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Asian Finance & Banking Review; Vol. 4, No. 1; 2020 
ISSN 2576-1161    E-ISSN 2576-1188 

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

 

     24 
 

 

Money Supply and Private Sector Funding in Nigeria: A Multi-Variant Study  
 
 

Zaagha Alexander Sulaiman  
Department of Banking and Finance 

Rivers State University, Port Harcourt, Nigeria 
E-mail: Zaaghi75@gmail.com 

Abstract 
This study examined the effect of money supply on private sector funding in Nigeria. The purpose of the study was to examine 
the extent to which monetary policy affect 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 sector was used as dependent variables while narrow money supply, broad money supply, large money supply, 
private sector demand deposit 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 money supply explains 
82.1 percent variation on credit to core private sector, 85.2 percent and 23.4 percent of the variation in credit to private sector 
and credit to small and medium scale enterprises sector. The study conclude that money supply has significant relationship with 
credit to private sector, credit to core private sector and credit to small and medium scale enterprises sector. From the findings, 
the study recommends that Central Bank of Nigeria should induce the variations of the amount of money changes through the 
nominal interest rates. That the monetary authorities should ensure adequate quantity of money supply that positively affect private 
sector funding in Nigeria.  
 
Keywords: Money Supply, Private Sector Funding, Private Sector Demand Deposits, Money Supply, Small and Medium 
Scale Enterprises.  

 
1. Introduction  
There are four sectors of the economy as formulated in the nation’s income accounting model and shown in the circular flow of 
income and products. These are the government (public), private sector, the household and the external sector. The private sector 
plays significant role in every economy. It drives growth, create jobs and pay the taxes that finance services and investment. In 
Nigeria the private sector generates 90 per cent of jobs, funds 60 per cent of all investments and provides more than 80 per cent 
of government revenues (Somoye, and Iio, 2009). The scale and diversity of the private sector operating in Nigeria strongly 
influences overall financial flows into the economy (Onoh, 2007). Private investors’ investment strategy is based on maximizing 
risk-adjusted returns. The goal is not to invest in the highest returning asset, but rather to invest in well-compensated risks. 

To achieve the above through the private sector, government plays a central role in formulating financial policies that 
facilities easy access/source of capital. It needs to provide good policies and strong financial and institutional framework to ensure 
that private sector can thrive and the benefits of growth reach all citizens. The majority of constraints to growth identified by the 
private sector are directly linked to government policies and actions. Government’s policy and legislative decisions determine 
private sector funding. Private sector funding is determined by government monetary policy, financial sector reforms and direct 
funding from the government (Sesay, and Abdulai, 2017).  

The pro-cyclical relationship between monetary policies and private sector funding can be illustrated through the credit 
channel which states that monetary policy works by affecting bank assets (loans) as well as banks’ liabilities (deposits). The key 
point is that monetary policy besides shifting the supply of deposits also shifts the supply of bank loans. For instance, an 
expansionary monetary policy that increases bank reserves and bank deposits increase the quantity of bank loans available. Where 
many borrowers are dependent on bank loans to finance their business activities, this increase in bank loans will cause a rise in 
investment (and also consumer) spending, leading ultimately to an increase in aggregate output. When monetary policy tightens, 
the reduction in available bank reserves forces banks to create fewer reservable deposits, banks must then either replace the lost 
reservable deposits with non-reservable liabilities, or shrink their assets, such as loans and securities, in order to keep total assets 
in line with the reduced volume of liabilities. 
 

The inadequacy of credit disbursement to small and medium scale businesses, firms, households and especially the very 
poor and the controversy surrounding the nature, direction and magnitude of relationship between private sector funding through 



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credit disbursement and poverty reduction/output growth within the monetary policy environment gave rise to the increasing 
desire for this investigation. It is observed that creating unhindered access to a wide range of credit services/products will not only 
enhances efficient financial intermediation prospect but will also significantly contribute to poverty reduction and output growth 
through the window of increased productivity, employment and enhance economic growth.  

The effect of   monetary policy has well been examined; however, there are three strands of studies on the effect of 
monetary policy. The first strands focused on the effect of monetary policy on economic growth (Adefeso and Mobolaji, 2010; 
Adofu, Abula and Audu, 2010; Amassoma, Nwosa and Olaiya, 2011, Nasko, 2016, Karimo and Ogbonna, 2017). The second 
strand focused on the effect of monetary policy on banking sector performance (Alper and Anbar, 2011; Enyioko, 2012; Okoye, 
and Eze, 2013, Udeh, 2015, Ogolo and Tamunotonye, 2019, Ayub and Seyed, 2016 and Jegede, 2014) while the third strand 
focused on the effect of monetary policy and capital market performance (Akani, 2017, Lawal, et al. 2017, Iddrisu, et. al 2017 
and Echekoba, 2018).These studies failed to establish the effect of monetary policy on private sector funding. From the above 
problems and knowledge gap, this study examined the effect of money supply on private sector funding in Nigeria. 

2. Literature Review 
Money Supply  
Monetary Policy refers to the specific/deliberate actions taken by the Central Bank to regulate the value, supply and cost of money 
in the economy with a view to achieving Government’s macroeconomic objectives. The objectives of monetary policy vary amongst 
various countries.  While the objective of monetary policy is predicated on achieving price stability in a country, other countries 
seeks to achieve price stability and other diverse macroeconomic objectives. The Central Bank of Nigeria, like other central banks 
in developing countries, achieves the monetary policy objective via the volume of money supply. The total volume (stock) of 
money in circulation among the public at a particular point in time is called money supply. Money supply is the entire stock of 
currency and other liquid instruments in circulation in an economy at a particular time. Money supply can include cash, coins, and 
balances held in checking and savings account, and other near money substitutes. Economists are of the view that an indebt analysis 
of money supply remains a key veritable instrument towards understanding macroeconomic paradigm and a tonic that guides 
macroeconomic policy. 

