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Bank Lending Channel of MonetaryPolicy: Dynamic Panel Data Study ofMalaysiaZulkefly Abdul Karim a , W. N. W. Azman-Saini b & Bakri Abdul Karimc

a Universiti Kebangsaan Malaysia , Selangor, Malaysiab Universiti Putra Malaysia , Selangor, Malaysiac Universiti Malaysia Sarawak , Sarawak, MalaysiaPublished online: 16 Aug 2011.

To cite this article: Zulkefly Abdul Karim , W. N. W. Azman-Saini & Bakri Abdul Karim (2011) BankLending Channel of Monetary Policy: Dynamic Panel Data Study of Malaysia, Journal of Asia-PacificBusiness, 12:3, 225-243, DOI: 10.1080/10599231.2011.570618

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Journal of Asia-Pacific Business, 12:225–243, 2011Copyright © Taylor & Francis Group, LLCISSN: 1059-9231 print/1528-6940 onlineDOI: 10.1080/10599231.2011.570618

Bank Lending Channel of Monetary Policy:Dynamic Panel Data Study of Malaysia

ZULKEFLY ABDUL KARIMUniversiti Kebangsaan Malaysia, Selangor, Malaysia

W. N. W. AZMAN-SAINIUniversiti Putra Malaysia, Selangor, Malaysia

BAKRI ABDUL KARIMUniversiti Malaysia Sarawak, Sarawak, Malaysia

This paper aims to investigate the relevance of bank-lendingchannel (BLC) of monetary policy in a small-open economy, i.e.Malaysia by using disaggregated bank-level data. A dynamic paneldata method namely generalized method of moments (GMM) pro-cedure has been used in estimating the dynamic of banks’ loansupply function. The empirical evidence revealed that the banks’loan supply is significantly and negatively influenced by monetarypolicy shocks, and therefore has supported the existence of BLC inMalaysia. Several bank-characteristics variables namely bank liq-uidity and bank capitalization (capital adequacy ratio) are alsostatistically significant in influencing the banks’ loan supply.

KEYWORDS bank-lending channel, monetary policy, dynamicpanel data

INTRODUCTION

The theory of transmission mechanism stated that monetary policy can influ-ence real-sector activity via several channels, namely interest rates channel,asset price channel, and credit channels.1 However, the role of bank credit

Address correspondence to Zulkefly Abdul Karim, School of Economics, Faculty ofEconomics and Management, National University of Malaysia, 43600 Bangi, Selangor,Malaysia. E-mail: mz@ukm.my

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226 Z. A. Karim et al.

has been given a special attention by the prior researchers in examining howmonetary policy (MP) transmitted to the economic activity via credit chan-nel. Bernanke and Gertler (1995) described two possible mechanisms ofthe credit channel theory, namely balance-sheet channel (BSC), and bank-lending channel (BLC). The BSC emphasized the impact of the changesin MP on the borrower’s balance sheet, whereas the BLC focused on thepossible effect of MP actions on the supply of loans by the banking system.

The BLC is based on the view that bank plays an important role in thefinancial system as external sources of financing for the firms. Because ofthe bank’s special role, certain borrower will highly dependent upon thebank loan and will not have access to the credit markets unless they borrowfrom the bank. As argued by Bernanke and Gertler (1989), MP can affect thebank portfolio behavior through the bank asset in term of loan, securities,as well as bank reserves. Therefore, it is believed that the BLC plays animportant role in affecting the economic activity. This is because any changesin the monetary policy stance will affect the bank behavior on the assetand liabilities sides. For instance, a tight MP will drain the reserves from thebanking system, which in turn will restrict the bank’s supply of loans, leadingto a decline in investment spending, and fall in economic activity (output).2

This article aims to investigate the role of credit channel (in particular,BLC) in the monetary transmission in Malaysia by using the disaggregatedbank-level data. By examining the relevance of BLC in economic activity, itcan provide some idea to the monetary authority in designing an appropriateMP to achieve their ultimate targets (e.g., price stability and sustainable eco-nomic growth). The relevance of BLC is examined by estimating the banks’loan supply function in dynamic panel data framework. Besides the MP vari-able, several macroeconomics variables (gross domestic product [GDP], andinflation), and bank characteristics (bank size, liquidity, and capitalization)are also consider in estimating the banks’ loan supply function.

The current study contributes to the existing literature by extendingthe analysis of the relevance of BLC of MP in a small-open economy byusing disaggregated data of a commercial bank in Malaysia. Specifically, thecurrent study tries to answer two main questions: First, how does the MPinstrument that is an overnight interbank rate affect the bank lending behav-ior? Second, do banks’ balance sheet variables, namely bank capital, bankliquidity, and asset size, matter in influencing the bank lending behavior? Inaddition, the current study uses the most recent panel data technique, that is,the generalized method of moment (GMM) proposed by Arellano and Bond(1991), Arellano and Bover (1995), and extended by Blundell and Bond(1998). This technique has an advantage for addressing the Nickell (1981)bias associated with the fixed effects in short panels (e.g., bias due to thepresence of the lagged dependent variable and bias due to the endogeneityof other explanatory variables).

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Bank Lending Policy in Malaysia 227

Consistent with the previous finding in developed countries, there is arelevance role of BLC of MP in Malaysia. Specifically, as postulated by BLCtheory, MP variable influences significantly and negatively the banks’ loansupply. In addition, several bank characteristics variables, namely bank liq-uidity and bank capitalization (capital adequacy ratio), also play a significantrole in influencing the banks’ loan supply.

