Monetary Policy, Bank Management and Industrial Sector Finance in ...

Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking

(ME14 DUBAI Conference) Dubai, 10-12 October 2014

ISBN: 978-1-941505-16-8 Paper ID_D482

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Monetary Policy, Bank Management and Real Sector Finance in

Nigeria: Who is to Blame?

Adolphus J. Toby,

Department of Banking and Finance,

Rivers State University of Science and Technology, Nigeria.

Email: [email protected]

Deborah Peterside,

Department of Banking and Finance,

Rivers State University of Science and Technology, Nigeria.

Email: [email protected]

___________________________________________________________________________________

Abstract

This study entails a critical analysis of the effects of monetary policy and selected bank

management decisions on commercial bank lending to agriculture and manufacturing in

Nigeria for the period 1980-2010. Relevant data generated from the Central Bank of Nigeria

(CBN) annual reports were analysed with the Software Package for Social Sciences (SPSS).

Four multiple regression models were specified, with the independent variables (IVs) tested

for multicollinearity employing the Variance Inflation Factors (VIFs) and tolerance values.

The descriptive results show that within the period, average bank liquidity ratio (BLR) was

46.4%, well above the prescribed average minimum of 27.7%. The average cash reserve

ratio (CRR) was 6.0%, in a period widely portrayed to support easy monetary policy regimes.

The average loan-to-deposit ratio (LTDR) was 69.5%, far below the prescribed prudential

maximum of 80.0%. While the incidence of funding risk exceeded the liquidity risk banks were

exposed to, the average margin reaped by banks was an average of 11.9%. Within the period,

the average sectoral allocations of commercial banks’ credit to the agricultural and

manufacturing sectors were 10.1% and 28.4% respectively. The inferential results show that

bank management decisions were significantly insensitive to the credit needs of the

agricultural and manufacturing sectors. The shoring up of banks’ core deposits through

increased deposit mobilisation was more significant in driving increased sectoral allocation

of credit to the agricultural and manufacturing sectors. The explanatory powers of bank

rates in determining the sectoral allocation of commercial banks’ credit to these two critical

sectors are more pronounced than the selected bank management ratios. For a period of 21

years (1980-2010), the regulatory authorities failed in adopting the relevant monetary policy

regimes to direct credit naturally, without coercion, to the agricultural and manufacturing

sectors in Nigeria. The seeming regulatory favouritism occasioned by the abolition of the

mandatory sectoral allocation of bank credit, easy monetary policy stance and prudential

paternalism gave the banks an ample opportunity to build their liquidity profiles at the

expense of funding the real sector. Rather, the banks reaped wide margins through rent-

seeking and the maximisation of shareholders’ wealth.

_________________________________________________________________________

Keywords: monetary policy, bank management, real sector, sectoral allocation of credit

mailto:[email protected]

mailto:[email protected]



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1. Introduction

Monetary policy is the deliberate use of monetary instruments (direct and indirect) at the

disposal of monetary authorities such as the central bank in order to achieve macroeconomic

stability (Ezema, 2009). A monetary policy shift tends, generally, to transmit a change for the

future in the expected behaviour of macroeconomic variables. In a developing or emerging

economy, monetary policy shift is often designed in response or reaction to undesirable

shocks in the monetary system and macroeconomy in order to restore equilibrium and achieve

a set of objectives. Ubi et al (2012) have suggested that monetary policy should be consistent

and transparently defined in response to the dynamics of the domestic and global economic

development.

The Central Bank of Nigeria’s monetary policy shifted from quantitative easing in

September 2010 to monetary tightening in 2011, in response to the apparent threats of

inflationary build-up. Tight monetary policy aimed at moderating the anticipated inflationary

pressures, expected to be triggered by the pre-election spending and the high liquidity

injections into the banking system through the purchase of non-performing loans (NPLs) by

the Asset Management Corporation of Nigeria (AMCON).

In the context of this study, bank management refers to the various decisions taken by

deposit money banks (DMBs) in order to influence the liquidity, funding and capital

adequacy of banks, and maximise shareholders’ wealth, subject to monetary policy

constraints. It also includes the extent of compliance with regulatory standards by DMBs. It

is hypothesised that these liquidity and funding decisions, constrained by the cash reserve

ratio and the monetary policy rate, could affect the sector’s contribution to the gross domestic

product (GDP). The earlier works of Toby (2011) have suggested that rural bank management

expanded aggregate credit in such a manner that constrained their liquidity profiles, and

created a critical gap in bank intermediation in the rural and SME sectors.

Sanusi (2011) has argued that economic development is about enhancing the productive

capacity of an economy by using available resources to reduce risks, remove impediments,

which otherwise could lower costs and hinder investments. Tawose (2012) suggests that the

contribution of the industrial sector to the GDP is significantly explained by commercial

banks’ loans and advances to the industrial sector, interest rate and inflation rate. Okoye and

Eze (2013) have found that the monetary policy rate has a critical and significant impact on

the bank lending rate in Nigeria.

