THE DETERMINANTS OF BANK CREDIT GROWTH IN
INDONESIA DURING 1992 –2004: A SUPPLY SIDE APPROACH
Abdul Mongid
Director of Research and Community Services
Perbanas School of Banking (STIE PERBANAS)
Nginden Semolo # 36
Surabaya, INDONESIA 60118
Center for Research and Community Service (PPPM)
STIE PERBANAS Surabaya Indonesia
Abstract
This study examines the role of monetary policy and banking sector variable
on growth of bank credit. The main question is whether monetary policy
variable and banking sector variable play an important role in the
credit growth. The finding suggest that from supply side, the monetary
policy using narrow money and discount rate (SBI) are significant to
explain the credit growth. From the banking variable perspective, total
banking liability is significant and positive mean that the bank function
as intermediary are working. Foreign bank asset tend to reduce the
credit growth as the result of regulation on net open position
limitation. However as this study are based on supply side. It means
not all question on the determinant of banking credit growth is not
answered fully. Very low R-squared provide indication that the model
is very weak to explain the credit growth. In addition, a substantial
weakness exists because we do not incorporate bank capital effects.
Keywords: bank credit growth, bank lending channel, monetary policy
and banking liability.
A. Introduction
The monetary policy mechanism by which monetary policy is transmitted to the
real economy has emerged as the pivotal discussion topic recently because the reality that
monetary policy has become the only game can be played by the central bank. However,
as the economic situation among countries are dispersed, the empirical investigation
resulted from various studies are also, in some cases, contradictionary and confusing.
That is why it is a debatable topic in macroeconomics in general and monetary study
especially.
1
However, till to date, there is no consensus among economists about how
monetary policy operates. The traditional money channel (or interest rate channel) states
that when the central bank reduces its reserves, commercial banks are forced to reduce
their demand for deposits due to higher costs of funds. If prices are sticky, in the short
run a decrease in real monetary holdings should lead to higher real interest rates. This in
turn translates into a contraction of interest-sensitive components of aggregate spending
(e.g., consumption and investment) and, therefore, into lower economic growth.
For Indonesia, the understanding and ability of Indonesia Monetary Authority
how monetary policy works is necessary mandate of the new Central Bank Act of 1999
especially to help the economic recovery. The understanding how monetary policy works
requires enhancements both the capacity and institutional buildings for better monetary
policy making process and implementation. As enacted in 1999,the new Central Bank
Act provides a clear mandate for Bank Indonesia in conducting its monetary policy to
maintain the value of Rupiah both in domestic and international term.
Currently, the main investigation efforts are directed to study on the role played by
banking industry in the transmission of monetary policy. The aim is to uncover a credit
channel of monetary policy. As the credit channel operates through shifts in loan-supply
schedules, uncovering the credit channel in monetary policy means nothing but to
looking on the impact of monetary policy instrument to banking industry.
Warjio and Agung (2002) mentioned the reasons why Indonesian monetary
authority is eager to study monetary policy channel because the understanding the
movements of financial and economic aggregates as result of monetary policy would
improve the understanding the link between the financial and the real sectors of the
economy. Second, a better understanding of the transmission mechanism would help
monetary authorities and analysts to interpret movements in financial aggregates. Finally,
more information about the transmission mechanism might lead to a better choice of
intermediate monetary targets. As concluded by Aghion, Bacchetta and Banerjetta (2000)
the interest rate shock may be necessary to prevent crisis economy from further recession
when the responses of such policy by credit supply not so strongly.
In the country experiencing multi crisis, banking, exchange rate and political crises,
Indonesian monetary authority worked hard to make the credit channel work. The
2
monetary policy currently is in questionable stage as Indonesia curretly lack of supported
institution and condition for succesful monetary policy, such bank capital constraint
(CAR), exchange rate instability and firm restructuring process. All these lessen the
effectiveness of monetary policy. Under dis-intermediation in the banking industry,
indicated by Loan to deposit ratio (LDR) currently at only 34%, the validity of the
monetary policy is on attack. The attacks based on the assumption that the work of credit
channel depends on the extent to which banks rely on deposit financing and adjust their
loan supply schedules following changes in bank reserves as responses to monetary
policy action.