The remainder of this article is organized as follow. The next sectiondiscusses the previous study relating to the transmission mechanism of MPvia bank-lending channel. The third section deals with the theoretical aspectof the bank-lending behavior. The fourth section we explain the estimationprocedures by using the dynamic panel data in GMM framework. The fifthsection reports the empirical findings, and the last section summarizes andconcludes.

LITERATURE REVIEW

The important role of credit channel in MP transmission can be traced backby Bernanke and Blinder (1988).3 They argued that there are three condi-tions for the existence of the BLC, namely (a) loans and open-market bondsmust not be perfect substitutes, (b) the central bank is able to affect the bankloan by changing the quantity of reserves, and (c) imperfect price adjustmentprevents any monetary shocks from being neutral. By using the traditional(IS-LM) model, where IS curve is replaced by the credit-commodity curve(CC), they produced CC-LM model where they stated that MP affects eco-nomic activity via credit channel or bank loan channel. For example, a tightMP through an increase in interest rates will drain the bank reserves anddeposit, and subsequently, the bank will contract the loan to the businessand consumer.

It is well documented in the literature that most of the studies relat-ing to the credit channel are focused on bank aggregate data. For example,Bernanke and Blinder (1992) used innovation in 3-month treasury bills rateto capture exogenous shifts in MP and found that the inverse relation-ship between bank loans and tight MP, and therefore supported the creditchannel view in the U.S. economy. However, there are several studies thathave used the disaggregated data bank in investigating the existence of theBLC particularly in the developed countries, especially in the U.S. econ-omy (for instance, Ashcraft, 2006; Kashyap, Stein, & Jeremy,1995a, 1995b,2000; Kishan & Opiela, 2000), in the UK study (Gambacorta, 2005; Huang,2003), and Euro area study (Angeloni, Kashyap, & Mojon, 2003; Altunvas,Fazyloz, & Molyneux, 2002).4 The general conclusion in most of the stud-ies is the tight MP leads to a decline in bank credit (loans), which in turnhas a negative impact on the economy. For example, Kashyap et al. (1995a,1995b) found that the growth in bank loans in the subsegment of the small

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228 Z. A. Karim et al.

commercial banks was most responsive to MP. A further study by Kashyapet al. (2000) divided banks by two categories, namely asset and liquidity size,and found that the smallest most nonliquid banks were most responsive toMP shocks. This finding was supported by Kishan and Opiela (2000) bydividing banks according to the size and capital strength. However, Ashcraft(2006) questioned the existence of the BLC the United States experienced.When using bank data, he identified a differential response of loan supplyto changes in the Federal Fund Rate (FFR) across banks. However, whenhe aggregated the bank data up to the state level, the loan market shareof affiliated banks tends to mitigate the negative response of loan supply tochanges in MP. In addition, the aggregate elasticity of output to bank lendingis very small (insignificant).

Studies in the U.K. economy and Euro area have also found a sameconclusion, which is the relevance role of BLC in monetary transmission.For example, Altunvas et al. (2002) found the importance of the role of BLCin Italy and Spain, and Huang (2003) showed that a BLC works in the UnitedKingdom through restricting banks’ loans to small bank-dependent firms.

In the Malaysian context, study relating to the role of the BLC in mon-etary transmission is still limited in the existing literature. So far, there areseveral studies that have examined the existence of the BLC by using aggre-gate data (e.g., Goh, Chong, & Yong, 2007; Ghazali & Abdul Rahman, 2005,Karim, Harif, & Adziz, 2006). Ghazali and Abdul Rahman (2005) investi-gated the role of commercial banks’ asset (loans) and liabilities (deposit)on selected Malaysian macroeconomic and financial variables. By employ-ing the aggregate data bank and a simple Granger causality test, they foundthat the significant role of bank asset (bank loan) in influencing the eco-nomic variables. This finding supports the active role of BLC in transmittingthe macroeconomic variables. In contrast, Goh et al. (2007) examined theexistence of BLC by using autoregressive distributed lagged model (ARDL)approach. By using monthly aggregate data spanning from January 1990 toMarch 2004, they found that deposits tend to fall in response to the con-tractionary (an increase in interest rates) MP shocks. However, the banksare able to protect customer loans from the reduction in deposits throughan adjustment in the liquid financial instrument. This finding stated that thetight MP did not depress growth in loans.

However, Said and Ismail (2008) used bank-level data in investigatingthe role of BLC in Malaysia. By using a static panel data framework span-ning from 1994 to 2004, they found that the relevance of BLC in Malaysia.However, their result is questionable because the coefficient of interestrates on bank loan was quite large.5 Therefore, it is expected there is amisspecification error in their model. In fact, as argued by Baltagi (2008),most economic relationships are dynamic in nature. Thus, by using dynamicpanel data framework in estimating the bank loans supply function, it isbelieved that the monetary authority can obtain an appropriate result for thepolicy purposes.

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Bank Lending Policy in Malaysia 229

BANK-LENDING CHANNEL: A THEORETICAL MODEL

The BLC can be analyzed by using a simple model of a profit-maximizingbank as develop by Stein (1998), and Ehrmann, Bambacorta, Martinez-Pages,Sevestre, and Worms (2003). The model can be simplified as follows:

Assume that, the balance sheet identity bank i is defined as

Li + Si = Di + Bi + Ci (1)

where,L is the volume of loans,S is the securities,D is the volume of the demand deposit from household,B is the level of nonsecured funding, andC is the capital of the bank.

