An Introspective Analysis of Inclusive Growth in Africa,With an Eminence on the Influence of Governance and Financial Development Interaction

Abstract

The study considered the relationship between the interaction of financial development with all six distinct governance indicators from the world development indicators, as well as a combination of them, and inclusive growth in 48 African economies from 2000 to 2019. The countries were classified into low-income countries and middle-income countries. The models were then estimated with Panel Quantile Regression with Fixed Effects estimations, and Dumitrescu & Hurlin causality tests after going through robust econometric tests. From the outcome, the interaction of financial development with corruption control is essential for inclusive growth in only middle-income countries. The interaction of financial development with political stability and the absence of violence/terrorism, and the interaction of financial development and governance in general are relevant to inclusive growth in Africa as a whole and middle-income countries. However, the interaction of financial development with voice and accountability, and the interaction of financial development with rule of law are relevant to inclusive growth in all country groupings.

JEL: G20; F62; R50

Keywords

inclusive growth financial development governance Africa

Introduction

Economic growth’s effect on tangible development in a country is a subject under discussion in literature (Kong et al., 2020). Over the past four decades, Africa have experienced steady annual growth rates between 1.6% and 4.9%, with individual countries recording double digit economic growth rates (World Bank, 2020a). Maintaining consistent growth has been one of the African countries’ priorities, particularly in recent years (Nketia & Kong, 2021). Equitable resource allocation and inclusion in the growth process, on the other hand, have always been a point of contention. In his book the wealth of nations Adams smith stated “what improves the circumstances of the greater part can never be regarded as an inconvenience to the whole. No society can surely be flourishing and happy, of which the far greater part of the members are poor and miserable” (Smith, 1776).

For growth to be beneficial to all within the economy, it must be inclusive rather than “wholesale” growth. The description of inclusive growth is related to creating institutional, social and economic opportunities that are available to everyone irrespective of their financial status (Asian Development Bank, 2011). An opinion shared by Rauniyar and Kanbur (2010), states that inclusive growth can be explained as economic growth that reduces disparity in available resource. This study thus views inclusive growth as an increase in an economy in which the population is able to appreciate the change in their financial and essential needs.

Finance’s position in achieving growth cannot be ignored. In recent years the association between finance and growth has attracted numerous concerns worldwide (see Appiah et al., 2020; Efogo, 2020; Juuti, 2020; Kong et al., 2020; Nketia & Kong, 2021; Nketia et al., 2020; Song et al., 2021). Finance and growth theory focused on how finance drives resource distribution decision growth by performing critical tasks, such as savings mobilization, available knowledge resources, corporate governance, allocation and acquisition, management and trade, diversification and risk management advancement (Nketia & Kong, 2021).

A number of panel studies have demonstrated a correlation between governance (institutions) and growth, which is critical for growth to be reflected in all sectors of an economy (Appiah et al., 2020; H. Khan et al., 2022; Nketia et al., 2020). In a July 2009 in his speech to Africans from Ghana’s parliament, the United States President, Barack Obama said that Africa requires good governance to be able to grow, as expected therefore “Africa doesn’t need strongmen, it needs strong institutions” (Obama, 2009). While discussing inclusive growth, the role of institutions cannot be overlooked, as enshrined in the Sustainable Development Goal (SDG) 16- peace Justice and strong institutions; corruption is minimized, transparency is increased and justice is administered by the judiciary without favoritism, to ensure the suppression of crime (United Nations, 2015).

In practical terms, the study is significant in many respects. The study would firstly enable governments in Africa to restructure financial policies so that they tackle financial sector challenges that are aimed at achieving full inclusive growth. Similarly, via African Development Bank (AfDB), the African Union (AU) will provide member states with a roadmap for financial development. The AU will also create a governance plan or goal indexes for the members who want to work for inclusive growth in Africa as a whole. AU member states may adapt financial development (FDV), and governance analyses according to the country’s level of income. In the framework of developing policies for Africa according to the level of income of countries, Africa’s international development partners including the World Bank, the International Monetary Fund (IMF) and the United Nations (UN) could recommend this direction of governance and financial sector development from this study. The study further contributes to achieving Africa’s 1, 8, 10, and 16 sustainable development goals.

