Monetary Policy and Bank Lending in Nigeria
Lateef Adewale Yunusa^{1}, Tolulope Oyakhilome Williams^{2}, Oluwatosin Joseph Adegbenle^{3}
Abstract: Objective: This study investigated the impact of monetary variables on bank lending in Nigeria.
Prior Work: Previous studies in Nigeria were reviewed and some of the results of this study were in line while some contradicted the prior works.
Approach: The study made use of macroeconomic time series variables between 19802018. The data obtained were subjected to Autoregressive Distributed Lag (ARDL) econometric technique.
Results: The findings of the study revealed inflation rate and interest rate have a significant negative effect on loans and advances; the exchange rate has a positive significant effect on loans and advances while liquidity ratio and money supply have a negative insignificant effect on loans and advances. More so, the Granger Causality test further revealed that the inflation rate does cause the lending rate in Nigeria while the interest rate doesn’t cause the inflation rate in Nigeria.
Implication: Higher inflation rate in the economy could lead to a reduction in the economic output. The monetary authority should, therefore, strive to maintain a reasonable interest rate that will help to achieve an optimal level of supply of money in circulation to avoid inflation in the economy and help in prediction of cost borrowing and lending.
Keyword: ARDL; Bank Lending; Interest rate; Inflation rate
JEL Classification: C32; E31; G21
1. Introduction
Resources allocation for efficient use is one of the challenges faced by all the sectors in the world. Allocation of resources is a very crucial issue, as human wants are unlimited but the means through which these wants can be satisfied are limited. In an economy, one of the means through which the wants could be satisfied despite the limited means is via the intermediation. This affords the disadvantaged individuals in the economy to have access to the idle fund from the surplus sector of the economy through the banks. The banks through its intermediation role link both the surplus and deficit sector.
The bank lends to the deficit unit from the fund deposited by the surplus sector. Lending is one of the most essential services rendered by the bank to its customers. Bank grants loans and advances to the government, corporate organisation and individuals (Cheboi, 2012). The income earned by banks from the loan and advances issued out to their customers is the major source of income to the bank which is simply called the interest income. Therefore, regardless of the economic condition, banks would always be interested in issuing loans and advances to their customers with the aim of profitability, liquidity, and solvency (Cheboi, 2012).
Banks often charge rates that are not too low because of its profitability and not too high in order not to scare the customers away. Banks are therefore always careful with their decision to lend out due to some factors that may affect the bank adversely such as inflation rate, interest rate, the level of deposit, liquidity of the bank, etc.
The higher the lending rate on loan, the higher the prices of output in the economy will be which will increase the level of inflation in the economy. Keynes (1936) in his work also asserted that as the supply of money increases, the interest rate decreases, if the reduction in interest rate persists, the amount of money in circulation will keep increasing which could lead to inflation in the economy. Conversely, if the supply of money decreases, the interest rate increases, which simply indicates a negative relationship between the supply of money and interest rate which makes the demand curve to be negatively sloppy.
Inflation is a situation of persistent increase in prices of commodities as a result of too much money chasing fewer commodities in an economy. It occurs when the aggregate supply is lower to the aggregate demand of commodities which results in the reduction of the purchasing power of the monetary unit Chioma, Adanma & Clementina (2014). Inflation was viewed as a phenomenon that hits the consumer’s pocket by reducing the purchasing power of the currency and sometimes acts as a hidden tax (Smith and Roche, 1999).
Emekekwue (2008) asserted that a particular level of inflation is needed for an economy to sustain its growth level, which means inflation has its good side. He further posited that above the certain level required to sustain an economy, inflation may become a hydraheaded monster that will frustrate the economy. Persistence inflation at an undesirable level may, therefore, frustrate both the lender and the borrower in an economy.
The inability of borrowers and lenders to forecast the lending rate on loans and advances is highly challenging to the financial system. The flow of funds will continue between the borrower and lender in viable terms of project and has high yield, in as much as lender and borrower can forecast the lending rate (Caglayan & Xu, 2016).
Empirically, the average inflation rate in 1981 in the developed economy was 11.9% but reduced to 1.4% in 2013. Similarly, in 1981, the average inflation rate in developing economies was 12.7% but reduced to 4.6% in 2013 (World Bank, 2014). The inflation rate in Nigeria in 1981 was 20.81% but reduced to 8.48% in 2013 and rose to 15.7% in 2016, 16.5% in 2017 and 12.09% in 2018 (CBN Statistical Bulletin, 2018). The recent increase in the inflation rate would make it difficult for both the lender and the borrower to forecast the cost of lending and borrowing respectively.