It is also assumed that the bank i acts in a loan market characterized bymonopolistic competition. The demand for nominal bank loan Ld

i is given by

Ldi = α0rL,i + α1y + α2p (2)

In Equation (2), the bank individual loan is determined by loan rate (rL,i),aggregate real output (y), and the domestic price level (p); which assumptionthat the coefficient α0 < 0 or negative, and α1 and α2 is > 0 or positive.Under the Basle requirement, the bank capital is linked to the level of theloans and the bank’s holding of securities to the level of the deposit (liquidityrisk). Therefore, it can be simplified as

Ci = kLi (3)

Si = cDi (4)

The demand deposit (D) is secured but does not bear interest. Thedemand deposit is according to the money-demand function, and demandedbecause of its role as a means of payment. Therefore, the demand deposit isnegatively related to the interest rates of an alternatives risk-free asset (rs),which translates the MP rate as

D = β0rs (5)

where β0 < 0.In Equation 5, the bank cannot influence the amount of the deposit.

This deposit is exogenous to the bank, and it will drop after the monetary

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230 Z. A. Karim et al.

tightening (after an increase in rs). However, the bank must have accessto an alternative source of funds, which is unsecured, and the bank has topay the interest (e.g., interbank loan and debt securities). Because banks areperceived to be risky, the suppliers on unsecured finance ask for an externalfinance premium. The external finance premium depends on a signal of thebank’s health (xi), which can be observed by all market participants. Thehigher the xi, the lower the external finance premium. Therefore, the interestrates of the unsecured financing

(rBi

)is

rBi = rs(μ− c0xi) (6)

where μ – c0xi ≥ 1 for ∀i.In Equation 6, bank i cannot raise unsecured funds if it offered less than

rBi , whereas it can raise any amount of funds if it pays at least rB

i . Given rBi

is a cost factor, bank i will not be ready to pay more than rBi . By assuming

Bi > 0, the profit of bank i (πi) is given by

πi = LirL,i + Sirs − BirBi − ψi (7)

where, ψ i captures bank-specific administrative costs and remuneration costsfor the required capital holdings. Inserting Equations 1–5 into Equation 7,and assuming equilibrium in the loan market, yields

πi = Li

(1

α0Li + α1

α0y + α2

α0p

)

+ sDirs − [(1 − k) Li − (1 − s)Di]rBi − ψi (8)

By setting the first-order condition to zero and inserting Equation 6 inEquation 8 yields6

Li − α1

2y + α2

2p + α0μ (1 − k)

2rs + α0c0 (1 − k)

2xirs + α0

2

∂ψi

∂Li(9)

Equation 9 is the standard loan equation in macro modeling. InEquation 9, a MP tightening through an increase in interest rates (rs) leadsto a reduction in deposit in Equation 5. However, the bank can keep theasset side of their balance sheet unchanged only if they increase othersources of funding (Bi) accordingly. However, the interest rate a bank hasto pay for these funds was increased by the MP tightening according to theEquation 6. Banks pass at least part of this higher cost on to their loan rate(rL,i), which in turn reduces the loan demand. Therefore, in Equation 9 it

is expected that the MP variable rs has a negative response to the bankloans.

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Bank Lending Policy in Malaysia 231

However, is it reasonable to assume that individual banks’ loan is alsoinfluenced by others factors such bank asset size, bank capital, and bankdeposit. Therefore, the current study also considers the bank balance sheetvariables in estimating the banks’ loan supply function. Following the lit-erature, there are three indicators widely used for measuring the bankcharacteristics such as bank size (Sit), liquidity

(Liqit

), and capitalization(

Capit). Specifically, the definitions of the variables are as follows:

Sit = log Ait − 1

Nt

∑i

log Ait (10)

Liqit = Lit

Ait− 1

T

∑t

(1

Nt

∑i

Lit

Ait

)(11)

Capit = Cit

Ait− 1

T

∑t

(1

Nt

∑t

Cit

Ait

)(12)

Size is measured by the log of total assets (Ai,t). Liquidity is measured asthe ratio of liquid asset Lit (cash, bank deposit, and securities) to total asset.Capitalization is given by the ratio of capital and reserves as a percentageof total assets. All the three criteria are normalized with respect to theiraverage across all the banks in the respective sample. All bank characteristicsinteract with the MP variables. For example, by interacting bank liquiditywith MP (LitxMP), we can examine how the bank-loan supply responds withthe bank liquidity after the MP tightening. Therefore, the augmented loansequation in the dynamic panel data based on Equation 9 can be specifiedas follows:

log(Lit) = ai +l∑

j=1

βj log(Li,t−j) +l∑

j=0

αj IBORt−j +l∑

j=0

φj log(GDPt−j)

+l∑

j=0

ϕj inft−j +l∑

j=0

λ1Xi,t−1IBORt−j + ηi + μi,t (13)

Specifically, in Equation 13, the bank loans supply (Lit) is determined bylagged dependent variable (Li,t−j), MP variable that is interbank overnightrate (IBOR), gross domestic product (GDP), inflation rate

(Inf

), bank char-

acteristics (Xi), and the interaction term of bank characteristics and MPvariables (Xi.IBOR). In addition, ηi is the bank-specific effect, where ηi ∼IID

(0, σ 2

η

)and μi,t are the remainder error terms, which is εi,t ∼ IID

(0, σ 2

ε

).