The objectives of this study are; first, to ascertain the impact of the interaction between financial development and all the six governance indicators on inclusive growth in Africa. Secondly, the study clarifies the causal relationship amid the interaction between financial development with governance, and inclusive growth in Africa. Thirdly, the study makes a novel contribution to literature on countries’ categorization by using the World Bank’s income classification by country instead of the usual geographical groupings. Finally, the study adopts the International Monetary Fund’s new financial development index for the study since it is a robust index which includes all financial aspects of every economy under the study. The remaining studies is as follows; literature review, Methods and materials, empirical results, discussion of results, and conclusion.

Literature Review

Inclusive Growth

Regardless of the rapid economic growth rate, Africa needs to achieve inclusive growth. To achieve equitable distribution of growth, all the economic aspects must participate to the process, implying that growth must be inclusive. Irrespective of the fact that there is no universally accepted definition of inclusive growth, the phrase “inclusive” often refers to the terms full, entire, or comprehensive (de Haan, 2015; Nketia & Kong, 2021; Obeng-Odoom, 2020). Significant international bodies have all largely contributed to advancing inclusive growth, including the United Nations Development Programme (UNDP), the African Development Bank (AfDB), the Asian Development Bank (ADB), the Organization for Economic Cooperation and Development (OECD), and the World Bank (Ngepah, 2017).

Nonetheless, it is essential to note that continuity and sustainability are contingent on inclusive growth (Raheem et al., 2018). The research followed the works of Kouton (2021), Oyinlola et al. (2020), Oyinlola and Adedeji (2019), Raheem et al. (2018) among others, who utilized GDP per person employed from the World Bank (2020a) as a proxy for inclusive growth; this metric combines economic growth with employment. This inclusive growth indicator shows two critical aspects of individual economic integration: the job opportunities that are readily accessible to a broad population, and the distribution of those available opportunities across sectors (IMF, 2020; World Bank, 2020a).

Financial Development and Inclusive Growth

The finance-growth theory is in four-folds; finance-led growth model (McKinnon, 1973; Shaw, 1973), growth-led finance model (Robinson, 1979), feedback model (Patrick, 1966), and the neutrality view model (Lucas, 1988). The finance-growth theories focuses on the effect of finance on growth through wealth distribution by performing fundamental functions such as saving mobilization, knowledge accessibility, corporate governance, investment strategy and expenditure control, trade facilitation, diversification, and risk management (Levine, 2005). Numerous studies have been made in the areas of economic growth and financial development; the majority of these studies have focused on the finance-led growth (supply-leading) and growth-led finance (demand-following), the outcome has a largely been positive and bidirectional relationship (Appiah et al., 2020; Efogo, 2020; Kong et al., 2020; Nketia & Kong, 2021; Nketia et al., 2020; Stolbov, 2013).

Considering inclusive growth rather than economic growth, Oyinlola and Adedeji (2019) examined the relationship between financial development (FDV), human capital, and inclusive growth (ICG) in 19 Sub-Saharan African (SSA) nations from 1999 to 2014. The findings indicate that both human capital and FDV have a disproportionately favorable direct influence on inclusive growth. In addition, Ayinde and Yinusa (2016) examined the relationship between FDV and inclusive growth from 1980 to 2013. The findings indicated that the influence of FDV on inclusive growth is conditional on the former’s measure up to the 90th percentile, and that causality also indicated a reverse of direction.

Governance and Inclusive Growth

Abera et al. (2019) employed fixed effect and system GMM estimates to assess the influence of governance/institutional quality (INQ) on economic growth in 14 East African nations from 2005 to 2016. The finding verifies the relevance of INQ on economic growth, although it again depends on the INQ measure used. On inclusive growth, Olanrewaju et al. (2020) examined INQ and inclusive growth in Nigeria from 1998 to 2017 using an ARDL method to cointegration. As an outcome, INQ had a large positive influence on inclusive growth. The study suggests that resilient institutions should be above the existing liberal democratic level in order to maximize the use of human capital resources. Additionally, Oyinlola et al. (2020) employed GMM to examine the connection between resource mobilization, inclusive growth and governance in 27 SSA economies from 1995 to 2015. The finding demonstrates that the degree of institutional tenacity greatly determines the region’s real inclusiveness of growth.

