Financial Institutions Dynamics,Investments and Development

Abstract

The study evaluates how specific features of financial institutions and investment sources influence enduring development among economies in the sub-region of Sub-Saharan Africa (SSA). Data for the interactions in question were compiled from 36 economies in SSA from 1996 to 2019, and various empirical estimates were carried out using the two-step system Generalized Method of Moments statistical framework. Results from the analyses suggest that growth in depth, improved access, and efficiency of financial institutions foster long-term development among economies in the sub-region. Investments in various forms were found to have a varied augmenting impact on long-term development. Further empirical analyses suggest that quality of governance has a significant positive moderating impact on how net foreign direct investment and domestic investments influence development among economies in the sub-region. Political instability is, however, found to negate gains to development from both investment growth and contributions from financial institutions.

JEL Codes: C33, G2, E13

Keywords

Financial institutions development investments governance quality political instability system GMM model

1. Introduction

The need to ensure appreciable and sustained economic growth and development amidst uncertain and volatile macroeconomic conditions continue to be a major issue for economies around the world. This unending drive to promote growth in the face of challenging macroeconomic conditions has become a key feature among most developing and emerging economies due mostly to weak economic structures and macroeconomic policies. Most emerging economies such as those found in sub-Saharan Africa (SSA) have had to deal with significantly different constraints to economic development over the years. These constraints often revolve around geo-political and socio-economic conditions. Ongoing efforts at managing conditions and factors with the potential to significantly influence variability in economic performance, and desired standard of living have given rise to several empirical studies. These studies highlight a plethora of both direct and proximate conditions with the potential to augment or constrain economic growth (Moral-Benito, 2012; Cuaresma et al., 2014; Oyebowale & Algarhi, 2020; Batrancea et al., 2021). Most of these studies, however, focus on gross domestic product (GDP) growth as a measure of economic growth or development, and how a variety of macroeconomic conditions tend to influence the condition (Sloboda & Sissoko, 2020; Ali & Sardar, 2020). Unlike most existing studies, some of which are noted above, this study is designed specifically to target a rarely reviewed niche in the economic growth/development discourse. This inquiry focuses on long-term development among emerging economies in SSA, and the role operational dynamics among financial institutions and investments (foreign, domestic, and government investments) play in such development agenda. It is important to point out that development, the dependent variable examined in this study, differs significantly from those often used in related studies. This study’s review employs a more holistic measure of development—the Human Development Index (HDI) instead of GDP growth.

The HDI, used as a proxy for development in this study, is more comprehensive in its measure of development compared with GDP growth often used in related studies. Compared to GDP growth, which mainly hinges on variability associated with consumption, investment, government expenditures as well as net exports, the HDI captures general economic performance in addition to the socioeconomic well-being of the general populace in an economy (Abaidoo & Agyapong, 2022). This study opted for the HDI as a proxy for development because according to Abaidoo and Agyapong (2021), it constitutes a true measure of long-term development compared with GDP growth. The HDI is preferred to GDP growth in this study because it captures the overall standard of living of the general population in addition to macroeconomic performance. Compared with HDI, GDP growth is a weak measure of development because an economy can experience significant growth in GDP without a commensurate improvement in the standard of living of its citizenry, a hallmark of long-term development. HDI captures the full spectrum of conditions critical for sustainable long-term development. For instance, HDI embodies three fundamental aspects of sustainable development—education, income levels, and life expectancy. Education highlights a critical element for long-term human development, and sustainable income levels suggest some measure of improved macroeconomic conditions. The unique approach adopted in this study (focusing on a more holistic measure of economic development) therefore augments the literature by highlighting the roles financial institutions and investment (foreign and domestic) play in long-term development (living standards, health, and human development) among economies in SSA.

Additionally, it is important to point out that unlike most related studies that examine the role of financial institutions in economic growth (Kaushal & Ghosh, 2016; Rateiwa & Aziakpono, 2017; Xu, 2022), this study rather reviews the extent to which key features of financial institutions (not the performance of such institutions) influence development as defined by HDI. This approach has the potential to provide a much broader scope of assessment of the discourse on how financial institutions influence development. For instance, we specifically examine the relative effects of financial institution depth, access, and efficiency on development, a micro-level analysis rarely found in the existing literature. Moreover, apart from the direct relationships noted above, we are also guided by the fact that most economies in the sub-region are often characterized by some form of unrest or political instability, and ineffective institutional structures (Aisen & Veiga, 2013; Hayat, 2019; Campos et al., 2020). Consequently, this study further examines how surmised relationships noted above may be moderated by political instability and the quality of institutions of governance. The governance quality variable, which defines the nature and performance of governmental institutions such as regulatory quality and rule of law employed as a moderator in this inquiry is unique to this study. The variable is a principal component analysis (PCA) index. Thus, in terms of research approach, and nature of underlying variables used, this study has the potential to contribute significantly to the existing literature by unearthing interactions (verifying the potential direct and moderating behavior of variables and indexes) hitherto unexamined in related studies.

