Financial Market Development and Economic Growth in Nigeria

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International Journal of Applied Economic Studies Available online at http://sijournals.com/IJAE/ ISSN: 2345-5721

Vol. 4, Issue 3 June 2016

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. [email protected] Omoshola D. Babafemi Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria. [email protected] Aminu Hassan Jakada Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria. [email protected]

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,

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada 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

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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

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada economic growth and financial market development variables which were pulled out from the framework of financial market development depicted in figure 1.0 below:

Financial Intermediaries

Financial Services

Financial Markets

Capital Market

Stock Market

Money Market

Forex Market

(MCAP)

(SVT)

(MQM, CPS)

(TR)

External Inflence

Capital Formation

(FDI)

Economic Growth

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

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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 MCAP SVT MQM CPS TR

– index of Gross Domestic Product (Real GDP) expressed in constant term – Total Market Capitalization as a ratio of nominal GDP – Stock Value Traded as a ratio of nominal GDP – Money and quasi money growth – annual percentage – Credit to Private Sector as a ratio of nominal GDP – 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 (ε t) 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

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada 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: p

Yt      Yt 1  ECTt 1   t i 1

(5)

Where  is the first difference operator, Yt is a p X 1 vector of variables that are integrated of order one, Y t-1 is one period lag of the integrated variables, ECT t-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.

Intercept

Trend & Intercept

-0.195698 -2.119408 InRGDP -2.043010 -2.465859 MCAP -1.784207 -2.274088 SVT -3.458387 -3.312924 MQM -1.917512 -2.685399 CPS -1.441885 -2.555985 TR Source: Author’s computation using Eviews9

NS^ NS^ NS^ NS^ NS^ NS^

FIRST DIFFERENCE Intercept

Trend & Intercept

-5.337394* -5.341846* -5.241936* -4.413320* -5.722589* -4.367743*

-5.239323* -5.252580* -5.194522* -4.554634* -5.658288* -4.278171*

Remark

LEVEL

Remark

Variables

Table 5.1: Augmented Dickey Fuller (ADF) Unit Root Test

S^ S^ S^ S^ S^ S^

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).

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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^

FIRST DIFFERENCE Intercept

Trend & Intercept

Remark

Intercept

LEVEL

Remark

Variables

Table 5.2: Phillips-Perron (PP) Unit Root Test

-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 H0 H1 r=0 r≤1 r≤2 r≤3 r≤4 r≤5

r>0 r>1 r>2 r>3 r>4 r>5

Eigenvalue

(𝝀𝒕𝒓𝒂𝒄𝒆 ) Statistic

0.05 Critical Value

Prob.*

0.866996 0.649960 0.388673 0.293524 0.128969 0.095495

132.6436* 68.08766 34.49704 18.74912 7.630216 3.211745

95.75366 69.81889 47.85613 29.79707 15.49471 3.841466

0.0000 0.0682 0.4748 0.5111 0.5057 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

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada Table 5.4: Johansen Multivariate Cointegration Test (Maximum Eigenvalue) Hypotheses H0 H1 Hypotheses r=0 r≤1 r≤2 r≤3 r≤4 r≤5

r>0 r>1 r>2 r>3 r>4 r>5

Eigenvalue

0.866996 0.649960 0.388673 0.293524 0.128969 0.095495

(𝝀𝒕𝒓𝒂𝒄𝒆 )Statistic (𝝀𝒕𝒓𝒂𝒄𝒆 ) 64.55594* 33.59061 15.74792 11.11890 4.418471 3.211745

0.05 Critical Value 0.05 40.07757 33.87687 27.58434 21.13162 14.26460 3.841466

Prob.*

0.0000 0.0540 0.6871 0.6354 0.8127 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.

INRGDP 1 Standard Errors Test Statistics

Table 5.5: Normalized Cointegrating Coefficients MCAP SVT MQM CPS 0.305733 1.589539 0.054630 -1.133393 (0.08348) (0.14813) (0.01899) (0.11561) [ 3.66234] [ 10.7308] [ 2.87702] [-9.80392]

TR -0.009625 (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 (3.662) (10.731) (2.877) (-9.804) (-2.100)

(6)

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

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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 Variables

Coefficient

Std. Error

t-Statistic

Prob.

