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International Journal of Applied Economic Studies Vol. 4, Issue 3 June 2016
Available online at http://sijournals.com/IJAE/
ISSN: 2345-5721
1
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM
Approach
Abubakar Hassan
Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria.
Omoshola D. Babafemi
Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria.
Aminu Hassan Jakada
Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria.
Abstract
There has been a series of intensive debate on whether financial market development has the potential to impact
positively on long run economic growth of an economy. Thus, this study empirically examines the impact of financial
market development on economic growth in Nigeria using annual time series data covering the period of 1981-2014. In
achieving this, the study employed the Vector Error Correction Model (VECM) as the econometric methodology. The
empirical results show that overall there is a positive effect of financial market development on economic growth in
Nigeria. Almost, all the financial markets, namely, stock, capital and money market have been found to have a
significant positive impact with the exception of only foreign exchange market having a negative impact on economic
growth. On the basis of the findings of the study, it was recommended that there is a need for a comprehensive financial
reform to overhaul the entire Nigerian financial system so as to boost business and investment activities in the country.
The study also recommended for the establishment of effective legal framework to complement the regulatory and
supervisory institutions as well as directing the financial reform and credit policy of the apex bank towards improving
credit to private sector. Finally, the study recommended a more flexible foreign exchange rate policy and
diversification of the export base of the country to make the forex market a positive contributor to the nation’s real
GDP.
Keywords: Financial markets, Economic growth, Cointegration, Error Correction Mechanism (ECM).
JEL Classification: C50, D53, G20, O43
1. Introduction
There have been a series of intensive debate among various economists, policymakers, academicians and even among
theoretical thinkers about the key roles of financial market in the growth process of an economy. Majority of the
researchers have considered financial market development as an integral part of economic growth. According to
Wachtel (2001), there are four medium through which financial market can support economic growth. First, financial
markets allocate funds from the surplus sectors to the deficit sectors of the economy ; second, they provide incentives
and innovative mechanisms for mobilising savings; third, they reduce the costs associated with evaluation and
implementation of projects through large scale economies and enhance monitoring through corporate governance; and
lastly, they reduce the problems associated with risk in business by ensuring symmetry information , thereby enhance
provision of liquidity and risk sharing.
Financial markets are the markets where stocks, bonds, commodities, foreign exchange and even derivatives are traded
to raise cash for government or businesses, reducing companies’ risks and increasing investors’ wealth (Amadeo, 2013).
The Nigerian financial market comprises the money market, capital market, stock market and foreign exchange market
as well as the institutions and channels that facilitate the smooth intermediation of financial transactions in the
economy. Financial markets are also synonymous with the financial services sector which is made up of the banking
system, other financial institutions, and the securities, insurance and pension sub-sectors (Central Bank of Nigeria,
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach
Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada
2
2009). According to Abiola and Okoduwa (2008), the financial market consists of two major segments, the money
market and the capital market. The money market provides finance on short-term basis to individuals while the capital
market provides finance to businesses, enterprises, corporate bodies, government agencies etc on a medium to long term
basis. They emphasize that money market and capital market plays a key role in the growth of financial system of every
economy and it an important medium of generating funds to finance projects and investments that would lead to
economic growth. Also, Al-faki (2006) contend that the capital market is a network of specialized financial institutions
with series of mechanism, processes and infrastructures that in various ways, facilitate the bringing together of suppliers
and users of medium to long term capital or fund for investment and economic development projects.
Although, several researchers have been conducted studies on the relationship between financial market development
and economic growth in Nigeria; however, the results of the studies are inconclusive in view of the mixed findings
reached, especially on the channels through which financial market development and economic growth are related. The
early study by McKinnon (1973) found that financial market development influences economic growth through a
process of capital accumulation (both domestic and foreign) and technological change, which is aided by incentives
namely, promotion of local saving rate. Berthelemy and Varoudakis (1996) argue that the competitiveness of the
banking sector has a direct effect on the steady state growth rate, through a process of well-functioning educational
system. Meanwhile, Greenwood and Jovanovic (1990) found that the financial system impacts on the economic growth
through the contribution of more productive investments and increased capital allotment.
It is on the basis of these inconclusive results of previous studies that this study is carried out. Therefore, the study seeks
to investigate empirically the role of financial market development on economic growth in Nigeria between the period
1981-2014, with focus on four major types of financial market, namely, capital, stock, money and forex market. The
remaining part of this paper is divided into four sections as follows. Section 2; examined existing literatures in line with
the research topic. Section 3; discusses the methodology to be employed, Section 4; deals with the analyses of the data
obtained and section 5; concludes the paper and provides policy recommendations for the study.