The rest of the study is structured as follows: the next section reviews literature that highlights the subject matter under review. The data and methodology section follows; it focuses on the sources and description of the variables, the model specifications, and the estimation technique employed. The next section presents the results and discussion of the empirical estimates, followed by the conclusion and recommendation section.

2. Literature Review

Theories that explain the dynamics of growth and development among economies have received significant review in the literature over the years. Broadly, the theoretical state of development of economies are defined by the neoclassical growth theories, endogenous growth theories, and new growth theories (Uddin et al., 2021). According to the neoclassical, the basis for the growth of an economy is the factor accumulation of labor and capital; it emphasizes the importance of technology in the growth discourse. However, according to the proponents of endogenous growth theory, human capital is the dominant medium through which technology and increased productivity could engender economic development. Apart from these traditional growth theories, the mechanics of growth, have also been discussed by heterodox economists. The heterodox theory of development highlights how power relations among groups and underutilization of resources may be the source of underdevelopment (Dutt, 2017). Consequently, the heterodox theory of growth emphasizes the need for full resource utilization and distribution (Dutt, 2017). The heterodox theorists emphasize fair resource distribution among economic agents, and government policies that place importance on improving the living conditions and well-being of the people (Friedman, 2006). The heterodox position largely aligns with the focus of this study, which seeks to review specific factors influencing variability in holistic development that goes beyond GDP growth by also focusing on the living standards and welfare, hence the use of HDI as a measure of development.

Several empirical studies have delved into the mechanics of financial sector operations and the impact they have on the development of economies. It must, however, be mentioned that per our review, most of these studies focus on the development of the financial sector as a whole or the role financial institutions such as banks play in economic growth, with economic growth/development proxied by GDP growth and its variants. For instance, Hassan et al. (2011) employed GDP per capita in examining the impact of financial development on growth in a study. The study found a positive relationship between financial development and economic growth in developing economies. A well-functioning financial system is necessary, among other relevant factors in contributing to the development of an economy (Hassan et al., 2011). Earlier work by Roubini and Sala-i-Martin (1992) concluded that policies that result in financial repression thwart the growth path of an economy. This conclusion emphasizes the significant role liberalized financial systems play in the economic growth discourse, highlighting the significance of access to financial institutions and the depth of financial institutions and services to economic development as reviewed in the present study. The stock market liquidity and development of the banking sector have a positive effect on growth, accumulation of capital, and productivity improvement (Levine & Zervos, 1998); this affirms the importance of the financial system in economic growth. Levine and Zervos (1998), however, found that stock market size, volatility, and international integration do not robustly affect growth. This latter revelation highlights the need for further inquiry based on new data on the relationship; that is, do the size, access, and efficiency of financial institutions have a significant impact on a broader measure of development of economies? Demetriades and Hussein (1996) also verified the effect of financial development and real GDP; the results from the study showed that there exists little support to indicate that finance is a leading factor in the developmental process of an economy; contrasting the position of earlier mentioned works. A unidirectional causality that flows from financial depth to economic growth was found by examining the relationship between growth and the financial system (Christopoulos & Tsionas, 2004); according to this conclusion, there is a basis to suggest that financial institution depth may have a significant influence on development. No clear evidence on the nexus between finance and growth was found in a related study (Andersen & Tarp, 2003), agreeing with the results from Demetriades and Hussein (1996). Andersen and Tarp (2003) therefore questioned the assertion that increased financial sector intermediation within a deregulated environment could act as an engine of growth. On the other hand, results from the system Generalized Method of Moments (GMM) estimation method in a study that employed data from 1980 to 2014 showed that financial development supports economic growth for the sub-region of SSA (Ibrahim & Alagidede, 2018).

For the Nepalese economy, development was represented by GDP growth in examining the impact of financial institutions on development. The results from the study showed a long-run association between financial institutions and economic development in Nepal (Dhungana, 2014). Focusing on the Nigerian economy, Murad and Idewele (2017) examined the impact of microfinance institutions on economic growth from 1992 to 2012. The study’s findings suggest that the operations of microfinance institutions have a significant positive effect on economic growth in the short run, but are insignificant in the long run. Again, in the Nigerian economy, performance of banks was found to exert a significant positive effect on economic growth (Obademi & Elumaro, 2014). For Middle East and North African economies, results from the GMM estimation technique showed that the relationship between the bank’s performance and economic growth is insignificant (Naceur & Ghazouani, 2007). In earlier research by Beck and Levine (2004), however, the performance of banks was found to promote economic growth from GMM estimate. Other relevant works suggest that the performance of financial institutions such as banks promotes economic development (Daly & Frikha, 2016; Moussa & Hdidar, 2019).

