




































Indian Journal of Finance and Banking; Vol. 2, No. 2; 2018 

                                                           ISSN 2574-6081  E-ISSN 2574-609X 

Impact Factor: 3.8 

 Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

26 
 

Determinants of Commercial Banks Credit to the Domestic 

Economy in Nigeria: Examinations of Dynamics Principles 
 

 

Akani, Henry Waleru
1 
& Oparaordu, Beauty

1 

 

 
1
Department of Banking and Finance, Rivers State University, Nkpolu-Port Harcourt, Rivers State, Nigeria 

Correspondence: Akani, Henry Waleru,
 
Department of Banking and Finance, Rivers State University, Nkpolu-Port 

Harcourt, Rivers State, Nigeria 

 

Received: July 20, 2018                           Accepted:  July 28, 2018                Online Published: August 8, 2018  

 

 

Abstract 

This study examined determinants of commercial banks credit to the domestic economy in Nigeria. The objective 

was to examine the extent to which banks variables, macroeconomic and monetary policy variables affects credit 

allocation of Nigerian Commercial Banks. Time series data was sourced from Central Bank of Nigeria Statistical 

bulletin and financial statement of commercial banks. Percentage of total commercial banks loans to gross domestic 

product was proxy for dependent variable while the banks specific variables are peroxide by operational efficiency, 

liquidity, number of commercial banks branches, Commercial Banks Deposit Liabilities and deposit rate. The 

independent variables in macroeconomic model comprises of real gross domestic product, public expenditure, 

openness of the economy, inflation rate and exchange rate while monetary policy variables comprises of treasury 

bills rate, real interest rate, monetary policy rate, growth of money supply and financial sector development. The 

study employed ordinary least square properties of augmented Dickey Fuller test, co-integration test, and granger 

causality test and vector error correction model. Findings from the study revealed that; banks specific variables 

shows that deposit liabilities and liquidity ratio have positive impact on total loans and advances while deposit rate, 

number of commercial banks branches and openness of the economy have negative impact. Model II found that; 

exchange rate, inflation rate and Real Gross Domestic Product have positive impact while public expenditure and 

openness of the economy have negative impact on total commercial bank loans and advances. Model III found that; 

financial sector development and monetary policy rate have negative impact while growth of money supply, real 

interest rate and Treasury bills rate have positive impact on total loans and advances of commercial banks. We 

conclude that monetary policy, bank specific variables or internal variables and macroeconomic variables are strong 

determinants of Nigerian commercial banks loans and advances. We therefore, recommend for the interplay and the 

strengthening of macroeconomic variables, monetary policy variables and banks specific variables (internal policies) 

in order to enhance commercial banks credit in Nigeria. 

 

Keywords: Determinants, Bank Credit, Domestic Economy, Macroeconomic Variables, Monetary Policy Variables 

and Banks Specific Variables.  

 

1. Introduction 

In a deregulated, monopolistically competitive and oligopolistic banking environment like Nigeria, bank credit is 

determined by internal and external factors. From the internal factors, commercial banks credit is determined by 

capital adequacy, number of bank branches, commercial banks deposit liabilities, operational efficiency and deposit 

rate. From the monetary policy perspective, commercial banks’ lending depend on monetary policy rate, treasury 

bill rate real interest rate, financial development and growth of money supply while macroeconomic variables 

includes commercial banks’ lending which depends on growth of the economy, inflation rate, real exchange rate, 

openness of the economy and public expenditure. Credit is a financial market activity where financial institutions are 

empowered by law with credit functions to extend credit facilities to deficit economic units. The monetary 



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authorities use credit policies to achieve macroeconomic growth. For instance, credit policies are used to achieve 

growth in some sectors of the economy, (Akani and Onyema, 2017). 

Bank loans are one of the most important long-term financing sources in many countries. Commercial banks are the 

most important savings mobilization and financial resource allocation institutions. Consequently, these roles make 

them an important phenomenon in economic growth and development. In performing this role, it must be realized 

that banks have the potential, scope and prospects for mobilizing financial resources and allocating them to 

productive investments. Olokoyo(2011), further notes that no matter the sources of the generation of income or the 

economic policies of the country, commercial banks would be interested in giving out loans and advances to their 

numerous customers bearing in mind, the three principles guiding their operations which are, profitability, liquidity 

and safety. Lending institutions play a major role in economic growth and development through provision of credit 

to execute economic activities.  

Lending which may be on short, medium or long-term basis is one of the services that commercial banks do render 

to their customers. In other words, banks do grant loans and advances to individuals, business organizations as well 

as government in order to enable them embark on investment and development activities as a mean of aiding their 

growth in particular or contributing toward the economic development of a country in general. Commercial banks 

are the most important savings mobilization and financial resource allocation institutions. Consequently, these roles 

make them a vital tool in economic growth and development. In performing this role, it must be realized that banks 

have the potential, scope and prospects for mobilizing financial resources and allocating them to productive 

investments.  

Lending practices in the world could be traced to the period of industrial revolution which increase the pace of 

commercial and production activities thereby bringing about the need for large capital outlays for projects. However, 

the emergence of banks in Nigeria in 1872 with the establishment of the African Banks Corporation (ABC) and later 

appearance of other banks in the scene during the colonial era witnessed the beginning of banks’ lending practice in 

Nigeria. Though, the lending practices of the then colonial banks were biased and discriminatory and could not be 

said to be a good lending practice as only the expatriates were given loans and advances. ( Amadi and Akani, 2004). 

The Bank and Other Financial Act Amendment (BOFIA) 1998, requires banks to report large borrowing to the 

Central Bank of Nigeria. The Central Bank of Nigeria also require that their total value of a loan credit facility or 

any other liability in respect of a borrower, at any time, should not exceed 20% of the shareholders’ funds 

unimpaired by losses in the case of commercial banks. Other banking enactment stipulated that banks loans should 

be directed to preferred sector of the economy in order to enhance economic growth and development. In full 

consideration of all these regulations the banks resorted to prudential guidelines necessary to avoid failures and to 

enhance maximum profitability in their banks’ lending activities.  

Empirical and theoretical evidence shows that there is a relationship between commercial banks credit and factors 

that determined commercial banks’ lending. The study of Akani and Onyema(2017) examined factors that determine 

credit growth in the economy, the study used net domestic credit, and this implies that, the study goes beyond 

commercial banks and other financial institutions that undertake the functions of lending and borrowings. 