2. Literature Review
Theoretically, economists agreed that financial market development plays a very vital role in economic growth and
development. However, the ongoing empirical research works concerning financial market development, its measures
and impact on economic growth have not reached any consolidative consensus (agreement). Levine et al (2000)
employed instrumental variable procedures and dynamic panel techniques to evaluate whether the exogenous
component of financial intermediary development influences economic growth and whether cross-country differences
in legal and accounting systems explain differences in the level of financial development of a sample of seventy four
(74) countries. Real per capita GDP was used to proxy economic growth and financial intermediaries’ indicators include
liquid liabilities, commercial – central bank and private credit. Using pure cross-sectional data covering 1960-1995,
they found that the exogenous component of financial intermediary development is positively associated with economic
growth. Also, their findings show that cross-country differences in legal and accounting systems account for differences
in financial development. They argued that countries with laws that give a high priority to secured creditors are getting
the full present value of their claims against firms; legal systems that rigorously enforce contracts including government
contracts, and accounting standards that produce high-quality comprehensive and comparable corporate financial
statements tend to have better developed financial intermediaries. Overall, their findings suggest that legal and
accounting reforms that strengthen creditor rights, contract enforcement, and accounting practices can boost financial
development and accelerate economic growth.
Another group of researchers, Beck et al (2000) examine the relationship between financial development and economic
growth and the sources of growth in terms of private saving rates, physical capital accumulation, and total factor
productivity using a pure cross-country instrumental variable procedure and a dynamic panel technique. The primary
measure of financial intermediary development employed was private credit, which measures the value of credits by
financial intermediaries to the private sector divided by GDP, and alternative measures used are liquid liabilities and
commercial-central Bank. The outcome of their study shows that financial intermediaries exert a large and positive
impact on total factor productivity, which translate to overall GDP growth and that the long-run links between financial
intermediary development and both physical capital growth and private savings rates are very weak. They concluded
that higher levels of financial development lead to higher rates of economic growth, and total factor productivity.
However, Dimitris and Efthymios (2004) investigate the long run relationship between financial depth and economic
growth, by employing panel unit root tests and panel cointegration analysis using data from 10 developing countries.
They estimated the long run relationship using fully modified Ordinary Least Square (FMOLS) technique. The
empirical results show that there is a single equilibrium relationship between financial depth, growth and ancillary
variables (investment share and inflation), and that the only causality relation implies unidirectional causality from
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
3
financial depth to growth. The empirical results further suggest that there is no short run causality between financial
deepening and economic growth (proxied by real output), thus, the effect is a fairly strong long run relationship between
financial depth and real output. They recommended that to promote growth, attention should be focused on long run
policies such as; the creation of modern financial institutions in the banking sector and stock markets. In conclusion
they state that long run causality runs from financial development to growth and the relationship is significant, and that
there is no evidence of bi-directional causality.
In contrast, Erdal et al (2007) empirically examines the relationship between financial development and economic
growth in Northern Cyprus by using Ordinary Least Square (OLS) Estimation Method. Annual growth rate of GDP was
used as proxy for economic growth and the financial development variables used are; the ratio of domestic investments
to GDP and ratio of deposit to GDP. Employing time series data from 1986-2004, the study found a negligible positive
relationship between financial development and economic growth in Northern Cyprus. Although, Granger causality test
showed that financial development does not cause economic growth, on the other hand there is evidence of causality
from economic growth to the development of financial intermediaries. Their empirical finding shows that there is a
causal relationship between annual growth rate of GDP and both the ratio of domestic investments to GDP and the ratio
of loan to GDP. They concluded that, there is no evidence to support the view that financial development promotes
economic growth in Northern Cyprus. By implication, financial development does not cause economic growth, rather,
economic growth causes financial development.
Recently, Victor and Samuel (2014) assessed the implication of financial sector development on the economic growth
in Nigeria using a time series data from 1990-2011. The variables used in their assessment include Real Gross Domestic
Product which proxies economic growth, and financial development variables- financial deepening which is given as a
ratio of money supply to Gross Domestic Product, liquidity ratio, interest rate and credit to the private sector. By
applying a cointegration technique they found that, on aggregate, financial sector development has significantly
improved the level of economic growth in Nigeria, although, credit to private sector did not play a significant role. They
concluded that further development of the financial sector should be targeted towards private sector credit by making
more funds available to the private sector through reduced interest rate on loans and to remove stringent collateral
conditions on credit facilities. While, Kolapo and Adaramola (2012) applied Johansen cointegration and Granger
causality test to examine the impact of the capital market on the economic growth in Nigeria between 1990-2010.