Money supply is generally classified as M0, M1, M2 and M3, based on the type and size of the account in which the 
instrument is kept. For example, M0 and M1 are often referred to as narrow money and include coins and notes that are in 
circulation and other money equivalents that can be converted easily to cash. M2 includes M1 and, in addition, short-term time 
deposits in banks and other money market funds. M3 includes M2 in addition to long-term deposits. Notably, the classifications 
differ amongst countries as each country may tend to use different classifications. Money supply depicts the interplay of different 
types of liquidity each type of money has in the economy. The crux of money supply is that it shows the different level of liquidity 
or spendability.  

The effect of money supply on the economy is pertinent as increase in the supply (stock) of money will lowers interest 
rates, which in turn, will stem investment and enhance access to credit by private sector players, small and medium scale enterprises, 
consumers and firms, thereby stimulating investment spending. Off-course, increase money supply that lowers interest rate will 
mean that businesses and individuals will increase consumption, increase production and investment drive and stem economic 
boom. The increased business activity raises the demand for labor. The reverse is the case if invariably money supply falls or when 
its growth rate declines. 
 

In Nigeria, the Central Bank defines money supply as comprising narrow and broad money. The definition of narrow 
money (M1) includes currency in circulation with non-bank public and demand deposits or current accounts in the banks. The 
broad money (M2) includes narrow money plus savings and time deposits, as well as foreign denominated deposits. The broad 
money measures the total volume of money supply in the economy. Thus, excess money supply (or liquidity) may arise in the 
economy when the amount of broad money is over and above the level of total output in the economy (CBN 2006).  

Notably, the raising or high level of money supply presently remains pivotal to the regulatory authority and policy drivers 
and requires adequate steps aim at regulating and controlling it frequently. The form of money supply called M0 is defined as the 
non-bank sectors holdings of notes and coins. It is calculated by subtracting the notes and coins held by banks from the total 
quantity of Risks bank notes and coins in circulation. Nnnana (2003) stressed that the broad measure of money supply 1 and 2 
includes M1 plus quasi money – i.e. the quantum of savings and time deposits of the public and private sectors with the banking 
system. An increase in the money supply is frequently assumed to positively affect stock prices and credit disbursement. When 
money stock grows, it stimulates the economy which leads to greater credit being available to firms and private sector to expand 
production, investment and then increases sale resulting in increased earnings for firms. This results in better dividend payments 
for firms leading to an increase in the price of stocks. However, money supply can also be negatively associated to stock prices. To 



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illustrate this argument, we first go through the link between money supply and inflation, since the expansion of the money supply 
is positively related to inflation in the economy which would increase the nominal risk free rate (Fama, 1981). This increase in 
the nominal risk free rate will lead to a rise in the discount rate which leads to a fall in return. Economists argue that inflation is a 
strictly a monetary phenomenon and occurs when the rate of growth of the money supply is higher than the growth rate of the 
economy (Chimobi and Igwe, 2010). 
 

The supply of bank money is inherently determined by banks in combination with their customers and owners. Easily 
reproducible micro-and macroeconomic accounting shows that when firms or households take loans from banks new deposits are 
created, when loans are repaid these deposits are destroyed, the two claims cancel out. Interest payments from non-banks to banks 
reduce aggregate deposits and increase bank equity by the same amount. Banks dividends to non-banks or bank expenses towards 
non-banks reduce bank equity and increase aggregate deposits by the same amount. When banks purchase assets from non-banks 
deposits are created in same amount.  

There are only two necessary conditions for the previously stated conditions to hold, first, that non-bank cash holding 
does not increase, and second, that banks use central bank money for settlement, both are observable. Le Bourva (1992) stated 
there are two opposing views concerning the supply of bank money. On the one hand the Quantity Theorists and Keynes believe 
the quantity to be fixed independently by the banking system, on the other, the Banking School and Wicksell believe that banks 
do not set a quantity but a price for money, interest rates. Keynes in this respect regards to his views presented in his General 
Theory, and not changing views expressed thereafter. How the Banking School views the behavior of banks is an analogy to the 
described supply of central bank money, but much harder to prove since the banking system consists of many banks. Noteworthy 
is that a central bank can force central bank money and bank money into the economy through quantitative easing even without 
the consent of banks.  
 
Private Sector Funding 
The private sector is said to be the engine of economic growth for a country, especially, for developing economies (William et al. 
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.  
 

Private sector funding entails the ways and means by which private firms and households (individuals) readily have access 
to fund to finance their investment and promote economic growth. It involves the pros and cons through which individuals and 
statutory firms’ gains access to the availability of credit (fund) to finance and promote their investment drive. 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 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. Financial resources by way of credit extension are essential lubricants 
that oil the wheels upon which the economy strives. It enables the funding of new investments and allows individuals to buy houses, 
cars, and make other investment plans. Though, excessive credit usually leads to financial crises as witness in the 2008 - 2009 
global financial crises but, in essence, credit availability remains the hallmark for the promotion of investment and economic 
development. As a vital engine for economic growth in developing economies, the private sector relies on the financial sector as a 
source of funds in advancing growth (Katusiime, 2018).  
 