Despite all the efforts devoted to maintaining stability in the lending rate obtainable in Nigeria by the monetary authority, the problem is yet to be resolved as evidenced by the previous studies in Nigeria (Chioma, Adanma & Clementina, 2014; Omondi, 2014; Olokoyo 2011, Somoye & Ilo 2009; Jegede, 2014). This necessitates the specific objective of the study to examine the impact of inflation on bank lending in Nigeria and also examine the relationship between the inflation rate and the lending rate charged by banks in Nigeria.
The rest of the paper is structured as follows, the section two deals with the literature and empirical review, the third section deals with the methodology, the fourth section four deals with the data analysis and findings while the last section is the concluding part.
2. Literature and Empirical Reviews
Monetary policy is the action taken by the Central Bank to stabilize the money supply and the cost in an economy (CBN Annual Report, 2009). Jegede (2014) viewed monetary policy as weapon used by the monetary authority to stabilize the economy.
Inflation can be viewed as the creation of money that increases the price of commodities and at the same time reduces the purchasing power of the currency of the country in question (Oritoni, 1981). Contrarily, the reduction in the purchasing power sometimes should not be worrisome as it was in the case of Nigeria in the early 60s as no nation is free of inflation (Chioma, Adanma and Clementina, 2014). Inflation is present in all the nations of the world; the rate of inflation in each county is what makes the difference among the countries. Inflation rate if it rises beyond single digit is an indication that the economy is in problem as the case of Nigeria today where the inflation rate was 9.01%, 15.7%, 16.5% and12.09% in 2015, 2016, 2017 and 2018 respectively (CBN Statistical Bulletin, 2018).
Ramady (2009) asserted that to mitigate the inflationary effect of increased money supply, the Saudi Arabian Monetary Agency (SAMA) raised the reserve requirements for commercial banks four times during 2007 and 2008 which reduced the liquidity generated by lower interest rates. This directive increased the statutory amount of money banks have to keep as statutory deposit with the central bank, which decreases the amount available with the commercial banks for lending, thereby forcing the banks to increase the base lending rate due to the contractionary policy which reduced the credit granted to the customer as well as inflation in the economy.
The allocation of scarce resources to the efficient channel for usage is one of the pressing issues faced by most economies (Caglayan & Xu, 2016). Under the condition of certainty, the price system is viewed to translate to the allocation of resources efficiently but under uncertainty, the price system may not translate to the allocation of resources efficiently in an economy.
Credit is the money lent by a bank to a borrower who admits to paying back in future both the principal plus interest which is a percentage of the principal (Jegede, 2014). Credit from the bank is extended to private individuals, the government and corporate organisations. Credit enables the borrower to finance its deficit and execute viable projects.
Nwankwo (1991) asserted that credit is one of the largest single incomeearning assets in the bank’s portfolio which explains the reason why banks allocate huge resources in monitoring, estimating and managing credit quality.
Adedoyin & Sobodun (1991) opines that "lending is undoubtedly the heart of banking business". Bank generate profit from the lending activities which could either make or mar the bank depending on the level of administration of the credit.
Bank lending technique can be classified into two; transactionbased lending and relationship lending (Berger and Udell, 2006). Transactionbased lending is lending which verification of borrower’s information is easier and faster. Transactionbased lending was further classified by Berger and Udell into four types; financial statement lending, assetbased lending, fixedasset lending, and credit scoring while relationshipbased lending is therefore based on the past relationship between the customer and the bank.
In Nigeria, the lending rate charged by banks on the credit granted to their customer is hardly negotiated between the bank and the customer, when the lending rate is increased by the Central Bank of Nigeria (CBN), most of the bank automatically apply the new rate to the outstanding loan without informing the borrower (Okafor, 2011). Oddly, if the rate is decreased by the CBN, the same bank will hide the information as regards the downward review of the lending rate from the borrower, thereby illegally exposing the customer to payment of a higher interest rate on loan.
Angel, Francisco & Hugo (2014) ascertained the longrun effects of inflation on the dynamics of private sector bank credit and economic growth in Mexico. ARDLbounds testing approach was used for the estimation. The result of the study shows that inflation rates have contributed negatively to the increase in private credit, liquid liabilities, and financial development and that the availability of private sector credit exert growth in the economy.
Omondi (2014) examined the relationship between the annual inflation rate and Kenya Commercial Bank base lending rate, new lending volumes and loans defaulting. The findings revealed that there was a positive relationship between inflation rate and the base lending rate charged by the bank. Also, inflation has a negative impact on lending volumes. Lastly, the study found that a rise in inflation leads to a high rate of loan defaulting activities in the bank.
Chioma, Adanma & Clementina (2014) examined the extent to which the profit of banks under the inflationary period affect the lending decision of such banks in Nigeria. A panel regression technique was used. The result revealed that there is a positive insignificant relationship between inflation and bank lending volume (lending decision) of commercial banks in Nigeria.
