Key words: economic growth; financial development; foreign direct investment; panel causality; panel cointegration. I.

Transcription

1 The study of the causal relationship between Foreign Direct Investment, Financial market development and Economic growth for eight (8) West African countries: Evidence panel data analysis Grafoute Amoro, (PhD candidate) School of Economics, Shanghai University, & Dr. Drama Bédi Guy Hervé Département d Economie, Université Peleforo GON de Korhogo République de Côte d Ivoire & Dr. Laaria Mingaine School of Management, Shanghai University Abstract This paper examines, the dynamic interrelationships among FDI, financial development, and real output in West African Regional Financial Market covering eight (8) countries namely Benin, Burkina-Faso, Cote d Ivoire, Bissau Guinea, Mali, Niger, Senegal and Togo. The study utilizes recent advances in panel cointegration, panel error correction models and panel granger causality test for a set of eight (8) countries using annual data running the period The study identified that there is strong cointegration between the variables indicating long-run relationship. The outcome from the empirical studies demonstrate that the panel data coefficients estimated were consistent at conventional level, this confirms the overall significance of our model. We also explore the directions of causality among FDI, financial development, and economic growth and obtain solid, convincing evidence that the financial development indicators have a larger effect on economic growth than does FDI. The panel Impulse Response Functions and panel Forecast Error Variance Decomposition investigation confirm the same results mentioned above. The study concluded that financial development and foreign direct investment can be combined to influence significantly economic growth. Therefore to achieve this goal, effort should be devoted to boost saving volume while the macroeconomic environment should be made to attract more foreign direct investment into the regional market. Key words: economic growth; financial development; foreign direct investment; panel causality; panel cointegration I. Introduction During the last past three decades, the number of empirical studies examining the impact of foreign direct investment (FDI) and financial sector development on economic growth has been growing ever since the emergence of endogenous growth theory. The relationship between growth, FDI and financial market development sector has been an important debate subject for both financial and macro economists. It has been extensively studied in developed capital markets and literature on the variables date back to 1970s. However, multifactor models have been developed as an explanatory factor of the variation in equity prices and these studies have typically focused on developed markets. Despite there are a numerous studies that investigate this area of research, both the academics and the practitioners have not arrived at a consensus on the causality s direction among these variables, which remained as a source of ambiguity between economic growth and financial markets, and on causal links between FDI and financial development. > RJEBS: Volume: 03, Number: 8, June-2014 Page 42

2 However, more recent studies have shown that the positive growth impact of FDI is dependent on the extent of financial sector development in host countries. Hermes and Lensink (2003), Alfaro et al. (2004) among others, have provided empirical evidence supporting this proposition. Despite this, the number of empirical studies examining this complementary impact has been relatively small especially for the West African Economic and Monetary Union countries (UEMOA). With more than thirty years of the literature to consider, the remains part of our study is organized as follows. The next sections involve the empirical foundation of our research. Then, we briefly highlight the econometric methodology and the selected sources in section 3. The section 4 deals with interpretation and discussion of the econometric results and the last section is a concluding part that presents recommendations and formulates policies which could help state government and authorities to reach optimal stabilization. II. Literature review Financial market development and growth During the last decades, the role of stock market as part of financial markets in economic development process has been in center of many debates of researchers. The interaction between stock market indices return movements and macroeconomic variables has been a debated issue in majority of economic studies and also is emphasized by growth theories. Stock market has been associated with economic growth through its role as source for new private capital. On the other hand, economic growth may be the catalyst for stock market growth. Most of important area of the debates issues in economics was whether the stock market can be served as an important indicator to forecast future economic growth or vice versa. Many economists believe that significant decrease in stock prices could be source of future recession, whereas large increase in stock prices may reflect the expectation towards future economic growth. However, there were controversy issues to doubt the stock market s predictive ability such as the 1987 stock market crashed followed by world recession and 1997 Asian financial crisis Har, Tan and Lim (2008). There were several possible arguments have been discussed as equity market may led economic growth such as (i) There was evidence that a more developed equity market may provide liquidity that lowers the cost of the foreign capital essential for development, thus, nation with greater development of equity market tends to generate more domestic savings for economic growth Benchivenga, Smith and Starr (1996); Neusser and Kugler (1998). (ii) The role of equity market provided incentive for managers to make investment decisions that may affect firm value in the long run Dow and Gorton (1997). (iii) The ability of equity markets to generate information about the innovative activity of entrepreneurs King and Levine (1993) or the aggregate state of technology Greenwood and Jovanovic (1997) (iv)the importance of stock market in providing portfolio diversification and enabling individual firms to engage in specialized production with efficiency gain Acemoglu and Zilibotti (1997) Patrick (1966) clearly dichotomized the direction of influence between financial development and economic growth into demand following and supply leading hypothesis. In the demand following hypothesis causality runs from economic growth to financial development while in the supply leading hypothesis causality runs from financial development to economic growth. Related empirical evidence by Goldsmith (1969), Hicks (1969), McKinnon (1973) and Shaw (1973) found credence for supply leading hypothesis while evidence from studies by Robinson (1952), Kuznet (1955), Friedman and Schwarts (1963), Lucas (1988), Kar and Pentecost (2000), Hermes and Lensink (2003) and Alfaro et al., (2004) found credence for demand following hypothesis. Contrary to the above, studies by Demetriades and Hussein (1996), Ünalmiş (2002), Claessens, Klingebiel and Schmukler (2002) and Yucel (2009) found a bi-directional causality between financial development and economic growth while Ram (1999) did not find any relationship between financial development > RJEBS: Volume: 03, Number: 8, June-2014 Page 43

