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                                            Australian Finance & Banking Review; Vol. 4, No. 1; 2020  
                                                                               ISSN 2576-1196   E-ISSN 2576-120X 

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

        18 
 

Deposit Money Bank Policy and Private Sector Funding: A Multi-Dimensional 
Study from Nigeria 

 
 

Zaagha Alexander Sulaiman 
Department of Banking and Finance 

Rivers State University, Port Harcourt, Nigeria 
E-mail: zaaghi75@gmail.com 

 
Murray Monday Ebike 

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

E-mail: fathermurraye@gmail.com 
 
Abstract 
This study empirically examined the effect of deposit money banks policy on private sector funding in Nigeria. Time series data 
was sourced from Central Bank of Nigeria Statistical Bulletin from 1985-2018. Credit to private sector, credit to core private 
sector and credit to small and medium scale enterprises was used as dependent variables while liquidity ratio and loan to deposit 
ratio was used as independent variables. Ordinary Least Square (OLS), Augmented Dickey Fuller Test, Johansen Co-integration 
test, normalized co-integrating equations, parsimonious vector error correction model and pair-wise causality tests were used to 
conduct the investigations and analysis. The empirical findings revealed that deposit money banks policy explains 40.8 percent 
variation on credit to core private sector, 28.1 percent and 58.9 percent of the variation in credit to core private sector and credit 
to small and medium scale enterprises sector.  The study conclude that deposit money banks policy has no significant 
relationship with credit to private sector and credit to core private sector but has significant relation with credit to small and 
medium scale enterprises sector. From the findings, the study recommends compliance to deposit money banks policies; this will 
enhance effective financial intermediation and increase funding of the private sector. There is also need for the regulatory 
authorities to harmonize the various deposit money banks policies with the objective of enhancing private sector funding. There 
is need to decentralize the operation of the deposit money banks in the urban cities. Policies should be formulated to extend the 
operation of the deposit money banks to the rural communities, this will enable the institutions to mobilize much deposit and 
increase credit to the private sector. 
 
Keywords: Deposit Money Banks Policy, Private Sector Funding, Liquidity Ratio, Loan to Deposit Ratio, Small and Medium 
Scale Enterprises    
 
1. Introduction  
The history of banking in Nigeria dates back to1892 when African Banking Corporation and bank of British West Africa now 
first bank was established. This means that banking business has existed for over one century in Nigeria (Olukayode and 
Somoye, 2018). Deposit money banks are empowered by law to undertake the business of lending and borrowing in the 
economy, the function bridge the savings and investment gap. This responsibility evolved over time and expanded to include 
investment management, maintenance of payments system, trade transactions, cards and e-payments. 

Banking activities are guided by policies directed toward achieving economic goals and enhance stability of the institutions. 
Liquidity policy gives information about the general liquidity shock absorption capacity of a bank. As a general rule, the higher 
the share of liquid assets in total assets, the higher the capacity to absorb liquidity shock. High value of this ratio can be 
interpreted as inefficiency, since liquid assets yield lower income liquidity bears high opportunity costs for the bank (Ogolo, 
2018). Increase on liquidity reserve reduces the earnings assets of commercial banks. This implies that increase liquidity policy 
can affect negatively deposit money banks credit to the private sector. 

Private sector actors are increasingly being recognized as a major driving force in enhancing economic growth and development. 
They drive economic growth through investment, employment and business creation, innovation and knowledge transfer, and 
other multiplier effects from their operations and activities. Ensuring that this growth is likely to contribute to long-term 
poverty reduction, however, requires private companies to include the poor as producers, suppliers, employees and consumers. 
Under the right circumstances, public-private partnerships that are based on the identification of complementary expertise and 
shared commercial and development interests are also an important tool that can harness the private sector’s contribution to such 

mailto:Zaaghi75@gmail.com
mailto:fathermurraye@gmail.com


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inclusive growth. While the importance of the private sector to economic growth has long been recognized, until very recently, 
private sector actors were not seen as development actors. As a result, they were typically treated as a secondary consideration in 
terms of potential vehicles of development financing and in terms of sources of ideas and input in development debates and 
policies, in comparison with the primary and more established group of actors: recipient governments and bilateral and 
multilateral development agencies (Eke, Chikulirim Eke and Inyang, 2015).  

Liquidity policy is more focused on bank’s sensitivity to selected types of funding such as deposits of households, enterprises 
and other financial institutions. It captures bank’s vulnerability related to these funding sources. Banks are able to meet its 
obligations in terms of funding if volume of liquid assets is high enough to cover volatile funding and if the value of this ratio is 
100 percent or more. Lower value indicates a bank’s increased sensitivity related to deposit withdrawals. 

Loan-to-deposit ratio policy was introduced for the purposes of curbing the incentives for excessive competition among 
domestic banks dependent upon wholesale funding to increase their business sizes before the global financial crisis, and 
improving liquidity conditions during crises (Lopez-Espinosa, Moreno, Rubia and Valderrama, 2012). Loan-deposit ratio 
policy  is effective as a macro prudential policy instrument; it reduces banks’ dependency on wholesale funding to thereby curb 
the interconnectedness among financial institutions, a systemic risk on the cross-sectional side, and also reduces the pro-
cyclicality of lending, a systemic risk on the time-series side. The policy is not only useful but also actually brings about ring-
fencing between retail and wholesale financing by encouraging banks to extend loans within the limits of their deposits. Bank 
loans shows pro-cyclicality in terms of the amounts of their supply and demand. During times of economic expansion, when 
loan demand is extremely high, a bank’s capability to mobilize funding in response to this need is a key factor determining the 
pro-cyclicality of its loans. 
 
Deregulation of financial sector in the last quarter of 1986 resulted in wide disparities in monetary policy targets with possible 
implications for commercial bank lending operations. Most studies focus on the effect of interest rate on economic growth 
(Oshikoya, 1992; Odhiambo, 2010) or on bank lending separately during regulation or deregulation periods (Amassoma et al. 
2011; Nwakama and Mbatogu, 2004; Owolabi, 2014). Others investigated the relationship between monetary policy 
instruments and Deposit Money Banks Loans and Advances (Ogolo, 2018 and Adeniyi et al. 2018) and the determinant and 
evidence of the impact of liquidity management on the performance of deposit money banks (Dhanuskodi, 2014; Alphonce, 
Silvanos, and Ziska, 52015; Daniel, 2017).While literature on the effect of interest rate on the economy and impact of LDR 
and bank liquidity on DMBs profitability is well documented in literature, the effect of deposit money banks policy on private 
sector funding is lacking. This study empirically examined the effect of deposit money banks policy on private sector funding in 
Nigeria. 

2. Literature Review 

Loan to Deposit Ratio 
The loan-to-deposit ratio regulation was introduced for the purposes of curbing the incentives for excessive competition among 
domestic banks dependent upon wholesale funding to increase their business sizes before the global financial crisis, and 
improving liquidity conditions during crises (Lopez-Espinosa, Moreno, Rubia and Valderrama, 2012). According to 
Dhanuskodi (2014), loan to deposit ratio is a useful instrument to determine bank liquidity, and by extension, it influences the 
profitability of banks. The regulation of loan to deposit ratio is basically an instrument for effective management of banks 
liquidity by limiting their loan size within the certain ratio of their deposits. During a period of economic expansion, however, 
this regulation is used to curb any expansion in lending (CGFS, 2012). 