Jegede (2014) examined the effect of monetary policy on commercial bank lending in Nigeria. Vector Error Correction Mechanism of Ordinary Least Square was used for the estimation. The findings of the study revealed that exchange rate and interest significantly influenced commercial bank lending, while liquidity ratio and money supply exert a negative impact on commercial banks’ lending.
Ojeaga, Odejimi, Okhiku & Ojeaga (2014) investigated the effect of bank lending on growth in Nigeria. A quantile regression estimation method was used in the study. The result of the study showed that bank lending, regulation, interest rate all had a negative effect on growth. Bank loan losses and money supply both had a positive significant effect on growth although this effect was weak.
Onaolapo & Habeeb (2017) appraised the impact of monetary policy on commercial banks’ lending behavior in Nigeria. Ordinary least square method (OLS), Augmented DickeyFuller test (ADF), cointegration test and Error correction model (ECM) were employed as estimation techniques. The result of this study revealed that the volume of deposit has a positive significant impact on loan and advances, foreign exchange and GDP both have a positive insignificant impact on loan and advances, interest rate and reserve requirement both have a negative significant impact on loan and advances.
Eke, Eke and Inyang (2015) examined the effect of interest rate deregulation on the lending operations of Nigerian commercial banks. The Chow test method estimation was used for the analysis. The result obtained for the interest rate regulation era showed that interest rate spread and statutory liquidity ratio had a negative and significant effect on the volume of commercial banks’ loans, while fixed exchange rate had a negative and insignificant impact on banks’ loans and advances. It was found that the Monetary Policy Rate (MPR) and inflation rate exert a positive and significant impact on banks’ loans for the period. For the deregulation era, the result showed that MPR and the exchange rate had a significant impact on banks’ loans and advances. While the former exerted a negative impact, the later had a positive influence on loans and advances. Interest rate spread, statutory liquidity ratio, and inflation rate were found not to have significantly impacted on commercial banks’ loans and advances for the period.
Olokoyo (2011) investigated the determinants of commercial banks’ lending behavior in the Nigerian context. Vector Error Correction Technique was used for the estimation. The result of this study revealed that the volume of deposits and investment portfolios of banks have a positive impact on bank loans and advances at 1%. Also, the result revealed that Foreign exchange and gross domestic products have a positive statistically significant impact on loans and advances at 10%. The coefficients of lending rate, minimum cash requirement ratio and liquidity ratio all have a positive impact on loan and advances.
Somoye and Ilo (2009) investigated the impact of macroeconomic instability on banking sector lending behavior in Nigeria. Cointegration and Vector Error Correction Modeling techniques were used for the estimation. The result of the study revealed that increases in broad money supply and inflation decreases bank lending, exchange rate depreciation increased bank lending, more so, deposit mobilization capacity of banks and bank size were the most important bank characteristics that explained their lending behavior.
3. Methodology
The data used for this study is mainly secondary data and time series from 19812018, which were obtained from the Central Bank of Nigeria (CBN) and the World Bank Development Index (WDI). Bank loan is proxy by commercial bank loans and advances which is the dependent variable while the independent variables are inflation rate, interest rate, foreign exchange rate, liquidity ratio, and money supply growth. The study makes use of inferential analysis with the PhillipsPerron test (PP) for unit root and the Autoregressive Distributed Lag (ARDL) to determine the existence of a longrun relationship among the variables. The error correction model (ECM) was also used to determine the shortrun relationship between the dependent and independent variables.
Model specification
The model for this study was adopted from the work of Eke, Eke & Inyang (2015). The model is explained mathematically as follows:
….......................................................................(3.1)
The Econometrics model is written as:
….......(3.2)
Where:
LOA is commercial banks’ loans and advances
MPR is the monetary policy rate
IRS is interest rates spread (Deposit minus lending rate)
SLR is the Statutory Liquidity ratio
EXR is the exchange rate and
INF is the inflation rate
The model for this study is therefore specified to examine the effect of inflation rate on bank lending in Nigeria is presented as
…(3.3)
Where:
LOA is commercial banks’ loans and advances
INF is inflation rate
INT is interest rate
FER is foreign exchange rate
LIQ is Liquidity ratio
MOS is money supply growth
The study further ascertained the direction of Causality between inflation rate and interest rate
……………………..(3.4)
…………………..….(3.5)
Where inflation rate and INT is interest rate, and are the disturbances which are assumed to be uncorrelated. In this framework, there are four possible hypotheses.
Case 1: Unidirectional causality from INF to INT. This is indicated if 0 and =0
Case 2: Unidirectional causality from INT to INF. This is indicated if =0 and 0.
Case 3: Bilateral causality. This is indicated if 0 and 0.
Case 4: No causality. This is indicated if =0 and =0.
Table 1 below shows the variables of the model and description of each variable.
Table 1. Variables Description and Measurement

