Thesis

1
The impact of banking sector concentration level on monetary
policy transmission through the bank lending channel
Abstract: This paper examines the impact of banking sector concentration level on monetary
policy transmission through the bank lending channel. Bank-level panel data for banks from 13
countries, developing countries and developed countries, from 1999 to 2011 are used in this study.
A model with loan growth rate and Lerner Index as a measurement of banking concentration is
estimated by using GMM estimator. The main finding is that banking sector concentration weakens
the effectiveness of monetary policy. And during crisis periods, this offsetting effect decreases.
Policy implications based on the findings are also discussed in this paper.
Key words: Banking concentration
Bank lending channel
Monetary policy transmission

2
1. Introduction
This paper studies the impact of banking sector concentration level on monetary policy
transmission through the bank lending channel and we focus on different responses of banks, in
term of the change in the amount of credit, to monetary policy shocks.
Monetary policy is the tool taken by central banks to realize their economic targets through
adjusting money supply and credit volume. Three main channels, namely, interest rate channel,
asset price channel and credit channel, suggest how monetary policy is transmitted to real economy.
These three channels all work by influencing the components of total output to impact the general
economic activities. Bank lending channel is one part of the credit channel. When government
adopts a contractionary monetary policy by reducing money supply, it leads to a decrease in the
scale of loans, followed by a decrease in total investment and finally a decrease in total output.
Thus, bank lending plays an important role in the monetary policy transmission since banking
sector is the intermediary that provides loans to the market. In this paper, we focus on the bank
lending channel to study the influencing factors of monetary policy transmission.
Banks make adjustment in their loans when facing a monetary policy shock and this will then affect
investment and finally impact the whole economy. However, banks perform very differently with
respect to the conditions of the banking market they are in. Under different circumstances, there
will be a large variance in banks’ efficiency, liquidity and other operational indicators, which
provides them with different capabilities to absorb monetary policy shocks. There are studies
concerning the relationship between market condition and bank efficiency. Ataullah and Le (2006)
use Indian banking industry as a case study to test the relationship between economic reforms and
bank efficiency in developing countries. They find a positive relationship between competition and
bank efficiency. In addition, Chortareas, Girardone and Ventouri (2013) investigate in a sample of
commercial banks in 27 European Union members and conclude that the higher degree of financial
freedom, the higher overall efficiency in banks. Berger, Hasan and Zhou (2006) analyze banks in
China and find that ownership structure has an impact on bank efficiency. Moreover, Gaganis and
Pasiouras (2013) investigate financial supervision regimes and bank efficiency. They show that as
the number of financial sectors that are supervised by the central bank increases, there will be

3
decrease in banks’ efficiency. However, some studies show doubts about the relationship between
market conditions and banks’ performance. Naceur and Omran (2011) examine the impact of bank
regulation, concentration and financial and institutional development on bank profitability. They
find that macroeconomic and financial development has no significant effect on cost efficiency
while regulatory and institutional variables will influence bank performance. Fungacova, Pessarossi
and Weill (2013) find no significant relationship between efficiency and competition in Western
banks.
Based on the hypothesis that the market conditions may have an impact on the performance of
banks, which are the intermediaries of monetary policy transmission, it is critically important to
study how market characters may affect monetary policy transmission. Favero, Giavazzi and Flabbi
(1999) study the monetary policy transmission mechanism through bank lending channel in Europe
and find that banks are able to use their excess liquidity to expand credit or use the strength of their
balance sheet to insulate loan from monetary policy fluctuations. Adams and Amel (2005)
investigate the relationship between competition and monetary policy transmission. They show that
the impact of monetary policy on loan originations is weaker in more concentrated markets.
Olivero, Li and Jeon (2009, 2010) study the impact of banking concentration and competition,
respectively, on monetary policy transmission and find a negative relationship between competition
in the banking sector and monetary policy transmission through bank lending channel. In addition,
Amidu and Wolfe (2013) also show that banking sector competition weakens the effectiveness of
monetary policy on bank lending. Jeon and Wu (2014) examine the impact of foreign bank
penetration on monetary policy transmission during the 2008-09 crisis. They show that foreign
bank penetration weakens the effectiveness of monetary policy transmission. Moreover, Omer,
Haan and Scholtens (2014) use Pakistan as a case study and show that excess liquidity in the
interbank market makes monetary policy, in the form of required reserve, less effective.
Generally, the market structure and conditions can largely influence the effectiveness of monetary
policy transmission. Thus, it is very meaningful to examine the impact of banking sector features
on the transmission monetary policy. This paper will mainly focus on banking sector concentration.
Concentration level in banking sector is expected to affect monetary policy transmission in the

