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FINANCIAL INTERMEDIATION AND ECONOMIC GROWTH IN AFRICA

FINANCIAL INTERMEDIATION AND ECONOMIC GROWTH IN AFRICA:A TEST FOR CAUSALITY USING PANEL DATA FRAMEWORK (1985-2017)

                BY

                   EKUNDAYO ADEYEMI ADEMOLA M.SC IN FINANCE

                       DEPARTMENT OF BANKING AND FINANCE

                       LAGOS STATE UNIVERSITY,OJO

                         

    INTRODUCTION

 

The efficient channelling of financial resources into productive projects provides basic ingredients for the growth of an economy. Banks and other financial institutions are fundamental to economic development as they provide these basic financial services. Their intermediation role is considered a catalyst for economic growth and development. Where the financial system functions properly, external financing constraints which may slow down industrial expansion will be reduced (Oluitan, 2012). The acceleration of rapid productive activities depends on the extent to which banks extend their credit to the public. Therefore, a well-functioning financial system helps to accelerate the pace at which a country’s economy grows and is sustained for a long period. This view was supported by Onwe, Adeleyeand Okorie (2019) that the efficient performance of the banking sector over time provides strong indication of financial stability in any nation.

The debate on the relationship between finance and growth has been extensively discussed in the literature. Patrick (1966) cited in Oluitan (2012) postulated two types of relationship. The first is the Supply-leading hypothesis; while, the second is the Demand-following hypothesis. The Supply-leading hypothesis assumes that the intermediation activities of the financial institutions help the real sector to increase their productive capacity. This subsequently helps to enlarge the productive base of the economy. The Demand-following hypothesis on the other hand, assumes that it is the enlargement of the economy that pushes the real sector to demand for fund from financial institutions so as to meet up with the increase in productivity. The main argument of this proponent is that, it is the economy that actually pushes the financial institutions to intermediate

 

The effects of financial intermediation on the development of any economy have generated heated debate in finance literature recently. While some studies opined that financial intermediation drives economic development (Odedokun, 1998; Nieh, 2009; Islam and Osman, 2011), others have argued that economic development drives financial intermediation. Some studies have robust statistical evidence for uni-directional causality from financial development to growth (for instance Jung, 1986; King and Levine, 1993), others have evidence for reverse causation from economic growth to financial development (see Demetriades& Hussein, 1996). However, the result of Calderon and Liu (2003) report bi-directional causality, but some results suggest no evidence of causality; thus, the existence of gap.

 OBJECTIVES OF THE STUDY

The main objective of the study is to examine the effects of financial intermediation on economic development in Africa. Specific objectives however, are as follows:

  1. To examine whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.
  2. To determine whether bi-directional causality exist between financial intermediation and economic development in ECOWAS and Sub-Sahara Africa.
  3. To examine whether financial intermediation proxies in the model co integrate with GDP growth rates both in the long-run and short-run in ECOWAS and Sub-Sahara Africa.

THEROTICAL AND EMPIRICAL REVIEW

Over eighty years ago, Schumpeter (1934) observed that financial markets play important role in the growth process. This process involves channelling funds to the most efficient investors and fostering entrepreneurial innovation. Thus, the links between financial development and economic growth is not a new theme in the economic literature. The position of Schumpeterwas that financial development leads economic growth

Capital flight is pervasive especially in the severely indebted low-income countries, which at the same time are overburdened by high levels of indebtedness(Boyce &Ndikumana, 2000). The illicit outflows of capital impose high costs on African economies and must be regarded by policy makers as an urgent matter of concern. 

The leading proponent of the supply-leading hypothesis is Schumpeter (1911). Others who supported this view according to Agu and Chukwu (2008) are Gurley and Shaw (1967), and McKinnon (1973). The theory suggested that financial development positively affects economic growth. Agu and Chukwu (2008) posited that this effect runs from financial development to economic growth as a consequence of improvement in the efficiency of capital accumulation or an increase in the rate of savings and investment rate.

In this section the study reviewed related studies on the effects of financial intermediation on economic growth in Africa. Gries, Kraft, and Meierrieks (2009) conducted a study on the causal relationship between financial deepening, trade openness and economic growth using 16 Sub-Saharan African countries as the study sample size. The study used the Hsiao Granger method, and the Vector Error Correction Model to analyse data collected. The results showed that finance lead to economic growth. Also, Bangake and Eggoh (2011), using a panel co-integration test, and panelVECM approach, examined the effects of financial development on economic growth in 71 countries from the period 1960 to 2004. Their findings showed evidence of bi-directional causality between financial development and economic growth.

Similarly, Altaee and Al-Jafari (2015) conducted a study on the relationship between trade openness, financial development and economic growth in Bahrain. The study covers the period from 1980 to 2012. The study analysed data collected using VECM model to test for causality among variables. Findings showed that trade openness and financial development lead to economic growth. The study recommended that Bahrain should strive to improve their financial sector so as to further enhance trade openness. This will help the country to achieve her quest for sustainable and higher economic growth.

Furthermore, Kar, Nazliogu, and Agir (2011) carried out a study on the relationship between financial development and economic growth. The study focused on the Middle East and North Africa countries for the period 1980 to 2007. The study used a simple linear model and result showed evidence of a two-way directional relationship between financial development indicators and economic growth. Also, Musamali, Nyamongo, and Moyi (2014) examined the relationship between financial development and economic growth using 50 African countries for period of twenty-eight years (that is, 1980 – 2008). Findings revealed a positive relationship between financial development and economic growth. Results further showed that domestic credit to private sector has greater effects on economic growth compared to broad money. The result further showed bi-directional relationship between financial sector and economic growth.