According to global economy report, if the banking industry credit to the private sector is about 70 percent of GDP and 
more, then the country has a relatively well developed financial system. However, in developed and advanced economies the amount 
(rate) of credit to the private sector can hover above 200 percent of GDP. Conversely, in some developing and poor countries 
(economies), the amount of credit disbursed to the private sector could be less than 15 percent of GDP. Thus, private sector 
funding remains a financial bane in poor economies as this constitutes major challenges confronting private sector investment and 
economic growth. Assefa (2014) opined that these countries, firms and households essentially do not have access to credit for 
investment and various purchases. The private sector represents the productive sector of the economy and should be fueled with 
sufficient funds so as to enhance the growth of the sector (Abdullahi, 2014). 
 
Funding Small and Medium Scale Enterprises Sector in Nigeria  



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In Nigeria, the national policy on micro, small and medium enterprises define SMEs 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). A widespread concern is that, the deposit money banks attitude towards the subsector; which supposed to be the major 
source of funding to small and medium sized businesses are not providing enough aids and therefore limiting the potentials that 
could be taped from the subsector. The deposit money banks in their mode of operations most of the time call for more sure form 
of financial security, if they are to grant credit facility to small or medium sized business that need funds for business activities. 
However, due to the nature of small and medium sized businesses, in most cases, they tend not meeting up the requirements for 
the granting of the facilities. This has become a major challenge to the small and medium sized business operations in Nigeria. 
Robinson and Victor (2015) assert that most SMEs growth was hindered as a result of inability to access fund from financial 
institutions. 

Theoretical Review 
Money View 
The theory commonly tag money view is predicated on the notion that reductions in the volume of outside money will cause the 
real rates of return; this in turn reduces investment because fewer profitable projects are available at higher required rates of return 
(Cecchetti, 1995). This is a movement along a fixed marginal efficiency of investment schedule. (Cecchetti, 1995) opine that the 
less substitutable outside money is for other assets the larger the interest rate changes. In fact, there is no reason to distinguish any 
of the “other” assets in investors’ portfolios. In terms of the simple portfolio model, the money view implies that the shift in assets 
prices for all of the assets excluding outside money are equal. Major implication of this traditional model of monetary policy 
transmission centers on the incidence of decline in investment. Since there are no externalities or market imperfections, it is only 
the least socially productive projects that go unfunded.  

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 funds. 
In the words of Prof Lerner in Jhingan (1992); 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 scooters, 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 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) describes 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 



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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  
Suleyman (2013) examined the monetary policies of the Central Bank of the Republic of Turkey on SMEs credit between 2003-
2011. Autoregressive Moving Average (ARMA) test and VAR estimation models were use. Results show that money supply has 
a strong effect for manufacturing sector credit volume. Also, result shows that increase in the credit volume of large enterprises 
does not have any effect on the credit volume for SMEs. On the contrary, as credit volume of SMEs increases, credit volume of 
large enterprises decreases, which reveals a reverse causality between credit volume tendencies of different size firms.  

Olukayode and Somoye, (2013) evaluates the impact of finance on entrepreneurship growth in Nigeria using endogenous 
growth framework, the results showed that finance and interest rate, significantly impacted on entrepreneurship in Nigeria. They 
argued that the formulation of effective macroeconomic policy targeted to entrepreneurship financing and growth is necessary and 
also, monetary authorities should intervene indirectly by reducing Monetary Policy Rates (MPR) which will directly reduce the 
transaction costs of funds to industrial sectors. 

Tsenkwo and Longdu’ut (2013) examined the relationship between Monetary Policy Rate (MPR) and Banking Rates: 
Evidence from Regression and Multivariate Causality Analysis. The study used descriptive statistics and econometrics analysis to 
subject the raw data from secondary source to series of refining like Unit Root Test, Ordinary Least Square Test, Stability Test, 
and Granger causality test. These tests were conducted, using Granger causality test, to know the direction of their relationships 
and how they are caused. The finding revealed that almost all the variables, with the exception of bank savings rate, exhibit a strong 
sign of co-moving in the long run with the tendency of converging. The research revealed that there exists unidirectional causality 
between monetary policy rate and bank lending rate; bank lending rate and bank savings rate. And there exist a bi-directional 
causality between monetary policy rate and bank savings rate. 
 

Otalu, Aladesanmi and Mary (2014) assessed the impact of monetary policy on the deposit money banks performance 
in Nigeria, and in their study, the interest rate and money supply, liquidity ratio and the cash reserve ratio were used as proxy for 
monetary policy. The study used regression analysis to examine the relationship between monetary policy and bank performance 
in Nigeria. The results of the diagnostic test showed that credit creation of commercial banks is significantly being influenced by 
the interest rate, money supply, liquidity ratio and the cash reserve. Precisely, money supply and cash reserve ratio appeared to have 
statistically influenced deposit money banks’ credit creation.  
 

Jegede (2014) empirically examined the effect of monetary policy on commercial bank lending in Nigeria between 1988 
and 2008, using macroeconomic time series variables of exchange rate, interest rate, liquidity ratio, money supply, and commercial 
bank loan and Advances. The study employs Vector Error Correction Mechanism of Ordinary Least Square econometric technique 
as the estimation method. Findings indicate that there exists a long run relationship among the variables in the model. The study 
specifically revealed that exchange rate and interest rate significantly influenced commercial banks’ lending, while liquidity ratio 
and money supply exert negative effect on commercial banks’ loan and advances. The study concludes that monetary policy 
instruments are not effective to stimulate commercial bank loans and advances in the long-run, while banks’ total credit is more 
responsive to cash reserve ratio and recommends that monetary authority should make efforts to develop indirect monetary 
instruments and exercise appropriate control over the monetary sector. 
 