The economic crisis that began in the mid-1997 completely changed monetary
and banking landscape in Indonesia. After nearly ten years of rapid expansion,
Indonesia’s banking system is crippled, with the vast majority of dominant local banks
now technically bankrupt without government supports. It then means that when banks
strive to survive, no actions will be done to responses the monetary policy action but only
responding to prevent from the death.
However, the study is not based on that presumption. We still believe that the
roles of monetary variables are still important to influences the bank managers to change
their loan or balance sheet position.
B. Theoretical Background
The debate on the monetarty policy via bank lending chanel and balance sheet
chanel emerged from the existence of assymentric information problem between lender
and borrowers. Bank lending channel focuses on the assumption that bank loan is very
important for successful monetary policy. In the monetary policy channel via the “bank
lending ”(Bernanke and Blinder,1988), monetary transmission mechanism was delivered
by banks upon changing their assets as well as their liabilities.
During a monetary contraction situation, banks will decrease their reserves and
reduce their deposits and the the loan. In a monetary expansion, banks increase the loan.
If the decrease in deposits is not offset by other funds which are not subject to reserve
requirements,or by a decrease in securities,this will result in a decrease in bank loans.If
bank loans fall and bank dependent borrowers are dominant in the economy,real
3
investment expenditure will fall. Since bank loans in Indonesia remain the main source of
external finance for business enterprises,a disrupting of bank loan supply can reduce the
economic activity.
Kim (1999:5-10), using Korean cases, found that bank credit channel is
significant after the crisis. The sharp drops in credit supply in line to the eastern asia
crisis reduced credit supply provided evidence that constractive monetary policy was
responded. There is strong evidence that a substantial excess demand for bank loans
becuause of a dramatic decrease in loan supply as impact of banking crisis.
Previously, Kasyap and Stein (1997) found supportive evidences that credit
channel is as monetary policy channel. The study was using USA bank panel data during
1976-1993. The study found evidences the impact of monetary policy that affected
bank’s propensity to lend, especially among illiquid banks. For Indonesian cases, Agung
(1998), using sample data from 1985-1995, proved that a monetary policy was able to
influence the bank supply of credit, in particular small banks, not large banks which were
able to shield their bank loan supply by finding the cheaper source of funds from
overseas. Further investigation using aggregate data during episode of crisis, Warjio and
Agung (2002) conclude that monetary policy is still effective to affect bank lending.
However different type of bank has different elasticity.
DeBondt (2000) finds evidence for the reaction of bank lending to monetary
policy mainly depends on bank size. On the other hand, Favero et al. (1999) do not find
such evidence using the same database in a cross-sectional analysis. Worms (2001),
Quoted from Kakes and Strum, (2002) – study using the Bundesbank´s bank balance
sheet statistics covering all German banks – conclude that we cannot reject the hypothesis
that the reaction of a bank’s lending to monetary policy depends on its size, although this
effect does not seem to be of macroeconomic importance.
The finding support Kashyap and Stein (1995) that as long as the banks do not face
a perfectly elastic demand for their managed liabilities, a bank lending channel will
operate. However, some argue that the regulatory action of central banks can also
significantly influence bank loan supply. For examples is the regulation on loan
classification and capital adequacy ratio (CAR). Interested paper on this issue is the work
4
of Peek and Rosengren (1995), The capital Crunch: Neither a borrower nor lender be,
Journal of Credit Money and Banking, 27, p 149-213
De Haan (2001) usig bank level data divides all bank operting in the Netherlands
into two classifications base on financial health and market orientation. The results for
loan supply suggest that a lending channel is operative in the Netherlands but become
less effective for secured bank debt. It means contractive monetary policy does not have
any negative effect on secured bank lending.
Altunbas, Fazylop and Molyneux (2002) add more confusions on the role of bank
channel in the monetary policy. By using bank specific data for European case, they
found that smaller and undercapitalized banks were more responsive to monetary policy.
Small banks which have relatively limited access to non-deposit funds such as securities
issues or foreign borrowings are expected to be more affected by the monetary shock and
to tend to cut their loan supplies immediately following the shock. However, across
European countries resulted in inconsistent results and suggest further verification and
examinations. Their findings are in line to Ehrmann et al. (2001) that testing for a
differential reaction of bank loans to monetary policy across banks for France, Germany,
Italy, Spain and the euro-area as a whole and conclude no evidence for a bank lending
channel using bank size as the discriminating variable. They also show that the data
summarized by BankScope is not necessarily a useful database for exercises of this kind.