Enyioko (2012) has found that the interest rate policies in Nigeria have not improved the

overall performance of banks significantly and have contributed marginally to the growth of

the economy. Nwosa and Saibu (2012) have demonstrated the interest rate channel was most

effective in transmitting monetary policy to sectoral output growth in the agriculture and



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manufacturing sectors in Nigeria between 1986 and 2009. The works of Udoh and Ogbungu

(2012) have shown that the inefficiency of the financial sector is responsible for the adverse

impact on industrial production. Sangosanya (2011) has employed the estimated dynamic

panel model to show that manufacturing firms finance mix, utilization of assets to generate

more sales, abundance of funds reserve and government policies are significant determinants

of manufacturing firms’ growth and thus dictated their dynamics in Nigeria.

Edoumiekumo, et al, (2013) have examined the responsiveness of real sector output to

monetary policy shocks in Nigeria. Applying a VAR model and covering the period 1970 to

2011, the study revealed that credit to the private sector (CPS) had a direct, instantaneous

impact on real sector development (GDP). Real GDP responded more to shocks in monetary

policy rate (MPR) and credit to private sector (CPS) in the long run. The study concludes that

monetary policy in Nigeria encouraged credit to the private sector and capital accumulation.

The works of Imoughele and Ismaila (2014) have found that interest rate, exchange rate

and external reserves impacted negatively on the manufacturing sector in Nigeria between

1996-2012. Financial analysts have equally argued that high interest rate is stifling the

growth of the real sector in Nigeria (Nnodim, 2014). Usman and Adegare (2014) study the

impact of monetary policy on industrial growth in Nigeria for the period 1970-2010. The

study found that the rediscount rate and deposit size have a significantly positive effect on

industrial output, but investment in treasury bills (TBs) has a negative impact on industrial

growth. Odior (2013) investigates the impact of macroeconomic factors on manufacturing

productivity in Nigeria over the 1975-2011 period. The findings show that credit to the

manufacturing sector in the form of loans and advances and foreign direct investment have

the capacity to sharply increase the level of manufacturing productivity in Nigeria, while

broad money stock has less impact.

Recently, banks are struggling to grapple with a 2014 tight monetary policy regime which

pegs the cash reserve ratio (CRR) and monetary policy rate (MPR) at 12 per cent, and a

special CRR on public sector deposits at 75 per cent. Consequently, their constrained balance

sheets are likely to hinder the flow of credit to the industrial sector, with emphasis on

agriculture and manufacturing. Toby and Peterside (2014) have shown empirically that the

role of the Nigerian deposit money banks in facilitating the contribution of the agriculture and

manufacturing sectors to economic growth is still significantly limited. The study argues that

the growing risk aversion of Nigerian banks towards these sectors is the primary reason for

the liquidity and funding shortages in the critical sectors of the economy. The research

analyses the sectoral allocation of bank credit to these sectors between 1980-2010, some of

the years being influenced by the era of mandatory sectoral allocation of bank credit.



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What is not yet known is the critical role of monetary policy and bank management in the

financial intermediation puzzle, with respect to the real sector of the Nigerian economy. The

major research questions are (1) what is the nature of the relationship between interest rate

(proxy of monetary policy) and industrial growth in Nigeria? (2) What is the nature of the

relationship between bank management and industrial growth in Nigeria. The study null

hypotheses are:

H01: There is no significant relationship between bank interest rates and commercial bank

lending to the agricultural sector.

H02: There is no significant relationship between bank interest rates and commercial bank

lending to the manufacturing sector.

H03: There is no significant relationship between selected bank management variables and

commercial bank lending to the agricultural sector.

H04: There is no significant relationship between selected bank management variables and

commercial bank lending to the manufacturing sector.

The next part of the paper discusses the background of the study, then the methodology

and model specifications, results and discussion. The paper concludes with financial policy

implications of the study.

1.1 Background of the Study

The Central Bank of Nigeria Annual Report (2010) provided the framework for

understanding the direction of this study. The monetary and credit developments in the 2006-

2010 period, the maturity structure of DMBs loans and advances portfolio, and the proportion

of bank credit to preferred and less preferred sectors are summarised in the report.

Monetary growth was sluggish in 2010, despite the monetary easing policy maintained by

the Central Bank of Nigeria. The stance of monetary policy was to inject liquidity into the

economy and restore confidence in the Nigerian Financial System. The measures taken

included the continuation of guarantees on inter-bank transactions and towards the end of the

year, the purchase of non-performing loans (npls) from the DMBs by the Asset Management

Corporation of Nigeria (AMCON).

The growth of the key monetary aggregate at the end of December, 2010 was below the

indicative benchmark and the growth rate attained at the end of the preceding year (Table 1).

Broad money (M2) grew by 6.7 per cent, compared with 17.5 per cent at the end of December,

2009, and the indicative benchmark of 29.3 per cent for fiscal 2010. The rather slow growth

in money stock was driven by the increase in domestic credit (net) and other assets (net) of

the banking system. Narrow money (M1) grew by 10.6 per cent at the end of December,

2010, compared with the growth of 3.0 per cent at the end of preceding year. Aggregate bank

credit to the domestic economy (net) grew by 13.4 per cent, compared with the growth of 59.6



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per cent at the end of December, 2009. The development was attributed, largely to the 64.2

per cent growth in net credit to the Federal Government. Claims on the private sector,

however, declined by 4.1 per cent, in contrast to the growth of 26.6 per cent recorded at the

end of December 2009. Base money, the CBN’s operating target for monetary policy, which

stood at N1,803.9 billion, grew by 9.1 per cent but was lower than the indicative benchmark

for the year by 25.9 percentage points.