Olokoyo(2011) does not disaggregated the factors based on macroeconomic, bank internal variables and monetary 

variables, the result therefore does not validate the effects of monetary, macroeconomic and internal policies 

variables on commercial banks’ lending in Nigeria. From the above knowledge gap, this study examined the 

determinants of commercial banks ‘credit to the domestic economy of Nigeria by disaggregating the variables into 

internal, monetary and macroeconomic variables. 

2. Literature Review 

Conceptual Foundation 

Concept of Bank Lending  

Lending which is considered to be the main function of banks in general and commercial banks, in particular, could 

be on a short, medium and long-term basis. It is the act of making funds available with the hope of receiving back 

the principal plus interest payment or/and any other fees imposed on carrying out the transaction. Credit is a 

financial market activity where financial institutions are empowered by law with credit functions to extend credit 

facilities to deficit economic units. 

Theories of Bank Credit 

Loan Pricing Theory 

Banks cannot always set high interest rates and trying to earn maximum interest income. Banks should consider the 

problems of adverse selection and moral hazard since it is very difficult to forecast the borrower type at the start of 

the banking relationship (Oputu, 2010). If banks set interest rates too high, they may induce adverse selection 

problems because high-risk borrowers are willing to accept these high rates. Once these borrowers receive the loans, 

they may develop moral hazard behaviour or so called borrower moral hazard since they are likely to take on highly 



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risky projects or investments (Chodecai, 2004). From the reasoning of Stiglitz and Weiss, it is usual that in some 

cases we may not find that the interest rate set by banks is commensurate with the risk of the borrowers. 

Firm Characteristics Theories 

These theories predict that the number of borrowing relationships will be decreasing for small, high-quality, 

informational opaque and constraint firms, all other things been equal. (Godlewski&Ziane, 2008) 

Theory of Multiple-Lending 

 Literatures explain that banks should be less inclined to share lending (loan syndication) in the presence of well-

developed equity markets and after a process consolidation. Both outside equity and mergers and acquisitions 

increase banks’ lending capacities, thus reducing their need of greater diversification and monitoring through share 

lending. Degryse et al (2004). This theory has a great implication for banks in Nigeria in the light of the recent 2005 

consolidation exercise in the industry. 

Hold-up and Soft-Budget-Constraint Theories 

Banks choice of multiple-bank lending is in terms of two inefficiencies affecting exclusive bank-firm relationships, 

namely the hold-up and the soft-budget-constraint problems. According to the hold-up literature, sharing lending 

avoids the expropriation of informational rents. This improves firms’ incentives to make proper investment choices 

and in turn it increases banks’ profits (Von Thadden, 2004; Padilla and Pagano, 1997). As for the soft-budget-

constraint problem, multiple-bank lending enables banks not to extend further inefficient credit, thus reducing firms’ 

strategic defaults. Both of these theories consider multiple-bank lending as a way for banks to commit towards 

entrepreneurs and improve their incentives. None of them, however, addresses how multiple-bank lending affects 

banks’ incentives to monitor, and thus can explain the apparent discrepancy between the widespread use of multiple-

bank lending and the importance of bank monitoring. But according to Carletti et al (2006), when one considers 

explicitly banks’ incentives to monitor, multiple-bank lending may become an optimal way for banks with limited 

lending capacities to commit to higher monitoring levels. Despite involving free-riding and duplication of efforts, 

sharing lending allows banks to expand the number of loans and achieve greater diversification. This mitigates the 

agency problem between banks and depositors, and it improves banks’ monitoring incentives. Thus, differently from 

the classical theory of banks as delegated monitors, their paper suggested that multiple-bank lending may positively 

affect overall monitoring and increase firms’ future profitability. 

The Signaling Arguments 

The signaling argument states that good companies should provide more collateral so that they can signal to the 

banks that they are less risky type borrowers and then they are charged lower interest rates. Meanwhile, the reverse 

signaling argument states that banks only require collateral and or covenants for relatively risky firms that also pay 

higher interest rates (Chodechai, 2004; Ewert and Schenk, 1998). 

Credit Market Theory 

A model of the neoclassical credit market postulates that the terms of credits clear the market. If collateral and other 

restrictions (covenants) remain constant, the interest rate is the only price mechanism. With an increasing demand 

for credit and a given customer supply, the interest rate rises, and vice versa. It is thus believed that the higher the 

failure risks of the borrower, the higher the interest premium (Ewert et al, 2000).  

Empirical Literature 

Akani and Onyema (2017), examined the determinants of credit growth in Nigeria. Annual time series data were 

sourced from Central Bank of Nigeria statistical bulletin from 1981-2016.Three multiple regression models were 

formulated to examine the effect of macroeconomic variables, monetary policy variables and international variables 

on the growth of Nigeria’s net domestic credit. The unit root test indicates that all the variables are stationary at first 

difference using the Augmented Dickey Fuller (ADF) test. The Johansen Cointegration test result shows that there 

exists a positive long run dynamic relationship between the dependent and the independent variables. TheGranger 

causality test shows a uni-variate relationship from the independent to the dependant variable. From the 

macroeconomic variable, public expenditure, inflation rate and capital formation have a negative relationship with 

growth of Nigeria net domestic credit while real gross domestic product, government revenue and balance of 

payment have a positive impact on the dependent variable, we conclude that macroeconomic variables have 

significant effect on the growth of Nigeria’s net domestic credit. From the monetary policy variables, treasury bill 

rate, interest rate and compliance to credit rules have a negative effect on net domestic credit while monetary policy 

rate, financial deepening and growth of broad money supply have a positive effect on the dependent variables. We 

also conclude that monetary policy variables have no significant relationship with the growth of net domestic credit 

in Nigeria. While from the international variables, exchange rate, international liquidity, foreign direct investment 

and openness of the economy have positive effect on net domestic credit whereas cross boarder credit and net 

foreign portfolio investment have negative relationship with net domestic credit. From the result, we conclude that 

international variables have no significant relationship with the growth of net domestic credit in Nigeria.  



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Gertler and Gilchrist (1994) on how bank business lending responds to monetary policy tightening, the study reveals 

that business lending does not decline when policy is tightened. They concluded that the entire decline in total 

lending comes from a reduction in consumer and real estate loans. Kashyap and Stein (1995) find evidence that 

business lending may respond to a tightening of monetary policy. They find that when policy is tightened, both total 

loans and business loans at small banks fall, while loans at large banks are unaffected. The differential response of 

small banks may indicate they have less access to alternative funding sources than large banks and so are less able to 

avoid the loss of core deposits when policy is tightened.  