Imoughele and Ismaila (2014) employed Co-integration and Error Correction Modelling (ECM) techniques to 
investigate the impact of commercial bank credit on Nigeria's SMEs between 1986 and 2012.The results revealed that SMEs and 
selected macroeconomic variables included in the model have a long run relationship with SMEs output. The study also reveals 
that savings time deposit and exchange rate have significant impact on SMEs output in Nigeria. The study also showed that interest 
rate has adverse effect on SMEs output. 



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Ayub and Seyed (2016) in their study examine the relationship existing between monetary policy and bank lending 
behavior and the influence of bank specific features on this relationship in the banks listed on the 8 Tehran Stock Exchange. The 
study used Iran’s bank loan aggregated series and bank’s size and capital structure data. The study used the growth rate of M2 as 
the indicators of Irans’ monetary policy. Using Vector error correction model (VECM) and quarterly data for the period 2007: 
Q1 to 2014: Q4. The results showed a bidirectional causal link between M2 and banks’ lending behavior trading on the Tehran 
Stock Exchange. It was also observed that the banks' capital structure as one of the banks specific feature variables have a negative 
impact on bank lending behavior in accepted banks in Tehran Stock Exchange. 

 
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.   

Echekoba and Ubesie (2018) did an assessment of financial deepening on the growth of Nigerian -economy 1990-2016 
using ordinary least square regression (OLS). The main objective of this study is to evaluate the effect of private sector credit, 
money supply and market capitalization on economic growth in Nigeria. Findings showed that the three independent variables of 
the study all have significant effect on Nigerian financial deepening. It was therefore recommended that policies aimed to reduce 
the high incidence of non performing credits to ensure that private sector credits are channel to the real sector of the economy. 
The monetary authorities should implement policies that increase the flow of investible funds and improves the capacity of banks 
to extend credit to the economy as this will make broad money supply and private sector, to significantly impact on economic 
growth in Nigeria. 
 

Adeniyi et al. (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. Hence, the study recommended that monetary 
authority should formulate policies that will stabilize interest rate so as to boost the investors’ confidence. 

 
Ogolo & Tamunotonye (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 the manufacturing sector while monetary policy rate have positive relationship with the dependent variable. They 
recommend that monetary policy should be harmonized with bank lending objectives to enhance commercial banks’ lending to 
the real sector of the economy. Commercial banks should develop policies of managing the negative effect of monetary policy 
variables on its lending. 
 

William, Zehou, and Hazimi (2019) investigated the factors that influence domestic credit to the private sector in 
Ghana. The study uses the Johansen cointegration and vector auto-regression model to analyze panel data spanning the period 
from 1961 to 2016. Findings from the study revealed that though there is no long-run association among the variables, there exist 
significant short-run relationship between domestic credit to the private sector, broad money and gross capital formation. Further 
diagnostic tests showed that gross capital formation Granger causes both domestic credit to the private sector and broad money, 
and domestic credit to the private sector Granger-causes broad money. They concluded that money supply and gross capital 



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formation are necessary factors to address in the quest for developing the financial strength of domestic banks in providing credit 
facilities to the private sector for economic growth. 

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 is 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 analyses 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 sec tor 
and money supply in Nigeria. The study also finds that there was significant relationship between private sector credit 
and economic growth in Nigeria. They recommend that there should be persistence increase of money supply to Nigerian 
economy in order to increase the flow of credit to the real sector of the Nigerian economy, financial institutions should 
distribute more credit to the real sector for productive purposes in order to increase Gross domestic product.  
 
Literature Gap 
Sesay and Abdulai (2018) examined 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. Gap and Focus of Present Study:  The above study is a foreign study and does not capture 
the effect of monetary policy on private sector funding, further the study only examined how monetary policy affect bank behavior 
of private investment. The present study will be carried out in Nigeria and focus on the effect of money supply on private sector 
funding in Nigeria. 

William, Zehou, and Hazimi (2019) investigate the factors that influence domestic credit to the private sector in Ghana. 
The study uses the Johansen cointegration and vector auto-regression model to analyze panel data spanning the period from 1961 
to 2016. Gap and Focus of Present Study: The above study is a foreign study and does not capture the effect of money supply on 
private sector funding. Furthermore, the study only examines the relationship between domestic credit to the private sector, broad 
money and gross capital formation. The present study will be carried out in Nigeria and focus on the effect of money supply on 
private sector funding in Nigeria. 

3. Methodology 
This study used ex-post facto quasi-experimental research design to examine the effect of money supply on private sector funding 
in Nigeria. This study employed secondary data sourced mainly from the Central Bank of Nigeria (CBN) statistical bulletin.  

Model Specification  
The study models are specified below: 

CPS = α + β1M1 + β2M2 + β3M3 + β4PSDD + et                                                                                 1 

CCPS = α + β1M1 + β2M2 + β3M3 + β4PSDD + et                                                                                 2 

SMES = α + β1M1 + β2M2 + β3M3 + β4PSDD + 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 
M1  = Narrow Money Supply 
M2  = Broad Money Supply 
M3  = Large Money Supply 
PSDD  = Private Sector Demand Deposit 
 
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. 



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(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 monetary 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.  

(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 zero i.e. 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) 



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32 
                         
 

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 

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 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 

Money Supply and Credit to Private Sector Money supply and Credit to Core Private Sector 
Variable                   Coefficient         t-test  Prob. Variable  Coefficient       t-test      Prob. 