Studies on the dynamics of investment and development have also mostly focused on evaluating the impact of investment on development defined by GDP growth. For instance, in a study focusing on 19 Latin American countries, foreign direct investment (FDI) was found to have an insignificant effect on economic growth (Alvarado et al., 2017). However, for high-income countries out of the total sample, FDI was found to have a significant positive impact on economic growth, while, for upper-middle-income countries, the magnitude of the impact was uneven and insignificant (Alvarado et al., 2017). FDI was found to boost long-run economic growth, but with adverse impact growth in the short run among developing economies (Dinh et al., 2019). Again, Dinh et al. (2019) found that domestic credit to the private sector, domestic investment, human capital, and money supply assists in the development of economies in the long-run. Results of another related study, which reviewed the relationship between domestic investment and economic growth in the Algerian economy from 1969 to 2015 using vector error correction model, found that poor management, weak development policies, and domestic investment exerts a significant negative effect on economic growth in the long-run (Bakari, 2018). This outcome contrasts with the findings by Zamilur and Ferdaus (2021) for the Pakistani economy, where domestic investment was found to have a positive effect on GDP growth. In a study based on 27 African economies, results show that for those with developed institutional structures, FDI tend to enhance economic growth; however, for economies with poor institutional dstructures, FDI was found to have insignificant influence on growth (Yeboua, 2021). This conclusion highlights the importance of governance quality in the development discourse; hence one of the core objectives reviewed in this current study. Again, Gizaw et al. (2021) focused on examining the subject matter for the Ethiopian economy and concluded that domestic investment (public or private) promotes GDP growth, but FDI has an insignificant effect on GDP growth.

As already noted, reviewed literature suggests that studies on the subject matter in question have focused primarily on economic growth/development using GDP growth and its variant forms. Again, reviewed submissions also highlight varied conclusions based on the scope of the study. This current study brings a different perspective by proxying development with HDI, in an attempt to have a holistic or broader definition of long-term development that goes beyond sustainable productivity growth; and how financial institution dynamics influence this broader measure of development if any.

3. Data and Methodology

Data and methodology adopted in achieving the noted objectives of the study are presented in this section. We present the sources of the data and a detailed description of variable construction and derivation processes; this is followed by a presentation of the descriptive statistics and variable diagnostic checks. The section concludes with a presentation and discussion of the functional forms of the models that are estimated and highlight key features and robustness of the estimation technique adopted.

3.1. Data Sources and Description

The study uses panel data, collected for 36 countries in SSA from 1996 to 2019 in its empirical estimation verifying key interactions already noted. The data for the various variables were compiled from relevant sources, which include the United Nations Development Programme (UNDP), the International Monetary Fund (IMF), World Development Indicators (WDI), the World Governance Indicators, and the Fraser Institute database. Data on HDI, representing holistic development was compiled from the UNDP database, while the government investment index was sourced from the Fraser Institute database. Again, data on financial institution features, that is, financial institution access, financial institution efficiency, and financial institution depth were collected from the IMF database. From the WDI, we also collected data for GDP growth, FDI (net inflow of FDI as a ratio of GDP), gross capital formation (representing domestic investment), trade (ratio of imports plus exports to GDP), population growth (ratio of the total population for year t to year t–1) and age dependency (ratio of people below 15 years and over 65 years to total working population). Finally, six governance variables as defined by the World Bank (government effectiveness, rule of law, voice and accountability, political stability, control of corruption, and regulatory quality) were compiled from the WDI database.

3.2. Governance Quality Index

As already alluded to, the quality of governance variable is unique to this study. It is a construct, which is used both as a control variable and as a moderator in verifying the influential role of institutions of governance in the surmised nexuses examined. The construction process of this variable uses PCA procedure following notable research works (Abaidoo & Agyapong, 2021; Ahamed & Mallick, 2019; Bali et al., 2014; Ellul & Yerramilli, 2013). The variables used for the estimation of the governance quality index include government effectiveness, rule of law, voice and accountability, political stability, control of corruption, and regulatory quality; these variables are indexes of equal scale constructed by the World Bank. Variables of varying scales are normally subjected to data normalization (Kumar et al., 2016; Sendhil et al., 2018; Agyapong & Abaidoo, 2022). However, because the variables employed for the estimation of the governance quality for this study are of equal scale, normalization procedures are not required. According to Abdi and Williams (2010), the PCA procedure is an efficient multivariate process of obtaining significant components of the variance of inter-correlated dependent factors, whilst discarding the insignificant portion of the variance in the process. Per the PCA procedure, the eigenvectors corresponding to the significant constituents of the variance of the various variables denote the weights that are employed for the index estimation process (Ahamed & Mallick, 2019). Compared to other methods such as budget allocation process, conjoint analysis, equal weight, and expert opinion, the PCA approach has been deemed robust because it eliminates the possibility of bias (Sendhil et al., 2018; Basel et al., 2020). We proceed to present equation (1) for the estimation and construction of the quality of governance index:

{GQ}_{i t} = \sum_{j}^{n} ((Y_{j ​, i t} * W_{j, i}) / \sum_{j}^{n} W_{j, i})

(1)

According to equation (1), the subscripts i and t denote the country and year, respectively, while j represents the variable in question. GQ, Y, and W, therefore, denote governance quality, the data point for the governance variable in question, and PCA-derived weight, respectively. A higher calculated index in this regard represents improved governance and vice versa.