Gambacorta and Iannoti (2005) studied the velocity and asymmetry in response of bank interest rates (lending, 

deposit, and inter -bank) to monetary policy shocks (changes) from 1985-2002 using an Asymmetric Vector 

Correction Model (AVECM) that allows for different behaviours in both the short-run and long-run. The study 

shows that the speed of adjustment of bank interest rate to monetary policy changes increased significantly after the 

introduction of the 1993 Banking Law, interest rate adjustment in response to positive and negative shocks are 

asymmetric in the short run, with the idea that in the long -run the equilibrium is unique. They also found that banks 

adjust their loan (deposit) prices at a faster rate during period of monetary  

Van den Heuvel (2005) in his study shows that monetary policy affects bank lending through two channels. They 

argued that by lowering bank reserves, contractionary monetary policy reduces the extent to which banks can accept 

reservable deposits, if reserve requirements are binding. The decrease in reservable liabilities will, in turn, lead 

banks to reduce lending, if they cannot easily switch to alternative forms of finance or liquidate assets other than 

loans. 

Punita and Somaiya (2009) examined the impact of monetary policy on profitability of banks in India between 1995 

and 2000 provided some dissenting evidence that lending rate has a positive and significant influence on banks’ 

profitability, which indicates a fall in lending rates will reduce the profitability of the banks. It was also found out 

that bank rate, cash reserve ratio and statutory ratio significantly affect profitability of banks negatively. Their 

findings were the same when lending rate, bank rate, cash reserve ratio and statutory ratio were pooled to explain the 

relationship between bank profitability and monetary policy instruments in the private sector. 

Amidu and Wolfe (2008) examined the constrained implication of monetary policy on bank lending in Ghana 

between 1998 and 2004. Their study revealed that Ghanaian banks’ lending behaviour are affected significantly by 

the country’s economic also support and change in money supply. Their findings also support the finding of 

previous studies that the central bank prime rate and inflation rate negatively affect bank lending. Prime rate was 

found statistically significant while inflation was insignificant. Based on the firm level characteristics, there study 

revealed that bank size and liquidity significantly influence bank’s ability to extend credit when demanded.  

Somoye and Ilo (2009) investigated the impact of macroeconomic instability on the banking sector lending 

behaviour in Nigeria between 1986 to 2005. Their study revealed the mechanism transmission of monetary policy 

stocks to banks operation. The result of cointegration and Vector Error correction suggests a long-run relationship 

between bank lending and macroeconomic instability. This study will empirically analyze the effect of monetary 

policy on the commercial banks’ lending in Nigeria with the intension of determining the influence of monetary 

policy instruments on commercial bank loan and advances.  

TUhomoibhi (2008) investigated the determinants of bank profitability macroeconomic evidence from Nigeria 

seeking to econometrically identify significant using a panel data set comprising 1255  observations of 154 banks 

over a period of 1980-2006, the indices over the same period regression result reveal that interest rate, inflation, 

monetary policy and exchange rate regime, significant macroeconomic determinants of banks profitability in Nigeria 

banking sector development, stock market development and financial structure are insignificant and the relationship 

between corporate tax policy and bank profitability in Nigeria is inconclusive. In  

Samad (2004) examined the study of Bahrans commercial banks performances during 1994-2001. The main focus of 

the study was to examine empirically the performance of Bahrains commercial banks with respect to credit (loan), 

liquidity and profitability during the period. By applying students’-test to the financial measure, it was shown that 

commercial banks liquidity performance is not at par with the banking industry. That is commercial banks are 

relatively less profitable and less liquid as expected.  

Although Chizea (1994) asserted that, there are certain aspects of fiscal and monetary policies which could affect the 

decision of the discerning and informed public to patronize the bank and the lending behaviour of commercial 

banks. Paramount amongst these measures is what could be called the interest rate disincentives. Interest rates have 

been so low in the country that they are negative in real terms. As inflation increased, the purchasing power of 

money lodged in deposit accounts reduce to the extent that savers per force pay an inflation tax. There is also the 

fear that the hike in interest rates would increase inflations rates and make a negative impact on the rate of 

investment.  



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Naceur and Goaid (2010) investigated the determinants of commercial banks interest margin and profitability 

(evidence from Tunisia). The study received the impact of banks characteristics, financial structure and 

macroeconomic indicators on bank’s net interest margin and profitability in Tunisia banking sector for the period of 

1980-2000. It shows that individual bank characteristic explains a substantial part of the within country variation in 

bank interest margin and net profit. High net interest margin and profitability tend to be associated with banks that 

hold a relatively high amount of capital and with large overheads size is found to impact negatively on profitability 

which implies that Tunis banks are operating above their optimum level. 

William (2009) will result to a near shut down in lending ratio volume to any bank with major credit concern 

because, new policy ensures that only the highest quality borrowers have access to a new bank credit within the 

year, but according to Ojo (1999) in a study on “roles and failure of financial intermediation by banks in Nigeria 

revealed that commercial banks can lend on medium and short term basis without necessarily jeopardizing their 

liquidity. If they must contribute meaningfully to the economic development, the maturity pattern of their loans 

should be on a long term nature rather than of short term period.  

Davis and Zhu (2005) examined the study of commercial property prices and bank performance during the 1989-

2002 periods. This paper seeks to fill the gap by undertaking an extensive analysis of a sample of 904 banks 

worldwide. It seeks to assess the effect of changes in commercial property prices on bank behaviour and 

performances in 15 industrialized economies. The result of this study suggest that commercial property price tend to 

be positively associated with bank lending and profitability, negatively associated with banks net interest margin, 

bad loan ratios. Such impact exists even when conventional independence variable determining banks performance 

are included as controls.  

Olokoya(2011) claimed in the study on the common determinants of commercial banks lending behavior in Nigeria 

which aimed to test and confirm the effectiveness of these factors/variables. It reveals that there exists functional 

relationship between the variables. From the regression analysis, the model was found to be significant and its 

estimators turned out as expected and it was discovered that commercial banks have greatest impact on their lending 

behavior. And suggested that commercial banks should focus on mobilizing more deposits, as it will enhance their 

lending performance through the formulation of critical, realistic and comprehensive strategies and financial plans 

Though Acha (2011) probed into „the effect of banks financial intermediation on economic growth‟ on a time frame 

of 1980 -2008, adopting the Granger causality test to ascertain the relationship that exist between savings 

mobilization and credit on one hand and economic growth on the other.  Osayameh (1991) supported this veiw by 

stressing that the days of arm chain banking are over, and that the increasing trend in bad debts and absence of basic 

business corporate advisory services in most Nigerian commercial banks, suggest an apparent lack of use of 

effective lending and credit administration technique in these banks. 