M2 0.000729 1.141947 0.2628 M2 0.000753 1.060002 0.2979 
M3 -0.000298 -1.400662 0.1719 M3 -0.000538 -2.273573 0.0306 
MI 0.001500 0.375437 0.7101 MI 0.002805 0.630556 0.5333 

PSDD -0.001213 -0.281657 0.7802 PSDD -0.002128 -0.443908 0.6604 
C 7.201741 10.96838 0.0000 C 7.289496 9.973294 0.0000 
R2 0.852010   R2 0.821964   

Adj R2 0.831598   Adj R2 0.797407   
F-Stat 41.73995   F-Stat 33.47208   
F-Prob 0.000000   F-Prob 0.000000   
DW 0.731266   DW    0.826918   

Source: Extract from E-view 9.0  
The regression result on the two sectors have high R-square indicating 85.2 percent variation on credit to private sector and 82.1 
percent variation on credit to core private sector. The models are statistically significant by the value of f-statistics and probability. 



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The coefficient of the variables shows that M2 and M1 have positive effect on credit to private sector and credit to core private 
sector while M3 and private sector demand deposit have negative effect on the dependent variables. 

Table 2: Unit Root Test 

Money Supply and Credit to Private Sector Money supply and Credit to Core Private Sector 
 

Money supply and Credit to Core Private Sector 

Variable  ADF 5% Prob. Variable  ADF 5% Prob. 

CPS -6.020482 -2.967767 0.0000 CCPS -9.705692 -2.960411 0.0000 
M2 -14.14646 -2.960411 0.0000 M2 5.025161 -2.621007 0.0000 
M3 -6.804960 -2.963972 0.0000 M3 -6.804960 -2.963972 0.0000 
M1 -5.634717 -2.986225 0.0001 M1 -5.634717 -2.986225 0.0000 

PSDD -5.455320 -2.986225 0.0002 PSDD 5.455320 -2.986225 0.0000 

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 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 I (1) because 
they become stationary after being differenced once. Therefore, the Engle and Granger (1987) can be employed. 

Table 3: Cointegration Test 

Hypothesized 
No. of CE(s) 

 

Eigenvalue Trace 
Statistic 

 

0.05 
Critical Value 

 

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

 

Eigenvalue Trace 
Statistic 

 

0.05 
Critical Value 

 

Prob
.** 

Money supply and Credit to Private Sector Money supply and Credit to Core Private Sector  

None *  0.977828 
 212.134

2 
 69.8188

9  0.0000 None *  0.979759 
 232.080

1  69.81889  0.0000 

At most 1 *  0.782885 
 90.2493

0 
 47.8561

3  0.0000 At most 1 *  0.810103 
 107.278

5  47.85613  0.0000 

At most 2 *  0.455343 
 41.3747

6 
 29.7970

7  0.0015 At most 2 *  0.601650 
 54.1177

7  29.79707  0.0000 

At most 3 *  0.332401 
 21.9316

0 
 15.4947

1  0.0047 At most 3 *  0.457598 
 24.6641

9  15.49471  0.0016 

At most 4 *  0.245195 
 9.00146

1 
 3.84146

6  0.0027 At most 4 *  0.147011 
 5.08827

4  3.841466  0.0241 

Source: Extract from E-view 9.0  
From table 3, the results of the Johansen co-integration test show that we adopt the alternate hypothesis of four co-integrating 
equations at the 5% level of significance. This implies that, there is linear combination of the variables that are stationary in the 
long run and also confirms the existence of a long-run relationship between money supply variables and credit to private sector 
and credit to core private sector. 

Table 4: Error Correction Model 

Money supply and Credit to Private Sector Money supply and Credit to Core Private Sector  

Variable  Coefficient                t-test  Prob. Variable  Coefficient                 t-test  Prob. 

C 0.103264 0.206326 0.8397 C 0.011006 0.022741 0.9822 

D(CPS(-1)) 0.970388 2.208733 0.0458 D(CCPS(-1)) 1.056725 2.963148 0.0110 

D(CPS(-2)) 0.583686 1.681936 0.1164 D(CCPS(-2)) 0.806126 2.114837 0.0543 

D(CPS(-3)) 0.450984 1.492833 0.1593 D(CCPS(-3)) 0.828710 2.233214 0.0437 

D(M2(-1)) 0.002332 1.923614 0.0766 D(M2(-1)) 0.003711 2.512611 0.0260 

D(M2(-2)) -0.001110 -1.176311 0.2606 D(M2(-2)) -0.001354 -1.581128 0.1379 

B D(M2(-3)) -0.004425 -2.089293 0.0569 D(M2(-3)) -0.004596 -2.065428 0.0594 

D(M3(-1)) 0.000771 1.602640 0.1330 D(M3(-1)) 0.000585 1.121600 0.2823 

D(M3(-2)) 0.001049 1.627066 0.1277 D(M3(-2)) 0.002438 3.350254 0.0052 

D(M3(-3)) 0.001307 3.425904 0.0045 D(M3(-3)) 0.001227 2.700478 0.0182 

D(MI(-1)) -0.000529 -0.044607 0.9651 D(MI(-1)) 0.006965 0.618165 0.5471 

D(MI(-2)) -0.036868 -2.403970 0.0318 D(MI(-2)) -0.038119 -2.688275 0.0186  



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D(MI(-3)) 0.019887 1.740477 0.1054 D(MI(-3)) 0.021494 2.041020 0.0621  

