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Banks Performance and Economic Growth: Evidence from West Africa

This paper uses empirical econometric models to analyze Bank performance on GDP using panel data from 15 countries in West Africa. Data from 1996 to 2017 was used to make a statistical inference. Unit root test, cointegration test, fully modified
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  International Journal of Management Sciences and Business Research, Oct - 2019 ISSN (2226 - 8235) Vol - 8, Issue 10   http://www.ijmsbr.com Page 168 Banks Performance and Economic Growth: Evidence from West Africa Author’s Details:   (1) Professor Tan Zhongming, Lecturer - (2) Isaac Akpemah Bathuure- Student,MSc Economics  (3) Dr.Ding Guoping, Lecturer - (1)(2)(3) Jiangsu University.School of Finance and Economics-Address:301 Xuefu Road, Zhenjiang,Jiangsu,China  Abstract: This paper uses empirical econometric models to analyze Bank performance on GDP using panel data from 15 countries in West Africa. Data from 1996 to 2017 was used to make a statistical inference. Unit root test, cointegration test, fully modified ordinary least square (FMOLS) and Granger causality test were performed. A total of nine variables are employed in the study. GDP per capita, dependent variable which proxy’s economi c growth, return on assets and return on equity as independent variables. Government effectiveness, corruption control, interest rate and inflation as control variables. It was discovered that there is stationarity at first difference and that long run relationship exists among the dependent and independent variables. The fully modified OLS results showed that all the independent variables and the control variables have 1% significate relationship with the dependent variable.The regulatory bodies of the various countries in the sub-region are to ensure efficient management of Banks to avoid the situation of institutional failure which is a major issue in most developing countries. Stringent measures must equally be put in place to fight corruption.  Keywords:  Bank performance, economic growth, financial sector, West Africa   1.   Introduction The health of a country’s economy to a larger extend depends on the soundness of the banking sector. Instability in the banking sector and other financial intermediaries in any country can bring the whole economy to a standstill. The strength of most businesses depends on the ability of banks to provide them the needed facilities to undertake major deals. Persistent increase profitability and solid solvency are some major indications of good banking performance. Safeguarding the stability of the financial system and minimizing the risks of negative spillovers from the banking sector to the rest of the economy is the key objective of banking supervision as there is enough evidence to show that banks can not only promote growth but also endanger it, Svetlana and Olga (2017).It is, however, important to note that the magnitude of role played by banks depends on the long term economic dynamics of the country (Nasir, Ali & Khokar, 2014; Levine, 1998; 1999). There has been a lot of happenings in the banking industry in the West African region in recent times. Within a period of two years, sixteen indigenous Ghanaian Banks collapsed as the main regulatory body-thus Bank of Ghana embarked on reforms to make the banking sector robust. Two major reasons for the collapse of most of these banks were poor cooperate with governance and inability to meet the new Bank of Ghana minimum capital requirement. The collapse of indigenous banks has become a set back to the effort of promoting indigenous Ghanaians to take control of the economy by building strong local institutions (Jerry, 2018).Four banks in Nigeria were fined N5.65bn in total by the Central Bank of Nigeria for illegally facilitating the transfer abroad of $8.134bn for MTN, a South African telecom firm (Rafiq Raji, 2018).The very institutions that are supposed to be facilitating economic growth and progress were stifling it .It is an undisputable fact that countries that have strong and stable monetary and financial systems  tend to build up their economic development much more rapidly(Ayman 2017)  With the intended implementation of a single currency in the sub-region, which will be controlled by the west African monetary Agency(WAMA),West Africa Monetary Institute(WAMI) and the Central Bank of the various countries. This will go a long way to boost economic growth though it can equally plunge the whole region into a financial crisis if care is not taken. Banks in the West African sub-region continue to struggle due to poor macroeconomic conditions and over-dependency of economies on the export of raw materials. The interrelationship between bank performance