May. 2014. Vol. 4, No.

1 ISSN 2307-227X
International Journal of Research In Social Sciences
© 2013-2014 IJRSS & K.A.J. All rights reserved
www.ijsk.org/ijrss

DOES FINANCIAL SECTOR DEVELOPMENT PROMOTE

INDUSTRIALISATION IN NIGERIA?
1
Ewetan, O. O. (PhD)., 2Prof. D. N. Ike

1. Dept. of Economics and Development Studies, Covenant University, Nigeria

2 Dept. of Economics, Caleb University, Nigeria

Abstract

This paper examines the long run and causal relationship between financial sector development and
industrialization in Nigeria for the period 1981 to 2011 using time series data. Results from a multivariate VAR and
vector error correction model provide evidence of long run relationship between financial sector development and
industrialization in Nigeria. The two measures of financial development had contrasting effects on industrial output.
Ratio of private sector bank credit to GDP has a positive relationship with industrial output while the ratio of broad
money stock to GDP has a negative relationship with industrial output. Granger causality test reveals long-run
unidirectional causal link running from industrialization to financial development. There is therefore the urgent
need for government to consolidate on past financial sector reforms to address the challenges of financial
intermediation in the domestic financial sector to improve loan disbursement to the industrial sector of the Nigerian
economy.

Keywords: Financial Sector Development, Industrialization, Cointegration, Granger Causality

JEL Classification: G00, 016
1.Introduction Ogunrinola, 2008; Okodua & Ewetan, 2013;
Acemoglu & Zilibotti, 1997).
While there is a vast theoretical and empirical The scholarly works of Schumpeter (1912),
literature on the links between financial sector Mckinnon (1973) and Shaw (1973) provide
development and economic growth that emerged evidence of strong links between financial
from the debate of Mckinnon (1973) and Shaw intermediation and economic growth. These
(1973) on financial intermediation and economic scholars argue that financial deepening and
growth, not much has been done to examine the savings, enhance investment particularly in the
links between financial development and industrial industrial and manufacturing sectors which
growth.There is also an extensive literature on the generate a positive impact on economic growth.
transmission mechanism between financial Financial deepening enhances financial sector
development and economic growth. One of these development which is usually accompanied by
transmission channels centers on the driving role relaxation of the credit access constraint facing
that financial development could play in a domestic firms, especially small and medium
country’s industrialization process through industries.
improved access to credit for industries (Kabango Theories of economic development
& Paloni, 2011). recognize industrialization as an integral and
Financial development connotes fundamental part of structural transformation of
improvements in the functioning of the financial economies. Many economists and institutions still
sector. These include increased access to financial consider it to be a precondition for increasing GDP
intermediation, greater diversification per capita, and improving the livelihood of the
opportunities, improved information quality, and people. In its Industrialization Report (2009), the
better incentives for prudent lending and United Industrial Development Organization
monitoring (Ewetan & Okodua, 2013; Alege & (UNIDO) stated: “Industrialization is integral to
economic growth and development, scarcely any

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May. 2014. Vol. 4, No.1 ISSN 2307-227X
International Journal of Research In Social Sciences
© 2013-2014 IJRSS & K.A.J. All rights reserved
www.ijsk.org/ijrss

