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JOURNAL OF BUSINESS MANAGEMENT AND ECONOMICS
Homepage: http://innovativejournal.in/jbme/index.php/jbme
Bank Characteristics, Bank Lending Channel, and Monetary Policy Transmission in
Taiwan
Jung-Chu Lin
Associate Professor, Department of Banking and Finance, Takming University of Science and Technology
No. 56, Se c. 1 , Hu ansh an Rd. , N e ihu D is tr ic t, Ta ipe i, Ta iw an 11451, R.O. C.
E-mail: [email protected]
DOI: http://dx.doi.org/10.15520/jbme.2016.vol4.iss6.198.pp01-13
Abstract: This study investigates the bank lending channel (BLC) as a mechanism for the monetary policy transmission in Taiwan where banks
are an important source of funding. Using a panel of bank-level data over the 1993-2013 period, I examine particularly whether the influence of
monetary policy impulses on bank lending differs with the three bank characteristics: bank size, capitalization, and liquidity. The empirical
results demonstrate the existence of the BLC in Taiwan. They also evidence that all the three bank characteristics play an important role to
capture the distributional effects of monetary policy stance, yet the capitalization seems to play the most significant role in distinguishing banks’
reactions to monetary policy changes. The results further indicate that larger and more capitalized, larger and more liquid, or less capitalized and
less liquid banks are less responsive to monetary policy changes.
JEL: E52, E58
Keywords: Bank lending channel, monetary policy transmission, bank size, bank capitalization, Taiwan
the “external finance premium” and subsequent cost and
INTRODUCTION
availability of credit (Ananchotikul and Seneviratne, 2015).
The traditional Keynesian view believes that, by influencing
The credit channel has traditionally been divided into two
real interest rates, monetary policy may impact investment
separate channels: the balance sheet channel (the broad
demand, thereby influencing the real economy. However,
credit channel) and the bank lending channel (BLC,
many studies, e.g. Bernanke and Gertler (1995), have
Bernanke and Gertler, 1995). The BLC stresses the potential
evidenced that investment decisions are more influenced by
effect of monetary policy changes on the supply of bank
other factors such as expected cash flows than by the cost of
credit and., in turn, the real economy due to imperfect
capital represented by interest rates. Therefore, other than the
substitutes between deposits and other sources of finance
traditional interest rate channel, a number of alternative
and the lack of similar substitutes for bank credit for
mechanisms for the transmission of monetary policy are
borrowers.
advanced and fall into two categories: the asset price (e.g.
foreign exchange rates, stock prices and real wealth prices)
As the primary financing source for the corporate sector and
channel and the credit channel.
households in emerging Asia, banks have been an important
conduit for the transmission of domestic monetary policy
The credit channel emphasizes the role of imperfect
(Ananchotikul and Seneviratne, 2015). The total size of
information and other frictions in credit markets that
banks’ private credit in emerging Asia is roughly 70-80
amplify the effects of monetary policy shocks by changing
percent of GDP. In Taiwan, the proportion of indirect
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
finance is currently as high as 78.88% and has been
The remainder of the paper is organized as follows. Section
climbing to its high of 90% in 1993. Although capital
2 outlines the critical literature. Section 3 explains the
markets have been growing quite rapidly in most emerging
empirical methodology and the model specification. Section
Asian countries, access to these markets as an alternative
4 describes the data. Sections 5 and 6 present the empirical
source of funding is still limited to very large firms.
results and the conclusion, respectively.
Therefore, discerning how banks react to a change in
LITERATURE REVIEW
monetary policy is important for understanding how
monetary policy affects the real economy.
Evidence indicates that banks are not passive intermediaries
between the central bank and end users of funds. The role of
This paper examines the effectiveness of the BLC as a
banks in monetary policy transmission has been extensively
mechanism for the transmission of monetary policy in
studied under the view of the credit channel. Earlier studies
Taiwan. Since banks across Taiwan are far from
attempt to test the existence of the BLC using aggregate data
homogeneous in key characteristics that may impact the
(Bernanke and Blinder, 1992; Kashyap et al., 1993) and
policy transmission, this paper especially analyzes whether
discover that banks systematically adjust their fund
the response of bank loans to shifts in monetary policy
allocation in response to the monetary policy shocks, which,
differs with the banks’ characteristics or financial strengths.
in turn, confirm the presence of the BLC for the transmission
Considering that the aggregate response to monetary policy
of monetary policy. Kashyap et al. (1993) also demonstrate
may mask significant variation of responses at the level of
that while a tight monetary policy decreases banks’ loan
individual banks, I use a bank-level data analysis to allow
supply, it increases the commercial paper issuance.
for the heterogeneity across banks and to separate out the
factors that determine bank lending behavior.