As part of its monetary policy effort towards ensuring that DMBs increase its financial intermediation function, stem financial 
inclusion and increase lending to the real sector of the Nigerian economy, the CBN increase the loan to deposit ratio to 60 
percent. The apex bank in a bid to ensure compliance opine that banks that fail to meet the deadline of March ending 2019 will 
attract 50 percent levy of additional CRR of the lending shortfall of the target LTDR. The LTDR was further increase to 65 
percent in the last quarter of 2019 to facilitate robust investment and disbursement of credit to the real sector of the Nigerian 
economy that will bring about a sound and resilient financial intermediation system. 
 
Since the introduction of the loan to deposit ratio policy, the effects that the regulatory authorities intended have found banks’ 
wholesale funding on the decline and liquidity conditions have improved. Empirical analysis shows that the loan-to-deposit ratio 
regulation is also effective as a macro prudential policy instrument; it reduces banks’ dependency on wholesale funding to 
thereby curb the interconnectedness among financial institutions, a systemic risk on the cross-sectional side, and also reduces the 
pro-cyclicality of lending, a systemic risk on the time-series side. It should be noted, however, that the loan-to-deposit ratio 
regulation is a strong but not precise policy instrument that directly limits the ratio of deposits DMBs to loans, two core 



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business areas of banks, and may overlap with the Basel III liquidity regulations. In addition, the regulatory authorities and the 
central bank need to work in close cooperation when making changes in policies regarding this regulation, given that the loan-to-
deposit ratio regulation can affect banks’ intermediary role and the channels of monetary policy transmission (Bouvatier, López-
Villavicencio and Mignon, 2010). 
 
The loan-to-deposit ratio regulation is a macro prudential policy instrument designed to curb systemic risks. The regulation is 
not only useful but also actually brings about ring-fencing between retail and wholesale financing by encouraging banks to 
extend loans within the limits of their deposits. Given however that this regulation is a powerful monetary policy tool, that puts 
direct constraints on banks' core businesses of deposits and loans, some unintended consequences could be created. 

Bank loans shows pro-cyclicality in terms of the amounts of their supply and demand. In particular, during times of economic 
expansion, when loan demand is extremely high, a bank’s capability to mobilize funding in response to this need is a key factor 
determining the pro-cyclicality of its loans. The reason why Nigeria domestic banks were able to meet the heightened demand 
for loans in the run up to the global financial crisis was that, in addition to deposits, they were capable of mobilizing funds to 
meet this demand through wholesale funding. Since introduction of the loan-to-deposit ratio regulation, banks have moved 
funds out of their wholesale funding CDs and into corporate deposits to be able to comply with the regulation (Berger   and 
Udell, 2004). 

Loan-to-deposit ratio regulation could affect banks' function of financial intermediation by hindering their flexible use of 
wholesale funding as assets for bank lending. Facing restraints on their assets for lending, banks generally tend to first reduce 
their lending to SMEs whose credit ratings are relatively low. According to Jeong, (2009) bank lending to large corporations and 
households has continued to rise since introduction of the loan-to-deposit ratio regulation, whereas their lending to SMEs has 
stagnated or declined. 
 
Bank Liquidity Ratio 

Bank for International Settlements (2008) defined liquidity as the ability of bank to fund increases in assets and meet 
obligations as they come due, without incurring unacceptable losses. Liquidity risk arises from the fundamental role of banks in 
the maturity transformation of short-term deposits into long-term loans. The term liquidity risk includes two types of risk: 
funding liquidity risk and market liquidity risk. Funding liquidity risk is the risk that the bank will not be able to meet efficiently 
both expected and unexpected current and future cash flow and collateral needs without affecting either daily operations or the 
financial condition of the firm. Market liquidity risk is the risk that a bank cannot easily offset or eliminate a position at the 
market price because of inadequate market depth or market disruption. Liquidity risk can be measured by two main methods: 
liquidity gap and liquidity ratios. The liquidity gap is the difference between assets and liabilities at both present and future 
dates.  

Positive gap between assets and liabilities is equivalent to a deficit (Bessis, 2009). Liquidity ratios are various balance sheet ratios 
which should identify main liquidity trends. These ratios reflect the fact that bank should be sure that appropriate, low-cost 
funding is available in a short time. This might involve holding a portfolio of assets than can be easily sold (cash reserves, 
minimum required reserves or government securities), holding significant volumes of stable liabilities (especially deposits from 
retail depositors) or maintaining credit lines with other financial institutions. Various authors like More (2010); Praet (2009); 
Rychtárik (2009) provide various liquidity ratios.  Bank liquidity can be measured as follows: 

Liquid assets          
Total assets 
The liquidity ratio should give us information about the general liquidity shock absorption capacity of a bank. As a general rule, 
the higher the share of liquid assets in total assets, the higher the capacity to absorb liquidity shock, given that market liquidity is 
the same for all banks in the sample. Nevertheless, high value of this ratio may be also interpreted as inefficiency, since liquid 
assets yield lower income liquidity bears high opportunity costs for the bank. Thus it is necessary to optimize the relation 
between liquidity and profitability. 

Liquid assets  
Deposits + short term borrowing 

The liquidity ratio is more focused on the bank’s sensitivity to selected types of funding (we included deposits of households, 
enterprises and other financial institutions). The ratio should therefore capture the bank’s vulnerability related to these funding 
sources. The bank is able to meet its obligations in terms of funding (the volume of liquid assets is high enough to cover volatile 



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funding) if the value of this ratio is 100 % or more. Lower value indicates a bank’s increased sensitivity related to deposit  
withdrawals. 

Loans  
Total assets  
The ratio measures the share of loans in total assets. It indicates what percentage of the assets of the bank is tied up in illiquid 
loans. Therefore, the higher this ratio the less liquid the bank is. 

Loans  
Deposits + short term borrowing 

The last liquidity ratio relates illiquid assets with liquid liabilities. Its interpretation is the same as in case of ratio the higher this 
ratio the less liquid the bank is. Although liquidity problems of some banks during global financial crisis re-emphasized the fact 
that liquidity is very important for functioning of financial markets and the banking sector, an important gap still exists in the 
empirical literature about liquidity and its measuring.  

Private Sector Funding 
The private sector is said to be the engine of economic growth for a country, especially, for developing economies (William, 
Zehou and Hazimi, 2019). The private sector remains the nucleus that drives economic growth. Private sector funding (credit) 
is no doubt a driver of the real economy, particularly in developing economies like Nigeria where the financial markets are 
porous and near well developed to mobilize the needed resources to accelerate the desired level of economic development. The 
private sector is the part of the economy that is run by individuals and companies for profit and is not state controlled. 
Therefore, it encompasses all for-profit businesses that are not owned or operated by the government. According to the global 
economic report (2019), domestic credit to private sector by banks refers to financial resources provided to the private sector by 
other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of non-equity 
securities, and trade credits and other accounts receivable, that establish a claim for repayment. It involves the pros and cons 
through which individuals and statutory firms’ gains access to the availability of credit (fund) to finance and promote (drive) 
investment. Private sector funding involves credit extended by the banking and financial institutions to the private sector of the 
economy alone and basically include firms and households excluding loans disbursed to the public sector.  
 
Credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations 
(deposit taking corporations except central banks), such as through loans, purchases of non-equity securities, and trade credits 
and other accounts receivable, that establish a claim for repayment. Credit to the core private sector refers to credit from the 
commercial banks and other credit institutions to the preferred sectors of the economy.  This is measured as annual loans and 
advances from credit institutions in Nigeria to the real sectors of the economy. This study adopts the total credit disbursement 
to the core private sector as a measure through which monetary policy affects private sector funding in the Nigerian economy. 

Funding Small and Medium Scale Enterprises Sector in Nigeria  
In Nigeria, the national policy on micro, small and medium enterprises define small and medium scale enterprises along the lines 
of international criteria. The policy mainly uses the employment base and asset size to categorize firms into micro, small and 
medium. Accordingly, for small-scale enterprises, the employment base should be between 10 and 49 with an asset base of over 
N5 million but less than N50 million. Medium scale enterprises are those that employ between 50 and 199 workers, with an 
asset base of over N50 million but less than N500 million. Importantly, the assets admitted for these classifications exclude land 
and buildings. Also, in case of conflict of classification between employment and asset size, the policy gives pre-eminence to the 
number of employees over asset size. Bank credit refers to loans, advances and discounts of specific sums, which are normally 
with terms and other conditions available to individuals, small and medium sized business to start, grow or sustain any economic 
activity (John and Onwubiko, 2013).  

Credit to small and medium scale enterprises sector refers to credit disbursed to small and medium scale enterprises by the 
deposit money banks. It is measured as annual loans and advances from the financial institutions in Nigeria to small and medium 
scale enterprises. The central bank of Nigeria statistical bulletin reports credit to small and medium scale enterprises as 
percentage of total credits in the economy. 
 
Theoretical Review 
The Loanable Funds Theory  
The neo-classical or the loanable fund theory examines interest rate in terms of demand and supply of loanble funds or credit. 
According to this theory, the rate of interest is the price of credit which is determined by the demand and supply for lonable 



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funds. In the words of Prof Lerner in Jhingan (2005); it is the price which equates the supply of credit, or saving plus the net 
increase in the amount of money in a period, to the demand for credit, or investment plus net hoarding in the period. The 
demand for loanble fund has primarily three source; government, businessmen and consumers who need them for purpose of 
investment, hoarding and consumption. The government borrows funds for constructing public works or for war preparations. 
The businessmen borrow for the purpose of capital goods and for starting investment projects. Such borrowings are interest 
elastic and depend mostly on the expected rate of profit as compared with the interest rates. The demand of loanable fund on 
the part of consumers is for the purchase of durable consumer goods like cars, houses etc. Individual borrowings are also interest 
elastic. The tendency to borrow is more at a lower rate of interest than at a higher rate. 

Loanable funds theory of interest rate determination views the level of interest in the financial market as resulting from the 
factors that affect the supply and demand of loanable funds (Saunders 2010).Interest rate in this theory is determined just like 
the demand and supply of goods is determined, supply of loanable funds increases as interest rate increases, other factors held 
constant. He goes further to explain that the demand for loanable funds is higher as interest rate fall, other factors held constant. 
Saunders (2010) identifies two factors among others causing demand curve for loanable funds to shift; economic conditions and 
the monetary expansion refers to the sum of money offered for lending and demanded by consumers and investors during a 
given period. The interest rate model is determined by the interaction between potential borrowers and potential savers.  
 
Credit Rationing Theory 
Access to credit is explained by credit rationing theory (Stiglitz and Weiss, 1981; Bester, 1985; Cressy, 1996; Baltensperger and 
Devinney, 1985). According to Stiglitz and Weiss (1981) credit rationing is said to occur when some borrowers receive a loan, 
while others do not. Credit rationing takes place at either financier level due to loan markets imperfection and information 
asymmetry or voluntarily by the borrowers (voluntary exclusion). At financier level, credit rationing occurs in a situation where 
demand for credit exceeds supply at the prevailing interest rate (Stiglitz and Weiss, 1981). There is scant literature on self-
rationing, however, in situations where credit rationing is voluntary, Arora (2014) described such borrowers as non-credit 
seekers due to personal, culture or social reasons or could be in the bracket of discouraged borrowers. Bester (1985) suggested 
that financiers may choose to reject some borrowers because of negative enticement effects. For example, for given collateral, an 
increase in the rate of interest causes adverse selection, since only borrowers with riskier investments will apply for a loan at a 
higher interest rate. Similarly, higher interest payments create an incentive for investors to choose projects with a higher 
probability of bankruptcy (Afonso and Aubyn, 1997, 1998; Matthews and Thompson, 2014). On the other hand, for a fixed 
rate of interest, an increase in collateral requirements may also result in a decline in the lender’s profits (Cressy, 1996). Stiglitz 
and Weiss (1981) showed that this happens if the more risk-averse borrowers, those that choose relatively safe investment 
projects, drop out of the market. According to Bester (1985) Andretti (1983), if financiers set collateral requirements and the 
rate of interest to screen investors' riskiness, then no credit rationing will occur at equilibrium. This is because increasing 
collateral requirements tends to result in adverse selection, even with risk-neutral investors (Bester, 1984a, 1985).  
 
Empirical Review  
Ajayi and Atanda (2012) investigated the impact of monetary policy instruments on banks performance between 1980 and 
2008. The study used Engle-granger two-step co-integration approach for its analysis. The result indicated that bank rate, 
inflation rate and exchange rate are credit enhancing variables, while liquidity ratio and cash reserves ratio exert negative impact 
on banks total credit. Although, it is only cash reserve ratio and exchange rate that are found to be significant at 5% critical 
value. The study found that monetary policy instruments are not significant to stimulate credit in the long-run, while banks total 
credit is more responsive to cash reserve ratio.  
 
Nto, Mbanasor and Osuala (2012) examined the influence of monetary policy variables on banks’ credit supply to SMEs in 
Nigeria. Time series data were collected on quarterly basis covering a period of 1995-2010 and were analyzed using Fully 
Modified Least Squares (FMOLS). The results indicated that policies on interest rate and liquidity ratio were negatively and 
positively significant to SMEs. The study recommends that government through CBN should strengthen existing policies on the 
monetary policy instruments so as to increase and stabilize credit supply to SMEs. 

Dhanuskodi (2014) examines the impact of Loan Deposit ratio on the profitability of Malaysian commercial banks for the 
period of 2009 to 2013 using all the 8 locally owned commercial banks in Malaysia. The study use loan deposit ratio of the 
banks as the independent variable and the dependent variable was profitability which measures through Return on Assets 
(ROA). Data were sourced mainly from the annual reports of the 8 banks. Ratio analysis along with descriptive, correlation 
analysis, paired T- test and regression analysis were used in the study. The result of the study indicated that there was a positive 
and non-significant impact of LDR on ROA in five banks (Bank 1, 2, 3, 4 and 8). Further, the study revealed that only one 
bank (Bank 5) had a negative and non-significant impact of LDR on ROA and bank 7 had positive and significant impact. 