Buccheit (1992) in his study based on Syndicated loans found out that when commercial banks jointly give out loans 

to a borrower, they are able to efficiently minimize their cost and manage time. They can better deliberate with the 

borrower(s) concerning the loan agreement for their various organizations. In addition, this paves the way for a 

constant follow-up of these borrowers to avoid default.  

Eichengreen et al (1998) believed that commercial banks will not hesitate to give out loans if they can effectively 

deal with the problem of asymmetric information through constant surveillance. Also, if they are able to mitigate 

lending risks to a greater extent by diversifying their portfolio assets and maximize their profits. Kashyap et al 

(1997) commercial banks would be willing to lend to individuals whose information are not perfect. This is because 

these firms will solely depend on the banks for their financial needs. In this case, the banks can exercise their full 

rights over them and obtain the necessary information to know if they will be able to meet their debt obligation. 

Moreover, with the information at hand, these banks will be efficient and guided in making good lending decisions.  

Ahiawodzi and Sackey (2013) banks use different strategies to assess their credit and it is vital for them to consider 

these guiding rules in carrying out their lending activities. This is because commercial banks do not trust the 

information they acquire from opaque borrowers who might end up defaulting. Some recent researchers found out 

that in addition to a political and environmental crisis, the banking crisis is also a major hindrance to the economic 

growth of countries. One way to tackle this issue is to implement or set up strict rules and regulations to govern 

banks’ lending activities. This policy does not only reduce the cost of the crisis in a society, but it as well enables 

banks to better maximize their profits and boost up economic growth (Quintyn et al., 2003). 

Daniel and Jones (2007) carried out a study based on financial liberalizationand Banking crisis in emerging 

countries were of the opinion that some causes ofthe financial crisis occurred because some banking systems were 

not well coordinated. They believed that a proper supervision of these banks would have permitted a good number 

of countries to experience a grace period of minimum risk followed with economic development before the outburst 

of the crisis. In South Africa, commercial banks do not easily make loans available to SMEs and less privileged 

individuals in the society.  



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Kumbirai et al (2013) did a study for the case of South Africa on “Banks’ ratio analysis performance discovered that 

in the process of meeting up with the 1994 constitutional democracy, the South African commercial banks had to 

experience series of updates in their regulatory policies. Gilbert et al (2009)supported this view by saying that “the 

implementation of these rules and regulations for banks was purposely done to bring about the equality across a 

nonvolatile financial domain and to curb the rising competition costs through regulatory requirements, innovation 

and new technologies during the financial crisis.  The financial crisis that occurred in recent years, negatively 

affected every part of the world, particularly in South Africa. Banks were not only reluctant to lend to one another, 

but became even more unwilling to give out loans to SMEs and individuals.   

Djiogap and Ngomsi (2012) carried out a study for the period of 2001-2010 on factors that influences banks’ 

Lending Behavior in the Central African Economic and Monetary Community on long-term basis. Six countries in 

the CEMAC zone and 35 commercial banks were considered. Using a panel data analysis, they found out that 

bank’s capital to asset ratio, long- term liabilities, GDP growth and its size were statistically significant. This implies 

that these variables are taken into consideration by banks in making long-term loans available to firms. They also 

carried out a multivariate test based on different countries which revealed that banks with inadequate capital, high 

non-performing loans and small banks functions. 

Olokoyo (2011) examined this topic for the case of the Nigerian Economy for the period of 1980-2005. From her 

findings, the predictor variables (volume of deposits, investment portfolio, foreign exchange, and GDP) were 

statistically significant and portrayed a positive relationship with commercial bank lending. This implies that these 

explanatory variables are very vital for banks’ lending decisions to give out loans and advances to borrowers. She 

suggested that commercial banks in Nigeria should improve their management skills and lending performance by 

building up new strategies and system that will pull deposits irrespective of its source.   

Panagopoulos and Spiliotis (1998) for the period of 1971-1993 also carried out a dissertation on the influencing 

factors of commercial banks’ lending decision in Greece and made use of the panel software analysis and regression 

model. Their findings exhibited that credit money, money wage bill, and loan customer relation had a strong 

significant impact on commercial banks’ lending behavior in Greece. These researchers asserted that “statistically it 

is senseless for Greek monetary authorities to keep pressurizing commercial banks to reserve a large percentage of 

their deposits in risk-free assets such as T-bills. They suggested that the Greek monetary authorities should set the 

maximum amount of bank’s lending rate. Malede (2014), examined the determinants of commercial banks’ Lending 

in Ethiopia over a 6-year period (2005-2011). He applied the panel data analysis and OLS to find out that credit risk, 

bank size, GDP, liquidity, lending rate and investment were statistically significant and had a positive relationship 

with commercial banks’  lending. He concluded that these explanatory variables greatly influenced banks’ lending 

decisions compared to deposit and cash required reserve which was insignificant. He suggested that commercial 

banks should throw more light on their credit risk and better manage their liquidity ratio because these variables 

prevent their willingness to lend.  

Tomak (2013) investigated on this topic for the case of Turkey starting from the period 2003-2012 considering 18 

banks for the sample size. His results showed that GDP and interest rate were statistically insignificant. On the other 

hand, banks total liabilities, NPL size and inflation rate were statistically significant and had a positive relationship 

with commercial banks’ lending behavior. Chodechai (2004), in his study on the “Determinants of bank lending in 

Thailand” supported Cole’s second view about past relationships as a criterion in banks’ lending decision. He 

discovered that when banks have such relationships with borrowers, they are more confident in accessing the 

borrowers’ privacy concerning their occupations and their financial state at every point in time. 

 Cole (1998) found out that commercial banks, unlike other lending institutions are very unwilling to give out loans. 

The reason is because during the period of the 1990s these lenders were pressurized by their regulators to make 

underwriting benchmark or requirement difficult to attain or meet up with. He further stressed that these banks 

would consider making credit available to firms with whom they have had a close relationship no matter how long. 

In addition to that, if they are informed about them being the sole providers of financial services to these firms, they 

will be willing to lend.  

Loutskina (2011), in her research study on the role of securitization in bank liquidity and funding management she 

found out that when banks are able to liquidate their loans in order to meet their liquidity needs, they will be more 

willing to make credit available to borrowers. According to her, since liquid funds and loans are very vital elements 

of bank assets there is a negative relationship between liquid funds and lending. That is to say, as the former 

decreases the later increases. This paragraph discusses the view of researchers under category 4 as specified in the 

1st paragraph above. Behr et al (2013) carried out investigations on financial constraints of Private firms” and 

discovered that banks’ lending behavior are influenced by soft information based on the quality of the borrower and 

continuous lending relationship. Ahiawodzi and Sackey (2013) investigated the rationing behavior of some 

commercial lending in Ghana. Their results displayed that experience, security value; sex, net profit, purpose, and 



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age were significant in determining the amount of loan given out. Imran and Nishat (2013) empirically identified 

commercial banks credit lending in Pakistanfor the period 1971-2010. From their findings domestic deposits, 

exchange rate, foreign liabilities, greatly influenced banks’ lending decisions to the private sector in the long run. 