country has grown without industrializing” Okodua, 2013; Okodua & Ewetan, 2013; Mccaig &
(UNIDO, 2009). Stengos, 2005; Beck & Levine, 2004; Levine,
Industrialization is said to be a significant Loayza, & Beck, 2000) offer strong and robust
measure of modern economic growth and evidence supporting the view that both well-
development but the Nigerian industrial sector has functioning banking systems and well developed
suffered from decades of low productivity. stock markets independently spur economic
Industrialization is generally argued as capable of growth. That is, banking systems and stock markets
increasing the pace of economic growth and provide different, but complimentary, growth-
ensuring swift structural transformations of the enhancing financial services to the economy.
economy. The critical role of the industrial sub- The extensive literature on the finance-
sector is predicated on the fact that it acts as an growth nexus reveals four possible scenarios on the
engine of growth by broadening the productive and nature of the relationship between financial
export base of the economy, reducing development and economic growth. These are
unemployment and minimizing rural-urban drift as finance-led growth referred to as supply-leading
well as helping to reduce poverty. hypothesis, growth driven finance referred to as
Despite the abundant natural and human demand-following hypothesis, bi-directional
resources, Nigeria has failed to achieve industrial relationship referred to as feedback, and no
development. Several policies and reforms by relationship between financial development and
various governments to turn around the industrial economic growth. Different techniques which
sector have largely been unsuccessful as the include cross-country, panel, time series, country
sectoral contribution of the industrial sector to the specific, industry level, and case study-study
gross domestic product has remain very low and analyses have been used to investigate the links
insignificant. between financial development and economic
Historically, economists accorded great growth (Levine, 1997, 2005; Aug, 2008; Beck,
importance to the role of the financial sector in the 2009; Ewetan & Okodua, 2013; Akinlo &
development of new markets and as catalyst for Egbetunde, 2004)
industrialization and economic growth Okodua and Ewetan (2013) examine the
(Gerschenkron, 1962). Although the nexus between effects of stock market performance on economic
financial development and economic growth has growth and find that in the long-run, overall output
long been a subject of intense scrutiny, few studies in the Nigerian economy is less sensitive to
have examined the relationship between financial changes in stock market capitalization as well as
development and industrialization as well as the the average dividend yield. On the contrary,
direction of causality between financial Thumrongvit, Kim, and Pyun (2013) in a study on
development and industrial production. This paper the effects of bond markets as a third key
therefore attempts to investigate the links as well as component of the financial system on economic
the direction of causality between financial sector growth find that government bonds positively
development and industrialization in Nigeria. relate to economic growth, while the effects of
corporate bonds change from negative to positive
2. Literature Review as domestic financial structures expend in size and
diversity. On the contrary Cecchetti and Kharroubi
The relationship between financial development (2012), argue that more finance does not always
and economic growth has been explored produce better outcomes, because the financial
extensively in the literature. Theoretically, sector competes with the rest of the economy for
financial intermediaries and financial markets scarce resources. They find that financial sector
mitigate the costs of acquiring information, size exhibit an inverted U-shaped effect on
enforcing contracts, and making transactions. The productivity growth. That is, further enlargement
positive effects on growth occurs through changes of the financial system beyond a certain point can
in the incentives and constraints facing economic reduce real growth
agents, improved information flows, capital Considering firm’s access to external
allocation, corporate governance, ameliorating risk, finance, Demirguc-Kunt and Maksimovic (2002)
pooling saving and easing exchange (Acemoglu & find that firms do not grow faster in either market-
Zilibotti, 1997; Khan, 2001; King & Levine, 1993). based or bank-based financial systems. Thus, the
Empirically both time series and cross-country overall level of financial development matters for
studies (Alege & Ogunrinola, 2008; Ewetan & economic growth, rather than the development of a

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May. 2014. Vol. 4, No.1 ISSN 2307-227X
International Journal of Research In Social Sciences
© 2013-2014 IJRSS & K.A.J. All rights reserved
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specific component of the financial systems 3. Methodology and Data