Table 1: Key Policy Targets and Outcomes, 2006-2010 (per cent)

Monetary Policy Indicators 2006

Target Outcome 2007

Target Outcome 2008

Target Outcome 2009

1/

Target Outcome 2010

2/

Target Outcome

Growth in base money

7.5

27.8

3.3

22.6

20.8

58.9

3.6

6.7

35.0

9.1

Growth in broad money (M2) 27.0 43.1 24.1 44.2 45.0 57.8 20.8 17.5 29.3 6.7

Growth in narrow money (M1) n.a. 32.2 - 36.6 - 55.9 32.2 3.0 22.4 10.6

Growth in aggregate bank

credit

-72.3 -69.1 -29.9 276.4 66.0 84.2 87.0 59.6 51.4 13.4

Growth in bank credit to

private sector

30.0 32.1 30.0 90.8 54.7 59.4 45,0 26.6 31.5 -4.1

Inflation rate 9.0 8.5 9.0 6.6 9.0 15.1 9.0 13.9 11.2 11.8

Growth in real GDP 7.0 6.0 10.0 6.5 7.5 6.0 5.0 7.0 6.1 7.9

1/ Revised

2/ Provisioned

Source: Central Bank of Nigeria Annual Report, 2010.

1.2 Liquidity Management

Monetary policy in 2010, as in the preceding year, was conducted against the background

of managing the devastating effects of a liquidity crunch in the domestic economy, arising

from the global financial and economic crises of 2007/2008 and internal problems in some

deposit money banks in Nigeria. Liquidity management was, therefore, geared towards

improving the liquidity and efficiency of the financial markets, without compromising the

primary objective of monetary and price stability. The CBN made use of open market

operations (OMO), complemented by macro prudential cash and liquidity ratios, standing

facilities, tenured repurchased transactions, sale of treasury instruments at the primary

segment of the market, and foreign exchange market intervention.

The monetary easing policy that commenced in the late 2009, which was aimed at

improving banking system liquidity, ensuring financial system liquidity, and a steady flow of

credit to the real sector of the economy continued in 2010. The monetary policy measures

implemented in 2010 substantially improved the liquidity conditions in the banking system,

thereby ameliorating to a large extent, the challenge of the credit crunch in the banking

system. The sustenance of banking reforms, unrestricted access to the discount window, and



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the guarantee of inter-bank transactions increased the level of confidence in the banking

system.

Analysis of the structure of DMBs’ outstanding credit at the end of December 2010

indicated that short-term maturity remained dominant in the credit market (Table 2).

However, there was an improvement in the share of long-term maturity. Outstanding loans

and advances, maturing one year and below accounted for 65.3 per cent of the total, compared

with 70.3 per cent at the end of December, 2009, while the medium-term (between 1-3 years)

and long-term (3-year and above) accounted for 14.6 and 20.1 per cent respectively,

compared with 14.3 and 15.3 per cent at the end of December, 2009.

Table 2: Maturity Structure of DMBs Loans and Advances and Deposit Liabilities (per cent)

Loans and Advances Deposits

Tenor/Period 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

0-30 days 54.4 49.2 46.6 50.1 46.1 66.6 74.1 72.7 73.3 76.3

31-90 days 11.0 11.3 13.4 6.4 10.0 16.6 12.3 13.1 15.0 14.4

0-181 days 6.3 5.8 7.8 7.3 3.9 3.5 4.3 6.2 4.7 3.4

181-365 days 6.4 9.5 7.5 6.5 5.3 1.4 2.6 2.7 2.7 2.8

Short-term 78.02 75.83 75.4 70.3 65.3 88.1 93.3 94.8 95.7 96.87

Medium-term

(above 1 year and

below 3 years)

8.3 13.5 14.5 14.3 16.6 5.4 3.3 5.2 4.1 2.06

Long-term

(3 years and above)

13.7 10.7 10.1 15.3 20.1 6.5 3.3 0.03 0.069 1.005

Total 100 100 100 100 100 100 100 100 100 100

Source: Central Bank of Nigeria Annual Report, 2010

Analysis of DMBs’ deposit liabilities shared a similar trend, with short-term deposits of

below one year maturity constituting 96.9 per cent of the total. The share of deposits of less

than 30-day maturity was 76.3 per cent, while long-term deposits of more than three (3) years

had a share of 1.0 per cent at the end of December, 2010, compared with 0.1 per cent at the

end of December, 2010, compared with 0.1 per cent at the end of December, 2009. The

structure of DMBs’ deposit liabilities explains banks’ preference for short-term claims on the

economy.