Therefore, the total error term is εi,t = ηi + μi,t .

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232 Z. A. Karim et al.

RESEARCH METHOD

Data

The current study uses annual commercial bank balance sheet data spanningfrom 1993 to 2008 (16 years). Few banks have been operated continuouslysince 1993, but some banks are operated continuously at some later point.Therefore, the data constitute an unbalanced panel. In general, there are 37commercial banks in the current study, which is equivalent to 375 bank-year observations. However, after considering the lagged effects, and somemissing variables, there are only 35 banks that have been estimated in thebaseline model, which is equivalence to 311 bank-year observations.7 Thebank balance sheet data set (loans, asset, liquidity, and bank capital) hasbeen collected from Bank Scope database, meanwhile the data of macroeco-nomic variables (GDP, inflation, and IBOR) are collected from InternationalMonetary Fund.

Dynamic Panel GMM

The inclusion of the lagged dependent variables in the baseline banks loansupply function in Equation 13 implies that there is correlation betweenthe regressors and the error term because the lag of banks loan supply(log(Li,t−j)

)depends on εi,t–1, which is a function of the bank-specific effect

(ηi). Therefore, due to this correlation, the dynamic panel data estimation inEquation 13 suffers from Nickell (1981) bias, which disappears only if T islarge or approaches infinity. Arellano and Bond (1991), Arellano and Bover(1995), and Blundell and Bond (1998) proposed GMM estimators to dealthe endogeneity problem (the correlation between the lagged dependentvariable and the error term).

To remove the firm specific effect (ηi) in Equation 13, Arellano andBover (1995) proposed a forward orthogonal deviation transformation orforward Helmert’s procedure. This transformation essentially subtracts themean of future observations available in the sample from the first T – 1observations. Its main advantage is to preserve sample size in panels withgaps. In contrast, a first-difference transformation has some weakness, whichis, if some explanatory variable (xit) is missing, then xi,t and xi,t+1 aremissing in the transformed data (Roodman, 2009a). However, under orthog-onal deviations, the transformed xi,t+1 need not go missing. This procedurecan be expressed as follows:

xi,t+1 = cit

[xit − 1

Tit

∑s>t

xis

](14)

where,

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Bank Lending Policy in Malaysia 233

Tit is the number of time-series observations on firm i,

cit is the scale factor that is√

TitTit+1 and

∑s>t

xis = xit + xi,t+1 + . . .+ xiT .

As noted by Hayakawa (2009), by using a Monte Carlo simulation study,the GMM estimator of the model transformed by the forward orthogonaldeviation tends to work better than if transformed by the first differ-ence. Therefore, based on this justification, the current study used forwardorthogonal deviation transformation to eliminate the firm-specific variable.

However, by transformation using forward orthogonal deviation, a newbias is introduced, that is, the correlation between the transformed errorterms and the transformed lagged dependent variable. Similarly, the trans-formed explanatory variables, that is the IBOR, the GDP, the inflation rate,and firm characteristics are also potentially endogenous because they arerelated to the transformed error term. Therefore, three assumptions can bemade regarding to the explanatory variables. First, an explanatory variable(Xit) can be a predetermined variable that is correlated with the past erroror E[Xitεis] �= 0 for s < t but E[Xitεis] = 0 for all s ≥ t. Second, an explanatoryvariable (Xit) can also be an endogenous variable, which is potentially corre-lated with the past and present error or E[Xitεis] �= 0 for s ≤ t but E[Xitεis] = 0for all s > t. Third, Xit is said to be strictly exogenous if E[Xitεis] = 0 for all tand s that is uncorrelated with either current, past, or future error.

Arellano and Bond (1991) and Arellano and Bover (1995) recommendthat the lagged levels or untransformed regressors are used as an instrumentfor the transformed variable. This refers to the difference GMM. However,Alonso-Borrego and Arellano (1999) and Blundell and Bond (1998) showedthat if the lagged dependent and the explanatory variables are persistentover time or nearly a random walk (it happens either as the autoregressiveparameter [β j ] approaches unity, or as the variance of the individual effects[ηi] increases relative to the variance of the idiosyncratic error [μi,t ]), thenlagged levels of these variables are weak instruments for the regressionequation in differences.

This happens either as the autoregressive parameter (α) approachesunity, or as the variance of the individual effects (ηi) increases relative tothe variance of the transient shocks (εit). Hence, to decrease the potentialbias and imprecision associated with the difference estimator, Blundell andBond (1998) proposed a system GMM approach by combining regression indifferences and regression in levels. In addition to the regression in differ-ences, the instruments for the regression in levels are the lagged differencesof the corresponding instruments.

However, as noted by Roodman (2009b), the system GMM can gen-erate moment conditions prolifically. Too many instruments in the systemGMM overfits endogenous variable even as it weakens the Hansen testof the instruments’ joint validity. Therefore, to deal with the instruments

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234 Z. A. Karim et al.

proliferation, the current study uses two main techniques in limiting thenumber of instruments—such as using only certain lags instead of all avail-able lags for instruments and combining instruments through addition intosmaller sets by collapsing the block of the instrument matrix. This techniquehas been used by previous researchers, for example, Calderon, Chong, andLoayza (2002), Beck and Levine (2004), Cardovic and Levine (2005), andRoodman (2009b).

