Dynamics of Financial Intermediation and Manufacturing Outputs Growth in Nigeria

(1986-2015): Empirical Evidence from Dynamic Ordinary Least Square (DOLS)

Umejiaku, R.I. (Ph.D)

Department of Economics,
University of Jos, Jos, Plateau State
roseumejiaku@yahoo.com

Ezie Obumneke
Department of Economics,
Bingham University, Karu, Nasarawa State
eobumneke@yahoo.com

Abstract
For decades, the Manufacturing sector has been recognized as an important engine of growth,
an antidote for unemployment, a creator of wealth, and a channel for sustainable development
capable of promoting industrialization in the economy. However, there has been a growing
concern on the decline of the outputs of the manufacturing sector in Nigeria in recent times,
despite the fact that the government embarked on several strategies aimed at improving
industrial production. Despite all attempts in developing the manufacturing sector, the situations
of manufacturing sector in Nigeria revealed that manufacturing sector has not improved
appreciably in terms of output growth. The study thus examines the dynamic effects of Financial
Intermediation on Manufacturing sector Outputs Growth in Nigeria using Dynamic Ordinary
Least Square (DOLS) regression technique. Preliminary investigation from the study suggests
that even though the variables were not stationary are levels individually, linear (long-run)
relationships were found to exist among them collectively. Findings from the study revealed that
credits to the manufacturing sector has not significantly enhanced increased in the level of
investments by manufacturing firms and had thus constrained manufacturing productivity in
Nigeria. The low investments have been traced largely to banks unwillingness to make credits
available to manufacturers, owing partly to the mis-match between the short-term nature of
commercial banks’ funds and the medium to long-term nature of funds needed by manufacturing
firms. The use of bank deposit liabilities for rent-seeking by banking industry in Nigeria to earn
income by capturing economic rent through manipulation and exploitation of the economic and
political environment (rather than earning profits through economic transactions and the
production of value addition wealth) has further constrained the output growth of manufacturing
firms. The study thus recommends that there is the need for monetary authorities to put in place
a revolving intervention fund to meet the long-term funding needs of the manufacturing sector
which Deposit Money Banks (DMBs) are unwilling and unable to provide. The financial sector
must equally seek ways of making resources more available to the productive sector of the
economy at zero or low cost of funds. By shoring up its funding for the priority sectors, the banks
can help to stabilize the manufacturing sectors and alleviate the impact of shocks.
Keywords: Manufacturing outputs, financial intermediation, dynamic ordinary least square,
bank deposit liabilities, Outputs growth and stock market capitalization
JEL: L6, G21 and L11

1
I. Introduction
The development of any economy is often viewed largely from the perspective of the growth and
vibrancy of its financial system. This shows how important investible funds are to the growth of
manufacturing firms. Therefore, the place of banking industry in manufacturing sector growth
and development in any nation cannot be over-emphasized. Deposit money banks more
effectively play intermediating role in financing industrial expansion than any other forms of
financial institutions in developing economies. Financial intermediation plays a significant role
in increasing economic activities of an economy through an effective conduit for channeling
funds from surplus to deficit units by mobilizing resources and ensuring an efficient
transformation of funds into the real productive sector of an economy (Nwite, 2014). More so,
financial intermediation leads to the transformation of the maturity of savers and investors’
portfolios, thus, providing sufficient liquidity to the system as the need arises. The third
important role is risks reduction from the system through diversification and techniques of risk
sharing and pooling (Nissanke and Stein, 2003). Through these functions, a modern financial
system may promote economic growth. Thus, financial systems through the intermediary roles
provide credit facilities to individuals, companies, as well as government for one kind of
economic activity or the other. It could be for industrialization purpose, manufacturing,
agricultural production, execution of contract and the likes.
In most modern economies, manufacturing sector serves as the vehicle for the production of
goods and services, the generation of employment and the enhancement of incomes. Hence,
Olorunfemi, Obamuyi, Adekunjo and Ogunleye (2013) described the manufacturing sector, as
the heart of the economy. The manufacturing sector also acts as a catalyst that accelerates the
pace of structural transformation and diversification of the economy, thus enabling a country to
utilize its factor endowments and to depend less on the foreign supply of finished goods or raw
materials. The manufacturing sector also creates investment capital at a faster rate than any other
sector of the economy while promoting wider and more effective linkages among different
sectors.
However, there has been a growing concern on the decline of the outputs of the manufacturing
sector in Nigeria in recent times, despite the fact that the government embarked on several
strategies aimed at improving industrial production and capacity utilization of the sector. The
unimpressive performance of the sector in Nigeria is mainly due to lack of access to finance