These inconsistent results may arise as the impact of the banking industrial strucutre
of Europe as mentioned by Lensink and Sterken (2002). Both suggest to view credit
market within industrial organisation of banking sector, considering structural factor in
the economy, business cycle and institutional factors among nations. However, using
individual firm data, Nilsen (1999) concludes the bank lending channel become less
important to transfer the effect of monetary policy because small firms still can increase
their demand on loan by shifting from investment loan to trade credit.
The importance of banking in the economy especially to the behavior of output that
driven by aggregate bank credit will be necessary condition for prompt economic
recovery. During the crisis, banks were forced to cut lending, and this resulting “credit
crunch” . This is, then believed as the propagation and deepening the crisis. So then
restoring the flow of credit should be a priority for policy-makers in the immediate
5
aftermath of banking crises. Bernanke (1983) argued that the contraction in credit
inhibited by the banking crisis was instrumental in the propagation of the Great
Depression in the U.S. Recent attempt to test for a credit crunch effect in Indonesia was
done by Hariadi (2002) and found the evidences of credit crunch during the crisis.
The study on the monetary policy strategy in Indonesia during the banking crisis
has been investigated extensively especially by Fane (2000). The strategy was mainly
controlling the growth of M0. In sum, achieving a modest target for domestic inflation
would not have been very different in practice from setting tight limits on the growth of
M0.
Bernanke and Gertler concluded that the effects of an adverse macroecoomic
shock may be amplified if it restricts the flow of credit (see, e.g., Bernanke, Gertler, and
Gilchrist, 1996). A decline in credit creation capacity could occur because a negative
shock worsens firms’ balance sheets and thus their access to credit or because it restricts
the ability of banks to supply loans. In a world of asymmetric information, firms with
healthier balance sheets can obtain credit on better terms. The firm’s ability to post
collateral and provide down payments will reduce the agency costs associated with
borrowing. If a negative shock worsens firms’ financial positions, it might impair their
access to credit because the assumption that agency risk increases.
Similarly, an adverse shock might reduce bank capital and thus banks’ ability to
provide loans. When either firms’ balance sheets deteriorate or banks’ loan supply
declines, the reduction in credit forces firms to reduce spending and output.
That is why Bernanke and Gertler believed that the effects of an adverse
macroecoomic shock might be amplified if it restricts the flow of credit (see, e.g.,
Bernanke, Gertler, and Gilchrist, 1996). A decline in credit creation could occur because
a negative shock worsens firms’ balance sheets and thus their access to credit or because
it restricts the ability of banks to supply loans. In a world of asymmetric information,
firms with healthier balance sheets can obtain credit on better terms. Their ability to post
collateral and provide down payments reduces the agency costs associated with
borrowing.
When a negative shock worsens firms’ financial positions, it might impair their
access to credit market. Similarly, an adverse shock might reduce bank capital and thus
6
banks’ ability to provide loans. When either firms’ balance sheets deteriorate or banks’
loan supply declines, the reduction in credit forces firms to reduce spending and output.
Krugman (2001) emphasized the role of deteriorating balance sheets in
propagating the Asian crisis. Firms were saddled with short-term foreign currency
denominated debt. This debt was largely unhedged against exchange rate risk.
Depreciating exchange rates thus multiplied businesses’ liabilities and reduced their
retained earnings (equity). Krugman (2001) also stated that, in the case of Indonesia,
bank runs and a freezing up of the credit system played an important role. Declines in
bank capital and disintermediation reduced the willingness of banks to supply loans.
Business borrowers facing a once in a generation crisis lost their credit lifelines and were
forced to curtail spending and production.
Djiwandono (2000), the head of the Indonesian Central Bank (BI) in 1997,
describes how banking sector problems developed in Indonesia. When spillovers from
Thailand’s currency crisis reached Indonesia, BI sought to preserve foreign currency
reserves first by widening the exchange rate bands and then by floating the exchange rate.
As pressure on the exchange rate continued, BI raised the interest rate on its certificates
(Sertifikat Bank Indonesia or SBI’s). The Minister of Finance at the same time directed
state enterprises to transfer 3.5 trillion rupiah of bank deposits into SBI certificates. These
moves damaged the banking sector. Banks were further harmed in October when IMF
demands for the closure of 16 banks led to a bank run. Using the framework of bank
lending, Mongid (2004) concluded that the bank lending chanell is valid during the
periods of 1992-2002 but become less effective after that.