However, the main obstacle in testing the BLC is the
identification
problem:
how
to
distinguish
between
For the bank-level analysis, I construct an unbalanced panel
movements in credit aggregates due to demand shifts
dataset comprising a cross-section of 36 banks and time
induced by effects of the traditional interest rate channel and
series over a 21-year period from 1993 to 2013 to carry out
movements due to supply factors that constitutes a distinct
an empirical examination that uses a dynamic panel
BLC? To overcome this problem, recent studies have
estimation approach. The empirical results demonstrate the
increasingly resorted to bank-level data, verifying the
existence of the BLC in Taiwan. The results also evidence
existence of the BLC by showing that banks with different
that all the three bank characteristics play a role to capture
characteristics respond differently to monetary policy
the distributional effects of monetary policy stance, yet the
changes depending on their ability to shield their loan
capitalization seems to play the most significant role in
supply from the negative shocks (Ananchotikul and
distinguishing banks’ reactions to monetary policy changes.
Seneviratne, 2015). Banks with weaker financial strengths
The results further indicate that larger and more capitalized,
(i.e. having a higher external finance premium) will be
larger and more liquid, or less capitalized and less liquid
forced to reduce their supply of loans more in a monetary
banks are less responsive to monetary policy changes. The
tightening. Using bank-level data, several studies find
study contributes to the understanding of the BLC in Taiwan
evidence for the existence of a BLC, while the financial
and the factors that determine bank lending behavior, and, in
characteristics are proxied by bank size (Altunbas et al.,
turn, allows us to draw important implications for monetary
2002; Ehrmann et al, 2003; Kakes and Sturm, 2002;
and financial sector policies.
Kashyap and Stein, 1995a, 1995b, 2000; Kishan and Opiela,
2000; Matousek and Sarantis, 2009), liquidity (Ehrmann et
2
Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
METHODOLOGY AND MODELS
al, 2003; Gambacorta, 2005; Kashyap and Stein, 2000;
Khwaja and Mian, 2008; Matousek and Sarantis, 2009; Stein,
Two approaches are usually employed to verify the
1998), and capitalization (Altunbas et al., 2002; Ehrmann et
mechanism of the BLC for monetary policy transmission by
al, 2003; Gambacorta, 2005; Kishan and Opiela, 2000, 2006;
showing that banks with different characteristics respond
Matousek and Sarantis, 2009; Peek and Rosengren, 1995a,
differently to monetary policy changes. The first approach
1995b). Bank ownership has also been used as a proxy for
involves classifying banks based on their characteristics and
financial characteristics when testing the existence of the
comparing their different responses to monetary policy
BLC. For instance, Houstan and James (1998) find that the
changes (Kashyap and Stein, 1995a, 1995b, 2000; Kishan
loan growth of banks affiliated with a multi-bank holding
and Opiela, 2000, 2006; Altunbas et al, 2002). This approach
company is less sensitive to their cash flow, liquidity, and
requires a large number of banks so that each classification
capital positions than in the case of unaffiliated banks.
of banks has a sufficient number of banks. While this
Bhaumik et al. (2011) use data of Indian banks and find that
approach is workable in the United States where bank
banks with different ownership respond very differently to
number in their thousands, it is not so applicable in countries
monetary policies in different monetary regimes. Wu et al.
like Taiwan where the number of banks is relatively limited.
(2011) find that foreign banks are less responsive to the
The second approach, therefore, uses a panel model to avoid
monetary shocks in host countries which can be ascribed to
the aforementioned issue associated with the small number
the access of foreign banks to funding from parent banks
of banks and to relate the response of bank loans to
through the internal capital market.
monetary policy both directly via the money channel and to
the bank characteristics by including macroeconomic
An emerging strand of research examines certain aspects of
variables and banks’ characteristics stepwise as explanatory
the banking sector features that may impact the strength of
variables (Bernanke and Blinder, 1988; Ehrmann et al 2003;
the BLC of monetary policy transmission (Ananchotikul and
Matousek and Sarantis, 2009). This paper adopts the second
Seneviratne, 2015). Olivero et al. (2011a, 2011b) investigate
approach.