Economic growth was proxied by Gross Domestic Product (GDP) while the capital market variables considered
include; Market Capitalization (MCAP), Total New Issues (TNI), Value of Transactions (VLT), and Total Listed
Equities and Government Stocks (LEGS). They found that the activities in the capital market impact positively on the
Nigeria economy. They recommended that regulatory authority should formulate policies that would encourage more
private limited liability companies and informal sector operators to access the market for fresh capital and to remove
trading impediments such as high transaction costs to encourage more active trading in stocks.
Finally, a study by Emeka and Aham (2013) examines the financial sector development-economic growth nexus in
Nigeria using data from 1980- 2009. They used ratio of broad money stock to GDP, private sector credit to GDP,
market capitalization to GDP, banks deposit liability to GDP and prime interest rate as proxies for financial sector
development, while real gross domestic product proxy economic growth. They employed cointegration and Error
Correction Mechanism (ECM) and found that there is a positive effect of financial sector development on economic
growth in Nigeria. The financial sector development indicators; stock market capitalization-GDP ratio, interest rate and
broad money stock-GDP ratio are found to stimulate economic growth, however, credits to private sector and financial
sector depth variables are ineffective and fail to accelerate economic growth. They recommended that, to sustain and
enhance the existing relationship between financial sector development and economic growth in Nigeria, there is a need
to adequately deepen the financial system through innovations, adequate and effective regulation and supervision, a
sound and efficient legal system, efficient mobilization of funds and making such funds available for productive
investment, and improved services.
3. Methodology
The study employs the conventional econometric techniques to critically scrutinize the relationship between financial
market development and economic growth in Nigeria within the framework of Vector Error Correction Model (VECM).
The study incorporates various measures of capital market, stock market, money market and foreign exchange market
as proxies for the financial market development and real GDP as a proxy for the economic growth.
Data Sources and Description
This study employs annual time series data from 1981-2014 (33 observations), and the data are sourced from the
Nigerian Stock Exchange (NSE) , Security and Stock Exchange Commission (SEC) Market Bulletins, the Central Bank
of Nigeria Statistical Bulletins, 2014 and World Development Indicators, 2015. The data used include the measure of
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach
Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada
4
economic growth and financial market development variables which were pulled out from the framework of financial
market development depicted in figure 1.0 below:
Fig. no. 1: A Framework of Financial Market Development
The figure 1.0 above shows that the primary role of financial market is to serve as an intermediary between buyers and
sellers of financial services ( assets & securities) which include equities, bonds, currencies and derivatives. Although,
there are several types of financial market, but in the case of Nigeria we identify four major segments of financial
market as shown in the figure 1.0 above. They include capital, stock, money and forex market and from these markets
we come up with the financial market development variables as indicated in the figure above.
The dependent variable is the growth rate of the real GDP (RGDP) which serves as a proxy of economic growth. The
financial market development variables are classified into four groups-Total Market Capitalizations as percentage of
Financial Intermediaries
Financial Services
Financial Markets
Capital Market
(MCAP)
Stock Market
(SVT)
Capital Formation
Economic Growth
Money Market
(MQM, CPS)
Forex Market
(TR)
External Inflence
(FDI)
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
5
GDP (MCAP) measures the size of the capital market; Stock Value Traded as percentage of GDP (SVT) measures the
liquidity of the stock market; Money and quasi money growth – annual percentage (MQM) and Credit to Private Sector
(CPS) as a ratio of nominal GDP measure the size and outreach of the banking and other related sectors in the economy
(i.e. financial deepening) ; and finally, Total Reserves as a percentage of total external debt (TR) measure the adequacy
of the Foreign Exchange (Forex) Market. These classifications allows us to capture all the four major segments of the
financial market and by so doing, makes our study unique and more comprehensive in comparison with some of the
other previous studies which classified the financial markets into only two i.e. stock/equity market and financial
intermediaries or credit market (King and Levine, 1993; Levine and Zervos, 1998; Levine et al, 2000).
Model Specification
From the forgoing, this study specified the following functional form of the relationship between financial market
development and economic growth by incorporating various proxies which reflect the financial market development as
the explanatory variables and real GDP as the proxy for economic growth to serve as dependent variable:
RGDP= f (MCAP, SVT, MQM, CPS, TR) (1)
where:
RGDP – index of Gross Domestic Product (Real GDP) expressed in constant term
MCAP – Total Market Capitalization as a ratio of nominal GDP
SVT – Stock Value Traded as a ratio of nominal GDP
MQM – Money and quasi money growth – annual percentage
CPS – Credit to Private Sector as a ratio of nominal GDP
TR – Total Reserves as a percentage of total external debt
The equation (1) above can be further transformed into a mathematical model as follows:
RGDP = α + β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR (2)
Equation (2) above shows the mathematical form of the relationship between RGDP (as the dependent variable) and
MCAP, SVT, MQM, CPS, and TR as the independent variables. The theoretical expectation of the model is that all the
coefficients are expected to be positive: β1 >0, β2 >0, β3 > 0, β4 >0, and β5 >0, Furthermore, the equation (2) can be
transformed into an econometric model as follows:
RGDP = α+ β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR + εt (3)
The above equation is the econometric model where the error term (εt) is added to account for the effect of all the
omitted variables not included in the model as well as the influence of any measurement error that might affect the
dependent variable. The error term is assumed to be normally, independently and identically distributed around zero
mean and constant variance [ i.e. εt~ NIID [ (0,1)].
