JEAS 05 2021 0084 proof 1 16 Relationship between financial inclusion, banking stability and economic growth a dynamic panel approach Richard Boachie, Godfred Aawaar and Daniel Domeher Accounting and[.]
The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1026-4116.htm Relationship between financial inclusion, banking stability and economic growth: a dynamic panel approach Richard Boachie, Godfred Aawaar and Daniel Domeher Accounting and Finance, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Banking stability and economic growth Received 24 May 2021 Revised 19 July 2021 Accepted 26 July 2021 Abstract Purpose – The purpose of this paper is to analyse the relationship between financial inclusion, banking stability and economic growth in sub-Saharan African countries given the interconnectedness between them Globally, financial inclusion has gained recognition as a critical channel for promoting economic growth by bringing a large proportion of the unbanked population into the formal financial system This cannot be achieved exclusive of the banking sector Design/methodology/approach – This paper focussed on 18 countries in sub-Saharan Africa Data on financial inclusion and the economy were obtained from the World Bank, and bank soundness indicators data were also obtained from International Monetary Fund covering the 11-year period from 2008 through 2018 Panel system generalised method of moments is employed for the regression analysis because it has the capability to produce unbiased and consistent results even if there is endogeneity in the model Findings – The results show that economic growth drives banking stability and not vice versa; confirming a unidirectional causality from gross domestic product to banking stability So, this study finds support for the demand-following hypothesis The paper further observed that financial inclusion positively and significantly influences the stability of banks and economic growth The study established that bank capital regulation negatively influences banking stability in sub-Saharan African countries Research limitations/implications – This study does not capture the unique country-specific relationship Practical implications – The policy implication is that policymakers in sub-Saharan African countries should focus on growth-enhancing policies that improve the level of financial inclusion The central banks in sub-Saharan African countries should take advantage of the positive effect of financial inclusion to develop regulatory frameworks and policies that make it attractive for banks to continue to expand their operations to the unbanked Originality/value – This is, as far as the authors know, the explanation of the interconnection of financial inclusion, banking stability and economic growth in sub-Saharan Africa Keywords Financial inclusion, Banking stability, Economic growth, GMM, Sub-saharan Africa Paper type Research paper Introduction Globally, financial inclusion (FI) has gained recognition as a means of drawing the unbanked population into the formal financial sector to promote economic growth The role of FI has become increasingly crucial for every economy especially emerging, frontier and developing countries in sub-Sahara Africa (SSA) Following the work of Abor et al (2018), this study defines FI or inclusive finance as the case where quality financial products are accessible to citizens most conveniently and cost-effectively through established policies and regulatory frameworks that safeguard the users at all times The financial system has made concerted efforts to draw those excluded financially to have access to a wide range of services through FI initiatives The expansion of access to finance has three potential benefits First, FI neutralises the barrier to socio-economic growth which can be hampered by financial exclusion (DemirgucKunt and Klapper, 2012; Adomako et al., 2016; Allen et al., 2016) Financial exclusion is empirically noted to have a devastating consequence on the economy because the financial Journal of Economic and Administrative Sciences © Emerald Publishing Limited 1026-4116 DOI 10.1108/JEAS-05-2021-0084 JEAS infrastructure may be obstructed even though it is a fundamental pillar for growth (Diamond and Dybyig, 1983; Angadi, 2003) Second, FI provides pathways in accessing financial services conveniently in a cost-effective way (Kim, 2016) This enables the poor in society to safely keep their funds away from the norm of housing it, helping them escape the risks of economic shocks (Kim et al., 2018) Third, FI helps in achieving a multiplier effect on the economy through credit creation from the pooled savings from a large segment of the unbanked population leading to an increase in economic activities and employment (Koku, 2015; Balach et al., 2016) However, FI also provides a pathway for banks to achieve inclusiveness especially when these banks are stable The interconnectedness between FI and banking stability thus becomes apparent When a greater number of people remain unbanked or excluded financially, predicting banking stability becomes difficult and on the other hand, achieving a higher level of FI becomes difficult if banks are unstable (Khan, 2011) An important question that arises is “does expanding access to formal financial services work in conjunction with policies aimed at improving bank stability or threaten bank stability?” These are two divergent schools of thought The first school of thought argues that the FI–bank stability nexus benefits the economy in two ways First, inclusive finance provides a more stable deposit base for banks Second, financial inclusiveness improves bank stability through process improvement in executing intermediary functions These benefits, according to the proponents, are consistent with the view that financial integration systems enhance bank stability (Khan, 2011; Ryan et al., 2014; Amatus and Alireza, 2015; Mostak and Sushanta, 2015; Neaime and Gaysset, 2018; Diallo, 2018), and inclusive finance reduces the unnecessary risk taken by individual banks The other school of thought also proposed that getting a greater percentage of the population included in the financial system is negatively related to banking stability They adduced two major reasons to support their argument First, inclusive finance reduces the quality of your credit portfolio, because the provision of credit to low-income markets will be affected by a wide range of information asymmetries related to customers who have no credit history or collateral; and second, the expansion of the financial channels can increase the credit risk and, above all, the concentration and liquidity risk This position proposes that inclusive finance has a negative influence on banks (Khan, 2011; Allen et al., 2013) Empirically, studies on FI so far have typically focussed on identifying the determinants and measures of inclusive finance (Demirguc-Kunt and Klapper, 2012; Allen et al., 2016; Mohammed et al., 