the role of market structure in the banking sector and
evidence
that
increases
in
bank
competition
and
When applying the second approach, I assume three bank
consolidation weaken the BLC, potentially due to a
characteristics: bank size, capital strength, and liquidity to
reduction in information asymmetries in the credit market
examine particularly whether the impact of monetary policy
and better access to alternative sources of funding for larger
changes on bank lending differs in those bank characteristics.
and more competitive banks, thereby making loan supply
Five groups of models of the bank loans, drawn upon
less sensitive to interest rate shocks. Wu et al. (2011)
Bernanke and Blinder (1988) and Matousek and Sarantis
indicate that the rise in foreign banks’ penetration has
(2009), are developed and specified as follows. While the
further weakened the BLC in emerging Asia and Latin
first group of models examine the response of the growth of
America. Moreover, Altunbas et al. (2009) find that the
bank loans to monetary policy stance that is measured by the
dramatic increase in securitization activity in Europe has
interest rate, groups 2 to 4 additionally allow for bank
weakened the effectiveness of the BLC, as it allows greater
characteristics:
access for banks to liquidity without the need to expand
capitalization,
and
liquidity,
respectively, and group 5 takes into account two or three
balances sheets and hence the ability to continue lending in
bank characteristics at a time in a model.
the face of a monetary tightening.
Group 1: the bank lending, interest rate changes, and other macroeconomic variables
Model 1.1:
size,
LDGit = α + β 0 ∆Rt + ε it
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
Model 1.2:
LDGit = α + β 0 ∆Rt + χ 0 ∆GDPt + δ 0 ∆CPI t + ε it
Model 1.3:
LDGit = α + β 0 ∆Rt + β1 ∆Rt −1 + χ 0 ∆GDPt + χ 1 ∆GDPt −1
+ δ 0 ∆CPI t + δ 1 ∆CPI t −1 + ε it
Model 1.4:
LDGit = α + β1 ∆Rt −1 + χ 0 ∆GDPt + χ 1 ∆GDPt −1 + ε it
Group 2: the bank lending, bank size, interest rate changes, and demand for loans
Model 2.1:
LDGit = α + β1 ∆Rt −1 + χ 0 ∆GDPt + χ 1 ∆GDPt −1 + φ1 Sizeit −1 + ε it
Model 2.2:
LDGit = α + β1 ∆Rt −1 + χ 0 ∆GDPt + χ 1 ∆GDPt −1 + φ1 Sizeit −1
+ ϕ1Sizeit −1∆Rt −1 + ε it
Model 2.3:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + φ1Sizeit −1
+ ϕ0 Sizeit −1∆Rt + ϕ1Sizeit −1∆Rt −1 + ε it
Model 2.4:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + φ1Sizeit −1
+ ϕ0 Sizeit −1∆Rt + ϕ1Sizeit −1∆Rt −1 + ε it
Group 3: the bank lending, bank capital, interest rate changes, and demand for loans
Model 3.1:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + γ 1CARit −1 + ε it
Model 3.2:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + γ 1CARit −1
+ η1CARit −1∆Rt −1 + ε it
Model 3.3:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + γ 1CARit −1
+ η0CARit −1∆Rt + η1CARit −1∆Rt −1 + ε it
Model 3.4:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + γ 1CARit −1
+ η0CARit −1∆Rt + η1CARit −1∆Rt −1 + ε it
Group 4: the bank lending, bank liquidity, interest rate changes, and demand for loans
Model 4.1:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + λ1LRRit −1 + ε it
Model 4.2:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + λ1LRRit −1
+ µ1LRRit −1∆Rt −1 + ε it
Model 4.3:
LDGit = α + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + λ1LRRit −1
+ µ0 LRRit −1∆Rt + µ1LRRit −1∆Rt −1 + ε it
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
Model 4.4:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + λ1LRRit −1
+ µ0 LRRit −1∆Rt + µ1LRRit −1∆Rt −1 + ε it
Group 5: the bank lending, bank characteristics, interest rate changes, and demand for loans