3.3. Political Instability Index

Political instability is also employed in the study to evaluate how political and civil unrests among economies in the sub-region influence the relationship between financial institutions, investment, and development. The variable compiled from WDI, however, defines a stable political atmosphere (a higher figure denotes a stable political environment and vice versa). As a result, we subject the data to a derivation process by taking the additive inverse of the variable to represent political instability. Equation (2) presents the functional form of the political stability variable; its inverse, captured in equation (3) denotes the political instability variable.

f ({PST}_{i t}) = f (X_{i 1996}, \dots, X_{i 2019})

(2)

f ({PINST}_{i t}) = f (- X_{i 1996}, \dots, - X_{i 2019})

(3)

From both equations, PST and PINST represent political stability and political instability, respectively, while X represents the data point for political stability. Similar to the definition of political stability, a higher index for political instability per equation (3) represents an atmosphere of heightened civil and political unrest and a lower index denotes a state of reduced political unrest.

3.4. Descriptive Statistics and Variable Acceptability Checks

In Table 1, the results of descriptive statistics for the data are presented. As shown by the average HDI and its corresponding standard deviation, it is evident that among economies in the sub-region, there exists an insignificant disparity in the level of development (higher mean compared with standard deviation). Again, according to the mean and standard deviation for the financial institution variables, the results indicate a significant variation in access to financial institutions and financial institution depth among the various economies; the level of financial institution efficiency, however, does not exhibit a significant difference among economies in the sub-region. On average, over the study period, economies in the sub-region recorded a net inflow of FDI of 4.4% of the total value of GDP, grew in terms of GDP by an average of 4.9% per year, while international trading activities received significant patronage, averaging 67.6% of the total value of GDP over the period under study. Again, from the table, it could be deduced that the quality of governance and institutional structures are relatively poor; a negative average index (–0.42) was recorded over the study period.

Table 1.

Descriptive Statistics.

Variables	Obs.	Mean	Std. Dev.	Min	Max	Skew.	Kurt.	VIF
HDI	781	.471	.111	.236	.796	.423	2.833
FIA	828	.075	.106	.002	.586	2.623	9.979	3.41
FIE	828	.517	.139	.111	.86	–.387	2.833	1.78
FID	828	.119	.181	.001	.885	2.852	10.729	2.35
FDI	819	.044	.101	–.087	1.618	8.429	100.059	1.27
GI	557	5.808	3.012	.003	10	–.147	1.857	1.05
GCF	763	.218	.094	–.024	.779	1.259	6.636	1.44
GDPG	823	.049	.077	–.364	1.5	9.021	165.45	1.14
Trade	788	.676	.334	.207	3.114	2.006	11.664	1.57
POPG	828	.026	.009	–.006	.081	.395	8.454	2.71
AgeD	828	.858	.141	.413	1.128	–.935	4.039	4.67
GQ	720	–.42	1.373	–8.133	12.908	4.115	38.727	2.39
PINST	720	.448	.857	–1.219	2.699	.248	2.307	1.82

Abbreviations: HDI = Human Development Index, FIA = Financial Institution Access, FIE = Financial Institutions Efficiency, FID = Financial Institutions Depth, FDI = Foreign Direct Investment (Net Inflow), GI = Government Investment, GCF = Gross Capital Formation, GDPG = GDP Growth, Trade = Trade Liberalization, POPG = Population Growth, AgeD = Age Dependency, GQ = Governance and Institutional Quality, PINST = Political Instability.

This section examines the suitability of the various variables for subsequent model specification and analysis. Results of a pairwise correlation matrix, showing the degree of association between pairs of variables are presented in Table 2. For variable suitability, and to avoid possible problems due to multicollinearity, Elith et al. (2006) posit that correlation between a pair of explanatory variables should not exceed 0.85. It is evident from the results that none of the pairs of variables has a correlation coefficient above 0.85; all the explanatory variables can therefore be considered suitable for the analysis. This conclusion is supported by the results of the variance inflation factor (VIF) shown in Table 1. According to Liao and Valliant (2012), for an explanatory variable to be suitable for model specification, its VIF should be less than 10. Again, it is evident that the explanatory variable with the highest VIF is age dependency (with a VIF of 4.67); this means that all the explanatory variables satisfy the recommended threshold.

Table 2.

Pairwise Correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
(1) HDI	1.000
(2) FIA	0.717	1.000
(3) FIE	0.192	0.373	1.000
(4) FID	0.533	0.667	0.403	1.000
(5) FDI	0.032	0.000	–0.025	0.003	1.000
(6) GI	0.218	0.067	–0.080	0.011	0.089	1.000
(7) GCF	0.359	0.235	0.092	0.062	0.306	0.014	1.000
(8) GDPG	–0.058	–0.047	0.137	–0.073	0.277	–0.115	0.150	1.000
(9) Trade	0.426	0.321	0.084	0.265	0.300	0.042	0.398	–0.005	1.000
(10) POPG	–0.367	–0.484	–0.122	–0.430	0.119	0.018	–0.008	0.200	–0.224	1.000
(11) AgeD	–0.818	–0.733	–0.185	–0.583	–0.011	–0.003	–0.196	0.045	–0.452	0.507	1.000
(12) GQ	0.312	0.240	0.043	0.179	–0.035	0.000	0.051	0.010	0.005	–0.132	–0.319	1.000
(13) PINST	–0.513	–0.502	–0.244	–0.348	–0.046	0.020	–0.206	–0.052	–0.284	0.252	0.479	–0.261	1.000

3.5. Model Specifications and Estimation Technique

In this study, we argue that holistic development is a function of specific relevant factors, following the principles of the Augmented Solow Growth model. We, therefore specify equation (4) as a function defining economic development.