4
following aspects. First, in a more concentrated market, large banks are more likely to have easier
access to additional funds such as issuing debt other than deposit to maintain their loan volumes,
which will offset the influence of a monetary policy tightening. Second, market concentration
means a larger profit margin for the banks than for those banks in a more competitive market. Thus,
banks in a concentrated market are more likely to keep a large and stable scale of loans (Severe,
2011). This will also reduce the effectiveness of a monetary policy changes. However, this higher
profit margin in a more concentrated market can also lead banks to be less cautious in lending,
which will limit their liquidity and as a result, they will be more influenced by a monetary policy
shock due to a lack of liquidity. In addition, large banks in a market with a higher degree of
monopoly are more likely to have stronger bargaining power over the government, which can also
reduce the effectiveness of a monetary policy change on those banks. On the other hand, in a
market with lower concentration level, one possibility is that banks will be more cautious in the use
of funds and maintain a stable and sufficient liquidity so that they will not be put into a
disadvantageous situation in the competition with other banks. Thus, their loan volume will not be
significantly influenced by a monetary policy shock. Another possible case is that competition
leads banks to make full use of their funds, which in turn causes a lack of fund in those banks when
facing a monetary policy change. Therefore, competition, from this aspect, can enhance monetary
policy transmission through bank lending channel. Thus, the observed impact of banking sector
concentration on monetary policy transmission, no matter negative or positive, is a consequence of
resultant forces of all those possible impacts.
Given the concentration level of a country’s banking sector, this paper has important policy
implication on how monetary policy should be conducted to reach its economic target.
The rest of the paper is organized as follows: Section 2 introduces our data, benchmark model and
econometric methodology. Section 3 reports empirical results, followed by robustness test in
Section 4. Section 5 concludes.

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2. Data, model and methodology
2.1 Data description
In this paper, cross-country annual bank-level balance sheet data, banking sector concentration
measurements and macroeconomic indicators are included to examine the factors that may affect
monetary policy transmission. The data of 1999-2011 period is used in this study. To better capture
the impact of concentration, banking sector concentration indicators of both developing countries
(i.e., Argentina, Brazil, China, India, Indonesia, Korea, Philippines and Russia) and developed
countries (i.e., Canada, Finland, Germany, Japan and USA) are used so that a large range of
concentration level will be examined. Bank-level balance sheet data such as loans, total assets,
liquid assets and equity are from Bureau van Dijk’s BankScope database. Due to a huge variation in
the numbers of banks in those countries, to avoid the situation that developed counties might
dominate the dataset with their large banking market scale, only the top 200 banks in every
developed country are selected and as for developing country, every bank with available data is
included in our dataset. To make sure that the bank-level data can more accurately reflect the
year-to-year fluctuations, only banks with available continuous data for at least 3 years are selected.
As for macroeconomic data, data for real GDP growth rate, CPI growth rate and money market rate
are from EIU Countrydata database. However, no complete report has been made for the money
market rate in China, therefore, bank lending interest rate is used to measure Chinese monetary
policy. To measure banking sector concentration level, we choose Lerner Index as the indicator,
which is calculated as (p-mc)/p, where p is the price and mc stands for marginal cost. This index is
built based on the theory that in a perfectly competitive market, price equals marginal cost. Thus, a
higher degree of concentration (or monopoly) implies a larger gap between price and marginal cost
and therefore a higher value in Lerner Index. The time series data for Lerner Index in those
countries can also be found in BankScope database. In total, 1378 banks are included in the dataset.
2.2 The model
We use the growth rate of loans as the indicator to study the monetary policy transmission through
bank lending channel. Multiple equations are involved in the model and some key variables and
interaction terms will be added to the model in following equations to compare with the benchmark.
The model is as follows:

6
Equation1.
Loani,j,t =c + αLoani,j,t-1 + βMMRcj,t + γCrisisj,t + θGDPcj,t + ϕCPIcj,t + δLiquidityi,j,t
+ λCapitalizationi,j,t + δDiversificationi,j,t + ηSizei,j,t + θConsolidationi,j,t
+ ωYear + εi,j,t
Equation2.
Loani,j,t =c + αLoani,j,t-1 + βMMRcj,t + γCrisisj,t + ρLernerj,t + θGDPcj,t + ϕCPIcj,t
+ δLiquidityi,j,t+ λCapitalizationi,j,t + δDiversificationi,j,t + ηSizei,j,t
+ θConsolidationi,j,t + ωYear + εi,j,t
Equation3.
Loani,j,t = c + αLoani,j,t-1 + βMMRcj,t + γCrisisj,t + ρLernerj,t + ζLernerj,t×MMRcj,t
+ θGDPcj,t + ϕCPIcj,t + δLiquidityi,j,t+ λCapitalizationi,j,t + δDiversificationi,j,t
+ ηSizei,j,t + θConsolidationi,j,t + ωYear + εi,j,t
Equation4.
Loani,j,t = c + αLoani,j,t-1 + βMMRcj,t + γCrisisj,t + ρLernerj,t + ζLernerj,t×MMRcj,t
+ πMMRcj,t×Crisisj,t + ηLernerj,t×Crisisj,t + ξLernerj,t×MMRcj,t×Crisisj,t
+ θGDPcj,t + ϕCPIcj,t + δLiquidityi,j,t+ λCapitalizationi,j,t + δDiversificationi,j,t
+ ηSizei,j,t + θConsolidationi,j,t + ωYear + εi,j,t
where Loani,j,t denotes the growth rate of loans at bank i in country j at year t and Loani,j,t-1 is its
one-year lag. DMMRj,t represents the monetary policy shock in country j in year t, which is
calculated by the percentage change of money market rate. An increase in the money market rate
indicates a monetary policy tightening, while a decrease means a monetary policy easing. The
coefficient of this variable is expected to be negative since banks are likely to reduce loans amid a
tightened monetary policy.1
Crisisj,t is a dummy variable for bank crisis which equals 1 if the
banking market in country j is hit by a crisis in year t. The data for this variable is from Laeven and
1
It has been suggested that the first order difference of money market rate may be an alternative indicator.
However, the author believes that growth rate is better because it takes the original level into consideration.

7
Valencia (2012). Additionally, since the 2008-09 crisis is a global-wide financial shock, this
dummy is equal to 1 for all countries in year 2008 and 2009. GDPcj,t and CPIcj,t are the real GDP
growth rate and CPI percentage change in country j in year t, respectively. GDP growth rate can
approximately measure the aggregate demand changes for loans in the economy. The following
variables are bank-level characteristics. Liquidityi,j,t is the liquidity level of bank i in country j in
year t, calculated as the liquid assets as a share of total assets. Capitalizationi,j,t denotes the capital
sufficiency of bank i in country j in year t, proxied by equity over total assets. Diversificationi,j,t is
calculated by other operating income over total assets to measure the operational diversification of
bank i in country j in year t. Sizei,j,t is the log value of total assets for bank i in county j in year t.2
Year is a series of year dummies to control unobserved changes that may influence the dependent
variable in those years.
Moreover, in equation2, Lernerj,t representing the Lerner Index in country j in year t is added as an
indicator for banking sector concentration and its coefficient indicates how concentration level
influences banks’ loan provision. A higher level of Lerner Index suggests a deteriorated
competition among financial intermediaries, or put differently, a higher concentration level. To
better understand the impact of banking sector concentration on monetary policy transmission, an
interaction term Lernerj,t×MMRcj,t is introduced in equation 3. The coefficient of this interaction
term will be negative if a higher concentration strengthens monetary policy transmission while a
positive sign of the coefficient means concentration weakens the effectiveness of monetary policy.
In equation 4, not only interaction term between Lerner Index and money market rate change but
also interactions between Lerner Index and Crisis dummy, money market rate change and Crisis
dummy are included to examine the interacting effect of those variables. Furthermore, a three-way
interaction term, Lernerj,t×MMRcj,t×Crisisj,t, is included in equation 4 to investigate the impact of
banking sector concentration on monetary policy shocks during crisis periods.
Apparently, some independent variables (bank-level characteristics such as size, capitalization,
liquidity, diversification,) are endogenous with dependent variable, loan growth rate. Thus, to solve
2
We use a dummy variable, Consolidationi,j,t, to control for the potential impact when our bank-level data
switch from unconsolidated type to consolidated.