Model 5.1:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + φ1Sizeit −1
+ ϕ 0 Sizeit −1∆Rt + ϕ1Sizeit −1∆Rt −1 + γ 1CARit −1 + η 0CARit −1∆Rt
+ η1CARit −1∆Rt −1 + ψ 0 Sizeit −1CARit −1∆Rt + ψ 1Sizeit −1CARit −1∆Rt −1 + ε it
Model 5.2:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + φ1Sizeit −1
+ ϕ0 Sizeit −1∆Rt + ϕ1Sizeit −1∆Rt −1 + λ1LRRit −1 + µ0 LRRit −1∆Rt
+ µ1LRRit −1∆Rt −1 + ν 0 Sizeit −1LRRit −1∆Rt + ν 1Sizeit −1LRRit −1∆Rt −1 + ε it
Model 5.3:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + γ 1CARit −1
+ η0CARit −1∆Rt + η1CARit −1∆Rt −1 + λ1LRRit −1 + µ0 LRRit −1∆Rt
+ µ1LRRit −1∆Rt −1 + τ 0CARit −1LRRit −1∆Rt + τ 1CARit −1LRRit −1∆Rt −1 + ε it
Model 5.4:
LDGit = α + β 0 ∆Rt + β1∆Rt −1 + χ 0 ∆GDPt + χ1∆GDPt −1 + φ1Sizeit −1
+ ϕ0 Sizeit −1∆Rt + ϕ1Sizeit −1∆Rt −1 + γ 1CARit −1 + η0CARit −1∆Rt
+ η1CARit −1∆Rt −1 + λ1LRRit −1 + µ0 LRRit −1∆Rt + µ1LRRit −1∆Rt −1 + ε it
be
LDGit is the loan and discount loan growth rate of bank i
for
the
three
considered
bank
characteristics—bank size, capitalization, and liquidity,
respectively and defined as follows:
at time t where i = 1, ..., N, N is the number of banks, t = 1, ...,
T, and T is the data period,
proxies
Size = Natural logarithm of banks’ total assets
LDGit which is used as an
CAR = (Total risk-based capital / risk-weighted assets) ×
indicator of the bank lending behavior and defined as
100%
follows:
LRR = (Current assets / deposits that should provide
liquidity reserve) × 100%
LDGit = {[(Loan and discount loan of bank i at time t) –
(loan and discount loan of bank i at time t-1)] / the absolute
Models of groups 2 to 5 also incorporate the interaction term
value of the loan and discount loan of bank i at time t-1}×
of the specific bank characteristics with the interest rate
100%
changes to capture the distributional effects of monetary
policy stance. As discussed in Matousek and Sarantis (2009),
∆Rt is the first-order difference of the nominal re-discount
it is assumed that small, less capitalized, and less liquid
interest rate that is used to indicate the monetary policy
banks should be more responsive to monetary policy
stance. ∆GDPt and ∆CPI t
changes compared to large, more capitalized, and more
are the nominal gross
domestic products (GDP) growth rate and the consumer
liquid banks. Thus, three related hypotheses
price index (CPI) growth rate, respectively, which are
∂ 2 LDGit ∂∆Rt −1∂Sizeit −1 > 0
proxies for the demand for loans. Size, capital adequacy
ratio (CAR), and liquidity reserve ration (LRR) are used to
,
∂ 2 LDGit ∂∆Rt −1∂Capit −1 > 0 ,
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
and ∂ 2 LDGit ∂∆Rt −1∂Liqit −1 > 0 should be tested to
effectively capture the distributional effects of monetary
prove the existence of the BLC. The first hypothesis implies
data other than macroeconomic data are used for empirical
that the lending of large banks is less responsive to a change
estimation in this study.
policy through the BLC. Hence, disaggregated bank-level
in monetary policy stance than the lending of small banks.
The second hypothesis implies that the more capitalized
All these data, on a yearly frequency, are obtained from the
banks are less sensitive to a change in monetary policy
Taiwan Economic Journal (TEJ) Data Bank. The pooled
stance than the less capitalized banks. Finally, the last
bank-level data forms an unbalanced panel comprising a
hypothesis suggests that more liquid banks are less affected
cross-section of 36 banks and time series over a 21-year
by a monetary tightening than less liquid banks.
period from 1993 to 2013 that amounts to 586 to 701
observations
DATA
in
total.