2017; Chikalipah, 2017; Assuming et al., 2019) Some studies also focussed on the impact of FI either on income, income inequality or poverty reduction (Inoue and Hamori, 2012; Park and Mercado, 2015, 2016; Sharma, 2016; Kim et al., 2018; Neaime and Gaysset, 2018; Inoue, 2019) and on economic development and growth where these studies established a positive impact of FI on economic growth (Inoue, 2019) and other studies support this position (Morgan and Pontines, 2014; Kim, 2016; Lenka and Sharma, 2017; Abor et al., 2018; Sethi and Acharya, 2018; Kim et al., 2018; Tang et al., 2019; Sethi and Kumar, 2019; Ananzeh and Othman, 2019) Furthermore, the study of Anarfo et al (2019) changed the narrative and constructed the FI index using principal components analysis (PCA) on two dimensions of FI – access and usage However, penetration and mobile money deepening factors were not fully factored in This impacted the results to be weak in application to SSA countries This study provides a robust composite index using three dimensions of FI variables – penetration, access and usage as indicated in Appendix Earlier, studies have established an interactive nexus of banking stability and FI (Morgan and Pontines, 2014; Ryan et al., 2014; Amatus and Alireza, 2015; Mostak and Sushanta, 2015; Neaime and Gaysset, 2018; Ahamed and Mallick, 2019) Most of these studies concentrated on the cross-country and sector level A few of them focussed on the banking sector and employed single or multiple banking stability proxies not banking stability index hence, creating a contextual gap The most adopted proxy for banking stability is the bank z-score The challenge is that the bank z-score is used to measure the stability of individual banks Adopting it for a panel of banks or countries is liable to produce bias and weak results This paper offers a solution by developing a composite index for banking stability using four key dimensions of banking soundness – capital adequacy, asset quality, earnings/profitability and liquidity Furthermore, these previous studies on FI acknowledge the role of the banking sector in the FI agenda, yet little is known about how FI interacts with the soundness of banks and their composite impact on the economy Given the importance of FI and the intermediation role played by banks and the existing empirical gaps, this study enriched the existing related literature by using the multidimensional nature of FI and banking stability to build a composite index to study the interaction between FI and banking stability and the effect on the growth of the economy Also, the study incorporated the moderating effect of bank regulation on the relationship between FI and stability of banks in SSA countries which lacks or requires more research for policy directions and decisions Based on the aforementioned contributions to knowledge, this paper achieved three objectives: (1) investigate the causal (lead–lag) relationship between banking stability and economic growth in SSA countries; (2) analyse the relationship of FI, banking stability and economic growth in SSA countries; (3) examine the moderation role of bank capital regulation on the relationship between FI and bank stability in SSA countries This study is significant for banking practitioners and inclusive finance policymakers The study recommends that policy regulatory framework should focus on mobile money deepening as major delivery channel for financial inclusiveness The central banks, other regulatory bodies and consultants should carefully examine economic policy directions in conjunction with bank regulations to ensure the parallel growth of the economy and the financial system The governments in SSA countries through the central bank should rely on the study findings in determining policy decisions towards reducing bank-level risk by reducing information asymmetry needed to access banking services The remainder of the paper is organised as follows: Section discusses the theories that underpin the study and empirical literature review on the study variables Section also addresses the methodology and data sources while Section addresses the results and discussions on the study variables The final Section draws out the conclusions and recommendations of the study Literature review and theoretical background FI cannot be achieved in a vacuum without the involvement of banking institutions The banking sector also operates within an economic environment The two divergent views on bank stability and economic growth factors have theoretical underpinning We used demandfollowing and supply-leading hypotheses to explain the bases for our argument Again, achieving inclusive finance means large unbanked populations with dynamic characteristics are being brought into the fold which potentially affects bank level of risk We also used information asymmetry theory to explain this In financial resource allocation by banks through debt contracts, there exist situations whereby one party holds more or better information than the other This presents a challenge for banks to differentiate between good and bad borrowers Therefore, Akerlof (1970) developed this theory to deal with the situation In a seminal contribution by Richard (2011), asymmetric information results in two-dimensional problems: adverse selection and moral hazard A moral hazard essentially involves behaving in a manner that puts the interest of the other parties to a contract at risk Adverse selection, on the other hand, assumes that lenders Banking stability and economic growth JEAS find it difficult to distinguish between borrowers with different levels of risk, thus limiting the performance of credit agreements These two problems create an increase in non-performing loan levels because of a wrong decision which in turn affects the performance and stability of the banks To mitigate the effect of adverse selection and moral hazard, banks insert collateral requirements into their loan contracts that might not be available or sufficient The performance of an economy has something to with banking or the broader financial system There are two divergent schools of thought regarding the leading influence in the nexus between the financial system and economic growth This theory propounded by Robinson (1952) assumes that economic growth promotes financial system growth instead of the other way round This suggests that when growth is weak, projected future demands may decline and will of course affect or suppress investment below the actual limit or speed needed to realise optimal economic growth The demand-following hypothesis further argues that economic growth drives financial infrastructure development and bank-branch facilities The demand-following framework places emphasis that credit financing and investment spending from domestic institutions generate its savings This theory captures the essence of economic growth factors causing the generation of savings by the populace in the banking sector The