0.0023)
Yes
Yes
2208
34920
0.7558
35
A
Consumer loans
±8
-0.0116***
(0.0007)
0.0015**
(0.0007)
0.0478***
(0.0003)
Yes
Yes
2208
34920
0.9283
B
Consumer loans
±8
-0.0020**
(0.0008)
0.0015*
(0.0009)
0.0430***
(0.0003)
Yes
Yes
2208
34920
0.8994
C&I loans
±8
-0.0101***
(0.0010)
-0.0018
(0.0012)
0.0945***
(0.0006)
Yes
Yes
2208
34920
0.8875
RE com. loans
±8
0.0107***
(0.0013)
0.0058***
(0.0016)
0.1613***
(0.0008)
Yes
Yes
2208
34920
0.9446
C&I loans
±8
0.0097***
(0.0014)
0.0028
(0.0019)
0.0840***
(0.0007)
Yes
Yes
2208
34920
0.8093
RE com. loans
±8
0.0454***
(0.0026)
0.0178***
(0.0035)
0.1428***
(0.0012)
Yes
Yes
2208
34920
0.8402
Table 8: Probability of default for high and low capitalized banks (subject to TARP or not)
This table shows results for regressions of Eq. (3) with banks’ quarterly probability of default as the dependent variable
for banks that are high or low capitalized and subject to TARP or not. Event is a dummy variable that is zero for the
pre-event period and one for the post-event period, where the event is the signing into law of the Emergency Economic
Stabilization Act in Q4 2008. Affected is a dummy variable that is one for banks affected by the event (the change of
insured deposits following the event is in the top quartile) and zero for banks unaffected by the event (the change is in
the lowest quartile). Event*Affected is the respective interaction term. In all columns results are presented for a period
of ±8 quarters around the event. All regressions include time fixed effects (TE) and bank fixed effects (FE). We show
clustered standard errors on bank level in parentheses. The ***, ** and * stand for significant coefficients at the 1%,
5%, and 10% levels, respectively.
Dependent variable: Probability of default
Non-TARP
TARP
high
low
high
low
0.0018***
0.0034*** 0.0058**
0.0051***
(0.0004)
(0.0004) (0.0027)
(0.0007)
0.0019***
0.0039***
0.0008
0.0017*
(0.0006)
(0.0006) (0.0034)
(0.0010)
-0.0001 -0.0006***
-0.0012 -0.0020***
(0.0002)
(0.0002) (0.0013)
(0.0003)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
873
1103
31
229
13728
17206
453
3519
0.5637
0.5590
0.5449
0.5782
Event
Event*Affected
Constant
FE
TE
No. of Banks
No. of Observations
adj. R2
Table 9: Loan loss provisions for high and low capitalized banks (subject to TARP or not)
This table shows results for regressions of Eq. (3) with banks’ quarterly loan loss provisions as the dependent variable
for banks that are high or low capitalized and subject to TARP or not. Event is a dummy variable that is zero for the
pre-event period and one for the post-event period, where the event is the signing into law of the Emergency Economic
Stabilization Act in Q4 2008. Affected is a dummy variable that is one for banks affected by the event (the change of
insured deposits following the event is in the top quartile) and zero for banks unaffected by the event (the change is in
the lowest quartile). Event*Affected is the respective interaction term. In all columns results are presented for a period
of ±8 quarters around the event. All regressions include time fixed effects (TE) and bank fixed effects (FE). We show
clustered standard errors on bank level in parentheses. The ***, ** and * stand for significant coefficients at the 1%,
5%, and 10% levels, respectively.
Dependent variable: loan loss provisions
Non-TARP
TARP
high
low
high
low
0.0015*** 0.0034***
0.0045** 0.0076***
(0.0020)
(0.0009)
(0.0002)
(0.0003)
0.0009*** 0.0022***
-0.0013
0.0009
(0.0023)
(0.0010)
(0.0003)
(0.0004)
0.0012*** 0.0018*** 0.0028*** 0.0016***
(0.0001)
(0.0001)
(0.0010)
(0.0003)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
873
1104
31
229
13740
17208
453
3519
0.2157
0.3311
0.2953
0.3045
Event
Event*Affected
Constant
FE
TE
No. of Banks
No. of Observations
adj. R2
36
Table 10: FDIC variable description
Notes: The source for all variables as well as their descriptions is the FDIC. For more details, refer to
http://www2.fdic.gov/SDI/main.asp.
Variable name
FDIC code Description
Insured deposits
depins
The estimated amount of FDIC insured deposits in domestic offices and in insured branches of Puerto Rico and US
territories and possessions.
Deposits
dep
The sum of all deposits including demand deposits, money
market deposits, other savings deposits, time deposits and
deposits in foreign offices.
Equity
eqtot
Total equity capital
Total assets
asset
The sum of all assets owned by the institution including
cash, loans, securities, bank premises and other assets. This
total does not include off-balance-sheet accounts.
Net interest income
netinc
Net interest income plus total noninterest income plus realized gains (losses) on securities and extraordinary items,
less total noninterest expense, loan loss provisions and income taxes.
Provisions for loans
elnatr
The amount needed to make the allowance for loan and lease
losses adequate to absorb expected loan and lease losses.