A visual inspection of the time series plots of the variables (see Appendix B) revealed that all the variables are trending
over time, most especially RGDP which exhibits some great elements of random walks with some extreme outliers.
This is because only RGDP is recorded in Naira not as a ratio of nominal GDP or annual percentage as such the natural
log of RGDP is taken in order to secure normality and homoskedasticity. Thus, equation (3) becomes log-linear model
through log transformation as follows:
InRGDP = α + β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR + εt (4)
Estimation Techniques
In order to analyze the econometric model specified above, unit root test based on the Augmented Dickey-Fuller (ADF)
and Philips-Perron (PP) test will be carried out first in order to find out whether the time series variables are stationary
or not. If the time series variables are stationary, this will prevent spurious result and problem of autocorrelation.
However, in most cases time series variables are non-stationary in nature; and thus running a regression analysis on
non-stationary variables will result in spurious results which in turn will lead to a wrong inference by establishing that
the variables are correlated when in reality they are not. Therefore, as would be expected if the variables of concern are
non-stationary at level but found to be stationary (of the same order) after taking first or second difference then a
cointegration test using Johansen Multivariate Cointegration would be applied accordingly. The purpose of the
cointegration test is to check the presence of a long-term equilibrium relationship among the variables in the model. In
other words, if the variables are cointegrated, there is said to be a long-term equilibrium relationship between the
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach
Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada
6
variables. Therefore, if the variables are integrated of the same order such as I(1) and they are cointegrated based on the
Johansen Multivariate Cointegration test, then Vector Error Correction Model (VECM) specified by Engle and Granger
(1987) will be applied to investigate the relationship between financial market development and economic growth.
The long run model can be formulated into an error correction model (ECM) which integrates short- and long- run
dynamics of the model. An ECM takes the following form:
ttt
p
i
t ECTYY
11
1 (5)
Where is the first difference operator, Yt is a p X 1 vector of variables that are integrated of order one, Yt-1 is one
period lag of the integrated variables, ECTt-1 is one period lag of the residual term (disequilibrium) from the long run
relationship, and ɛt is white noise error term. While α, β and π are the coefficients of VECM, with α representing the
intercept, β represents short run coefficients and π is the long-run coefficient of the one period lag of the disequilibrium
term. Equation (4) can be estimated by the usual Ordinary Least Square (OLS) method since all its terms are I (1) and
therefore standard hypothesis testing using t-ratios and related diagnostic tests can be conducted on the error term.
Theoretically, the coefficient of the one period lag of the disequilibrium term should be negative (i.e. π < 0) and
significant if the disequilibrium is to be corrected in subsequent period and long run equilibrium restored. In this light,
the coefficient of the error term represents the speed of adjustment to the long run equilibrium i.e. it shows by how
much any deviation from the long run relationship is corrected in each period.
4. Empirical Results & Discussion
In this section, the empirical results of the study will be presented and discussed. The empirical results include unit root
test results, cointegration test results and estimated VECM results. The study critically analyses and discusses these
results as well as compares and contrasts them with the previous empirical evidences reviewed in section two.
Unit Root Test Results
In this subsection the stationarity properties of all the variables of interest are examined using time series plot,
Augmented Dickey Fuller (ADF) test and Phillips-Perron (PP) test. From the visual results of the time series plot
contains in Appendix B, it appears that all the variables exhibit clear patterns which suggest presence of non-stationary.
To confirm this suspicion, the two popular conventional unit root (ADF and PP) tests are conducted and their results are
presented in Table 5.1 and Table 5.2, respectively.