3 and economic growth. To come back to West African case Loesse (2009) re-examine the cointegrating and causal relationship between financial development and economic growth in the case of ECOWAS 1 countries. To this end, he used the Pesaran, Shin and Smith, (2001) approach to cointegration and the procedure for non-causality test of Toda and Yamamoto (1995) and also utilizing data from the World Bank (2007) and covers the period He calculated exact bounds critical values and showed that there is a positive long-run relationship between financial development and economic growth in four countries, namely, Cote d'ivoire, Guinea, Niger and Togo, and a negative one in Cape Verde and Sierra Leone. In addition, finance development causes growth only in Cote d'ivoire and Guinea. Tachiwou (2010) examined the impact of stock market development on growth in West African monetary union by utilizing time series data over the period and analyzed both the short run and long run relationship by constructing an Error-Correction Model. He found that stock market development positively affects economic growth in West African monetary union both in the short run and long run. Foreign Direct Investment and Growth The recent economic challenges and failure of some countries that have experienced both increases in FDI and stock market activities have made scholars to raise issues with respect to the relation between FDI and stock market development. On one hand, there is the view that FDI tends to be larger in countries that are riskier, financially underdeveloped and institutionally weak, Haussmann and Fernandez-Arias (2000). Under this view, FDI is a substitute for stock market development FDI takes place to overcome the difficulties of investing through capital market. According to this view, FDI should be negatively correlated with the development of stock market. In contract to this view, evidence from some countries showed that FDI flows into countries with good institutions and fundamentals and fuels the development of stock market through different channels. In addition to the traditional growth theory, the Solow s growth model reiterated the role of FDI in influencing growth through the introduction of new technologies, such as new production processes and techniques, managerial skills, ideas, and new varieties of capital goods Grossman and Helpman (1991); Barro and Sala-i-Martin (1995). Related, plethora studies on FDI-growth relationship exists, nevertheless, ambiguity still existed on the direction of causality between FDI and economic growth. Kumar and Pradhan (2002) observed that in most cases, the direction of causation between growth and FDI is not pronounced; Hansen and Rand (2006) found that foreign direct investment and growth have a positive relationship, but the direction of causality is unclear while Mwlima (2003), Carkovic and Levine (2002) concluded that FDI does not have a robust independent influence on growth. Empirical studies Isimbabi (1997; Hermes and Lensink (2003); Omran and Bolbol (2003); Kholdy and Sohrabian (2005); Ljungwall and Li (2007); Choog and Lim (2009) on the tri-variate relationship between financial development, foreign direct investment and economic growth, focused on the role of financial development in enhancing the positive relationship between FDI and economic growth, but failed to examine the extent of causality between these variables. In addition, most of these studies with exception to Choog and Lim (2009) used cross-sectional analysis. Evidence from cross sectional analysis have been found to provides pool estimates of the link between variables and such estimate disregard country specific factors. Another pitfall of cross-sectional studies according to Abu-Bader and Abu-Qran (2007) is that when economic growth is regressed on a wide spectrum of variables, researchers tend to interpret a significant coefficient of the measure of financial development as a confirmation of causality from financial development to economic growth. A significant coefficient of financial measures in such a The Economic Community of West African States (ECOWAS) is a regional group of fifteen countries, founded in Its mission is to promote economic integration in all fields of economic activity. > RJEBS: Volume: 03, Number: 8, June-2014 Page 44