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Hyunggeun, Hyunwoo and Dokyung  (2015) conduct an empirical analysis of the usefulness of the loan-to-deposit ratio using 
panel data regression model. The results of cross-sectional analysis indicate that as a bank’s share of wholesale funding declines, 

the indicator (ΔCoVaR value) of interconnectedness among financial institutions is reduced. Findings show that loan-to-deposit 
ratio regulation has served as an effective macro-prudential policy tool by weakening the interconnectedness among financial 
institutions and the pro-cyclicality of bank lending. Analysis of the loan-to-deposit ratio regulation’s effects on the monetary 
policy transmission channels suggests that, among the various transmission channels, the loan-to-deposit ratio influences the 
effectiveness of the bank lending channel, by changing bank conditions for asset management and funding. This implies that the 
supervisory authorities and the central bank, which are in charge of the loan-to-deposit ratio regulation, need to maintain a closer 
cooperative relationship. 

Alphonce,  Silvanos and Ziska  (2015) study was to find out the relationship between liquid assets and profitability of 
commercial banks in Zimbabwe. A quantitative correlation approach was adopted for the study in which testable hypotheses 
were formulated based on literature review findings. Eight years historical financial statements data relating to two periods; 2005 
to 2008 and 2009 to 2012 was collected from selected commercial banks in Zimbabwe. The study found that working capital 
was weakly related to profitability, while capitalization strongly influenced commercial bank profitability. An inverse relationship 
was found between the ratio of loans to deposits and commercial bank profitability. It was therefore concluded that the 
composition of current assets strongly influences commercial bank profitability. This study recommends that RBZ should 
monitor the capitalization levels of commercial banks and create policies to ensure growth and commercial banks should 
monitor the structure or composition of current assets in order to ensure profitability. 

Anigbogu, Okoli and Nwakoby (2015) investigated the effect of financial intermediation on small and medium enterprises 
performance in Nigeria between 1980-2013 using an econometric model of the Ordinary Least Square (OLS). Findings 
revealed that with the exception of bank interest rate to SMEs, all other variables namely bank lending rate to SMEs, exchange 
rate and monetary policy have a positive and significant influence on small and medium enterprises performance in Nigeria.  

Ovat (2016) examined the role played by commercial banks’ credit in facilitating the growth of SMEs in Nigeria. The study 
adopted co-integration and error correction mechanisms and based on the findings, exchange rate and lending rate are 
statistically significant to SMEs credit. Also, inflation rate was found to be significant but negative to SMEs credit. He opined 
that SMEs should be made to have easy access to credits from commercial banks. In order to achieve this, the monetary 
authority should ensure that the lending rate at which commercial banks lend to the SMEs is reduced to the barest minimum. 
More so, devaluation of the national currency should not be encouraged as devaluation makes the cost of imported raw materials 
and capital goods used by the SMEs very expensive and hence impedes their production, rather local sourcing of raw materials 
should be encouraged to reduce the pressure on exchange rate.  

D’Pola, and Touk, (2016) empirically examine the impact of commercial bank credit on the performance of Small and Medium 
Size Enterprises (SMEs) in Cameroon between 1980 and 2014 using Ordinary Least Square (OLS) method to estimate the 
multiple regression model. The study use SMEs output as approximated by wholesale and retail trade output as a component of 
the GDP. The results revealed that commercial bank credit and real interest rate have a negative and significant impact on the 
performance of SMEs in Cameroon.  
 
Sesay and Abdulai (2017) empirically investigate monetary policy effects on private sector investment in Sierra Leone. The study 
examines the rate at which changes in monetary policy in Sierra Leone has affected the behavior of private sector investments, 
theories and empirical studies are reviewed in a way to identify a suitable model for private sector investment for the period 
1980-2014 using recent econometric techniques (OLS, VECM, VAR). Results of the findings suggest that money supply and 
gross domestic saving exert positive and statistically significant effect on private sector investments whereas Treasury bill rate, 
inflation and gross domestic debt exert a negative effect. An important policy implication emerging from this study is to 
facilitate the establishment of financial institutions to increase credit delivery to the private sector so as to enhance private 
investment. 

João, Barroso and Gonzalez (2017) estimated the impact of reserve requirements (RR) on credit supply in Brazil exploring a 
large loan-level dataset. The authors used a difference-in-difference strategy, first in a long panel, then in a cross-section. In the 
first case, they estimate the average effect on credit supply of several changes in RR from 2008 to 2015 using a macro 
prudential policy index. In the second, they use the bank-specific regulatory change to estimate credit supply responses from (1) 
a countercyclical easing policy implemented to alleviate a credit crunch in the aftermath of the 2008 global crisis; and (2) from 
its related tightening, findings show evidence of a lending channel where more liquid banks mitigate RR policy. Exploring the 
two phases of countercyclical policy, they found that the easing impacted the lending channel on average two times more than 
the tightening. Foreign and small banks mitigate these effects and banks are prone to lend less to riskier firms. 



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Adeniyi1, Adeyemi, Salawudeen and  Fagbemi (2018) investigated the relationship that exists between monetary policy 
instruments and Deposit Money Banks Loans and Advances in Nigeria. Annual time series data covering a period from 1981-
2016 were used and the Toda and Yamamoto granger non-causality model was employ to examine the relationship existing 
between Deposit Money Banks loan and advances and monetary policy variables in Nigeria. Findings revealed that structural 
changes in monetary policy system exerted positive significant impact on loan and advances of Deposit Money Banks in Nigeria. 
Findings also revealed bidirectional relationship existing between MPR and loan and advances of Deposit Money Banks in 
Nigeria. Precisely, MPR proved to be a significant variable which causes Deposit Money Bank loans and advances in Nigeria. 
Other explanatory variables (broad money supply, liquidity ratio, inflation rate and cash reserve ratio does not granger cause loan 
and advances of Deposit Money Banks in Nigeria within the study period. It concluded that the structural change in monetary 
policy system and monetary policy rate have significant impact on loan and advances of deposit money banks in Nigeria. 
 
Ogolo (2018) empirically examined the effects of monetary policy on commercial banks’ lending to the real sector from 1981-
2014 using multiple regression models aided by Software Package for Social Sciences. The study modeled commercial banks 
credit to agricultural and manufacturing sector as the function of interest rate, monetary policy rate, treasury bill rate, exchange 
rate, broad money supply and liquidity ratio. The regression results from model one found that interest rate, monetary policy 
rate have positive relationship with commercial banks’ lending to the agricultural sector while Treasury bill rate, exchange rate, 
broad money supply and liquidity ratio have negative effect on the dependent variable. Model two found that interest rate, 
Treasury bill rate, exchange rate, broad money supply and liquidity ratio have negative effect on commercial banks’ lending to 
the manufacturing sector while monetary policy rate have positive relationship with the dependent variable.  
 
Courage and Leonard (2019) examined the effect of commercial bank sectorial credit to the manufacturing and agricultural sub-
sectors on economic growth in Nigeria with time series data from 1981 to 2015, using co-integration and error correction 
mechanism. The study specifies a three equation model to analyze the variables which include; real GDP, bank sectorial credit to 
manufacturing and agriculture subsectors, monetary policy rate, financial market development, sourced from CBN statistical 
bulletin and also the interaction variables, Empirical result revealed that commercial bank credit to the manufacturing and 
agricultural subsectors significantly affects economic growth in Nigeria both in the short run and in the long run. Furthermore, 
development of the financial sector enhances the growth effects of commercial banks credit to the manufacturing and 
agricultural subsectors of the economy.  