2
which has been the major problem facing the Nigerian manufacturing sectors. MAN
(manufacturing association of Nigeria, 2008) emphasized that low capacity utilization has
undermined the competitiveness of manufacturing industries, whose fortunes have been
worsened by the impact of globalization. A 2004 survey conducted by the Manufacturers
Association of Nigeria (MAN) revealed that only about ten percent (10%) of industries run by its
members are fully operational. Essentially, this means that 90 percent of the industries are either
ailing or have closed down. Capacity utilization was 70.1 per cent in 1980 but declined rapidly to
29.3 per cent in 1995 before rising gradually (with fluctuating trends) to 52.8 per cent in 2005
and 48.0 per cent in 2009. The share of manufacturing in the aggregate GDP declined from 5.3
per cent in 1981 to 4.1 per cent in 1993, 3.4 per cent in 2005, and marginally increased to 4.1 per
cent in 2009. Similarly, direct manufacturing employment declined over the years. A survey of
about 300 manufacturing companies carried out by MAN showed that 2,752,832 people were
engaged by the sector in 2001; 1,043,982 in 2005, and 1,026,305 in 2008. Given the fact that
manufacturing industries are well-known catalysts for real growth and development of any
nation, this reality clearly portends a great danger for the Nigerian economy. Jide (2012), also
remarked, that in addition to policy somersault, funding remains a challenge to all stakeholders
in the manufacturing sector
Another constraint hindering the performance of Nigerian manufacturing sectors, most especially
in the area of financing their operations is the government’s fiscal operation. The largest single
spender in the economy is government who often finance its deficit through the ways and means
of Central Bank of Nigeria (CBN). This mode of deficit financing directly increase the monetary
base and the level of excess liquidity which has an adverse effect on cost of credit and price
level. An examination of the financial deficits through the money market, reveal some negative
impact on the manufacturing industry and the Nigerian economy. It affects banking industry and
the Nigerian economy such that once the government gets the money from Treasury Bills (TB),
through mopping of liquidity in the system; it deprives the manufacturing sector of having
loanable funds, making the cost of the fund very high for manufacturing firms.
In-spite of the financial intermediation that has been moderately witnessed in Nigeria (with the
ongoing financial sector reforms), the manufacturing sector has shown such dismal performance,
that has greatly affected its potential output growth and size.

3
II. Literature Review
2.1 Concept of Financial intermediation
Financial intermediation is a process which involves surplus units depositing money with
financial institutions which then lend to deficit units (Mathews & Thompson, 2008). In other
words, financial intermediation is a system of channeling funds from lenders (surplus economic
unit) to borrowers (deficit economic unit) through financial institutions.
Financial intermediaries can be classified into institutional investors, pure intermediaries like
investment banks and Deposit Money Banks. Among all the financial intermediaries, banks are
the major financial intermediaries that accept deposits and make loans directly to the borrowers.
Financial intermediation involves the transformation of mobilized deposits liabilities by financial
intermediaries such as banks into bank assets or credits such as loan and overdraft. It is simply
the process whereby financial intermediaries take in money from depositors and lend same out to
borrowers for investment and other economic development purposes (Andrew and Osuji, 2013).
Financial intermediation is thus a system of channeling funds from lenders (economic surplus
unit) to borrowers (economic deficit unit) through financial institutions.
2.2 Theory of Supply Leading Hypothesis
The theory posits that a well-developed financial sector provides critical services to reduce
transaction, information and monitoring costs and increase the efficiency of intermediation. It
mobilizes savings, identifies and funds good business projects, monitors the performance of
managers, facilitates trading and the diversification of risks, and fosters exchange of goods and
services. These services lead to efficient allocation of resources; lead to a more rapid
accumulation of physical and human capital; and lead to faster technological innovation. This
eventually results into faster and long-term economic growth (Schumpeter, 1912).
Patrick's (1966) argues that the direction of causality between financial development and
economic growth changes over the course of development. In his view, financial development is
able to induce real innovation of investment before sustained modem economic growth gets
underway, and, as modem economic growth occurs, the supply-leading impetus gradually
becomes less and less important as the demand-following financial response becomes dominant.
As Patrick puts it, this sequential process is also likely to occur within and among specific
industries or sectors. For instance, one industry may initially be encouraged financially on a
supply-leading basis and, as it develops, have its financing shift to demand-following, while

