Credit Channel In Monetary Transmission Policy
TUGAS KELOMPOK SOFTSKILL
EKONOMI KOPERASI
CREDIT CHANNEL
IN MONETARY TRANSMISSION POLICY
Nama Kelompok:
Anis Pratiwi D
Hilma Noor F
Shelvy Septiani
Kelas:
2EB28
Universitas Gunadarma
PTA 2015/2016
THE EFFECTIVENESS OF BANK CREDIT CHANNEL OF MONETARY POLICY TRANSMISSION: THE NIGERIAN EXPERIENCE
Ogun, T. P. and Akinlo, A. E.
Obafemi Awolowo University, Nigeria.
Abstract
Using
Structural Vector Autoregressive (SVAR) technique, the paper tested the
effectiveness of bank credit channel of monetary transmission with the
adoption of deregulatory measures in Nigeria. Secondary data obtained
from the International Financial Statistics and Central Bank of Nigeria
for the period 1986:1 to 2006: 4 were employed in the analysis of the
responses of bank balance sheet variables to monetary policy shock. The
study found that bank deposits, securities holdings and total loans and
advances responded slowly to monetary policy shock during the simulation
period. Monetary policy shock also contributed very little to the
forecast errors of these bank balance sheet variables. The paper
concludes that the bank credit channel is ineffective in Nigeria.
Introduction
In recent years the
issue of the effectiveness of the bank credit channel of monetary
policy transmission has attracted substantial research interest
(Bernanke and Blinder (1992), Bernanke (1993), Romer and Romer (1990)).
The bank credit channel of monetary policy transmission (a sub-channel
of the credit view) holds that because of asymmetric information in the
credit market, banks are able to play special role by providing loans to
some categories of firms which under normal circumstance would have
found it difficult to obtain finance from the capital market. This
implies that monetary policy affecting the supply of bank credit
adversely affect the investment behaviour and the performance of these
firms.
The renewed
interest in the role of bank credit for the transmission mechanism has
been fueled by the wave of financial deregulation that has blown across
several countries recently. Earlier studies which empirically tested for
the existence of the bank credit channel in United States and some
industrial countries have shown that this channel of
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
monetary
transmission exists. However it has been argued recently that financial
deregulation has altered the structure of financial markets in a way
that should have weakened this channel overtime (Thornton, 1994),
(Bernanke and Gertler, 1995)). Two main reasons advanced by the
proponents of the above view are: first, the deregulation of financial
markets have subjected banks to stiff competition from non-bank
intermediaries and this have opened up financial options for some firms
who initially relied on banks for finance. This implies that the
deregulation of financial markets have reduced the share of bank credit
in total amount of funds available to the private sector (Edwards,
1993), (Gorton and Pennachi, 1993)). Second, it has been maintained that
banks’ access to financial markets has increased significantly,
resulting in greater proportion of bank funds coming from sources that
are not affected directly by the central bank action. Under these
conditions proponents of the above view stressed that banks are no
longer special.
Though the above
view is to a large extent tenable in industrialized countries, the lack
of extensive studies on monetary transmission mechanism, continuous
change in economic structure and substantial data problem such as
shortness of time series and structural breaks have made studies on the
role of bank credit in monetary transmission quite mixed in developing
countries (Bank of Korea, 1998), Kim (1999), Carrasquilla (1998), Carcia
(2001). Montiel (1991) also noted that the process of monetary policy
transmission in developing countries has also been bisected by problems
such as limited menu of financial assets to private agents, absence of
organized markets for securities and securities to mention only few.
This paper
investigates the effectiveness of the bank credit channel of monetary
policy transmission by focusing on the Nigerian economy. The Nigerian
case is particularly relevant for two main reasons. First, despite the
adoption of financial liberalization program in 1987, available data
reveal that some features prevalent under the regulated regime still
persist. The banking sector which has been the dominant sector in the
financial market before the adoption of deregulatory measures still
leads. This sector remains the provider of the bulk of financial
liabilities to the private sector of the economy and its activities also
dominate that of non-bank financial intermediaries
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
(Iniodu, and
Udomesiest, 2004). These features, therefore, seems to lend credence to
bank credit channel of monetary transmission. Second, though several
studies have been conducted to test for the effectiveness of the bank
credit channel of monetary policy transmission in developed countries,
no known study, to our knowledge have empirically investigated the
existence or otherwise of bank credit channel of monetary transmission
in Nigeria.