Shittu (2012) examined the impact of financial intermediation on economic growth in Nigeria. The study considered the period from 1970 – 2010 (that is, 30 years).The study analysed data collected using co-integration test and VECM technique. Result showed that financial intermediation significantly affects economic growth in Nigeria. Similarly, Ndako (2017) conducted a study on financial development, investment and economic growth in Nigeria. Data collected was analysed using the VAR framework of Johansen. The study covers the period from 1960 to 2014 (that is, 54 years). Results revealed that financial development, investment and economic growth have long run relationship inNigeria. Results also revealed that investmentis a critical factor through which financial development affects economic growth.

Al-Qudah (2017)investigated the correlation between financial development and economic growth using quarterly data in Jordan for the period 1993 to 2014 (that is, 84 Quarters in 21 years). Results revealed that financial development significantly positively influence economic growth in the long run. It also showed evidence of bi-directional causality between financial development and economic growth. Moreover, Jung (2017) carried out a study on financial development and economic growth in Korea for the period 1961 to 2013. Data collected was analysed using the VAR model. Results showed that real GDP per capita, financial development, real exports and real imports co-integrates. Results also showed support of supply leading view of financial development and economic growth in Korea.

 RESEARCH GAP

While Sahoo (2014) found no causality between stock market capitalization and growth, the result of Calderon and Liu (2003) report bi-directional causality. While Ekpenyong and Acha (2011) showed that bank intermediation insignificantly impacts on growth, some results suggest no evidence of causality (seeAcha, 2011, whose results showed no evidence of causality between savings/credit and economic growth). Others (see Andabai, 2014) showed that causality flows from private sector credit to growth; thus, the existence of gap.

EMPIRICAL MODEL

Research design guides the researcher in solving the research problem. This study uses longitudinal research design. Secondary data used for the study was collected from CBN Statistical Bulletin (various publications), the World Development Indicator (WDI) 2017 dataset and the International Financial Statistics (IFS). The study will use panel data to be collected from the twenty sampled Africa countries.

The model adopted for this study followed the work of Marshal and Solomon (2015) but was modified below as:

GDPGRi,t = f (BCPSi,t, GFCFi,t, LOGMSSi,t, FSCPSi,t,INFRi,t and EXCHRi,t)         … equation (i)

GDPGRi,t = β0 + β1BCPSi,t + β2GFCFi,t + β3LOGMSi,t4FSCPSi,t5INFRi,t6 EXCHRi,t   +  μi,t                                                                                                                                                                             … equation (ii)

Where:

β0 = Constant

GDPGRi,t= Gross domestic product growth rate as a percentage of GDP of country i at period t

BCPSi,t= Bank credit to private sector as a percentage of GDP of country i at period t

GFCFi,t = Gross fixed capital formation as a percentage of GDP of country i at period t

LOGMSi,t=  Log value of Money supply of country i at period t

FSCPSi,t= Financial sector credit to private sector as a percentage of GDP of country i at periodt

INFRi,t = Inflation rate of country i at period t

EXCHRi,t          = Exchange rate of country i at period t

β1, β3…β3        = The coefficients of variations

μi,t        =  Error term

        DATA PRESENTATION, ANALYSIS AND INTERPRETATION OF RESULTS

 

The Unit root test was conducted on the data collected using Eviewsversion 10.0, before the actual analysis. The unit root test helps the researcher to determine the level of data stationarity and hence, the most appropriate tool of analysis; so as to avoid invalid result output. The descriptive statistics, Unit root test, Generalized Moment method, Johansen and Fishers Co-integration test and the Vector Error Correction Mechanism test are conducted on the data collected for this study in line with the formulated hypotheses.

 

THE UNIT ROOT TEST

This test was conducted to determine whether the panel data used is stationary or not. Regression results conducted in the absence of Unit Root test may be spurious because the estimated parameters would be bias and inconsistent where the series is not stationary. This test is conducted using the Levin-Lin-Chu test. The results of this test are presented in table 4.2.

Table4.2: Unit Root Result for Variables used for the Study

Variable t –Statistics P-Value
GDPGR -5.0382* 0.0000
D(BCTPS) -10.5451* 0.0000
EXCHR (-9.01)* 0.0000
D(FSCPS) -11.3543* 0.0000
GFCF -1.8311** 0.0335
INFR -7.3689* 0.0000
D(MSS) 2.6766** 0.0163

Note: * = Implies significant at 1%, ** = Implies significant at 5%.

Source: Field Survey 2019.

The Unit Root test was conducted using Levin, Lin and Chu technique under the assumption of determining the trend and intercept. The specified variables are GDP growth rate (GDPGR), Bank Credit to Private Sector (BCTPS), Exchange Rate (EXCHR), Financial Sector Credit to Private Sector (FSCPS), Gross Fixed Capital Formation (GFCF), Inflation Rate (INFR) and Money Supply (MSS). The results of the Unit Root test presented in table 4.2 shows the LLC statistics with their corresponding P-values. The test was conducted using the Akaike information criteria at lag 1. Result shows that the probability value in reference to each variable is smaller than the alpha value at 5%. Thus, the null hypothesis that the panel contains a unit root is rejected at 5% level of significance.

 

Generalized Moment Method (GMM) Test

The study examines both short-run and long-run dynamic relationship between the explanatory and the explained variables. Model 1was estimated after testing between the Pooled regression, Random Effects and Fixed Effects models to arrive at the most adequate.

 

Test of Hypothesis 1

Ho: Financial intermediation does not significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.

Table4.4: Results of Generalized Moments Model on the influence of Financial

Intermediation  Indicators on GDP Growth rates in ECOWAS &Sub-Sahara Africa.