4
another may remain in the supply-leading phase. This would be related more to the timing of the
sequential development of industries, particularly in cases where the timing is determined more
by governmental policy than by private demand forces (Patrick, 1966).
2.3 Schumpeterian Growth Models
Schumpeterian growth models gather specific kind of economic growth models, which are
produced by the endogenous process innovation and introduction of product. The
“Schumpeterian growth” term honours the name of Joseph Schumpeter, who defined the
progress of capitalism via creative process. Schumpeterian growth models are nearer in essence
to Schumpeter’s thinking than other those that put emphasis on learning by doing, physical
capital accumulation and human capital accumulation as foundations of growth process in the
economy (Dinopoulos, 2006).The Schumpeterian model of growth has the natural property that
neoteric discovery make products obsolete and old technologies. In addition, this creative
obliteration lineament in sequence has both normative and positive consequences. On the
normative side, in case of development and future research that recent innovation has positive
externalities. In case of positive side, it suggests a negative link between future and current body
of research, which emerges in the presence of matchless in the likelihood of cyclical patterns of
growth and also balances the growth equilibrium (Aghion, Howitt & Peñalosa, 1998).
Previous Schumpeterian growth models had analyzed the link between long run growth and trade
patterns by using a variety of approaches. They produced product cycle trade that is constructed
on the observation which many goods are discovered in the developed countries and their
manufacture shifts to developing countries as they are in the low level of technology and they
have imitated in this case. Moreover, the determinants of growth rate examined by this model
had also analyzed Schumpeterian models in open economies. According to Prior endogenous
growth models, there are three comprehensive channels which convey from the impact of
openness policy with regard to economic sector to long run growth. First channel is trade, the
long run rate of growth and innovation as well as the profitability of R&D investment increase
due to increasing of the size of the market.
Secondly, economic openness, through simplifying the information exchange, raises the
productivity of researchers and the field of knowledge spillovers. Thirdly, there is the trade
openness (Dinopoulos, 2006).

5
2.4 Empirical Discourse
Various studies have been carried out on financial intermediaries and output growth of firms’
overtime. For example, Odhiambo (2008) examined the dynamic causal relationship between
financial depth and economic growth in Kenya. The study focuses on the period, 1969 to 2005,
and included savings as an intermitting variable. To achieve this task, this study adopted two
econometric techniques; the dynamic tri-variate granger causality test and the error correction
model (ECM Modeling). This study concludes that one-way direction causality, from economic
growth to finance, exists in Kenya. In other words, finance plays a minor role in the attainment
of economic growth in Kenya.
Bangake and Eggoh (2011) also support the view of an existing two-way directional causality
between financial development and economic growth among developing countries. This study
focuses on seventy-one countries, which included eighteen developing countries, for the period
1960 to 2004. The study carried out its empirical analysis using both the Panel Cointegration
tests and the Panel cointegration estimation (that is, Dynamic OLS and panel VECM approach).
It establishes that both financial development and economic growth have influence on one
another, but suggests that a long-run policy approach may prove beneficial among the
developing countries.
Ikenna (2012) has employed time series data from 1970-2009 on an Autoregressive Distributed
Lag (ARDL) – Based Test Model to test for the long and short run impact of financial
deregulation and the possibility of a credit crunch in the real sector. The results suggest that
deregulating the Nigerian financial system had an adverse boomerang effect on the credits
allocated to the real sectors in the long run, and in the short run financial liberalization was
in all insignificant and negative. Ikenna also concludes that Deposit Money Banks (DMBs) in
Nigeria have strong discriminatory credit behaviour towards the real sector (agriculture and
manufacturing) and the SMEs as credit crunch is found to be present in these sectors both in the
short and long run.
Tonye and Andabai (2014) investigated the relationship between financial intermediation and
economic growth in Nigeria from 1988 to 2013 using Error Correction Mechanism. It was
observed that there is long run equilibrium between economic growth and financial
intermediation. It also indicates 96% short run adjustment speed from long run disquilibrium.