The remaining
aspect of this paper is organized into four sections. Section 2 focuses
on the econometric framework and the model. In section 3 discusses the
measurements and sources of data used in estimation. Section 4 focuses
on the empirical results. The policy implication of the paper is
considered in section 5.
Econometric Framework and the Model
To test for the
effectiveness of the bank credit channel in Nigeria, this study, drawing
from Bernanke and Blinder (1988, 1992) augmented the existing IS-LM
framework by including bank portfolio of assets and liabilities and
other variables in our model. Given that the dynamics of the economy
could be typically approximated by a system of linear equations
containing these variables, a Structural VAR (SVAR) model (assuming lags
but no exogenous variables) is specified as
A 0 y t A1 y t 1 .......A k y t k CD t Be t
|
(1)
|
where yt =(y1t, y2t,…..ynt)’ is an nx1 vector of non-policy and policy variables and the Ai and C are parameter matrices of order nxn. Dt contains
all deterministic variables which may consist of a constant, a linear
trend, seasonal dummy variables as well as other specified dummy
variables. Moreover, et is
an nx1 vector of structural shock or innovations in policy and
non-policy variables are assumed to be a white noise process with (0,1n). The reduced-form of equation (1) which is estimated in the study is
y
|
t
|
y
|
t 1
|
......
|
u
|
t
|
(2)
| |||
0
|
1
|
t
|
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
where i A 1Ai (i = 0, 1….ρ)andtherelationshipbetweenthereduced-form and SVAR residuals is given as
ut =A-1Bet and Aut =Bet
|
(3)
|
Equation (3) above
is called the AB model by Amisano and Giannini (1997). To identify the
reduced-form VAR above, these authors imposed restrictions on the
contemporaneous matrices A and B. When B =In, we have the A-Model
and monetary policy shocks are identified by imposing restrictions on
the contemporaneous relationship between the VAR residuals; that is,
matrix A in the AB-Model. On the other hand, if A =In, we have the B-model and the identification scheme is recursive and B = Δ, whereΔΔ’=Σand isalowertriangular.A generalcaseofAB-Model exists where restrictions is placed on both A and B matrices.
For the A and
B-Models, at least n(n-1)/2 restrictions have to be imposed for
identification of a system with n endogenous variables, for the AB
model, at least n2+n
(n+1)/2 restrictions are needed (see Breitung, Bruggemann and
Lutkepohl, 2004). In this study, the restrictions is imposed in the A
matrix in the AB model while matrix B is assumed to be diagonal.
The vector of endogenous variables is defined as
yt
|
= (lcpit, leat lrt, lsht , ltlat lbtdt ltbrt lnert)’
|
(4)
| |
where
| |||
lcpit
|
=
|
consumer price index(2000=100);
| |
eat
|
=
|
economic activity (captured by lrgdpt or
|
lipit);
|
lrgdpt
|
=
|
real gross domestic product
| |
lipit
|
=
|
industrial production(2000=100);
| |
lrt
|
=
|
average lending rate of banks;
| |
lsht
|
=
|
securities holdings of banks;
| |
ltlat
|
=
|
banks’ total loans and advances;
|
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
lbtdt
|
=
|
bank deposits;
|
ltbrt
|
=
|
treasury bill rate;
|
nert
|
=
|
nominal exchange rate(=N=/$).