hypothesis is relevant to this research because it captures the essence of economic growth factors causing the generation of its savings which are kept in the banks as deposits This hypothesis is the other school of thought in the financial system and growth nexus The proponent Schumpeter (1911) and supported by Gurley and Shaw (1967), McKinnon (1973) and King and Levine (1993) assumes that the efficiency of the financial sector is the driving force of economic growth The key argument for supply-based assumptions is that increased credit or credit deepening is a decisive factor in economic growth The effective arbitration occurring in the banking sector influences the optimal allocation of resources within the economy (Hurlin and Venet, i2008) The supply-based model shows that the causal link goes from finance to economic growth and that there is no feedback on economic growth Improved financial intermediation leads to a reduction in asymmetric information, transaction and monitoring costs In addition, a well-functioning financial sector can enhance the creation of financial services and the accessibility of anticipated demand for real economy participation They posit that finance promotes economic growth and further state that the promotion of economic growth occurs in the early stage of economic development FI as a strategy has the focus of drawing the unbanked population to access and use formal financial services In examining the relationship with economic growth in developing economies, the study of Inoue and Hamori (2016) focusses on SSA countries from 2004 through 2012 and finds that commercial bank branches have a positive relationship for the selected countries Balach et al (2016) also supported the findings by focussing on 97 crosssection countries from 2004 through 2012 using two FI proxies to assess the impact on economic growth The study established a positive effect for both proxies and on selected countries being studied Several studies support these findings establishing a positive relationship (Sharma, 2016; Kim et al., 2018; Sethi and Kumar, 2019) Furthermore, the FI and banking stability relationship has empirical underpinning For instance, the study on SSA by Anarfo et al (2019) investigated the dynamic link between FI and financial sector development which is moderated by economic growth The findings from the study indicate a reverse causality between financial sector development and FI (Anarfo et al., 2019) Again, the study posits that FI is a driver for financial sector development and vice versa In a recent development, Anarfo et al (2019) took into account the impact of the regulatory framework on financial stability and FI in SSA countries and found that financial regulation influences financial stability and FI Several studies find support for this position (Fratzscher et al., 2016; Fernandez et al., 2016; Owen and Pereira, 2018) Some studies established a negative relationship between FI variables and bank stability (Koong et al., 2017; Le et al., 2019) Moreover, another strand of literature focussed on banking stability and economic growth (Jayakumar et al., 2018) An example is the study of Manu et al (2011), who examine the relationship between financial stability and economic growth in Africa and employed a dynamic fixed-effect model approach, and the results reveal that financial stability impacts positively on economic growth (Manu et al., 2011) In support of the findings, Jokipii and Monnin (2013) confirmed the positive significance in banking stability and output growth nexus using the vector autoregression (VAR) methodology approach in 18 OECD countries for a period spanning from 1980 through to 2008 Several studies find support for a positive relationship (Puatwoe and Piabuo, 2017; Jayakumar et al., 2018) even though some studies also find a negative relationship between banking stability and economic growth (Inoue, 2019; Alsamara et al., 2019) The study tested the hypothesis to establish the relationship between banking stability and economic growth (Sethi and Acharya, 2018) and to investigate the direction of their relationship The results of the study will either confirm demand-following or supply-leading hypotheses propounded by Robinson (1952) and Schumpeter (1911), respectively The findings inform the SSA countries whether economic growth leads or lags in the relationship with banking stability We hypothesise as below: H1 There is a lead–lag relationship between banking stability and economic growth Another strand of literature focussed on the direction of causality between banking stability and economic growth (Jayakumar et al., 2018) Several studies find support for a positive relationship (Puatwoe and Piabuo, 2017; Jayakumar et al., 2018) even though some studies also find a “negative relationship between banking stability and economic growth” (e.g Inoue, 2019) However, the study of Alsamara et al (2019) confirmed a bi-directional causality between banking stability and economic growth We then hypothesise as below: H2 There is a bi-causality between banking stability and economic growth Furthermore, the literature established a relationship between FI and economic growth For instance, the study of Andrianaivo and Kpodar (2011) adopted the generalised method of moments (GMM) approach in a panel of 44 African countries and confirmed the positive and significant effect of financial inclusion on economic growth Inoue and Hamori (2016) find that commercial bank branches have a positive relationship with the economy of the selected countries Balach et al (2016) also supported the findings by focussing on 97 cross-section countries from 2004 through 2012 Several studies find support for these findings (Lenka and Sharma, 2017; Kim et al., 2018; Sethi and Kumar, 2019; Anarfo et al., 2019) We then hypothesise as below: H3 FI significantly influences economic growth Regarding banking stability and economic growth, the study of Manu et al (2011) examines the relationship between financial stability and economic growth in Africa and established that financial stability impacts positively on economic growth (Manu et al., 2011) In support of the findings, Jokipii and Monnin (2013) confirmed the positive significant influence of banking stability on economic growth in 18 OECD countries The study of Jayakumar et al (2018) also established a positive significant effect of banking stability on the economy We then hypothesise as follows: H4 Banking stability significantly affects economic growth We observe in the literature the relationship between banking stability and FI The study of Anarfo et al (2019) established a reverse causality between financial sector development and FI The same study also posits that FI is a driver for financial sector development and vice Banking stability and economic growth JEAS versa Several studies find support for this position (Fratzscher et al., 2016; Fernandez et al., 2016; Owen and Pereira, 2018) Based on these positions, we