6
Toby and Peterside (2014) examined the role of banks in financing the agriculture and
manufacturing sectors in Nigeria using OLS regression method. Their results showed that the
role of banks in facilitating the contribution of the agriculture and manufacturing sectors to
economic growth is still significantly limited. The rise of numerous public intervention funding
programs in these sectors is evidence of the lagging banking intermediation. They stated that the
growing risk aversion of Nigerian banks is indicative of the liquidity and funding shortages in
the agriculture and manufacturing sectors. Their study recommends that monetary policy should,
therefore, emphasize mandatory sectoral allocation of credit with appropriate incentives to boost
the flow of bank credit to these sectors.
Basher (2013) examined the linkage between open markets, financial sector development and
economic growth to know if markets along with financial sector development affect economic
growth in Nigeria. The study made use of Granger causality test, Johansen cointegration test and
vector error correction model. It was found that the causation between open markets, financial
sector development and growth in Nigeria is weak and insignificant, and such cannot be used to
forecast economic growth in Nigeria. This study also does not consider effects of financial
intermediation on economic development using credit to private sector, lending rate and interest
rate margin as independent variables in the country.
III. Methodology
3.1 Research Design
Ex-post facto research design was adopted for the paper as it aids to tests hypotheses concerning
cause-and-effect relationships; as well as combining theoretical consideration with empirical
observation. The use of this design allowed for the testing of expected relationships between and
among variables and the making of predictions regarding these relationships.
3.2 Technique of Analysis
Although ordinary least squares (OLS) estimators are consistent, they do not have asymptotic t-
distributions (Enders, 1995; Wooldridge, 2013). This is due to the fact that the independent
variables series may be endogenous and arbitrarily correlated with the co-integration errors. The
endogeneity invalidates the strict exogeneity assumption; an important condition needed to
obtain asymptotic t-statistics for the regression estimators of the right-hand side series
(Wooldridge, 2013). Thus, the temptation to conduct significance tests on OLS estimators should
be avoided (Enders, 1995). To overcome this issue, Phillips and Hansen (1990) propose an

7
estimator which employs a semi-parametric correction to eliminate the problems caused by the
long run correlation between the cointegrating equation and stochastic regressors innovations.
A simple approach to constructing an asymptotically efficient estimator that eliminates the
feedback in the cointegrating system was advocated by Saikkonen (1992) and Stock and Watson
(1993). Termed Dynamic OLS (DOLS), the method improves on the classical (ordinary) least
square (OLS) by coping with small sample and dynamic sources of bias. It is a robust single
equation approach which corrects for regressor endogeneity (peculiar with co-integrating
relationships) by the inclusion of leads and lags of first differences of the regressors, and for
serially correlated errors (residuals) by a generalized least squares (GLS) procedure to provide
optimal estimates of cointegration regressions (Al-Azzam and Hawdon, 1999). Augmenting the

cointegrating regression with lags and leads of so that the resulting cointegrating equation
error term is orthogonal to the entire history of the stochastic regressor innovations is expressed
as:

(Where; are deterministic trend regressors, stochastic regressors). Under the assumption
that adding lags and leads of the differenced regressors soaks up all of the long-run

correlation between and , least-squares estimates of using equation (1) have the
same asymptotic distribution as those obtained from FMOLS and CCR.