|
In the model
represented by equation (4) above, all variables are in their
logarithmic form except the lending rate (lr) and the Treasury bill rate
(tbr). To achieve identification in the SVAR, we adopt the
non-recursive scheme. Being guided by Kim (1999) and Holtermoller (2002)
and given that matrix B is diagonal and of order 8x8; matrix A has the
following structure:
tbr
|
lbtd
|
lcpi
|
lrgdp
|
lsh
|
lr
|
ltla
|
lner
|
1
|
*
|
0
|
0
|
0
|
0
|
0
|
*
|
*
|
1
|
*
|
*
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
*
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
*
|
*
|
0
|
0
|
1
|
*
|
0
|
0
|
*
|
*
|
0
|
0
|
0
|
1
|
*
|
0
|
0
|
0
|
*
|
*
|
0
|
*
|
1
|
0
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
1
|
The identification
scheme above is over-identified with six restrictions and the asterisks
(*) symbolizes freely estimated parameters. The first line represents
monetary policy rule or the reaction function of the central bank. The
second line, which is banks’ total deposits or quasi-money equation,
proxies the standard money demand equation; with real GDP and the
Treasury bill rate as the scale and opportunity variables respectively.
The hypothesis of price stickiness is incorporated in the third and
forth lines of the scheme. The banking sector’s behaviour, in terms of
securities holdings is represented by line five. Banks’ securities
holdings depend on the Treasury bill rate and the lending rate. Line six
and seven depict loans supply and demand respectively, while the last
line is the arbitrage equation. It is important to note that in the
recursive identification schemes, the nominal exchange rate reacts
immediately to the monetary shock.
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
The necessary
conditions that must prevail for the bank credit channel to exist
following Bernanke and Blinder (1992) include (i) the bank deposits as
well as securities fall immediately in response to monetary shock; (ii)
total bank credit declines but only after a lag of two three quarters;
(iii) banks are able to maintain lending in the face of decline in
deposit by selling securities; and (iv) the eventual decline in bank
lending corresponds in timing with a decline in economic activity. As
additional condition, Friedman and Kuttner (1993), Suzuki (2001) and
Holtermoller (2002) also argued that the price of credit should rise
while quantity of credit should decline under monetary tightening.
Data Measurement and Sources
Quarterly
time-series data from 1986:1 to 2006:4 were utilized in estimating the
SVAR model. Variables can be categorized into policy variables,
non-policy variables or bank balance sheet variables. The monetary
policy variables used in the study is the Treasury bill rate which is a
short time interest rate. The innovation to this variable in the SVAR is
interpreted as the unanticipated monetary policy shock. The Treasury
bill rate series were obtained from the Central Bank of Nigeria
Statistical Bulletin (various issue). Economic activity is proxied by
real GDP or industrial production. The real GDP is measured as nominal
GDP deflated by consumer price index (2000 =100). The real GDP and index
of industrial production series were obtained from International
Financial Statistics (IFS). In the absence of quarter GDP series, the
Annual series were decomposed into quarterly series using the Galdalfo’s
(1981) algorithm. The general price level is measured by the composite
consumer price index (2000=100). The composite consumer price index was
also obtained from the IFS. Bank balance sheet variables include total
bank deposits, bank securities holdings and loans and advances. Total
bank deposit is measured as the total deposits with deposit money banks,
while bank securities holding is the sum of investments of deposit
money banks in treasury bills, treasury certificates and other
securities. With respect to bank credit, this is measured as total loans
and advances of deposit money banks. The bank balance sheet data are
sourced from IFS and the CBN Statistical Bulletin. The lending rate of
bank is
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
obtained by taking
the average of the prime lending rate and maximum lending rates of
banks. This series is also obtained from the CBN Statistical Bulletin.
Estimation Technique and Empirical of Analysis
Estimation Technique
To estimate our
model data were initially subjected to unit root test using both the
Augmented Dickey Fuller (ADF) and the Philips-Perron procedures. To
determine the order of the reduced-form VAR, the Akaike Information
Criterion (AIC), Schwarz Bayesian Criterion (SBC), and Hannan-Quinn
Criterion (HQ) were adopted. Since all variables were found to be I(1)
series a cointegration test was done using the multivariate approach
proposed by Johansen (1988) and Johansen and Juselius (1990).
Given the existence
of 4 cointegrating vectors as obtained from the trace statistics, the
conventional approach is to estimate a Structural Vector Error
Correction Model (SVECM). However to avoid the problem of
misspecification which could arise due to incorrect imposition of
long-run identifying restrictions coupled with the need for imposition
of short run restriction to achieve identification, a different
procedure was pursued in this study. Following Benkwitz et. al. (2001),
the reduced-form VAR in levels was consistently estimated and
appropriate confidence intervals for the impulse responses were obtained
using Bootstrap procedure. This procedure involves three steps: first,
the estimated coefficients and the fitted residuals from the estimated
model were initially saved. Second, the residuals were reshuffled with
replacement, and in the last step, an artificial data set were created
using the estimated VAR model as the true data generating process. In
the study, a series of 1000 of such simulations were undertaken.