The current study has used a one-step system GMM estimation.However, for robustness checking, a two-step estimation in the system GMMwas also considered. The success of the GMM estimator in producing unbi-ased, consistent, and efficient results is highly dependent on the adoptionof the appropriate instruments. Therefore, there are three specifications testsas suggested by Arellano and Bond (1991), Arellano and Bover (1995), andBlundell and Bond (1998). First, the Hansen test of overidentifying restric-tions, which tests the overall validity of the instruments by analyzing thesample analogue of the moment conditions used in the estimation process.If the moment condition holds, then the instrument is valid, and the modelhas been correctly specified. Second, it is important to test that there is noserial correlation among the transformed error term. Third, to test the validityof extra moment’s conditions on the system GMM, the difference in Hansentest is used. This test measures the difference between the Hansen statisticgenerated from the system GMM and the difference GMM. Failure to rejectthe three null hypotheses gives support to the estimated model.

ESTIMATION RESULTS

Full Sample

Table 1 reports the estimation results of the determinants of the banks’ loansupply by using system GMM estimation. The main results are the systemGMM with one-step and two-step estimation. As can be seen in Table 1,in one-step estimation, MP variable (IBOR) is negatively and statisticallysignificant in influencing the banks’ loan supply. Specifically, a one per-centage point increase in the IBOR leads to a contemporaneously decreasein the banks’ loan supply by a 0.389 percentage point. The lagged effectof MP is also statistically significant in influencing the banks’ loan supply,which means the banks’ loan has decreased by a 0.104 percentage point inresponse to a one percentage point increase in IBOR. This finding indicatethat the relevance of BLC in MP transmission mechanism in Malaysia. Theresults are also robust by using two-step system GMM estimation, whichis contemporaneous and lagged effect MP variables are negatively and sig-nificantly influenced by the banks’ loan supply. The significance of BLCin monetary transmission indicated that MP enables real sector economyinfluence through bank-balance sheet variables.

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TAB

LE1

The

Det

erm

inan

tsofB

anks

’Loan

Supply

Funct

ion:Sy

stem

GM

MEst

imat

ion

(Full

Sam

ple

)

One-

step

estim

atio

nTw

o-s

tep

estim

atio

n

Coef

fici

ent

Robust

SEp

valu

eCoef

fici

ent

Corr

ecte

dSE

pva

lue

Lagg

eddep

enden

tva

riab

le0.

937

0.05

40.

000∗∗

∗0.

945

0.05

10.

000∗∗

∗In

terb

ank

ove

rnig

htra

te(I

BO

R)

−0.3

890.

190

0.04

1∗∗−0

.357

0.15

60.

022∗∗

Lagg

edofIB

OR

−0.1

040.

036

0.00

4∗∗∗

−0.0

850.

027

0.00

2∗∗∗

Log

ofgr

oss

dom

estic

pro

duct

(LG

DP)

0.35

60.

539

0.50

90.

069

0.54

10.

898

Lagg

edofLG

DP

0.80

70.

439

0.06

6∗0.

447

0.50

00.

372

Inflat

ion

0.09

250.

047

0.05

1∗0.

073

0.04

50.

108

Lagg

edofIn

flat

ion

0.08

70.

053

0.10

10.

061

0.04

00.

128

Char

1×

MP

0.06

50.

024

0.00

7∗∗∗

0.06

50.

023

0.00

4∗∗∗

Char

2×

MP

0.00

30.

001

0.02

0∗∗0.

003

0.00

10.

035∗∗

Char

3×

MP

0.00

30.

004

0.50

5−0

.000

20.

003

0.95

6N

um

ber

ofin

stru

men

ts25

25N

um

ber

ofobse

rvat

ions

311

311

Num

ber

ofgr

oups

3535

AR(2

)p

valu

e0.

387

0.36

8H

anse

nte

stp

valu

e0.

152

0.15

5

Note

s:G

MM

=ge

ner

aliz

edm

ethod

of

mom

ent;

Char

1=

ban

ksi

ze;Char

2=

ban

kliq

uid

ity;Char

3=

ban

kca

pita

lizat

ion;M

P=

monet

ary

polic

y;A

R(2

)=

seco

nd

ord

erau

tore

gres

sive

.The

dep

enden

tva

riab

leis

the

log

ofban

kslo

an.The

indep

enden

tva

riab

les

are

the

IBO

R,lo

gofG

DP,

inflat

ion

rate

,an

dban

kch

arac

terist

ics

(Char

).Char

×M

Pis

the

inte

ract

ion

effe

ctbet

wee

nm

onet

ary

polic

y(I

BO

R)

with

the

ban

kch

arac

terist

ics.

All

pva

lue

of

the

diffe

rence

inH

anse

nte

sts

of

exoge

nei

tyof

inst

rum

ents

subse

tshav

eal

sobee

nre

ject

edat

leas

tat

10%

sign

ifica

ntle

velbutnotre

ported

her

e.Yea

rdum

mie

san

dco

nst

antar

enotin

cluded

tosa

vesp

ace.

The

full

resu

ltsar

eav

aila

ble

upon

reques

t.∗ S

ignifi

cantat

p≤

0.10

,∗∗

Sign

ifica

ntat

p≤

0.05

,∗∗

∗ sig

nifi

cantat

p≤

0.01

.In

stru

men

tfo

rorthogo

nal

dev

iatio

neq

uat

ion:La

gs2

to3

for

endoge

nous

variab

les

(lag

ged

dep

enden

tva

riab

le),

lags

1to

2fo

ral

lpre

det

erm

ined

variab

les

(log

ofG

DP,

inflat

ion

rate

,an

dal

lban

kch

arac

terist

ics)

,an

dal

lla

gsfo

rst

rict

lyex

oge

nous

variab

le(I

BO

R).