(Levine, 1997, 2005; Ang, 2008; Beck 2009).
There is mixed evidence within the The study investigates the existence of a long-run
literature supporting either a positive or negative relationship and dynamic interaction among the
link between financial sector development and study variables using annual time-series data from
industrialization. For instance, Larrain (2006) and 1981 to 2011. Empirical models are first specified
Raddatz (2006) used the methodology of Rajan and to capture the hypothesized relationships in the
Zingales (1998) to revisit the effect of financial study. These are then estimated using appropriate
development on industrial growth volatility, using estimation techniques. Data for all variables were
cross-industry (firm) data. Larrain (2006) finds a obtained from the Central Bank of Nigeria (CBN)
significantly negative coefficient on the interaction Annual Statistical Bulletin (2011) edition. Data for
term, arguing that lower volatility output occurs in the study is analyzed using the econometric
sectors with higher external dependence and in software, Stata 10.0.
countries with better financial development.
Raddatz (2006) finds that financial development 3.1 Model Specification
reduces the volatility of industries that require large
amount of liquidity. Udoh & Ogbuagu (2012) The baseline model estimated for this study is first
employed an aggregate production framework and specified in its functional form below:
autogressive distributed lag (ARDL) cointegration
technique and find that both the long-run and short- Yt= f (Lt ,Kt, MCYt, CPSt, INTt,,C) (1)
run dynamic coefficients of financial sector
development variables have negative and Where: Yt is the aggregate output of the industrial
statistically significant impact on industrial sector at a point in time t, Kt is the total capital
production in Nigeria.Similarly, Lin and Huang stock at a point in time t, Lt is the stock of labour
(2012) find that banking sector volatility exerts a at a point in time t. Total Factor Productivity (TFP)
negative effect on the growth of industries that rely as a function of financial depth is captured by M2
more on external finance. to GDP (MCY) ratio and the ratio of private sector
On the contrary, Loayza and Ranciere Bank credit to GDP (CPS), the interest rate (INT),
(2006) find a positive long-run linkage between and C is the error term. This functional relationship
financial development and output growth, is stated as follows:
coexisting with a mostly negative short-run
At= f(MCYt,CPSt,INTt,,C) (2)
association between financial fragility, namely,
banking crises, financial sector volatility, and Equation (2) expressed in its non-linear form
output growth. Similarly, Ang (2008) used an becomes:
augmented neoclassical growth framework and
find evidence suggesting that financial At= MCYtα3CPStα4INTtα5Ct (3)
development exerts positive impact on economic
development in Malaysia. Beck and Levine (2002) Equation (1) in its functional form is specified
using industry-level data found evidence that below
greater financial development accelerates the
growth of financially dependent industries. Yt= CtKtα1Ltα2MCYtα3CPStα4INTα5 (4)
Recently, Gehringer (2013) finds that financial
liberalization generates a strongly positive effect In order to obtain a more explicit and estimable
on productivity growth, investment, industrial linear function of equation (4), the variables on
output, and economic growth for the EU members. both sides are transformed into their natural logs
Apparently, there are few studies on the (L) to obtain the following:
relationship between financial development and
industrialization in Nigeria. This study is therefore lnYt = α0 + α1 lnKt + α2lnLt+ α3 lnMCYt + α4
another attempt to shed more light on the links lnCPSt + α5 lnINTt+ εt (5)
between financial development and
industrialization in Nigeria. The coefficient estimates in this case are
interpreted as constant elasticities which essentially
capture the sensitivity of the dependent variable to
a unit variation in each of the explanatory

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May. 2014. Vol. 4, No.1 ISSN 2307-227X
International Journal of Research In Social Sciences
© 2013-2014 IJRSS & K.A.J. All rights reserved
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variables. Theoretically, the InYtis expected to be under study. The size and statistical significance of
more than proportionately sensitive to marginal the coefficient of the error correction term in each
variations in each of the explanatory variables ECM model measures the tendencies of each
holding all other constant in each case. variable to return to the equilibrium. A significant
coefficient implies that past equilibrium errors play
a role in determining the current outcomes. The
3.2 Model Estimation Technique short run dynamics are captured through the
individual coefficients of the difference terms. The
In terms of econometric methodology, the short run dynamics are captured through the
multivariate cointegration approach offers useful individual coefficients of the difference terms.
insights towards testing for causal relationship. In Financial development (FD) does not Granger
principle, two or more variables are adjudged to be cause economic growth (GY) if all , and
cointegrated when they share a common trend. Economic growth (GY) does not Granger cause
Hence, the existence of cointegration implies that financial development (FD) if all = 0.
causality runs in at least one direction. However According to Akinlo and Egbetunde (2010), and
there could be exceptions to this expectation. The Mehra, (1994) these hypotheses can be tested using
cointegration and error correction methodology is standard F statistics.
extensively used and well documented in the
literature (Banerjee, et al. 1993; Johansen and
Juselius, 1990; Johansen, 1988; Engle and Granger, 3.4 Stationarity Tests
1987). Johansen (1988) multivariate cointegration
model is based on the error correction There is the possibility of co-integration when each
representation given by: variable is integrated of the same order d 1. This
∆Xt = µ + ∑ ∆ + + necessary, but rarely sufficient, condition implies
(6) that the series share a common trend. Therefore to
Where Xt is an (nx1) column vector of variables, ascertain whether mean reversion is characteristic
is an (nx1) vector of constant terms, Г and Π of each variable the paper used both Augmented
represent coefficient matrices, ∆ is a difference Dickey-Fuller (ADF) test by Dickey and Fuller
operator, and −N(0,∑). The coefficient matrix Π (1979, 1981), and Phillip-Perron (PP) test by
is known as the impact matrix, and it contains Phillips (1987) and Phillips Perron (1988) to infer
information about the long-run relationships. the stationarity properties of the study series. This
Johansen’s methodology requires the estimation of is conducted, with intercept only and intercept and
the VAR equation (6) and the residuals are then trend respectively, on the levels and first difference
used to compute two likelihood ratios (LR) test of the series.
statistics that can be used in the determination of
the unique cointegrating vectors of Xt. The
cointegrating rank can be tested with two statistics, 3.5 Granger Causality Test
the trace test and the maximal eigenvalue test.
Granger causality tests are performed to find out
3.3 Vector Error Correction Model (VECM) the direction of the causal link between financial
development and economic growth. The Granger
The error correction version pertaining to the six causality approach measures the precedence and
variables (Y, K, L, MCY, CPS, INT) used in the information provided by a variable (X) in
study is stated below: explaining the current value of another variable
∆Yt =α0 + ∑ 1t∆Yt-1 + ∑ 2t∆Kt-1 +
(Y). The basic rationale of Granger causality is that
∑ ∆L +∑ ∆MCY + the change in financial sector development Granger
3t t-1 4t t-1
causes the change in economic growth if past
∑ 5t∆CPSt-1 + ∑ 6t∆INTt-1 + λ1ECMt-1 + εi
(7) values of the change in financial sector
Where ECMt-1 is the error correction term and is development improve unbiased least-square
the mutually uncorrelated white noise residual. The predictions about the change in economic growth.
coefficient of the ECM variable contains The null hypothesis H0 tested is that X does not
information about whether the past values of granger-cause Y and Y does not granger-cause X.
variables affect the current values of the variable