The
dynamics
of
the
three
macroeconomic data: nominal GDP growth rate, CPI
As macroeconomic time series do not help identify a BLC, a
growth rate and re-discount rate decided by the central bank,
sub-channel of the credit channel (Bernanke and Blinder,
each of them is a proxy for loan demand, macroeconomic
1992; Matousek and Sarantis, 2009), aggregate data do not
stance and monetary policy stance, respectively—are
allow us to distinguish between supply and demand factors
depicted as Figure 1.
that affect the bank lending activities and may mask
significant variation of responses at the level of individual
banks. Disaggregated data on banks, on the other hand, may
14
12
10
8
Nominal GDP growth rate -- yearly (%)
6
CPI growth rate -- yearly (%)
4
Re-discount rate -- yearly (%)
2
13
20
11
20
09
20
07
20
05
20
03
20
01
20
99
19
97
19
95
19
93
19
19
91
0
-2
-4
Figure 1 The dynamics of the macroeconomic variables and the re-discount rate of the central bank, 1991 to 2013
BLC, the growth of bank loans significantly responds to
EMPIRICAL RESULTS
monetary policy stance that is measured by the interest rate
The estimates of the models of groups 1 to 5 are presented
changes, resulting in a negative relationship between them.
in Tables 1 to 5, respectively. The parameter estimates of the
The
models of group 1 show that, as predicted by the view of
relationship
remains
negative
after
the
bank
characteristic, size, is introduced in the models (Table 2) but
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
becomes positive when the other two characteristics, bank
The interaction terms of the specific characteristics with the
capitalization and liquidity, are considered separately into
interest rate changes are incorporated in models of groups 2
the models (Tables 3 and 4). When pairs of size and
to 5 to capture the distributional effects of monetary policy.
capitalization and size and liquidity are considered in the
In the specifications with bank size only (Table 2), the
models, the bank loans appear to respond positively to the
coefficients of the interaction terms between the bank size
interest rate changes. However, the picture changes when
and the monetary policy indicator (interest rate change) are
the pair of capitalization and liquidity or all the three
statistically significant positive, which support the first
characteristics altogether are considered in the models
hypothesis, i.e. the lending of large banks is less responsive
(Table 5).
to a change in monetary policy stance than the lending of
small banks. In the specification with bank size and
Moreover, the growth of bank loans also responds
capitalization at the same time (Model 5.1), the coefficient
significantly to the demand for loans that is measured by the
of the interaction term among the bank size, the
GDP growth rate, resulting in a positive relationship
capitalization, and the monetary policy indicator displays a
between them (Table 1). Such relationship remains
significant positive sign, which indicates that larger and
unchanged no matter which bank characteristic is considered
more capitalized banks are less sensitive to a change in
in the models (Tables 2 to 4). Even in the models estimated
monetary policy stance than smaller and less capitalized
with all possible pairs of characteristics and with all the
banks. In the specification with bank size and liquidity at the
three characteristics altogether, bank loans still respond
same time (Model 5.2), the coefficient of the interaction
positively to the GDP changes.
term among the bank size, the liquidity, and the monetary
policy indicator also displays a significant positive sign,
In the cases where each bank characteristic enters on its own,
which indicates that larger and more liquid banks are less
the results show that bank size itself has a significant
sensitive to a change in monetary policy stance than smaller
negative sign in all models of group 1, which are identical to
and less liquid banks. It is meaningful to highlight that the
the results of Matousek and Sarantis (2009) and support the
findings of a significant role for bank size are in line with
hypothesis that small banks have higher dynamic of lending
those reported in studies on other emerging countries
activities compared to large banks. When two or three
(Horvath et al., 2006; Matousek and Sarantis, 2009;
characteristics enter simultaneously, bank size still has a
Pruteanu, 2004; Wrobel and Pawlowska, 2002), which is
significant negative relationship with the LDG. Moreover,
probably due to the fact that both Taiwan and these
capitalization shows the significant positive coefficients for
emerging countries have proportionately more small banks
all models that consider capitalization alone or capitalization
in their countries.
with other bank characteristics as explanatory variables,
which support the hypothesis that more capitalized banks
Bank capitalization appears to play an important role in
have higher dynamic of lending activities compared to less
distinguishing banks’ response to changes in monetary
capitalized banks. Finally, liquidity has the expected positive
policy. Its interaction term with the interest rate changes is
coefficient and is significant in most models whether it
consistently statistically significant in the specifications with
enters the regression on its own or with other bank
the only characteristic and with two or three characteristics.
characteristics, indicating that more liquid banks have
In the specifications with bank capitalization only (Table 3),
higher dynamic of lending activities compared to less liquid
the coefficients of the interaction terms between the bank
banks.
capitalization and the interest rate changes are statistically
significant negative, which do not support the second
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
hypothesis. In the specification with capitalization and
et al. (2002) and Gambacorta (2005), both who find a
liquidity at the same time (Model 5.3), the coefficient of the
significant role for capitalization.