4 regression can be equally compatible with causality running from financial development to economic growth, with causality running from economic growth to financial development or with bi-directional causality between the two variables. Such inadequate assessments of the causal relationship in a static cross section setting have led to a search for more dynamic times series analyses to unravel whether financial development causes economic growth or vice versa. As we mentioned above, several studies on finance-growth nexus in West Africa have only focused on the causal link between financial development and economic growth, some taking into account the role of stock market development. But in the context of West African Economic and Monetary Union financial market, not many studies can be traced in literature; moreover this paper use some additional variables which has not been previously used by researchers in the area. III. Selected data sources, Model specification and Econometric methodology. Selected Data sources and Measurement For this study variables representing financial market development are divided into two groups: The first category represented the banking sector or primary credit market while the second category represented the stock market or secondary credit market. Following Guariglia and Poncet (2006), three groups of bank based variables were identified. The three groups of indicators are measures of financial depth; misallocation of financial resources; and market-oriented financing respectively. These indicators allow us to account for both size and quality effect of financial development and intermediaries. To measure financial deepening or banking sector size, two measures are defined. The first is defined as the ratio of saving deposits to GDP (FD 1). The second indicator is defined as the ratio of total credits to GDP (FD 2). These two indicators measure the financial resources that are available for investment in West African Economic and Monetary Union Central s Bank (BCEAO). To evaluate the specific impact of misallocation of funds, the study relied on an indicator measuring the role of government interventionism induced distortions in financial sector. The indicator is the ratio of loans to deposits, which served as a proxy for cash relending (FD 3). The study followed the previous literature and considers this to be a measure of the Central Bank s credit to banks aimed at helping the Central Bank to meet their lending quotas Guariglia and Poncet (2006). Thus, this is a measure of the interventionism of the Central Bank. In West African Economic and Monetary Union, while the volume of deposits is determined by economic activity, the volume of lending is largely determined by policy objectives of BCEAO and is set through credit guidelines independently of banks to finance the lending target from deposits. The second category of financial markets development represents the stock market. Brasoveanu, Dregota, Catarama and Semenescu, (2008) classified the stock market indicators into categories namely (i) size variable and (ii) liquidity variable. These sets of stock market development variables had been used severally in the literature. The size variable is proxy by market capitalization ratio (FD 4) which is defined as market capitalization/gdp as used by Campos, Hanousek and Filer (1999), while the Liquidity variables are proxy by (i) value traded ratio (FD 5) defined as trading volume/gdp and (ii) turnover ratio (FD 6) defined as trading volume/market capitalization as used by Levine and Zervos (1998). The annual data utilized for our study will be selected from the World Development Indicator published by the World Bank covering the whole period for the eight (8) West African countries because annual data of the relevant variables are available. In so doing, the Foreign Direct Investment (FDI) variable is measured by the direct investment items in the balance of payment account of the concerning countries while economic growth is measured by the real gross domestic output (GDP) calculated by dividing the nominal gross domestic output by the consumer price index. The openness is obtained by export plus import as a ratio of GDP. The private credit, liquidity liabilities, export and import will be collected from the World Bank Indicator database Online. Note that > RJEBS: Volume: 03, Number: 8, June-2014 Page 45

5 for our paper we use only liquidity liabilities FD1 and the private credit FD2 as financial development indicators. Model Specification This study investigates the causal relationship between Foreign Direct Investment, Financial market development and economic growth for eight (8) West African countries by using the technique of panel data analysis. Yen Li Chee et al., (2010) have researched the impact of FDI and financial sector development on economic growth of 44 Asian and Oceania countries by using panel data methods (fixed effects-estimator and random effects-estimator). Therefore, the econometric model is specified as follow: LnGDP LnFDI LnFD1 LnFD2 LnX LnI LnOP (1) it 0 1 it 2 it 3 it 4 it 5 it 6 it it Where represents the natural logarithm, GDP is the gross domestic product, FDI represents the foreign direct investment, FD 1 is the ratio of the total domestic saving deposit to GDP, FD 2 is the ratio of domestic credit provided by banking sector to GDP, X is the real export value, I is the real import value, OP represents trade openness to capture the trade component of economic globalization which is depicted by export plus import as a ratio of GDP and it is a normally distributed error term. Note that the variables utilized for N countries indexed by i observed in T period indexed byt. Following standard economic theory, 2, 3, 4, 5 and 6 are expected a priori to be positive and 1 can either be positive or negative depending on the tendency of government expenditure. Econometric Methodology Applied. Before conducted our empirical study, the time series properties of the variables need to be examines. Non-stationary time series data has often been regarded as a problem in empirical analysis. Working with non-stationary variables leads to spurious regression results from which further inference is meaningless when these variables are estimates in their levels. In order to overcome this problem there is a need for testing the stationarity of these micro-economic variables. As the involvement of macroeconomic applications in the panel data analyses has been growing recently, the Dickey-Fuller and Augmented Dickey-Fuller tests are required to be extended for testing stationarity in panel data analysis. When dealing with panel data, because the procedure is more complex, the ADF and DF tests can result in inconsistent estimators. Thus, the stationarity of the series should be tested by using three different types of tests, namely (LLC) Levin Lin and Chu (2002), Im, Pesaran and Shin (2003) (IPS) and Hadri (2000) to verify the stationarity. In the analysis, firstly the (LLC) test is employed because the model allows heterogeneity of individual deterministic effects and heterogeneous serial correlation structure of the error terms assuming homogeneous first order autoregressive parameters. Barbieri (2005) In addition the model provides two-way fixed effects, one of which comes from the term i and the other one emanates from t. Moreover, these two parameters allow for heterogeneity, as the coefficient of lagged Y t is limited to be homogenous through all individual units of the panel. > RJEBS: Volume: 03, Number: 8, June-2014 Page 46

