Modern Economy, 2012, 3, 837-845 Published Online November 2012 (
The Determinants of Interest Rate Spreads in Nigeria:
An Empirical Investigation
Anthony E. Akinlo*, Babatunde Olanrewaju Owoyemi
Department of Economis, Obafemi Awolowo University, Ile-Ife, Nigeria
Email: *
Received September 17, 2012; revised October 20, 2012; accepted November 5, 2012
The paper examines the determinants of interest rate spreads in Nigeria using a panel of 12 commercial banks for the
period 1986-2007. The results suggest that cash reserve requirements, average loans to average total deposits, remu-
neration to total assets and gross domestic product have positive effect on interest rate spreads. However, non-interest
income to average total assets, treasury certificate and development stocks have negative relationship with interest rate
spreads. In general, the findings that suggest a reduction in cash reserve ratio, high bank overhead costs amongst others
will help to moderate the high interest rate spreads in Nigeria.
Keywords: Interest Rate Spread; Determinants; Nigeria
1. Introduction
The interest rate spreads (measured as the difference be-
tween deposit and lending rates) not only indicate the
level of inefficiency of the banking sector but show the
level of development of the financial system. Bank inter-
est rate spreads have several important implications for
growth and development of any economy. Specifically
high interest rate spreads tend to discourage potential
savers and thus limiting the quantum of funds available
to potentials investors. A reduction in lending arising
from low savings often leads to low investment and thus
the economic growth rate [1-3]. Incidentally, interest rate
spreads in Nigeria increased by a large amount over the
study period1. However, not many studies have been un-
dertaken to analyze the main factors underlying the high
interest rate spreads in the country. There is the need to
fill this gap. Hence, the main objective of this paper is to
investigate the issue of the determinants of interest rate
spreads in Nigeria.
The paper is organized as follows. In Section 2, we
provide an overview of the development in the banking
sector and interest rate spreads in Nigeria. Section 3 pro-
vides a survey of empirical literature on determinants of
interest rate spreads. Section 4 provides the methodology,
data sources and variable measurements. Section 5 re-
ports the empirical findings of the study. The last section
concludes the article.
2. An Overview of the Banking Sector and
Interest Rate Spreads in Nigeria
Banking activity started in Nigeria with the establishment
of the First Bank by the African Banking Corporation in
1892. Subsequently, several other banks were established
but failed except National bank of Nigeria, African Con-
tinental Bank, the Bank of British West Africa, Barclays
Bank and the British and French Bank. The development
led to enactment of the 1952 Banking Ordinance to
regulate the activities of banks in country.
After independence in 1960, further legislation in the
form of the Banking Act of 1969 was passed to regulate
the activities of banks in Nigeria. During this period,
most banks in Nigeria maintained their pre-independence
era focus on financing trade transactions. Following the
indigenization decree promulgated in the 1970s, most of
the foreign owned banks in the country were nationalized,
and the federal government assumed a significant own-
ership of stake in these banks. Commercial and merchant
banks were empowered by law to accept deposits from
individuals and companies and to conduct general bank-
ing business.
However, with the adoption of the Structural Adjust-
ment Programme (SAP) in 1986, many far reaching re-
forms were introduced to enhance the efficiency of the
banking industry. These included gradual changes from
the regime of strict direct controls to indirect monetary
controls and elimination of the policy of selective alloca-
tion of credit by the early 1990s. The commercialization
and privatization programmes of this period led to a di-
*Corresponding author.
1Evidence of high interest rate spreads in Nigeria over the years has
been documented in the works of [4,5] amongst others.
opyright © 2012 SciRes. ME
vestment of the Federal Government’s holdings in sev-
eral banks. Moreover, several new commercial banks
were granted licenses. The number of banks in operation
in Nigeria increased from 58 1990 to 90 in 2001. The
first community bank commenced operations in Decem-
ber 1990. However, many of the banks became distressed,
principally because regulatory capacity building could
not keep up with the expansion in banks. To address the
distress situation in the banking sub-sector, several pol-
icy reforms were introduced. One of the reforms was the
2004 directive by the Central Bank of Nigeria (CBN)
mandating all (deposit money) banks operating in Nige-
ria to increase their capital base to a minimum of N25
billion by December 2005. Of the 89 banks that operated
prior to this directive, only 25 banks emerged. Fourteen
banks licenses were revoked, as they were unable to meet
the minimum capital requirement. Four of these banks
(Assurance Bank Plc, Leadbank Plc, Allstates Trust Bank
Plc and Trade Bank Plc) were subsequently acquired by
three capitalized banks under the ‘Purchase and Assump-
tion’ strategy.