interaction terms between the bank capitalization and the
interest rate changes turns out to be statistically significant
positive while the coefficient of the interaction term among
CONCLUSION
the capitalization, the liquidity and the monetary policy
This paper investigates the role of the BLC as a mechanism
indicator displays a significant negative sign. The negative
for the transmission of monetary policy in Taiwan. Since
sign of the coefficient of the interaction term among the
banks across Taiwan are far from homogeneous in key
capitalization, the liquidity and the monetary policy
characteristics that may impact the policy transmission, this
indicator indicates that more capitalized and more liquid
paper especially analyzes whether the response of bank
banks are more responsive to a change in monetary policy
loans to monetary policy shifts differs with banks’
stance than less capitalized and less liquid banks, which may
characteristics. Considering the aggregate response to
be explained by the empirical finding of Lin (2012). Lin
monetary policy may mask significant variation of responses
(2012) demonstrates that in Taiwan, those having higher
at the level of individual banks, this paper uses a bank-level
capitalization and liquidity are private banks with smaller
data analysis to allow for the heterogeneity across banks and
scales relative to state-owned banks. These private banks’
to distinguish the influence of different factors on bank
status and attitude to monetary policy make them more
lending behavior. As Taiwan has been committed to
responsive to monetary policy changes.
enhancing its integration into the regional economic
cooperation and hopefully even taking part in the regional
In the specifications where bank liquidity enters the
monetary union which may be led by the great China in the
regression on its own (Table 4), the coefficients of the
future, an understanding of how banks respond to changes in
interaction terms between the bank liquidity and the interest
monetary policy stance is timely and critical to policy
rate changes are statistically significant negative, which do
making.
not support the third hypothesis. In the specification with
capitalization and liquidity at the same time (Model 5.3), the
To examine particularly whether the impact of monetary
coefficient of the interaction terms between the bank
policy
liquidity and the interest rate changes turns out to be
changes
on
bank
lending
differs
in
bank
characteristics, this paper employs a model for bank loans
statistically significant positive. In fact, the second and third
that relates the response of bank loans to monetary policy to
hypotheses establish only in Model 5.3 where bank
the money channel and to the bank characteristics. The
capitalization and liquidity are considered at the same time.
presence of the bank lending channel is tested by
considering three characteristics: bank size, capitalization
Models of group 5 comprehensively consider two or three
and liquidity and by empirical models that incorporate the
bank characteristics in their specifications at the same time.
interaction term of the specific characteristics with the
When all the three bank characteristics enter the regression
interest rate change to capture the distributional effects of
(Model 5.4), only the coefficient of the interaction term
monetary policy stance. The three related hypotheses, if
between the capitalization and the interest rate changes is
larger, more capitalized or more liquid banks are less
statistically significant, indicating that in Taiwan, bank
responsive to a monetary policy change, are also tested to
capitalization appears to play the most important role in
provide further evidence of the existence of the BLC.
distinguishing banks’ response to monetary policy changes.
Such finding is in line with the results reported by Altunbas
The empirical results demonstrate the existence of the BLC
8
Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
[11]. Houston, J.F., James, C., (1998). Do bank internal capital
markets promote lending? Journal of Banking and Finance
22, 899-918.
in Taiwan. The results also display that all the three bank
characteristics are able to capture the distributional effects of
monetary policy stance. However, when all the three bank
[12]. Kakes, J., Sturm, J.-E. (2002). Monetary policy and bank
lending: Evidence from German banking groups. Journal of
Banking and Finance 26, 2077-2092.
characteristics enter the regression, the results display that
only bank capitalization may discriminate banks’ response
[13]. Kashyap, A.K., Stein, J.C., and Wilcox, D.W. (1993).
Monetary Policy and Credit Conditions: Evidence from the
Composition of External Finance. American Economic
Review, 83(1), 78-98.
to monetary policy, indicating that in Taiwan, bank
capitalization appears to play the most important role in
distinguishing banks’ response to monetary policy changes.
[14]. Kashyap, A.K., Stein, J.C. (1995a). The impact of monetary
policy on bank balance sheets. Carnegie-Rochester
Conference Series on Public Policy 42, 151-195.
Regarding the three hypotheses, the results support only the
first hypothesis, implying that in Taiwan, the lending of
[15]. Kashyap, A.K., Stein, J.C. (1995b). The role of banks in the
transmission of monetary policy. NBER Reporter, Fall,
National Bureau of Economic Research.
large banks is less responsive to a change in monetary policy
stance than the lending of small banks. The results indicate
[16]. Kashyap, A.K., Stein, J.C. (2000). What do a million
observations on banks say about the transmission of
monetary policy? American Economic Review 90, 407-428.