Net charge-offs
ntlnls
Total loans and leases charged-off (removed from balance
sheet because of uncollectibility), less amounts recovered on
loans and leases previously charged-off.
Total loans
lnlsgr
Total loans and lease financing receivables, net of unearned
income.
C&I loans
lnci
Commercial and industrial loans. Excludes all loans secured
by real estate, loans to individuals, loans to depository institutions and foreign governments, loans to states and political
subdivisions and lease financing receivables.
Real-estate loans
lnre
Loans secured primarily by real estate, whether originated
by the bank or purchased.
Commercial RE loans
lnrenres
Nonresidential loans primarily secured by real estate.
Consumer loans
lncon
Loans to individuals for household, family, and other personal expenditures including outstanding credit-card balances and other secured and unsecured consumer loans.
Nonaccrual assets
naasset
Total assets, which are no longer accruing interest. Total assets include real estate loans, installment loans, credit cards
and related loans, commercial and all other loans, lease financing receivables, debt securities and other assets.
Other real estate owned
ore
Includes direct and indirect investments in real estate. The
amount is reflected net of valuation allowances.
Non-performing loans
p9asset
Total assets past due 90 or more days and still accruing interest. Total assets include real estate loans, installment loans,
credit cards and related plan loans, commercial loans and all
other loans, lease financing receivables, debt securities and
other assets.
37
B
Further figures
38
.25
.15
.05
4
−.05
ND
3.8
−.15
−.25
3.4
3.2
3.6
−.15
−.25
0
.002
−.05
ND
.004
.05
.006
.15
4.2
.25
.008
Figure 3: Variables with normalized differences
2002q4
2004q4
2006q4
affected
ND
2008q4
2010q4
2002q4
2004q4
unaffected
2008q4
.25
.012
2010q4
.15
−.25
.002
−.15
.004
−.05
.006
ND
.05
.008
.05
ND
−.05
−.15
−.25
2006q4
affected
ND
2002q4
2010q4
.25
.15
.05
ND
−.05
−.15
.015
.01
2010q4
−.25
−.15
−.25
.02
−.05
ND
.025
.05
.03
.15
.035
.25
.015
.01
2008q4
2008q4
unaffected
(d) Other real estate owned/assets
.005
2006q4
affected
ND
2006q4
affected
ND
0
2004q4
2004q4
unaffected
(c) Charge-offs/assets
2002q4
2010q4
.01
.25
.15
.008
.006
.004
.002
0
2004q4
2008q4
unaffected
(b) Log(zscore)
.01
(a) Probability of default
2002q4
2006q4
affected
ND
2002q4
2004q4
unaffected
2006q4
affected
ND
(e) Loan loass provisions/assets
2008q4
2010q4
unaffected
(f) Non-performing loans/assets
The figure shows the development of the mean values of the probability of default (a), the log of zscore (b), of
the ratio of charge-offs to deposits (c), the ratio of “other real estate owned” to assets (d), the ratio of nonaccrual assets to assets (e), and the ratio of non-performing loans to assets (f) and the normalized differences
for the period from the second quarter of 2001 to the second quarter of 2011. The mean values for affected
and for unaffected banks are represented by a solid line and dotted line, respectively. The blue dotted line
represents the normalized differences. The two vertical solid lines surround the fourth quarter of 2008 in which
the Emergency Economic Stabilization Act took place. The vertical dotted line depicts the previous quarter
39
when banks may have begun to anticipate the adoption of the law.
2002q4
2004q4
2006q4
2008q4
affected
ND
.25
.15
.5
−.05
ND
.05
.45
−.15
−.25
.35
−.25
.6
−.15
.62
.4
−.05
.64
ND
.05
.66
.15
.68
.7
.25
Figure 4: Variables with normalized differences continued
2010q4
2002q4
2004q4
unaffected
2006q4
affected
ND
2010q4
2002q4
2004q4
2006q4
2008q4
affected
ND
.25
.15
.05
ND
−.05
.09
−.25
−.15
.08
.07
.03
−.25
−.15
.04
−.05
.05
ND
.05
.06
.1
.15
.07
.11
(b) Real estate loans/assets
.25
(a) Total loans/assets
2008q4
unaffected
2010q4
2002q4
2004q4
unaffected
2006q4
affected
ND
2010q4
(d) C&I loans/assets
−.25
.12
−.15
.14
−.05
.05
ND
.16
.15
.18
.25
.35
.2
(c) Consumer loans/assets
2008q4
unaffected
2002q4
2004q4
2006q4
affected
ND
2008q4
2010q4
unaffected
(e) Commercial RE loans/assets
The figure shows the development of the mean values of the ratio of total loans to assets (a), the ratio of
real estate loans to assets (b), of the ratio of consumer loans to assets (c), the ratio of C&I loans to assets
(d), and the ratio of commercial RE loans to assets (e) and the normalized differences for the period from the
second quarter of 2001 to the second quarter of 2011. The mean values for affected and for unaffected banks
are represented by a solid line and dotted line, respectively. The blue dotted line represents the normalized
differences. The two vertical solid lines surround the fourth quarter of 2008 in which the Emergency Economic
Stabilization Act took place. The vertical dotted line depicts the previous quarter when banks may have begun
to anticipate the adoption of the law.
40