Dev = f (DF)

(4)

From equation (4), Dev refers to development, while DF is a vector of variables surmised to influence the level of development of an economy. Specifically, according to the present study, we surmise that development is influenced by the dynamics of financial institutions, level of investment, and other relevant factors. We hence proceed to specify equation (5) to verify this assertion.

\begin{array}{l} {Dev}_{i t} = ω + α {Dev}_{i t - 1} + γ {FIA}_{i t} + μ {FIE}_{i t} + δ {FID}_{i t} + λ {FDI}_{i t} + \\ φ {GI}_{i t} + \emptyset {GCF}_{i t} + y {ContV}_{i t} + ε_{i t} \end{array}

(5)

According to equation (5), the subscripts i and t denote country and time, respectively. FIA, FIE, FID, FDI, GI, and GCF are, respectively, referred to as financial institution access, financial institution efficiency, financial institution depth, FDI (net inflow), government investment, and gross capital formation (domestic investment), denoting the variables of concern. ContV refers to control variables comprising GDP growth, trade, population growth, age dependency, and governance quality. εit is a vector of error terms made up of time-specific, country-specific, and residual components. We also verify the influence of governance quality in the relationship between the variables of focus and development in equations (6) and (7).

\begin{array}{l} {Dev}_{i t} = ω + α {Dev}_{i t - 1} + γ {FIA}_{i t} + μ {FIE}_{i t} + δ {FID}_{i t} + λ {FDI}_{i t} + \\ φ {GI}_{i t} + \emptyset {GCF}_{i t} + y {ContV}_{i t} + ψ ({FIV}_{q, i t} \times {GQ}_{i t}) + ε_{i t} \end{array}

(6)

\begin{array}{l} {Dev}_{i t} = ω + α {Dev}_{i t - 1} + γ {FIA}_{i t} + μ {FIE}_{i t} + δ {FID}_{i t} + λ {FDI}_{i t} + \\ φ {GI}_{i t} + \emptyset {GCF}_{i t} + y {ContV}_{i t} + ψ ({InV}_{p, i t} \times {GQ}_{i t}) + ε_{i t} \end{array}

(7)

From equations (6) and (7), FIV_q denotes financial institution variable q (q is either financial institution access, financial institution efficiency, or financial institution depth), InV_p refers to investment variable p (p denotes either FDI, gross capital formation or government investment), and GQ refers to governance quality. The remaining variables follow the definitions in equation (5). Similarly, we evaluate the impact of political instability on the relationship between financial institution dynamics, investment, and development in the sub-region by specifying equations (8) and (9).

\begin{array}{l} {Dev}_{i t} = ω + α {Dev}_{i t - 1} + γ {FIA}_{i t} + μ {FIE}_{i t} + δ {FID}_{i t} + λ {FDI}_{i t} + \\ φ {GI}_{i t} + \emptyset {GCF}_{i t} + y {ContV}_{i t} + ψ ({FIV}_{q, i t} \times {PINST}_{i t}) + ε_{i t} \end{array}

(8)

\begin{array}{l} {Dev}_{i t} = ω + α {Dev}_{i t - 1} + γ {FIA}_{i t} + μ {FIE}_{i t} + δ {FID}_{i t} + λ {FDI}_{i t} + \\ φ {GI}_{i t} + \emptyset {GCF}_{i t} + y {ContV}_{i t} + ψ ({InV}_{p, i t} \times {PINST}_{i t}) + ε_{i t} \end{array}

(9)

According to equations (8) and (9), PINST denotes political instability while the remaining variables follow the definitions in the preceding functions.

The panel estimation technique adopted in this study is the GMM approach. The GMM estimation technique stands out as one of the robust panel estimation methodologies and has been extensively used in the literature (Abaidoo & Agyapong, 2021; Adeleye et al., 2017; Fiordelisi & Molyneux, 2010; Beck et al., 2000). According to Abaidoo and Agyapong (2021), the extensive use of GMM in panel estimation is due to its ability to control for un-observed group-specific effects, control for endogeneity, and permit the inclusion of lagged dependent variables; these features make estimated results robust and efficient. Again, according to Wooldridge (2001), other panel estimation techniques could fail some auxiliary assumptions; notably, the assumption of homoscedasticity, but the GMM can overcome this problem without loss of efficiency. The two-step system GMM variant is employed in this study as against the one-step because according to Hwang and Sun (2018), the two-step is associated with a smaller asymptotic variance; this makes it asymptotically efficient when compared with the one-step approach. Finally, the GMM framework further produces relevant post-estimation tests that attest to model validity and robustness of the results and the inferences made.