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 May. 2014. Vol. 4, No.1 ISSN 2307-227X
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© 2013-2014 IJRSS & K.A.J. All rights reserved
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4. Empirical Results and Discussions 4.1 Stationarity Test

To avoid spurious regression outcomes, the paper
This section presents the results of the unit root, used the Augmented Dickey-Fuller (ADF) test to
cointegration, vector error correction, and Granger establish the existence of unit root in each of the
causality tests conducted. time series. Table 1 below summarizes the results
of the ADF test conducted,

Table 1 : Test for Stationarity

Levels 1st Difference Order of
Integration

Series ADF Critical LAG Rema ADF CV at Remarks LAG I(1)

Value at rks 5%
5%

LnY 2.591 -2.886 0 NS -3.718* -2.889 S 0 I(1)

LnK -1.865 -2.996 0 NS -4.960* -2.889 S 0 I(1)

LnMCY -0.578 -2.786 0 NS -4.615* -2.889 S 0 I(1)

LnCPS -0.556 -2.786 0 NS -4.760* -2.889 S 0 I(1)

LnINT -2.256 -2.786 0 NS -6.507* -2.889 S 0 I(1)

LnL 0.248 -2.786 0 NS -4.767* -2.889 S 0 I(1)
Note: *, ** and *** indicate 1%, 5%, and 10% significance respectively.
Source: Author’s Estimation using Stata 10.0

A variable is stationary when the absolute value of 4.2 Cointegration Result

the ADF is greater than the absolute value of the
critical value at a given level (1%, 5%, 10% denoted The cointegration test is used to establish the
as *, **, ***, respectively). NS and S refer to non- existence of a long run relationship among the
stationary and stationary respectively.Since all the variables. Table 2 reports the cointegration test
variables were not stationary in levels they were all results.
differenced once, and all the variables became
stationary meaning that the variables are I(1) series.
Table 2 : Test for Cointegration among Series
Max-Eigen
5% Critical 5% Critical
Eigen value Trace Statistic value Statistic Value
Maximum rank
0 . 118.0919 99.15 45.6298* 38.47

1 0.88744 74.2738* 67.52 28.7276 35.75

2 0.72787 46.6599 48.21 25.3576 28.18

3 0.69117 22.0814 28.68 13.7423 23.84

4 0.46329 9.5273 18.41 9.3732 16.19

5 0.35987 0.3640 4.76 0.2831 4.97

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Source: Author’s Estimation using Stata 10.0

The trace statistic indicates the presence of two betweenindicators of financial sector development,
cointegrating equations while the max-eigen statistic industrial sector output, real interest rate, labour and
indicates the presence of one cointegrating equation, capital. The trace statistic and max-eigen statistic
both at the 0.05 level of significance. Thus, the reject the null hypothesis of no cointegration at 5 per
results confirm the existence of cointegration cent level of significance.