D(PSDD(-1)) -0.002181 -0.166101 0.8706 D(PSDD(-1)) -0.013800 -1.154855 0.2689  

D(PSDD(-2)) 0.038496 2.274134 0.0406 D(PSDD(-2)) 0.040595 2.595134 0.0222  

D(PSDD(-3)) -0.017678 -1.329483 0.2065 D(PSDD(-3)) -0.025661 -2.037529 0.0625  

ECM(-1) -1.717053 -3.714111 0.0026 ECM(-1) -1.672063 -4.105179 0.0012  

R2 0.791987   R2 0.844319    

Adj R2 0.535971   Adj R2 0.652713    

F-Stat 3.093507   F-Stat 4.406521    

F-prob 0.022942   F-prob 0.005035    

DW 1.756215 
  

DW 
            
1.923447 

   

Source: Extract from E-view 9.0  
It is important to note that the corresponding sign of Error Correction Term (ECT) is negative but 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 models proved that the 
variables can explain 79.1 and 84.4 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 171 and 
167 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.  

Money  Supply and Credit to private sector  Money supply and Credit to core private sector  

 M2 does not Granger Cause CPS 32 
 4.0679

8 0.0286  M2 does not Granger Cause CCPS 
32  0.7868

5 0.4654 

 CPS does not Granger Cause M2 32 
 0.1997

3 0.8202  CCPS does not Granger Cause M2 
32  4.1493

8 0.0268 

 M3 does not Granger Cause CPS  32 
 0.6526

5 0.5287  M3 does not Granger Cause CCPS 
32  8.1663

9 0.0017 

 CPS does not Granger Cause M3 
  21.271

4 3.E-06  CCPS does not Granger Cause M3 
32  17.149

5 2.E-05 

 MI does not Granger Cause CPS  32 
 2.1730

3 0.1333  MI does not Granger Cause CCPS 
32  0.8891

5 0.4227 

 CPS does not Granger Cause MI 
  0.3650

1 0.6976  CCPS does not Granger Cause MI 
32  0.1546

6 0.8575 

 PSDD does not Granger Cause CPS 
  1.9697

0 0.1590 
 PSDD does not Granger Cause 
CCPS 

32  0.9314
8 0.4063 

 CPS does not Granger Cause PSDD 
  0.8096

4 0.4555 
 CCPS does not Granger Cause 
PSDD 

32  0.2103
5 0.8116 

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 5 above. The researcher’s 
interest here is to establish the direction of causality between the dependent variables and the independent variables from 1985-
2018. In the models there is uni-directional causality from broad money supply to credit to private sector and unidirectional 
causality from credit to core private sector to broad money supply and from M3 to credit to core private sector. 

 
 
 
 
 
 
 



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Table 6: Short Term Regression Results 
Money Supply and Credit to Small and Medium Scale Enterprises  

Money supply and Credit to Small and Medium Scale Enterprises Sector 

         Variable                           Coefficient                t-test                                                                Prob. 

PSDD -0.001888 -0.146513 0.8846 

M1 0.001038 0.087725 0.9307 

M3 0.000601                0.963978 0.3433 

M2 -0.000860 -0.456413 0.6516 

C 8.035331 4.167254 0.0003 

R2 0.234860   

Adj R2 0.125554   

F-Stat 2.148652   

F-Prob 0.101072   

DW                                                       0.792969   

Source: Extract from E-view 9.0  

To find out how well the model fits a set of observations, the R2 indicates that 23.4 percent of the variation in money supply to 
credit 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 is necessary. From the results it could 
be deduced that M2 and private sector demand deposit (PSDD) have negative effect on credit to small and medium scale 
enterprises sector while M1 and M3 have positive effect on credit to small and medium scale enterprises sector in Nigeria. 

Table 7: Unit Root Test 
Money Supply and Credit to Small and Medium Scale Enterprises 

Money supply and Credit to SMEs 

           Variable               ADF               5% Prob. 

SMEs -5.921945 -2.976263 0.0000 

M2 5.025161 -2.954021 0.0000 

M3 -6.804960 -2.963972 0.0000 

M1 -7.235884 -2.986225 0.0001 

PSDD -5.455320 -2.986225 0.0000 

 
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 table 7 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 8: Cointegration Test 

Hypothesized 
No. of CE(s) 

 

Eigenvalue Trace 
Statistic 

 

0.05 
Critical 
Value 

 

Prob.** 

Money supply and Credit to SMEs 
None *  0.986093 192.7224 69.81889  0.0000 

At most 1*  0.712074  64.46241  47.85613  0.0007 
At most 2  0.490986 27.11087  29.79707  0.0989 
At most 3  0.000693  0.202187  15.49471  0.5949 
At most 4 0.002532 0.076056 3.841466                                                                0.7827 

Source: Extract from E-view 9.0  
 



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From table 8, the results of the Johansen co-integration test show that we adopt the alternate hypothesis of three co-integrating 
equations at the 5% level of significance. This implies that, there is linear combination of the variables that are stationary in the 
long run and also confirms the existence of a long-run relationship between money supply variables and credit to small and medium 
scale enterprises sector. 

Table 9: Error Correction Model 

 

Furthermore, 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 model 3 in table 9 above proved that 
the independent variables can explain 62.5 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 
42.4 percent annually. The coefficient of the variables defines the effect of the independent variables on the dependent variables 
at various lags. 

 

 

 

 

 

Money supply and Credit to SMEs 

                  Variable                                                                                 Coefficient                           t-test                               
Prob. 