Table 3 shows that as at the end of December, 2010, credit to the core private sector by

the DMBs declined by 4.8 per cent, in contrast with the growth of 25.1 per cent at the end of

December, 2009. Of the amount outstanding, DMBs’ credit to priority sectors constituted

30.4 per cent, of which agriculture, solid minerals, exports and manufacturing received 1.7,

15.3, 0.6 and 12.8 per cent respectively. The less priority sectors accounted for 47.8 per cent

of outstanding credit, compared with 46.9 per cent at the end of December, 2009, while

unclassified sectors accounted for the balance of 21.8 per cent.



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Table 3: Bank Credit to the Core Private Sector, 2006-2010

Share in Outstanding (Per cent)

Sector 2006 2007 2008 2009 2010

1. Priority Sectors 30.3 25.9 26.2 25.2 30.4

Agriculture 2.2 3.2 1.4 1.4 1.7

Solid minerals 10.1 10.7 11.3 12.7 15.3

Exports 1.2 1.4 1.0 0.5 0.6

Manufacturing 16.9 10.4 12.5 10.6 12.8

2. Less Preferred Sectors 46.0 41.2 42.0 46.9 47.8

Real Estate 5.9 6.2 6.2 8.3 8.7

Public Utilities 0.9 0.6 0.6 0.8 0.7

Transport and Comm. 7.6 6.8 7.2 8.3 10.7

Finance and Insurance 4.6 9.4 9.5 13.1 11.3

Government 4.5 3.7 1.9 3.7 4.9

Imports and Domestic

Trade

22.5 14.5 16.4 12.8 11.7

3. Unclassified 23.7 32.0 31.8 27.9 21.8

Total (1 + 2 + 3) 100 100 100 100 100

Source: Central Bank of Nigeria Annual Report, 2010.

2. Literature Review

From a macroeconomic perspective, the nature of banking activities and banks’ position

as intermediaries makes these institutions relevant for the transmission of monetary policy.

Two important channels of monetary policy transmission depend on the functioning of the

banking sector: the traditional interest rate channel and the credit channel. The interest rate

channel operates when the central bank’s adjustments to the nominal interest rate have an

impact on the real interest rate (assuming a degree of price stickiness) and thus on the pattern

of investment and consumption. This channel will only work, however, if banks transmit the

changes in the monetary policy rate to their customers. The credit channel, in turn, assumes

some capital market imperfections, such as asymmetric information, that induce a contraction

of the quantity of credit when the central bank imposes a restrictive monetary policy.

It is shown in Peersman and Smets (2002) that on average the negative effect of an

interest rate tightening on output is significantly greater in recessions than in booms. Francis

et al (2011) find that the relevance of the interest rate and credit channel appears to be more

robust to business cycle uncertainty. Peersman (2013) demonstrates that within-year

differences in the responses of output and prices following monetary policy shocks are not



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more pronounced in the service sector, where labour costs represent a larger fraction of the

total production costs.

A resurgence of interest in the role of banks in the transmission of monetary policy has

resulted in a spate of theoretical and empirical studies. A number of studies have offered an

explanation on the manner in which monetary policy actions affect investment, prominent

among them are the classical school (Mayumster, 2007), the Keynesian view (Barro, 1997),

and the credit channel approach (Kahn, 2010; Bernaike and Gertler, 1995). The recent works

of Huang et al (2014) show that bank concentration magnifies industrial growth volatility, but

reduces the volatility in sectors with higher external liquidity needs.

Arnold et al (2006) have presented evidence on the industry effects of bank lending in

Germany and identifies the industry effects of bank lending associated with changes in

monetary policy and industry-specific bank credit demand. The study estimates individual

bank lending functions for 13 manufacturing and non-manufacturing industries and five

banking groups using quarterly bank balance sheet and bank lending data for the period

1992:1-2002:4. The research concludes that the industry composition of bank credit

portfolios is an important determinant of bank lending growth and monetary policy

effectiveness.

The works of Granley and Salmon (1997) have demonstrated, using UK data, that the

effects of an unanticipated monetary policy tightening seem to be unevenly distributed across

sectors of the economy. Manufacturing as a whole responds quite sharply to a monetary

tightening, but some large industrial sector enterprises, notably the utilities, show a subdued

reaction.

Tobins and Mambo (2012) explore the relationship between monetary policy and private

sector investment in Kenya by tracing the effects of monetary policy through the transmission

mechanism to explain how investors responded to changes in monetary policy. Based on the

empirical results, the study suggests that tightening of monetary policy by -1 per cent has the

effect of reducing investment by -2.63 per cent while the opposite loose monetary policy

tends to increase investment by 2.63 per cent.

Cambazogha and Karaalp (2012) analyse the effectiveness of the narrow credit view on

employment output for Turkey using monthly variables for the period 2005-2010. The results

indicate that changes in money stock (M2) impact on real variables such as employment and

output through the credit stock. Dickson and Liu (2007) show that there was an increasing

influence of interest rates on output over 1984 to 1997 and non-state-owned enterprises were

reacting to monetary policy changes in China.

Kapan and Minolu (2013) have shown that banks with strong balance sheets were better

able to maintain lending during the 2007-2009 global financial crisis. In particular, banks that



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were ex-ante more dependent on market funding and had lower structural liquidity reduced

the supply of credit more than other banks. It has been argued that financial intermediation

stimulates the funding of liquidity needs through credit lines (Allen and Gale, 2004; Shittu,

2012).