4. Empirical Results and Analysis

This section presents and analyzes various estimations consistent with the objectives of the study. First, the study sought to evaluate the effect of financial institution dynamics and investment activities on a more holistic measure of development of economies in the sub-region, as well as the possible influence of governance quality on the surmised relationships in Table 3. Results presented in Table 3 indicate that at 1% alpha level, the first lag of development is positive and significant; this signifies that the extent of development among economies in the current year has a positive cascading impact on the subsequent year. This presents a significant piece of input in policy initiatives for managers of economies in the sub-region; it suggests that efforts at development do not only affect the current year but also have the potential to bolster further development in subsequent years. For financial institution dynamics, the results suggest that financial institution access, financial institution efficiency, and financial institution depth have a significant positive impact on development. These outcomes are consistent with conclusions by Hassan et al. (2011), Christopoulos and Tsionas (2004), Ibrahim and Alagidede (2018), Obademi and Elumaro (2014), and Moussa and Hdidar (2019), on the impact of the financial system on GDP growth. On the other hand, these results contradict conclusions by Naceur and Ghazouani (2007), Andersen and Tarp (2003), and Demetriades and Hussein (1996) for studies that assessed the influence of the financial system on GDP growth for various economies. The above result suggest that for SSA economies, growth among financial institutions in the form of increased access to services offered by financial institutions, improved efficiency of operational activities of financial institutions, and increased penetration of financial institutions improve the socio-economic development of the citizenry, all other things being equal.

Table 3.

Financial Institutions, Investment Dynamics and Development: Moderating Role of Governance Quality.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Lag 1 HDI	0.938^***	0.876^***	0.926^***	0.902^***	0.906^***	0.918^***	0.893^***
	(26.96)	(25.40)	(28.47)	(18.69)	(22.39)	(16.81)	(31.04)
FIA	0.107^***	0.0766^*	0.0702^*	0.0879^***	0.0577^*	0.0279	0.0530
	(3.90)	(1.87)	(2.02)	(2.80)	(1.74)	(0.81)	(1.07)
FIE	0.0188^**	0.0159^*	0.00635	0.0180^*	0.0146	0.0131^**	0.0184^*
	(2.23)	(2.02)	(0.43)	(1.88)	(1.46)	(2.14)	(1.93)
FID	0.0426^*	0.0103	0.0329	0.0308	0.0158	0.0234	0.0872^***
	(1.84)	(0.35)	(1.32)	(1.24)	(0.62)	(0.67)	(3.11)
FDI	0.00485	–0.00319	0.00859	0.00151	0.0335^*	0.00393	0.00780
	(0.61)	(–0.57)	(1.15)	(0.25)	(1.80)	(0.78)	(1.30)
GI	0.000266	0.000873^**	–0.0000787	0.000342	0.000638	0.000924^*	0.00132^***
	(0.53)	(2.16)	(–0.14)	(0.64)	(1.21)	(1.87)	(3.07)
GCF	0.0215^**	0.000338	0.0166	0.0154	0.000141	0.00579	0.0396^**
	(2.18)	(0.04)	(1.61)	(1.58)	(0.02)	(1.00)	(2.20)
GDPG	0.0570^***	0.0650^***	0.0800^***	0.0699^***	0.0417^*	0.0566^***	0.0569^**
	(3.22)	(5.64)	(5.03)	(4.13)	(1.83)	(4.53)	(2.65)
Trade	0.00802^*	–0.000884	0.00108	0.00140	0.00121	–0.000728	–0.00224
	(1.83)	(–0.24)	(0.24)	(0.33)	(0.25)	(–0.25)	(–0.42)
POPG	0.791^**	0.881^**	1.080^***	0.966^***	0.855^**	0.699^***	0.812
	(2.66)	(2.54)	(3.03)	(3.20)	(2.43)	(3.00)	(1.54)
AgeD	0.0454	–0.0147	–0.00472	–0.00816	0.0111	–0.00914	0.0116
	(1.52)	(–0.62)	(–0.16)	(–0.21)	(0.47)	(–0.26)	(0.45)
GQ	–0.0120^**	0.00412	–0.00481	–0.00535	0.00226	–0.00547	–0.0113
	(–2.28)	(0.88)	(–0.47)	(–0.98)	(0.41)	(–1.16)	(–1.46)
FIA*GQ		–0.00305
		(–0.17)
FIE*GQ			–0.00760
			(–0.41)
FID*GQ				–0.0230
				(–1.21)
FDI*GQ					0.0337^*
					(1.74)
GI*GQ						0.00113^*
						(1.95)
GCF*GQ							0.0679^***
							(3.12)
Constant	–0.0696	0.0337	–0.00827	0.00116	–0.000767	0.0143	–0.0185
	(–1.56)	(0.87)	(–0.24)	(0.02)	(–0.02)	(0.28)	(–0.60)
Obs.	505	505	505	505	505	505	505
Countries	36	36	36	36	36	36	36
F-Stat	2764.1	4134.3	2013.7	6132.9	16276.9	13985.9	6013.1
P-Value	6.60e–46	4.55e–49	6.41e–44	6.82e–52	6.95e–59	8.49e–58	9.44e–52
Instruments	48	40	49	47	40	40	45
AR(1) P	0.0302	0.0301	0.0338	0.0270	0.0349	0.0364	0.0146
AR(2) P	0.0724	0.129	0.110	0.126	0.0597	0.236	0.0726
Hansen P	0.805	0.905	0.947	0.898	0.919	0.942	0.658

Notes: *p < 0.1, **p < 0.05, ***p < 0.01. t statistics in parentheses.