C 2.697326 0.877753 0.3988 

D(SMES(-1)) -0.092164 -0.265953 0.7952 

D(SMES(-2)) -0.064598 -0.199608 0.8454 

D(SMES(-3)) -0.054213 0.185505 0.8562 

D(PSDD(-1)) 0.055488 0.513111 0.6180 

D(PSDD(-2)) -0.038363 -0.319586 0.7553 

D(PSDD(-3)) 0.104251 0.748408 0.4699 

D(M1(-1)) -0.056087 -0.552928 0.5914 

D(M1(-2)) 0.040457 0.353998 0.7300 

D(M1(-3)) -0.095419 -0.772243 0.4562 

D(M3(-1)) -0.002449 -0.557153 0.5886 

D(M3(-2)) 0.000104 0.044017 0.9657 

D(M3(-3)) 0.000682 0.356550 0.7282 

D(M2(-1)) -0.001067 -0.166555 0.8707 

D(M2(-2)) 0.006089 0.646836 0.5310 

D(M2(-3)) -0.001169 -0.057870 0.9549 

ECM(-1) -0.424263 -1.323800 0.2124 

R2 0.625345   

Adj R2 0.455970   

F-Stat 4.331540   

F-prob 0.007725   

DW 
                                  
2.123434 

  



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Table 10: Granger Causality Test 

Source: extract from e-view 
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.  There is no causality among the variables. 

5. Discussion of Findings  
Model I examined the relationship between money supply and credit to the private sector in Nigeria.  The estimated regression 
model from result of the vector error correction result in table 4 shows that the relationship between money supply and credit to 
private sector is high and significant. This is because of an R2 of 0.791987 meaning that the model explains approximately 79 
percent of the total variations in the credit to the private sector. The error correction model shows a negative value of -1.717053 
which is appropriate and is significant. This means that 171 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 money supply are positive and also proved negative at various lags.   

Furthermore, it was found that the result of the error correction in table 4.9, the F*- cal = 3.093507 > F*- tab = 2.24 
at 5% n=31 is statistically significant which is supported with a probability value of 0.0022942< 0.05 at 5% is significant, we 

therefore reject the null hypothesis, that is β1-β4 (money supply) is statistically significant with credit to private sector in Nigeria. 

Findings from the study corroborates findings from Olorunmade, et al (2019) that there is significant relationship 
between total credits to private sector and money supply in Nigeria. The implication is that there is enough money supply that 
enhance the disbursement of credit to the real sector. Otalu, Aladesanmi and Mary (2014) opine that money supply and cash 
reserve ratio appeared to have statistically significant influenced deposit money banks’ credit creation. Akani (2017) noted that if 
banks set interest rates too high, they may induce adverse selection problems because high-risk borrowers are willing to accept 
these high rates. The findings of this study is supported by Keynesian liquidity preference theory as it could be used to determines 
the interest rate by the demand for and supply of money which is a stock theory. It emphasizes that the rate of interest is purely a 
monetary phenomenon. It further validates loanable funds theory which is a flow theory that determines the interest rate by the 
demand for and supply of loanable funds. Findings also confirm bank lending of monetary policy transmission. Mishkin (1995) 
argued that to be successful in conducting monetary policy, the monetary authorities must have an accurate assessment of the 
timing and effect of their policies on the economy, thus requiring an understanding of the mechanism through which monetary 
policy affects the economy. The bank lending channel represents the credit view of this mechanism. According to this view, 
monetary policy works by affecting bank assets (loans) as well as banks’ liabilities (deposits). The key point is that monetary policy 
besides shifting the supply of deposits also shifts the supply of bank loans. For instance, an expansionary monetary policy that 
increases bank reserves and bank deposits increase the quantity of bank loans available. Where many borrowers are dependent on 
bank loans to finance their activities, this increase in bank loans will cause a rise in investment (and also consumer) spending, 
leading ultimately to an increase in aggregate output, (Y). The schematic presentation of the resulting monetary policy effects is 
given by the following:  

M ↑ → Bank deposits ↑ → Bank loans ↑ →I ↑ → Y ↑     
(Note: M= indicates an expansionary monetary policy leading to an increase in bank deposits and bank loans, thereby 

raising the level of aggregate investment spending, I, and aggregate demand and output, Y,). In this context, the crucial response 
of banks to monetary policy is their lending response and not their role as deposit creators. The two key conditions necessary for 
a lending channel to operate are: (a) banks cannot shield their loan portfolios from changes in monetary policy; and (b) borrowers 

Null Hypothesis                             
Obs F-Statistic Prob.  

Money supply and Credit to SMEs 

PSDD does not Granger Cause SMES 32  0.87193 0.4305 

SMES does not Granger Cause PSDD 32  0.03088 0.9696 

 M1 does not Granger Cause SMES 32  0.94385 0.4026 

 SMES does not Granger Cause M1   0.01320 0.9869 

 M3 does not Granger Cause SMES 32  0.37423 0.6916 

 SMES does not Granger Cause M3   0.53915 0.5899 

M2 does not Granger Cause SMES  0.87246 0.4303 

SMES does not Granger Cause M2  0.29899 0.7442 



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cannot fully insulate their real spending from changes in the availability of bank credit. The importance of the credit channel 
depends on the extent to which banks rely on deposit financing and adjust their loan supply schedules following changes in bank 
reserves; and also the relative importance of bank loans to borrowers. Consequently, monetary policy will have a greater effect on 
expenditure by smaller firms that are more dependent on bank loans, than on large firms that can access the credit market directly 
through stock and bond markets (and not necessarily through the banks). 