Ubesie1, Echekoba, Chris-Ejiogu and Ananwude  (2019) studied the effect of sectoral allocation of deposit money banks’ credit 
on the growth of the Nigerian real economy from 2008Q1 to 2017Q4 using the Ordinary Least Square (OLS) regression 
technique. Result of the analysis revealed that deposit money banks' credit to agriculture, industries, building and  construction 
and wholesale & retail trade have no significant effect on agricultural, industrial, building and  construction and wholesale & 
retail trade contribution to real gross domestic product. Deposit money banks should remove the disparagement that the 
agricultural sector is not viable, and lend to farmers with genuine needs for funds at a low interest rate. The Central Bank of 
Nigeria can equally play a critical role in reducing the interest rate charged by deposit money banks in extending credit to the 
economy by cutting down the monetary policy rate to a single digit compared to the current double digit of 14%. 
  
Olorunmade, Samuel, and Adewole, (2019) examined the determinant of private sector credit and its implication on economic 
growth in Nigeria. The fluctuation in the supply of money and credit i s the basic causal factor at work in cyclical process; 
when money supply falls, prices decrease, profit decrease, production activities become sluggish and production falls and 
when money supply expands, price rise, profit increase and the total output increases and finally growth takes place.Sample 
regression analysis were used to analyse data obtained from Central Bank of Nigeria statistical bulletin from 2000 to 2017. It 
was revealed in the determinant of credit supply that there was significant relationship between Total credits to private sector 
and money supply in Nigeria. The study also finds that there was significant relationship between private sector credit 
and economic growth in Nigeria.  
 
Literature Gap 
Dhanuskodi (2014) examines the impact of Loan Deposit ratio on the profitability of Malaysian commercial banks for the 
period of 2009 to 2013 using all the 8 locally owned commercial banks in Malaysia. Gap and Focus of Present Study: The 
above study is a foreign study and does not capture the effect of DMBs monetary policy on private sector funding. The study 
only captures the impact of monetary policy instrument (LDR) on banks profitability. The present study will be carried out in 
Nigeria and focus on the effect of DMBs policy on private sector funding. Daniel (2017) examined the evidence of the impact 
of liquidity management on the performance of deposit money banks. 24 banks were surveyed which constitute the entire 
deposit money banking industry in Nigeria between 1986 and 2011. Secondary data were collected and analyzed using SPSS. 
Gap and Focus of Present Study: The above though carried out in Nigeria; it does not capture the impact of DMBs monetary 



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25 
                         

policy on private sector funding. The study only examined the effect of liquidity management on the performance of deposit 
money banks. This present study empirically focused on the effect of deposits money banks policy on private sector funding in 
Nigeria.   

3. Methodology 
This study used ex-post facto quasi-experimental research design to examine the effect of deposit money banks policy on private 
sector funding in Nigeria. This study employed secondary data sourced mainly from the Central Bank of Nigeria (CBN) 
statistical bulletin from 1985 to 2018.  
Model Specification  
The study models are specified below: 

CPS = α + β1BLR + β2LTDR + et                                                                                                               1 

CCPS = α + β1BLR + β2LTDR + et                                                                                                                                    2 

SMES = α + β1BLR + β2LTDR + et                                                                              3 

Where: 
CPS      = Credit to the Private Sector 
CCPS             = Core Credit to the Private Sector  
CSMES = Credit to Small and Medium Scale Enterprises Sector 
BLR  = Bank Liquidity ratio 
LTDR     = Loan to Deposit Ratio 
Et                     =         Error Term  
 
Techniques of Data Analysis  
The main tool of analysis is the Ordinary Least Squares (OLS) using the multiple regression method for a period of 34 years, 
annual data covering 1985– 2018. Statistical evaluation of the global utility of the analytical model, so as to determine the 
reliability of the results obtained were carried out using the coefficient of correlation (r) of the regression, the coefficient of 
determination (r2), the student T-test and F-test. 

(i) Coefficient of Determination (r2) Test: This measure the explanatory power of the independent variables on the 
dependent variables.R2 gives the proportion or percentage of the total variation in the dependent variable Y that is 
accounted for by the single explanatory variable X. The higher the R2 value the better. For example, to determine 
the proportion of monetary policy to private sector funding in our model, we used the coefficient of 
determination. The coefficient of determination varies between 0.0 and 1.0. A coefficient of determination says 
0.20 means that 20% of changes in the dependent variable are explained by the independent variable(s). 
Therefore, we shall use the R2 to determine the extent to which variation in DMBs policy variables are explained 
by variations in private sector funding variables over the periods covered in this study. 

(ii) Correlation Co-Efficient (R): This measures the degree of the relationship between two variables x and y in a 
regression equation. That is, it tries to establish the nature and magnitude of the relationship when two variables 
are been analyzed. Thus correlation co-efficient show whether two variables are positively or negatively correlated. 
That is, it takes the value ranging from – 1, to + 1. 

(iii) F-Test: This measures the overall significance. The extent to which the statistic of the coefficient of 
determination is statistically significant is measured by the F-test. The F-test can be done using the F-statistic or 
by the probability estimate. We use the F-statistic estimate for this analysis.  

(iv) Student T-test: measures the individual statistical significance of the estimated independent variables. This is a 
test of significance used to test the significance of regression coefficients (Gujurati, 2003). Generally speaking, the 
test of significance approach is one of the methods used to test statistical hypothesis. A test of significance is a 
procedure by sample results are used to verify the truth or falsity of a null hypothesis (Ho) at 5% level of 
significance.  



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(v) Durbin Watson Statistics: This measures the collinearity and autocorrelation between the variables in the time 
series. It is expected that a ratio of close to 2.00 is not auto correlated while ratio above 2.00 assumed the 
presence of autocorrelation.  

(vi) Regression coefficient: This measures the extent in which the independent variables affect the dependent variables 
in the study. 

(vii) Probability ratio: It measures also the extent in which the independent variables can explain change to the 
dependent variables given a percentage level of significant. 

 
Stationarity (Unit Root) Tests 
Stationary test therefore checks for the stationarity of the variables used in the models. If stationary at level, then it is integrated 
of order zeroi, 1(0). Thus, test for stationarity is also called test for integration. It is also called unit root test. Stationarity 
denotes the non-existence of unit root. We shall therefore subject all the variables to unit root test using the augmented Dickey 
Fuller (ADF) test specified in Gujarati (2004) as follows. 

