Too-big-to-fail status and interest costs of banks. The evidence from

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Krzysztof Jackowicz*
Oskar Kowalewski**
£ukasz Koz³owski***
Krzysztof Jackowicz, Oskar Kowalewski, £ukasz Koz³owski
Too-big-to-fail status and interest costs of banks.
The evidence from Central European countries
Too-big-to-fail status and interest costs of banks...
Introduction
The too-big-to-fail (TBTF) doctrine claiming that certain banks are too
large to let them fail (i.e. possess the TBTF status) has potentially two undesired side effects. First, this doctrine privileges banking organisations
with large scales of operations and, as a consequence, creates an uneven
playing field. Second, it diminishes incentives to monitor banks’ financial
health and, therefore, harms the market discipline exercised by depositors
and other creditors. In this article, we concentrate on the impact of the
TBTF doctrine on competition in the banking sector. Thus, we are interested in the first of the aforementioned side effects. We employ static and
dynamic modelling techniques to establish whether interest costs paid by
Central European (CE) banks are affected by the TBTF statuses of some financial organisations. Using a comprehensive dataset and different methods of identifying TBTF banks, we find very little evidence that large
banks incur lower interest costs in CE countries.
The remaining article is structured as follows. In Section 1, we review
shortly the relevant literature. In Section 2, we present hypotheses, and in
Section 3, we describe the dataset and the econometric strategy. In Section
4, we provide empirical results. In the last section, we conclude the article
and indicate directions for further research.
1. Literature review
The existence of the TBTF doctrine was publicly admitted in 1984 by
the Comptroller of the Currency testifying before the U.S. Congress
[Athavale, 2000]. The early results concerning the influence of this statement were ambiguous. On the one hand, Ellis and Flannery [1992] found
*
Dr hab., Katedra Bankowoœci i Ubezpieczeñ, Akademia Leona KoŸmiñskiego, e-mail:
[email protected], ul. Jagielloñska 57/59, 03-301 Warszawa
**
Dr hab., Katedra Bankowoœci i Ubezpieczeñ, Akademia Leona KoŸmiñskiego, World
Economy Research Institute, e-mail: [email protected]
***
Dr, Bank Gospodarki ¯ywnoœciowej S.A., e-mail: [email protected]
209
no evidence that the interest rates offered by certificates of deposit were
affected by the potential TBTF statuses of some banks. On the other hand,
Athavale [2000] demonstrated that investors reduced the required rates of
return on negotiable certificates of deposit after 1984. However, the later
works usually documented that the TBTF status negatively influences
market discipline. Pop and Pop [2009] established that the 2003 bail-out of
Resona, the fifth largest banking group in Japan, was interpreted by the
market as a credible signal that some organisations are too important to let
them fail, even during non-crisis periods. The rescue operation orchestrated by regulatory authorities significantly increased the share prices of
large banks and diminished risk premiums on the credit default swap
markets. Similar conclusions were reached by Balasubramnian and Cyrce
[2011], who showed that the bail-out of Long-Term Capital Management
in 1998 resulted in the reduced sensitivity of subordinated notes yields to
the conventional measures of default risk in the case of the biggest banking organisations. Oliveira et al. [2011] remarked, in turn, that during the
recent crisis, large and important banks in Brazil recorded significant inflows of formally uninsured deposits. Interestingly, there is also evidence
that distortions in market discipline and competition caused by the TBTF
doctrine weaken when banks attain mainly through mergers and acquisitions the too-big-to-be-rescued (TBTR) status, i.e. the status of banks too
large to be effectively rescued using available public funds. For example,
Völz and Wedow [2011] established that the TBTR banks are disciplined
through higher premiums on the credit default swap market. To the best
of our knowledge, the impact of the TBTF doctrine on banking in CE countries has not been studied thoroughly. Therefore, our article attempts to
fill the existing gap in the literature.
2. Hypotheses
Because of the limited length of this article, we are able to test only
two basic hypotheses. First, large banks, seen as TBTF, may enjoy a competitive advantage in the deposit market. As a result, we posit that these
banks pay lower interest rates. Thus, we verify the following H1:
H1: TBTF banks report lower interest costs than other banks.
Second, the literature does not provide clear guidance with regard to
the problem of discriminating between banks seen as TBTF and banks
treated as small enough to be liquidated. As a result, we test H2:
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H2: Interest costs sensitivity to the TBTF status depends on the TBTF definition used.