8
the endogeneity problem, we apply Arellano and Bond’s (1991) generalized method of
moments(GMM) for estimation. Before estimation, outliers of relevant variables have been
removed from the dataset.
3. Empirical results
We estimate the 4 equations as shown above with generalized method of moment (GMM) method
following procedures shown by Mileva (2007) with Stata. Data considered to have endogenous
problem (liquidity, capitalization, diversification, and size) are sorted out from other variables.3
Table1
Estimation results of the 4 equations
(1)Equation1 (2)Equation2 (3)Equation3 (4)Equation4
Loan Loan Loan Loan
L.Loan 0.0542***
0.0522***
0.0521***
0.0523***
(4.88) (4.63) (4.62) (4.62)
MMRc -0.0853***
-0.0807**
-0.247***
-0.161
(-3.58) (-3.24) (-3.96) (-1.66)
Crisis -0.214***
-0.175**
-0.199***
-0.0306
(-3.84) (-3.02) (-3.39) (-0.42)
Lerner 0.225 0.389**
0.643***
(1.66) (2.66) (3.86)
Lerner×MMRc 0.757**
0.585
(2.91) (1.65)
Lerner×Crisis -0.940***
(-3.55)
MMRc×Crisis 0.0141
(0.11)
Lerner×MMRc×Crisis -1.166
(-1.76)
3
It is worth to note that money market rate is used as an indicator of monetary policy shock. However, this
measurement of monetary policy changes is actually, to some extent, correlated with loans. That is, when
banks’ loans are at a comparatively high level, it is more likely that they will face a lack of liquidity and have
to find alternative sources of funds from other markets such as the money market to maintain their liquidity
and operation, which can lead to an increase in the money market rate. In turn, changes in the money market
rate can also affect the banks’ decision on supplying loans. Thus, the growth rate of money market rate and
the growth rate of loan are endogenous. However, since we use money market rate as a measurement of
monetary policy which should be an independent decision, to make this data better suit this research, it is
considered as an exogenous variable. As for other variables such as GDP growth rate and CPI percentage
change, they are all considered to be exogenous in this model.

9
GDPc 0.0180***
0.0170***
0.0121**
0.0136**
(5.22) (4.43) (2.88) (3.25)
CPIc -0.0269***
-0.0264***
-0.0253***
-0.0295***
(-7.26) (-7.02) (-6.74) (-7.08)
Liquidity 0.135 0.167 0.157 0.147
(1.23) (1.51) (1.41) (1.33)
Capitalization -0.476*
-0.474*
-0.485*
-0.475*
(-2.42) (-2.40) (-2.46) (-2.40)
Diversification -0.109 -0.0839 -0.0483 -0.0909
(-1.19) (-0.91) (-0.52) (-0.95)
Size -0.202***
-0.196***
-0.199***
-0.206***
(-11.51) (-10.81) (-10.95) (-10.71)
_cons 1.932***
2.361***
2.378***
1.674***
(4.54) (11.27) (11.38) (3.57)
N 9074 8802 8802 8802
Note: Year dummies and the dummy consolidation are included in the regressions although not reported. The numbers in
the parentheses denote standard errors of the coefficients.
*The statistical significance at the 5% level.
**The statistical significance at the 1% level.
***The statistical significance at the 0.1% level.
Table1 shows the estimation results of the 4 equations. The first equation in the model is considered
as the benchmark estimation. From the results we can see that, firstly, loan growth rate is highly
correlated with its previous level with a positive coefficient at 1% significance level. This is
reasonable that banking operation strategies are always continuous and it takes some time to switch
from one strategy to another. Secondly, there is a significant negative relationship between loan
growth rate and money market rate percentage change. This is consistent with the intuition that as
money market rate increases, which represents a tightened monetary policy, banks will reduce their
credit to firms and individuals. As for crisis, there is a negative coefficient at 1% significant level,
suggesting that during bank crises, due the fluctuations in the market, difficulties in bank operation
and uncertainty for the future, banks will be more cautious in providing loans. GDP growth rate has
a positive effect on bank loan growth rate, which can be interpreted as a higher demand for loans
leads to an increase in loan supplied by banks. Higher inflation causes banks to reduce their loans.4
In the second equation, Lerner Index is added in the model. As the estimation result for the second
4
The effects of banks’ characteristics are not focused in this paper and their impacts are not interpreted in
detail.