and  International Journal of Management Sciences and Business Research, Oct - 2019 ISSN (2226 - 8235) Vol - 8, Issue 10   http://www.ijmsbr.com Page 169 economic growth in the sub-region is a topic of much importance for both bank supervisors as well as macroeconomic policymakers in general. To reduce vulnerability to external shocks threatened by the dependence of several economies, especially Nigeria(the biggest economy in the region), on oil or other mineral extraction, West African countries must increase domestic input into their products through manufacturing, especially processing minerals and agricultural products (African Development Bank, 2018) The impact of Bank performance and the financial sector as a whole on economic development has always been an issue of debate among economists and academics. McKinnon (1973) argued that country with a patchy financial system where individuals have to rely on their own savings for investment that country will not be able to rally funds to shift to more productive technologies and hence development prospects would suffer. However, Lucas (1988) has a different opinion. He argued that the development of financial institutions had been overstressed as a factor for economic development. Svetlana and Olga (2017) evaluated the links between banks and GDP growth in Latvia using Granger causality and Johansen cointegration tests based on quarterly data from 2001 to 2015.They reviewed several indicators for banking development to establish their relevance for GDP growth: Ale (2016) undertook a study to find the influence of the banking industry on the growth of the economy using the Multilayer Perception to define functions of Universal Bank, Cooperative Bank, and Thrift Bank as predictors of Gross Domestic Product growth. Series from 2003- 2013 were used. It was found that Universal banks have been growing tremendously, taking huge shares of growth compared to the other two bank types. Meantime, the Gross Domestic Product was found to be steadily growing over the same period with a significant spike in 2004.His findings corroborated with Mohammed Ziaur Rhman et al. (2015) who posited that combined causality existed among the variables used in their study Ahmad and Malik (2009) examined the effects of financial sector development on economic growth. Using GMM with data from 35 developing countries over the period of 1970-2003.they concluded that financial sector development affects per capita GDP hence economic growth.   Specifically, this paper is motivated by the zeal to contributes to existing knowledge and fills up a gap in the econometric-based empirical literature on the performance of Banks in west Africa on Economic growth .While a lot of econometric studies have been done on the impact of Bank performance and profitability and other related areas, little work has been done when it comes to Economic growth covering all west African countries. The study intends to investigate the relationship between variables that are included directly in the efficiency and performance of the banking sector and contribute to the economic growth of countries in the West African sub-region. The study is divided into four-folds; section 1 introduces the study and reviews existing literature, section 2 describes the data and methodology for the study, section 3 reports the finding results and discussion and section 4 concludes the study. 2.   Data and Methodology 2.1 Data The study used panel data from 15 West African countries from the period 1996 to 2017. The study’s objective is to assess the impact that banks in West Africa have on their economic growth hence it uses country level data from World Bank’s World Development Indicator, World Governance Indicators and Global financial development database. The dependent variable for the study is economic growth and it is measured by proxy of gross domestic product per capita. However, the independent variables that study employed are a return on assets and return of equity. In order to firmly ascertain the impact of banks' performance, the study considered the two variables as proxies to measure banks' performance due to the bias that only one of the variables can put out. To control for macroeconomic consequences, the study has considered some macroeconomic factors and  International Journal of Management Sciences and Business Research, Oct - 2019 ISSN (2226 - 8235) Vol - 8, Issue 10   http://www.ijmsbr.com Page 170 other external factors as well as to control for economic growth. The control variables used for the study are corruption control, inflation, interest rate, regulation quality and unemployment. 