Financial Development, Governance and Inclusive Growth

A handful of studies have been conducted in the area of financial development, governance/institutional quality and inclusive growth especially in Africa. To begin with, in the study by Gyamfi et al. (2022), data were analyzed with related variables in selected African economies. Their study showed that FDV and ICG had a non-linear relationship. FDV also contributes to inclusive growth, and strong financial market participation management institutions need to be integrated into inclusive growth. If poor institutions exist, the FDV would be negative to inclusive growth. Besides, Kebede and Takyi (2017). The causal relation between economic growth, the FDV and INQ was analyzed to determine if INQ is a cause or effect of SSA economic growth. They used evidence from 27 countries between 1996 and 2014, and took the co-integration of the Pedroni panel, GMM systems and the Wald panel causality measure. As a result, a long-term relationship was established among INQ and economic growth. Moreover, economic growth was affected positively by the unidirectional causality from economic growth to INQ in terms of effects, INQ, FDV, debt and trade openness.

Materials and Methods

Data and Variables

The data used in this analysis spans the years 2000–2019; this timeframe was chosen due to data availability for the countries under the study. The research covers 48 African nations. The countries were divided into two categories; low-income countries (LIC) and lower & upper middle-income countries (MIC). The classifications were based on the World Bank (2020a)’s classification of countries by income level. The list of countries under the study as well as, LIC and MIC groups are in Appendix A. The variables were from different sources, variables descriptions and sources are in Appendix B. To measure inclusive growth, the study used the Gross Domestic Product (GDP) per person employed from World Bank (2020a). This index, blends employment and economic growth, thus considering the opportunities available to the general public and how those opportunities are spread across different sectors (Kouton, 2021; Oyinlola & Adedeji, 2019; Raheem et al., 2018). This inclusive growth measurement is also used by the United Nations to monitor the eighth Sustainable Development Goal (SDG).

In addition, the study used the Svirydzenka (2016) Financial Development Index, which is the first principal component of two sub-indices: financial markets and financial institutions. The index is very robust for the study because the two components considered financial depth, access, and efficiency in the computations (Efogo, 2020; Ouyang & Rajan, 2019; Oyinlola & Adedeji, 2019). Besides, the study considered all the six indicators from the World Governance Indicators (World Bank, 2020b) the indicators were complied by Kaufmann et al. (2011). The institutional quality (governance) indicators include; Political Stability, Rule of Law, Voice and Accountability, Government Effectiveness, Regulatory Quality, and Control of Corruption (Appiah et al., 2020; Gyamfi et al., 2022; Nketia & Kong, 2021; Nketia et al., 2020). Finally, the control variables for the study were; government expenditure (EXP) this variable mostly indicates the amount of funding that different governments spend on the economy, but it also indicates the size of the government. Inflation (INF), this variable reflects fluctuations in the pricing of products and services between nations. Foreign direct investment (FDI) this is the variable that indicates the amount of external investment made in the economy. Gross capital formation (GCF) as a proxy for capital. Capital accumulation’s effect on growth is mostly determined by the kind of investment, which includes interest rates, foreign trade, and savings. The accumulation of resources determines a nation’s productive capacity, which ultimately impacts economic progress. The control variables were used to resolve the issue of omitted variable bias, these control variables have also been used in related studies ( see, Gyamfi et al., 2022; Kouton, 2021; Oyinlola & Adedeji, 2019; Oyinlola et al., 2020; Raheem et al., 2018). The interaction variables’ description are displayed in Table 1.

Table 1.

Definition of the Interaction Variables.