Inflation has an insignificant role in the long run. Also, domestic deposits in the short run do not apply with private 

credit because banks do not loan from the current account deposit. 

3. Research Methodology 

This study used quasi experimental research design approach for the data analysis. The data for this study are 

secondary data sourced from the Central Bank of Nigeria Statistical Bulletin, Stock Exchange Fact book, Economic 

and Financial Review and Financial Statement of quoted commercial banks. From theories, principles and empirical 

findings, the model below is specified in this study.  

Model I 

CBC/GDP = f (NBB, CBDL, LIQR, OPE, DR)                                                                     1 

Transforming equation 1 into a testable form, we have; 

CBC/GDP = 0 NBB1 CBDL2 LIQR3 OPE4 DR5  2 

 Where; 

CBC/GDP  =    Percentage of Commercial Bank Credit to Gross Domestic Product( % GDP ) 

NBB   =     Number of Commercial Banks Branches 

CBDL   =  Commercial Banks Deposit Liabilities 

LIQR   = Liquidity Reserve 

OPE   = Operational Efficiency of Managementproxied by total cost to total revenue 

DR   = Deposit Rate 

0    = Regression Intercept 

1   - 5   = Coefficient of the independent variables to the  

Dependent variable 

µ   = Error term 

Model II 

CBC/GDP = f (MPR, TBR, RINTR, FD, G-M2)                                                                     3 

Transforming equation 3 into a testable form, we have; 

CBC/GDP = 0 PRM1 BRT2 INTRR3 DF4  2G6 M             4 

Where; 

CBC/GDP  =     Percent of Commercial Banks Credit to Gross Domestic Product ( % GDP ) 

MPR   = Monetary Policy Rate 

TBR   = Treasury Bill Rate 

RINTR  = Real Interest Rate 

FD   = Financial Sector Development 

G-M2   = Growth of Broad Money Supply 

0    = Regression Intercept 

1   - 6   = Coefficient of the independent variables to the  

Dependent variable 

µ   = Error term 

Model III 

CBC/GDP = f (RGDP, INFR, EXR, OPE, PEX)                                                                        5                                                         

Transforming equation 4 into a testable form, we have; 

CBC/GDP  = 0 RGDP1 NFRI2 XRE3 PEO4 PEX5            6 

Where; 

CBC/GDP  =     Percent of Commercial Banks Credit to Gross Domestic Product  

RGDP   = Real Gross Domestic Product 

INFR   = Inflation Rate 

EXR   = Exchange Rate 

OPE   = Openness of the Economy 

PEX   = Public Expenditure 



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0    = Regression Intercept 

1   - 5   = Coefficient of the independent variables to the  

Dependent variable 

µ   = Error term 

A-Priori Expectation  

Model I: 00,,, 35421    

Model II: 00,,, 35421    

Model III: 00,,, 35421  
 

 

Estimation Procedure 

Unit Root Test 

Most of time series have unit root as demonstrated by many studies including Nelson and Plosser (1982), Stock and 

Watson (1988) and Campbell and Peron (1991). Therefore, their means of variance of such time series are not 

independent of time. Conventional regression technique based on non-stationary time series produce spurious 

regression and statistic may simply indicate only correlated trends rather true relationship Granger and Newbold 

(1974). Spurious regression can be detected in regression model by low Durbin Watson and relatively moderate R
2
. 

Therefore, to distinguish between correlation that arises from share trend and one associated with an underlying 

causal relationship; we use both the Augmented Dickey fuller (Dickey and Fuller, 1979, 1981)  

ttt XX   1                                                                                                                 7                              

 

The null hypotheses for the ADFstatistic test are H0.Non stationary (unit root) and H0: Stationary respectively  

 

Co-integration 

To search for possible long run relationship amongst the variables, we employ the Johansen and Juselius (1990) 

approach. Thus, the study constructed a p-dimensional (4x1) vector auto regression model with Gaussian errors that 

can be expressed by its first differenced error correction form as 

ttktkttt YYYYY    1112211 .....                                              8 

Where Yt are the data series studied, 

t  is i. i. d, N(0,∑) i + -1 + A1+A1  + A2 + A3 + ……. + Ai for i = 1,2,3……..,k-1, П = I – A1 – A2 - ……-Ak.                                                                                                                                                                              

9     

The П matrix conveys information about the long term relationship among the Yt variables studied. Hence, testing 

the cointegration entails testing for the rank r of matrix П by examine whether the eigenvalues of П are significantly 

different from zero. 

Johansen and Juselius (1990) proposed two tests statistics to determine the number of cointegrating vectors (or the 

rank of П), namely the trace and the maximum eigen-value (-trace) is computed as; 

)1(
1 


n

rj jInTtrace                                                                                                 10                                                        

The trace tests the null hypothesis that “at most” r co-integration vector, with “more than” r vectors being the 

alternative hypothesis. The maximum eigenvalue test is given as: 

)1( 1max  rTIn                                                                                               11                            

It tests the null hypothesis of r co-integrating vectors against the alternative hypothesis of r + 1 co-integration 

vectors. In the equation (10) and (11), is the sample size and  is the largest canonical correlation. 

 

Granger Causality  

In case we do not find any evidence for co-integration among the variables, the specification of the Granger 

causality will be a vector autoregression (VAR) in the first difference form. However, if will find evidence of co-

integration, there is the need to augment the Granger-type causality test model with a one period lagged error term. 

This is a crucial step because as noted by Engel and Granger (1987). 



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34 
 

 XXYY
n

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

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                                                                     12 

and 

t

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                                                             13 

Error Correction Model (ECM) 

Co-integration is a prerequisite for the error correction mechanism. Since co-integration has been established, it is 

pertinent to proceed to the error correction model. 

 

4. Results and Discussion of Findings 

 

Table 1:  Presentation of Results 

Variable Coefficient Std Errs. T-Statistics Prob. 