Etyiyy t

m

i
tt ++++= −

−
− 1

1
121 

                                                                                         
4 

Where:  

ty   = change time t 

1− ty  = the lagged value of the dependent variables  

t   = White noise error term  

If in the above  =0, then we conclude that there is a unit root. Otherwise there is no unit root, meaning that it is stationary. 
The choice of lag will be determined by Akaike information criteria. 
Co-integration Test (The Johansen' Test) 
It has already been warned that the regression of a non-stationary time series on another non stationary time series may lead to a 
spurious regression. If the residual is found to be stationary at level, we conclude that the variables are co-integrated and as such 
has long-run relationship exists among them. 

tijt

j

i

iit

i

i

tOt CPSCPSwCPS 1

11

 +++= −

=

−

=

                                                                     5 

tijt

j

i

iit

i

i

tOt CCPSCCPSwCCPS 1

11

 +++= −

=

−

=

                                                           6 

tijt

j

i

iit

i

i

tOt SMEsSMEswSMEs 1

11

 +++= −

=

−

=

                                                             7 

Granger Causality Test 
Causality means the impact of one variable on another, in other-words; causality is when an independent variable causes changes 
in a dependent variable. The pair-wise granger causality test is mathematically expressed as:  

111

1

11

1

uxYxY t

x
n

i

t

y
n

i

ot ++ −

=

−

=

                                                                         8 

and  

1
V

1y
xxdp1

n

1i

1Yt
y
1

dp
n

1i
o

dp
t

x +
−


=

−
=

+                                                                                 9 



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Where xt and yt are the variables to be tested white ut and vt are the white noise disturbance terms. The null 

hypothesis 011 == yy dp , for all I’s is tested against the alternative hypothesis 01 x  and .01 ydp if the co-efficient 

of 
x

1 are statistically significant but that of ydp1  are not, then x causes y. If the reverse is true then y causes x. however, 

where both co-efficient of 
x

1 and 
ydp1 are significant then causality is bi – directional. 

Vector Error Correction (VEC) Technique 
The presence of co-integrating relationship forms the basis of the use of Vector Error Correction Model. E-views econometric 
software is used for data analysis, implement vector Auto-regression (VAR)- based co-integration tests using the methodology 
developed by Johansen (1991,1995). The non-standard critical values are taken from (Osterward, 1992). 

4. Results and Discussion of Findings  
Table 1: Short Term Regression Results 

DMBs Policy and Credit To Private Sector  DMBs Policy and Credit To Core Private Sector 
Variable  Coefficient  t-test  Prob. Variable  Coefficient  t-test  Prob. 

LTDR -0.067112 -0.927541 0.3608 LTDR 0.058381 1.613331 0.1171 

BLR -0.395088 -1.713754 0.0966 BLR -0.077273 -0.677874 0.5030 

C 23.33372 3.595452 0.0011 C 5.842477 1.662310 0.1069 

R2 0.109700   R2 0.811046   

Adj R2 0.052261   Adj R2 0.792150   

F-Stat 1.909860   F-Stat 42.92288   

F-Prob 0.165126   F-Prob 0.000000   

DW 0.218049   DW 0.557335   

Source: Extract from E-view 9.0  

The results of the short-run estimation are presented in table 1 above along with the corresponding diagnostic tests in tables 
below. To find out how well the model fits a set of observations, the R2 indicates that 10 percent and 81 percent of the variation 
in credit to private sector and core credit to the private sector is explained within the model. Nonetheless, the R2 cannot 
determine whether the coefficient estimates and predictions are biased, hence further assessment of the residuals is necessary.  
From the results it could also be deduced that loan to deposit ratio and bank liquidity ratio have negative effect on credit to 
private sector. Furthermore, from the results it could be deduced that loan to deposit ratio has positive effect while bank 
liquidity ratio has negative effect on credit to core credit to private sector. 

 
Table 2: Unit Root Test 

DMBs Policy and Credit To Private Sector  DMBs Policy and Credit To Core Private Sector 
Variable  ADF 5% Prob. Variable  ADF 5% Prob. 

CPS -4.639432 -2.960411 0.0000 CCPS -9.705692 -2.960411 0.0000 

LTDR -4.216364 -2.954021 0.0000 LTDR -4.216364 -2.954021 0.0000 

BLR -7.235884 -2.967767 0.0000 BLR -7.235884 -2.967767 0.0000 

Source: Extract from E-view 9.0 (2020) 

The time series properties of the variables used in the analysis was investigated using Augmented Dickey-Fuller test. The test was 
run with specification of trend and intercept in the model. The ADF statistics for the test are presented in the table 2 above. It 
can be seen from the table above that the unit root test results, using the ADF unit root test suggest that all series are stationary 
at order I (1) because they become stationary after being differenced once. Therefore, the Engle and Granger (1987) can be 
employed. 

Table 3:  Co-integration Test 

Hypothesized 
No. of CE(s) 

 

Eigenvalue Trace 
Statistic 

 

0.05 
Critical 

Prob.** Hypothesized 
No. of CE(s) 

 

Eigenvalue Trace 
Statistic 

 

0.05 
Critical 

Prob.** 



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Value 
 

Value 
 

DMBs Policy and Credit To Private Sector  DMBs Policy and Credit To Core Private Sector 
None *  0.543161  25.06957  21.13162  0.0132 None *  0.719047  79.69140  47.85613  0.0000 

At most 1  0.319038  12.29597  14.26460  0.1000 At most 1 *  0.576459  39.06521  29.79707  0.0032 
At most 2  0.034507  1.123715  3.841466  0.2891 At most 2  0.288596  11.57389  15.49471  0.1785 

Source: Extract from E-view 9.0  

From table 3 above the results of the Johansen co-integration test show that we adopt the null hypotheses of no co-integrating 
equation at the 5% level of significance. This implies that, there is no linear combination of the variables that are stationary in 
the long run and also confirms the existence of a long-run relationship between monetary policy variables and credit to private 
sector and credit to core private sector. 

Table 4: Error Correction Model 

DMBs Policy and Credit To Private Sector   DMBs Policy and Credit To Core Private Sector 
Variable  Coefficient  t-test  Prob. Variable  Coefficient  t-test  Prob. 

C 0.564197 1.407637 0.1754 D(CCPS(-1))   0.131719 1.275962 0.2202 

D(CPS(-1)) 0.279625 1.206350 0.2425 D(CCPS(-2))   0.148021 0.262307 0.7964 

D(CPS(-2)) -0.415204 -1.849144 0.0801 D(CCPS(-3))  -0.764760 0.335028 0.7420 

D(CPS(-3)) -0.057267 -0.242831 0.8107 D(LTDR(-1))  -0.033402 -1.360830 0.1924 

D(LTDR(-1)) 0.011215 0.310737 0.7594 D(LTDR(-2))   0.071733 -0.657523 0.5202 

D(LTDR(-2)) 0.009938 0.294156 0.7718 D(LTDR(-3))   0.001541 1.491720 0.1552 

D(LTDR(-3)) 0.078231 2.279508 0.0344 D(BLR(-1))  -0.094156 0.028357 0.9777 

D(BLR(-1)) 0.049728 0.468390 0.6448 D(BLR(-2))   0.055822 -0.690108 0.5000 

D(BLR(-2)) -0.124536 -1.172818 0.2554 D(BLR(-3))  -0.076869 0.353759 0.7281 

D(BLR(-3)) -0.034666 -0.355103 0.7264 ECM(-1)   0.379914 -0.576592 0.5722 

ECM(-1) -0.031462 -0.385968 0.7038     

R2 0.408549   R2   0.281129    

Adj R2 0.097260   Adj R2   0.102954    

F-Stat 1.312440   F-Stat   0.481317    

F-prob 0.292124   F-prob   0.905265    

DW 1.757685   DW   2.025376    

Source: Extract from E-view 9.0 

    The corresponding sign of Error Correction Term (ECT) is negative for the models but not significant. This means that 
there is a long run causality running from independent variables to the dependent variable. The negative sign of (ECT) indicates 
a move back towards equilibrium following a shock to the system in the previous year.  The R2 from the models proved that the 
independent variables can explain 40 and 28 percent changes on the dependent variables. The models are statistically not 
significant from the value of f-statistics and probability. However, the ECM coefficient indicates that the models can adjust at 
the speed of 3 and 37 percent annually. The coefficient of the variables defines the effect of the independent variables on the 
dependent variables at various lags. 