Variable	Definition	Source
FDCOC	The interaction of Financial Development and Control of Corruption	Authors’ computation
FDGVE	The interaction of Financial Development and Government Effectiveness	Authors’ computation
FDPSV	The interaction of Financial Development and Political stability and absence of violence and terrorism	Authors’ computation
FDRQT	The interaction of Financial Development and Regulatory Quality	Authors’ computation
FDROL	The interaction of Financial Development and Rule of Law	Authors’ computation
FDVAA	The interaction of Financial Development and Voice and Accountability	Authors’ computation
FDINQ	The interaction of Financial Development and General governance (the composite of all institutional quality indicators)	Authors’ computation

Preliminary Tests

Table 2 shows the results of descriptive statistics and correlation tests used to establish how the interacted variables will react to the other variables. The table provides a statistical summary of all countries, the statistics shows that from the averages GCF displayed the highest mean of 9.12 representing a higher average data than the other set of data. Again, all the interaction variables displayed a negative mean as a result of the governance indicators which mostly were negative. Nonetheless, With the exception of FDI, which had a maximum of 23.18 and a minimum of 9.34 showing a rather larger outliners, the table reveals that all of the variables’ minimum and maximum values were within a small range, denoting that the data followed a particular trend. Apart from FDI, GCF, and ICG, all of the variables’ standard deviations showed lower figures which denotes that the values are not further dispersed from their means but rather they are clustered around the mean.

Table 2.

Descriptive Statistics and Correlation Matrix for All Countries.

	ICG	FDCOC	FDGVE	FDPSV	FDRQT	FDROL	FDVAA	FDINQ	EXP	FDI	INF	GCF
Observation	960	960	960	960	960	960	960	960	960	960	960	960
Mean	9.12	−0.06	−0.07	−0.06	−0.06	−0.06	−0.05	0.07	2.58	19.24	4.70	21.51
Maximum	11.68	0.32	0.46	0.48	0.49	0.39	0.43	1.33	4.00	23.17	8.55	25.30
Minimum	7.22	−0.35	−0.29	−0.53	−0.35	−0.30	−0.54	−0.28	−0.05	9.35	1.00	15.76
Std. Dev.	1.05	0.10	0.10	0.14	0.10	0.10	0.13	0.26	0.44	2.10	0.80	1.70
Skewness	1.35	1.14	1.89	1.34	1.80	1.65	0.99	2.68	−1.84	−1.00	1.88	−0.01
Kurtosis	2.10	5.45	9.08	5.11	10.28	8.36	5.85	10.79	6.34	4.64	7.16	2.74
Jarque-Bera	52.20	448.90	2052.10	197.40	2635.20	1588.40	481.10	3576.80	560.00	266.60	815.50	2.70
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.26
ICG	1.00
FDCOC	0.16	1.00
FDGVE	0.28	0.82	1.00
FDPSV	0.20	0.74	0.66	1.00
FDRQT	0.18	0.75	0.92	0.64	1.00
FDROL	0.25	0.85	0.88	0.78	0.85	1.00
FDVAA	0.09	0.77	0.77	0.61	0.76	0.74	1.00
FDINQ	0.42	0.70	0.92	0.51	0.83	0.80	0.71	1.00
EXP	−0.01	−0.01	0.02	0.02	0.04	0.01	0.03	0.03	1.00
FDI	−0.35	−0.12	0.02	−0.15	−0.01	−0.05	−0.07	0.19	0.01	1.00
INF	−0.05	−0.00	−0.01	−0.01	−0.00	0.01	−0.01	0.00	−0.18	0.04	1.00
GCF	0.45	−0.16	0.06	−0.27	0.01	−0.06	−0.09	0.26	−0.02	0.60	0.05	1.00

Note. Std Dev is Standard deviation. Values are in natural logarithm.

Concerning skewness, FDI and EXP are moderately negative polarized, while the rest of the variables are approximately symmetric. The kurtosis reveals that INF EXP and FDI are leptokurtic, while the other ones are mesokurtic. The kurtosis and skewness suggest that the variables are not distributed uniformly across the panel. Except for GCF, the Jarque-Bera test and probability suggest that all of the variables are not normally distributed at 5%. The table shows that the variables are not highly correlated, except the interaction variables correlations against each other. Since the interaction variables will be used separately for each model, the estimation outcomes will be void of multicollinearity.