ECOWAS RESULT

Dependent Variable: GDPGR
Method: Panel GMM EGLS (Cross-section weights)
Date: 02/18/20   Time: 09:27
Sample: 1985 2017
Instrument specification: C GOVT EXP, AGE DEPENDENCY
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
FSPS 0.098238 0.026057 3.770074 0.0002
GFCF 0.037958 0.012328 3.079047 0.0022
LOGMSS 0.302333 0.031191 9.692873 0.0000
BCTPS -0.115824 0.035638 -3.250015 0.0012
INFR -0.001393 0.014370 -0.096972 0.9228
EXCHR -6.74E-05 0.000182 -0.369809 0.7117
Weighted Statistics
R-squared       0.254390     Mean dependent var 5.384403
Adjusted R-squared 0.159204     S.D. dependent var 5.218880
S.E. of regression 4.850145     Sum squared resid 11503.19
Durbin-Watson stat 1.670547     J-statistic 4.729341
Instrument rank 8     Prob(J-statistic) 0.093980
Unweighted Statistics
R-squared 0.710874     Mean dependent var 4.102357
Sum squared resid 12051.29     Durbin-Watson stat 1.594980

Source: Authors computation using E-view 8

SUB-SAHARA RESULT

Dependent Variable: GDPGR
Method: Panel GMM EGLS (Period weights)
Date: 02/18/20   Time: 09:04
Sample (adjusted): 1986 2017
Instrument specification: C GOVT EXP, AGE DEPENDENCY
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
C 1.939083 0.804258 2.411020 0.0163
GDPGR(-1) 0.974509 0.103149 9.447546 0.0000
FSPS 0.040920 0.013280 3.081387 0.0022
GFCF 0.016601 0.009228 1.799078 0.0726
LOGMSS -0.134913 0.077876 -1.732393 0.0839
BCTPS -0.097817 0.024913 -3.926303 0.0001
INFR 0.000317 0.000258 1.231069 0.2189
EXCHR -2.14E-09 1.80E-10 -11.87896 0.0000
Weighted Statistics
R-squared 0.428167     Mean dependent var 1.539536
Adjusted R-squared 0.449347     S.D. dependent var 5.329547
S.E. of regression 6.416179     Sum squared resid 19430.99
Durbin-Watson stat 2.846706     J-statistic 2.27E-22
Instrument rank 8
Unweighted Statistics
R-squared 0.396717     Mean dependent var 3.693941
Sum squared resid 20922.13     Durbin-Watson stat 2.817336

Source: Field Work 2019 using E-view 8

In comparing the results on the influence of financial intermediation on economic growth rate in ECOWAS and Sub-Sahara Africa, it can be seen that financial sector credit to private sector (FSCPS) positive and significantly influence GDP growth rate in ECOWAS and Sub-Sahara Africa. Results also showed that gross fixed capital formation (GFCF) and money supply (MSS) in both regions have significant positive influence on GDP growth rate. Furthermore, while bank credits to private sector (BCTPS) and exchange rate (EXCHR) have significant and negative influence on GDP growth rate in ECOWAS region, its influence (bank credit to private sector) in Sub-Sahara region of Africa is negative and significant. The implication of this result is that while bank credit to private sector and exchange rate negatively affects economic growth in both regions. However, inflation rate has positive but insignificant influence on GDP growth rate in Sub-Sahara Africa.

The coefficient of determination in both regions at 0.2543 (25.43%% in ECOWAS) and 0.4281 (42.81% in Sub-Sahara Africa) showed a moderate positive relationship between financial intermediation and economic growth in the regions. Overall financial intermediation indicators jointly contributed 25.43% (R2) and 42.81% respectively to GDP growth rate in both regions.

Furthermore, since the observed P-value of regression estimates are less than the critical value of 5%, the Null hypothesis which states that financial intermediation does not significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa is rejected. Thus, the study observes that financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.

Test of Hypothesis 2& 3

H02: Bi-directional causality does not exist between financial intermediation and economic development in ECOWAS and Sub-Sahara Africa.

Test of Hypothesis 3

H03: Financial intermediation proxies in the model do not co=integrate with GDP growth rates both in the long-run and short-run in ECOWAS and Sub-Sahara Africa.

GDPGRi,t = β0 + β1BCPSi,t + β2GFCFi,t + β3LOGMSi,t4FSCPSi,t5INFRi,t + β6 EXCHRi,t   +  μi,t                                                                                                                    … 2                                                        

JOHANSEN AND FISHERS COINTEGRATIONTEST

This test was conductedto examine whether Financial Intermediation components namely: Bank Credit to Private Sector (BCTPS), Exchange Rate (EXCHR), Financial Sector Credit to Private Sector (FSCPS), Gross Fixed Capital Formation (GFCF), Inflation Rate (INFR) and Money Supply (MSS)in both regions exhibits long-run co-movement with GDP growth rate. The decision rule for the Co-integration test is that: The null hypothesis of no co-integration is rejected and the alternative accepted, if the observed P value is less than 5%. The results of the Johansen and Fishers co-integration test are shown in table 4.5 and 4.6 respectively for both regions.

 

Table 4.5: Results of Johansen and Fishers Cointegration Test(ECOWAS)             .

                  Hypothesized       Fisher Stat.                     Fisher Stat.          

                  No of CE(s)     (from trace test)  P-Value   from Max-eigen test)    P- Value

GDPGR, BCTPS, FSCPS, GFCF, LOGMSS, EXCHR, INFR

None               435.7*             0.0000             289.3*             0.0000

At most 1        207.0*             0.0000109.8*              0.0000

At most 2        114.5*             0.0000             59.54*                   0.0010

At most 3        69.57*             0.000138.75                      0.1313

At most 4        46.54**           0.0276             41.05***               0.0861

At most 5        24.55               0.7466             20.38               0.9062

                        At most 6        37.88               0.152737.88                      0.1527      .