Variables 
Description 
Measurement/Justification 
Dependent 
LOA

Loan and advances 
This is the money lent by bank to borrower (private individual, corporate organisation or government) which will be repaid in future 
Independent 
FER

Foreign exchange rate 
This is the rate at which foreign currency will be exchanged for a local currency. 

LIQ 
Liquidity ratio 
This measures the ability of the bank to meet up with its obligation to the customers 

INF 
Inflation Rate 
Inflation is the persistent increase in a nation’s general prices levels 

MS

Money supply 
This is the measure of the broad money in circulation in an economy 

INT 
Interest rate 
Interest rate is the proportion of the principal which compensated the owner of fund for time and risk taken 
Source: Authors Compilation, 2019.
The table below shows the variables of the model, and expected effect of the various variables on bank lending.
Table 2. Variables, Denotations and Expected Signs
Variable 
Denotation 
Unit 
Expected sign 
Foreign exchange rate 
FER 
Rate 
(+) 
Liquidity ratio 
LIQ 
Ratio 
(+) 
Inflation rate 
INF 
Rate 
() 
Money supply 
MS 
Naira 
(+) 
Interest rate 
INT 
Rate 
() 
Source: Authors Compilation, 2019.
4. Data Analysis and Findings
4.1. Unit Root Test
The unit root test helps to determine the nature of data used to prevent spurious results and it will help in determining the technique appropriate for analysis. PhillipsPerron Test (PP) was employed for this study to determine the order of integration of the time series data. The unit root result was presented in table five (3) below.
Table 3. Unit Root Test: PhillipsPerron Test (PP)
Variables 
Level TStat 
Critical Value @ 5% 
First Difference TStat 
Critical Value @ 5% 
Order of Integration 
LOA 
2.594494 
2.943427 
8.873145 
2.945842 
I(1) 
INT 
3.135315 
2.943427 
7.974725 
2.945842 
I(0) 
INF 
2.756391 
2.943427 
9.447935 
2.945842 
I(1) 
FER 
2.360795 
2.943427 
3.324554 
2.945842 
I(1) 
LIQ 
3.223066 
2.943427 
9.466231 
2.945842 
I(0) 
MOS 
2.212247 
2.943427 
5.839320 
2.945842 
I(1) 
Source: Authors compilation, 2019
The PhillipsPerron (PP) unit root result showed that variable INT and LIQ are stationary at level but LOA, INF, FER, and MOS are all nonstationary at levels but after first differencing, they become stationary at first difference I(1) and none of the variables is integrated at order 1(2). ARDL method was used to investigate both short and longrun relationships among the variables. This will help to avoid spurious results.
4.2 Optimal Lag Length Selection
The implication of the lag length selected explains the effect of the outcome of the previous year on the current year. The selection of an optimal lag length is very essential before carrying out the Autoregressive Distributed Lag (ARDL) test, the result of which is presented in table 4 below.
Table 4. Lag length selection criteria
Lag 
LogL 
LR 
FPE 
AIC 
SC 
HQ 
0 
704.4699 
NA 
5.69e+10 
41.79235 
42.06170 
41.88420 
1 
535.0022 
269.1545 
22973847 
33.94131 
35.82681 
34.58432 
2 
469.8317 
80.50472* 
5094380. 
32.22540 
35.72705* 
33.41956 
3 
426.9552 
37.83227 
6332444. 
31.82089 
36.93869 
33.56621 
4 
347.3499 
42.14395 
2390379.* 
29.25588* 
35.98982 
31.55234* 
Source: Authors compilation, 2019
The result in table 4 shows different lag length criteria (LR, FPE, AIC, SC, and HQ). The Schwarz information criterion depicting lag order length of (2) for the model is selected. After establishing the lag order length, the ARDL, short and longrun equation results were estimated and explained in the next section.
Table 5. Autoregressive Distributed Lag (ARDL) Estimates
Variable 
Coefficient 
Std. Error 
tStatistic 
Prob.* 
lnLOA(1) 
0.726891 
0.305822 
2.376843 
0.0288 
lnLOA(2) 
0.315531 
0.319028 
0.989038 
0.3358 
INF 
0.005724 
0.003480 
1.644627 
0.1174 
INF(1) 
0.003373 
0.002946 
1.144834 
0.2673 
INF(2) 
0.003598 
0.003639 
0.988912 
0.3358 
INT 
0.021614 
0.013063 
1.654650 
0.1153 
INT(1) 
0.010488 
0.012088 
0.867633 
0.3970 
INT(2) 
0.003430 
0.010959 
0.312976 
0.7579 
FER 
0.001838 
0.001693 
1.085813 
0.2919 
FER (1) 
0.004349 
0.002556 
1.701776 
0.1060 
FER(2) 
0.004121 
0.002218 
1.858022 
0.0796 
LIQ 
0.002599 
0.003903 
0.666049 
0.5138 
LIQ(1) 
0.008282 
0.004223 
1.960982 
0.0655 
LIQ(2) 
0.005928 
0.004218 
1.405584 
0.1769 
MOS 
0.035829 
0.010183 
3.518504 
0.0025 
MOS(1) 
0.013324 
0.008685 
1.534122 
0.1424 
MOS(2) 
0.002651 
0.008525 
0.310992 
0.7594 
C 
0.512246 
0.549761 
0.931762 
0.3638 
Rsquared 
0.998698 
Mean dependent var 
6.451611 