Table 5: Granger Causality Test 

Null Hypothesis Obs F-Statistic Prob.  Null Hypothesis Obs F-Statistic Prob.  

DMBs Policy and Credit To Private Sector  DMBs Policy and Credit To Core Private Sector 
 LTDR does not Granger Cause 
CPS 32  0.13568 0.8737 

 LTDR does not Granger Cause 
CCPS 

32 
 0.14328 0.8672 

 CPS does not Granger Cause 
LTDR 32  0.17254 0.8424 

 CCPS does not Granger Cause 
LTDR 

32 
 0.08213 0.9214 

 BLR does not Granger Cause CPS 32  0.42941 0.6553  BLR does not Granger Cause CCPS 32  1.13195 0.3372 
 CPS does not Granger Cause BLR   1.28036 0.2943  CCPS does not Granger Cause BLR 32  1.62170 0.2162 

Source: Extract from E-view 9.0  



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Pair wise causality tests were run on the models with an optimal lag of 2. The results are presented in table 5 above. The 
researcher’s interest here is to establish the direction of causality between the independent variables and the dependent variables 
from 1985-2018. The models shows that there is no causality running from the dependent variables to independent variables 
and vice versa.  

Table 6: Short Term Regression Results  

Deposit Money Banks Policy and Credit to Small and Medium Scale Enterprises Sector 

Variable                                   Coefficient                                        t-test                                       Prob. 

LTDR -0.118892 -1.268007 0.2145 

BLR 0.770832 2.813736 0.0086 

C -1.099937 -0.133762 0.8945 

R2 0.241691   

Adj R2 0.191137   

F-Stat 4.780853   

F-Prob 0.015765   

DW 
                                        
0.525461 

  

Source: Extract from E-view 9.0 
The results of the short-run estimation are presented in table 5 above along with the corresponding diagnostic tests in tables 
below. To find out how well the model fits a set of observations, the R2 indicates that 24.1 percent of the variation in DMBs 
funding to small and medium scale enterprises sector is explained within the model. Nonetheless, the R2 cannot determine 
whether the coefficient estimates and predictions are biased, hence further assessment of the residuals necessary. From the result 
it could be deduced that loan to deposit ratio has negative effect while bank liquidity ratio has positive effect on credit to small 
and medium scale enterprises sector. 

Table 7: Unit Root Test 

Deposit Money Banks Policy and Credit to Small and Medium Scale Enterprises Sector 

Variable  ADF 5% Prob. 

SMEs -5.921945 -2.976263 0.0000 
LTDR -4.739563 -2.967767 0.0007 
BLR -4.216364 -2.954021 0.0023 

Source: Extract from E-view 9.0  

The time series properties of the variables used in the analysis was investigated using Augmented Dickey-Fuller test. The test was 
run with specification of trend and intercept in the model. The ADF statistics for the test are presented in the table above. It can 
be seen from the table above that the unit root test results, using the ADF unit root test suggest that all series are I (1) because 
they become stationary after being differenced once. Therefore, the Engle and Granger (1987) can be employed. 

Table 8: Co-integration Test  

Hypothesized 

No. of CE(s) 
 

Eigenvalue Trace 

Statistic 
 

0.05 

Critical Value 
 

Prob.** 

None *  0.496177  37.62841  29.79707  0.0051 
At most 1 *  0.361479  17.06252  15.49471  0.0288 
At most 2  0.113212  3.604479  3.841466  0.0576 

     

Source: Extract from E-view 9.0  

From table 8, the results of the Johansen co-integration test show that we adopt the null hypotheses of no co-integrating 
equation at the 5% level of significance. This implies that, there is no linear combination of the variables that are stationary in 
the long run and also confirms the existence of a long-run relationship between DMBs policy variables and credit to small and 
medium scale enterprises sector in Nigeria. 

 



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Table 9: Error Correction Model 

Deposit Money Banks Policy and Credit to Small and Medium Scale Enterprises Sector 

Variable  Coefficient  t-test                                             Prob. 

C -17.29070  0.022741 0.9822 
D(SMES(-1)) -0.043787  2.963148 0.0110 
D(SMES(-2)) -0.139630  2.114837 0.0543 
D(SMES(-3)) -0.285653  2.233214 0.0437 
D(LTDR(-1)) -0.020892  2.512611 0.0260 
D(LTDR(-2))  0.208784 -1.581128 0.1379 
D(LTDR(-3)) -0.038395 -2.065428 0.0594 
D(BLR(-1)) -0.410439  1.121600 0.2823 

BLR(-2)  0.108664  3.350254 0.0052 
BLR(-3)  0.805284  2.700478 0.0182 
ECM(-1) -0.428681  0.618165 0.5471 

R2  0.589935    
Adj R2  0.348721    
F-Stat  2.445688    
F-prob  0.050320    
DW  1.580857    

Source: Extract from E-view 9.0  
The corresponding sign of Error Correction Term (ECT) is negative but not significant. This means that there is a long run 
causality running from independent variables to the dependent variable. The negative sign of (ECT) indicates a move back 
towards equilibrium following a shock to the system in the previous year.  The R2 from the model proved that the variables can 
explain 58.9 percent changes on the dependent variables. The models are statistically significant from the value of f-statistics and 
probability. However, the ECM coefficient indicates that the models can adjust at the speed of 42.8 percent annually. The 
coefficient of the variables defines the effect of the independent variables on the dependent variables at various lags. 

Table 10: Granger Causality Test 

Source: Extract from E-view 9.0  

Pair wise causality tests were run on the models with an optimal lag of 2. The results are presented in table 10 above. The 
researcher’s interest here is to establish the direction of causality between the dependent variables and the independent variables 
from 1985-2018. The models shows that there is no causality from the dependent variables to independent variables and vice 
versa.  

Discussion of findings  

In model 1, the estimated regression model from result of the vector error correction result in table 4 the relationship between 
deposit money bank rates policy and credit to private sector is moderate and not significant. This is because of an R2 of 
0.408549 meaning that the model explains approximately 40.8 percent of the total variations in the credit to private sector. The 
error correction model shows a negative value of -0.031462 which is appropriate and is significant. This means that 3 percent of 
the deviation from long run equilibrium relationship in the credit to private sector is corrected every year since credit to private 
sector is estimated annually. Some of the values of the coefficient of independent variables, that is deposit money bank policy 
rates are positive and also proved negative at various lags.   

The negative findings of the study confirm the findings of Ogolo (2019) who found that interest rate, Treasury bill rate, 
exchange rate, broad money supply and liquidity ratio have negative effect on commercial banks’ lending to the manufacturing 

Null Hypothesis Obs F-Statistic Prob.  