The trend of interest rate spreads is as shown in Fig-
ure 1. It shows that the interest rate spreads was low at
2.5 in 1986. It increased to 5.2 in 1987 following gov-
ernments’ liberalization of the entering requirements into
the banking business and the total removal of interest rate
control. The spreads experienced a fall in 1988 which
could be linked to the establishment of the Nigerian De-
posit Insurance Corporation and relaxation of bank port-
folio restrictions. It rose to 8.2 and 8.9 in 1989 and 1990.
This was the period when banks were permitted to pay
interest on demand deposits. Auction markets for gov-
ernment securities were introduced; capital adequacy
standards were reviewed upward and the extension of
credit based on foreign exchange deposits was banned.
All these might have in one way or the other influenced
the interest rate spreads. It dropped to 6.51 in 1991 when
embargo was placed on bank licensing. The Central Bank
was to regulate and supervise all financial institutions
and interest rate re-administered. Interest rate spreads
became double-digit from in 1992 and 1993 standing at
15.1 and 19.43 respectively. This was the period when
government once again removed interest rate control,
commenced the privatization of government-owned
banks, deregulated the capital market and removed the
credit controls. Moreover, in 1993, the monetary author-
ity introduced the indirect monetary instruments and took
over five banks for restructuring. The interest rate
spreads decelerated to a single-digit value between 1994
and 1996 following government re-imposed control of
interest and exchange rate. However, spreads maintained
double digit value through the period 1997 and 2007 at-
taining a peak of 24.62 in 2002. On the average over the
86 88 90 92 94 96 9800020406
Figure 1. Plot of interest rate spreads 1986-2007.
study period, interest rate spreads maintained upward
3. Empirical Evidence on the Determi na n t s
of Interest Rate Spreads
Some empirical studies have been conducted on the de-
terminants of interest rate spread in the developed coun-
tries. However, in developing countries, Nigeria inclu-
sive, not very many studies have been conducted on the
subject matter. In this subsection, we provide a summary
of the findings of the few existing empirical studies on
the determinants of interest rate spreads. The study by [6])
examined the determinants of bank net interest margins
for a sample of US banks using annual data for 1989-
1993 in a country specific basis. The results for the
pooled sample suggested that the proxies for default risk
(ratio of net loan charge—offs to total loans), the oppor-
tunity cost of non-interest bearing reserves, leverage (ra-
tio of core capital to total assets), and management effi-
ciency (ratio of earning assets to total assets) are all sta-
tistically significant and positively related to bank inter-
est margins. The ratio of liquid assets to total liabilities, a
proxy for low liquidity risk, was inversely related to the
bank interest margins. [7] in a cross country study found
that between 1988-1995 interest margins in six European
countries and the US were affected by the degree of bank
capitalization, bank market structure, and the volatility of
interest rates.