Table 5.1: Augmented Dickey Fuller (ADF) Unit Root Test
Va
ria
ble
s
LEVEL
Rem
ark
FIRST DIFFERENCE
Rem
ark
Intercept Trend & Intercept Intercept Trend & Intercept
InRGDP
MCAP
SVT
MQM
CPS
TR
-0.195698
-2.043010
-1.784207
-3.458387
-1.917512
-1.441885
-2.119408
-2.465859
-2.274088
-3.312924
-2.685399
-2.555985
NS^
NS^
NS^
NS^
NS^
NS^
-5.337394*
-5.341846*
-5.241936*
-4.413320*
-5.722589*
-4.367743*
-5.239323*
-5.252580*
-5.194522*
-4.554634*
-5.658288*
-4.278171*
S^
S^
S^
S^
S^
S^
Source: Author’s computation using Eviews9
Note: * denotes significance at 1% level, and S stands for ‘Stationary’, NS stands for ‘Non stationary’, (^) indicates test conducted
with intercept and trend & intercept. The rejection of null hypothesis (series is non stationary) is based on the Mackinnon critical
values (1991).
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
7
Table 5.2: Phillips-Perron (PP) Unit Root Test
Va
ria
ble
s
LEVEL
Rem
ark
FIRST DIFFERENCE
Rem
ark
Intercept Trend & Intercept Intercept Trend & Intercept
InRGDP
MCAP
SVT
MQM
CPS
TR
-0.182272
-2.062496
-1.781755
-2.277499
-1.823547
-1.618013
-2.229381
-2.504381
-2.173600
-1.992160
-2.557302
-2.257261
NS^
NS^
NS^
NS^
NS^
NS^
-5.348326*
-5.833423*
-5.723716*
-6.272375*
-8.960685*
-4.178196*
-5.237902*
-5.717418*
-6.367932*
-8.828720*
-9.950748*
-4.050078*
S^
S^
S^
S^
S^
S^
Source: Author’s computation using Eviews9
Note: * denotes significance at 1% level, and S stands for ‘Stationary’, NS stands for ‘Non stationary’, (^) indicates test conducted
with drift and linear trend. The rejection of null hypothesis (series is non stationary) is based on the Mackinnon critical values (1991).
From the tables above, both the ADF and PP test revealed that all the variables are non stationary in level but found to
be stationary at first difference. This is because in level none of the null hypothesis was rejected for all the variables of
interest but at the first difference all the null hypotheses for all the variables were rejected at 1% significant level.
Therefore, this suggest that all the variables are integrated of order one i.e. they are all I(1s). This outcome satisfies the
condition for conducting cointegration test which requires that all the variables must be integrated of the same order
either at first difference or higher difference. Hence, the next sub-section present the results for the cointegration test.
Cointegration Test Results
After identifying the order of integration in levels and at first difference using both ADF and PP test, the results from
the two unit root tests suggested that the long run relationship among the variables may exist. Therefore, it is very
appealing to investigate if the individual variables of interest can actually converge in the long run. To investigate this,
the study employed Johansen Multivariate Cointegration technique. The results of the cointegration test are presented in
table 5.3 and table 5.4 for the Trace criterion and the Maximum Eigenvalue criterion, respectively.
Table 5.3: Johansen Multivariate Cointegration Test (Trace)
Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05
H0 H1 Eigenvalue Statistic Critical Value Prob.*
r=0 r>0 0.866996 132.6436* 95.75366 0.0000
r≤1 r>1 0.649960 68.08766 69.81889 0.0682
r≤2 r>2 0.388673 34.49704 47.85613 0.4748
r≤3 r>3 0.293524 18.74912 29.79707 0.5111
r≤4 r>4 0.128969 7.630216 15.49471 0.5057
r≤5 r>5 0.095495 3.211745 3.841466 0.0731
Trace test indicates 1 cointegrating eqn(s) at the 0.01 level
* denotes rejection of the hypothesis at the 0.01 level
**MacKinnon-Haug-Michelis (1999) p-values
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach
Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada
8
Table 5.4: Johansen Multivariate Cointegration Test (Maximum Eigenvalue)
Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05 H0 H1 Eigenvalue Statistic Critical Value Prob.*
Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05
r=0 r>0 0.866996 64.55594* 40.07757 0.0000
r≤1 r>1 0.649960 33.59061 33.87687 0.0540
r≤2 r>2 0.388673 15.74792 27.58434 0.6871
r≤3 r>3 0.293524 11.11890 21.13162 0.6354
r≤4 r>4 0.128969 4.418471 14.26460 0.8127
r≤5 r>5 0.095495 3.211745 3.841466 0.0731
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.01 level
* denotes rejection of the hypothesis at the 0.01 level
**MacKinnon-Haug-Michelis (1999) p-values
From the above tables, it can be observed that both the Trace test and Maximum Eigenvalue test rejected the first null
hypothesis at 1% level of significance, implying presence of one cointegrating equation among the variables.