Likewise, Table 3 portrays a statistical summary of low-income African countries. The maximum mean for GCF was 20.74, while the mean for all of the interaction variables was negative. All of the variables have lower standard deviations meaning that the values are closer to the mean. The variables ICG and INF are positively skewed, while the rest are negatively skewed. According to the kurtosis, ICG, FDINQ, AND GCF are mesokurtic, while the other variables are leptokurtic. The kurtosis and skewness of the variables indicate that they are not distributed normally in general. The study used the Jarque-Bera test together with the probability test to examine the normality hypothesis for the residuals. The results in Table 2 indicate that the null hypothesis that the variables were normally distributed was rejected at 5% except for GCF. This conclusion is consistent with the skewness and kurtosis findings, which established that the data distributions were abnormally shaped. The correlation outcome shows a lower correlation of less than .8 with each variable, except for the interaction variables, which are strongly correlated against each other. However, since each interaction variables will be used separately in the estimation model, it will not produce bias estimations. This implies that the study will not answer the multicollinearity problem.

Table 3.

Descriptive Statistics and Correlation Matrix for Low-Income Countries.

	ICG	FDCOC	FDGVE	FDPSV	FDRQT	FDROL	FDVAA	FDINQ	EXP	FDI	INF	GCF
Observations	4,220	420	420	420	420	420	420	420	420	420	420	420
Mean	8.18	−0.07	−0.09	−0.07	−0.07	−0.08	−0.06	−0.03	2.59	18.52	4.67	20.74
Median	8.15	−0.07	−0.08	−0.06	−0.06	−0.07	−0.06	−0.02	2.63	18.78	4.62	20.94
Maximum	9.03	0.07	0.03	0.09	0.02	0.01	0.05	0.17	3.73	22.62	8.51	24.17
Minimum	7.22	−0.25	−0.29	−0.30	−0.35	−0.30	−0.27	−0.26	−0.05	9.34	1.00	15.76
Std. Dev.	0.42	0.04	0.05	0.08	0.05	0.05	0.05	0.07	0.42	2.18	0.80	1.46
Skewness	1.09	−1.18	−1.09	1.53	−1.53	−1.97	−1.61	1.30	−1.04	−1.05	1.69	0.23
Kurtosis	7.08	5.81	4.49	6.80	7.97	5.05	5.66	6.94	8.44	5.56	7.41	1.84
Jarque-Bera	15.23	140.73	121.48	20.29	595.99	139.08	33.62	6.49	593.84	119.94	373.55	3.99
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.14
ICG	1.00
FDCOC	0.05	1.00
FDGVE	0.09	0.79	1.00
FDPSV	0.26	0.50	0.51	1.00
FDRQT	0.19	0.69	0.83	0.61	1.00
FDROL	0.16	0.78	0.85	0.65	0.84	1.00
FDVAA	0.24	0.36	0.35	0.62	0.45	0.50	1.00
FDINQ	0.15	0.73	0.90	0.47	0.72	0.79	0.34	1.00
EXP	0.06	0.03	0.04	0.09	0.09	0.08	0.08	0.01	1.00
FDI	0.13	0.07	0.09	0.06	0.09	0.15	0.12	0.21	0.02	1.00
INF	−0.08	−0.06	−0.08	−0.05	−0.03	−0.06	0.04	−0.07	−0.17	0.04	1.00
GCF	0.08	0.11	0.19	−0.06	0.16	0.19	0.07	0.35	−0.02	0.23	0.04	1.00

Note. Std Dev is Standard deviation. Values are in natural logarithm.

Table 4 summarizes the data for middle-income African countries. GCF had the highest mean (22.11, Max=25.30, Min=17.96). All of the variables’ standard deviations showed lower figures which explains that the values are closer to the mean (average) except for ICG, FDI, and GCF. For the skewness, EXP FDI and GCF are negatively skewed, while the remaining variables are positively skewed. The kurtosis shows that ICG and GCF are mesokurtic, while the other variables are leptokurtic. The kurtosis and skewness suggest that the variables are not distributed uniformly across the panel. According to the Jarque-Bera test and probability, all the variables are not normally distributed at 5% with the exception of GCF. The correlations outcome shows lower correlation of the variables, with the exception of the interaction variables. The interaction variables would not cause any problems with multicollinearity because each interaction variable will be used separately in the model estimation.