An estimator of the asymptotic variance matrix of may be computed by computing the usual
OLS coefficient covariance, but replacing the usual estimator for the residual variance of with an

estimator of the long-run variance of the residuals, or a robust HAC estimator of the
coefficient covariance matrix may be computed.
3.3 Model specification
The fundamental theories of growth are quite explicit on the roles of capital, labour, and
technological progress. However, the Schumpeterian growth models were more explicit on the
relationship between finance and growth. Shittu(2012) gave a brief explanation of these models
as follows;

8
Where technological progress (T) is defined as a function of research and development (q), while
the two parameters define the probability that each unit spent on R&D yields a successful
innovation (γ) and the extent to which each innovation raises the productivity parameter (δ),
respectively. The economic determinants of the R&D are assumed to be taken as exogenous by
the entrepreneur. Thus, these may include; the discounted value of expected returns, the real
interest rate, capital per efficiency unit, and institution features of the economy.

From the equation above; the R&D intensity (q) is assumed to be positively related to the
discounted value of expected return as measured by γ and δ, negatively related to real interest
rate (r), and positively related to capital per efficiency unit (k), while product market competition
and (comp.) are examples of institutional features within the economy. Ɛ depicts all other
institutional features of the economy not cited in the equation. Thus, an increase in the saving
rate in the economy will increase the capital efficiency per unit, which in turn stimulates more
R&D activities via innovation. This will bring about growth in the economy. Thus, in a steady
state, T is similar to economic growth.
Therefore, following Schumpeterian growth models and expanding the empirical specifications
of Oleka, Sabina & Onyeze (2014), financial intermediation – Manufacturing output model is
thus specified as:

Where;
MO = Manufacturing Outputs
BDL = Bank deposit liabilities
CMS = Credit to Manufacturing Sector
SMC = Stock Market Capitalisation of listed companies
3.4 Variable Description
Bank Deposit Liabilities: Generally, banks mobilize deposits from the general public as part
of their intermediation role by way of demand deposits, savings deposits and time deposits
accounts (Nwaeze, Onydikachi and Nwabekee, 2014)
Credit to Manufacturing Sector: The relevance of this indicator arose from the fact that
credit is an important link in money transmission especially among manufacturing firms in
developing economies and it reflects the efficiency of financial intermediation process.

9
Stock Market capitalization of listed companies - the development of an economy's financial
markets is closely related to its overall development. In our case, analyzing developing
economies, commercial banks tend to dominate the financial system, while at higher levels
domestic stock markets tend to become more active and efficient relative to domestic banks
giving the structure of financial. Modern communications technology and increased financial
integration have resulted in more cross-border capital flows, a stronger presence of financial
firms around the world, and the migration of stock exchange activities to international
exchanges. Many firms in emerging markets now cross-list on international exchanges, which
provides them with lower cost capital and more liquidity-traded shares. However, this also
means that exchanges in emerging markets may not have enough financial activity to sustain
them, putting pressure on them to rethink their operations. (Dima and Opris, 2013)
The co-integrating model is specified as:

The apriori expectations are:

The variables are as previously defined.
ln = natural logarithm

= error term

= are long run cumulative multipliers or long-run effects of changes in the

explanatory variables on the dependent variable.
p and k = represents lag length and lead length respectively of each explanatory variable
IV. Results and Discussion
4.1 Unit Root Test
The presence of unit root in the underlying series points to the fact that there is non-stationarity
in that series. If the series are non-stationary, using standard econometric techniques can point to
misleading results, so standard economic theory requires the variables to be stationary.
The study employs Augmented Dickey- Fuller or ADF, (p) test (Dickey and Fuller 1979; 1981)
to test for unit root in the variables.
An ADF test here consists of estimating the following regression:

10
Where is the time series under consideration, is pure white noise error, t is trend, is drift

and . The number of lagged difference terms to include is often determined empirically,
the idea being to include enough terms so that the error term is serially uncorrelated. If the null
hypothesis that δ = 0 is rejected, it means the series is stationary. Unfortunately, under the null
hypothesis that δ = 0 (i.e., ρ = 1), the t value of the estimated coefficient of does not follow the t
distribution even in large samples; that is, it does not have an asymptotic normal distribution.
Dickey and Fuller have shown that under the null hypothesis that δ = 0, the estimated t value of

the coefficient of follows the τ (tau) statistic. These authors have computed the critical
values of the tau statistic on the basis of Monte Carlo simulations.
Results of the unit root tests are presented in table 1:

Table 1: Summary of Unit Root Test Results

Variables ADF Test Statistic Order of Integration
MO -3.355482(-3.229230)*** I(1)
CMS -3.296853(-3.229230)*** I(1)
BDL -9.332791(-4.440739)* I(0)
SMC -5.415015(-4.374307)* I(1)
Source: Authors Compilation (2017), E-views 9.0 Output

The ADF test indicates that three of the variables (SMC, CMS and MO) were found stationary at
first difference and hence the unit roots for ADF test were rejected at the first difference for the
three variables. This means that they are all integrated at order one I(1). Only BDL was found
stationary at levels, and as such integrated at order zero, I(0)
4.2 Cointegration Test
The problem of non-stationarity of most of the economic time series which is likely to render
standard ordinary least squares (OLS) estimator bias necessitated taking the first differences of
the time series before implementing standard OLS. However, this may lead to the loss of
information that is important for the long-run equilibrium. Cointegration approach has therefore
been developed by Engle and Granger (1987) overcame this problem.
Cointegration is the statistical implication of the existence of a long-run relationship between
economic variables. The test stipulates that if variables are integrated of the same order, a linear

11
combination of the variables will be integrated of that same order. The idea behind cointegration
analysis is that, although macro variables may tend to trend up and down over time, groups of
variables may drift together. If there is some tendency for some linear relationships to hold
among a set of variables over long periods of time, then cointegration analysis helps us discover
it. If the variables are integrated of different orders, however, there is some linear combination of
the two series that is stationary. In other words, instead of being I(1), the linear combination is
I(0).
Advances in econometrics have resulted in number of techniques in estimating cointegration
equation (e.g. Engle and Granger (1987), Johansen (1991), Pesaran, Shin and smith, (2001), etc.
The study employs the Pesaran, Shin and smith, (2001) to examine the long-run relationship
between the variables. The ARDL approach was applied because the regressors are a mixture of
I(1) and I(0).
In testing for cointegration, we estimate the cointegration equation by ordinary least square
(OLS) in order to test for the existence or otherwise of a long-run relationship among the
variables. This is done by conducting an F-test for the joint significance of the coefficients of
lagged levels of the variables. Two asymptotic critical values bounds provide a test for
cointegration when the independent variables are I(d) (where 0 ≤ d ≤1): a lower value assuming
the regressors are I(0) and an upper value assuming purely I(1) regressors. Suppose the F-
statistic is above the upper critical value, the null hypothesis of no long-run relationship is
rejected regardless of the orders of integration for the time series. On the other hand if the F-
statistic falls below the lower critical values, the null hypothesis are accepted, implying that there
is no long-run relationship among the series. Lastly, if the F-statistic falls between the lower and
the upper critical values, the result is inconclusive.
Table 2: Results of the Bound Test for Cointegration
ARDL Bounds Test
Date: 01/24/17 Time: 08:51
Sample: 1990 2015
Included observations: 26
Null Hypothesis: No long-run relationships exist

Test Statistic Value k

F-statistic  5.027729 3

Critical Value Bounds

12
Significance I0 Bound I1 Bound

10% 2.72 3.77

5% 3.23 4.35
2.5% 3.69 4.89
1% 4.29 5.61

Source: Authors Compilation (2017), E-views 9.0 Output

From table 2, the calculated F-Statistic that the joint hypothesis that the lagged level variables of
the coefficients is zero equals 5.027. This figure is greater than the upper bound of the critical
values of all the conventional levels 10% (3.77) and 5% (4.35). This means that joint null
hypothesis of all the lagged level variables of the coefficients being zero is rejected even at 5%.
This suggests that there is cointegration between manufacturing outputs and the independent
variables (measuring financial intermediaries) and hence a long run relationship between
manufacturing outputs and financial intermediaries.
4.3 Estimated Results and Discussion of Findings
With results of the Bound test clearly depicting long-run cointegration relationship among the
variables, equation (7) from methodology is estimated using Dynamic Ordinary Least Squares
Method (DOLS). Since the key to DOLS estimation is the construction of long-run covariance
matrix estimator, the study computes the long-run covariance matrix by employing a kernel
approach with a Quadratic-Spectral kernel and Newey-West bandwidth.
Results of the cointegration regression using DOLS is presented in table 3 (see appendix).
The line equation is presented as:

SEE = (0.11) (0.15) (0.14) (0.29)

t* = 17.08 -7.58 3.29 2.04
= 59.57

Prob(F-statistic) =0.00000;

The results showed that only three variables namely, BDL and SMC conformed to apriori
expectations; while CMS didn’t comply. The CMS shows a negative effect on MO within the
period under review. This is in agreement with Toby and Peterside (2014) who pointed out that
low level of investments have constrained productivity in Nigeria. The low investments have
been traced largely to banks unwillingness to make credits available to manufacturers, owing

13
partly to the mis-match between the short-term nature of commercial banks’ funds and the
medium to long-term nature of funds needed by industries. Ikenna also concludes that Deposit
Money Banks (DMBs) in Nigeria have strong discriminatory credit behaviour towards the real
sector (agriculture and manufacturing) and the SMEs as credit crunch is found to be present in
these sectors both in the short and long run. The works of Oputu (2010) present a fragmented
“rent-seeking” banking industry in Nigeria that earns income by capturing economic rent through
manipulation and exploitation of the economic and political environment, rather than earning
profits through economic transactions and the production of value addition wealth.
The BDL and SMC were however found to have had positive and significant effects on MO in
Nigeria. They were both found to be highly significant at 5% and thus suggest that BDL and
SMC are key variables that affect MO in Nigeria. A 10% increase in BDL and SMC, on the
average, increased MO by 2.3 and 2.9 percent respectively between 1986 and 2015.
4.4 Diagnostic Test
Diagnostic test underlying the DOLS regression from estimating equations (7) indicates the
goodness of fit of the model. This was captured by the adjusted coefficient of determination of
0.9312 which indicates that 93.12 percent of the systematic variation in the dependent variable is
explained by the regressors. The Correlogram Q-Statistics and Correlogram Squared Residuals
(alternative for LM ARCH Effect) indicates no autocorrelation (see appendix page).Thus the
results could be reliably deployed for policy purposes. Normality test by histogram- Jaque-Bera
test indicates that the residuals are normally distributed.
V. Conclusion and Recommendations
Manufacturing sector is reputed to be an important engine of growth, an antidote for
unemployment, a creator of wealth, and the threshold for sustainable development, thus it is
expected to dominate, shape, and define the core path of industrialization. However, banks
intermediary roles in financing the manufacturing sectors in Nigeria is still limited, hence the
increase in direct intervention funding in the industrial sector. Toby and Peterside (2014) pointed
out that low level of investments have constrained productivity in Nigeria. In addition to the
consequences of a maturity mismatch, the near-absence of long-term deposits has continued to
constrain the ability of banks to create long-tenured risk assets crucial for manufacturing sector
development.

14
The monetary authorities should therefore put in place a revolving intervention fund to meet the
long-term funding needs of the manufacturing sector which Deposit Money Banks (DMBs) are
unwilling and unable to provide. The financial sector must equally seek ways of making
resources more available to the productive sector of the economy at zero or low cost of funds. By
shoring up its funding for the priority sectors, the banks can help to stabilize the manufacturing
sectors and alleviate the impact of shocks.