Constant ΔInRGDP t-1 ΔMCAP t-1 ΔSVT t-1 ΔMQM t-1 ΔCPS t-1 ΔTR t-1 ECM t-1

-0.006992 1.061019* 0.079659* 0.093512* 0.000961 -0.045224*** -0.000986** -0.065728*

0.062705 0.273209 0.023369 0.023293 0.002505 0.017691 0.000437 0.014888

-0.111510 3.883549 3.408758 4.014633 0.383515 -2.556291 -2.255655 -4.414832

0.9121 0.0007 0.0023 0.0005 0.7047 0.0173 0.0335 0.0002

STATISTICAL TESTS: R2 Adjusted R2 Schwarz criterion F-statistic

0.568291 0.442376 -0.519630* 4.513291** DIAGNOSTIC TESTS:

B-G Serial Correlation LM Test ARCH Test B-P-G -Heteroskedasticity Test Jarque-Bera Test

0.339169 0.373935 0.987693 0.830339

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 ECT t-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.

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada 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 Ftest. 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 PaganGodfrey 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.

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REFERENCES Diaz E, Prieto MA (2000). Bacterial promoters triggering biodegradation of aromatic pollutants.Curr.Opin.Biotech. 11(2): 467-475. Abiola, A. and Okoduwa, H. (2008). “The Role of the Nigerian Stock Market in Achieving Vision 2020” Journal of Banking, Finance and Economics, 2(1) Al-Faki, M. (2006). “The Nigerian Capital Market and Socio Economic Development” A paper presented at the 4th Distinguished Faculty of Social Science, Public Lectures, University of Benin Nigeria, 9-16 Amadeo, K (2013). “An Introduction to the Financial Markets, Definition, Types and Function” [Online] Available frohttp://useconomy.about.com/od/themarkets/a/capital_markets.htm Accessed 15 September, 2014 Beck, T., Levine, R., Loayza, N., 2000.” Finance and the Sources of Growth”.Journal of Financial Economics, 58, 261-300 Berthelemy, J. C. and Varoudakis, A. (1996). “Economic Growth, Convergence Clubs, and the Role of Financial Development” Oxford Economic Papers, New Series, 48(2), 300-328. Central Bank of Nigeria. (2009). “Annual Report and Statement of Account” Dimitris, K. C. and Efthymios, G. T. (2004). “Financial Development and Economic Growth: Evidence from Panel Unit Root and Cointegration Tests” Journal of Development Economics 73, 55– 74 Emeka, N. and Aham, K. (2013). “Financial Sector Development-Economic Growth Nexus: Empirical Evidence from Nigeria” American International Journal of Contemporary Research, 3(2), 87 Erdal, G., Okan, V. S., and Behiye, T. (2007). “Financial Development and Growth: Evidence from Norther Cyprus” International Research Journal of Finance and Economics, Issue 8 Greenwood, J. and Jovanovic, B. (1990). “Financial Development, Growth, and the Distribution of Income”. The Journal of Political Economy, 98 (5), 1076-1107. Ivanov, V., & Kilian, L. (2005). Studies in Nonlinear Dynamics & Econometrics A Practitioner’s Guide to Lag Order Selection For VAR Impulse Response Analysis A Practitioner’s Guide to Lag Order Selection For VAR Impulse Response Analysis ∗, 9(1). King, G. and Levine,” R. (1993). Finance and Growth: Schumpeter might be right” The Quarterly Journal of Economics, 108(3), 717-737. Kolapo, F. and Adaramola, A. (2012). “The Impact of the Nigerian Capital Market on Economic Growth (1990-2010)” International Journal of Developing Societies, 1(1), 11-19. Levine, R. and Zervos, S. (1998). “Stock Markets, Banks and Economic Growth” American Economic Review, 88 (3), 537-558. Levine, R., Loayza, N. and Beck, T. (2000). “Financial Intermediation and Growth: Causality Analysis and Causes” Journal of Monetary Economics, 46 (1), 31-77. McKinnon, R. (1973). Money and Capital in Economic Development. Washington D.C: The Bookings Institution. Victor, E. and Samuel, J. (2014). “An Empirical Assessment of Financial Sector Development and Economic Growth in Nigeria” International Review of Management and Business Research, 3(1).