The results further indicate that gross capital formation has a significant positive impact on development among economies in the sub-region; this outcome implies that growth in domestic investments among economies in the sub-region provides a significant boost to appreciable development, holding other factors constant. We also observe that the net inflow of FDI and government investment are insignificant per results in column (1). Notwithstanding the preceding observation, net inflow of FDI is found to have a significant positive impact on development at a 10% alpha level in column (5); government investment is also found to have a significant positive effect on development in columns (2), (6), and (7). Per these observations, it can be inferred that domestic and foreign investment to some degree augments the developmental agenda of economies in the sub-region of SSA. Comparing these findings to the extant literature, which predominantly evaluates the effect of investment on GDP, this conclusion is consistent with results by Dinh et al. (2019), Zamilur and Ferdaus (2021), and Gizaw et al. (2021) for domestic investment, however, contradicts conclusions by Bakari (2018) and Gizaw et al. (2021) for FDI. Focusing on the control variables, the results indicate that GDP growth, trade, and population growth exert a significant positive impact on holistic development. It is therefore evident that output levels, trading activities in the form net export, and population dynamics have significant roles in the developmental agenda among economies in the sub-region. On the other hand, and quite strikingly, governance quality is found to rather have significant negative impact on development among economies in the region. This outcome could be attributed to the effective or lack thereof of strong institutions tasked with the mandate of supporting the development agenda of governments among economies in the sub-region. Quality governance could mostly augment development efforts if the executors of such policies (institutions of governance) are strong and efficient. Most of the institutions in the sub-region, however, tend to be weak and ineffective in executing their mandate in support of needed growth and development.

In columns (2) to (7), where the moderating role of quality of governance is examined, it is evident that governance quality is insignificant in influencing the effects of financial institution dynamics (access, efficiency, and depth) on development. This outcome suggests that the extent to which these structural features influence development may rely less on the quality of governance. On the other hand, we observe in columns (5) and (6) that quality of governance has a significant positive moderating impact on the effect of net inflow of FDI, government investment, and gross capital formation (domestic investment) on the development of economies in the region. This is consistent with the result by Yeboua (2021), who concluded that for countries with developed institutional structures, FDI enhances economic growth. This outcome suggests that improved governance and institutional structures are critical in the pursuit of development by economies in SSA through increased inflow of FDI, domestic investment, and government investment. In other words, policymakers have a role to play in providing a conducive environment in their quest to promote development among economies in SSA.

Results presented in Table 4 evaluate the impact of political instability on the relationships between financial institutional features, investment variables, and the development of economies in the sub-region. From the table, we observe that political instability has a significant negative moderating effect on the relationships between the various financial institution variables (access, efficiency, and depth), investment variables (net inflow of FDI, domestic investment, and government investment), and development. This outcome suggests that political and civil unrest are inimical to developmental interventions, and in this instance, will constrain how financial institutions and investments ultimately influence development among economies in the sub-region. In other words, political instability can negate the positive role played by financial institutions and investments in the developmental agenda of economies in SSA.

4.1. Model Robustness Checks

Empirical estimates presented in all columns of Tables 3 and 4 are accompanied by relevant post-estimation tests that verify the validity of the estimated results. To confirm the validity of the instruments used in the estimations, we analyze the Hansen test results for the various estimations. The p-values of the Hansen test as shown in the tables indicate that at the 5% alpha level, we fail to reject the null hypothesis (null hypothesis: the instruments are valid for purpose) for each of the estimations; this confirms instrument validity for the various estimations reviewed. Additionally, we also verify and confirm the existence of no serial correlation in the error terms of the various estimations. This is done by analyzing the test result of the AR(2) presented in the various estimations. The null hypothesis states that the error terms do not exhibit serial correlation whilst the alternative counters such a claim. Again, at the 5% alpha level, we fail to reject the null hypothesis for each of the estimations presented in both tables, and therefore conclude that the estimations presented are not characterized by questionable conclusions due to serial correlation. Comparing the number of instruments to the number of observations for each of the estimations, we can also confirm and satisfy one of the key requirements for the GMM procedure; that is, the number of instruments should be less than the number of observations for the validity of the GMM estimator. The results from these post-estimation checks support the robustness of various estimations and conclusions thereof.

Table 4.

Financial Institutions, Investment Dynamics and Development: Influence of Politically Unstable Environment.