15
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Appendix
Date: 01/24/17 Time: 09:18
Sample: 1986 2015
Included observations: 27

Autocorrelation Partial Correlation AC  PAC  Q-Stat  Prob*

     . |**. |      . |**. | 1 0.279 0.279 2.3524 0.125

     .**| . |      .**| . | 2 -0.236 -0.341 4.0969 0.129
     . *| . |      . | . | 3 -0.161 0.028 4.9421 0.176
     . | . |      . | . | 4 0.045 0.020 5.0103 0.286
     . |* . |      . | . | 5 0.099 0.031 5.3568 0.374
     . *| . |      .**| . | 6 -0.170 -0.239 6.4391 0.376
     . *| . |      . | . | 7 -0.164 0.024 7.4970 0.379
     . | . |      . | . | 8 0.015 -0.031 7.5059 0.483
     . | . |      . *| . | 9 -0.038 -0.154 7.5701 0.578
     .**| . |      . *| . | 10 -0.206 -0.199 9.5255 0.483
     .**| . |      . *| . | 11 -0.249 -0.171 12.566 0.323
     . | . |      . *| . | 12 -0.040 -0.080 12.651 0.395

*Probabilities may not be valid for this equation specification.

Source: Authors Compilation (2017), E-views 9.0 Output

Date: 01/24/17 Time: 09:18

Sample: 1986 2015
Included observations: 27

Autocorrelation Partial Correlation AC  PAC  Q-Stat  Prob*

     . | . |      . | . | 1 -0.052 -0.052 0.0821 0.774

     . |* . |      . |* . | 2 0.139 0.136 0.6849 0.710
     . *| . |      . *| . | 3 -0.091 -0.079 0.9522 0.813
     . |**. |      . |**. | 4 0.306 0.289 4.1501 0.386
     . *| . |      . *| . | 5 -0.133 -0.108 4.7819 0.443
     . |* . |      . |* . | 6 0.187 0.127 6.0791 0.414
     . |* . |      . |* . | 7 0.117 0.208 6.6130 0.470
     . | . |      .**| . | 8 -0.050 -0.221 6.7152 0.568
     . *| . |      . *| . | 9 -0.159 -0.101 7.8100 0.553
     . |* . |      . |* . | 10 0.109 0.082 8.3572 0.594
     . |* . |      . | . | 11 0.121 0.068 9.0718 0.615
     . | . |      . | . | 12 -0.054 -0.011 9.2262 0.684

*Probabilities may not be valid for this equation specification.

Source: Authors Compilation (2017), E-views 9.0 Output

18
6
Series: Residuals
Sample 1988 2014
5 Observations 27

4 Mean 7.13e-16
Median -0.014585
Maximum 0.178224
3 Minimum -0.124159
Std. Dev. 0.086258
Skewness 0.193840
2
Kurtosis 1.978070

1 Jarque-Bera 1.343966
Probability 0.510695

0
-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

Source: Authors Compilation (2017), E-views 9.0 Output

Wald Test:
Equation: Untitled

Test Statistic Value df Probability

F-statistic  59.57150 (3, 14)  0.0000

Chi-square  178.7145  3  0.0000

Null Hypothesis: C(1)=C(2)=C(3)=0

Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(1) -0.800573  0.105652

C(2)  0.509314  0.154502
C(3)  0.294859  0.144246

Restrictions are linear in coefficients.

Source: Authors Compilation (2017), E-views 9.0 Output

Table 3: Results of DOLS for estimating MO equation

Dependent Variable: LOG(MSO)
Method: Dynamic Least Squares (DOLS)
Date: 01/24/17 Time: 08:57
Sample (adjusted): 1988 2014
Included observations: 27 after adjustments
Cointegrating equation deterministics: C
Fixed leads and lags specification (lead=1, lag=1)
HAC standard errors & covariance (Bartlett kernel, Newey-West fixed
        bandwidth = 3.0000)

19
 Variable Coefficient Std. Error t-Statistic Prob.

LOG(CMS) -0.800573 0.105652 -7.577468 0.0000

LOG(BDL) 0.509314 0.154502 3.296488 0.0053
LOG(SMC) 0.294859 0.144246 2.044138 0.0602
C 6.309054 0.369206 17.08816 0.0000

R-squared 0.962983    Mean dependent var 7.718484

Adjusted R-squared 0.931253    S.D. dependent var 0.448331
S.E. of regression 0.117550    Sum squared resid 0.193454

Source: Authors Compilation (2017), E-views 9.0 Output

20