The positive findings of the study confirm the findings of Zuzana, Riikka and Laurent (2015) who found no evidence 
of the bank lending channel through the use of reserve requirements. The author noted   that changes in reserve requirements 
influence loan growth of banks.  The findings of João, Barroso and Gonzalez (2017) 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. The findings of Mohammed (2014) that there was co-integration between re-positioning of commercial banks and 
capacities of SMEs to deliver services and also a significant dispersion resulting from lending conditions and macroeconomic 
variables and the findings of Ovat (2016) that exchange rate and lending rate are statistically significant to SMEs credit.  

Model 2 examined the relationship between money supply and Credit to core private sector in Nigeria.  From the 
estimated regression model, the vector error correction result in table 4 shows that the relationship between money supply and 
credit to core private sector is high and significant. This is because of an R2 of 0.844319 meaning that the model explains 
approximately 84.4 percent of the total variations in the credit to core private sector, the error correction model shows a negative 
value of -1.672063 which is appropriate and is significant. This means that 167 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 money supply are positive and also proved negative at various lags.   

Furthermore, it was found that the result of the error correction in table 4, the F*- cal = 4.406521 > F*- tab = 2.24 at 
5% n=31 is statistically significant which is supported with a probability value of 0.005035< 0.05 at 5% is significant, we 

therefore reject the null hypothesis, that is β1-β4 (money supply) is statistically significant with credit to core private sector in 
Nigeria. 

The findings of this study is supported by Keynesian liquidity preference theory as it could be used to determines the 
interest rate by the demand for and supply of money which is a stock theory. It emphasizes that the rate of interest is purely a 
monetary phenomenon. It further validates loanable funds theory is a flow theory that determines the interest rate by the demand 
for and supply of loanable funds. The significant effect of monetary policy on bank credit to core private sector confirms the 
classical opinion on the relevant of money in the economy. The finding also supports the bank lending channel transmission of 
monetary policy. The monetary transmission mechanism describes how policy induced changes in the nominal money stock or the 
short-term nominal interest rates impact real variables such as aggregate output and employment. Bernanke and Gertler (1995) 
noted that bank lending channel centers on the possible effect of monetary policy actions on the supply of loans by depository 
institutions.   
 

It is also evidence that, the positive findings of the study confirm the findings of Zuzana, Riikka and Laurent (2015) 
who found no evidence of the bank lending channel through the use of reserve requirements. The author noted that changes in 
reserve requirements influence loan growth of banks.  The findings of João, Barroso and Gonzalez (2017) 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. It also confirms the positive findings of Sesay and Abdulai (2017) that suggest that money 
supply and gross domestic savings exert positive and statistically significant effect on private sector investment. They opine that 
financial institutions should increase the credit delivery to the private sector in a bid to facilitate private sector investment drive.  
Olorunmade et al. (2019) also finds significant relationships between private sector credit and economic growth and significant 
relationship between total credits to private sector and money supply in Nigeria. Their study revealed that private sector credit 
impact positively on the growth of the economy in Nigeria. The findings of Mohammed (2014) that there was co-integration 
between re-positioning of commercial banks and capacities of SMEs to deliver services and also a significant dispersion resulting 
from lending conditions and macroeconomic variables and the findings of Ovat (2016) that exchange rate and lending rate are 
statistically significant to SMEs credit. Abdullahi (2014) study shows that the positive relation observed between credit to the 
private sector and money supply indicates that such credit only increases money supply in the economy and that the funds were 
not properly annexed into productive activities. 

Model 3 examined the relationship between money supply and credit to private sector in Nigeria.  It is evidence that the 
estimated regression model from result of the vector error correction result in table 9 the relationship between money supply and 
credit to small and medium scale enterprises sector is moderate and not significant. This is because of an R2of 0.625345 meaning 
that the model explains approximately 62.5percent of the total variations in the credit to small and medium scale enterprises sector. 



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39 
                         
 

It is also evidence that the error correction model shows a negative value of -0.424263 which is appropriate and is significant. 
This means that 42.4 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 money supply variables   are positive and also proved negative at 
various lags.   

The findings of the study confirm the findings of Otalu, Aladesanmi and Mary (2014) that money supply and cash 
reserve ratio appeared to have statistically significant influenced deposit money banks’ credit creation and Jegede (2014) who 
specifically revealed that liquidity ratio and money supply exert negative effect on commercial banks’ loan and advances. The study 
concludes that monetary policy instruments are not effective to stimulate commercial bank loans and advances in the long-run. 
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. Findings are also consistent with the works of Olorinmade et al. (2019) that there was significant 
relationship between total credits to the private sector and money supply in Nigeria. This implies that the volume of money supply 
is enough to facilitate and guarantee the disbursement of credits to Small and Medium Scale Enterprises sector in Nigeria. Dada 
(2014) 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. Abdullahi 
(2014) study shows that the positive relation observed between credit to the private sector and money supply indicates that such 
credit only increases money supply in the economy and that the funds were not properly annexed into productive activities. 

6. Conclusion and Recommendations 
With the F*- cal =3.093507 > F*- tab = 2.24 the study conclude that there is significant relationship between money supply and 
credit to private sector in Nigeria. From the result, the F*- cal =4.406521 > F*- tab = 2.24, the study conclude that there is 
significant relationship between money supply and credit to core private sector in Nigeria. The F*- cal =4.331540> F*- tab = 
2.24, the study conclude that there is significant relationship between money supply and credit to small and medium scale 
enterprises sector in Nigeria. 
 
7. Recommendations  
The study recommends that the monetary authorities should ensure adequate quantity of money supply that affect positively 
private sector funding in Nigeria.  This is because money does not affect only the absolute price and quantity of trade, but it affects 
also the level of financial intermediation. 
 
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