2.1 Monetary Transmission Mechanism, Credit Frictions and Macroprudetial

Regulation

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 (Ireland, 2005). Specific channels of monetary

transmission operate through the effects that monetary policy has on interest rates, exchange

rates, equity and real estate prices, bank lending, and firm balance sheets. Recent research

shows how these channels work in the context of dynamic, stochastic general equilibrium

models.

Bernanke and Gertler (1995) classify three channels of monetary policy as the balance

sheet channel, the bank-lending channel and the credit channel. The balance sheet channel

focuses on monetary policy effects on the liability side of the borrowers’ balance sheet and

income statement, including variables such as borrowers’ networth, cash flow and liquid

assets whilst the bank lending channel centres on the possible effect of monetary policy

actions on the supply of loans by depository institutions.

However, most of the previous empirical literature on the effects of credit aims to

distinguish between different transmission mechanisms, such as the balance sheet channel, the

bank lending channel and the bank capital channel (see Oliner and Rudebusch, 1996; van den

Heuve, 2002). Since these different channels have similar predictions for aggregate

quantities, many empirical studies use micro-level data from banks and/or firms rather than

the aggregate data (Bayoumi and Melander, 2009). One consequence of these empirical

studies is that the general conditions of the banking sector and the specific characteristics of

individual banks can have predictable impacts on the monetary transmission mechanism. In

fact, recent studies have emphasised a risk-taking channel of monetary policy that places

more emphasis on the willingness of banks to expand their balance sheet (Borio and Zhu,

2012; Adrian and Shin, 2011). The works of Adrian and Shin (2011) provide an overview of

how changes in risk appetite, which is partly a function of monetary policy, generates a

critical link between monetary policy changes, the actions of financial intermediaries, and the

impact on the real economy.

Boivin et al (2010) have argued that the monetary transmission mechanism is one of the

most studied areas of monetary economics for two reasons. First, understanding how

monetary policy affects the economy is essential to evaluating what the stance of monetary

policy is at a particular point in time. Second, in order to decide on how to set policy



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instruments, monetary policy makers must have an accurate assessment of the timing and

effects of their policies on the economy.

Over the last two decades, beginning with the pioneering works of Bernanke and Gertler

(1989), economists began to introduce credit frictions into models that allowed for borrowing

and lending in equilibrium. A number of studies have shown that these credit frictions could

amplify the macroeconomic fluctuations introduced by certain shocks, hence the credit

frictions are often referred to as the “financial accelerator” (see Kiyotaki and Moore, 1997,

Carlstrom and Fuersto, 1997 and Bernanke, et al, 1999). The recent papers have contributed

to this literature by adding a relatively simple realistic, and well-defined financial

intermediation sector into a large-scale dynamic stochastic general equilibrium (DSGE)

model (Gertler and Kiyotaki, 2009; Curdia and Woodford, 2010). These works analyse the

relationship between the financial intermediation sector and macroeconomic volatility by

examining both the indirect effect of the sector on the propagation of non-financial shocks

and the direct effects of financial shocks that inhibit financial intermediation.

Tayler and Zilberman (2014) examine the macroprudential roles of bank capital

regulation and monetary policy in a Dynamic Stochastic General Equilibrium (DSGE) model

with endogenous financial frictions and a borrowing cost channel. The model identifies

various transmission channels through which credit risk, commercial bank losses, monetary

policy and bank capital requirements affect the real economy. These mechanisms generate

significant financial accelerator effects, thus providing a rationale for a macroprudential

toolkit. Following credit shocks, counter cyclical bank capital regulation is more effective

than monetary policy in promoting financial, price and overall macroeconomic stability. For

supply shocks, macroprudential regulation combined with a strong response to inflation in the

central bank policy rule yield the lowest welfare losses. The findings emphasise the

importance of the Basel III regulatory accords and cast doubts on the desirability of

conventional Taylor rules during periods of financial stress.

3. Data Sources and Methodology

The data for this study were generated from the Central Bank of Nigeria for the periods

1980-2010. The study variables are commercial bank lending to agriculture (CBLA),

commercial bank lending to manufacturing (CBLM), bank liquidity ratio (BLR), cash reserve

ratio (CRR), loan-to-total deposit ratio (LTDR), savings rate (SR), prime lending rate (PLR)

and maximum lending rate (MLR). The descriptive statistics are calculated for each of these

variables (mean and standard deviation).

The delineation of the variables into dependent and independent variables is specified in

the following multiple regression models:

(1) CBLA = + 1BLR + 2CRR + 3LTDR + i



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ί

ί

ί

(2) CBLM = + 1BLR + 2CRR + 3LTDR + i

(3) CBLA = + 1SR + 2PLR + 3MLR + i

(4) CBLM = + 1SR + 2PLR + 3MLR + i

The Software Package for Social Sciences (SPSS) was used to compute the variables in

the equation, the residual statistics, collinearity diagnostics and the relevant model

summaries.

The problem of multicollinearity, a situation in which the explanatory variables in

equations (1)-(4) are highly linearly correlated, is resolved by computing the VIF and

tolerance values as in equation (5).