The

estim

atio

nal

soco

llapse

sth

ein

stru

men

tsm

atrix

aspro

pose

dby

Cal

der

on,Chong,

and

Loay

za(2

002)

,an

dRoodm

an(2

009b

).

235

Dow

nloa

ded

by [

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ity O

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3:45

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ust 2

014

236 Z. A. Karim et al.

The interaction effect between MP and bank characteristics is also sta-tistically significant in influencing the banks’ loan supply. For example,the positive coefficient of the interaction between bank liquidity and MP(Char2 × MP) on banks’ loan supply has indicated that a low value of bankliquidity interacts with monetary tightening and tends to reduce the banks’loan supply. The underlying reasoning is that banks with a more liquidbalance sheet can use their liquid assets to maintain their loan portfolio andtherefore are affected less heavily by a monetary contraction (Ehrmann et al.,2003). The interaction term between bank capitalization and MP (Char3 ×MP) is also positive and significant, which means that banks with high cap-italization ratio (high capital adequacy ratio) are enable to offer more loansduring monetary tightening. This findings signal that sound bank character-istics in term of liquidity and bank capitalization play an important role ininfluencing the banks’ loan supply. However, the interaction term betweenbank asset and MP (Char1 × MP) is not statistically significant. This indicatesthat bank asset or bank size is not relevant in influencing the banks’ loansupply.

The results of the both specification tests, that is second order autore-gressive (AR[2]) for testing the serial correlation and the Hansen test fortesting the validity of instrument adopted, are also valid. As shown inTable 1, the p values for the AR(2) and Hansen tests are higher than 0.10,that is, statistically insignificant at the 10% significance level. This implies thatthe empirical model has been correctly specified because there is no serialcorrelation (autocorrelation) in the transformed residuals, and the instru-ments (moment conditions) used in the models are valid. The additionalmoment conditions such as difference in Hansen tests are also statisticallyinsignificant in all models but not reported to save space.

Subsample

Table 2 and Table 3 report the estimation results of two subsample periods,namely periods 1993 to 1999 and 2000 to 2008. The motivation for splittingthe sample is to examine the differential of MP effects on bank loans supplyfunction across two subsample periods. Period of 1993 to 1999 representsbefore economic recovery, and period of 2000 to 2008 represents after eco-nomic recovery that Malaysia experienced. In general, the main results arerobust, which indicates that the relevance of BLC in two subsample periods.However, there is a significant difference of MP effects on banks’ loan supplyacross subsamples. MP contraction has a small effect on bank loan supplyduring 1993 to 1999 (before economic recovery). For example, in 1993 to1999, a 1% increase in the IBOR led to a decrease contemporaneously inbank loan supply by 0.166% (in one-step estimation). In contrast, during theperiod 2000 to 2008 (after economic recovery), 1% increase in interbank rateled to a decrease in bank loan supply by 0.650% (in one step estimation),

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ust 2

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TAB

LE2

The

Det

erm

inan

tsofB

anks

’Loan

Supply

Funct

ion:Sy

stem

GM

ME

Est

imat

ion

(Subsa

mple

1990

–199

9)

One-

step

estim

atio

nTw

o-s

tep

estim

atio

n

Coef

fici

ent

Robust

SEp

valu

eCoef

fici

ent

Corr

ecte

dSE

pva

lue

Lagg

eddep

enden

tva

riab

le0.

922

0.05

90.

000∗∗

0.93

90.

099

0.00

0∗∗In

terb

ank

ove

rnig

htra

te(I

BO

R)

−0.1

660.

092

0.04

3∗−0

.090

0.01

70.

000∗∗

Lagg

edofIB

OR

−0.0

280.

029

0.32

4−0

.010

0.03

50.

777

Log

ofgr

oss

dom

estic

pro

duct

(LG

DP)

1.22

62.

100

0.56

00.

197

1.49

10.

895

Lagg

edofLG

DP

−1.9

471.

880

0.30

1−0

.904

1.27

90.

480

Inflat

ion

0.03

850.

111

0.73

0−0

.019

0.07

90.

807

Lagg

edofin

flat

ion

0.08

70.

053

0.10

10.

061

0.09

60.

128

Char

1×

MP

0.13

70.

100

0.17

20.

093

0.09

50.

332

Char

2×

MP

−0.0

030.

001

0.02

7∗−0

.025

0.00

20.

218

Char

3×

MP

0.00

20.

003

0.34

8−0

.002

0.00

30.

489

Num

ber

ofin

stru

men

ts24

24N

um

ber

ofobse

rvat

ions

154

154

Num

ber

ofgr

oups

3535

AR(2

)p

valu

e0.

346

0.34

2H

anse

nte

stp

valu

e0.

239

0.23

9

Note

s:G

MM

=ge

ner

aliz

edm

ethod

of

mom

ent;

Char

1=

ban

ksi

ze;Char

2=

ban

kliq

uid

ity;Char

3=

ban

kca

pita

lizat

ion;M

P=

monet

ary

polic

y;A

R(2

)=

seco

nd

ord

erau

tore

gres

sive

.The

dep

enden

tva

riab

leis

the

log

ofban

kslo

an.The

indep

enden

tva

riab

les

are

the

IBO

R,lo

gofG

DP,

inflat

ion

rate

,an

dban

kch

arac

terist

ics

(Char

).Char

×M

Pis

the

inte

ract

ion

effe

ctbet

wee

nm

onet

ary

polic

y(I

BO

R)

with

the

ban

kch

arac

terist

ics.