3. The dataset and econometric strategy
We analyse the interest costs of banks licensed in 11 CE countries:
Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. Our dataset covers the period from 1994 to 2009. All of the bank-specific information was retrieved
from the Bankscope database. Using these data, we created an unbalanced
panel of 4,125 bank-year observations for 416 banks. The information on
ownership structures comes from our previous studies [Jackowicz et al.,
2012; Hasan et al., 2012], in which the reader can also find a more detailed
description of the sample.
We estimate static and dynamic panel models that explain the real interest costs scaled by total liabilities (RINTCOSTS). Equations (1) and (2)
present the general construction of these models:
RINTCOSTSit = f ( MD it -1 , BCit , OSit , EV it , TxC jt )
(1)
RINTCOSTSit = g(RINTCOSTSit -1 MD it -1 , BCit , OSit , EV it , TxC jt ) (2)
In Equations (1) and (2), MD it -1 denotes a set of explanatory variables
used to test the existence of market discipline exercised through interest
costs, BCit is a set of variables designed to control for determinants of interest cost related to the character of bank’s activities, OSit is a set of binary
variables that encode the ownership structures of banks, EV it is a set of experimental variables that verify our hypotheses regarding the impact of
the TBTF status, and TxC jt is a set of dummies that control for specific economic and regulatory conditions in year t in country j.
To obtain a reliable test of the TBTF status significance in Central European banking systems, we have to control for other bank-specific, country and time-specific determinants of interests costs. First, we introduce
three variables that illustrate the influence of market discipline. We assume that a bank with high profitability (ROA), a low share of loans in assets (LOANS), and a solid capital base (EQUITY) should report lower interest costs. Second, the costs of deposits critically depend on the
prevailing component of a bank’s activities. We expect that retail banks,
i.e., banks with a high cost-to-income ratio (CIR), low shares of commission and fee incomes in operating income (NONINTR), and developed
211
distribution channels, as measured by the relative size of a given bank’s
tangible fixed assets (RFIXA), should pay lower interest costs. Third, we
assume that foreign banks (FGN) and state-owned banks (GOV) enjoyed
a reputational advantage over domestically controlled competitors, in the
case of foreign-owned banks, at least until the recent fiscal crisis. Fourth
and finally, the level of interest costs is affected by macroeconomic and regulatory factors. Therefore, we include in all regressions binary variables
(TxC jt ) that control for specific conditions of banking business conduct in
a given year and in a given country.
Differentiating between TBTF banks and other banks in not a trivial
task. Hence, to assure that our empirical results thoroughly portray the relationship between bank size and interest costs, we have decided to use
several methods for identifying TBTF banks. Consequently, the exact
composition of the set of experimental variables is changeable. Below, we
describe the alternative methods applied in this article.
The uniform approach. First, we compute the quotient of a bank’s assets
and GDP of a given country in a given year (ASSETS/GDP). Next, we define a series of binary variables according to Equation (3):
ì1 when ASSETS/ GDPit > k %
TBTFk = í
î0 otherwise
(3)
Thus, the TBTFk variables equal one when the bank size surpasses
a threshold – which is uniformly set for all CE countries. However, this
simple method has a potential drawback; it does not account for the differences in the financial intermediation in the studied countries.
The continuous approach. To present the scale of operation continuously, we use the variable ASSETS/GDP and its squared values
(ASSETS/GDP2). The latter underline the significance of large differences
in the size of banks.
The relative continuous approach. Contrary to the uniform measure and
the continuous measure, the third method assesses the scale of a bank’s
operation by comparing it with the assets of the biggest bank or all banks
in a given country. We name the variables determined in this manner
ASSETSvsBIGG and ASSETSvsALL, respectively. As in the previous case,
we incorporate the squared values of the relative continuous measures
into our model (ASSETSvsBIGG2 and ASSETSvsALL2).
The arbitrary approach. Because depositors are usually not sophisticated investors, in this approach, we identify TBTF organisations alterna-
212
tively, as the biggest bank or the group of banks composed of the three or
five biggest banks in a given country. We obtain the following binary variables in this manner: TBTFone, TBTFthree, and TBTFfive. Additionally,
due to the limited number of banks in certain CE countries, we impose the
condition that the assets of a TBTF bank in relation to GDP must exceed
5%. In this way, we avoid the situation in which the majority of banks are
designated TBTF in small countries.