In the analysis,
impulse response functions (IRFs) and forecast error variance
decomposition (FEVD) were used. The IRFs trace out the response of
current and future values of each of the variables to a one-unit
increase in the current value of one of the VAR errors, assuming that
the errors are equal to zero. The FEVD, on the other hand is the
percentage of the variance of the error made in forecasting a variable
due to a specific shock at a given horizon. All the variables entered
the reduced-form VAR model in their logarithmic form except the treasury
bill rate (tbr) and average lending rate of banks (lr).
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
Figure 1: Impulse response functions of SVAR Model (Non-Recursive Identification
Restrictions)
(a)
|
b
|
(c)
|
d
|
(e)
|
f
|
(g)
|
h
|
Note:
Solid lines indicate SVAR impulse response while broken lines indicate
95% Hall’s Percentile confidence intervals calculated with 1000
Bootstrap procedure. Author’s Calculation
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
Empirical of Analysis
Impulse response analysis
In testing for the
existence of bank credit channel, there is the need to examine how banks
adjust their portfolio in response to monetary policy shock. Banks’
assets are captured by bank securities holdings (lsh) and total loans
and advances (ltla). The only banks’ liability included in the study is
total deposits (lbtd).
Using the
non-recursive identification scheme, the response of non-policy
variables as well as bank portfolio to policy variable are indicated by
the impulse response functions (IRFs) in Figure 1. Panel (a) shows that a
one standard deviation in treasury bill rate is estimated as
approximately 1.7% unanticipated increase in this variable. This shock
from treasury bill rate can be interpreted as unanticipated monetary
policy shock. With the unanticipated hike in treasury bill rate (tbr),
the impulse response function in panel (c) shows that bank deposits
declined immediately by approximately 2% and the maximum impact of
monetary policy occurs when this variable decline by about 2.2% after 13
quarters. The insignificant impact of monetary policy on bank deposit
could be attributed to the adoption of liability management in Nigerian
banks. Panel (e) also indicates that bank securities holding, following
the hike in treasury bill rate immediately rises by 2% declined by only
0.9% and 1.2% after the first and second quarters respectively and a
positive innovation is recorded afterwards. With respect to bank credit,
the point estimates of the IRF in panel (f) further indicates that this
variable initially rises by 1% and decline by approximately 0.4 after
the second quarter and the maximum impact of the hike in treasury bill
rate occurs when this variable decline by 2.2% after the 19th quarter.
To examine the
impact of monetary policy on economic activity and prices, the IRF in
panel (b) shows that the unanticipated monetary policy shock measured as
the hike in treasury bill rate produces negative innovation in consumer
prices. Prices decline by approximately 2.4% after the 5th quarter
and this is in line with theoretical expectation. This corroborates the
Fisher’s quantity theory of money; the hike in treasury bill rate leads
to a fall in bank deposits (a component of money supply) and ultimately
leads to a fall in prices. IRF in panel (d) further indicates that real
GDP(Lrgdp) falls immediately
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
following an unanticipated hike in treasury bill rate, with a maximum impact of 1.3% decline occurring after 3 quarters.
Since one of the
basic objectives of monetary policy is to ensure exchange rate
stability, we also examine the behaviour of this variable to shock in
treasury bill rate. The IRF in panel (h) above indicates that nominal
exchange rate (panel h) initially depreciates only to appreciate later.
The initial depreciation in exchange rate following monetary policy
shock contradicts theoretical expectation as a rise in domestic interest
rate is expected to lead to the appreciation of nominal exchange rate.
This result is not surprising since this problem has been identified as
the exchange rate puzzle in the literature. The existence of this
puzzle could be ascribed to round-tripping of foreign exchange by
Nigerian banks. Round tripping as a term refers to a financial
malpractice in which banks obtain supply of foreign exchange from the
Central Bank at the official rate and resell the same in parallel market
at higher rate.