6 n Y Y Y t with i 1,... N ; t 1..., T i, t i i, t 1 k i, t k i t it k 1 (2) ( LLC ) model tests the hypothesis of non-stationary, i.e. the presence of unit roots. There are two major shortcomings of the LLC test. Firstly, it relies on the assumption of the independence across units of panel where a cross sectional correlation may be present. Barbieri (2005) Secondly and more importantly, Autoregressive parameters are considered to be identical across the panel in this model. Im, Pesaran and Shin (2003) broadened the LLC test to overcome the second limitation of it by presenting a more flexible and computationally simple test structure that permits the among individuals, i.e. by allowing heterogeneity. The IPS test made the estimation for each of the i section possible. As a result their model is such that; n Y Y Y t with i 1,... N ; t 1..., T i, t i i i, t 1 k 1 i, t k i t it k 1 (3) IPS (Im, Pesaran and Shin (2003) tests the null of non-stationarity. That is; H 0: i = 0 for all i This alternative test clarifies that a fraction of the panel can have unit roots. This is the contrasting point of IPS to LLC. The IPS model is constructed under the restrictive assumption that T should be the same across individuals. (Hadri, 2000) test is distinctive from other two tests mentioned above for testing the absence of unit roots, i.e. variance of the random walk equals to zero. He proposes a parameterization which provides an adequate representation of both stationary and nonstationarity variables and permits an easy formulation for a residual based Lagrange-Multiplier (LM) test of stationarity. Barbieri (2005) in his model, the disturbance terms are heteroskedasticity across. He provides a LM where the series are stationary Çelik, Deniz and Eken (2008), such that; Y r t e where it io i it t t 1,... N ; t 1,... N e it it it r 1 (4) (5) Beyond testing for the unit root, there is a need to establish whether the non-stationary variables are cointegrated. The panel cointegration tests are implemented through two main tests, namely Pedroni (1997,1999 & 2000) and Larson et al., (2001) to test for the presence of long run equilibrium relationship between economic variables. Pedroni (1997,1999 & 2000) concentrated on the homogeneity of the two simple variables in his first analysis. Nonetheless, it has some limitations. Thus, in the second study, he analyzed multi regressors models. As a result, Pedroni developed seven test statistics to test the null of no cointegration between two variables; Larson et al., (2001) constructed their model on Johansen (1988) maximum likelihood estimator tests on residuals, i.e. a panel extension of VAR cointegration analysis. This model permitted to avoid from unit root tests on residuals, widening the unique cointegrating vector assumption. As a consequence, the model is written as follow: n Y Y Y (5) i, t i, i t1 ik, i k1, i t k 1 The Larson et al., (2001), model is based on the estimation of the above model separately for each cross sectional unit by employing the maximum likelihood models to compute the trace for each. To this end the null and alternative hypothesis will be; > RJEBS: Volume: 03, Number: 8, June-2014 Page 47

7 H0 rank ( i ) r i r for all i 1,..., N H1 rank( i ) p for all i 1,..., N Where p is the number of variables we adopt to test cointegration among them. We can evaluate the Larson et al., (2001) process in two phases. First, after the computation of trace statistics, the rank trace statistic LR NT should be calculated by taking the average of N cross sectional units. Second, the LN RT statistics is used to calculate LR NT by adopting the formulation below. As it can be realized, one can say that when Y LR is greater than the critical value of 1.96, it moves to the upper cointegration vector number by rejecting the one it has Çelik, Deniz and Eken (2008). ( ) / var( ) (6) Y N LR E Z Z LR NT k k With this cointegration test still error correction is better than and being adopted. Following this procedure, the vector error correction model is very crucial in the cointegration literature as it drives from the fact that, if macro variables are integrated in order one and are cointegrated, they can be modeled as having been generated by the vector error correction model. The produces better short run forecasts that hold together in economic meaningful ways. Thus, we suggest the reparametrization of the initial vector auto regression in the familiar formulated in equation (7). The general model can be written as: p 1 (7) Y Y i B v it it p it 1 it it i 1 Where is and vector of the time series of interest, ~, and contains the conditioning variable set. The order of VAR is assume finite and the parameters, and are assume constant. The long-run re pon e matrix i and if the ca e can be expre as the product of two matrix and : where contains the cointegrating vectors and is the loading matrix which contains the coefficients with which the cointegrating relationships enter in the equation.as we mentioned earlier Pedroni (1997,1999 & 2000) and Larson et al., (2001) methodology target is to test the existence of long-run equilibrium relationship among the variables. The general overparameterized model is estimated with maximum lags denoted. An error correction term is introduced in the model; hence equation (8) is re-specified to include panel error-correction term in this form: p p p P p p p lngdp lngdp LnFDI LnFd1 LnFd 2 LnX LnI LnOP it 0 k it j k it j k it j k it j k it j k it j k it j j 1 j 1 j 1 j 1 j 1 j 1 j 1 ˆ ˆ ˆ ˆ ˆ ˆ LnGDPit 1 0 1LnFDI it 1 2LnFd1it 1 3LnFd 2it 1 4LnX it 1 5LnI ˆ it 1 6LnOPit 1 t it After the panel vector error correction model model is estimated, and then we utilize two short-run dynamic analyses called Forecast Error Variance Decompositions (FEVD) and Impulse Response Functions (IRFs) for our research. Both analyses help us to examine the behavior of an error > RJEBS: Volume: 03, Number: 8, June-2014 Page 48