Yea

rdum

mie

san

dco

nst

ant

are

not

incl

uded

tosa

vesp

ace.

All

p-va

lue

ofth

ediffe

rence

inH

anse

nte

sts

ofex

oge

nei

tyofin

stru

men

tssu

bse

tshav

eal

sobee

nre

ject

edat

leas

tat

10%

sign

ifica

ntle

velbutnotre

ported

her

e.The

full

resu

ltsar

eav

aila

ble

upon

reques

t.∗ S

ignifi

cantat

p≤

0.05

,∗∗

Sign

ifica

ntat

p≤

0.01

.In

stru

men

tfo

rorthogo

nal

dev

iatio

neq

uat

ion:La

gs2

to3

for

endoge

nous

variab

les

(lag

ged

dep

enden

tva

riab

le),

lags

1to

2fo

ral

lpre

det

erm

ined

variab

les

(log

ofG

DP,

inflat

ion

rate

,an

dal

lban

kch

arac

terist

ics)

,an

dal

lla

gsfo

rst

rict

lyex

oge

nous

variab

le(I

BO

R).

The

estim

atio

nal

soco

llapse

sth

ein

stru

men

tsm

atrix

aspro

pose

dby

Cal

der

on,Chong,

and

Loay

za(2

002)

,an

dRoodm

an(2

009b

).

237

Dow

nloa

ded

by [

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ia L

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at 0

3:45

11

Aug

ust 2

014

TAB

LE3

The

Det

erm

inan

tsofB

anks

’Loan

Supply

Funct

ion:Sy

stem

GM

MEst

imat

ion

(Subsa

mple

2000

–200

8)

One-

step

estim

atio

nTw

o-s

tep

estim

atio

n

Coef

fici

ent

Robust

SECoef

fici

ent

Robust

SECoef

fici

ent

Robust

SE

Lagg

eddep

enden

tva

riab

le0.

748

0.06

50.

000∗

0.76

40.

096

0.00

0∗In

terb

ank

ove

rnig

htra

te(I

BO

R)

−0.6

500.

083

0.00

0∗−0

.370

0.09

10.

000∗

Lagg

edofIB

OR

0.12

70.

164

0.43

70.

087

0.12

90.

500

Log

ofgr

oss

dom

estic

pro

duct

(LG

DP)

1.39

11.

123

0.21

61.

366

1.67

60.

415

Lagg

edofLG

DP

−0.7

601.

334

0.56

9−0

.738

2.03

80.

717

Inflat

ion

−0.0

270.

031

0.38

2−0

.023

0.04

10.

573

Lagg

edofin

flat

ion

0.03

20.

071

0.64

80.

032

0.07

40.

666

Char

1×

MP

0.30

10.

277

0.27

70.

180

0.47

70.

706

Char

2×

MP

−0.0

060.

001

0.00

0∗−0

.006

0.00

20.

012∗

Char

3×

MP

−0.0

110.

003

0.00

1∗−0

.012

0.00

50.

251

Num

ber

ofin

stru

men

ts25

25N

um

ber

ofobse

rvat

ions

137

137

Num

ber

ofgr

oups

2121

AR(2

)p

valu

e0.

887

0.89

7H

anse

nte

stp

valu

e0.

410

0.41

0

Note

s:G

MM

=ge

ner

aliz

edm

ethod

of

mom

ent;

Char

1=

ban

ksi

ze;Char

2=

ban

kliq

uid

ity;Char

3=

ban

kca

pita

lizat

ion;M

P=

monet

ary

polic

y;A

R(2

)=

seco

nd

ord

erau

tore

gres

sive

.The

dep

enden

tva

riab

leis

the

log

ofban

kslo

an.The

indep

enden

tva

riab

les

are

the

IBO

R,lo

gofG

DP,

inflat

ion

rate

,an

dban

kch

arac

terist

ics

(Char

).Char

×M

Pis

the

inte

ract

ion

effe

ctbet

wee

nm

onet

ary

polic

y(I

BO

R)

with

the

ban

kch

arac

terist

ics.

Yea

rdum

mie

san

dco

nst

ant

are

not

incl

uded

tosa

vesp

ace.

All

p-va

lue

ofth

ediffe

rence

inH

anse

nte

sts

ofex

oge

nei

tyofin

stru

men

tssu

bse

tshav

eal

sobee

nre

ject

edat

leas

tat

10%

sign

ifica

ntle

velbutnotre

ported

her

e.The

full

resu

ltsar

eav

aila

ble

upon

reques

t.∗ S

ignifi

cantat

p≤

0.01

.In

stru

men

tfo

rorthogo

nal

dev

iatio

neq

uat

ion:La

gs2

to3

for

endoge

nous

variab

les

(lag

ged

dep

enden

tva

riab

le),

lags

1to

2fo

ral

lpre

det

erm

ined

variab

les

(log

ofG

DP,

inflat

ion

rate

,an

dal

lban

kch

arac

terist

ics)

,an

dal

lla

gsfo

rst

rict

lyex

oge

nous

variab

le(I

BO

R).