Table 3 : Long Run Normalized Cointegration Estimates

Normalized Cointegrating Coefficients (standard error estimates)

LnY LnL LnK LnCPS LnINT LnMCY

1.000000 -1.45215 0.0026032 -0.2975154 -0.1076028 0.0077553

(0.0572477) (0.016781) (0.0818639) (0.0220378) (0.093274)

{-26.36} {0.12} {-3.56} {-3.62} {0.09}

P>|z| 0.000 0.818 0.001 0.000 0.834

Note: Standard error and Z-Statistics are stated in parenthesis as () and {} respectively
Source: Author’s Estimation using Stata 10.0

Table 3 above shows the normalized cointegration and insignificant relationship with the output of the
coefficients of the variables in the study model. The industrial sector at 0.05 level of significance which
results in the table are explained with respect to the deviates from a priori expectation.
signs and magnitude of the variables in the
normalized cointegration result. The probability 4.3 Error Correction Model
(P>|z|) statistic is used to determine whether or not a
variable is significant at a 5% level. The null The error correction term measures the speed of
hypothesis states that the variable is not statistically adjustment to restore equilibrium in the dynamic
different from zero and is thus insignificant while the model. The error correction coefficient shows how
alternative hypothesis states that the variable is quickly/slowly variables return to equilibrium and it
statistically different from zero and is thus should have a statistically significant coefficient with
significant. With a P-value less than 0.05, the null a negative sign between 0 and 1.A highly significant
hypothesis cannot be accepted that the variable is error correction term is further proof of the existence
statistically different from zero and is thus of a stable long-term relationship (Bannerjee et al.
significant. The coefficient of the variables shows if 1993). The Z statistic and the probability (P) statistic
the independent variable has a positive or negative are used to test the null hypothesis that the coefficient
relationship with the dependent variable is statistically different from zero. Coefficients
The coefficient values of credit to the private having a p-value of 0.05 and less are termed
sector (CPS), the deposit rate (INT), and labour force significant therefore the null hypothesis cannot be
(L) have a positive and significant relationship with accepted and it is concluded that the coefficient is
the industrial sector output (Y) in accordance with a significantly different from zero). However, if the p-
priori expectation at 0.05 level of significance while value is greater than 0.05, the null hypothesis cannot
the gross fixed capital formation (K) and the ratio of be rejected and it is concluded that the coefficient
broad money stock to GDP (MCY) have a negative value is not significantly different from zero.

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Table 4 : Vector Error Correction Estimates

Variable LnY LnL LnK LnCPS LnINT LnMCY

CE1 -0.283476 0.020132 -0.327673 3.056923 2.653366 1.632864

Standard (0.0960767) (0.0144654) (1.573296) (0.7412366) (0.8843342) 0.4744643
Error

Z-Statistic [-3.03] [0.30] [-0.28] [3.40] [2.41] [3.06]

P>|z| 0.002 0.692 0.796 0.001 0.021 0.002

Source: Author’s Compilation using Stata 10.0

Table 4 above shows that the error correction equilibrium relationship between the output of the
coefficient of industrial output (Y) is -0.283476.Thus, industrial sector and the explanatory variables.
the speed of adjustment is -0.2834 suggesting that
about 28.3 percent of errors generated in the current 4.4 Granger Causality Test
period within the model are automatically corrected
in subsequent periods. The coefficient also has a p- The Granger Causality test shows the causal
value of 0.002 and so the null hypothesis that the relationship which exists between the dependent
variable is not statistically different from zero is variable and each of the independent variables in the
rejected and it is concluded that the variable is equation.
significant at a 5% level. The significance of the error
correction mechanism supports cointegration and
suggests that there exists a steady long-run