Table 4.

Descriptive Statistics and Correlation Matrix for Middle-Income Countries.

	ICG	FDCOC	FDGVE	FDPSV	FDRQT	FDROL	FDVAA	FDINQ	EXP	FDI	INF	GCF
Observations	540	540	540	540	540	540	540	540	540	540	540	540
Mean	9.85	−0.15	−0.05	−0.04	−0.05	−0.05	−0.06	0.14	2.57	19.80	4.72	22.11
Median	9.76	−0.07	−0.07	−0.04	−0.06	−0.06	−0.09	0.02	2.64	19.98	4.63	22.07
Maximum	11.68	0.32	0.45	0.48	0.49	0.39	0.43	1.33	4.00	23.17	8.55	25.30
Minimum	7.22	−0.35	−0.29	−0.53	−0.33	−0.30	−0.54	−0.28	0.69	11.45	2.60	17.96
Std. Dev.	0.78	0.12	0.13	0.18	0.13	0.12	0.17	0.32	0.46	1.87	0.10	1.63
Skewness	1.10	1.80	1.46	1.20	1.47	1.25	1.76	1.85	−1.71	−1.91	1.03	0.09
Kurtosis	4.25	3.38	5.78	3.76	6.60	5.30	3.72	6.05	5.10	4.38	6.94	1.53
Jarque-Bera	13.46	61.25	367.10	16.68	487.23	259.98	63.03	518.72	144.16	118.22	444.97	5.74
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.06
ICG	1.00
FDCOC	0.15	1.00
FDGVE	0.24	0.82	1.00
FDPSV	0.20	0.76	0.67	1.00
FDRQT	0.19	0.75	0.93	0.64	1.00
FDROL	0.23	0.86	0.88	0.79	0.85	1.00
FDVAA	0.06	0.80	0.81	0.61	0.79	0.76	1.00
FDINQ	0.32	0.71	0.93	0.53	0.86	0.81	0.75	1.00
EXP	−0.01	−0.02	0.02	0.00	0.03	0.00	0.02	0.05	1.00
FDI	−0.23	−0.26	−0.09	−0.32	−0.09	−0.19	−0.18	0.11	0.01	1.00
INF	−0.04	−0.01	−0.01	−0.00	−0.01	−0.03	−0.02	−0.00	−0.19	0.03	1.00
GCF	0.30	−0.31	−0.07	−0.44	−0.07	−0.23	−0.18	0.15	−0.01	0.72	0.04	1.00

Note. Std Dev is Standard deviation. Values are in natural logarithm.

Econometric Approach

To begin the econometric tests, the CD test was used to verify the existence of CD within the cross-section of the panel. The analysis used the Breusch and Pagan (1980) LM test, Pesaran (2004) scaled LM test, and Pesaran (2015) CD test to check for CD. Following the CD test, the study examined the probability of slope heterogeneity. The regression analysis may be hindered if slope homogeneity test is ignored. To prevent bias in regression analysis, the Pesaran and Yamagata (2008) test was used to assess whether or not there was heterogeneity within the slope values. The Breusch-Pagan (Breusch & Pagan, 1979) heteroscedasticity test was used to determine if the residuals of the models were heteroscedastic or not.

Panel unit root tests using second-generation panel unit root tests were used to begin the study’s econometric analysis. CIPS panel unit root by Pesaran (2007) and Cross-sectional Augmented DF (CADF) panel unit root test by Im et al. (2003) are the tests to consider. Where there is cross-sectional dependence within the panel, the Westerlund (2007) cointegration approach is suggested (Dauda et al., 2019; Liu et al., 2021). For this test, G_t (between groups), G_a (among groups), P_t (between panels), and P_a (among panels) are the four tests that are equally distributed in this test (among panels). Specifically, G_t and G_a represent group averages tests using t-statistics, while P_t and P_a represent cointegration for the entire panel. The Westerlund (2007) error correction model helps to ascertain the error correction in the heterogeneous panel, additionally, the test is void of common factor constraint. The classic panel data methodologies such as fixed & random effects, and instrumental variable estimators do not solve the cross-sectional reliance of error components and yield inaccurate conclusions (Z. Khan et al., 2020; Su et al., 2021; Wang et al., 2021).