Note:* = significant at 1%,** = significant at 5%, *** = significant at 10%,

SOURCE:Source: Authors computation using E-view 8

 

 

Table 4.6: Results of Johansen and Fishers Co-integration Test (Sub-Sahara Africa) .

                  Hypothesized       Fisher Stat.                     Fisher Stat.          

                  No of CE(s)     (from trace test)  P-Value   from Max-eigen test)    P- Value

GDPGR, BCTPS, FSCPS, GFCF, LOGMSS, EXCHR, INFR

None               481.9*             0.0000             345.3*             0.0000

At most 1        248.5*             0.0000142.9*              0.0000

At most 2        127.1*             0.0000             86.96*                   0.0000

At most 3        59.10*             0.000539.15***                0.0785

At most 4        36.18               0.1381             27.96                     0.4665

At most 5        25.21               0.6165             22.87               0.7394

                        At most 6        25.54               0.598225.54                      0.5982      .

Note:* = significant at 1%,*** = significant at 10%,

SOURCE:Source: Authors computation using E-view 8

 

The test was conducted under the assumption of no intercept and trend. Results show the trace statistics and the max-eigen statistics with their corresponding P values. The P values for ‘None’ ‘At most 1’, ‘At most 2’ and ‘At most 3’are below alpha value at 10% level of significance in both regions. This shows that the null hypothesis of no co-integration is rejected. Thus, financial intermediation co-integrated in the long-run with GDP growth rate in both regions of Africa.

VECTOR ERROR CORRECTION MECHANISM (VECM) TEST

The VECM test is conducted to examine whether financial intermediation components namely: Bank Credit to Private Sector (BCTPS), and Gross Fixed Capital Formation (GFCF) in both regions exhibit long-run and short-runrelationship with GDP growth rate. This test also showwhether any sudden shock that could cause disequilibrium can be corrected at certain speed within a year.  The result is shown in table 4.6 while, the E-view output is presented in Appendix II.

Table 4.6:  Results of Vector Error Correction Mechanism Test  .

Descriptor                      Coefficient       Std Error              t- statistics      P-value          

GDPGR, BCTPS, GFCF(ECOWAS)

ECM(-1)                                 -0.7665*          0.0582             -13.159            0.0000

GDPGR(-1)                            -0.0895**        0.0451             -1.9806            0.0478

BCTPS (-1)                             -0.1433            0.0926             1.5458            0.1224

GFCF (-1)                               0.0555*          0.0107             5.1700              0.0000

Constant                                  0.1290*          0.0282               4.5653           0.0000

GDPGR, GFCF, BCTPS(SUB-SAHARA AFRICA)

ECM(-1)                                 -0.7229*          0.0583             -12.398           0.0000

GDPGR(-1)                            -0.0478            0.0465             -1.0266            0.3047

GFCF(-1)                                -0.0211            0.0258             -0.8161            0.4145

BCTPS (-1)                             -0.0729*          0.0864             -0.8435            0.0139

Constant                                  0.0706            0.2473             0.2855            0.7752          .

Note: * = significant at 1%, ** = significant at 5%.

SOURCE: Source: Authors computation using E-view 8

Table 4.6 showed the result of the VECM test for both regions. In ECOWAS and Sub-Sahara Africa regions, it can be seen that the coefficient of the ECM at -0.7665and -0.7229 with their corresponding P value of 0.0000 and 0.000 showed that the ECM coefficients gives the right approiri sign and are significant at 5% level of significance. Thus, the null hypothesis that there is no long-run causality flowing from financial intermediation (BCTPS, and GFCF)to GDP growth rate in both regions is rejected. However, result showed that while GFCF exerts significant positive influence on GDP growth rate in ECOWAS region, it insignificantly negatively influences GDP growth rate in Sub-Sahara Africa. Similarly, while BCTPS exerts insignificant negative influence on GDP growth rate in ECOWAS region, it significantly negatively influences GDP growth rate in Sub-Sahara Africa. The result further shows that short-run dynamic influence flows from GDP growth rate to financial intermediation only in ECOWAS region. Thus, result confirms the existence of bidirectional relationship between financial intermediation and economic development in ECOWAS region. Results showed that any sudden shock that could cause disequilibrium can be corrected at the rate of 76.65% and 72.29% respectively in both regions within a year. The short-run dynamic test is conducted using the Wald coefficient test reported in table 4.7.The E-view output is however, presented in Appendix B.

 CONCLUSION AND RECOMMENDATIONS

 

The study examined the effects of financial intermediation on economic growth in Africa. The study specifically sought to evaluate whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. Secondly, to determine whether financial intermediation proxies in the model co-integrate with GDP growth rates in the long-run in ECOWAS and Sub-Sahara Africa.

 

The study investigated the effects of financial intermediation on economic growth in Africa. Results have shown that financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. However, the coefficient of determination in both regions at 0.25% (in ECOWAS) and 0.42% (in Sub-Sahara Africa) showed a weak relationship between the dependent and independent variables. The study indeed contributes to literature on the effects of financial intermediation on economic growth in Africa.

Secondly, results from Johansen and Fishers co-integration test, showed that long-run effects flows from financial intermediation proxies to GDP growth rate in both regions of Africa.

 

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User-specified lags: 1
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ABSTRACT

The study examined effects of financial intermediation on economic growth in Africa: evidence from ECOWAS and Sub-Sahara regions. The study sought to examine whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. Also, to identify whether financial intermediation proxies in the model co-integrate with GDP growth rates in the long-run in ECOWAS and Sub-Sahara Africa. The study used secondary data collected from the World Bank statistics for the period 1985 to 2017.

Keywords:Financial intermediation, Economic growth, ECOWAS, Sub-Sahara, Africa.