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responsive to monetary policy changes.
[17]. Khwaja, A.I., Mian, A. (2008). Tracing the Impact of Bank
Liquidity Shocks: Evidence from an Emerging
Market. American Economic Review, 98(4), 1413-1442.
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Table 1 Bank loans, interest rate, and other macroeconomic variables
Rate
Model 1.1
Model 1.2
Model 1.3
3.4960
-0.5133
1.4810
(4.1776)***
(0.5441)
(1.3227)
-4.0823
-2.9924
(3.1519)***
(3.1440)***
1.3949
0.7383
1.0673
(7.1067)***
(2.6629)***
(6.2240)***
1.3549
1.3856
Rate(-1)
GDP
Model 1.4
GDP(-1)
***
(7.5502)***
(5.6597)
CPI
0.7332
1.0341
**
(2.2708)
CPI(-1)
(1.1747)
0.5064
(1.0970)
10.7821
1.5257
-2.8574
-2.6555
(17.5170)***
(1.3410)
(2.2962)**
(2.4676)**
No. of observations
701
668
640
673
Adjusted R-squared
0.0230
0.1465
0.2249
Constant
0.2230
* **
The absolute values of the t-statistics for the coefficients of the independent variables are shown in the parentheses. ,
and
***
indicate significance at the 10%, 5%
and 1% levels, respectively. (-1) indicates a one period lag for the variable.
Table 2 Bank loans, bank size, interest rate, and other macroeconomic variables
Model 2.1
Model 2.2
Model 2.3
Rate
Model 2.4
16.4591
(1.7583)*
Rate(-1)
-23.0198
-1.9319
**
GDP
GDP(-1)
Size(-1)
(2.3924)
(2.4677)**
1.0064
1.0177
0.7045
0.6886
***
**
-22.8051
(2.4886)
(6.0465)
(6.1310)
(2.9313)
(2.8678)***
1.0922
1.0793
1.2509
1.2444
(5.9071)***
(5.8528)***
(6.0329)***
(6.0099)***
-3.5737
-3.2006
-3.3309
-3.5695
(6.9451)***
(5.9475)***
(6.1445)***
(6.3971)***
0.1475
-1.1232
(1.7989)*
(1.5443)
1.6393
1.5060
1.5634
(2.2915)**
(2.0975)**
(2.1786)**
Size(-1)*Rate
Size(-1)*Rate(-1)
-22.1248
(2.0551)
***
**
10
***
Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
No. of observations
665
665
665
Adjusted R-squared
0.2833
0.2879
0.2903
665
0.2925
* **
The absolute values of the t-statistics for the coefficients of the independent variables are shown in the parentheses. ,
and
***
indicate significance at the 10%, 5%
and 1% levels, respectively. (-1) indicates a one period lag for the variable. Size(-1) indicates the natural logarithm of bank assets with a one period lag.
Table 3 Bank loans, bank capitalization, interest rate, and other macroeconomic variables
Model 3.1
Model 3.2
Model 3.3
Rate
Model 3.4
-8.3419
(3.2113)***
Rate(-1)
7.2576
-1.7909
**
GDP
CAR(-1)
6.8865
(2.2358)
(2.7848)
(2.2415)
(2.6330)***
0.8321
0.8335
0.3789
0.5452
***
GDP(-1)
5.8633
***
***
**
(5.9287)
(5.9982)
(1.9777)
(2.7666)***
0.8868
0.9149
1.1439
1.0225
(5.4042)***
(5.6247)***
(6.5498)***
(5.7641)***
1.5415
1.3475
1.4274
1.5291
(12.7662)***
(10.2962)***
(10.8269)***
(11.3586)***
0.2484
0.9134
(3.4142)***
(4.1649)***
-0.8301
-0.8067
-0.8453
(3.6448)***
(3.5713)***
(3.7652)***
CAR(-1)*Rate
CAR(-1)*Rate(-1)
**
No. of observations
616
616
616
616
Adjusted R-squared
0.3662
0.3787
0.3894
0.3986
The absolute values of the t-statistics for the coefficients of the independent variables are shown in the parentheses. *, ** and *** indicate significance at the 10%, 5%
and 1% levels, respectively. (-1) indicates a one period lag for the variable. CAR(-1) indicates the bank capital adequacy ratio with a one period lag.