CBDL 0.944815 0.320126 2.951387 0.0068 

DR -0.073630 0.329448 -0.223496 0.8250 

LIQR 0.070625 0.348065 0.202907 0.8408 

NBB -0.182644 0.060617 -3.013069 0.0059 

OPE -0.115707 0.105006 -1.101914 0.2810 

C 8.531951 9.269151 0.920467 0.3661 

R2 0.777896    

ADJ. R2 0.552630    

F-STATISTICS 4.337907    

F-PROB 0.000890    

Durbin-Watson stat 2.401563    

 

EXR 0.082146 0.024577 3.342392 0.0026 

INFR 0.033077 0.056301 0.587491 0.5621 

OPE -0.217520 0.076679 -2.836782 0.0089 

PEX -0.111390 0.060587 -1.838514 0.0779 

RGDP 0.401942 0.324098 1.240187 0.2264 

C 16.33847 4.380158 3.730109 0.0010 

R-squared 0.657973    

Adjusted R-squared 0.589567    

F-statistic 9.618715    

Prob(F-statistic) 0.000032    

    Durbin-Watson stat 1.059613    

FD -0.306369 0.371902 -0.823789 0.4178 

G_M2 0.737820 0.298887 2.468555 0.0208 

MPR -0.325075 0.664682 -0.489068 0.6291 

RINTR 0.074515 0.269627 0.276362 0.7845 

TBR 0.063271 0.496470 0.127442 0.8996 

C 9.164022 10.51041 0.871900 0.3916 

R-squared 0.335405    

Adjusted R-squared 0.202486    

F-statistic 2.523382    

Prob(F-statistic) 0.055642    

    Durbin-Watson stat 0.594821    

Source: Extracts from E-view  (2018) 

 

Model I examined the bank specific variables and commercial domestic credit in Nigeria, an  examination of the 

above table proved that the independent variables formulated in model I can explain 77.7 and 55.2 percent variation 



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on total commercial banks credit in Nigeria while the estimated regression model proved significant from the F-

statistics. Also the Durbin Watson statistics is greater than 2.0 but less than 2.5, this prove the presence of serial 

autocorrelation. The F-statistics and the F-probability proves that commercial bank deposit liability and liquidity are 

statistically significant while other variables in the model are statistically not significant. In ascertaining the 

direction of the relationship, the study found that commercial banks deposit liability and liquidity ratio have positive 

impact on commercial bank loans and advances while deposit rate, number of commercial bank branches and 

operational efficiency of the industry have negative impact on the dependent variable. The positive effect of the 

deposit liability confirm the a-priori expectation of the results and justifies various reforms formulated in the 

Nigerian banking sector to effectively intermediate between the deficits and surplus the economic unit such as the 

rural banking scheme in 1975, the universal banking scheme in 2001-2004 the banking sector consolidation and 

recapitalization in 2004/2005. However, the negative impact of liquidity reserve confirms the a-priori expectation of 

the results. According to Nwankwo (1998) there is inverse relationship between liquidity and earning assets of 

commercial banks. This is what Toby (2004) described as optimal liquidity and lending position. The negative 

impact of deposit rate, number of bank branches and operational efficiency is contrary to our expectations as the 

variables are expected to have a positive impact on the dependent variable. The negative impact could be traced to 

poor banking habits and high banking density as noted in Akani and Lucky (2018). 

Model II which examined the effect of macroeconomic variables on commercial bank credit found that the 

independent variables can explain 65.7 and 55.9 variations on total commercial bank credits within the period under 

study. This is justified by the validity of the F-statistics and probability. However, the Durbin Watson explains 

variation justifies the presence of serial autocorrelation in the model. Further, the coefficient of the independent 

variable which measures the direction of the relationship found that all the independent variables have positive 

relationship with the dependent variable except openness of the economy and public expenditure. However, 

Exchange rate, openness of the economy and public expenditure are statistically significant while inflation rate and 

real GDP are statistically not significant. The positive effect of the variables confirm various macroeconomic policy 

reforms such as financial sector deregulation with the objective of increasing the operational efficiency of business 

institutions. It also agreed with the findings of Akani and Onyema (2017). The negative of the variables is contrary 

to our expectation and could be traced to policies such as the treasury single account system and other 

macroeconomic instability. 

Model III which examined the effect of monetary policy variables on commercial banks loans and advances found 

that the independent variables can explain 33.5 and 20.2 percent while the F-statistics validates the model. The 

coefficient of the variables found that financial sector development and monetary policy rate negatively related to 

total commercial banks loans and advances while growth of money supply, real interest rate and Treasury bill rate 

positively relates to the dependent variable. The model found that growth of broad money supply is statistically 

significant while other variables in the model are statistically not significant. The positive effect of the variables 

confirms our a-priori expectation. The negative impact of financial sector development and monetary policy rate is 

contrary to our expectation and could be traced to monetary policy shocks. The above results enable us to test for 

stationary of the variables using the Augmented Dickey Fuller unit root test. 

 

Table 2: Unit Root Test Summary Results at First Difference 

Variable ADF Statistics Mackinnon Prob. Order Of Intr. 

1% 5% 10% 

CBC/GDP -4.241819 -3.689194 -2.971853 -2.625121  0.0026 1(1) 

DR -8.634975 -3.679322 -2.967767 -2.622989 0.0001 1(1) 

LIQR -1.429812 -3.679322 -2.986225 -2.622989 0.0001 1(1) 

NBB -9.109359 -3.699871 -2.976263 -2.627420  0.0000 1(1) 

OPE -6.935697 -3.808546 -2.971853 -2.625121  0.0000 1(1) 

CBDL -10.61917 -3.679322 -2.967767 -2.622989 0.0000 1(1) 

 

Unit Root Test Summary Results at First Difference 

CBC /GDP -5.111894 -3.752946 -2.998064 -2.638752 0.0000 1(1) 

INFR -5.818042 -3.679322 -2.967767 -2.622989 0.0000 1(1) 

OPE -6.358758 -3.699871 -2.976263 -2.627420 0.0000 1(1) 

PEX -5.972811 -3.699871 -2.976263 -2.627420 0.0000 1(1) 

RGDP -6.650857 -3.724070 -2.986225 -2.632604 0.0000 1(1) 



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EXR -7.098366 -3.679322 -2.967767 -2.622989 0.0000 1(1) 

CBC /GDP -4.241819 -3.689194 -2.971853 -2.638752 0.0000 1(1) 

FD -5.101366 -3.679322 -2.967767 -2.625121  0.0026 1(1) 

G_M2 -5.431477 -3.689194 -2.971853 -2.622989 0.0003 1(1) 

MPR -6.663459 -3.699871 -2.976263 -2.625121  0.0001 1(1) 