Adjusted Rsquared 
0.997467 
S.D. dependent var 
2.601105 

S.E. of regression 
0.130900 
Akaike info criterion 
0.921912 

Sum squared resid 
0.308427 
Schwarz criterion 
0.130153 

Log likelihood 
34.59442 
HannanQuinn criter. 
0.645567 

Fstatistic 
811.8749 
DurbinWatson stat 
2.251163 

Prob(Fstatistic) 
0.000000 



Source: Authors compilation, 2019
The result of ARDL from table 5 revealed that the first period lag of LOA has a significant positive effect on LOA and the second period lag of LOA has a positive significant effect on the present LOA. INF has an insignificant positive effect on LOA in Nigeria; the first period lag of INF has a negative insignificant effect on LOA while the second period lag of INF has a positive insignificant effect on LOA. INT has a positive insignificant effect on LOA, the first period lag of INT has a negative insignificant effect on LOA while the second period lag of INT has a positive insignificant effect on LOA. FER has a negative insignificant impact on LOA, the first period lag of FER has a positive insignificant effect on LOA while the third period lag of FER has a negative insignificant effect on LOA. LIQ ha a positive insignificant effect on LOA, the first period lag of LIQ has a negative insignificant effect on LOA while the second period lag of LIQ has a positive insignificant effect on LOA. MOS has a positive significant effect on LOA, the first period lag of MOS has a negative insignificant effect on LOA while the second period lag of MOS has a negative insignificant effect on LOA and the first period lag of MOS has a positive significant and negative significant impact on LOA respectively.
The Durbin Watson value of 2.251163 indicates no serial autocorrelation among the explanatory variables in the model. The RSquare of 99.86% implies that 99.86% variation in the dependent variable can be explained by the independent variables. The Adjusted Rsquare depicts that if an additional variable is added to the independent variable, the independent variable will still be able to explain a 99.74% variation in the dependent variable. The F statistics (811.8749) which is greater than its prob(Fstatistics) 0.000000 at a 5% level of significance indicates that the linear relationships between the independent and dependent variables are statistically significant (i.e. the overall model).
4.3. ARDL Cointegration and Long Run Result
The ARDL Cointegration longrun analysis will help to determine the existence of longrun relationship among the variables in question. If the variables are cointegrated, this means that the variables have a stochastic trend in common.
Table 6. Cointegrating Form
Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(lnLOA(1)) 
0.315531 
0.319028 
0.989038 
0.3358 
D(INF) 
0.005724 
0.003480 
1.644627 
0.1174 
D(INF) 
0.003598 
0.003639 
0.988912 
0.3358 
D(INT) 
0.021614 
0.013063 
1.654650 
0.1153 
D(INT(1)) 
0.003430 
0.010959 
0.312976 
0.7579 
D(FER) 
0.001838 
0.001693 
1.085813 
0.2919 
D(FER(1)) 
0.004121 
0.002218 
1.858022 
0.0796 
D(LIQ) 
0.002599 
0.003903 
0.666049 
0.5138 
D(LIQ(1)) 
0.005928 
0.004218 
1.405584 
0.1769 
D(MOS) 
0.035829 
0.010183 
3.518504 
0.0025 
D(MOS(1)) 
0.002651 
0.008525 
0.310992 
0.7594 
ECT (1) 
0.042421 
0.034202 
1.240318 
0.0408 
Source: Authors compilation, 2019
The result from table 6 shows the ARDL Cointegration result. The estimated coefficient of the ECT1 is 0.042421. The error correction term is the shortrun estimate and has a negative significant value of (0.042421). This means that the error correction term is the speed of adjustment correcting back the shock at the rate of 4.24 percent annually. This means that a 4.24% gap between longrun equilibrium value and the actual value of the dependent variable (LOA) has been corrected. The negative sign signifies the existence of cointegration among the variables. The negative significant vector shows that the vector is in line with the a priori expectation and statistically significant.