Specifically, the trace test statistics indicates the existence of one cointegrating equation, and likewise the maximum
Eigenvalue statistics reveals the same at 1% level of significance in both cases. Therefore, we can conclude that there is
long run relationship among the variables. Note that the outcome of our cointegration test is similar to one obtained by
Victor and Samuel (2014) who discovered the existence of cointegration between financial development variables
(including financial deepening, liquidity ratio, interest rate and credit to the private sector) and real GDP in Nigeria
from 1990 to 2011. The next sub-section will therefore present the long run relationship for the variables.
The long-run relationship
From the Johansen Multivariate Cointegration technique, the normalized cointegrating equation is obtained which
shows the long run relationship between real GDP and financial market development variables. The table below
contains the coefficients of the first normalized cointegrating equation.
Table 5.5: Normalized Cointegrating Coefficients
INRGDP MCAP SVT MQM CPS TR
1 0.305733 1.589539 0.054630 -1.133393 -0.009625
Standard Errors
Test Statistics
(0.08348)
[ 3.66234]
(0.14813)
[ 10.7308]
(0.01899)
[ 2.87702]
(0.11561)
[-9.80392]
(0.00458)
[-2.10032]
Source: Author’s computation using Eviews9
The table 5.5 above shows the coefficients of the first normalized cointegrating equation with the standard error in
brackets and test statistics in parenthesis. The test statistics (or t-values) are computed by taking the ratio of the
coefficient of each variable by its respective standard error. From the above table, we can observe that all the variables
are highly statistically significant. Using the normalized cointegrating coefficients and their t-values we can now
construct the long run equation as follows:
InRGDP = -3.596 + 0.306MCAP + 1.589SVT + 0.054MQM ̶ 1.133CPS ̶ 0.009TS (6)
(3.662) (10.731) (2.877) (-9.804) (-2.100)
Equation (6) above shows the estimated long run relationship that exists among the variables of interest. As expected,
there is highly statistically significant positive relationship between total market capitalizations (MCAP), stock value
traded (SVT), money and quasi money (MQM) and economic growth (InRGDP). This implies that the three markets,
namely, capital market, stock market and money market play a very vital and positive role in the growth process of the
Nigerian economy. In contrast, credit to private sector (CPS) and total reserves (TR) turn out with an unexpected
negative sign which is contrary to our apiriori expectation, but however the two variables are also highly statistically
significant. Given the fact that our model is log-linear model, we can interpret the coefficients of the long run equation
as long run elasticities. Meaning each coefficient of the variables measures the contribution of each financial market to
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
9
real GDP. For instance, a 1% increase in total market capitalization will result in almost 4% increase in real GDP all
things being equal. By this standard, we can regard stock market contribution as the most significant in the development
of financial market followed by capital market while money market takes the third position and with forex market
taking the last as its contributions to real GDP is even negative. Our findings are very well supported empirically by
some of the previous studies such as Abiola and Okoduwa (2008); Al-faki (2006); Beck et al (2000) and Emeka and
Aham (2013) with the exception of Erdal et al (2007) whose findings are contrary to ours.
Our next empirical analysis would therefore involve the estimation of the Vector Error Correction Model, since the just
concluded cointegration test revealed the presence of long run relationship among the variables.
Vector Error Correction Results
Having met the two conditions (i.e. all the variables of interest are integrated of the same order and found to be
cointegrated) for estimating VECM, this study estimates the Vector Error Correction Model which is presented in table
5.5 below:
Table 5.6: Parsimonious Error Correction Estimates/Short Run Dynamics
Source: Author’s computation using Eviews9
The table 5.6 above presents the VECM results which include the parsimonious error correction estimates and the short
run dynamics among the variables as well as the statistical and diagnostic test results. Having found cointegration
among the variables, then it follows that the coefficient of the error correction term (ECT) should be negative and
statistically significant for the disequilibrium to be corrected in subsequent period and long run equilibrium restored.
This condition is met by our model as the coefficient of the one period of the error correction term ECT t-1 is negative (-
0.0657 approximately) and it is highly statistically significant at 1 percent level. The negativity of the ECTt-1 signals that
the system is stable enough and is capable of converging to the long run equilibrium after some shocks/disturbances in
the system. The value -0.066 implies that about 6.6% of the disequilibrium is restored within one year. However, this
means that the speed of adjustment is very sluggish as it will take 15 years on average for long run equilibrium to be
fully restored after some major shocks in the financial market. But given the underdeveloped nature of the financial
systems especially in a developing country like Nigeria, the outcomes of our model make some little sense at least.
Apart from the underdeveloped nature of the financial systems, there is also coexistence of a very huge nonbankable
population alongside huge informal sector operating in Nigeria constraining the ability of the financial markets in
playing vital roles in the growth process of the Nigerian economy.