The study by [8] observed that for the Eastern Carib-
bean countries, unlike the evidence gathered above, the
impact of loan loss provisioning has been to reduce bank
interest margin rather than increase it once the tendency
of banks to under provision in the case of government
loans was accounted for. Like in other countries, operat-
ing expenses seem to have a large impact on bank
spreads in the Eastern Caribbean region. Over the sample
period, the ratio of operating expenses to total asset ex-
plains 23 percent of the estimated spreads. Using
monthly data for Argentinean banks from June 1993 to
Copyright © 2012 SciRes. ME
July 1997 period, [9] studied the determinants of the in-
termediation spreads for loan and deposits dominated in
both domestic as well as foreign currencies. Both in-
termediation margins are related to the average tax ratio,
the cost of reserve requirements, operating costs, prob-
lem loans, exchange rate risk, and market structure
measured using Herfindahl index. The only marked dif-
ference between domestic and foreign currency markets
is a positive and significant impact of the market struc-
ture on spreads for the former markets and a non-sig-
nificant impact for the latter. For both markets, the in-
termediation spreads are mostly affected by operating
costs and problems loans. The quantitative effects of both
factors are nearly the same for the domestic currency
The study by [10] found that interest margin was posi-
tively related to bank’s market power, operating costs,
credit risk and degree of interest rate risk. However, in-
crease in bank’s equity was found to have an adverse
effect on margin when the bank faced little interest rate
risk. The paper by [11] examined the impact of financial
liberalization of the Colombian economy on interest rate
margin in the banking system. The results obtained were
mixed. They found that liberalization increased banking
sector competition significantly, lowered market power
and reduced financial taxation from its highest level of
the late 1970s. Moreover, they found that banks were
responsive to changes in loan quality, possibly reflecting
an improvement in the banking supervision and/or re-
In his work, [12] applied the two-step procedure for a
sample of five Latin American countries during the mid
1990’s (Argentina, Bolivia, Colombia, Chile and Peru).
Their results showed positive coefficients for capital ratio
(statistically significant for Bolivia and Colombia), cost
ratio (statistically significant for Argentina and Bolivia),
and the liquidity ratio (statistically significant for Bolivia,
Colombia, and Peru). As for the effect of nonperforming
loans, the evidence was mixed. Apart from Colombia,
where the coefficient for nonperforming loans was posi-
tive and statistically significant, for the other countries
the coefficient was negative (statistically significant for
Argentina and Peru). In the second stage, [12] ran a re-
gression for the measure of “pure” bank spreads on mac-
roeconomic variables reflecting interest rate volatility,
inflation rate and GDP growth rate. Their results showed
that interest rate volatility increased bank spreads in Bo-
livia and Chile; the same happened with inflation in Co-
lombia, Chile and Peru. For the other cases, the coeffi-
cients were not statistically significant.
The study by [13] examined the determinants of banks
spreads in Uganda from 1999-2005. They found that
spreads and margins have been mainly driven by time
invariant bank characteristics as well as overhead costs
and sectoral compositions of loans.
Likewise, [5] provided empirical evidence on the de-
terminants of interest rate spreads in a liberalized finan-
cial system for the period 1989-2000, using selected
banks in Nigeria. Ex-ante interest rate spreads equations
were estimated using bank balance sheet and income
statement as well as macroeconomic data. The results
showed that macroeconomic and monetary policy/finan-
cial regulation factors were more important determinants
of commercial banks’ interest spreads than bank level
factors. Inflation rate, GDP, financial deepening, cash
reserve requirement, risk premium, Treasure bill rate,
loan asset quality, liquidity risk and non interest expenses
were the most important factors that affected commercial
banks’ interest rate spreads during the period.
The study by [14] used unique bank-by-bank balance
sheet and income statement information to investigate the
intermediation efficiency in the Nigerian pre-consoli-
dated banking sector during 2000-2005. He analyzed
whether the Central Bank of Nigeria’s policy of recent
banking consolidation can be justified and rationalized
by looking at the determinants of spreads. Indeed, spreads
decomposition and panel estimations showed that the
reform of the banking sector could be the first step to
raise the intermediation efficiency of the Nigerian bank-
ing sector. He found that larger banks enjoyed lower
overhead costs and that increased concentration in the
banking sector was not detrimental to spreads. The re-
sults equally showed that increased holdings of liquidity
and capital positively impacted spreads in 2005, while
stable macroeconomic environment enhanced more effi-
cient channeling of savings to productive investments.
The work by [15] explored the factors behind consis-
tently high interest rate spreads and margins in Uganda.
The results showed that small size of Ugandan banks,
high treasury bill rates and institutional deficiencies were
major determinants of bank spreads and margins. More-
over, the results showed that macroeconomic factors
such high inflation rate and exchange rate appreciation
had significant impact on interest rate spreads in Uganda.
4. Methodology
In examining the determinants of interest rate spreads in
Nigeria, the study employs panel data procedures since
the sample contains data across banks and over time. In
the estimation, three estimation models were adopted,
namely, pooled OLS, fixed-effects and random effects.