The shortrun coefficient further showed that the previous LOA has a negative insignificant impact on the LOA. INF has an insignificant negative effect on LOA which implies that a percentage increase in INF will lead to 0.003598 unit decrease on LOA. INT has a positive insignificant impact on LOA while the first period lag of INT has a negative insignificant impact on LOA which means a unit increase in INT will lead to 0.021614 and 0.003430 increase and decrease LOA respectively. FER has a negative insignificant impact on LOA which implies that a percentage increase in EXC will lead to 0.005049 unit decrease in LOA; the first period lag of FER has a positive insignificant impact on LOA which means that a percentage increase in EXC will lead to 0.004121 unit increase in LOA. LIQ has a positive insignificant impact on LOA which implies that a ratio increase in LIQ will lead to 0.002599 while the first period lag of LIQ has a negative insignificant impact on LOA which implies that a ratio increase in LIQ will lead to 0.005929. MOS has a positive significant impact on LOA which implies that a unit increase in MOS will lead to 0.035829 unit increase in LOA while the first period lag of MOS has a positive insignificant impact on LOA which implies that a unit increase in MOS will lead to 0.002651 unit increase in LOA
Table 7. Long Run Coefficients
Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
INF 
0.140246 
0.129532 
1.082717 
0.0392 
INT 
0.343133 
0.428595 
0.800600 
0.0338 
FER 
0.037965 
0.012469 
3.044638 
0.0070 
LIQ 
0.005799 
0.153856 
0.037688 
0.9704 
MOS 
0.467992 
0.452233 
1.034848 
0.3144 
C 
12.075199 
13.352723 
0.904325 
0.3778 
Source: Authors compilation, 2019
The longrun coefficient further showed that INF has a significant negative effect on LOA which implies that a percentage increase in INF will lead to 0.140246 unit decrease in LOA. This finding is in line with the findings of Somoye & Ilo (2009) but contrary to the findings of Eke, Eke & Iyang (2015) and Omondi (2014). This result is also in line with the a priori expectation of negative impact.
INT has a negative insignificant impact on LOA which implies that a percentage increase in INT will lead to 0.343133 unit decrease in LOA. This finding is in line with the findings Onalapo & Habeeb (2017) but contrary to the findings of Eke, Eke & Iyang (2015). This result is also in line with the a priori expectation of negative impact.
FER has a positive significant impact on LOA which implies that a percentage increase in FER will lead to 0.037965 unit increase in LOA. This finding is in line with the findings of Jegede (2014) but contrary to the findings of Somoye & Ilo (2009). This result is also in line with the a priori expectation of negative impact.
LIQ has a negative insignificant impact on LOA which implies that a ratio increase in LIQ will lead to 0.005799 unit decrease in LOA. This finding is in line with the finding of Jegede (2014). This result is contrary to the a priori expectation of positive impact.
MOS has a negative insignificant impact on LOA which implies that a unit increase in MOS will lead to 0.467992 unit decrease in LOA. Jegede (2014) and Somoye & Ilo (2009). This result is contrary to the a priori expectation of positive impact.
The study also went further to investigate the relationship between the inflation rate and the lending rate in Nigeria. The relationship was estimated with the granger causality test but before the estimation, the appropriate lag length needs to be determined to ascertain the effect of the outcome of the previous year on the current year.
Table 8. Lag Length Selection Criteria
Lag 
LogL 
LR 
FPE 
AIC 
SC 
HQ 
0 
238.4706 
NA 
4767.661 
14.14533 
14.23512 
14.17595 
1 
227.1523 
20.63938 
3102.629 
13.71484 
13.98420* 
13.80670 
2 
220.5723 
11.22464 
2674.770 
13.56308 
14.01201 
13.71617 
3 
216.8344 
5.936685 
2737.172 
13.57849 
14.20699 
13.79283 
4 
209.3597 
10.99218* 
2262.377* 
13.37410* 
14.18217 
13.64968* 