8 shock to each variable on its own future dynamics as well as on the future dynamics of the other variables in the VECM system Gunasekarage, Pisedtasalasai, Power (2004). In fact, FEVD is used to detect the causal relations among the variables. It also explains the degree at which a variable is explained by the shocks in all the variables in the system Mishra (2004). While Impulse Response Function is used to detect the dynamic interaction among variables. For computing the IRFs, it is necessary that the variables in the system are in ordered and that a moving average process represents the system. Finally, as Granger-causality tests require stationary data, all time series will be tested for the presence of unit roots, applying a battery of now standard panel unit root tests. When these tests fail to detect unit roots, the panel estimation models can be set up. As only long-run coefficients are estimated, the restriction of identical coefficients of the lagged X it and Y it variables across countries will be imposed 2. Thus, we will estimate a time-stationary VAR model adapted to a panel context as in Holtz-Eakin et al., (1998) of the form: m X X Y it 0 l it l t it l i it l 1 l 0 m (9) m Y Y X it 0 l it l t it l i it l 1 l 0 m (10) Where X it and Y it represent the variables utilized for N countries indexed by i observed in T period indexed by t. The disturbances uit and it are assumed to be independently distributed across countries with a zero mean. They may display heteroscedasticity across time and countries. IV. Empirical Results and Interpretations Empirical results In this section, we first start by analyzing the summary of descriptive statistics of the variable, so table 1 presents the results. Sample mean, standard deviation, skewness and kurtosis, and the Jacque-Bera statistic and p-value have been reported. From the p-values, the null hypothesis that LnGDP, LnFD, Ln FD1, LnFD2, LnX, LnI and LnOP are normally distributed at the significance level cannot be rejected. Table 1: Summary Panel statistics for the variables LNGDP LNFDI LNFD2 LNFD1 LNX LNI LNOP Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera > RJEBS: Volume: 03, Number: 8, June-2014 Page 49

9 Probability Sum Sum Sq. Dev Observations Source: Computation from Data used in Regression Analysis. We second the univariate augmented Dickey-Fuller (ADF) and Phillip-Peron (PP) unit root tests need to be extended for testing stationarity in panel data analysis. When dealing with panel data, because the procedure is more complex, the ADF and PP tests can result in inconsistent estimators. Thus, the stationarity of the series should be tested by using three different types of tests, namely (LLC) Levin, Lin and Chu (2002); (IPS) Im, Pesaran and Shin (2003) and Hadri (2000). The results are depicted in table (2a, 2b) bellow. Table 2a: Panel Unit Roots at Level > RJEBS: Volume: 03, Number: 8, June-2014 Page 50

10 Table 2b: Panel Unit Roots at First Difference However the tests indicate that all variables contain a unit root at level while they are all first difference stationary. Thus, according the empirical foundation, we conclude that all variables follow the I(1) process. Third, the Hausman(1978) test is formulated to assist in making choice between the fixed effects and random effects approaches. According Ahn and Moon (2001), the Hausman(1978) test is view as the distance measure fixed effects and random effects estimators. Thus we actually test H 0, than random are consistent and efficient, versus H that random effect are inconsistent. If the value of the statistic is large, then the difference between the estimates is significant, so reject the null hypothesis that the random effects model is consistent then we use fixed effects estimator. In contrast, a small value of Hausman statistic implies that the random effect is more appropriate. Following the procedure mentioned above, > RJEBS: Volume: 03, Number: 8, June-2014 Page 51

11 we reject the null hypothesis test H 1 that the random effects model is consistent and we use the fixed effects estimators. The results are shown in table 3. Table 3: Test Cross-section fixed Effect Beyond testing for the unit root, there is a need to establish whether the non-stationary variables are cointegrated so we follow method developed by Johansen-Fisher and Pedroni (1997, 1999 & 2000) panel cointegration test to verify the presence of long run equilibrium relationship between economic variables. The results are illustrated in table 4a and 4b. > RJEBS: Volume: 03, Number: 8, June-2014 Page 52