hypothesise as follows: H5 FI significantly affects banking stability To avoid arbitrariness in the conduct of banking business, each economy has a regulatory framework to guide and protect stakeholders The strictness of the regulation and its effectiveness have implications for expanded access to finance and achieving banking stability In the spirit of Dev (2006), other literature affirms the positive effect of regulation on stability and FI (Chiwira et al., 2013; Fratzscher et al., 2016; Sethi and Acharya, 2018) In a recent development, Anarfo and Abor (2020) considered the impact of the regulatory framework on financial stability and FI in SSA countries and found that financial regulation influences financial stability and FI Based on this assumption, the hypothesis is tested: H6 Banking regulation significantly moderates the relationship between FI and banking stability Research methodology In estimating a panel data set, the GMM has been adjudged to have the ability to avoid biases according to Levine et al (2000) This method is applicable in different statistical analytical circumstances The GMM estimation technique can correct the problem of endogeneity The assumption further shows that when the number of periods (T) of available data may be small and the number of observations (N) may be large, the GMM can produce unbiased results (Roodman, 2009) The study adopts the dynamic system GMM model as follows: Yi;t ẳ ỵ Xi;t ỵ i ỵ i;t where i ¼ n; t ¼ T (1) Based on the assumption of non-serial correlation among the error terms, μi;t and weakly exogenous explanatory variables, Arellano and Bond (1991) suggest the following moment conditions: (2) E Yi;ts ị i;t ẳ 0; i ẳ n; s ≥ 2; t ¼ T E ðXi;t−s Þ Δμi;t ¼ 0; i ¼ n; s ≥ 2; t ¼ T (3) The two equations above are a two-step GMM estimator recommended by Arellano and Bond (1991) The first equation adopts periodic cross-country-independent homoscedastic errors The second equation adopts the residual derived from the first equation to run an estimate of the covariance matrix and variance analysis which relaxes the axiom of independent homoscedasticity When these moment conditions are applied in the GMM estimator, the result is efficient estimates Based on the estimation technique, the following general model is employed: Yit ẳ ỵ X1it ỵ X2it ỵ μi;t where i ¼ n; t ¼ T (4) where Y the variable we are trying to predict; α the intercept; β1 and β2 the slope (Bun and Windmeijer, 2010); X1, X2 the variables used to predict Y; μ the error term The intercept (α) is the value of the dependent variable when the independent variable is equal to zero while the slope of the regression line (β1 and β2) represents the rate of change in Y as X changes (Bun and Windmeijer, 2010) Following the works of Beck et al (2008), Kocisova (2014), Mostak and Sushanta (2015) and with modifications, we adopt the following equations: GDPit ẳ ỵ FIIit ỵ BSIit ỵ it (5) FIIit ẳ BSIit ị ỵ Xit ỵ it (6) BSIit ẳ FIIit ị ỵ Xit ỵ it (7) GDP is gross domestic product FII is financial inclusion index BSI is banking stability index The study draws data from several sources to investigate the relationship between FI, banking stability and economic growth The FI data set is obtained from the Global Financial Index (FINDEX) of the World Bank, and the banking stability data set is obtained from the Global Financial Development Database (GFDD) of IMF Finally, the data for GDP, the proxy for economic growth is compiled from World Bank economic data This paper targeted 18 countries in SSA for the study These countries have a mixture of lower-income, lowermiddle-income and upper-middle-income countries This suggests that all the characteristics of countries in SSA are represented Our data cover from 2008 to 2018, thus giving us 198 as our panel observations The review of the empirical literature shows that economic growth as the dependent variable is mostly represented by gross domestic product (GDP annual growth rate) Our independent variables used here are banking stability and FI A detailed description is found in Appendix We followed empirical work to define the variables used in this study (such as Ghosh, 2010; Sarma, 2012; Kocisova, 2014) Principal component analysis (PCA) The study adopted a panel PCA estimation technique to construct banking stability and FI indices made up of eight selected measures of banking stability and FI The PCA results are presented in Appendixes and The study adopted indexes for banking stability and FI to provide a common measure for countries in the SSA region and to provide a benchmark or reference point for future studies using similar variables for individual specific countries (Ghosh, 2010) In line with this estimation techniques, the ith factor index can be specified for FI Eqn (8) and (9) and banking stability Eqn (10) and (11) as: FIIi ¼ Wi1 X1 ỵ Wi2 X2 ỵ Wi3 X3 ỵ ỵ Wip Xp (8) where FIIi is the financial inclusion index; Wi is the weight (factor loading) of the parameter of the factor score; X is the original figure of the respective components, and P is the number of FI variables in the equation Eqn (9) below specifies the eight proxies used in the composite index construction The variables are as follows: Number of Mobile Money Accounts per 100,000 adults (NMMA), Number of Commercial Bank Accounts per 1,000 adults (BAPTA), Mobile Banking, Registered Agent Outlets Per 100,000 Adults (MBRA), Domestic credit to the private sector by banks % of GDP (DCTP), Bank branches per i100,000 adults (BBPA), Automated teller machines (ATMs) per 100,000 adults (ATMPA), Borrowers from commercial banks per 1,000 adults (BFCB) and Depositors with commercial banks per 1,000 adults (DBPA) The index is specified as follows: Z FII ẳ NMMA; BAPTA; MBRA; DCTP; BBPA; ATMPA; BFCB; DBPAị (9) BSIi ẳ Wi1 X1 ỵ Wi2 X2 ỵ Wi3 X3 ỵ ỵ Wip Xp (10) where BSIi is the banking stability index; Wi represents the weight of factor score, X represents the original figure a component and P represents the number of variables used for the index construction Eight banking stability indicators used are; Regulatory Tier capital Banking stability and economic growth JEAS to risk-weighted assets (T1CWA), Nonperforming loans net of provisions to capital (NPLPC), Nonperforming loans to total gross loans (NPLGL), Return on assets (ROA), Return on equity (ROE) and Interest margin to gross income (IMGI), Liquid assets to total assets (LATA) and Liquid assets to short-term liabilities (LASL) The index is specified as follows: Z BSI ¼ ðT1CWA; NPLPC; NPLGL; ROA; ROE; IMGI; LATA; LASLÞ (11) Empirical results and discussion Descriptive statistics The presentation in Table provides a summary of the statistical properties of the variables All the variables have positive mean values and close mean–median scores reflect the approximately normal distribution of the data All the variables are skewed which shows that they are asymmetrical When the skewness is close to 0, then the data is normally distributed or