2.2 Model specification The econometric model that the study has considered can be written as:  Economic growth = ƒ  (Banks performance + control variables)  (1) In equation (1), Economic growth is measured by proxy of gross domestic product per capita; banks' performance is measured by proxies of return on assets and return on equity, control variables are corruption control, inflation, interest rate, unemployment rate, regulatory quality and government effectiveness. Some of the variables were transformed into their natural logarithm to avoid fluctuation in the data series thus all the variables except the regulatory quality and government effectiveness. After taking the natural logarithm of the variables then the econometric model transformed into equation (2) below;                                     (2)                                      (3) In the equations (2) and (3),    represents the intercept of regression   is the error term or stochastic error or disturbances which the independent variables could not represent; i represents the cross-section of the countries and t represents the time period of 1996 to 2017. 2.3   Methodology The study employed panel data methodologies to run its analysis to make a statistical inference. The methodologies used for the study are; panel unit root tests, cointegration test, fully modified ordinary least square (FMOLS) and granger causality tests. The first step of the study is to compute for the summary statistics to find the mean, median and standard deviation of the variables. Moreover, to also do Skewness, Kurtosis and Jarque-Bera test to find the sequence of the distribution. Subsequently, panel unit root test is performed to ascertain stationarity among the variables. However, the purpose of the unit root tests is to reject the null hypothesis, which posits that there is unit root in the variables hence further extension of the regression model to analyse the data will make the regression process spurious. The following unit root tests are used; Levin et al. (2002) LLC test, Im-Pesaran & Shim test IPS (Im et al., 2003) and ADF-Fisher and PP-Fisher tests (Maddala & Wu, 1999). After it has been ascertained there is no evidence of unit root in the variables then the study tests for cointegration to establish the long run relationship that exists among the variables thus the dependent and independent variables. Furthermore, the regression analysis then becomes the next step; the main regression method for the study is the fully modified ordinary least square (FMOLS) for its statistical inference. Finally, the study then performs granger causality test to ascertain the direction of causality among the dependent and independent variables. The expected direction is either unidirectional or bidirectional and also the null hypothesis posits that none of the variables granger causes another. Therefore, the test of granger causality will either validate the null hypothesis or reject it. 3. Empirical results and findings discussion 3.1 Summary statistics Table 1 reports the summary statistics of the variables and it can be reported that the average rate of corruption control in West Africa was -0.54 annually, average rate of economic growth was 6.58% annually and the  International Journal of Management Sciences and Business Research, Oct - 2019 ISSN (2226 - 8235) Vol - 8, Issue 10   http://www.ijmsbr.com Page 171 average growth in banks performance was 0.28% as in return on equity and 2.26% as in return on assets. Details of the statistics can be found in table 1. However, the Jarque-Bera test confirms that data is not in normal distribution hence it is leptokurtic as the Kurtosis test reports. Table 1 Summary Statistics corrup lngdppc lninf lnint lnroe lnunemp regqty lnroa goveff Mean -0.540 6.581 4.264 1.573 0.284 1.367 -0.540 2.263 -0.684 Median -0.641 6.439 4.463 1.687 0.324 1.454 -0.479 2.680 -0.682 Maximum 1.143 8.171 5.331 2.925 2.259 2.511 0.128 4.837 0.366 Minimum -1.702 4.811 0.000 -1.114 -3.927 -1.255 -2.024 -1.538 -1.885 Std. Dev. 0.537 0.621 0.900 0.764 0.805 0.748 0.437 1.253 0.516 Skewness 0.649 0.662 -3.621 -0.855 -1.138 -1.067 -0.562 -0.875 0.056 Kurtosis 3.293 3.008 17.178 3.273 7.505 4.561 2.774 2.686 1.903 Jarque-Bera 24.381 24.072 3484.863 41.204 350.219 96.140 18.083 43.446 16.732 Probability 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Observations 330 330 330 330 330 330 330 330 330 3.2 Panel unit root tests The study conducted panel unit root tests to ensure stationarity among the variables to avoid spurious regression and the results of the tests can found in table 2. From table2, it can be established that four tests were used to test for stationarity. Again, at level form there was evidence of stationarity in all the variables except that corrup was not stationary with LLC and ADF-Fisher tests, goveff was not stationary with LLC test and lngddpc was not stationary with LLC and IPS tests but at first difference, all the variables