12 Table 4a: Johansen-Fisher Panel Cointegration Test. Null Alternative Trace Max-Eigen Hypothesis Hypothesis Fisher Statistic Prob.** Fisher Statistic Prob.** trace max r=0 r= r 1 r= r 2 r= r 3 r= r 4 r= r 5 r= r 6 r= Source: Computation from Data used in Regression Analysis. This table displays Johansen-Fisher tests for cointegration. The ( max statistics for testing panel cointegration. ) and ( trace ) are Johansen s maximum eigenvalue and trace eigenvalue Table 4b: Pedroni s panel Cointegration test Pedroni(1999,2004) Constant Constant and Trend Homogenous Alternative Panel v-statistics (0.999)*** 1.127( 0.129) Panel rho( )-statistics (0.350) 2.133( 0.983)*** Panel PP-statistics (0.000)*** ( 0.144) Panel ADF-statistics (0.010)*** 2.158( 0.984)*** Panel v-statistics- Weighted (0.999)*** ( 0.992)*** Panel rho( )-statistics- Weighted (0.481) 2.699(0.996)*** Panel PP-statistics- Weighted (0.001)*** ( 0.045)* Panel ADF-statistics- Weighted (0.436) 0.868( 0.807) Heterogenous Alternative Group rho( )-statistics 2.009(0.977)*** 3.422( 0.999)*** Group pp-statistics (0.007)*** ( 0.031)* Group ADF-statistics (0.365) 2.189(0.985)*** Source: Computation from Data used in Regression Analysis. Note: All statistics are from Pedroni s procedure (1999) where the adjusted values can be compared to the N(0,1) distribution. The Pedroni (2004) statistics are one-sided tests with a critical value of (k < implies rejection of the null), except the v-statistic that has a critical value of 1.64 (k > 1.64 suggests rejection of the null). *, ** indicates rejection of the null hypothesis of no-co-integration at 1% and 5%, levels of significance. > RJEBS: Volume: 03, Number: 8, June-2014 Page 53

13 Fifth, after getting the long-run cointegration relationship using Johansen Johansen-Fisher and Pedroni (1997, 1999, 2000) procedures, the panel error-correction model can be expressed and estimated with a more appropriate simple dynamic representation of the equation (11). Y Y... Y ECM D (11) it 1 it 1 k it k it 1 it it Where ECM it 1 denotes the lag of error-correction term and Dit is incorporated dummies to capture all qualitative data. Thus, an error correction term lagging one period error-correction term is included as one of the independent variables in the general over parameterized error correction model of maximum sustainable yield equation. This term capture the long run relationship by attempt to correct deviations from the long run equilibrium path. Its coefficient can be interpreted as the speed of adjustment or the amount of disequilibrium transmitted each period to amount of Economic growth with lag length ( k 12 ). Results on table 5 3 represent the estimation of the over parameterized model. After the Vector Error Correction Model (VECM) model is estimated, then we utilize two short-run dynamic analyses called Forecast Error Variance Decompositions (FEVD) and Impulse Response Functions (IRFs) for our study. Both analyses help us to examine the behavior of an error shock to each variable on its own future dynamics as well as on the future dynamics of the other variables in the VECM system Gunasekarage, Pisedtasalasai and Power (2004). In fact, FEVD is used to detect the causal relations among the variables. It also explains the degree at which a variable is explained by the shocks in all the variables in the system Mishra (2004). While Impulse Response Function is used to detect the dynamic interaction among variables. For computing the IRFs, it is necessary that the variables in the system are in ordered and that a moving average process represents the system. The results are reported respectively in table 6 and figure 1. Table 6: Panel Forecast Error Variance Decomposition for 192 observations Variance Decomposition Period S.E. LNGDP LNFDI LNFD1 LNFD2 LNX LNI LNOP LnGDP Cholesky Ordering: LNGDP LNFDI LNFD1 LNFD2 LNX LNI LNOP Source: Computation from Data used in Regression Analysis. > RJEBS: Volume: 03, Number: 8, June-2014 Page 54

14 > RJEBS: Volume: 03, Number: 8, June-2014 Page 55

15 Figure 1: Panel Impulse Response Function results Finally, the next step is to examine the existence of block causality among variables of each model. Thus, we adopted time-stationary VAR model adapted to a panel context following the methodology proposed by (Holtz-Eakin et al (1998). As only long-run coefficients are estimated, the restriction of identical coefficients of the lagged X it and X it Granger-causesY it but not vice versa, then the causality from Y it variables across countries will be used. In the case that X it to Y it is unidirectional. On the other hand, if both variables Granger-cause each other, then it can be stated as bi-directional causality or feedback Brooks (2002). However, it is worth noting that Granger-causality basically means a correlation between the current value of one variable and the past (lags) value of others. It does not mean that movements of one variable physically causes movements of another Brooks (2002).The results are depicted in table 7 bellow see. Table 7: Panel Granger Causality Tests > RJEBS: Volume: 03, Number: 8, June-2014 Page 56