10
equation shows, Lerner Index has a positive relationship with loan growth rate. Since an increase in
Lerner Index indicates a deterioration of competition in the banking sector, this positive
relationship can be interpreted that when the banking sector becomes more concentrated, banks are
likely to lend more loans. As mentioned in previous parts, concentrated market can provide banks
with larger profit margin so that banks will issue more loans to gain a higher profit. At the same
time, the monopoly power of the large banks in concentrated banking market makes them simply
less cautious in providing loans. In the third equation, an interaction term Lernerj,t×MMRcj,t is
added. The estimation result of this interaction term shows a positive coefficient at 1% significance
level. The second order derivative of MMRc on loan growth rate is -0.0853+ 0.757*Lerner,
indicating that the effectiveness of monetary policy transmission through the bank lending channel
will be dampened with banking sector concentration. As stated in previous analysis, banking sector
concentration can affect the operation of banks in many ways and further influence the
transmission of monetary policy through the bank lending channel.
In the last equation, interaction terms between either two variables among Lernerj,t, MMRcj,t and
Crisisj,t and a three-way interaction term of these three variables are added. The estimation result
gives a positive coefficient (0.585) for Lernerj,t×MMRcj,t at 10% significance level, a negative
coefficient (-0.94) for Lernerj,t×Crisisj,t at 0.1% significance level, a negative coefficient (-0.0141)
for MMRcj,t×Crisisj,t without significance and a negative coefficient (-1.166) for Lernerj,t×MMRcj,t
×Crisisj,t at 10% significance level. The first coefficient is in line with previous explanation. The
second coefficient shows that during crisis periods, banking sector concentration will enhance the
negative impact of crisis on bank lending. The third coefficient indicates that during crisis periods,
the effect of monetary policy changes will be stronger. As for the three-way interaction term, the
negative coefficient indicates that during crisis periods, the offsetting effect of banking sector
concentration on monetary policy transmission will be weakened. That is, during crisis periods,

11
although being weakened by banking sector concentration, monetary policies will be more effective
than they are in none-crisis periods with the same level of banking sector concentration. A possible
explanation for this phenomenon is that during crisis period, banks become more cautious and have
already reduced their loan offering. Therefore, the offsetting effect of banking sector concentration
will be buffered.
4. Robustness test
In this section, we conduct some robustness tests using an estimation method different from GMM
and different measurements of the same variable in the empirical study to check if our results will
be qualitatively unchanged.
First, instead of GMM, fixed effect estimator is used. The results are shown in Table 2 to compare
with the results using GMM. In this test, since we focus on the impact of banking sector
concentration on monetary policy transmission, which is mainly studied in the third equation in the
model, a comparison between the results from those two methods is made at this stage.
Table 2
Estimations using GMM method and fixed effect method
(1)GMM (2)fixed effect
Loan Loan
L.Loan 0.0521***
-0.0875***
(4.62) (-8.13)
MMRc -0.247***
-0.271***
(-3.96) (-4.25)
Lerner 0.389**
0.347*
(2.66) (2.02)
1.029***
(2.91) (3.88)
Crisis -0.199***
-0.162**
(-3.39) (-2.61)
GDPc 0.0121**
0.0266***
(2.88) (5.30)
CPIc -0.0253***
0.00630
(-6.74) (1.67)
Liquidity 0.157 0.0712

12
(1.41) (0.72)
0.138
(-2.46) (0.72)
Diversification -0.0483 -0.134
(-0.52) (-1.17)
Size -0.199***
0.173***
(-10.95) (6.24)
_cons 2.378***
-1.014
(11.38) (-1.79)
N 8802 8802
Note: The numbers in the parentheses denote standard errors of the coefficients.
We can see in Table 2 that the sighs of the coefficient of the key variables, such as money market
rate change rate, Lerner Index, crisis dummy and the interaction term of MMRc and Lerner Index,
are the same using these two different estimators. One difference is the negative sign of the
coefficient of one-year lagged loan growth rate using the fixed effect estimator. The result using
fixed effect estimator also shows that as banking sector becomes more concentrated, the
effectiveness of monetary policy transmission will be weakened.
In the following tests, some new measurements of banking sector concentration and monetary
policy change are used to replace the original ones. Firstly, as discussed before, the first-order
difference of money market rate can be an alternative measurement of monetary policy change
compared to the growth rate of money market rate. So in the following tests, the first-order
difference of money market rate (denoted by DMMR) is used to measure monetary policy shocks.
In addition, a new measurement of banking sector concentration level has been introduced. Instead
of using Lerner Index, CR5 indicator (denoted by CR5) is used in this test. CR5 is calculated as the
sum of the top 5 banks’ assets over the banking sector total assets. A higher CR5 value indicates a
more concentrated banking market. Table 3 shows the results of the robustness tests using new
substitutes of the indicators.