Table 5 : Granger Causality Wald Tests

Variables Excluded Chi2 df Prob>chi2
LnY LnCPS 1.0039 2 0.707
LnY LnMCY 0.0392 2 0.895
LnY ALL 3.0783 4 0.573
LnCPS LnY 8.0396 2 0.019
LnCPS LnMCY 3.5587 2 0.113
LnCPS ALL 13.2034 4 0.018
LnMCY LnY 6.6063 2 0.039
LnMCY LnCPS 3.4088 2 0.184
LnMCY ALL 9.2277 2 0.063
Source: Computed by the Author using Stata 10.0

Table 5 above presents the result of the Granger further inspection, we notice that industrial output
causality test carried out to determine the direction of has a significant P-value with the ratio of bank credit
causality between industrialization and financial to the private sector and broad money stock with P-
sector development in Nigeria. The P-value of the values of 0.019 and 0.039. Therefore, in both
joint effect of bank credit to the private sector as a instances we cannot accept the null hypothesis that
ratio of GDP, and broad money stock as a ratio of the output of the industrial sector does not Granger
GDP on industrial output is 0.562. Therefore, we cause financial development which is captured by
cannot reject the null hypothesis that financial sector these two financial depth variables. This therefore
development does not Granger cause industrialization means that industrial output or industrialization
in Nigeria. This therefore suggests that the supply- Granger causes financial development in Nigeria and
leading hypothesis and bidirectional causality do not confirms the applicability of demand-following
hold between these two variables in Nigeria. Upon hypothesis in the Nigerian economy.

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5. Conclusion and Policy Implications and Growth.Journal of Economic Surveys,

22, 536-576.
This paper examined the relationship between 5. Beck, T. (2009). The Econometrics of
financial sector development and industrialization in Finance and Growth. In K. Patterson & T. C.
Nigeria over the period, 1981 to 2011 using the Mills (Eds.),The Palgrave Handbook of
multivariate cointegration approach and generated a Econometrics, 2, 1180-1212.
number of findings. First, significant evidence 6. Beck, T., & Levine, R. (2004). Stock
supports the existence of a long-run relationship Markets, Banks and Growth: Panel
between financial development and industrialization Evidence. Journal ofBanking & Finance, 28,
in Nigeria.Second, credit to the private sector had a 423-442.
significant positive effect on industrialization in 7. Beck, T., & Levine, R. (2002). Industry
Nigeria, while ratio of broad money stock to GDP Growth and Capital Allocation: Does
had had an insignificant negative effect on Having a Market-Based or Bank-Based
industrialization in Nigeria. Third, the granger System Matter?. Journal of Financial
causality test providesevidence in support of the Economics, 64(2), 147-180.
demand-following hypothesis in the Nigerian 8. Ceccheti, S. C., &Kharroubi, E.
economyas industrialization granger causes financial (2012).Reassessing the Impact of Finance on
sector development, and further confirms the finding Growth. BISWorking Papers, No. 381.
of Udoh and Ogbuagu (2012). This suggests that 9. Central Bank of Nigeria (CBN). (2011).
despite the several financial sector reforms that have Statistical Bulletin, Abuja: Central Bank of
been carried out in the Nigeria economy, the financial Nigeria.
sector has failed to impact positively on the industrial 10. Demirguc-Kunt, A., &Maksimovic, V.
sector of the economy. (2002). Funding Growth in Bank-Based and
This paper therefore recommends that Market-Based Financial Systems: Evidence
government should consolidation previous financial From Firm Level Data. Journal of Financial
sector reforms to make the financial sector strong to Economics,65, 337-36
support industrialization in Nigeria. Sound and 11. Ewetan, O. O., &Okodua, H. (2013). Is
developed financial system, improves financial There a Link Between Financial Sector
intermediation and the efficiency of economic Developmentand Economic Growth in
activities. Moreover, the unidirectional causality from Nigeria? International Journal of Financial
industrialization to financial development supports Economics, 1(4),108-118.
the recent industrial revolution policy of Jonathan 12. Gehringer, A. (2013). Growth, Productivity
administration, if well implemented should promote and Capital Accumulation: The Effects of
financial sector development in Nigeria. FinancialLiberalization in the Case of
European Integration. International Review
References of Economics and Finance, 25, 291-309.
13. Johansen, S. (1988). Statistical Analysis of
1. Acemoglu, D., &Zilibotti, F. (1997). Was
Cointegrating Vectors. Journal of
P:rometheus Unbound by Change?
EconomicsDynamic and Control, 12, 231-
Risk,Diversification, and Growth. Journal of
254.
Political Economy, 105, 709-751.
14. Johansen, S., &Juselius, K. (1990).
2. Akinlo, A. E., &Egbetunde, T. (2010).
Maximum Likelihood Estimation and
Financial Development and Economic
Inferences onCointegration with Application
Growth: TheExperience of 10 Sub-Saharan
to the Demand For Money. Oxford Bulletin
African Countries Revisited. The Review of
of Economicsand Statistics, 52, 169-210.
Finance andBanking, 02(1), 17-28.
15. Kabango, G., &Paloni, A. (2011). Financial
3. Alege, P. O., &Ogunrinola, I. O. (2008).
Liberalization and Industrial Development
Financial Sector Reforms and Growth of the
inMalawi. Discussion Papers Series 2010-
NigerianEconomy. Lagos Journal of
08, University of Glasgow.
Banking, Finance and Economic Issues,
16. Khan, A. (2001). Financial Development
2(1), 50-76
and Economic Growth. Macroeconomic
4. Ang, J. B. (2008). A Survey of Recent
Dynamics, 5,413-433.
Developments in the Literature of Finance
17. King, R. G., & Levine, R. (1993). Finance,
Entrepreneurship, and Growth-Theory and