16 Source: Computation from Data used in Regression Analysis. Empirical Results Interpretation First, we computed respectively the descriptive statistics and normally test for our microeconomic variables in order to verify weather our data sets are normally distributed and display stable mean return see table 1. All variables exhibit a positive mean return. Also the sum squared deviation row represents the net change over the sample period. In terms of skewness, GDP, FDI, FD1, FD2, X and I are negatively skewed which implies that they have a long left tail excepted OP. Thus we reject the null hypothesis of normality which means that all the variables are relatively normally distributed as indicated by the p values of Jarque Bera statistic. Second, as a precondition for panel cointegration tests, panel unit root tests namely (LLC) Levin, Lin and Chu (2002); (IPS) Im, Pesaran and Shin (2003) and Hadri (2000) were implemented as individual intercept and intercept and trend for gross domestic product (GDP), Foreign Direct Investment (FDI), the ratio of the total domestic saving deposit to GDP (FD1), the ratio of domestic credit provided by banking sector to GDP (FD2) the real export value (X), the real import value(i) and the trade openness(op). Note that working with non-stationary economic variables lead to spurious regression results from which further inference is meaningless when these variables are estimates in their levels. This has often been regarded as a problem in empirical analysis. In order to overcome this problem there is a need for testing stationarity of these microeconomic variables. Table 2a shows that the presence of unit roots could not be rejected at level. Nevertheless, when one takes the first difference of the variables, it can be noted that all of the variables have unit root in not only individual intercept case, but intercept and trend situations, as well. IPS test has the same null hypothesis of having unit roots as LLC test. However, it assumes individual unit root process as stated above. This test also indicated a positive result in testing the presence of unit roots of series just like the LLC test results. In addition to the previous tests, Hadri test is also implemented highlight positive result as table 2b points out. As a result, these outcomes obtained from panel unit root tests allowed us to go on to cointegration tests. In another words, all tests mentioned above indicate that all variables are non-stationary at level while they are all stationary at first difference. Thus, according the empirical foundation; we found that all variables follow the I (1) process. Third, before conducting cointegration test, we performed Hausman (1978) test for the panel data in order to make an appropriate choice between both fixed and random effects method that investigates whether the regressors are correlated with the individual effect. Following the procedure mentioned earlier, we fail reject the null hypothesis test H 0 that the random effects model is consistent and we use the fixed effects estimators. The results are shown in table 3. The general output of panel estimation 2 reports a significant F-statistics which implies that there is overall improvement of the model. The R of means that the explanatory variables include in the model explain more 99.6 percent of all variations of real gross domestic product ( LnGDP ). The variable such as LnX, LnI and LnOP are significant at conventional level 1%, 5% and 10% excepted LnFDI, LnFD 1 and LnFD 2. The model is consistent at first difference since the probability of rejecting is zero given by Prob (F Statistic but the model faces a problem of serial correlation with low DW Note that the advantage of the use of the fixed e timator i that it con i tent even the estimators are correlated with the individual effect. Fourth, as our variables follow the I (1) process the important step of our analysis is to run the cointegration test. The concept of cointegration implies that there is a short-run and long run relationship between economic variables in other words, the independent and dependent variables are stable during the study period. In so doing, we performed simultaneously both Johansen-Fisher and Pedroni (1997,1999 & 2000) panel cointegration test. The Johansen-Fisher cointegration test reveals that real > RJEBS: Volume: 03, Number: 8, June-2014 Page 57

17 GDP, FDI, FD1, FD2, X, I and OP are cointegrated at the 5% level of significance. Both the maximum eigenvalue( ( trace max max statistics( trace ) test identify two (2) cointegrating vectors with 181.0; ) table 4a. The results of Pedroni s panel cointegration test are demonstrated in table 4b. Pedroni computed seven statistics to test the null of no cointegration among series. For these series, the critical value is except -statistic which has That s to say, when the test statistic is lower than -1.64, (greater than 1.64 for -statistic), then the null hypothesis is rejected. Table 4b reports these seven statistics for real growth and it determinants. As it can easily be understood from table 4b, there is a strong cointegration between seven (7) variables in both individual and individual intercept and trend situations. The Pedroni s panel cointegration test for both cases unweighted and weighted confirm also the cointegrating relationship between all variables. Hence, according our empirical suggestions mentioned earlier, if we use real GDP growth as independent variable, we reject the null hypothesis of single cointegration at 5% significance level. This mean that the relevant variables utilized for real GDP growth function in eight (8) countries of West African regional financial market are quite stable. This result also implies that long-term relationship exist between real GDP growth and it main determinants. Therefore, the long-run independent variables use in specifying the real GDP growth function for this study seems to be good. Regarding Jansen, Thornton and Dickey (1991), the vector that makes economic sense is that the estimated coefficients are close to and have the same signs as those predicted by economic theory. According Jansen, Thornton and Dickey (1991) cointegration analysis does not give estimates with structural interpretation regarding the magnitude of the parameters of the cointegrating vectors. Because cointegrating vectors merely imply long run, stable relationships among jointly endogenous variables, they generally cannot be interpreted as structural equations. Therefore, we continue our study by analyzing more deeply the effects of all relevant macro-variables on real GDP growth in the zone. According the VAR model literature, the Vector Error Correction Model (VECM) is very crucial as it drives from the fact that, if macro-variables are integrated in order one I(1) and are cointegrated, they can be modeled as having been generated by Vector Error Correction Model. The error correction model produces better short run forecasts that hold together in economic meaningful ways. Even in the absence of cointegration, the VECM model produces good forecasts Lesage (1990). Thus, is important to use error correction model in the study. Fourth, following the cointegration technique procedure, the short-run dynamics of the long-run real output function is analyzed by computing the panel error-correction model with lags length ( k 12 ) and report a insignificance F-test statistics which implying that there is no improvement in the overall significance of the model. The result shown positive sign, not meaningful and relatively very low panel error coefficient term ( PECTit 1 ). These signify that the panel adjustment process to an exogenous shock is rather not acceptable. These result perhaps because there is some dissimilarity among each country (note that our study concerns a group of eight countries). Moreover, the panel variance decomposition provides further evidence of relationships among the variables under investigation. The panel variance decomposition shows the proportion of the forecast error of one variable due to the other variables. Therefore, the panel variance decomposition makes possible to determine the relative importance of each variable in creating fluctuations in other variables Ratanapakorn and Sharma (2007). Table 6 shows that the real output LnGDP is relatively strongly exogenous in relation to other variables, such as LnFDI, LnFD 1, LnFD 2, LnX, LnI and LnOP > RJEBS: Volume: 03, Number: 8, June-2014 Page 58