13
Table3
Estimation results of the robustness tests
(1)Lerner & MMRc (2)Lerner & DMMR (3)CR5 & DMMR
Loan Loan Loan
L.Loan 0.0521***
0.0459***
0.0617***
(4.62) (4.16) (5.72)
Crisis -0.199***
-0.113 -0.233***
(-3.39) (-1.87) (-4.20)
MMRc -0.247***
(-3.96)
Lerner 0.389**
0.203
(2.66) (1.52)
(2.91)
DMMR 0.00729 -0.0538**
(1.57) (-3.13)
Lerner×DMMR -0.207***
(-5.07)
CR5 -0.296**
(-2.92)
CR5×DMMR 0.0620*
(2.36)
GDPc 0.0121**
0.0147***
0.0210***
(2.88) (3.72) (5.77)
CPIc -0.0253***
-0.0261***
-0.00898**
(-6.74) (-7.03) (-2.75)
Liquidity 0.157 0.159 0.110
(1.41) (1.47) (1.03)
-0.510**
-0.347
(-2.46) (-2.62) (-1.76)
Diversification -0.0483 -0.172 -0.151
(-0.52) (-1.89) (-1.66)
Size -0.199***
-0.191***
-0.125***
(-10.95) (-10.63) (-6.17)
_cons 2.378***
2.009***
1.139*
(11.38) (9.26) (2.52)
N 8802 8947 9219
Note: The numbers in the parentheses denote standard errors of the coefficients.
In the results, when first-order difference of money market rate is used and other variables are

14
maintained the same as shown in the second column, although it shows a negative effect of
concentration on monetary policy effectiveness, the coefficient on the stand-alone monetary policy
indicator (DMMR) is counter-intuitively positive and statistically insignificant. The third column
shows the results of robustness test using the first-order difference of money market rate as
measurement of monetary policy shocks and CR5 as indicator for banking sector concentration
level. The estimation result shows a negative coefficient of DMMR at 1% significance level, which
means a tighter monetary policy leads to a decrease in loans provided by banks. The interaction
term between DMMR and CR5 shows a positive coefficient at 5% significance level, implying as
well that an increase in concentration will weaken the effectiveness of monetary policy. One
difference is that, in the first equation, the result shows that as banking sector becomes more
concentrated, banks will increase their loan offering. However, in the third equation, as banking
sector concentration level increases, banks are likely to issue fewer loans. Generally, the robustness
test show that there is no qualitative difference using different estimation methods and variable
measurements. Thus, it is very likely that banking sector concentration has a negative effect on
monetary policy transmission and as concentration in banking sector increases, the negative effect
will be stronger.
5. Conclusion
In this paper we use bank-level data, multiple macroeconomic indicators and banking sector
concentration indicators to examine the impact of banking sector concentration on the monetary
policy transmission through bank lending channel. A model with panel data of 13 countries,
including both developing countries and developed countries, from 1999 to 2011 is built and
estimated with GMM method. We find that banking sector concentration will weaken the
transmission of monetary policy and more specifically, as banking sector becomes more
concentrated, the negative effect will be stronger. When different measurements of banking sector
concentration are introduced, similar conclusions are still reached. In addition, crisis, as a dummy
variable, has been included in this study which investigates the impact of crisis on bank lending
channel. We find that during crisis periods, the offsetting effects of banking sector concentration on
monetary policy transmission will be weakened. Moreover, interaction terms of some key variables
are used to analyze the interactive effects of those variables.

15
Our research has important policy implication. Focused on policy effectiveness, government should
consider the banking sector concentration level when making monetary policy decisions. When the
banking sector is comparatively more concentrated, monetary policy has to be more aggressive in
order to reach desired economic targets. And during crisis periods, this negative effect of banking
sector concentration can be less pronounced.
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