The adoption of pooled OLS is based on the assumption
that there is no group or individual effects among the
banks. However, as panel contains observation on the
same cross-sectional units over several time periods, the
possibility of cross-sectional effects on each firm or on a
set of group of banks is very high. In order to take care of
Copyright © 2012 SciRes. ME
this problem, the study adopts other estimation tech-
niques namely, fixed effects and random effects. Random
effects assume that the individual or group effects are
uncorrelated with other explanatory variables and can be
estimated. The fixed effects, on the other hand, takes into
consideration the individuality of each bank or cross-
sectional unit included in the sample by allowing the
intercept vary for each bank while assuming that the
slope coefficients are constant across banks.
4.1. Model Specification
The general model specification is represented by the
following equation2:
,,itit Wt,t it
 
 Z IRS X (1)
where IRS is defined as interest rate spreads for bank i
indexes bank i and t indexes time t; Xit is a vector of
bank-specific variables for bank i and time t; Wt contains
time varying, banking industry-specific variables; Zt is a
vector of time-variant macroeconomic variables, and εit is
error term for bank i and time t. In this equation, it is
assumed that the error term is distributed independently
and identically in a manner that the variance is equal to
4.2. Sources and Measurement of Data
The study utilized data obtained from 12 banks selected
from the 25 banks that survived the consolidation exer-
cise of 2004. The banks were selected based on the
availability of the relevant data on the various variables
used in the study. Only banks with complete data set for
all the variables used in the estimation were selected for
the study. Bank-specific and industry-specific data were
sourced from annual reports and statement of accounts of
the selected banks. However, data on macroeconomic
variable were sourced from Statistical Bulletin (2007)
published by the Central Bank of Nigeria (CBN).
4.3. Definition and Measurement of Variables
Interest Rate Spreads (IRS): This refers to the differ-
ence between bank’s lending and deposit rate. It was
calculated as average bank lending rate minus average
bank deposit rate3.
Cash Reserve Requirements (CRR): This is the pre-
scribed percentage of Commercial banks’ total deposits
that must be kept with the monetary authority as a cau-
tion. The cash deposit is expressed as a ratio of each
bank’s total demand deposit liabilities.
Average Capital Employed to Average Total Assets
(KPTEMP): This refers to a bank’s net worth, capital
asset ratio or capital adequacy.
Loan to Deposit Ratio (LDEPRAT): This is the ratio
of commercial bank loans to total deposit. It is referred to
a bank’s liquidity risk.
Average Loans to Average Total Assets (LNASS):
This refers to the size of a bank’s loan asset.
Non Interest Expenses to Average Total Assets
(NXPVTA): This refers to bank’s expenses management
Remuneration to Total Assets (REMUTA): This re-
fers to bank’s personnel cost.
Minimum Rediscount Rate (MRR): This is the most
favorable rate of interest at which the Central bank lends
to financially sound deposit money banks.
Gross Domestic Product (GDP): This is the produc-
tive capacity of an economy. The real domestic product
is the nominal value of the GDP deflated by the con-
sumer price index.
Development Stocks (DS): This is one of the tradi-
tional money market/government-issued financial instru-
ments that provide the government with short term funds
as well as provide financial institutions with opportuni-
ties for local investment of idle funds. It has about
thirty-six month’s maturity.
Treasury Certificates (TC): This is also a traditional
money market/government-issued financial instrument. It
was introduced to have a longer maturity period (one to
two years), but not long enough to be used as a govern-
ment long term security.
Treasury Bonds (TB): It is also a government-issued
financial instrument.
Changes in Inflation (CINFL AT): This refers to vari-
ability in the level of inflation over selected number of
5. Empirical Results
5.1. Descriptive Statistics
Table 1 provides the descriptive statistics of the data
series employed in the study. For almost all the variables,
the mean and median values lie within their maximum
and minimum values showing a good level of consis-
tency. The kurtosis of the eight of the twelve variables
included in the analysis exceeds 3 meaning that the series
are leptokurtic (peaked) relative to the normal4. Also, the
probability that the Jarque-Bera statistic exceeds (in ab-
solute value) the observed value is generally low for all
the series. This suggests the rejection of the hypothesis of
ormal distribution at 5%.