Table 4 Bank loans, bank liquidity, interest rate, and other macroeconomic variables
Model 4.1
Model 4.2
Model 4.3
Rate
Model 4.4
1.3326
(0.7282)
Rate(-1)
GDP
GDP(-1)
LRR(-1)
-4.9517
0.8514
0.7881
0.5883
(5.1199)***
(0.4400)
(0.4076)
(0.3011)
1.1148
1.0807
1.3175
1.2579
(6.6505)***
(6.4939)***
(5.6496)***
(5.0870)***
1.9379
1.9846
1.8534
1.8822
(10.1082)***
(10.4191)***
(8.7934)***
(8.7736)***
0.5027
0.4494
0.4697
0.4639
(8.6681)***
(7.5494)***
(7.6868)***
(7.5258)***
-0.0849
-0.1422
(1.4480)
(1.4494)
LRR(-1)*Rate
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
LRR(-1)*Rate(-1)
-0.3140
-0.2727
-0.2672
(3.4526)***
(2.8640)***
(2.7957)***
No. of observations
607
607
607
607
Adjusted R-squared
0.3323
0.3442
0.3454
0.3449
The absolute values of the t-statistics for the coefficients of the independent variables are shown in the parentheses. *, ** and *** indicate significance at the 10%, 5%
and 1% levels, respectively. (-1) indicates a one period lag for the variable. LRR(-1) indicates the bank liquidity reserve ratio with a one period lag.
Table 5 Bank loans, bank characteristics, interest rate, and other macroeconomic variables
Rate
Rate(-1)
Model 5.1
Model 5.2
Model 5.3
Model 5.4
7.3585
28.9113
-14.8582
-5.0002
(0.2534)
(1.3083)
(2.7278)
(0.5555)
149.0903
64.1016
-16.5318
-7.1726
***
GDP
GDP(-1)
Size(-1)
Size(-1)*Rate
Size(-1)*Rate(-1)
CAR(-1)
***
CAR(-1)*Rate(-1)
(3.1639)
(3.3614)
(0.8379)
0.4854
0.9865
0.7821
0.6470
(2.5049)**
(4.2357)***
(3.9103)***
(3.1632)***
0.8769
1.4265
0.9258
0.9428
(5.0320)***
(6.9789)***
(5.0991)***
(5.0942)***
-1.2541
-4.1257
-1.8354
(2.6514)***
(7.0271)***
(3.4049)***
-1.2268
-2.2352
-0.2679
(0.5322)
(1.3013)***
(0.4037)
-11.3269
-4.7923
0.9836
(4.9533)***
(3.0330)***
(1.5530)
1.5704
1.3688
(10.1479)
(11.1547)
(9.7379)***
-0.1084
1.6394
1.0619
***
(0.0455)
(3.4387)
(4.4105)***
-14.1344
1.2311
-0.5496
***
(2.7720)
(2.0058)**
0.5027
0.0586
0.1996
(8.0115)***
(1.0849)
(3.3923)***
-0.5417
0.1074
-0.1323
(0.4567)
(0.3423)
(1.4250)
-4.4498
0.7587
-0.0657
(4.8589)***
(3.6209)***
(0.6720)
(5.8239)
LRR(-1)
LRR(-1)*Rate
LRR(-1)*Rate(-1)
0.0818
(0.4286)
Size(-1)*CAR(-1)*Rate(-1)
1.0617
(5.5200)***
Size(-1)*LRR(-1)*Rate
1.4354
***
***
Size(-1)*CAR(-1)*Rate
***
(5.1538)
***
CAR(-1)*Rate
***
0.0408
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Jung-Chu Lin al, Journal of Business Management and Economics, 4 (06), June, 2016
(0.4536)
Size(-1)*LRR(-1)*Rate(-1)
0.3200
(4.5710)***
CAR(-1)*LRR(-1)*Rate
-0.0217
(0.8492)
CAR(-1)*LRR(-1)*Rate(-1)
-0.0596
(3.8983)***
No. of observations
613
603
589
Adjusted R-squared
0.4419
0.4422
0.4560
586
0.4398
* **
The absolute values of the t-statistics for the coefficients of the independent variables are shown in the parentheses. ,
and
***
indicate significance at the 10%, 5%
and 1% levels, respectively. (-1) indicates a one period lag for the variable. Size(-1) indicates the natural logarithm of bank assets with a one period lag. CAR(-1)
indicates the bank capital adequacy ratio with a one period lag. LRR(-1) indicates the bank liquidity reserve ratio with a one period lag.
13