 

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FINANCIAL INTERMEDIATION AND ECONOMIC GROWTH IN AFRICA

FINANCIAL INTERMEDIATION AND ECONOMIC GROWTH IN AFRICA:A TEST FOR CAUSALITY USING PANEL DATA FRAMEWORK (1985-2017)

                BY

                   EKUNDAYO ADEYEMI ADEMOLA M.SC IN FINANCE

                       DEPARTMENT OF BANKING AND FINANCE

                       LAGOS STATE UNIVERSITY,OJO

                         

    INTRODUCTION

 

The efficient channelling of financial resources into productive projects provides basic ingredients for the growth of an economy. Banks and other financial institutions are fundamental to economic development as they provide these basic financial services. Their intermediation role is considered a catalyst for economic growth and development. Where the financial system functions properly, external financing constraints which may slow down industrial expansion will be reduced (Oluitan, 2012). The acceleration of rapid productive activities depends on the extent to which banks extend their credit to the public. Therefore, a well-functioning financial system helps to accelerate the pace at which a country’s economy grows and is sustained for a long period. This view was supported by Onwe, Adeleyeand Okorie (2019) that the efficient performance of the banking sector over time provides strong indication of financial stability in any nation.

The debate on the relationship between finance and growth has been extensively discussed in the literature. Patrick (1966) cited in Oluitan (2012) postulated two types of relationship. The first is the Supply-leading hypothesis; while, the second is the Demand-following hypothesis. The Supply-leading hypothesis assumes that the intermediation activities of the financial institutions help the real sector to increase their productive capacity. This subsequently helps to enlarge the productive base of the economy. The Demand-following hypothesis on the other hand, assumes that it is the enlargement of the economy that pushes the real sector to demand for fund from financial institutions so as to meet up with the increase in productivity. The main argument of this proponent is that, it is the economy that actually pushes the financial institutions to intermediate

 

The effects of financial intermediation on the development of any economy have generated heated debate in finance literature recently. While some studies opined that financial intermediation drives economic development (Odedokun, 1998; Nieh, 2009; Islam and Osman, 2011), others have argued that economic development drives financial intermediation. Some studies have robust statistical evidence for uni-directional causality from financial development to growth (for instance Jung, 1986; King and Levine, 1993), others have evidence for reverse causation from economic growth to financial development (see Demetriades& Hussein, 1996). However, the result of Calderon and Liu (2003) report bi-directional causality, but some results suggest no evidence of causality; thus, the existence of gap.

 OBJECTIVES OF THE STUDY

The main objective of the study is to examine the effects of financial intermediation on economic development in Africa. Specific objectives however, are as follows:

  1. To examine whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.
  2. To determine whether bi-directional causality exist between financial intermediation and economic development in ECOWAS and Sub-Sahara Africa.
  3. To examine whether financial intermediation proxies in the model co integrate with GDP growth rates both in the long-run and short-run in ECOWAS and Sub-Sahara Africa.

THEROTICAL AND EMPIRICAL REVIEW

Over eighty years ago, Schumpeter (1934) observed that financial markets play important role in the growth process. This process involves channelling funds to the most efficient investors and fostering entrepreneurial innovation. Thus, the links between financial development and economic growth is not a new theme in the economic literature. The position of Schumpeterwas that financial development leads economic growth

Capital flight is pervasive especially in the severely indebted low-income countries, which at the same time are overburdened by high levels of indebtedness(Boyce &Ndikumana, 2000). The illicit outflows of capital impose high costs on African economies and must be regarded by policy makers as an urgent matter of concern. 

The leading proponent of the supply-leading hypothesis is Schumpeter (1911). Others who supported this view according to Agu and Chukwu (2008) are Gurley and Shaw (1967), and McKinnon (1973). The theory suggested that financial development positively affects economic growth. Agu and Chukwu (2008) posited that this effect runs from financial development to economic growth as a consequence of improvement in the efficiency of capital accumulation or an increase in the rate of savings and investment rate.

In this section the study reviewed related studies on the effects of financial intermediation on economic growth in Africa. Gries, Kraft, and Meierrieks (2009) conducted a study on the causal relationship between financial deepening, trade openness and economic growth using 16 Sub-Saharan African countries as the study sample size. The study used the Hsiao Granger method, and the Vector Error Correction Model to analyse data collected. The results showed that finance lead to economic growth. Also, Bangake and Eggoh (2011), using a panel co-integration test, and panelVECM approach, examined the effects of financial development on economic growth in 71 countries from the period 1960 to 2004. Their findings showed evidence of bi-directional causality between financial development and economic growth.

Similarly, Altaee and Al-Jafari (2015) conducted a study on the relationship between trade openness, financial development and economic growth in Bahrain. The study covers the period from 1980 to 2012. The study analysed data collected using VECM model to test for causality among variables. Findings showed that trade openness and financial development lead to economic growth. The study recommended that Bahrain should strive to improve their financial sector so as to further enhance trade openness. This will help the country to achieve her quest for sustainable and higher economic growth.

Furthermore, Kar, Nazliogu, and Agir (2011) carried out a study on the relationship between financial development and economic growth. The study focused on the Middle East and North Africa countries for the period 1980 to 2007. The study used a simple linear model and result showed evidence of a two-way directional relationship between financial development indicators and economic growth. Also, Musamali, Nyamongo, and Moyi (2014) examined the relationship between financial development and economic growth using 50 African countries for period of twenty-eight years (that is, 1980 – 2008). Findings revealed a positive relationship between financial development and economic growth. Results further showed that domestic credit to private sector has greater effects on economic growth compared to broad money. The result further showed bi-directional relationship between financial sector and economic growth.