otherwise From Table 1, it can be seen that BSI and FII are positively skewed, and this shows that they are asymmetrical Further, GDP and MOD exhibit a negative skewness which implies that they have a long-left tail Again, kurtosis values according to Table show that the data is normally distributed except for FII which is deviated from Panel unit root results We assert that adopting a fitting econometric model for the study variables requires that the panel unit root test is conducted As a first step to the analysis and discussions of the study results, the available data used are analysed for their statistical properties The study employs the Augmented Dickey–Fuller (ADF) test as well as the Phillips–Perron test for individual unit root process and Levin et al (2002) (LLC) test for a common unit process for all the variables The authors assume that the “test performs well when N lies between 10 and 250 and when T lies between and 250” (Baltagi et al., 2007) When the “T is very small, the test is undersized and has low power” This paper has N being 18 and T being 11 which is suitable for our data set “Besides, panel data approach gives options for estimation ranging from no trend and non-constant estimations with a constant and deterministic trend testing for similar effects” (Anarfo et al., 2019) thus provides a “greater level of flexibility in computing the coefficients” From our results on panel unit root presented in Table 2, it is realised that all the variables are stationary at levels and have no unit roots BSI Table Results of descriptive statistics on variables FII GDP MOD Mean 1.97E-16 0.791988 1.489647 2.803109 Median 0.022369 0.651245 1.518199 2.849147 Maximum 2.902328 2.588181 3.594348 3.742482 Minimum 3.107133 0.009117 0.115685 0.562326 Std Dev 1.002535 0.987153 0.834745 0.374974 Skewness 0.172839 0.500057 0.255925 1.619703 Kurtosis 3.122445 2.138376 3.807921 9.943446 Jarque–Bera 1.109508 14.37666 7.546491 484.3179 Probability 0.574214 0.000755 0.022977 0.000000 Sum 3.16E-14 156.8136 294.9502 555.0157 Sum Sq Dev 198.0000 67.77636 57.96810 27.69925 Observations 198 198 198 198 Note(s): BSI – banking stability index; FII – financial inclusion index, GDP – gross domestic product, MOD – moderating variable (i.e regulatory capital risk-weighted average) Variables Statistic p-values** Cross sections Obs BSI @Level Levin, Lin and Chu t* ADF – Fisher χ 2.35347 53.3400 0.0093 0.0314 18 18 171 171 83.2583 0.0000 18 180 FII @Level Levin, Lin and Chu t* ADF - Fisher χ 7.50145 64.9814 0.0000 0.0022 18 18 172 172 PP - Fisher χ 100.575 0.0000 18 180 GDP @Level Levin, Lin and Chu t* ADF – Fisher χ 7.81349 87.4435 0.0000 0.0000 18 18 175 175 PP – Fisher χ 102.923 0.0000 18 180 MOD @Level Levin, Lin and Chu t* ADF – Fisher χ 4.57528 57.1056 0.0000 0.0141 18 18 176 176 PP – Fisher χ 80.3915 0.0000 18 180 PP – Fisher χ Note(s): ** Probabilities for Fisher tests are computed using an asymptotic χ distribution All other tests assume asymptotic normality Banking stability and economic growth Table Panel unit root test Selection and computation of lag order The appropriate model is selected based on a technique reported in Table According to Andrews and Lu (2001), the selection criteria are based on three models and the overall coefficient of determination, VAR selection at the first-order model is preferred The results presented in Table show that all the models – AIC, SC and HQ are first-order selections The VAR model adopted for the study was established as stable (see Appendix: Figure A1) The stability graph showed that all the roots of AR characteristics were within the circle with no outlier Lag LogL LR FPE 99.09967 NA 0.942634 48.05719 93.57787* 0.069128* 44.60563 5.944365 0.071469 42.86373 2.806385 0.081543 40.11803 4.118555 0.088450 36.66073 4.801805 0.092875 32.50407 5.311285 0.094674 31.07512 1.667112 0.113636 22.31783 9.243807 0.092166 21.57655 0.700096 0.118978 Note(s): * Indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan–Quinn information criterion AIC SC HQ 5.616648 3.003177* 3.033646 3.159096 3.228779 3.258929 3.250226 3.393062 3.128768 3.309808 5.704621 3.267097* 3.473512 3.774909 4.020539 4.226635 4.393879 4.712661 4.624314 4.981301 5.647353 3.095292* 3.187171 3.374031 3.505125 3.596685 3.649392 3.853638 3.650754 3.893204 Table VAR lag order selection criteria JEAS Table VAR Granger causality tests Table Panel Granger causality tests Discussion of regression results In investigating the interaction between FI, banking stability and economic growth, the system GMM estimator was adopted (Wang and Lee, 2018) From the results shown in Tables and 5, the lead–lag relationship between banking stability and economic growth has been presented using VAR Granger Causality/Block Exogeneity Wald Tests The results show from the upper panel of Table that banking stability is influenced by economic growth measured by GDP and not vice versa Thus, economic growth lead and banking stability lag This also supports Alsamara et al (2019), whose study finds that growth in GDP leads to stability in the banking sector This result finds support for the demand-following hypothesis The economic implication of this study is that when the economy is performing and indicators demonstrate strength, it creates its own savings capacity where the citizenry keeps the banks as deposits The booming economic activities result in surplus savings out of which the banks create assets to generate interest income Impliedly, the good financial performance of banks is borne out of the performance of the economy Further, confidence by the population in the banking sector is heightened when the economy booms because the stable deposits by the banks increase banks’ liquidity strength Another detrimental cause of banking instability is the level of non-performing loans (NPLs) and a major cause is attributed to the state of the economy Thus, a lower NPL is a single reflection of a sound banking system This study supports that growth-enhancing policies should be the optimal focus for economic policymakers to ensure the long-term stability of financial intermediaries Further in Table 5, we have presented the Panel Granger Causality tests between GDP and BSI to explain the direction of causality The p-values show that GDP Granger causes BSI and not vice versa This means there is a unidirectional relationship between GDP and BSI This study supports the findings of Zang and Kim (2007) which examine the causal link between financial sector development and economic growth in East Asian countries and established strong evidence that economic growth precedes financial development These findings are the test results for H2 Moreover, the GMM regression estimator is adopted to test the interactive relationship between the three studied variables The results of the GMM in Table have shown a positive significant relationship between the FII and economic growth (GDP) of 18 SSA countries with other variables held constant The coefficient