The method based on medoids. Following Oliveira et al. [2011], we identify banks’ clusters using the k-medoids method. We choose three variables to determine the composition of the TBTF bank groups. The variables NONINTR and RFIXA select retail banks, and a third variable,
ASSETS/GDP, selects banks with large scales of operation. Alternatively,
we isolate three, five or ten groups of banks. In each case, we consider one
group of banks as TBTF. For these banks, the binary variables TBTFmed3,
TBTFmed5, and TBTFmed10 are equal to one.
4. Empirical results
To estimate static panel models, we use a random effects GLS estimator [Verbeek 2000, p. 316-318]. In the case of dynamic panel models, we
apply the GMM-SYS procedure proposed by Blundell and Bond [1988].
Table 1 presents our baseline results obtained for the uniform approach to
identify TBTF banks. As we can see, both the static and dynamic models
have good econometric properties. All independent variables in Panels A
and B of Table 1 are jointly statistically significant at the level lower than
1%. The static models explain almost 90% of the sample variance in
RINTCOSTS. In the case of the dynamic models, the Sargan test of
overidentifying restrictions, based on a two-step GMM estimator, confirms the validity of the instruments. The AR(1) and AR(2) tests suggest
that the disturbances are not serially correlated because there is evidence
of significant negative first-order serial correlation in the differenced residuals and no evidence of second-order correlation in the differenced residuals [Doornik, Hendry 2009, p. 39]. As a result, statistical inference is
possible.
The independent variables from groups MD, BC and OS impact the
level of real interest costs in the expected directions when they are statistically significant. In Panel A of Table 1, lower interest costs are recorded by
banks with a solid capital base (EQUITY) and owned by foreign-investors
(FGN). In contrast, a higher share of loans in assets (LOANS) augments
213
the real interest costs. After introducing the lagged dependent variable in
Panel B of Table 1, the variables LOANS and FGN preserve their significance. Additionally, in specifications (7) to (10), banks with developed distribution channels (DRFIXA) report lower real interest costs.
The evidence in Table 1 does not support H1. All the coefficients estimated for the TBTFk variables are insignificant when the random effects
estimator is applied. Moreover, three coefficients have unexpected, positive signs. After substituting the GMM procedure for the GLS method, all
the coefficients for the TBTFk variables become negative. However, the
null hypothesis that the parameters are equal to zero can be rejected at the
10% level only for banks with assets exceeding 18% of a given country’s
GDP (TBTF18). Moreover, the p-value in the t-test for the TBTF24 variable
is 15%, so it surpasses slightly the conventional levels.
Table 1. The determinants of interest costs of banks – the uniform approach to
identifying TBTF banks
A. Static panel models
(1)
ROA
LOANS
(2)
CIR
NONINTR
RFIXA
GOV
ASSETS/GDP
(5)
-0.0111
-0.0110
-0.0112
-0.0109
(0.0145)
(0.0145)
(0.0145)
(0.0145)
(0.0145)
0.0179
***
-0.0121
0.0181
***
(0.0022)
***
-0.0118
0.0181
***
(0.0022)
***
-0.0118
0.0180
***
(0.0022)
***
-0.0117
0.0181
***
-0.0116
(0.0039)
(0.0039)
(0.0039)
(0.0039)
0.0015
0.0014
0.0014
0.0015
0.0016
(0.0016)
(0.0016)
(0.0016)
(0.0016)
(0.0016)
0.0023
0.0026
0.0026
0.0026
0.0026
(0.0025)
(0.0025)
(0.0025)
(0.0025)
(0.0025)
-0.0015
-0.0008
-0.0007
-0.0011
-0.0012
(0.0024)
(0.0023)
(0.0023)
(0.0024)
(0.0024)
-0.0002
-0.0001
-0.0001
-0.0001
-0.0001
-0.0029
(0.0014)
***
-0.0029
(0.0014)
**
-0.0029
(0.0014)
**
-0.0029
***
(0.0022)
(0.0039)
(0.0014)
FGN
(4)
-0.0115
(0.0022)
EQUITY
(3)
***
(0.0014)
**
-0.0029
(0.0011)
(0.0011)
(0.0011)
(0.0011)
(0.0011)
-0.0127
-0.0049
-0.0056
0.0018
0.0044
(0.0124)
(0.0132)
(0.0143)
(0.0144)
(0.0140)
***
214
Table 1. (cont. tab.)