Variables Coefficient Std. Error t-Statistic Prob.
Constant -0.006992 0.062705 -0.111510 0.9121
ΔInRGDP t-1 1.061019* 0.273209 3.883549 0.0007
ΔMCAP t-1 0.079659* 0.023369 3.408758 0.0023
ΔSVT t-1 0.093512* 0.023293 4.014633 0.0005
ΔMQM t-1 0.000961 0.002505 0.383515 0.7047
ΔCPS t-1 -0.045224*** 0.017691 -2.556291 0.0173
ΔTR t-1 -0.000986** 0.000437 -2.255655 0.0335
ECM t-1 -0.065728* 0.014888 -4.414832 0.0002
STATISTICAL TESTS:
R2 0.568291
Adjusted R2 0.442376
Schwarz criterion -0.519630*
F-statistic 4.513291**
DIAGNOSTIC TESTS:
B-G Serial Correlation LM Test 0.339169
ARCH Test 0.373935
B-P-G -Heteroskedasticity Test 0.987693
Jarque-Bera Test 0.830339
Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach
Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada
10
The short run coefficients are similar to that of the long run coefficients and are all relatively highly statistically
significant with the exception of MQM which is found to be statistically insignificant. Like the long run relationships,
MCAP, SVT and MQM all possess the correct signs and magnitudes while CPS and TR turn out with the incorrect
signs. As can be expected, InRGDP is the most important determinant of the real GDP in the short run, and then
followed by SVT, MCAP and MQM (just like in the long run equation) while CPS and TR still having negative impact
on real GDP. Overall, the short run coefficients are largely statistically and highly significant with a fairly good fit (
judging from the R2 and the adjusted R2 ) and also the model is overall highly statistically significant according to the F-
test. Note that the optimal lag chosen for this study is based on Schwarz information criterion which is more accurate
for a sample size smaller than 120 (Ivanov & Kilian, 2005). The results of our VECM is almost similar to the empirical
findings of Emeka and Aham (2013) who employed the same VECM technique and found that there is a positive effect
of financial sector development on economic growth in Nigeria. In their studies, the financial sector development
indicators; stock market capitalization-GDP ratio, interest rate and broad money stock-GDP ratio are found to stimulate
economic growth, however, credits to private sector and financial sector depth variables are ineffective and fail to
accelerate economic growth. Clearly, their findings are similar to our findings in this study.
Robustness Check
In addition to the individual test of significance and other statistical tests conducted, the model is further evaluated
based on econometric criterion. Generally, the model is econometrically satisfactory as it was found to be statistically
significant (having highly statistically significant coefficients) and theoretically meaningful (possessing correct signs
and magnitudes). Specifically, the model passed all the three major econometric tests, namely autocorrelation test,
heteroskedasticity test and normally test according to the Breusch-Godfrey Serial Correlation test, Breusch Pagan-
Godfrey Heteroskedasticity test and Jarque-Bera Normality test, respectively. Overall, these additional diagnostic tests
provide strong evidence of the robustness of our model and hence assuring valid inferences to be drawn with high level
of confidence.
5. Conclusion
This paper investigated the influence of financial market development on economic growth in Nigeria during the period
of 1981 to 2014 using a Vector Error Correction Model (VECM). As the study involves time series data, the two
popular conventional unit root (ADF and PP) tests were first applied to uncover the true order of integration of the
variables involved. The results of the two unit root tests revealed that all the variables are integrated of the same order at
first difference (i.e. I [1s]). Thereafter, Johasen Multivariate Cointegration test was performed to find the long run
relationship between the financial market development variables and economic growth. The findings from the
cointegration test showed that there exists positive and highly statistically significant long run relationship between the
three financial market development variables (MCAP, SVT and MQM) and real GDP. While the other two financial
market development variables (CPS and TR) are also found to be highly statistically significant but negatively related to
real GDP which is contrary to the apriori expectation of our model.
In the same vein, the results of the Vector Error Correction Model (VECM) showed the same outcomes and in addition
revealed that the coefficient of the Error Correction Term (ECT) is negative and statistically significant. Overall the
results of our empirical analysis are very robust according to all the three methodical criteria employed. That is the
results are theoretically meaningful, statistically significant and econometrically satisfactory.