2This model has been adopted by few existing studies including [15-
3The ex-ante ante approach in calculating the interest rate spreads was
used. This approach uses the rates quoted on loans and on deposits and
draws inferences from the difference between them.
4The few exception are Gross domestic Product (GDP), development
stocks (DS), treasury bonds (TB) and treasury certificates (TC).
Copyright © 2012 SciRes. ME
Copyright © 2012 SciRes. ME
Table 1. Descriptive statistics.
Mean 61555.757 0.15871 0.502605 0.542839 0.0805730.13809390702.715.67626 3009.171 210967.1 11579.37 0.807634
Median 1826 0.070665 0.467513 0.286839 0.020186 0.069746 377830.814.31 2960 134387.6 0 1.5
Maximum 43537 2.131332 2.034027 5.9 6.6020411.211406562043.726 4909 430608.2 37342.731.5
Minimum 0 0.20076 0.121213 0.090174 1.89E050.01721925599712.75 980 0 0 43.5
Std. Dev. 9421.071 0.333611 0.239431 0.969675 0.580736 0.231762 81991.11 3.240154 1232.408 179291.9 15278.39 17.67513
Skewness 1.960768 4.483397 2.851987 3.544564 10.96457 3.433519 0.4023671.707390.01967 0.208101 0.812722 0.77919
Kurtosis 6.087207 23.37996 17.92046 14.95635 123.3166 13.57072 2.420211 5.889912 1.751833 1.326232 1.8244413.84996
JarqueBera 135.9632 2705.948 1392.724 1054.605 81640.14 867.3095 5.369655 109.2339 8.512096 16.23704 21.96437 17.19916
Probability 0 0 0 0 0 0 0.058233 0 0.014178 0.000298 0.0000170.000184
Sum 806404.2 20.79101 65.84131 71.1119 10.555118.08977511820482053.59394201.4 27636691 1516897105.8
Sum Sq. Dev. 1.15E+10 14.46855 7.452511 122.2352 43.84313 6.9827578.74E+111364.8181.97E+08 4.18E+12 3.03E+1040613.31
Observations 131 131 131 131 131 131 131 131 131 131 131 131
5.2. Correlation Matrix
Table 2 shows the degree of association among the vari-
ables. In general, the results show that while some are
positively related to interest rate spreads, few others are
negatively related. Specifically, cash reserve require-
ments, ratio of non-interest expenses to average total
assets, non-interest income to average to average total
assets, gross domestic product and treasury bills have
positive relationship with interest rate spreads. However,
ratio of loan to total deposit, remuneration to total assets,
minimum rediscount rate, development stocks, treasury
certificates and changes in inflation have negative rela-
tionship with interest rate spreads5.
5.3. Panel Unit Root Tests
To ascertain the unit root characteristics of the panel data,
we estimate unit root test for each variable in the model.
Specifically, the study used Levin, Lin and Chu; Im,
Pesaran and Shin W-stat, ADF-Fisher Chi-square and
PP-Fisher Chi-square tests. The results as contained in
Table 3 show stationarity for all the variables at different
levels of difference. Specifically, IRS, CRR and TC were
stationary at second difference. However, KPTEMP,
TASS, GDP, TB were stationary at first difference while
MRR, DS and CINFLAT were stationary at level.
5.4. Regression Results
The results for the determinants of interest rate spreads
using pooled and fixed effects panel methods respec-
tively are presented in Table 4. Column 1 refers to the
estimation with pooled OLS and column 2 shows estima-
tion results with fixed effects approach6. The results ob-
tained from pooled OLS are quite consistent with that
obtained using the fixed effects in both signs and magni-
tude. The results from the two estimation approaches show
that cash reserve ratio is positively related to interest rate
spreads. The coefficient is significant in both results. The
results show that a 1 percent increase in cash reserve
ratio will increase interest rate spreads by 0.43 percent in
the fixed effects model. This results support the findings
of [18-21]. The finding is very much in support of the
assertion that statutory minimum reserve requirements
are implicit taxes that increase interest rate spreads be-
cause banks tend to shift them to customers by either
increasing the lending rate or reducing the deposit rate.