TBTF6
0.0025
(0.0015)
TBTF9
0.0003
(0.0019)
TBTF15
0.0005
(0.0026)
TBTF18
-0.0016
(0.0025)
TBTF24
-0.0030
(0.0030)
Constant
-0.1091
***
(0.0130)
Observations
2613
Wald (joint)
84.94
R2
0.896
-0.1087
***
(0.0131)
82.47
***
(0.0131)
2613
***
-0.1084
0.896
82.44
***
(0.0131)
2613
***
-0.1089
0.896
82.81
***
(0.0131)
2613
***
-0.1091
2613
***
83.46
0.896
0.896
(9)
(10)
***
B. Dynamic panel models
(6)
DRINTCOSTS
DROA
0.3774
(7)
***
DEQUITY
DCIR
DNONINT
DRFIXA
DGOV
***
0.3771
***
0.3764
***
0.3765
(0.0480)
(0.0480)
(0.0480)
(0.0480)
(0.0480)
-0.0188
-0.0195
-0.0193
-0.0198
-0.0190
(0.0200)
DLOANS
0.3769
(8)
0.0158
(0.0201)
***
0.0158
(0.0200)
***
0.0158
(0.0201)
***
0.0153
(0.0200)
***
0.0157
(0.0052)
(0.0052)
(0.0052)
(0.0052)
(0.0052)
0.0013
0.0014
0.0011
0.0009
0.0010
(0.0063)
(0.0063)
(0.0063)
(0.0063)
(0.0063)
-0.0011
-0.0012
-0.0012
-0.0009
-0.0010
(0.0023)
(0.0023)
(0.0023)
(0.0023)
(0.0023)
0.0016
0.0017
0.0017
0.0016
0.0017
(0.0026)
(0.0027)
-0.0028
-0.0030
(0.0018)
(0.0018)
(0.0018)
(0.0019)
(0.0021)
-0.0005
-0.0005
-0.0005
-0.0006
-0.0005
(0.0011)
(0.0011)
(0.0011)
(0.0011)
(0.0011)
(0.0027)
*
-0.0030
(0.0026)
*
-0.0045
***
***
(0.0026)
**
-0.0043
**
215
DFGN
DASSETS/GDP
DTBTF6
-0.0038
***
-0.0038
***
-0.0038
***
-0.0040
***
-0.0040
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
0.0041
0.0075
0.0031
0.0181
0.0123
(0.0134)
(0.0146)
(0.0141)
(0.0139)
(0.0143)
***
-0.0009
(0.0013)
DTBTF9
-0.0015
(0.0017)
DTBTF15
-0.0010
(0.0020)
DTBTF18
-0.0034
*
(0.0020)
DTBTF24
-0.0038
(0.0026)
Constant
0.0532
***
0.0532
***
0.0533
***
0.0533
***
0.0533
(0.0088)
(0.0088)
(0.0088)
(0.0088)
(0.0088)
Observations
2475
2475
2475
2475
2475
Wald (joint)
217.6
Sargan test
(two-step)
169.1
AR(1) test
-4.437
AR(2) test
0.269
***
225.4
***
167.9
***
-4.438
0.2729
206.5
***
169.4
***
-4.437
0.2714
210.3
***
164.4
***
-4.43
207.9
***
***
162.3
***
0.2623
-4.432
***
0.2654
Notes: All models include the time and country dummies (TxC). This table presents the
GLS random effects and the one-step GMM-SYS estimates. The robust standard errors are
given in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Source: Own calculations.
To check the robustness of our main empirical results and to verify
H2, we have used, as noted in Section 2, four others approaches to identify
TBTF banks. Because the econometric properties of the models and the estimations outcomes for the variables from groups MD, BC and OS are
highly stable, we have decided to present only the results for the set of experimental variables (EV)1 in Table 2. In the vast majority of cases, the coefficients obtained for the experimental variables are negative, as stipu1
The full estimation results are available from the authors upon request.
216
lated by H1. However, they are never statistically significant at the
conventional levels. Therefore, our evidence falsifies H2 because our results, reported in Tables 1 and 2, are not sensitive to the method used to
identify TBTF banks.