 LTDR does not Granger Cause SMES 32  0.96925 0.3932 
 SMES does not Granger Cause LTDR 32  1.71470 0.2005 
 BLR does not Granger Cause SMES 32  1.43567 0.2569 
 SMES does not Granger Cause BLR 32  0.86750 0.4323 
    
 
   



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sector.  Ajayi and Atanda (2012) that liquidity ratio and cash reserve ratio exert negative impact on banks total credit and Jegede 
(2014) who finds that liquidity ratio and money supply exert negative effect on commercial bank’s loans and advances. Daniel 
(2017) opine that the correlation results reveal positive impacts between return on equity and liquidity management variables: 
liquidity and cash reserve ratios, whereas loan to deposit ratio shows negative impact. Also, Alphonce, et al. (2015) opine that 
the negative impact implies that an inverse relationship exists between the ratio of loans to deposits and a bank’s profitability. 
 
From Model 2, the estimated regression model from result of the vector error correction model in table 4 the relationship 
between deposit money bank policy rates and credit to core private sector is moderate and not significant. This is because of an 
R2 of 0.281129 meaning that the model explains approximately 28.1 percent of the total variations in the credit to core private 
sector. The error correction model shows a positive value of 0.379914 which is appropriate and is significant. This means that 
37 percent of the deviation from long run equilibrium relationship in the credit to core private sector is corrected every year 
since credit to core private sector is estimated annually. Some of the values of the coefficient of independent variables that is 
DMBs policy rates are positive and also proved negative at various lags.   

The positive findings confirm the findings of Dhanuskodi (2015) that there was a positive and non-significant impact of LDR 
on ROA in five banks out of the 8 studied banks. It also confirms the findings of Atemnkenf and Josep (2006), cited in Husain 
and Abdullah (2008), whose findings illustrated a positive correlation between the loan to deposit ratio and bank’s profitability. 
Conversely, Daniel (2017) opine that the correlation results reveal positive impacts between return on equity and liquidity 
management variables (liquidity and cash reserve ratios). The positive findings of the study also confirm the findings of 
Anigbogu, Okoli and Nwakoby (2015) that with the exception of bank interest rate to SMEs, all other variables namely bank 
lending rate to SMEs, exchange rate and monetary policy have a positive and significant influence on small and medium 
enterprises performance in Nigeria. Dada (2014) opines that commercial banks credit to SMEs and the saving and time deposit 
of commercial banks exert a positive and significant influence on SMEs and Suleyman (2013) that money supply has a strong 
effect for manufacturing sector credit volume.  

The negative findings of the study confirm the findings of Ajayi and Atanda (2012) that liquidity ratio and cash reserve ratio 
exert negative impact on banks total credit and Jegede (2014) who finds that liquidity ratio and money supply exert negative 
effect on commercial bank’s loans and advances.  
 
In model 3, it is evidence that the estimated regression model from result of the vector error correction result in table 9 that the 
relationship between deposit money banks policy rate and credit to small and medium scale enterprises sector is high and 
significant. This is because of an R2 of 0.589935 meaning that the model explains approximately 58.9 percent of the total 
variations in the credit to small and medium scale enterprises sector. It is also evidence that the error correction model shows a 
negative value of -0.428681 which is appropriate and is significant. This means that 42 percent of the deviation from long run 
equilibrium relationship in the credit to small and medium scale enterprises sector is corrected every year since credit to small 
and medium scale enterprises sector is estimated annually. Some of the values of the coefficient of independent variables, that is 
deposit money bank policy rates are positive and also proved negative at various lags.   

The positive effect of the variables as shown confirm the a-priori expectation of the study and validates the objectives of 
monetary policy. The findings also confirm the findings of Akambi and Ajagbe (2012) that increase in interest rate will leads to 
a decrease in the lending rate while liquidity ratio and cash ratio were statistically significant to the profit of the selected banks. 
Van den Heuvel (2015) that monetary policy affects bank lending through two channels, Djiogap and Ngomsi (2012) that 
bank’s capital to asset ratio, long-term liabilities, GDP growth and its size were statistically significant.  Olokoyo (2011); 
Adelegan (2018) predictor variables (volume of deposits, investment portfolio, foreign exchange, and GDP) were statistically 
significant and portrayed a positive relationship with commercial bank lending.  Malede (2014) that these explanatory variables 
greatly influenced banks’ lending decisions compared to deposit and cash required reserve which was insignificant. Nto et al, 
(2012) study finds policies on interest rate and liquidity ratio were negatively and positively significant to SMEs and 
recommend that CBN should stabilize the supply of credit to SMEs by strengthening policies on monetary policy instruments. 
The negative and positive findings confirm the study of Daniel (2017) that the empirical analysis show that there is a significant 
relationship between liquidity management and the performance of Deposit Money Banks in Nigeria and opine that the 
correlation results reveal positive impacts between return on equity and liquidity management variables: liquidity and cash reserve 
ratios, whereas loan to deposit ratio shows negative impact. Also, Alphonce, et al. (2015) stress that the negative impact implies 
that an inverse relationship exists between the ratio of loans to deposits and a bank’s profitability. 
 
 

 



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5. Conclusion  

Empirical findings proved that the F*- cal = 1.312440 < F*- tab = 2.24 at 5% n=31 is statistically not significant which is 
supported with a probability value of 0.292124 > 0.05 at 5% is significant, we therefore reject the alternate hypothesis, that is 

β1-β2 (deposit money bank policy rates) is statistically not significant with credit to private sector in Nigeria. Therefore, we 
conclude that there is no significant relationship between deposit money bank policy rates and credit to private sector in Nigeria. 

It was proved that, the F*- cal = 0.481317< F*- tab = 2.24 at 5% n=31 is statistically not significant which is supported with 

a probability value of 0.905265> 0.05 at 5% is significant, we therefore reject the alternate hypothesis, that is β1-β2 (deposit 
money bank policy rates) is not statistically significant with credit to core private sector in Nigeria. Therefore, we conclude that 
there is no significant relationship between deposit money policy bank rates and credit to core private sector in Nigeria.  

The study  found that, the F*- cal =2.445688 > F*- tab = 2.24 at 5% n=31 is statistically significant which is supported with 

a probability value of 0.050320< 0.05 at 5% is significant, we therefore reject the null   hypothesis, that is β1-β2 (deposit 
money bank policy rates) is statistically significant with credit to small and medium scale enterprises sector in Nigeria. 
Therefore, we conclude that there is significant relationship between deposit policy bank rates and credit to small and medium 
scale enterprises sector in Nigeria. 

6. Recommendations  

The study recommends compliance to deposit money banks policies, this will enhance effective financial intermediation and 
increase credit to private sector. There is also needs for the regulatory authorities to harmonize the various deposit money banks 
policies with the objective of enhancing private sector funding.  

▪ There is need to decentralize the operation of the deposit money banks in the urban cities. Policies should be 
formulated to extend operation of the deposit money banks to the rural communities, this will enable the financial 
institutions to mobilize the much needed deposit and increase credit to the private sector. 

▪ The study recommends that central bank should reduce deposit money banks policy rates. This is because changes in 
interest rates and bank credits may lead to changes in the real sector through investment and influence of aggregates 
demand.  
 

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