The ratio of average capital employed to average total
assets has negative effect but only significant at 10 per-
cent in the pooled OLS. A 1 percent increase in the ratio
of average capital employed to average total assets will
lead to 0.82 percent reduction in the interest spreads.
Loan to deposit ratio (LDEPRAT) has a positive sign but
significant only in the fixed effect model. The result
shows that a 1 percent rise in LDEPRAT would increase
IRS by 0.53 percent. The average loan to average total
assets (LNASS) coefficient is positive but not signifi-
cant7. The coefficient of the ratio of non interest expenses
to average total assets (NXPVTA) is positive but not
significant in the two estimation approaches. The ratio of
remuneration to total assets (REMUTA) has a significant
6The [22] test conducted showed that fixed-effects model provide a
better results. Therefore, we only report the fixed effects model here.
7The ratio of average loans to average total assets is only significant at
20 percent in the fixed effect model.
5In general examining simple bivariate correlation in a conventional
matrix does not take account of each variable’s correlation with all
other explanatory variables; hence caution should be exercised in in-
terpreting the result of the correlation matrix.
Table 2. Correlation matrix.
IRS 1.000
CRR 0.754 1.000
KPTEMP 0.601 0.346 1.000
LDEPRAT 0.065 0.046 0.057 1.000
LNASS 0.501 0.258 0.812 0.009 1.000
NXPVTA 0.537 0.323 0.785 0.073 0.719 1.000
REMUTA 0.036 0.034 0.030 0.099 0.031 0.0251.000
NIYAVTASS 0.303 0.133 0.608 0.010 0.897 0.615 0.0151.000
MRR 0.065 0.083 0.007 0.097 0.027 0.031 0.0600.002 1.000
GDP 0.453 0.539 0.209 0.204 0.0770.143 0.110 0.022 0.0191.000
DS 0.499 0.565 0.197 0.301 0.106 0.1680.103 0.0004 0.1230.836 1.000
TB 0.479 0.539 0.189 0.243 0.1290.169 0.0790.022 0.0540.753 0.941 1.000
TC 0.329 0.363 0.130 0.154 0.169 0.126 0.022 0.089 0.3660.326 0.560 0.633 1.000
CINFLAT 0.039 0.007 0.003 0.131 0.027 0.029 0.016 0.022 0.0330.033 0.130 0.138 0.1981.000
Table 3. Panel unit root tests individual effects 1986-2007.
Variables Level LLC ρ-V IPS ρ-V ADF ρ-V PP ρ-V
IRS 2 2.37 0.99 2.7 0.00** 63.04 0.00** 124.27 0.00**
CRR 2 2.70 0.00** 12.58 0.00** 125.33 0.00** 234.22 0.00**
KPTEMP 1 11.47 0.00** 9.2 0.00** 111.38 0.00** 135.47 0.00**
LDEPRAT 1 23.37 0.00** 10.36 0.00** 67.52 0.00** 134.7 0.00**
LNASS 1 6.48 0.00** 5.29 0.00** 73.82 0.00** 145.59 0.00**
NXPVTA 1 4.06 0.00** 7.17 0.00** 92.49 0.00** 176.04 0.00**
REMUTA 1 3,784,800 1.00 5.43 0.00** 72.88 0.00** 175.24 0.00**
NIYAVTASS 1 6.45 0.00** 5.81 0.00** 78.28 0.00** 136.91 0.00**
MRR 0 9.46 0.00** 6.58 0.00** 83.82 0.00** 144.78 0.00**
GDP 1 8.31 0.00** 5.04 0.00** 65.1 0.00** 221.07 0.00**
DS 0 2.66 0.00** 5.43 0.00** 70.4 0.00** 33.98 0.09***
TB 1 8.71 0.00** 7.06 0.00** 89.5 0.00** 175.06 0.00**
TC 2 16.95 0.00** 13.39 0.00** 164.51 0.00** 282.25 0.00**
CINFLAT 0 9.19 0.00** 5.95 0.00** 76.44 0.00** 70.57 0.00**
Notes: The null hypothesis (Ho) is that there is no unit root, (H1) some do not have a unit root process. Significance levels are denoted *: 1%, **: 5%, ***: 10%:
and indicate rejection of the null hypothesis. 0, 1, and 2 represent level, first and second difference respectively. Probabilities for Fisher tests are computed
using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. LLC denotes Levin, Lin and Chin, IPS denotes Im Pesaran Shin
W-Stat, ADF indicates Augmented Dickey Fuller test, PP denotes Phillip Peron is also reported, and ρ-V indicates Probability value.