Another crucial
aspect of the analysis is the investigation of the impact of monetary
policy on the average lending rate (lr) of banks. As the IRF in panel
(g) shows, the shock in treasury bill rate initially leads to a 10
percent point increase in the average lending rate (lr) of banks within
the simulation period.
The results of SVAR
model have shown that bank deposits declined negligibly following the
hike in treasury bill rate. Bank securities holdings, though fell later,
immediately rises after the hike in treasury bill rate. There was an
initial increase in bank credit after monetary policy shock; this was
followed by a permanent negative shock to this variable.
A question that
arises at this juncture is: to what extent did the above findings fit
the scenario where the bank credit channel is operative? The evidence
draw from the IRFs indicates that the responses of bank portfolio of
assets and liabilities to monetary policy shock are either very
negligible or contrary to theoretical expectation. The fact that the
average lending rate (figure 1g) of banks increased immediately
following tight monetary policy coupled with the initial rise in bank
credit following monetary policy shock indicates that the bank credit
channel is very weak in the Nigerian economy. The robustness of the
above findings was also investigated by estimating a SVAR model with
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
real values of bank
assets and liabilities. Though not presented in this study, the results
unambiguously reinforced earlier findings. The inability of monetary
policy to have a strong impact on bank lending could be attributed to
the excess liquidity in the banking system during this period. Moreover,
the adoption of liability management in Nigerian banks could also be
another crucial factor. In support of the above view, Ojo(1992) observed
that in the 1980s and 1990s aggregate bank credit consistently exceeded
its targeted level. The results of the impulse response analysis also
suggest that monetary policy affect prices than real activity in
Nigeria.
Forecast error variance decomposition (FEVD)
To shed more light
on the findings under the IRFs, this section tests the importance of the
bank credit channel in the light of the recent deregulation of the
financial markets in Nigeria. In analyzing the FEVD, results are
reported for forecast horizons of 1, 4, 12, and 24 quarters. Since the
contributions of shocks in treasury bill rate(a measure of monetary
policy) is the most important, Table 1 indicates that this shock
contributes only 5% and 6% to forecast error variance(FEV) in bank
deposits after the 4th and 12th quarter. Shock in treasury bill also contributes 1% and 0% to the FEV in bank securities holdings after 4th and 12th quarters
respectively, while it only accounted for 1% and 7% of the FEV in bank
credit. Monetary policy shock also accounted for 35% of variation in the
average lending rate of banks after the 1st quarter but its contribution declined to 18% and 17% after the 12th and
24th quarters. These results corroborate the results obtained using
IRFs as analytical tool and clearly show that the bank credit channel is
weak in Nigeria.
With respect to economic activity and prices, monetary policy shock contributes 4% and 5% to the FEV in real GDP after the 4th and 12th quarters
respectively, while it accounted for 11% and 12% of the FEV of consumer
prices within the same forecast horizon. This indicates that monetary
policy affects prices than real output in the Nigerian economy. While
monetary policy has little impact on real GDP, the FEVD indicates that
shock in nominal exchange rate is the dominant factor which contributes
20% and 27% to this variable. This evidence underscores the role of
foreign exchange in the Nigerian
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African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
economy. Given a monocultural economy where the bulk of domestic resources emanate
from oil exports, this result is not at all surprising.
Overall, the results under the FEVD have reinforced that obtained under the
IRFs. It clearly indicated that the bank credit channel of monetary transmission is not
important during the study period characterized by financial deregulation and innovation.
The study also suggests that monetary policy exerts more influence on prices than real
activity. Moreover the dominant factor contributing to the variation in real GDP is the
nominal exchange rate.