Shittu (2012) examined the impact of financial intermediation on economic growth in Nigeria. The study considered the period from 1970 – 2010 (that is, 30 years).The study analysed data collected using co-integration test and VECM technique. Result showed that financial intermediation significantly affects economic growth in Nigeria. Similarly, Ndako (2017) conducted a study on financial development, investment and economic growth in Nigeria. Data collected was analysed using the VAR framework of Johansen. The study covers the period from 1960 to 2014 (that is, 54 years). Results revealed that financial development, investment and economic growth have long run relationship inNigeria. Results also revealed that investmentis a critical factor through which financial development affects economic growth.

Al-Qudah (2017)investigated the correlation between financial development and economic growth using quarterly data in Jordan for the period 1993 to 2014 (that is, 84 Quarters in 21 years). Results revealed that financial development significantly positively influence economic growth in the long run. It also showed evidence of bi-directional causality between financial development and economic growth. Moreover, Jung (2017) carried out a study on financial development and economic growth in Korea for the period 1961 to 2013. Data collected was analysed using the VAR model. Results showed that real GDP per capita, financial development, real exports and real imports co-integrates. Results also showed support of supply leading view of financial development and economic growth in Korea.

 RESEARCH GAP

While Sahoo (2014) found no causality between stock market capitalization and growth, the result of Calderon and Liu (2003) report bi-directional causality. While Ekpenyong and Acha (2011) showed that bank intermediation insignificantly impacts on growth, some results suggest no evidence of causality (seeAcha, 2011, whose results showed no evidence of causality between savings/credit and economic growth). Others (see Andabai, 2014) showed that causality flows from private sector credit to growth; thus, the existence of gap.

EMPIRICAL MODEL

Research design guides the researcher in solving the research problem. This study uses longitudinal research design. Secondary data used for the study was collected from CBN Statistical Bulletin (various publications), the World Development Indicator (WDI) 2017 dataset and the International Financial Statistics (IFS). The study will use panel data to be collected from the twenty sampled Africa countries.

The model adopted for this study followed the work of Marshal and Solomon (2015) but was modified below as:

GDPGRi,t = f (BCPSi,t, GFCFi,t, LOGMSSi,t, FSCPSi,t,INFRi,t and EXCHRi,t)         … equation (i)

GDPGRi,t = β0 + β1BCPSi,t + β2GFCFi,t + β3LOGMSi,t4FSCPSi,t5INFRi,t6 EXCHRi,t   +  μi,t                                                                                                                                                                             … equation (ii)

Where:

β0 = Constant

GDPGRi,t= Gross domestic product growth rate as a percentage of GDP of country i at period t

BCPSi,t= Bank credit to private sector as a percentage of GDP of country i at period t

GFCFi,t = Gross fixed capital formation as a percentage of GDP of country i at period t

LOGMSi,t=  Log value of Money supply of country i at period t

FSCPSi,t= Financial sector credit to private sector as a percentage of GDP of country i at periodt

INFRi,t = Inflation rate of country i at period t

EXCHRi,t          = Exchange rate of country i at period t

β1, β3…β3        = The coefficients of variations

μi,t        =  Error term

        DATA PRESENTATION, ANALYSIS AND INTERPRETATION OF RESULTS

 

The Unit root test was conducted on the data collected using Eviewsversion 10.0, before the actual analysis. The unit root test helps the researcher to determine the level of data stationarity and hence, the most appropriate tool of analysis; so as to avoid invalid result output. The descriptive statistics, Unit root test, Generalized Moment method, Johansen and Fishers Co-integration test and the Vector Error Correction Mechanism test are conducted on the data collected for this study in line with the formulated hypotheses.

 

THE UNIT ROOT TEST

This test was conducted to determine whether the panel data used is stationary or not. Regression results conducted in the absence of Unit Root test may be spurious because the estimated parameters would be bias and inconsistent where the series is not stationary. This test is conducted using the Levin-Lin-Chu test. The results of this test are presented in table 4.2.

Table4.2: Unit Root Result for Variables used for the Study

Variable t –Statistics P-Value
GDPGR -5.0382* 0.0000
D(BCTPS) -10.5451* 0.0000
EXCHR (-9.01)* 0.0000
D(FSCPS) -11.3543* 0.0000
GFCF -1.8311** 0.0335
INFR -7.3689* 0.0000
D(MSS) 2.6766** 0.0163

Note: * = Implies significant at 1%, ** = Implies significant at 5%.

Source: Field Survey 2019.

The Unit Root test was conducted using Levin, Lin and Chu technique under the assumption of determining the trend and intercept. The specified variables are GDP growth rate (GDPGR), Bank Credit to Private Sector (BCTPS), Exchange Rate (EXCHR), Financial Sector Credit to Private Sector (FSCPS), Gross Fixed Capital Formation (GFCF), Inflation Rate (INFR) and Money Supply (MSS). The results of the Unit Root test presented in table 4.2 shows the LLC statistics with their corresponding P-values. The test was conducted using the Akaike information criteria at lag 1. Result shows that the probability value in reference to each variable is smaller than the alpha value at 5%. Thus, the null hypothesis that the panel contains a unit root is rejected at 5% level of significance.

 

Generalized Moment Method (GMM) Test

The study examines both short-run and long-run dynamic relationship between the explanatory and the explained variables. Model 1was estimated after testing between the Pooled regression, Random Effects and Fixed Effects models to arrive at the most adequate.

 

Test of Hypothesis 1

Ho: Financial intermediation does not significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.

Table4.4: Results of Generalized Moments Model on the influence of Financial

Intermediation  Indicators on GDP Growth rates in ECOWAS &Sub-Sahara Africa.