(5) VIF = 1

1-R2

where VIF is Variance Inflation Factor, R2 is the coefficient of determination of the

regression equation. Note that tolerance = 1-R2

. A tolerance of 0.20 or 0.10 or less and/or a

VIF of 5 or 10 and above means there is high multicollinearity among the independent

variables (IVs), (Kutner, et al, 2004, O’Brien, 2007).

4. Empirical Results

The descriptive measures of mean and standard deviation are presented in Table 4 for the

study variables. Within the 1980-2010 period, average bank liquidity ratio (BLR) was 46.4

per cent, higher than the prescribed average of 27.7 per cent. The standard deviation of 3.46 is

associated with the bank liquidity ratio for the period.

The average cash reserve ratio for the study period was 6.0 per cent, with a standard

deviation of 3.46 per cent. In addition, the average loan-to-deposit ratio was 69.5 per cent, far

below the prudential maximum of 80.0 per cent. A standard deviation of 10.28per cent is

associated with the LTDR.

The average savings rate (SR) for the 1980-2010 period was 8.4 per cent, while the

average prime lending rate (PLR) was 17.4 per cent for the same period. The average

maximum lending rate (MLR) was 20.3 per cent. The computed standard deviation for SR is

4.91 per cent, PLR 5.35 per cent and MLR 6.25 per cent.

Only an average of 10.1 per cent of total credit to the real sector was allocated to

agriculture during the 1980-2010 period. The absolute variation in commercial bank lending

to the agricultural sector was 6.31 per cent in the same period. The average sectoral

allocation of credit to the manufacturing sector was 28.4 per cent in the 1980-2010 period,

with a standard deviation of 10.53 per cent.

Table 4: Bank Management, Interest Rates and Commercial Banks’ Lending to the

Agriculture and Manufacturing Sectors

S/N Description Mean Std. Dev.



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1 Bank Liquidity Ratio (Actual) 46.4 3.46

2 Bank Liquidity Ratio (Prescribed Minimum) 27.7 N/R

3 Cash Reserve Ratio (Prescribed) 6.0 3.46

4 Loan-to-Deposit Ratio (Actual) 69.5 10.28

5 Loan-to-Deposit Ratio (Prescribed Maximum) 80.0 N/R

6 Savings Rate 8.4 4.91

7 Prime Lending Rate 17.4 5.35

8 Maximum Lending Rate 20.3 6.25

9 Sectoral Allocation of Commercial Banks’ Credit

to Agriculture

10.1 6.31

10 Sectoral Allocation of Commercial banks’ Credit

to Manufacturing

28.4 10.53

Source: Author’s computation based on data from CBN Statistical Bulletin (1980-2010)

N/R - Not Relevant

4.1 Collinearity Diagnostics

The test of multicollinearity is summarised in Table 5. In all the four models, the

Variance Inflation Factors (VIFs) are less than 5.0, while the tolerance values are all above

0.2. Hence, employing the rules of thumb established by Kutner et al, (2004) and O’Brien

(2007), it is safe to say that the independent variables in Models 1-4 are not linearly

correlated, and hence the problem of multicollinearity does not exist.

Table 5: Relationship between Lending and Agricultural Contribution to GDP

Model Variables

Independent Variables*

CBLA MBLA

B -0.0015 0.2771

SE B -.1061 0.1534

95% Confdnce -0.2264 -0.0480

Interval B 0.2233 0.6022

Beta () -0.0039 0.4822

SE Beta 0.2669 0.2669

Correl. 0.2708 0.4800

Part. Cor. -0.0032 0.3963

Partial -0.0036 0.4116

t-test -0.0150 1.8070

Sig.t (0.05) 0.9886 0.0897 B Constant = 30.7326 SE B Constant = 1.4269 Interval BConstant = 33.7574

t Constant = 21.539 Sig t. Const = 0.000 * Dependent variable is agricultural contribution to GDP (ACGDP)

4.2 Inferential Results

The results in Table 6 show the relationship between bank management and commercial

bank lending to agriculture (CBLA). With a beta coefficient () of -0.2052, we find that as

bank liquidity ratio (BLR) increases by 100 per cent, commercial bank lending to agriculture

in the 1980-2010 period fell by 20.52 per cent and vice versa. With a critical t-value of



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0.2917, a t-test statistics of -1.0760 shows that both the beta and correlation coefficients are

significant and show an inverse relationship between BLR and CBLA. Moreover, with a beta

coefficient of -0.1736, we find that as the cash reserves ratio increases by 100 per cent,

commercial bank lending to agriculture reduces by 17.36 per cent and vice versa. The inverse

correlation between CRR and commercial bank lending to agriculture is statistically

significant at the 5% level, as the t-test of -0.9080 falls outside the critical region of 0.3717.

The sensitivity of commercial bank lending to agriculture (CBLA) to the loan-to-deposit ratio

(LTDR) of banks is further explained by a beta coefficient of -0.3661. This means that as the

loan-to-deposit ratio (LTDR) rises by 100 per cent, the sectoral allocation of bank credit to

the agricultural sector falls by 36.61 per cent and vice-versa. The beta and correlation

coefficients are significant at the 5 per cent level of significance with the t-test statistic of -

1.9780 falling outside the critical regions of 0.0582.