became stationary hence the study can conclude that at first difference there is an evidence of stationarity in the variables. Therefore, the study rejects the null hypothesis that there is unit root in the variables. Table 2 Panel Unit root tests Note: *** connotes 1% significance level, ** connotes 5% significance level, * connotes 10% significance level 3.3 Panel cointegration test The study performed a cointegration test to find evidence of long run relationship that exists between the independent and dependent variables. The results of the test can be found in table 3 and it reports that at none to at most 3 with trace test there was no evidence that the variables are cointegrated. Also, at none to at most 1 with Max-eigen test there was no evidence of cointegration, but from at most 4 to at most 7 with trace test and at most 2 to at most 7, there was evidence of cointegration or long run relationship between the dependent and independent variables. Therefore, the null hypothesis that there is no cointegration among the variables is rejected. corrup goveff lngdppc lnif lnint lnroe lnunemp regqty lnroa LEVEL LLC 0.291 1.186 -1.098 -5.814*** -2.441*** -7.001*** -0.854* -0.696*** -7.476***IPS -1.645** -1.571** 0.923 -1.313* -2.283*** -7.123*** 0.300* -3.438*** -6.823***ADF-Fisher 37.687 40.470* 49.201*** 41.183* 58.127*** 120.725*** 28.722** 59.6811*** 113.988***PP-Fisher 172.041*** 190.131*** 45.829*** 48.601*** 60.387*** 376.187*** 10.59* 149.184*** 124.547*** FIRST DIFFERENCE LLC -57.615  -71.630***  -7.615*** -10.694*** -20.317*** -17.078*** -8.262*** -50.647*** -16.995***IPS -53.480*** -61.904*** -9.917*** -8.447*** -20.000*** -18.399*** -7.374*** -44.420*** -17.314***ADF-Fisher 2514.35*** 2456.07*** 149.472*** 146.076*** 301.429*** 279.890*** 108.258*** 1563.32*** 262.887***PP-Fisher 2582.92*** 2449.03*** 182.808*** 197.594*** 426.152*** 1229.76*** 97.632*** 1568.52*** 1173.28***  International Journal of Management Sciences and Business Research, Oct - 2019 ISSN (2226 - 8235) Vol - 8, Issue 10   http://www.ijmsbr.com Page 172 Table 3 Cointegration test Hypothesized Fisher Stat.* Fisher Stat.* No. of CE(s) (from trace test) Prob. Sig. (from max-eigen test) Prob. Sig. None 20.790 0.894 20.790 0.894 At most 1 20.790 0.894 20.790 0.894 At most 2 16.640 0.977 71.900 0.000 *** At most 3 1.386 1.000 259.300 0.000 *** At most 4 276.300 0.000 *** 276.300 0.000 *** At most 5 321.400 0.000 *** 240.100 0.000 *** At most 6 160.500 0.000 *** 114.900 0.000 *** At most 7 107.800 0.000 *** 107.800 0.000 *** Note: *** connotes 1% significance level 3.4 Results of regression analysis (Fully modified OLS) Table 4 displays the regression analysis results by using a fully modified ordinary least square (FMOLS). From the table, the results can be interpreted as robust or strong results because, from all indications, all the independent variables (lnroa) and (lnroe) and the control variables showed 1% significant relationship with the dependent variable (lngddpc). Banks' return on assets showed a positive and statistically significant relationship with economic growth with coefficient of 0.0123, confirming that a percentage increase in banks' return on assets will lead to 0.012% increase in economic growth. With regards to return on equity, it showed a negative and statistically significant relationship with economic growth with coefficient of -0.008. In the other words, a percentage increase in the return on equity of banks will lead to 0.008 decreases in economic growth. Taking into account the control variables, corruption control, inflation, unemployment rate and regulatory quality showed positive and statistically significant relationship with economic growth at coefficient of 0.161, 0.102, 0.038 and 0.148, respectively. These coefficients signal that a percentage increase in corruption control, inflation, unemployment and regulatory quality will lead to 0.161%, 0.102%, 0.038%and 0.148% increase, respectively. However, interest rate and government effectiveness showed a negative and statistically significant relationship with economic growth with coefficients of -0.046 and -0.233 respectively. The negative relationships confirm that a percentage increase in interest rate and government effectiveness will lead to 0.046% and 0.233% decrease in economic growth respectively. Table 4 Results of analysis with FMOLS coefficient T-statistic coefficient T-statistic Corrupt 0.161 7.894*** 0.178 9.030*** Goveff -0.233 -11.223*** -0.236 -11.594*** Lnif 0.102 23.447*** 0.100 23.348*** Lnint -0.046 -7.416*** -0.0345 -6.272*** Lnunemp 0.038 3.994*** 0.0378 4.045*** Regqty 0.148 7.277*** 0.126 6.385*** Lnroa 0.0123 3.677*** Lnroe -0.008 (-1.857)* Note: *** connote 1% significance level, * connote 10% significance level
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