ECOWAS RESULT

Dependent Variable: GDPGR
Method: Panel GMM EGLS (Cross-section weights)
Date: 02/18/20   Time: 09:27
Sample: 1985 2017
Instrument specification: C GOVT EXP, AGE DEPENDENCY
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
FSPS 0.098238 0.026057 3.770074 0.0002
GFCF 0.037958 0.012328 3.079047 0.0022
LOGMSS 0.302333 0.031191 9.692873 0.0000
BCTPS -0.115824 0.035638 -3.250015 0.0012
INFR -0.001393 0.014370 -0.096972 0.9228
EXCHR -6.74E-05 0.000182 -0.369809 0.7117
Weighted Statistics
R-squared       0.254390     Mean dependent var 5.384403
Adjusted R-squared 0.159204     S.D. dependent var 5.218880
S.E. of regression 4.850145     Sum squared resid 11503.19
Durbin-Watson stat 1.670547     J-statistic 4.729341
Instrument rank 8     Prob(J-statistic) 0.093980
Unweighted Statistics
R-squared 0.710874     Mean dependent var 4.102357
Sum squared resid 12051.29     Durbin-Watson stat 1.594980

Source: Authors computation using E-view 8

SUB-SAHARA RESULT

Dependent Variable: GDPGR
Method: Panel GMM EGLS (Period weights)
Date: 02/18/20   Time: 09:04
Sample (adjusted): 1986 2017
Instrument specification: C GOVT EXP, AGE DEPENDENCY
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
C 1.939083 0.804258 2.411020 0.0163
GDPGR(-1) 0.974509 0.103149 9.447546 0.0000
FSPS 0.040920 0.013280 3.081387 0.0022
GFCF 0.016601 0.009228 1.799078 0.0726
LOGMSS -0.134913 0.077876 -1.732393 0.0839
BCTPS -0.097817 0.024913 -3.926303 0.0001
INFR 0.000317 0.000258 1.231069 0.2189
EXCHR -2.14E-09 1.80E-10 -11.87896 0.0000
Weighted Statistics
R-squared 0.428167     Mean dependent var 1.539536
Adjusted R-squared 0.449347     S.D. dependent var 5.329547
S.E. of regression 6.416179     Sum squared resid 19430.99
Durbin-Watson stat 2.846706     J-statistic 2.27E-22
Instrument rank 8
Unweighted Statistics
R-squared 0.396717     Mean dependent var 3.693941
Sum squared resid 20922.13     Durbin-Watson stat 2.817336

Source: Field Work 2019 using E-view 8

In comparing the results on the influence of financial intermediation on economic growth rate in ECOWAS and Sub-Sahara Africa, it can be seen that financial sector credit to private sector (FSCPS) positive and significantly influence GDP growth rate in ECOWAS and Sub-Sahara Africa. Results also showed that gross fixed capital formation (GFCF) and money supply (MSS) in both regions have significant positive influence on GDP growth rate. Furthermore, while bank credits to private sector (BCTPS) and exchange rate (EXCHR) have significant and negative influence on GDP growth rate in ECOWAS region, its influence (bank credit to private sector) in Sub-Sahara region of Africa is negative and significant. The implication of this result is that while bank credit to private sector and exchange rate negatively affects economic growth in both regions. However, inflation rate has positive but insignificant influence on GDP growth rate in Sub-Sahara Africa.

The coefficient of determination in both regions at 0.2543 (25.43%% in ECOWAS) and 0.4281 (42.81% in Sub-Sahara Africa) showed a moderate positive relationship between financial intermediation and economic growth in the regions. Overall financial intermediation indicators jointly contributed 25.43% (R2) and 42.81% respectively to GDP growth rate in both regions.

Furthermore, since the observed P-value of regression estimates are less than the critical value of 5%, the Null hypothesis which states that financial intermediation does not significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa is rejected. Thus, the study observes that financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa.

Test of Hypothesis 2& 3

H02: Bi-directional causality does not exist between financial intermediation and economic development in ECOWAS and Sub-Sahara Africa.

Test of Hypothesis 3

H03: Financial intermediation proxies in the model do not co=integrate with GDP growth rates both in the long-run and short-run in ECOWAS and Sub-Sahara Africa.

GDPGRi,t = β0 + β1BCPSi,t + β2GFCFi,t + β3LOGMSi,t4FSCPSi,t5INFRi,t + β6 EXCHRi,t   +  μi,t                                                                                                                    … 2                                                        

JOHANSEN AND FISHERS COINTEGRATIONTEST

This test was conductedto examine whether Financial Intermediation components namely: Bank Credit to Private Sector (BCTPS), Exchange Rate (EXCHR), Financial Sector Credit to Private Sector (FSCPS), Gross Fixed Capital Formation (GFCF), Inflation Rate (INFR) and Money Supply (MSS)in both regions exhibits long-run co-movement with GDP growth rate. The decision rule for the Co-integration test is that: The null hypothesis of no co-integration is rejected and the alternative accepted, if the observed P value is less than 5%. The results of the Johansen and Fishers co-integration test are shown in table 4.5 and 4.6 respectively for both regions.

 

Table 4.5: Results of Johansen and Fishers Cointegration Test(ECOWAS)             .

                  Hypothesized       Fisher Stat.                     Fisher Stat.          

                  No of CE(s)     (from trace test)  P-Value   from Max-eigen test)    P- Value

GDPGR, BCTPS, FSCPS, GFCF, LOGMSS, EXCHR, INFR

None               435.7*             0.0000             289.3*             0.0000

At most 1        207.0*             0.0000109.8*              0.0000

At most 2        114.5*             0.0000             59.54*                   0.0010

At most 3        69.57*             0.000138.75                      0.1313

At most 4        46.54**           0.0276             41.05***               0.0861

At most 5        24.55               0.7466             20.38               0.9062

                        At most 6        37.88               0.152737.88                      0.1527      .

Note:* = significant at 1%,** = significant at 5%, *** = significant at 10%,

SOURCE:Source: Authors computation using E-view 8

 

 

Table 4.6: Results of Johansen and Fishers Co-integration Test (Sub-Sahara Africa) .