24
May. 2014. Vol. 4, No.1 ISSN 2307-227X
International Journal of Research In Social Sciences
© 2013-2014 IJRSS & K.A.J. All rights reserved
www.ijsk.org/ijrss

EvidenceJournal of Monetary Economics, In: B. B. Rao (ED.), CointegrationFor the

32, 513-542. Applied Economist, 147-59, New York, St
18. Larrain, B. (2006). Do Banks Affect the Martin’s Press.
Level and Composition of Industrial 27. Okodua, H., &Ewetan, O. O. (2013). Stock
Volatility? Journal of Finance, 67, 1897- Market Performance and Sustainable
1925. Economic in Nigeria: A Bounds Testing Co-
19. Levine, R. (2005). Finance and Growth: integration Approach. Journal of Sustainable
Theory and Evidence. In P. Aghion, & S. N. Development,6(8), 84-92.
Durlauf(Eds.), Handbook of Economic 28. Raddatz, C. (2006). Liquidity Needs and
Growth, 1A, 865-934. Vulnerability to Financial
20. Levine, R. (1997). Financial Development Underdevelopment. Journal of Financial
and Economic Growth: Views and Agenda. Economics, 80, 677-722.
Journal ofEconomic Literature, 35(2), 688- 29. Rajan, R., &Zingales, L. (1998).Financial
726. Dependence and Growth. American
21. Levine, R., Loayza, N., & Beck, T. (2000). EconomicReview, 88(3), 559-586.
Financial Intermediation and Growth: 30. Schumpeter, J. (1912). The Theory of
Causality andCauses. Journal of Monetary Economic Development, New York,
Economics, 46, 31-77. OUP.Shaw, E. S. (1973). Financial
22. Lin, P. C., & Huang, H. C. (2012).Banking Deepening in Economic Development, New
Industry Volatility and Growth. Journal of York, Oxford University Press.
Macroeconomics, 34, 1007-1019. 31. Thumrongvit, P., Kim, Y., &Pyun, C. S.
23. Loayza, N., &Ranciere, R. (2006).Financial (2013). Linking The Missing Market: The
Development, Financial Fragility, and Effect of Bond Markets on Economic
Growth.Journal of Money, Credit, and Growth. International Review of Economics
Banking, 38, 1051-1076. and Finance, 27, 529-541.
24. McCaig, B., &Stengos, T. (2005). Financial 32. Udoh, E., &Ogbuagu, U. R. (2012).
Intermediation and Growth: Some Financial Sector Development and Industrial
Robustness Results. Economics Letters, 88, production inNigeria. Journal of Applied
306-312. Finance and Banking, 2(4), 49-68.
25. McKinnon, R. I. (1973). Money and Capital 33. United Nations Industrial
in Economic Development. Washington DC, DevelopmentOrganisation (2009).
The Brookings Institution Press. Industrial Development report, 1-27.
26. Mehra, P. Y. (1994). Wage, Growth and the
Inflation Process: An Empirical Approach.

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