The result in Table 8 shows different lag length criteria (LR, FPE, AIC, SC, and HQ). The Schwarz information criterion depicting lag order length of one (1) for the model is selected. After establishing the lag order length, the Granger causality test will be estimated and explained in table 9 below.
Table 9. Granger Causality Test Result
Null Hypothesis: 
FStatistic 
Prob. 
Decision Rule 
INT does not Granger Cause INF 
0.89323 
0.4826 
Fail to reject Null 
INF does not Granger Cause INT 
4.69092 
0.0058 
Reject Null 
Source: Authors compilation, 2019
The result from the Granger causality test was shown in table 9 above. It revealed that INF does cause INT in Nigeria. The result further revealed that INT doesn’t cause INF in Nigeria which implies that the lending rate does not cause the inflation rate in Nigeria. The monetary authority should endeavor to encourage and maintain a stable rate of inflation as this will contribute to the level of the interest rate applicable in Nigeria. This will also encourage more investment in the market.
The result revealed that the inflation rate does cause the lending rate in Nigeria which led to the rejection of the null hypothesis based on the pvalue (0.0058). The result also showed that the lending rate doesn’t cause inflation rate in Nigeria which informed the failure of rejecting the null hypothesis based on the pvalue of (0.4826).
5. Conclusion and Recommendation
The study examines the impact of inflation on bank lending in Nigeria for the periods of 1980 to 2018. Previous studies were reviewed on inflation and bank lending in Nigeria to ascertain the relationship between the dependent and independent variables as well as their respective impact. Previous researches revealed that several measures have been used to proxy the independent variable which differs from country to country with mixed results. However, previous studies in Nigeria have employed various methodological techniques; this study employed the Autoregressive Distributed lag (ARDL) as dictated by the stationarity level of the data. The longrun result revealed that the inflation rate and interest rate have a significant negative effect on loans and advances; the exchange rate has a positive significant effect on loans and advances while liquidity ratio and money supply have a negative insignificant effect on loans and advances. More so, the Granger Causality test further revealed that the inflation rate does cause the lending rate in Nigeria while the interest rate doesn’t cause the inflation rate in Nigeria.
Persistency of a higher inflation rate in the economy could lead to a reduction in the economic output due to limited funds that will be available for lending as large amount of money will be chasing fewer goods in the economy. This result could also be related to Keynes’s assertion that as the supply of money increases, the interest rate decreases if the reduction in interest rate persists, the amount of money in circulation will keep increasing which could lead to inflation in the economy. The monetary authority should, therefore, strive to maintain a reasonable interest rate that will help to achieve an optimal level of supply of money in circulation to avoid inflation in the economy.
Therefore, there is a need for the government through the Central Bank of Nigeria to ensure a working monetary policy instrument and make a periodic review of the policies. The study thus opines that the interest rate obtainable in the economy should be stable and at a lower rate as this has a significant impact on lending in the economy. The lower the interest rate, the more borrowers will be willing to borrow from the bank and vice versa. The monetary authority should endeavor to maintain a stable level of inflation rate as this will help maintain favorable economic activities. Monetary authority should manage the lending rate properly for it to be attractive and affordable for investors to borrow money from the bank.
Adedoyin, O. & Sobodun, U. N. (1996). Commercial banks’ lending activities in Nigeria. Nigerian Financial Review, 9(3), 36  37.