Table 2. The determinants of interest costs of banks – the other approaches to
identifying TBTF banks
Static panel model
Dynamic panel model
The continuous approach
(11)
ASSETS/GDP
(12)
(13)
-0.004
(14)
(0.013)
0.000
-0.001
DASS
ETS/G
DP
(0.025)
ASSETS/GDP2
(16)
0.008
DASS
ETS/G
DP
(0.011)
ASSETS/GDP
(15)
(0.014)
0.001 -0.000
-0.012 -0.012 DASS
ETS/G
DP2
(0.066) (0.030)
(0.003) (0.021)
The relative continuous approach
(17)
ASSETSvsBIGG
ASSETSvsALL
(18)
(19)
-0.004
-0.001
(0.003)
(0.007)
-0.009
(21)
DASS
ETSvs
BIGG
-0.013 DASS
ETSvs
ALL
(0.008)
ASSETSvxBIGG
2
(20)
(0.016)
-0.004
(0.006)
DASS
ETSvx
BIGG2
(22)
(23)
-0.001
0.010
(0.004)
(0.009)
(24)
-0.001
-0.007
(0.009)
(0.019)
-0.014
(0.009)
217
ASSETSvsALL2
0.003
0.007 DASS
ETSvs
ALL2
(0.024)
(0.030)
The arbitrary measure
(25)
TBTFone
(26)
(27)
0.001
(28)
DTBT
Fone
(0.002)
TBTFthree
(29)
(30)
-0.002
(0.002)
-0.000
DTBT
Fthree
-0.003
(0.002)
TBTFfive
(0.002)
-0.001 DTBT
Ffive
-0.002
(0.003)
(0.001)
The approach based on medoids
(31)
TBTFmed3
(32)
(33)
0,001
(34)
DTBT
Fmed3
(0,002)
TBTFmed5
(36)
-0,000
(0,002)
-0,000
DTBT
Fmed5
(0,002)
TBTF med10
(35)
-0,002
(0,002)
-0,001 DTBT
F
med10
(0,003)
-0,001
(0,002)
Notes: All models include the variables from the groups MD, BC and OS as well as the time
and country dummies (TxC). The lagged dependent variable is present in the dynamic
models. This table presents the GLS and the one-step GMM-SYS estimates. The robust
standard errors are given are given in parentheses. ***, **, and * indicate significance at the
1%, 5%, and 10% levels, respectively.
Source: own calculations.
218
Conclusion
After controlling for the determinants of real interest costs other than
those related to the sheer size of the bank, we find almost no evidence that
TBTF banks have a competitive advantage in the CE deposit markets.
Moreover, our results are robust to the changes in the methods used to
identify TBTF banks. Thus, the empirical evidence contradicts both of the
hypotheses stated in Section 2. The apparent insignificance of the TBTF
status in CE countries is surprising when we consider the previous empirical findings reported in Section 1. We believe that this difference may be
related to three factors. First, during the last 20 years, CE countries underwent deep institutional and economic changes. Among other reforms,
they introduced explicit deposit insurance systems and considerably increased coverage limits. Consequently, the TBTF status could play a more
important role during early stages of transformation than later on. Second, Hasan et al. [2012] showed that CE banks controlled by distressed
foreign owners had some difficulties in attracting new deposits during the
2007-2009 period. This situation could reverse the usual negative direction
of the relationship between the TBTF status and interest costs because
large banks in CE countries are predominantly owned by foreign investors from the countries most severely hit by the recent crisis. Third, the
reputational capital of banks is shaped not only by the stability of financial
results and the size of operations but also by the ownership structure.
Thus, in our sample, the effect of being a state- or foreign-owned organisation (at least until 2007) may overshadow the implications of the large
scale of the bank’s activities. Each of these explanations indicates a promising field for further research. We plan to investigate these topics in our
future works because we were unable to address them properly in this
short research note. The aforementioned further research should, in our
opinion, also use the latest advances in financial networks modelling [Karas, Schoors, 2012; Krause, Giansante, 2012; Markose et al., 2012]. It would
be interesting, for example, to verify how banks’ interest costs are influenced by the measures based on the concepts of centrality and K-coreness.
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Too-big-to-fail status and interest costs of banks. The evidence from
Central European countries (Summary)
The article explores whether the TBTF status influences interest costs reported by banks in Central European countries. We use a comprehensive dataset
covering the period from 1994 to 2009 and the different methods of identifying
TBTF banks. After controlling for other determinants of interest costs, we find
220
very little evidence that the big banks in Central European countries incur lower
interest costs. Our results are robust to the changes in the estimation procedures.
Keywords
banking, TBTF, deposit market, competition

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