C. Methodology
C.1 The Framework of The Study.
The point of departure of the study is based on assumption adopted from Luísa
Farinha and Carlos Robalo Marques , The Bank Lending Channel Of Monetary Policy:
Identification And Estimation Using Portuguese Micro Bank Data, ECB Working
Paper 102, December 2001 which states that the monetary policy works by affecting bank
7
assets (loans) and banks’ liabilities (deposits). The key point is that monetary policy
besides shifting the supply of deposits also shifting the supply of bank loans. In this
context, the crucial response of banks to monetary policy is their lending response and
not their role as deposit creators. The two key necessary conditions that must be satisfied
for a lending channel to operate are: (a) banks cannot shield their loan portfolios from
changes in monetary policy; and (b) borrowers cannot fully insulate their real spending
from changes in the availability of bank credit.
The first condition assumes that banks are not able to completely offset the
decrease in deposits brought about by monetary policy shocks, by resorting to alternative
sources of funds (at least not without incurring in increasing costs). Because of the extra
premium that banks have to pay to bring in alternative external funds, banks will make
fewer loans after the fall in reserves brought about by monetary policy. Of course, it is
expected that banks hedge against changes in monetary policy, by holding securities as a
buffer against a reserve outflow. But such buffer is not expected to fully offset the effects
of a monetary policy contraction, as buffer stocks are costly for banks (in terms of
interest foregone).
The second condition assumes that some spending, which is financed with bank
loans, will not occur if banks cut the loans, else the real consequences of the credit
channel will be null. In summary, while the traditional theory emphasizes the households’
preferences between money and other liquid assets (bonds) the credit view argues that the
banking behavior is also very important to the transmission of monetary policy.
According to Warjio and Agung (2002) there are two necessary conditions for the
validity of the bank lending channel; bank loans and securities must be imperfect
substitutes for some borrowers, or some borrowers are bank dependent, second the
central bank must be able to constrain the supply of bank loans using all available
instruments. It seems these conditions are valid in Indonesia Case especially if we refer to
Agung (1998).
C.2 Data
The analysis in this study is based on monthly data extracted from the Monthly
Statistical Bulletin published by Bank Indonesia and from Asia Recovery Information
8
Center (ARIC) database, Asia Development Bank. The time period under consideration
starts with February 1993 and ends with February 2004. This study is based on aggregate
data, as detailed data at the individual bank level are not available. This approach is
common for a large number of studies on the European countries that face similar data
problem. Among others, empirical studies that are dependent on aggregate data such as
de Bondt (1999) and Kakes (2000). We will treat all data to fullfil stationarity using
Augmented Dickey-Fuller test. When the data is stasionair at the level, no further
treatments were conducted. If not the data is then differentiated.
C.3. Variables
Variables employed in this study are discount rate (SBI), Braod Money ( M2),
Total banking liability (TLIABI), Foreign Currency Denominated Liability (FLB)
Forreign curency denominated assets (FAB), Foreign Gap of banking sector and total
liability of banking. The dependent variable is credit growth (CREGR).
Figure 1. Framework of Analysis
Monetary Policy
Variables:
Money Supply (M1)
SBI
Result:
Credit
Growth
Control Variable
Total Liability
Foreign Assets
Forex GAP
C.4 Model of Analysis
To estimate the impact of monetary policy and banking system variables to the
bank lending, the model of analysis used in this study is linear regression using the credit
growth as dependent variable. The model is formulated below:
9
Cregr = 1 M1 + 2 SBI + 3 FAB + 4 FLB+ 5 Forex Gap + 6 Crisis + ε
Definition:
M1 = Total Narrow Money, Total Central Bank Liability
SBI = SBI Rate of Central Bank
FAB = Total Foreign Asset of banking system
Forex Gap = Total Foreign Liability divided by total foreign denominated Assets.