Ángel, T. Z.; Francisco, V. M. & Hugo, T. V. (2014). Growth, bank credit, and inflation in Mexico: Evidence from an ARDLbounds testing approach. Latin American Economic Review, 23 (1), 122. http://dx.doi.org/10.1007/s4050301400080.
Berger, A. N. & Udell, G. F. (2006). A more complete conceptual framework for SME finance. Journal of Banking and Finance, 30(11), 29452966.
Caglayan, M. & Xu, B. (2016). Inflation volatility effects on the allocation of Bank loans. Journal of Financial Stability, 24, 2739. https://doi.org/10.1016/j.jfs.2016.04.008.
CBN Annual Report (2009). 50 years of central banking in Nigeria. A publication of Central Bank of Nigeria.
Central Bank of Nigeria (2018). Statistical bulletin official data manual of the central bank of Nigeria.
Cheboi, P. K. (2012). Response strategies to change in the economic Environment by Cooperative Bank of Kenya Limited. University of Nairobi.
Chioma, O. E.; Adanma, E. S. & Clementina, O. N. (2014). Empirical study of the impact of inflation on bank performance: Implication for investment decision making in banking industry in Nigeria. Humanity & Social Sciences Journal, 9 (2), 6171. DOI: 10.5829/idosi.hssj.2014.9.2.1146.
Eke, F. A.; Eke, I. C. & Inyang, O. G. (2015). Interest rate and commercial banks’ lending operations in Nigeria: A structural break analysis using chow test. Global Journal of Social Sciences, 14, 922. DOI: http://dx.doi.org/10.4314/gjss.v14i1.2.
Emekekwue, P.E. (2008). Corporate financial management, Enugu: Snapp Press Ltd.
Jegede, C. A. (2014). Effects of monetary policy on the commercial banks’ lending in Nigeria. Review of Public Administration and Management, 3(5), 134146.
Keynes, (1936). The General Theory of Employment, Interest, and Money. New York: Harcourt Brace. Reprinted in Moggridge D. (ed.) The Collected Writings of Keynes, vol. 7, London: Macmillan.
Nwankwo, G.O. (1991). The money and capital market in Nigeria. Ibadan: Publisher.
Ojeaga, P.; Odejimi, O., Okhiku, J. & Ojeaga, D. (2014). Does commercial bank lending incite growth? The impact of commercial lending on real sector growth in Nigeria.
Okafor, E. O. (2011). 50 Years of Banking sector reform (1960 – 2010) past lessons: Future imperative. Enugu: Ezu Book Publication.
Olokoyo, F.O. (2011). Determinants of Commercial Banks’ Lending Behaviour in Nigeria. International Journal of Financial Research, 2(2): 6672.
Omondi, O. G. (2014). Effects of inflation on commercial banks’ lending: A case of Kenya commercial bank limited. Unpublished project repot submitted.
Onaolapo, O. F. & Habeeb, S. G. (2017). Appraisal of monetary policies on commercial bank lending behavior in Nigeria banking industry from 19802014. Global journal of humansocial science: Economic,17(4).
Oritoni, Y.H. (1981). The negative effects of inflation on economic growth in Japan, Discussion Paper series No 5, Tokyo: The Bank of Japan.
Ramady, M. (2009).External and Internal Determinants of Inflation: A Case Study of Saudi Arabia. Middle East Journal of Economics and Finance, 2, 2538.
Somoye, R.O.C. & Ilo, B.M. (2009). The impact of macroeconomic instability on the Banking sector lending behaviour in Nigeria. Journal of Money, Investment and Banking, Issue 7.
Sonneveld, P.; van Kan, J.J.I.M; Huang, X. & Oosterlee, C. W. (2009). Nonnegative matrix factorization of a correlation matrix. Linear Algebra and its Applications, 431(34),334–349. https://doi.org/10.1016/j.laa.2009.01.004.
Ramsay, J. O., Ten Berge, J. & Styan, G.P.H. (1984). Matrix correlation. Psychometrika, 49(3), 403423. DOI:10.1007/bf02306029.
1 Department of Banking and Finance, Faculty of Administration and Management Sciences, Olabisi Onabanjo University,Nigeria, Address: Ago Iwoye, Ogun State, Nigeria, Corresponding author: yunusalateef@gmail.com.
2 Faculty of Administration and Management Sciences, Olabisi Onabanjo University,Nigeria, Address: Ago Iwoye, Ogun State, Nigeria, Email: brainywilly1@gmail.com.
3 Email: oluwatosinadegbenle@gmail.com.
AUDŒ, Vol. 16, no. 1/2020, pp. 113129