From the forgoing, the study concluded that the three financial markets namely, capital, stock and money markets play
very important roles in the growth process of the Nigerian economy, while the foreign exchange market although a very
important segment in the financial market has not been developed to serve the Nigerian economy in achieving economic
growth and development. Thus, the study recommended that there is a need for a comprehensive financial reform to
overhaul the entire Nigerian financial system so as to boost business and investment activities in the country. This will
go a long way in strengthening the three financial markets (stock, capital and money market) that were found to have a
positive impact on economic growth and development of the Nigerian economy. In addition to the financial sector
reform, there is also the need to put in place effective legal framework that would complement the functioning of the
existing supervisory and regulatory financial institutions such as the Central Bank of Nigeria (CBN) and National
Deposit Insurance Corporation (NDIC). Also, the financial reform and credit policy of the apex bank should be geared
toward improving the credit to private sector by making more loanble funds available to the domestic investors through
soft loans bearing attractive interest rate and lessen the stringent collateral security requirements on the private sector
credits. For the foreign exchange market, the monetary authority in particular the CBN should embark on more flexible
foreign exchange rates policy and stop frequent interferences with the forex market forces. There is also the need for
government to diversify the export base of the country away from oil to other key areas such as agriculture, mining and
manufacturing. These will indeed increase the foreign exchange earnings of the country and thereby making forex
market becomes a positive contributor to real GDP and hence accelerates the economic growth rate and development of
the country.
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
11
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APPENDIX A
Time Series Data
YEARS RGDP MCAP SVT MQM CPS TR
1981 94.32502 15.34181 3.286509 5.899972 9.085659 36.41999
1982 101.0112 15.62870 2.969967 9.545308 10.56154 16.06369
1983 110.0640 16.07058 3.179967 14.02163 10.60114 7.122871
1984 116.2722 17.29213 2.494148 11.60280 10.71876 9.413961
1985 134.5856 16.56882 2.600575 8.992736 9.711546 10.14114
1986 134.6033 17.68634 2.005894 1.953095 11.32769 6.076326
1987 193.1262 14.27749 2.174744 22.41116 10.91669 5.160509
1988 263.2945 14.56802 1.709113 32.91320 10.37865 3.149426
1989 382.2615 12.00824 1.098724 12.92800 7.953513 6.776045
1990 472.6487 11.18315 0.719350 32.70103 7.097808 12.34010
1991 545.6724 13.81803 0.604758 37.38021 7.578257 13.95300
1992 875.3425 12.69358 0.365571 63.26025 6.640023 4.127298
1993 1089.680 15.17315 0.330372 53.75797 11.66560 5.343595
1994 1399.703 16.45296 0.228620 34.49514 10.24676 4.983555
1995 2907.358 9.943428 0.110066 19.41171 6.191351 5.012880
1996 4032.300 8.577088 0.074399 16.17816 5.917133 13.78140
1997 4189.250 9.865254 0.066838 16.03900 7.548060 27.33379
1998 3989.450 12.23592 0.067678 22.31776 8.822173 24.07671
1999 4679.212 13.44141 0.051291 33.12106 9.214550 19.41784
2000 6713.575 13.08479 0.031280 48.06752 7.900013 31.19609
2001 6895.198 18.40878 0.120374 26.37680 11.09412 33.88668
2002 7795.758 19.31773 0.162909 18.82110 11.93590 23.80989
2003 9913.518 19.69958 0.254198 13.51137 11.06101 20.19823
2004 11411.07 18.68203 1.560766 20.67703 12.45864 43.25154
2005 14610.88 18.05444 2.501355 22.60363 12.58233 111.1724
2006 18564.59 20.45781 4.864044 36.35072 12.33864 449.0795
2007 20657.32 24.82123 14.40932 64.41681 17.81495 431.4932
2008 24296.33 32.96055 10.53229 53.36007 28.56968 411.4237
2009 24794.24 37.99238 8.190451 14.54323 36.89332 286.9596
2010 54204.80 20.35787 3.577672 9.968683 18.59843 232.7190
2011 63258.58 19.24243 3.794687 13.14230 16.92602 208.2164
2012 71186.53 19.51969 6.216131 17.41589 20.42738 252.7783
2013 80222.13 18.89581 5.555693 12.44962 19.66704 213.9867
2014 89043.62 19.85602 5.890000 5.350532 19.23662 139.6119
International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016
13
APPENDIX B
Time Series Plot
0
20,000
40,000
60,000
80,000
100,000
1985 1990 1995 2000 2005 2010
RGDP
8
12
16
20
24
28
32
36
40
1985 1990 1995 2000 2005 2010
MCAP
0.0
2.5
5.0
7.5
10.0
12.5
15.0
1985 1990 1995 2000 2005 2010
SVT
0
10
20
30
40
50
60
70
1985 1990 1995 2000 2005 2010
MQM
0
10
20
30
40
1985 1990 1995 2000 2005 2010
CPS
0
100
200
300
400
500
1985 1990 1995 2000 2005 2010
TR