Copyright © 2012 SciRes. ME
Table 4. Pooled OLS and fixed effects 1986-2007.
Variable Pooled OLS 1 t-statistic Fixed Effects 2 t-statistic
Constant - - 0.989741 0.079724
LOG (CRR) 0.606898*** 19.07599 0.428597*** 12.92405
KPTEMP 0.820387* 1.731124 0.371179 0.979037
LDEPRAT 0.274806 1.157877 0.531888** 2.594412
LNASS 0.941695*** 4.015137 0.294473* 1.465834
NXPVTA 0.210381 0.384788 0.141088 0.327951
D (REMUTA) 2.43E05** 2.365429 2.06E05** 2.588548
NIYAVTASS 2.867447** 2.975764 1.078530 1.347358
MRR 0.018147 0.888362 0.017399 1.129850
LOG (GDP) 0.273711** 2.685783 0.832121 1.069112
LOG (DS) 0.091433 0.610702 0.756797** 2.224561
D (TB) 5.42E07 0.607967 1.11E07 0.164564
D (TC) 1.96E05*** 2.976032 2.48E05*** 3.260388
CINFLAT 0.004690* 1.569960 0.003215 1.050968
R2 squared 0.872880 0.924384
F-statistic 108.4750
Durbin-Watson 0.610638 0.856808
Schwarz criterion 2.531685 2.194683
Note: ***, ** and * denote significance at 1%, 5% and 10% respectively.
positive effect on interest rate spreads. The results show
that a 1% rise in ratio of remuneration to total assets will
cause interest rate spreads to increase by 0.0000243%.
This means that as profitability of the bank decreases due
to increase in remuneration or other expenses, the banks
recoup the losses by increasing the spreads, that is, either
charging more on loans or paying less to deposits or
some combination of the two. Non interest income to
average total assets (NIYAVTASS) has a negative coef-
ficient. However, the coefficient is significant only in the
pooled OLS estimation. The coefficient of minimum
rediscount rate (MRR) is positive but not significant.
Gross domestic product (GDP) has a positive relationship
interest rate spreads but significant only in the pooled
OLS estimation. The results show that a 1 percent in-
crease in gross domestic product will lead to 0.27 per-
cent increase in interest rate spreads. The positive rela-
tionship between GDP and interest rate spreads contra-
dicts the business cycles effect discussed by [23]8. De-
velopment stocks (DS), treasury bills, treasury certifi-
cates and changes in inflation have negative effects on
interest rate spreads. However, the coefficients of treas-
ury bills and changes in inflation are not significant at 5
percent level in either of the estimation techniques9. In
the fixed effects results, a 1 percent increase in develop-
ment stock will reduce interest rate spreads by 0.76 per-
cent, while a 1 percent increase in treasury certificate
will reduce interest rate spreads by 0.000025 percent.
5.5. Further Consideration
The adoption of pooled OLS is premised on the assump-
tion of the exogeneity of the explanatory variables.
However, this approach breaks down when the assump-
tion of exogeneity is relaxed. Hence, relaxing the as-
sumption requires that we adopt another estimation ap-
proach capable of correcting biases introduced by in-
cluding the lagged dependent variable in the right hand
side of the equation. Consequently, a Generalized
9The coefficient of changes in inflation is only significant at 20 percent
in the pooled OLS estimation results. The negative effect of inflation
on interest rate spreads could be attributed to conduct of monetary policy
in Nigeria and some other developing countries. Generally, lending
rates tend to vary more than deposit rate, such that a loose monetary
olicy that leads to higher inflation would be associated with lower
lending rates, and, as a result, lower the interest rate spreads. See [20]
for details.
8The authors argue that during recession the creditworthiness of the
orrower declines and therefore he can borrow only at a higher interest
rate, and this raises the spread.