Table 6: Relationship between Bank Lending and Manufacturing Contribution to GDP

Model Variables


CBLM MBLM

B -0.0734 0.0099

SE B 0.0592 0.0698

95% Confdnce -0.1989 -0.1382

Interval B 0.0521 0.1579

Beta () -0.2960 0.0337

SE Beta 0.2388 0.2388

Correl. -0.2976 0.0483

Part. Cor. -0.2956 0.0337

Partial -0.2959 0.0353

t-test -1.3390 0.1410

Sig.t 0.2331 0.8896

B Constant = 42.0221 SE B Constant = 3.4957 Interval BConstant = 49.4326

t Constant = 12.021 Sig. t. Const = 0.000

* Dependent variable is Manufacturing contribution to GDP (MCGDP)

Table 7 shows the beta and correlation coefficients for the manufacturing sector. A beta

coefficient of -0.2064 means that as bank liquidity ratio (BLR) rises by 100 per cent, we

should expect commercial bank lending to the manufacturing sector to decline by 20.64 per

cent and vice-versa. The beta coefficients are -0.1750 for the cash reserve ratio (CRR) and -

0.3106 for the loan-to-deposit ratio (LTDR). These beta coefficients are significant at the 5%

level of significance.

Table 7: Effects of Bank Management on Commercial Bank Lending to the

Manufacturing Sector

Model Variables


LTDR BLR CRR



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B -0.3183 -0.2258 0.5333

SE B 0.1920 0.2114 0.5896

Beta () -0.3106 -0.2064 0.1750

SE Beta 0.1874 0.1932 0.1935

Correl. -0.3039 -0.0688 0.1822

Part. Cor. -0.2958 -0.1906 0.1614

Partial -0.3040 -0.2014 0.1715

t-test -1.6580 -1.0680 0.9040

Sig.t 0.1089 0.2949 0.3737

* The dependent variable is commercial bank lending to manufacturing (CBLM)

The results in Table 8 show the effects of bank rates on commercial bank lending to the

agricultural sector. The beta coefficient of the maximum lending rate (MLR) is -0.1195. This

means that as the MLR rises by 100 per cent, the commercial bank lending to agriculture

(CBLA) falls by 11.95 per cent and vice-versa. In terms of the prime lending rate (PLR), the

beta coefficient is 0.1272, and the correlation coefficient is 0.3344. The sensitivity of

commercial bank lending to agriculture to the prime lending rate (PLR) is positive and

significant. As the prime lending rate rises by 100 per cent, CBLA rises by 12.72 per cent and

vice versa. Commercial bank lending to agriculture is more sensitive to a fall in the prime

lending rate (PLR), than an equivalent fall in maximum lending rate (MLR).

The critical beta coefficient of 0.8417 for savings rate (SR), and the corresponding

correlation coefficient of 0.8556, show that commercial bank lending to agriculture is much

more sensitive to changes in the savings rate. As the savings rate rises by 100 per cent, CBLA

rises by 84.17 per cent and vice versa. The positive correlation between SR and CBLA is

significant at the 5% level.

Table 8: Effects of Bank Rates on Commercial Bank Lending to the Agricultural Sector

Model Variables


MLR SR PLR

B -0.1209 1.0809 0.1502

SE B 0.1816 0.1364 0.2189

Beta () -0.1195 0.8417 0.1272



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SE Beta 0.1795 0.1062 0.1854

Correl. 0.2157 0.8556 0.3344

Part. Cor. -0.0657 0.7823 0.0677

Partial -0.1271 0.8363 0.1309

t-test -0.6660 7.7250 0.6860

Sig.t 0.51110 0.0000 0.4985

* The dependent variable is commercial bank lending to agriculture (CBLA)

MLR (Maximum Lending Rate), SR (Saving Rate), PLR (Prime Lending Rate)

The results in Table 9 show a negative beta coefficient of -0.1508 and an insignificant

inverse correlation coefficient of 0.10288. However, as the maximum lending rate (MLR)

rises by 100 per cent, the commercial bank lending to manufacturing falls by 15.08 per cent

and vice versa. The prime lending rate (PLR) has a beta coefficient of 0.0942 and a

correlation coefficient of 0.2030, and both coefficients are insignificant at the 5% level, since

the computed t-statistic of 0.3400 falls within the critical region of 0.7363. However, the

savings rate (SR) has a beta coefficient of 0.6434, and a correlation coefficient of 0.6367.

Both coefficients are significant since the t-test statistic of 4.0560 falls outside the critical

regions of 0.0004 at the 5% level. The results suggest that as savings rate rises by 100 per

cent, we should expect commercial bank lending to manufacturing to rise by 64.34%, and

vice versa. There is a significant and positive correlation between SR and CBLM.