of FII with GDP as the dependent variable χ2 df p-values Dependent variable: BSI Excluded GDP All 8.020642 8.020642 3 0.0456 0.0456 Dependent variable: GDP Excluded BSI All 2.511202 2.511202 3 0.4733 0.4733 Null hypothesis Obs F-statistic Prob GDP does not Granger cause BSI BSI does not Granger cause GDP 144 2.67355 0.83707 0.0498 0.4757 shows a positive value of 0.284404 with a corresponding p-value of 0.0046 which is less than 0.05, indicating significance This result is consistent with (Sharma, 2016; Kim et al., 2018; Sethi and Kumar, 2019) The study of Sharma (2016) shows a positive relationship between various dimensions of FI and economic growth (Inoue, 2019) FI concepts provide the unbanked a sure pathway to financial independence where their formal introduction opens up a gateway to access capital for commercial activities that contribute to the national output It also reduces the dependence on the national-social welfare scheme and as such excess funds could be used to open up the economy Banks are offered the opportunity to have access to huge stable deposits for onward lending to borrowers On the other hand, BSI has a negative insignificant relationship with GDP, hence no influence on economic growth The increased activities by the greater unbanked population whose economic contributions were not captured by being alien to the formal financial system have been revealed to have a significant influence on the growth of SSA countries’ economies Therefore, their characteristics as identified in literature together with their relative importance should drive policymakers to develop all-inclusive economic policy and banking regulations that seamlessly draw them into the banking sector to achieve inclusive growth Further, the results presented in Table show that the FII as an independent variable has a positive significant relationship with the banking stability index The positive coefficient of 0.671815 has corresponding p-value of 0.0000 < 0.05 This result is consistent with findings from the study by Anarfo et al (2019) which shows that FI is a key driver for stability for institutions within the financial system The channels of delivery of financial services to the unbanked population by financial institutions are critical to chalk these successes Banking institutions that develop a comprehensive and flexible delivery channel as mobile money is doing for African economies stand the chance to receive many stable deposits that can be used to create an asset to deepen the private sector and to enhance the financial performance of the banks involved Impliedly, banking institutions should be involved in financial literacy services to improve customer trust in the formal banking system Furthermore, we moderated the relationship between banking stability and FI The moderating variable used in the study is the regulatory capital risk-weighted average The result is presented in Table The statistics in Table shows that the moderating variable has a negative significance on banking stability The coefficient of 0.646139 has a Dependent variable: GDP Variable Coefficient Std Error t-Statistic p-values GDP(1) FII BSI J-statistic Prob(J-statistic) 0.401255 0.284408 0.039393 17.64473 0.281803 0.037472 0.098957 0.085821 10.70800 2.874051 0.459009 0.0000 0.0046 0.6469 Dependent variable: BSI Variable Coefficient Std Error t-Statistic p-values BSI(1) FII J-statistic Prob(J-statistic) 0.374000 0.671815 14.83546 0.536721 0.020023 0.107927 18.67823 6.224731 0.0000 0.0000 Banking stability and economic growth Table Panel system generalised method of moments Table Panel system generalised method of moments JEAS corresponding p-value of 0.0000 < 0.05 which means the null hypothesis is rejected However, the negative sign shows that a unit increase in capital requirement has the propensity to cause 0.65-unit instability in the banking space (by 0.65 units), all other things held constant It is observed that when central banks embarked on stricter capital regulations, the banking institutions become reserve in providing credit to the private sector Credit deepening is observed to provide a strong indicator for economic growth The banks become interested in less risky investments such as government and corporate bonds that are AAA rated all in a bid to minimise their risk exposure These findings also support the findings of Schliephake (2016), whose study confirms that stricter capital regulation has the potential to harm the stability of the banking sector especially in an environment of high-level competition In addition, results in Table show that the moderate variable (MOD) with regulatory capital to risk-weighted assets as a proxy influences the relationship between FI and banking stability The positive coefficient of 0.28 with a p-value of 0.000 < 0.05 indicates that FI has a positive significant influence on banking stability This shows that unit growth in financial inclusion causes 0.28-unit growth in banking stability Impliedly, when customers observe that the central banks have strengthened their monitoring activities of the banks and taking stricter measures to safeguard their hard-earned funds, they build trust and drawing them into the banking sector becomes easier This study confirms the findings in a recent study by Anarfo et al (2019) which highlighted that when capital regulations among others are tightened, it can impact access to finance in SSA countries with a spillover to impacting banking stability Recommendations and policy implications The role played by FI in the economic growth and stability of banks cannot be overemphasised in SSA countries There is evidence of the positive influence of FI on economic growth The policy implication is that policymakers in SSA countries should focus on growth-enhancing policies that would drive the delivery channels such as mobile money deepening and seamless interoperability of mobile and banking system to improve the level of financial inclusion The central banks in SSA countries should take advantage of the positive effect of FI to provide support for ICT infrastructural development and to enhance cyber to further deepen the trust of the unbanked population in the formal banking system This will increase stability in the banking sector because liquidity constraints will be minimised resulting from stable deposits from the expanded financial access Another implication has to with designing the policy framework for banks, we suggest that it should not be done in isolation but harnessed with the entire economic policies to ensure a holistic impact for all stakeholders By so doing, the central banks can achieve bank stability and expanded access together The study broadens the benefit scope by examining the lead–lag nexus of banking stability and economic growth where we established a unidirectional relationship from economic growth to banking stability and not vice versa We find support for the demand-following hypothesis Table Panel system