RINTR -8.036245 -3.699871 -2.976263 -2.627420 0.0000 1(1) 

TBR -6.055479 -3.689194 -2.971853 -2.627420 0.0000 1(1) 

Source: Extracts from E-view (2018) 

 

Having identified the presence of the serial autocorrelation, we test for unit root. From the table above, we found 

that all the variables are stationary at first difference which implies that the variables are integrated in the order of 

1(1). We accept alternate hypothesis, we therefore proceeds to co-integration test ascertain the presence of long run 

relationship or not 

 

Table 3: Johansen Co-Integration Test Results: Trace Statistics 

Hypothesized  

No. of CE(s) 

Eigen value Trace Statistics 0.05  

Critical Value 

Prob.** Decision 

None *  0.856928  136.3457  95.75366  0.0000 Reject H0 

At most 1 *  0.680575  79.95784  69.81889  0.0062 reject H0 

At most 2  0.517663  46.86213  47.85613  0.0618 reject H0 

At most 3  0.444551  25.71788  29.79707  0.1374 Accept H0 

At most 4  0.248740  8.666488  15.49471  0.3971 Accept H0 

At most 5  0.012758  0.372370  3.841466  0.5417 Accept H0 

Model II 

None *  0.814542  118.4784  95.75366 None * Reject H0 

At most 1  0.573135  69.61551  69.81889 At most 1 reject H0 

At most 2  0.497174  44.92817  47.85613 At most 2 reject H0 

At most 3  0.398347  24.99033  29.79707 At most 3 Accept  H0 

At most 4  0.273015  10.25617  15.49471 At most 4 Accept H0 

At most 5  0.034213  1.009532  3.841466 At most 5 Accept H0 

Model III 

None * 0.833949 130.4656 95.75366 0.0000 Reject H0 

At most 1 *  0.683583  78.39719  69.81889  0.0088 reject H0 

At most 2  0.584981  45.02707  47.85613  0.0900 reject H0 

At most 3  0.269891  19.52360  29.79707  0.4558 Accept H0 

At most 4  0.234638  10.40132  15.49471  0.2511 Accept H0 

At most 5  0.087219  2.646533  3.841466  0.1038 Accept H0 

Source: Extracts from E-view (2018) 

 

Using the Johansen co-integration test, the above table 3.1, the results found that there is one co-integrating equation 

in model I and model III but no co-integrating equation in model II. The presence of co-integrating equation in 

model I and III is expected and in line with a prior expectation and implies the presence of long run relationship 

between bank specific variables and Total commercial banks loans and advances and monetary policy variables and 

total commercial banks loans and advances. The absence of co-integrating equation in model II is contrary to our 

expectation and could be trade to macroeconomic challenges such as business cycle. The inability of the above 

result to give us the direction of  long run relationship enable us to test for normalized co-integration relationship.  

 

Table 4: Normalized Co-integrating Equation 

 

Model I   

CBC_GDP CBDL DR LIQR NBB OPE  

 1.000000 -0.207833  0.397391  4.038972  0.179184 -1.151830  

  (0.30775)  (0.31598)  (0.55580)  (0.06388)  (0.16727)  



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Model III 

  

CBC_GDP FD G_M2 MPR RINTR TBR  

 1.000000  4.858640 -6.101998 -0.573168  8.894031 -9.716535  

  (1.21036)  (1.33026)  (2.06855)  (1.00374)  (1.63751)  

Source: Extracts from E-view (2018) 

 

From model I, the study found that deposit liabilities and openness of the economy have negative long run 

relationship with the dependent variable while deposit rate, liquidity and number of bank branches have positive 

long run relationship. Model II found that exchange rate, inflation rate, public expenditure and real gross domestic 

products have negative relationship with the dependent variable while openness of the economy have positive 

impact on the dependent variable. 

It is evidenced in model III that growth of money supply, momentary policy rate and treasury bill rate have negative 

long run while financial sector development and real interest rate have positive long run relationship with total loans 

and advances of commercial banks.  

 

Table 5: Parsimonious Error Correction Results 

Model I 

Variable Coefficient Std. Error t-Statistic Prob.   

     C -0.141512 1.067818 -0.132524 0.8966 

D(CBC_GDP(-1)) 0.708665 0.322713 2.195958 0.0468 

D(CBDL(-1)) 0.488425 0.510527 0.956707 0.3562 

D(CBDL(-2)) 0.343939 0.633565 0.542863 0.5964 

D(CBDL(-3)) -0.273246 0.584257 -0.467681 0.6478 

D(DR(-1)) 0.047427 0.553806 0.085639 0.9331 

D(DR(-2)) 0.229004 0.440427 0.519959 0.6118 

D(DR(-3)) 0.405319 0.446344 0.908086 0.3804 

D(LIQR(-1)) -0.454314 0.461854 -0.983675 0.3432 

D(NBB(-1)) 0.076272 0.069359 1.099667 0.2914 

D(NBB(-2)) 0.009103 0.069192 0.131561 0.8973 

D(OPE(-1)) 0.097118 0.113525 0.855474 0.4078 

D(OPE(-2)) -0.060787 0.149747 -0.405934 0.6914 

ECM(-1) -0.562017 0.306053 -1.836339 0.0893 

     R-squared 0.442772 F-statistic 0.794598 

Adjusted R-squared -0.114456 Prob(F-statistic) 0.657690 

      Durbin-Watson stat 1.931567 

Model II 

C 2.043938 1.053284 1.940538 0.0727 

D(CBC_GDP(-1)) 0.562908 0.225122 2.500456 0.0254 

D(EXR(-1)) -0.038479 0.079060 -0.486711 0.6340 

D(EXR(-2)) -0.042698 0.075722 -0.563882 0.5818 

D(EXR(-3)) -0.143669 0.070481 -2.038403 0.0609 

D(INFR(-1)) -0.025638 0.048227 -0.531606 0.6033 

D(OPE(-2)) -0.059369 0.096083 -0.617901 0.5466 

D(OPE(-3)) -0.091805 0.111644 -0.822302 0.4247 

D(PEX(-1)) 0.171279 0.075279 2.275251 0.0391 

D(PEX(-2)) 0.040041 0.069050 0.579885 0.5712 

D(PEX(-3)) 0.057348 0.075812 0.756449 0.4619 

Model II   

CBC_GDP EXR INFR OPE PEX RGDP  

 1.000000 -0.123970 -0.375509  0.443366 -0.038397 -2.972428  

  (0.02303)  (0.05699)  (0.07302)  (0.05577)  (0.35682)  



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D(RGDP(-1)) -0.270962 0.206818 -1.310150 0.2112 