TLIABI = Total liability of banking system
Cregr = Credit growth = Credit (n) – Credit (n-1) / Credit (n-1) X 100%
Estimation is carried out using Eview Statistical Packard Programme. In the hipotheses
testing, we teste on the work of monetary aggregate such as discount rate (SBI) , narrow
Money (M1) to influence bank credit growth. We are also to test on the validity of
foreign currency gap as determinant of credit growth. The most inportant test is to
examine the total libaility in influencing the credit growth.
D. Results
D.1 Decriptive
From table1, we can see some interested points. The mean of foreign exhange gap
is more than 1. It means in average total libaility denominated in foreign exchange are
higher than foreign asset. The mean value is 1.15 and the standard deviation is 0.49. This
figure indicate that Indonesian Banks are vulnerable against the exchange rate movement.
However the minimum is only 36% but the maximum gap is almost double of it libaility.
Using Jarque-Bera test, the foreign exchnage gap is not normaly distributed.
Table 1 Descriptive Statisitcs
Category
FOREX
NARROW
SBI
TOTAL
INTEREST LIABILITY
FOREIGN
CREDIT
ASSET
GROWTH
GAP
MONEY
Mean
1.150141
104495.3
17.14692
647744.6
62647.76
0.150436
Median
0.934400
101197.0
13.67000
736776.0
76387.00
1.099400
Maximum
1.969300
224318.0
70.81000
1167894.
187525.0
13.34930
10
Minimum
0.364300
29051.00
7.450000
185168.0
10681.00
-28.38930
Std. Dev.
0.492703
57565.30
12.57794
337937.7
45057.63
6.235707
Skewness
0.136182
0.384660
2.856364
-0.042070
0.267614
-0.017269
Kurtosis
1.471452
1.832730
10.84526
1.358364
2.014246
11.73639
Jarque-Bera
13.35896
10.83047
521.9329
14.97386
6.972411
422.9721
Probability
0.001256
0.004448
0.000000
0.000560
0.030617
0.000000
133
133
133
133
133
133
Observations
Narrow money is also indicating that the mean and the median is very closed.
The mean is IDR 104 trillions and its standard deviation is IDR 57 Trillions. The narrow
money grow very fast after the crisis as result of increasing cash holding by society to
balance the price increase. The data on narrow money is not normally distributted. SBI,
discount rate that mostly used by central bank of Indonesia to influence money in
circulation, has mean value 17%. The highest is 70.8% and it was during crisis in 1998 as
effort to reduce speculation on US dollar. The stadard deviation is 12.6%. Using JarqueBera statitistic , the data is not normally distributed.
The total liability in the banking sector increase from time to time. The mean is
IDR 647 trillions and the median is IDR 74 trilion. At the beginning of the observation,
the value IDR 185 trillions. The standard deviation is IDR 338 trillions. Using JarqueBera statitistic , the data is not normally distributed. The credit rowth, as dependent
variable, has mean value 0.15% and the standar deviation is 6.2%. However, the data is
not normally distributed.
Credit growth variable has very interesting feature. The mean is 0.15% and
median is 1%. The maximum growth rate of credit is 13.3% and lowest growth is
negative 28%. The range is 42%. The variability coefficient is very high, 2%. It means
the variability is almost 50 time of its means. Similar to other variable, the credit growth
is not normally distributed.
11
D.2 Hypotheses Testing
The first step of the process is to determine the existence of serial corelation. This
test is an alternative to the Q-statistics for testing serial correlation. The test belongs to
the class of large sample tests known as Lagrange multiplier (LM) tests. As the data for
the study is time series data span from 1992 to 2004, the threat of serial correlation is
viable. To test the existence of serial correlation we use Breusch-Godfrey Serial
Correlation Langgrane Multiplier test. The result is 3.6 for F-statititics and decisively we
reject the existence of serial correlation. Unlike the Durbin-Watson statistic for AR(1)
errors, the LM test is used to test for because there is not lagged dependent variables. The
null hypothesis of the LM test is that there is no serial correlation up to lag order , where
is a pre-specified integer. The local alternative is ARMA() errors, where the number of
lag terms
= max(). Note that this alternative includes both AR() and MA() error
processes, so that the test may have power against a variety of alternative autocorrelation
structures.
See
Godfrey
(1988),
for
further
discussion.
See
table
2.