                  Hypothesized       Fisher Stat.                     Fisher Stat.          

                  No of CE(s)     (from trace test)  P-Value   from Max-eigen test)    P- Value

GDPGR, BCTPS, FSCPS, GFCF, LOGMSS, EXCHR, INFR

None               481.9*             0.0000             345.3*             0.0000

At most 1        248.5*             0.0000142.9*              0.0000

At most 2        127.1*             0.0000             86.96*                   0.0000

At most 3        59.10*             0.000539.15***                0.0785

At most 4        36.18               0.1381             27.96                     0.4665

At most 5        25.21               0.6165             22.87               0.7394

                        At most 6        25.54               0.598225.54                      0.5982      .

Note:* = significant at 1%,*** = significant at 10%,

SOURCE:Source: Authors computation using E-view 8

 

The test was conducted under the assumption of no intercept and trend. Results show the trace statistics and the max-eigen statistics with their corresponding P values. The P values for ‘None’ ‘At most 1’, ‘At most 2’ and ‘At most 3’are below alpha value at 10% level of significance in both regions. This shows that the null hypothesis of no co-integration is rejected. Thus, financial intermediation co-integrated in the long-run with GDP growth rate in both regions of Africa.

VECTOR ERROR CORRECTION MECHANISM (VECM) TEST

The VECM test is conducted to examine whether financial intermediation components namely: Bank Credit to Private Sector (BCTPS), and Gross Fixed Capital Formation (GFCF) in both regions exhibit long-run and short-runrelationship with GDP growth rate. This test also showwhether any sudden shock that could cause disequilibrium can be corrected at certain speed within a year.  The result is shown in table 4.6 while, the E-view output is presented in Appendix II.

Table 4.6:  Results of Vector Error Correction Mechanism Test  .

Descriptor                      Coefficient       Std Error              t- statistics      P-value          

GDPGR, BCTPS, GFCF(ECOWAS)

ECM(-1)                                 -0.7665*          0.0582             -13.159            0.0000

GDPGR(-1)                            -0.0895**        0.0451             -1.9806            0.0478

BCTPS (-1)                             -0.1433            0.0926             1.5458            0.1224

GFCF (-1)                               0.0555*          0.0107             5.1700              0.0000

Constant                                  0.1290*          0.0282               4.5653           0.0000

GDPGR, GFCF, BCTPS(SUB-SAHARA AFRICA)

ECM(-1)                                 -0.7229*          0.0583             -12.398           0.0000

GDPGR(-1)                            -0.0478            0.0465             -1.0266            0.3047

GFCF(-1)                                -0.0211            0.0258             -0.8161            0.4145

BCTPS (-1)                             -0.0729*          0.0864             -0.8435            0.0139

Constant                                  0.0706            0.2473             0.2855            0.7752          .

Note: * = significant at 1%, ** = significant at 5%.

SOURCE: Source: Authors computation using E-view 8

Table 4.6 showed the result of the VECM test for both regions. In ECOWAS and Sub-Sahara Africa regions, it can be seen that the coefficient of the ECM at -0.7665and -0.7229 with their corresponding P value of 0.0000 and 0.000 showed that the ECM coefficients gives the right approiri sign and are significant at 5% level of significance. Thus, the null hypothesis that there is no long-run causality flowing from financial intermediation (BCTPS, and GFCF)to GDP growth rate in both regions is rejected. However, result showed that while GFCF exerts significant positive influence on GDP growth rate in ECOWAS region, it insignificantly negatively influences GDP growth rate in Sub-Sahara Africa. Similarly, while BCTPS exerts insignificant negative influence on GDP growth rate in ECOWAS region, it significantly negatively influences GDP growth rate in Sub-Sahara Africa. The result further shows that short-run dynamic influence flows from GDP growth rate to financial intermediation only in ECOWAS region. Thus, result confirms the existence of bidirectional relationship between financial intermediation and economic development in ECOWAS region. Results showed that any sudden shock that could cause disequilibrium can be corrected at the rate of 76.65% and 72.29% respectively in both regions within a year. The short-run dynamic test is conducted using the Wald coefficient test reported in table 4.7.The E-view output is however, presented in Appendix B.

 CONCLUSION AND RECOMMENDATIONS

 

The study examined the effects of financial intermediation on economic growth in Africa. The study specifically sought to evaluate whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. Secondly, to determine whether financial intermediation proxies in the model co-integrate with GDP growth rates in the long-run in ECOWAS and Sub-Sahara Africa.

 

The study investigated the effects of financial intermediation on economic growth in Africa. Results have shown that financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. However, the coefficient of determination in both regions at 0.25% (in ECOWAS) and 0.42% (in Sub-Sahara Africa) showed a weak relationship between the dependent and independent variables. The study indeed contributes to literature on the effects of financial intermediation on economic growth in Africa.

Secondly, results from Johansen and Fishers co-integration test, showed that long-run effects flows from financial intermediation proxies to GDP growth rate in both regions of Africa.

 

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User-specified lags: 1
Newey-West automatic bandwidth selection and Bartlett kernel

 

ABSTRACT

The study examined effects of financial intermediation on economic growth in Africa: evidence from ECOWAS and Sub-Sahara regions. The study sought to examine whether financial intermediation significantly influence economic growth rate in ECOWAS and Sub-Sahara Africa. Also, to identify whether financial intermediation proxies in the model co-integrate with GDP growth rates in the long-run in ECOWAS and Sub-Sahara Africa. The study used secondary data collected from the World Bank statistics for the period 1985 to 2017.

Keywords:Financial intermediation, Economic growth, ECOWAS, Sub-Sahara, Africa.

 

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