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada Wachtel, P. (2001). “Growth and Finance: What do we know and how do we know it?” International Finance, 4, 335362.

APPENDIX A Time Series Data YEARS 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

RGDP 94.32502 101.0112 110.0640 116.2722 134.5856 134.6033 193.1262 263.2945 382.2615 472.6487 545.6724 875.3425 1089.680 1399.703 2907.358 4032.300 4189.250 3989.450 4679.212 6713.575 6895.198 7795.758 9913.518 11411.07 14610.88 18564.59 20657.32 24296.33 24794.24 54204.80 63258.58 71186.53 80222.13 89043.62

MCAP 15.34181 15.62870 16.07058 17.29213 16.56882 17.68634 14.27749 14.56802 12.00824 11.18315 13.81803 12.69358 15.17315 16.45296 9.943428 8.577088 9.865254 12.23592 13.44141 13.08479 18.40878 19.31773 19.69958 18.68203 18.05444 20.45781 24.82123 32.96055 37.99238 20.35787 19.24243 19.51969 18.89581 19.85602

SVT 3.286509 2.969967 3.179967 2.494148 2.600575 2.005894 2.174744 1.709113 1.098724 0.719350 0.604758 0.365571 0.330372 0.228620 0.110066 0.074399 0.066838 0.067678 0.051291 0.031280 0.120374 0.162909 0.254198 1.560766 2.501355 4.864044 14.40932 10.53229 8.190451 3.577672 3.794687 6.216131 5.555693 5.890000

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MQM 5.899972 9.545308 14.02163 11.60280 8.992736 1.953095 22.41116 32.91320 12.92800 32.70103 37.38021 63.26025 53.75797 34.49514 19.41171 16.17816 16.03900 22.31776 33.12106 48.06752 26.37680 18.82110 13.51137 20.67703 22.60363 36.35072 64.41681 53.36007 14.54323 9.968683 13.14230 17.41589 12.44962 5.350532

CPS 9.085659 10.56154 10.60114 10.71876 9.711546 11.32769 10.91669 10.37865 7.953513 7.097808 7.578257 6.640023 11.66560 10.24676 6.191351 5.917133 7.548060 8.822173 9.214550 7.900013 11.09412 11.93590 11.06101 12.45864 12.58233 12.33864 17.81495 28.56968 36.89332 18.59843 16.92602 20.42738 19.66704 19.23662

TR 36.41999 16.06369 7.122871 9.413961 10.14114 6.076326 5.160509 3.149426 6.776045 12.34010 13.95300 4.127298 5.343595 4.983555 5.012880 13.78140 27.33379 24.07671 19.41784 31.19609 33.88668 23.80989 20.19823 43.25154 111.1724 449.0795 431.4932 411.4237 286.9596 232.7190 208.2164 252.7783 213.9867 139.6119

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Vol. 4, Issue 2 April 2016

APPENDIX B Time Series Plot RGDP

MCAP

100,000

40 36

80,000

32 28

60,000

24 40,000

20 16

20,000

12 0

8 1985

1990

1995

2000

2005

2010

1985

1990

SVT

1995

2000

2005

2010

2000

2005

2010

2000

2005

2010

MQM

15.0

70

12.5

60 50

10.0

40 7.5 30 5.0

20

2.5

10

0.0

0 1985

1990

1995

2000

2005

2010

1985

1990

1995

CPS

TR

40

500

400

30

300 20 200 10

100

0

0 1985

1990

1995

2000

2005

2010

1985

13

1990

1995