Table 2 Correlation Test Using Breusch-Godfrey Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test
F-statistic
Obs*R-squared
3.606351
13.96087
Probability
Probability
0.008138
0.007421
Tabel 3 presents the result generated by Eviews Stattical Package progra. From the
table we could see that the ability of the model to explain the variability of credit growth
is 27%. It means the model only capable to explain 27% of credit growth in Indonesia
during the period of observation. The figure is relatively very low and other varibel are
more attributable to credit growth. However, base on Anova (F-test) we found that this
model in general can explain the varibility of credit growth. F-ratio is 9.52 and the
probability is 1%.
Before data processing, we conduct unit root tests using the Dickey-Fuller (DF) and
augmented Dickey-Fuller (ADF) tests. This tests is in Eviews. The Augmented DickeyFuller (ADF) Test is the t-statistic for the lagged dependent variable. The null hypothesis
of a unit root is rejected against the one-sided alternative if the t-statistic is less than (lies
12
to the left of) the critical value. We found that at level data, the data is not stationary.
Then further action was done using first different and we found it was stationary. In the
modeling we use first difference.
Tabel 3 Estimation Results Using Eviews
Dependent Variable: CREGR
Method: Least Squares
Date: 08/21/04 Time: 11:48
Sample: 1993:02 2004:02
Included observations: 133
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-4.323045
6.167701
-0.700917
0.4846
FOREXGAP
2.713010
3.160710
0.858355
0.3923
NARMONEY
-0.000212
5.88E-05
-3.600769
0.0005
SBI
-0.166116
0.065525
-2.535139
0.0125
TLIABI
5.19E-05
1.10E-05
4.706076
0.0000
FAB
-0.000117
4.30E-05
-2.709540
0.0077
R-squared
0.272610
Mean dependent var
0.150436
Adjusted R-squared
0.243972
S.D. dependent var
6.235707
S.E. of regression
5.421939
Akaike info criterion
6.262847
Sum squared resid
3733.472
Schwarz criterion
6.393239
F-statistic
9.519350
Prob(F-statistic)
0.000000
Log likelihood
Durbin-Watson stat
-410.4794
2.354683
From the table 3 we can explain when foreign gap of increase by 1%, credit growth will
increase by 0.2% at positive direction. For varible narrow Money, any increase by 1% will
decrease very low ( less than 1 per mile). Discount rate, measured by Bank Indonesia
Certificate for one month duration has negative impact as exopected. Any increase in
discount rate will decrease the credit growth by 0.16%. As credit is mainly financed by
banking liability, any increase in total banking liability will increase credit growth by 0.05%.
The increase is very low but in accordance to the theorethical expecatation. Foreign asset of
banking has negative sign indicating if the gap increase, credit groth will decrease atlthouh
13
the ealsticity is very low ( less than 1 per mile). In general only narrow money has different
result from expecatation.
Foreign Exchange gap is not significant. The t-Statitistic is only
0.85. The null
hypotheses that the foreign gap is not statitically significant is accepted. It means for further
analysis we should not consider the foreign gap. Regulation concerning the net open position
impossed to reduce exchange rate exposure is attributatble to the situation. Limitation that the
gap is not allowed to exceed 20% of capital are important feature of the regulation on net
open position.
Narrow money as a measure to investigate the ability of monetary policy variable to
influesce credit growth end to very confusing result. Narrow money is consisting of central
bank liability such as money in circulation and state department account. The coefficient is
negative although we expect it is positive. The null hipothese stating that the variable is not
significant is rejected. It means the narrow money is mportant to impluece the credit growth.
However, as the result is negative future reaseach should be focus on this agregate variable.
Discount rate or SBI has produced result as expected. The coefficeint is negative and
statitically significant. It means any increase in discount rate poolicy will hamper the bank
credit growth. This result is consitent with previous study that discount rat policy is still very
important to manage economic variable. The result also provide another firmed evidence that
monetary policy using interest rate is still effective. The finding consistent with Mongid
(2004) that found the palussible result on bank credit as monetary policy channel. It suggest
that Bank Indonesia use this instrument in carefull way to reduce the fatiquness.
Total liability of the banking, as the source of fund for making credit produce significant
result. Total liability of banking system mostly consist of time deposit, saving account and
currents account. The interesting feature of this variable is that the number increase from
time to time. From perspective of intermediation theory, the result is correct as the sign is
positive. It means any increase of bank funds will be transformed into credit. However, the
coefficient the coefficient is very low indicating the low sensitivity of the variable. It may
provide evidence that banking industry in Indonesia need an structural overhaul to make it
more sensible to the deposit change.