Table 9: Effects of Bank Rates on Commercial Bank Lending on the Manufacturing Sector

Model Variables


MLR SR PLR

B -0.2546 1.3790 0.1856

SE B 0.4526 0.3400 0.5457

Beta () -0.1508 0.6434 0.0942

SE Beta 0.2680 0.1586 0.2770

Correl. 0.10288 0.6367 0.2030

Part. Cor. -0.0829 0.5779 0.0501

Partial -0.1977 0.6153 0.0653

t-test -0.5630 4.0560 0.3400

Sig.t 0.5783 0.0004 0.7363

* The dependent variable is commercial bank lending to manufacturing (CBLM)

The model summary results are shown in Table 10. The coefficient of determination (R2),

and the F-ratio show the significance of the variation in the dependent variables (CBLA and

CBLM). Model 1 has an R2 of 0.1615, meaning that 16.15 per cent of the variation in

commercial bank lending to agriculture (CBLA) is explained by changes to critical bank

management ratios. The explanatory power of the independent or predictor variables in

Model 2 is 14.05 per cent. With a coefficient of determination of 0.7370, Model 3 shows that

73.70 per cent of the variations in agricultural lending is explained by bank rates. Model 4



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s

has an R2 of 0.4133, showing that only 41.33 per cent of the variations in both credits to

manufacturing is explained by changes in bank rates. In all the four models, the computed R2

are significant since the F-ratio falls outside the critical regions.

Table 10: Effects of Bank Policy on Commercial Bank Lending to Agriculture and

Manufacturing: Model Summary Results

Summary Model 1 Model 2 Model 3 Model 4

Mult R 0.4019 0.3748 0.8585 0.6429

R2 0.1615 0.1405 0.7370 0.4133

Adj. R2 0.0684 0.0450 0.7077 0.3481

F-ratio 1.7340 1.4710 25.2160 6.3400

Sig. F 0.1840 0.2450 0.000 0.0020

RsqCh 0.1615 0.1405 0.7370 0.4133

S.E. 6.1914 10.4620 8.6438 3.46781

Model 1 CBLA = a + b1 BLR + b2CRR + b3LTDR + i

Model 2 CBLM = a + b1 BLR + b2CRR + b3LTDR + i

Model 3 CBLA = a + b1 SR + b2PLR + b3 MLR + i

Model 4 CBLM = a + b1 SR + b2PLR + b3MLR + i

5. Summary and Conclusion

Within the 1980-2010 period under investigation, the average bank liquidity ratio (BLR)

was 46.4 per cent, well above the prescribed average of 27.7 per cent. The average cash

reserve ratio (CRR) was 6.0 per cent, portraying easy monetary policy regime. However, the

average loan-to-deposit ratio (LTDR) was 69.5 per cent, below the prescribed the prudential

maximum of 80.0 per cent. The incidence of funding risk exceeded the liquidity risk that

banks were exposed to. The average margin that banks reaped from the difference between

savings rate and maximum lending rate was 11.9 per cent. An average of 10.1 per cent of the

total bank credit was allocated to the agricultural sector during the 1980-2010 period. The

average allocation of credit to the manufacturing sector was 28.4 period. However, the

difference for the manufacturing sector in the sectoral allocation of bank credit over the

period is unexplained.

In the four specified models, the Variance Inflation Factors (VIFs) are less than 5.0, while

the tolerance values are all above 0.2. Hence, the problem of multicollinearity does not exist

among the independent variables (IVs). The beta and correlation coefficients show a

significantly inverse relationship between bank liquidity ratio (BLR) and commercial lending

to agriculture. There is also a significantly inverse correlation between cash reserves ratio

(CRR) and commercial bank lending to agriculture (CBLA). However, CBLA is more

sensitive to the banks’ loan-to-deposit ratio (LTDR).



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The banks’ sectoral allocation of credit to the agricultural and manufacturing sectors was

not significantly sensitive to changes in the prime lending rate (PLR) and the maximum

lending rate (MLR). However, the allocation of bank credit to these two critical sectors was

largely influenced by changes in the savings rate. The explanatory powers of bank rates in

determining the allocation of bank credit to agriculture and manufacturing were more

significant in the overall model results.

Overall, the robust liquidity profiles of the banks, far above the prescribed minimum, did

not improve their funding of the real sector. Most enterprises in the agricultural and

manufacturing sectors groaned under increasing funding risk, although for most of the period,

the Central Bank of Nigeria (CBN) pursued an easy monetary policy regime, with less

variation in the cash reserve requirements. The banks seemed to be more interested in

reaping from wide margins between savings rate and maximum lending rate. The significant

variation in the maximum lending rate could suggest the rationing of credit in sectors

considered too risky to invest in, and the systematic exclusion of the non-prime borrowers.

Bank management decisions were significantly insensitive to the credit needs of the

agricultural and manufacturing sectors. Apparently, bank lending rates did not assume a

declining trend, inspite of easy monetary policy. The limited sectoral allocation of bank

credit to these two sectors could have been explained by increasing risk aversion by the

banks, in sheer preference for rent-seeking and short-term financing.

For a period of 21 years, the regulatory authorities failed to utilise the relevant monetary

policy regimes to direct bank credit to the agricultural and manufacturing sectors. The

seeming paternalism might have led to the abolition of the mandatory sectoral allocation of

credit, easy monetary policy stance and prudential lapses, thereby giving the commercial

banks an ample opportunity to build their liquidity profiles and resolve the banking dilemma

at the expense of funding the real sector.

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