18 because almost 85 percent of its variance was explained by its own shock after 10 years. LnFDI explains 0.43 percent impact on Real GDP. Movements in other variables explained the forecast variance at 0.94 percent, 5.87 percent, 5.59 percent, 1.40 percent and 1.15 percent respectively for LnGDP. Turning to the impulse response function displayed in figure 1, we observe that the ratio of total domestic credits to GDP ( LnFD 2) seem to have immediate effect on LnGDP after 2 years with long run association with positive standard deviation innovation in LnGDP compare to LnFD 1. The result implies that the domestic credit efficiently allocate resources by adjusting to general increase in real GDP growth in long run. The response of LnFDI to LnGDP is relatively low, negative and under the base line thus FDI does not play a significant role in the zone s economy. A shock in LnOP leads to a negative effect on LnGDP in long run. The estimated coefficient examination associated with the impulse response function result can draw up the following conclusion: Trade liberalization has negatively affected the real sector in West Africa Monetary Zone since the majority of the regional countries export is composed of natural raw material and agricultural product. In addition, the financial system hasn t benefited from the regional commercial openness as it has a negative impact. This means that the trade volume still relatively low due to the lack of substantial investment. From figure 1 we can also observe the positive and significant response of LnX on LnGDP thus export is the key determinant of real sector growth for the whole regional economy. In the last step of our analysis, we run the panel Granger-causality following the method of Holtz-Eakin et al (1998). The panel Granger causality test statistic is reported in table 7 reveals that there is a unidirectional causality result as follow: i) Changes in the ratio of saving deposits to GDP (LnFD1) Granger-cause changes in foreign direct investment (LnFDI). The causality analysis also highlights that there is strong bi-directional relationship between the ratio of domestic credit provided by banking sector to GDP ( LnFD 2) and real GDP growth. All the results mentioned above show that foreign direct investment have net negative effect on economic growth. This implies that despite the upsurge in the financial inflow into regional economies, the expected positive effect remains a mirage. There were studies that have found similar negative effect on growth in other countries that shared similar experience with our case. Chong and Lim (2009) reported similar negative effect of foreign direct investment on economic growth in Malaysia. Also, an important observation from the causality result is that the bank based variable the ratio of domestic saving and credit on GDP influence economic growth even the regional domestic saving rate is restively low about 21% according the recent report of the Regional Central Bank; market capitalization (a stock market variable) was also found to have strong causal influence on economic in previous studies Tachiwou (2009). In recent times, the substantial upsurge in foreign direct inflows was not accomplished by substantial increase in economic growth. Also financial development recently witnessed upsurge in activities due to financial reform. V. Conclusion and Policy Recommendation. Studies on the tri-variate causal nexus among economic growth financial development and foreign direct investment have been dominated by cross sectional analysis. Such cross sectional analysis disregard country specific factors such as difference in the level of financial development; adequate measure to proxy financial development and difference in macroeconomic objectives of specific policy, thereby limiting the significance of policy references from such cross sectional analysis. This study examined the causal relationship among economic growth foreign direct investment and financial development in eight (8) countries of West African Economic and Monetary Union (UEMOA) over the period 1987 to 2010 within a panel data analysis. The study has established that FDI might not be a major factor in stimulating economic growth. The possible explanation for the adverse effect of foreign direct investment might be that fact that the bulk > RJEBS: Volume: 03, Number: 8, June-2014 Page 59