Copyright © 2012 SciRes. ME
Method of Moments (GMM) estimator in [24] approach
was adopted to generate consistent estimates. Basically,
such panel estimation techniques allow one to control for
endogeneity or simultaneity of some of the explanatory
variable in particular GMM estimators, as well as for
potential biases due to correlation between the explana-
tory variables and the regression residual. Moreover, the
use of GMM estimation technique provides the robust-
ness check for the results obtained through the pooled
OLS and fixed-effects approaches. The results of the
Generalized Method of Moments (GMM) for the sample
period are as shown in Table 5. In general, the results
from the GMM seem to diverge from the results from
pooled OLS and fixed effects. Many areas of differences
are discernible. In particular, some of the variables that
are significant in pooled OLS and fixed effects are either
not significant or barely significant. Moreover, the signs
of the coefficients of some variables are different. The
lagged value of the dependent variable, interest rate
spreads (IRS (1)) has a positive effect and is significant.
This confirms the positive inertia effect of interest rate
spreads. The coefficient of cash reserve requirement (CRR)
is negative as compared with the positive sign obtained
in the pooled OLS and fixed effects estimation tech-
niques. However, the coefficient is not significant.
Therefore, firm inference cannot be drawn from it. The
ratios of average capital employed to average total assets,
non interest expenses to average total assets, and remu-
neration to total assets have negative effects on interest
rate spreads as against positive effects obtained under
pooled OLS and fixed effects models. However, their
Table 5. Panel generalized method of moments 1986-2007.
Variable Pooled OLS t-statistic
IRS (1) 1.330263 6.096511***
CRR 0.071689 0.388612
KPTEMP 1483.884 1.556921*
LDEPRAT 115.6666 0.368751
LNASS 162.0543 0.221647
NXPVTA 2790.196 1.239996
REMUTA 75.38301 0.441414
NIYAVTASS 3849.895 1.166095
MRR 13.75431 0.592386
GDP 0.002142 1.100810
DS 0266873 1.554071*
TB 0.001709 1713846*
J-statistic 1.48E16
Instrument rank 12.000000
Note: *** and * denote significance at 1% and 10% respectively.
coefficients are not significant. In like manner, the coef-
ficient of minimum rediscount rate is negative as op-
posed to the positive sign obtained under pooled OLS
and fixed effects models. Non-interest income to average
total assets (NIYAVTASS) has a non significant positive
effect on interest rate spreads.
Gross domestic product (GDP) has negative relation-
ship with interest rate spreads while treasury bills and
development stocks have positive relationship with in-
terest rate spreads. The signs of the coefficients of these
three variables are contrary to those obtained under
pooled OLS and fixed effects models. In general, as the
coefficients of many of these variable are not signifi-
cance, firm can conclusion cannot be based on them.
This simply suggests that pooled OLS and fixed effects
tend to perform better in the estimation of the model
adopted in our work. Thus attention should be focused on
the results generated through these estimation techniques.
6. Concluding Remarks
The purpose of this study is to examine the determinants
of interest rate spreads in Nigeria over the period 1986-
2007. Based on data availability, some of the potential
determinants of interest rate spreads analyzed include
cash reserve ratio, ratio of average capital to average
total assets, ratio of loans to total deposit, ratio of aver-
age loans to average total assets, and ratio of non interest
expenses to average total assets. Others include ratio of
remuneration to total assets minimum rediscount rate,
gross domestic product development stocks, treasury
certificates, treasury bills and changes in inflation. The
specified model was estimated using both pooled OLS
and fixed-effects methods. However, for robustness check
the model was estimated using the GMM approach. The
results show among other things that cash reserve ratio
and loans to total deposits ratio are positively related to
interest rate spreads. In the same way, ratios of average
loans to average total assets and remuneration to total
assets are positively related to interest rate spreads. The
same pattern holds for gross domestic product. However,
development stocks and treasury certificates are nega-
tively related to interest rate spreads in Nigeria.
Generally, the results show that policy measures that
would lead to a reduction in remuneration and other ex-
penses and a reduction in the ratio of loan to total assets
have potentials of reducing the interest rate spreads in
Nigeria. Also, reduction in the statutory reserve require-
ments prescribed by the Central banks would help to re-
duce the interest rate spreads.
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Copyright © 2012 SciRes. ME
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