Foreign asset bank (FAB), as the variable to capture the size of fund in foreign
denominated liability provide signifiicant result. The sign is negative indicating that the
14
increase in foreign currency deposit decrease the number of new credit disbursement. The
result is very consistent with prudential measure called Net Open Position (NOP) that
prohibited bank to to have foreign exchange exposure more than 20% of its capital. It is also
indicating that banks may face dificulty to instantly produce loan in other currency.
Theorethically, net open position is not a barrier for the banking firm to produce loan. If
matching principle is applicable, meaning the foreign denominated depsoits used to finance
foreign denominated loan, Net Ope Position Regulation is netral again loan growth. However
, in real life the ability of bank to find borrowers that wiling to borrow in foreign currency is
not easy.
Interesting finding of this study is that base money is contracting variable. It mean the
ability of central bank to create money may reduce the credit. It is very interesting result
because it should have positive impact to credit due to fund availability. The finding should
be treated carefully as most of money circulated are in M1 statitistics. Interpretation of the
result will end to the conclusion that when money in circulation increase, the credit demand
will decrease. However this conclusion is very artificial as the demand drift on currency
increase when the festival time happen such as during christmast and Id day.
Figure 2. Residuals plot of the model
40
20
0
30
- 20
20
- 40
10
0
- 10
- 20
94
95
96
97
Residual
98
99
00
Actual
01
02
03
04
Fitted
Looking a residulas, we can see that the model preformed well in explaining the credit
growth during 1993 to 1997 and then 2001 to 2004. During periods associated as crisis time
we can see that the credit growth is very volatile indicating that during the crisis, credit
growth mostly negative.
15
However as the result is based on supply side, we can not use the conclusion in very
rough that to increase the credit growth, Bank Indonesia should increase the SBI / discount
rate at very low level. Or then askeing the banking industry to reduce deposits in foreign
currency. The suggestion to collect more public funds is a feasible way to increase the
banking intermediation role. As we know that corelation or R-squared is very low, it is very
realisitc to look other variable in demand side. It means that we look more on real sector in
the economy to increase the credit growth.
E. Conclusion
This study examines whether monetary policy variable and banking sector variable play
an important role in the credit growth. The finding suggest that from supply side, the
monetary policy using narrow money and discount rate (SBI) are significant to explain the
credit growth. It means monetary policy transmission mechanism in Indonesia appear
plausible. The finding provides the monetary authority an additional leverage in conducting
monetary policy. Restrictive monetary policy stance is found to cause the commercial banks
credit growth decrease significantly. From the banking variable perspective, total banking
liability is significant and positive mean that the bank function as intermediary are working.
Foreign bank asset tend to reduce the credit growth as the result of regulation on net open
position limitation.
However as this study are based on supply side, the determinant of banking credit
gworh is not answered totally. Low R-squared provide indication that the model is very weak
to explain the credit growth. That is necessary for future research to be directed to look at
demand side of credit growth.
This study contain a substantial weakness because we do not incorporate bank capital
effects. It is generally agreed that bank capital is an important factor in bank asset and
liability management and that its importance has likely increased since the implementation of
the risk-based capital requirements of the 1988 Basle Accord publicly known as Capita
Adequacy Ratio (CAR). The implementation of these regulations has often been blamed for
credit crunch. In addition, there is some more general evidence that the cost of loans depends
16
on bank capital. It means higher bank capital lowers the rate charged on loans, even after
controlling for borrower characteristics, other bank characteristics and loan contract terms.
So bank capital is a significant determinant of loan supply, then it should be incorporated in
the model.
When we take into account capital adequacy regulations, the study will allows to
address the question what role bank lending plays in the monetary transmission and
monetary policy affects the real economy at least in part through a direct effect on the supply
of bank loans. A necessary condition for this channel to be operative is that banks change
their loan supply in reaction to shocks to their reserves. An important point was presented by
Heuvel (2003) that in the presence of capital requirements bank lending plays a special role
in the transmission of monetary policy even if banks face a perfect market for some
nonreservable liability. The model demonstrates that capital requirements generate a
mechanism by which monetary policy shifts the supply of bank loans through its effect on
bank capital, rather than reserves.
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