generalised method of moments Dependent variable: BSI Variable Coefficient Std Error t-Statistic p-values BSI(1) FII MOD J-statistic Prob(J-statistic) 0.273790 0.280706 0.646139 11.39842 0.723873 0.027309 0.059229 0.134843 10.02556 4.739366 4.791776 0.0000 0.0000 0.0000 This hypothesis postulates that when the economy is performing and indicators demonstrate strength, it creates its own savings capacity where the citizenry keeps in the banks as deposits These deposits are used by the banks to create assets from which the level of their performance is measured We further contribute to the literature by establishing the triad relationship between economic growth, banking stability and FI using system-GMM estimation Limitations of the study and suggestions for future studies The limitation of this study has to with the adoption of an index for the explanatory variables, and thus a unique country-specific relation is not captured The paper acknowledges the possibility that certain relationships observed in this study may vary from one country to another Therefore, future studies may seek to ascertain the extent to which these relationships exist in each specific country in the SSA region by using the findings in this study as a benchmark References Abor, J.Y., Amidu, M and Issahaku, H (2018), “Mobile telephony, financial inclusion and inclusive growth”, Journal of African Business, Vol 19 No 3, pp 430-453, doi: 10.1080/15228916.2017.1419332 Adomako, S., Danso, A and Damoah, O.J (2016), “The moderating influence of financial literacy on the relationship between access to finance and firm growth in Ghana”, Venture Capital, Vol 18 No 1, pp 43-61 Ahamed, M.M and Mallick, S.K (2019), “Is financial inclusion good for bank stability? International evidence”, Journal of Economic Behavior and Organization, Vol 157, pp 403-427 Akerlof, G (1970), “The market for lemons: quality uncertainty and the market mechanisms”, The Quality Journal of Economics, Vol 84 No 3, pp 488-500 Allen, F., Carletti, E., Cull, R., Qian, J., Senbet, L.W and Valenzuela, P (2013), “Improving access to banking: evidence from Kenya”, World Bank Policy Research Working Paper No 6593, World Bank Group eLibrary, available at: https://elibrary.worldbank.org/doi/pdf/10.1596/18139450-6593 Allen, F., Demirguc-Kunt, A., Klapper, L.F and Martinez, P.M.S (2016), “The foundations of financial inclusion: understanding ownership and use of formal accounts”, Journal of Financial Intermediation, Vol 27, pp 1-30, doi: 10.1016/j.jfi.2015.12.003 Alsamara, M., Mrabet, Z., Jarallah, S and Barkat, K (2019), “The switching impact of financial stability and economic growth in Qatar: evidence from an oil-rich country”, The Quarterly Review of Economics and Finance, Vol 73, pp 205-216 Amatus, H and Alireza, N (2015), “Financial inclusion and financial stability in SubSahara Africa (SSA)”, International Journal of Social Sciences, Vol 36 No 1, pp 2305-4557 Ananzeh, I.E.N and Othman, M.D (2019), “Analyzing the effect of financial development on economic growth-The Jordanian experience”, Investment Management and Financial Innovations, Vol 16 No 1, pp 119-127 Anarfo, E.B and Abor, J.Y (2020), “Financial regulation and financial inclusion in Sub-Saharan Africa: does financial stability play a moderating role?”, Research in International Business and Finance, Vol 51, 101070 Anarfo, E.B., Abor, J.Y., Osei, K.A and Gyeke-Dako, A (2019), “Financial inclusion and financial sector development in Sub-Saharan Africa: a panel VAR approach”, International Journal of Managerial Finance, Vol 15 No 4, pp 444-463, doi: 10.1108/IJMF-07-2018-0205 Andrews, D.W and Lu, B (2001), “Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models”, Journal of Econometrics, Vol 101 No 1, pp 123-164 Andrianaivo, M and Kpodar, K (2011), ICT, Financial Inclusion, and Growth: Evidence from African Countries, IMF Working Papers, 2011(073) Banking stability and economic growth JEAS Angadi, V.B (2003), “Financial infrastructure and economic development: theory, evidence and experience”, Reserve Bank of India Occasional Paper, Vol 24 Nos 1/2, pp 191-223 Arellano, M and Bond, S (1991), “Some tests of specification for panel carlo application to Data: evidence and an employment equations”, The Review of Economic Studies, Vol 58 No 2, pp 277-297 Asuming, P.O., Gyamfuah, L.O and Mohammed, J.I (2019), “Financial inclusion in sub-saharan Africa: recent trends and determinants”, Journal of African Business, Vol 20 No 1, pp 112-134, doi: 10 1080/15228916.2018.1484209 Balach, R., Law, S.H and Habibullah, M.S (2016), “The role of financial inclusion in financial development: international evidence”, Abasyn Journal of Social Sciences, Vol 9, pp 330-344 Baltagi, B.H., Bresson, G and Pirotte, A (2007), “Panel unit root tests and spatial dependence”, Journal of Applied Econometrics, Vol 22 No 2, pp 339-360 Beck, T., Demirg uỗ-Kunt, A and Honohan, P (2008), “Access to financial services: measurement, impact and policies”, The World Bank Research Observer, Vol 22 No 3, pp 1-42, doi: 10.1093/ wbro/lkn008Source:RePEc Bun, M.J and Windmeijer, F (2010), “The weak instrument problem of the system GMM estimator in dynamic panel data models”, The Econometrics Journal, Vol 13 No 1, pp 95-126 Chikalipah, S (2017), “What determines financial inclusion in Sub-Saharan Africa? African”, Journal of Economic and Management Studies, Vol No 1, pp 8-18, doi: 10.1108/AJEMS-012016-0007 Chiwira, O., Ruramayi, T and M, B (2013), “Financial inclusion and financial stability: the important role of financial regulation in explaining the relationship (empirical review)”, Journal of Research in International Business and Management, Vol No 4, pp 139-149 Demirguc-Kunt, A and Klapper, L (2012), “Measuring financial inclusion: the global findex database”, Development Research Group Policy Research Working Paper No 6025, World Bank, Washington, DC Dev, S.M (2006), “Financial inclusion: issues and challenges”, Economic and Political Weekly, Vol 41 No 41, p 4310e4313 Diallo, B (2018), “Bank efficiency and industry growth during financial crises”, Economic Modelling, Vol 68, pp 11-22 Diamond, D.W and Dybvig, P.H (1983), “Bank runs, deposit insurance, and liquidity”, Journal of Political Economy, Vol 91 No 3, pp 401-419 Fernandez, A.I., Gonzalez, F and Suarez, N (2016), “Banking stability, competition, and economic volatility”, Journal of Financial Stability, Vol 22, pp 101-120 Fratzscher, M., K€onig, P.J and Lambert, C (2016), “Credit provision and banking stability after the great financial crisis: the role of bank regulation and the quality of governance”, Journal of International Money and Finance, Vol 66, pp 113-135 Ghosh, S (2010), “Credit growth, bank soundness and financial fragility: evidence from Indian banking sector”, South Asia Economic Journal, Vol 11 No 1, pp 69-98 Gurley, J.G and Shaw, E.S (1967), “Financial structure and economic development”, Economic Development and Cultural Change, Vol 15 No 3, pp 257-268 Inoue, T (2019), “Financial inclusion and poverty reduction in India”, Journal of Financial Economic Policy, Vol 11 No 1, pp 21-33, doi: 10.1108/JFEP-01-2018-0012 Inoue, T and Hamori, S (2012), “How has financial deepening affected poverty reduction in India? Empirical analysis using state-level panel data”, Applied Financial Economics, Vol 22 No 5, pp 395-408 Inoue, T and Hamori, S (2016), “Financial access and economic growth: evidence from sub-saharan Africa”, Emerging Market Finance, Vol 52, pp 743-753 Jayakumar, M., Pradhan, P.R., Dash, S., Maradana, P.R and Gaurav, K (2018), “Banking competition, banking stability, and economic growth: are feedback effects at work?”, Journal of Economics and Business, Vol 96 No 2018, pp 15-41 Jokipii, T and Monnin, P (2013), “The impact of banking sector stability on the real economy”, Journal of International Money and Finance, Vol 32, pp 1-16 Khan, H.R (2011), Financial Inclusion and Financial Stability: Are They Two Sides of the Same Coin? Address by Shri H R Khan, Deputy Governor of the Reserve Bank of India, at BANCON 2011, organized by the Indian Bankers Association and Indian Overseas Bank, Chennai, India, November, available at: www.bis.org/review/r111229f.pdf Kim, J.H (2016), “A study on the effect of financial inclusion on the relationship between income inequality and economic growth”, Emerging Markets Finance and Trade, Vol 52 No 2, pp 498-512 Kim, D.W., Yu, J.S and Hassan, M.K (2018), “Financial inclusion and economic growth in OIC countries”, Research in International Business and Finance, Vol 43, pp 1-14 King, R.G and Levine, R (1993), “Finance and growth: schumpeter might be right”, The Quarterly Journal of Economics, Vol 108 No 3, pp 717-737 Kocisova, K (2014), Banking Stability Index: A Cross-Country Study, Faculty of Economics, Department of Banking and Investments, Kosice, pp 197-208 Koku, P.S (2015), “Financial exclusion of the poor: a literature review”, International Journal of Bank Marketing, Vol 33 No 5, pp 654-668 Koong, S.S., Law, S.H and Ibrahim, M.H (2017), “Credit expansion and financial stability in Malaysia”, Economic Modelling, Vol 61, pp 339-350 Le, T.H., Chuc, A.T and Taghizadeh-Hesary, F (2019), “Financial inclusion and its impact on financial efficiency and sustainability: empirical evidence from Asia”, Borsa Istanbul Review, Vol 19 No 4, pp 310-322 Lenka, K.S and Sharma, R (2017), “Does financial inclusion spur economic growth in India?”, The Journal of Developing Area, Vol 51 No 3, pp 215-228 Levin, A., Lin, C.F and Chu, C.S.J (2002), “Unit root tests in panel data: asymptotic and finite-sample properties”, Journal of Econometrics, Vol 108 No 1, pp 1-24 Levine, R., Loayza, N and Beck, T (2000), “Financial intermediation and growth: causality and causes”, Journal of Monetary Economics, Vol 46 No 1, pp 31-77 Manu, L.P., Adjasi, C.K., Abor, J and Harvey, S.K (2011), “Financial stability and economic growth: a cross-country study”, International Journal of Financial Services Management, Vol No 2, pp 121-138 McKinnon, R (1973), Money and Capital in Economic Development, the Brookings Institution, Washington DC Mohammed, J.I., Mensah, L and Gyeke-Dako, A (2017), “Financial inclusion and poverty reduction in Sub-Saharan Africa”, African Finance Journal, Vol 19 No 1, pp 1-22 Morgan, P.J and Pontines, V (2014), “Financial stability and financial inclusion”, ADBI Working Paper 488, available at: https://ssrn.com/abstract=2464018 Mostak, M and Sushanta, M (2015), Is Financial Inclusion Good for Bank Stability? International Evidence, University of London Neaime, S and Gaysset, I (2018), “Financial inclusion and stability in MENA: evidence from poverty and inequality”, Finance Research Letters, Vol 24 No C, pp 230-237 Owen, A.L and Pereira, J.M (2018), “Bank concentration, competition, and financial inclusion”, Review of Development Finance, Vol No 1, pp 1-17 Park, C.Y and Mercado, R.V Jr (2015), “Financial inclusion, poverty, and income inequality in developing Asia”, ADB Economics Working Paper Series No 426, Asian Development Bank, New Delhi Banking stability and economic growth JEAS Park, C.Y and Mercado, R.V Jr (2016), “Does financial inclusion reduce poverty and income inequality in developing Asia?”, in Gopalan, S and Kikuchi, T (Eds), Financial Inclusion in Asia: Issues and Policy Concerns, Palgrave Macmillan, London, pp 61-92 Puatwoe, J.T and Piabuo, S.M (2017), “Financial sector development and economic growth: evidence from Cameroon”, Financial Innovation, Vol No 1, p 25 Richard, A (2011), “Information asymmetry, information precision and the cost of capital”, Review of Finance, Vol 16 No 1, pp 1-29 Robinson, J (1952), The Rate of Interest and Other Essays, Macmillan, London Roodman, D (2009), “How to xtabond2: an introduction to difference and system GMM in Stata”, The Stata Journal, Vol No 1, pp 86-136 Ryan, R.M., O’Toole, C.M and McCann, F (2014), “Does bank market power affect SME financing constraints?”, J Bank Finance, Vol 49, pp 495-505 Sarma, M (2012), “Index of financial inclusion–a measure of financial sector inclusiveness”, Working Paper Jawaharlal Nehru University, Centre for International Trade and Development, School of International Studies, Delhi Schliephake, E (2016), “Capital regulation and competition as a moderator for banking stability”, Journal of Money, Credit and Banking, Vol 48 No 8, pp 1787-1814 Schumpeter, J.A (1911), The Theory of Economic Development, Harvard University Press, Cambridge, MA Sethi, D and Acharya, D (2018), “Financial inclusion and economic growth linkage: some cross country evidence”, Journal of Financial Economic Policy, Vol 10 No 3, pp 369-385, doi: 10.1108/ JFEP-11-2016-0073 Sethi, D and Kumar, S (2019), “Financial inclusion matters for economic growth in India-Some evidence from cointegration analysis”, International Journal of Social Economics, Vol 46 No 1, pp 132-151, doi: 10.1108/IJSE-10-2017-0444 Sharma, D (2016), “Nexus between financial inclusion and economic growth: evidence from the emerging Indian economy”, Journal of Financial Economic Policy, Vol No 1, pp 13-36 Tang, J., Rubab, S and Wen, X (2019), “Determining the nexus between financial inclusion and economic development in Pakistan”, Journal of Money Laundering Control, Vol 22 No 2, pp 195-209, doi: 10.1108/JMLC-12-2017-0068 Wang, W and Lee, L.F (2018), “GMM estimation of spatial panel data models with common factors and a general space–time filter”, Spatial Economic Analysis, Vol 13 No 2, pp 247-269 Zang, H and Kim, Y.C (2007), “Does financial development precede growth? Robinson and Lucas might be right”, Applied Economics Letters, Vol 14 No 1, pp 15-19 Further reading International Financial Statistics (2015), Financial Stability Report [Data Set], International Monetary Fund, available at: https://www.imf.org Appendix The Appendix file is available online for this article Corresponding author Richard Boachie can be contacted at: boachierichard@gmail.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com