ECM(-1) -0.795299 0.252028 -3.155597 0.0070 

     R-squared 0.582549 F-statistic 1.628070 

Adjusted R-squared 0.224733 Prob(F-statistic) 0.190805 

     Durbin-Watson stat 1.934872 

Model III 

C 0.249340 0.584395 0.426663 0.6757 

D(CBC_GDP(-1)) 0.331146 0.171123 1.935138 0.0721 

D(FD) -0.763798 0.169172 -4.514913 0.0004 

D(FD(-1)) -0.154201 0.188692 -0.817211 0.4266 

D(FD(-2)) -0.112717 0.180436 -0.624694 0.5416 

D(G_M2) 0.301395 0.148708 2.026754 0.0608 

D(MPR) 0.006336 0.177273 0.035744 0.9720 

D(MPR(-1)) 0.309556 0.292876 1.056953 0.3073 

D(MPR(-2)) 0.246760 0.281118 0.877782 0.3939 

D(RINTR) -0.221470 0.188584 -1.174388 0.2585 

D(RINTR(-1)) -0.372632 0.211311 -1.763429 0.0982 

D(TBR(-2)) 0.500065 0.223421 2.238215 0.0408 

ECM(-1) -0.427213 0.127072 -3.361979 0.0043 

     
R-squared 0.700118 F-statistic 2.918309 

Adjusted R-squared 0.460213 Prob(F-statistic) 0.026596 

      Durbin-Watson stat 1.901181 

Source: Extracts from E-view (2018) 

 

From the error correction result, model I found a speed of adjustment of 56.2 percent, model II found a speed of 

adjustment of 79.5 percent while model III found a speed of adjustment of 42.7 percent. The independent variables 

show positive and negative impact of the variables on the dependent variables at various lags. However, the result 

shows that total commercial bank loans and advances is positive. 

 

Table 6: Granger Causality Test 

 

Model I 

 Null Hypothesis: Obs F-Statistic Prob.  

 CBDL does not Granger Cause CBC_GDP  29  2.15789 0.1375 

 CBC_GDP does not Granger Cause CBDL   1.62069 0.2187 

 DR does not Granger Cause CBC_GDP  29  1.30077 0.2908 

 CBC_GDP does not Granger Cause DR   0.41874 0.6626 

 LIQR does not Granger Cause CBC_GDP  29  1.46458 0.2511 

 CBC_GDP does not Granger Cause LIQR   2.10770 0.1435 

 NBB does not Granger Cause CBC_GDP  29  0.44604 0.6454 

 CBC_GDP does not Granger Cause NBB   4.22038 0.0269 

 OPE does not Granger Cause CBC_GDP  29  1.00600 0.3806 

 CBC_GDP does not Granger Cause OPE   0.74080 0.4873 

 

Model II 

 EXR does not Granger Cause CBC_GDP  29  1.23298 0.3092 

 CBC_GDP does not Granger Cause EXR   0.41550 0.6647 

 INFR does not Granger Cause CBC_GDP  29  0.61703 0.5479 

 CBC_GDP does not Granger Cause INFR   0.40108 0.6740 

 OPE does not Granger Cause CBC_GDP  29  1.05771 0.3629 

 CBC_GDP does not Granger Cause OPE   1.69627 0.2046 

 PEX does not Granger Cause CBC_GDP  29  4.51464 0.0217 



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 CBC_GDP does not Granger Cause PEX   0.38882 0.6820 

 RGDP does not Granger Cause CBC_GDP  29  0.06756 0.9348 

 CBC_GDP does not Granger Cause RGDP   1.89492 0.1721 

 

 

Model III 

 FD does not Granger Cause CBC_GDP  29  11.8474 0.0003 

 CBC_GDP does not Granger Cause FD   0.58076 0.5671 

 G_M2 does not Granger Cause CBC_GDP  29  5.28936 0.0125 

 CBC_GDP does not Granger Cause G_M2   0.78999 0.4653 

 MPR does not Granger Cause CBC_GDP  29  2.56373 0.0979 

 CBC_GDP does not Granger Cause MPR   0.25737 0.7752 

 RINTR does not Granger Cause CBC_GDP  29  0.08543 0.9184 

 CBC_GDP does not Granger Cause RINTR   0.51837 0.6020 

 TBR does not Granger Cause CBC_GDP  29  0.38331 0.6857 

 CBC_GDP does not Granger Cause TBR   0.35358 0.7058 

 

Source: Extracts from E-view (2018) 

 

Model I found that the variable have no causal relationship except a unidirectional relationship from commercial 

banks loans and advances to number of commercial banks branches. Model II found also that there is no causal 

relationship among the variable’s except a unidirectional relationship from public expenditure to commercial banks  

loans and advances while model III found a unidirectional relationship from financial development to financial loans 

and advances and a unidirectional relationship between growth of money supply to commercial banks loans and 

advances. Other variables in the model have no causal relationship. 

5. Conclusion 

Commercial banks remain dominant in the banking system in terms of their shares of total assets and deposit 

liabilities. Their total loans and advances, a major component of total credits to the private sector are still on the 

increase in spite of the major constraints posted by the government regulations, institutional constraints and other 

macro-economic factors. From the bank internal variable, deposit liability and number of commercial banks 

branches determine commercial banks loans and advances while other variables in the model does not determine 

commercial loans and advances. From model II, the study concludes that exchange rate, openness of the economy 

and public expenditure are strong determinants of commercial bank loans and advances while Real Gross Domestic 

Products and Inflation Rate does not determine bank loans and advances. Model III found that growth of money 

supply determine commercial bank loans and advances while other variables in the model does not determine 

commercial bank loans and advances. 

6. Recommendation 

There should be closer consultation and cooperation between commercial banks and the regulatory authorities so 

that the effect of regulatory measure on commercial banks will be taken into account at the stage of policy 

formulation andNigerian commercial banks should ensure good planning which encompasses budgeting, reviews 

and incentives. 

Banks should try as much as possible to strike a balance in their loan pricing decisions. This will help them to be 

able to cover cost associated with lending and at the same time, maintain good banking relationship with their 

borrowers and macroeconomic policies should be properly formulated to encourage bank lending. 

7. Policy Implication 

In view of the nexus between commercial banks credit to the domestic economy of Nigeria within the period under 

review and considering the strong relationship that exist between banks specific variables, monetary variables and 

macroeconomic variables and commercial banks credit to domestic to the economy. Therefore, there is need to 

strengthening and allow the interplay of regulatory cum supervisory framework to achieve the desire results.    

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