The

estim

atio

nal

soco

llapse

sth

ein

stru

men

tsm

atrix

aspro

pose

dby

Cal

der

on,Chong,

and

Loay

za(2

002)

,an

dRoodm

an(2

009b

).

238

Dow

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ded

by [

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Bank Lending Policy in Malaysia 239

which is greater than the full sample period. This finding shows that MP isvery effective in influencing bank lending behavior during a good economiccondition.

SUMMARY AND CONCLUSION

The role of BLC in monetary transmission has been studied extensively inadvanced countries, however little attention has been given to investigatingthis issue in a small-open economy. Therefore, to fill this gap in the previousliterature, the current study focuses on the BLC of MP in a Malaysian contextby estimating the banks’ loan supply function using a dynamic panel dataframework.

This article found that relevance of the role of BLC of MP in theMalaysian economy. This means that the tightening of MP is significantlyinfluenced by the banks’ loan supply, which indicates that the role of com-mercial banks is very important in transmitted MP to the real economicactivity. The finding also indicated that significant role of bank characteris-tics variable, namely bank liquidity and bank capitalization, in influencingthe banks’ loan supply. This indicated that, during tight MP, the banks areable to maintain their loans to the customers by using their liquidity andbank capital as alternatives of loan financing because they are affected lessheavily during monetary contraction.

This article has several implications for implementing of MP. First,because BLC plays a pivotal role in monetary transmission, the monetaryauthority has to monitor the stability in the interest rates to stabilize thebanks’ loan supply. This is because any changes in MP variable will affectthe bank loan and subsequently will affect the firm investment and eco-nomic activity. Second, the monetary authority affects the microeconomicsaspect of bank behavior in formulating their policy.

NOTES

1. The detailed explanation about the channels of the monetary policy (MP) transmissionmechanism can be found in Mishkin (1996).

2. Schematically, the MP effects on BLC can be summarized as follows: M ↓ ⇒ bank deposit ↓⇒ bank loans ↓ ⇒ I ↓ ⇒ Y ↓, which is contractionary MP leads to a fall in bank reserves and bankdeposit; subsequently will decline in the bank loans, in turn contract the investment spending, and fallin output.

3. The excellence literature review about the credit channel can be found in Mateut (2005), andEgert and MacDonald (2009).

4. In the Euro area, the role of banks in monetary transmission have been investigated by Ehrmann,Gambacorta, Martinez-Pages, Sevestre, and Worms (2003) in France, Germany, Italy, and Spain; Worms(2003) in Germany; Hernando and Martinez-Pages (2003) in Spain; Loupias, Savignac, and Sevestre (2003)in France; Brissimis, Kamberoglou, and Simigiannis (2003) in Greece; Gambacorta (2003) in Italy; Haan(2003) in Netherlands; Kaufmann (2003) in Austria; Farinha and Marques (2003) in Portugal, and Topi

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240 Z. A. Karim et al.

and Vilmunen (2003) in Finland. All studies have found that the existence of bank-lending channel inthe Euro area.

5. Specifically, the finding of the results indicate that a one percentage point increase in interestrates lead to a decline the bank loans by 458 321.8%. Therefore, it is believed that their estimation modelhas a misspecification error that relates to the econometric model, and bank-loan supply equation.

6. The detailed derivation of Equation 9 is available upon request.7. The appendix provides the detailed clarification about the data set.

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Bank Lending Policy in Malaysia 243

APPENDIX 1

Numberof the ObservationsBank Bank’s name Data (in years)

1 Affin Bank 1993–2008 162 Alliance Bank 1993–1999; 15

2001–20083 Arab-Msian Bank 1995–2001 74 Bangkok Bank Berhad 1994–2007 145 Bank of America Malaysia Berhad 1997–2007 116 Bank of Nova Scotia Berhad 1995–2007 137 Bank of Tokyo - Mitsubishi (Malaysia)

Berhad1994–2007 14

8 Bank Utama 1994–1999 69 BBMB 1993–1998 6

10 BCB/CIMB 1994–2007 1411 BHL Bank 1995–1999 512 BSN Com. 1995–1999 513 Chung Kiaw Bank 1994–1996 314 Citibank Berhad 1995–2007 1315 Deutsche Bank (Malaysia) Bhd. 1994–2007 1416 EON Bank Berhad 1994–2007 1417 Hoch-Hua Bank 1994–1999 618 Hock Hua 1994–1999 619 Hong Leong Bank Berhad 1995–2008 1420 HSBC Bank Malaysia Berhad 1994–2007 1421 Inter. Bank of Msia 1995–1999 522 JP Morgan Chase Bank Bhd 1995–2007 1323 Maybank 1993–2008 1624 OCBC Bank (Malaysia) Berhad 1994–2007 1425 Oriental-Bank 1995–2000 626 Oversea Union Bank 1994–1999 627 Pacific Bank 1994–1999 628 Phileo-Allied Bank 1995–2000 629 Public Bank 1993–2007 1530 RHB 1994–1996; 12

1999–200731 ROYAL SCOTLAND 1996–2007 1132 Sabah Bank 1994–1998 433 Sime Bank 1995–2002 734 Southern Bank Berhad 1994–2005 1135 Standard Chartered Bank Malaysia 1994–1999; 13

Berhad 2001–200736 United Overseas Bank (Malaysia) Bhd. 1994–2007 1437 Wah Tat 1994–1999 6

Total observations(bank-years)

375

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