	(1)	(2)	(3)	(4)	(5)	(6)
L.HDI	0.955^***	0.998^***	0.962^***	0.888^***	0.931^***	0.920^***
	(25.83)	(233.54)	(35.43)	(27.21)	(28.86)	(32.60)
FIA	0.0415	–0.00519^**	0.0558^*	0.0291	0.0784^***	0.0592
	(1.11)	(–2.04)	(2.03)	(0.76)	(3.15)	(1.50)
FIE	0.0250^***	0.00116	0.00385	0.0138^*	0.0159^*	0.0196^**
	(2.86)	(0.30)	(0.42)	(1.74)	(1.85)	(2.16)
FID	0.0391	0.00104	–0.0377	0.0550	0.0245	0.0288
	(1.31)	(0.76)	(–1.17)	(1.42)	(0.75)	(0.73)
FDI	–0.00467	–0.00119	–0.00628	0.0208^**	–0.00111	0.000241
	(–0.62)	(–0.26)	(–0.89)	(2.11)	(–0.11)	(0.05)
GI	0.000888	–0.0000913	0.000664^*	0.000461	0.000869	0.000494
	(1.65)	(–0.75)	(1.86)	(1.09)	(1.27)	(1.13)
GCF	0.00797	–0.000244	0.00727	–0.00114	0.00620	0.0103
	(0.78)	(–0.05)	(1.00)	(–0.16)	(0.51)	(0.98)
GDPG	0.0545^***	0.0905^***	0.0545^***	0.0371^**	0.0379	0.0656^***
	(5.14)	(7.89)	(6.28)	(2.54)	(1.22)	(7.42)
Trade	0.00220	–0.00164	0.00877^**	–0.00114	0.00751	–0.00235
	(0.54)	(–1.48)	(2.72)	(–0.39)	(1.11)	(–0.58)
POPG	0.791^**	–0.0832	0.882^**	0.563^*	1.258^***	1.172^**
	(2.33)	(–1.46)	(2.27)	(1.80)	(2.90)	(2.25)
AgeD	0.0501	–0.000994	0.00843	–0.0149	0.0292	0.00871
	(1.43)	(–0.21)	(0.30)	(–0.63)	(1.02)	(0.30)
GQ	0.00251	–0.00106	–0.00237	0.00611	–0.00345	0.000652
	(0.48)	(–1.55)	(–0.51)	(1.35)	(–0.56)	(0.11)
FIA*PINST	–0.0335^***
	(–3.50)
FIE*PINST		–0.00229^***
		(–3.58)
FID*PINST			–0.0290^***
			(–2.75)
FDI*PINST				–0.0291^*
				(–1.95)
GI*PINST					–0.000743^**
					(–2.19)
GCF*PINST						–0.0223^**
						(–2.10)
Constant	–0.0669	0.00765^**	–0.0229	0.0397	–0.0499	–0.0169
	(–1.34)	(2.31)	(–0.56)	(1.05)	(–1.19)	(–0.37)
Obs.	505	505	505	505	505	505
Countries	36	36	36	36	36	36
F-Stat	26691.6	270357.2	21599.7	15253.8	9151.3	5774.9
P-Value	1.99e–62	5.10e–79	6.53e–61	2.03e–58	9.27e–55	1.84e–51
Instruments	42	57	41	40	41	40
AR(1) P	0.0293	0.0307	0.0343	0.0342	0.0354	0.0317
AR(2) P	0.243	0.421	0.136	0.114	0.236	0.327
Hansen P	0.818	0.961	0.745	0.943	0.833	0.840

Notes: *p < 0.1, **p < 0.05, ***p < 0.01. t statistics in parentheses.

5. Conclusions and Recommendations

This study approached the subject on the development among emerging economies from a much broader perspective that captures the improved standard of living and welfare of the general populace. The main objective was to assess the impact of structural financial institutionsal features and investment on the development of economies in the sub-region using annual data from 36 countries from 1996 to 2019. Empirical analyses were carried out using the two-step system GMM estimation technique; a panel methodology deemed more efficient compared with other panel estimation techniques.

Examined results suggest that structural financial institutional features such as access to financial institution services, improved operational efficiency, and penetration of financial institutions bolster the development of economies in SSA. Additionally, we find that investment growth, in terms of net inflow of FDI, government investment, and domestic investment help in accelerating the pace of development among economies in the sub-region. Furthermore, quality of governance is found to have a significant positive moderating impact on how domestic investment, government investment, and net FDI inflow impact development. In other words, investment bolsters development in an environment characterized by improved governance structures, all else held constant. Finally, coefficient estimates also suggest that political instability has an adverse moderating effect on how investments and financial institutional features affect development in SSA. Political instability, therefore, has the potential to derail any gains that may accrue from any of the examined variables on development for economies in SSA.

The aforementioned conclusions could provide significant policy direction to relevant stakeholders among economies in the region as well as a different perspective on research in the area of development among emerging economies. For instance, policymakers can take a cue from the study to promote socio-economic development in the sub-region by initiating policies that foster the growth of financial institutions—measures that ensure accessibility of services provided by financial institutions, efficiency, and the extent of penetration of their services. Again, measures that augment growth in investment activities could be pursued by policymakers in an effort to support sustained development in the sub-region. Improvement in institutions of governance could also be pursued since reviewed evidence suggest that the condition or feature is critical in the development discourse. Political instability is found to be inimical to the developmental agenda; consequently, governments and policymakers can develop and promote measures that minimize the potential for unrest and political agitations. Evidently, the scope of the study is limited to economies in SSA. As a result, the presented conclusions may not apply to other regional blocs; we, therefore, recommend further studies on the subject for other global economic blocs or regions.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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