Table 1(a) SVAR Forecast Error Variance
| ||||||||||
Decomposition
| ||||||||||
Proportion of forecast error variance in tbr accounted for by:
| ||||||||||
Forecast horizon
|
tbr
|
lbtd
|
lcpi
|
lrgdp
|
lsh
|
lr
|
ltla
|
lner
| ||
(quarters)
| ||||||||||
1
|
0.86
|
0.14
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
| ||
4
|
0.53
|
0.12
|
0.00
|
0.02
|
0.21
|
0.1
|
0.01
|
0.01
| ||
12
|
0.38
|
0.11
|
0.01
|
0.07
|
0.23
|
0.15
|
0.01
|
0.05
| ||
24
|
0.36
|
0.11
|
0.02
|
0.07
|
0.22
|
0.14
|
0.03
|
0.05
| ||
Proportion of forecast error variance in lbtd accounted for by:
| ||||||||||
tbr
|
lbtd
|
lcpi
|
lrgdp
|
lsh
|
lr
|
ltla
|
lner
| |||
1
|
0.05
|
0.91
|
0.03
|
0.01
|
0.00
|
0.00
|
0.00
|
0.00
| ||
4
|
0.05
|
0.88
|
0.02
|
0.03
|
0.01
|
0.00
|
0.00
|
0.01
| ||
12
|
0.06
|
0.78
|
0.01
|
0.06
|
0.02
|
0.00
|
0.01
|
0.06
| ||
24
|
0.07
|
0.72
|
0.00
|
0.06
|
0.02
|
0.00
|
0.03
|
0.10
| ||
Proportion of forecast error variance in lcpi accounted for by:
| ||||||||||
tbr
|
lbtd
|
lcpi
|
lrgdp
|
lsh
|
lr
|
ltla
|
lner
| |||
1
|
0.00
|
0.00
|
0.85
|
0.15
|
0.00
|
0.00
|
0.00
|
0.00
| ||
4
|
0.11
|
0.06
|
0.52
|
0.12
|
0.02
|
0.01
|
0.16
|
0.00
| ||
12
|
0.12
|
0.15
|
0.30
|
0.05
|
0.04
|
0.02
|
0.30
|
0.01
| ||
24
|
0.11
|
0.21
|
0.22
|
0.02
|
0.06
|
0.01
|
0.31
|
0.04
| ||
Proportion of forecast error variance in lrgdp accounted for by:
| ||||||||||
tbr
|
lbtd
|
lcpi
|
lrgdp
|
lsh
|
lr
|
ltla
|
lner
| |||
1
|
0.00
|
0.00
|
0.00
|
1.00
|
0.00
|
0.00
|
0.00
|
0.00
| ||
4
|
0.04
|
0.01
|
0.00
|
0.73
|
0.01
|
0.02
|
0.00
|
0.20
| ||
12
|
0.05
|
0.05
|
0.02
|
0.49
|
0.06
|
0.02
|
0.03
|
0.27
| ||
24
|
0.04
|
0.09
|
0.05
|
0.45
|
0.06
|
0.01
|
0.05
|
0.25
| ||
26
African Economic and Business Review Vol. 8, No. 2, Fall 2010 ISSN 1109-5609
© 2010 The African Education and Business Research Institute, Inc.
Policy Implications and Conclusion
Three main policy
implications emerged from this study. First, the ineffectiveness of the
bank credit channel presupposes that monetary policy has little or no
effect on bank lending in Nigeria. This means that when the Central Bank
of Nigeria engaged in monetary tightening banks do not find it
prohibitively costly to expand loan supply. The main explanation for
this is that Nigerian banking industry is characterized by excess
liquidity. Another reason why banks could expand credit during monetary
tightening is the wide adoption of liability management in the Nigerian
banking industry recently. By managing the liability side of their
balance sheet, banks no longer need to depend on demand deposit as a
primary source of bank funds and can target their assets growth by
issuing liabilities as the need be. Second, since the bank credit
channel is weak and ineffective in Nigeria, it implies that the Central
Bank of Nigeria cannot affect the real spending of borrower directly
through this channel. The effectiveness of monetary policy as a
stabilization tool therefore depends on other channels of monetary
transmission. Last, the study also implies that macroeconomic policy
formulation based exclusively on the credit channel as a transmission
channel of monetary policy will be largely inadequate. This connotes
that a search for more informative variable other than bank credit is
essential for appropriate monetary management.
In conclusion, this
study has found that though Nigeria is a bank-dominated economy, the
bank credit channel is very weak and ineffective channel of monetary
transmission under the period of financial deregulation.
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