Model Specification

The findings of the preliminary tests’ normality and correlation tests, as well as the econometric method tests that preceded the model estimation, informed the choice of the Panel Quantile Regression with Fixed Effects (MMQREG) to be used. MMQREG is well-known for its resilience in the presence of outliers and its capacity to integrate all significant associations that Pooled Ordinary Least Squares (POLS) and other traditional econometric techniques have failed to capture. Unlike prior quantile regression models, MMQREG forecasts effects using moment constraints, which do not make any assumptions about the moment’s feature or correlation. Therefore, this study considers estimating provisional quantiles (location-scale) $Q_{y} (τ ∖ X)$ in the form:

Y_{it} = α_{i} + X_{it}^{'} β + (δ_{i} + Z_{it}^{'} γ) U_{it}

(1)

The function $P {δ_{i} + Z_{i, t}^{'} γ > 0} = 1$ .

The distinct fixed effects are recorded by the factors $(α_{i}, δ_{i})$ and Z is a k-vector of recognized variables in X instruments. The study considered the combined effect of FDV and INQ on ICG in Africa as a whole, as well as at different income levels. The study’s basic quantile models, which are based on the general quantile regression model, are as follows:

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDCO C_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(2)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDGV E_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(3)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDPS V_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(4)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDRQ T_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(5)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDRO L_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(6)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDVA A_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(7)

\begin{matrix} Q_{t} (lnIC G_{it}) = α_{t} + β_{1 t} lnFDIN Q_{it} + β_{2 t} lnEX P_{it} \\ + β_{3 t} lnIN F_{it} + β_{4 t} lnFD I_{it} + β_{5 t} lnGC F_{it} + ε_{it} \end{matrix}

(8)

To understand the models, $Q_{t}$ denotes the tth distributional point’s quantile regression parameters, t denotes the distributional point for the independent variables, and $lnIC G_{it}$ it denotes the panel of inclusive growth in natural logarithm. Furthermore, $α_{t}$ denotes set effects. The coefficients of the variables are again denoted by $β_{nt}$ (where n is the order of the coefficient). The variables $lnEX P_{it}, lnIN F_{it}, lnFD I_{it}, and lnGC F_{it}$ are considered as the control variables. Again, “it” signifies panel (cross-section and time respectively). Lastly, stochastic term is denoted by $ε_{it}$ . To obtain detailed data from the quantile regression estimates, three different quantiles (25th, 50th, and 75th) are used. The study’s outliners are the low quantile (25th) and high quantile (75th), respectively, while the study’s median regression is the 50th quantile.

Causality Test

Traditionally, the non-causality test for heterogeneous panel data models developed by Granger (1969) is used to estimate the causal interaction between variables in a model, this measure is problematic and could yield biased findings (Musah et al., 2020). As a result, there is a need to update the test, especially when there is unbalanced panel data and the existence of a CD. The Dumitrescu and Hurlin (2012) panel causality test is adopted in Granger stead. The underlying linear model for the test where two stationary variables are considered with N (cross-section) and T (time) for specifics i = 1…N, and t=1…T, the function will be:

y_{it} = α_{i} + \sum_{k = 1}^{K} γ_{i}^{(k)} y_{i, t - k} + \sum_{k = 1}^{K} β_{i}^{(k)} x_{i, t - k} + ε_{i, t}

(9)

The research also looked for a causal relationship between interaction terms and inclusive development, using the following model:

\begin{matrix} lnFDIN Q_{it} = α_{1 i} + \sum_{i = 1}^{n} γ_{1 it} lnIC G_{i (- 1)} + \sum_{i = 1}^{n} γ_{2 it} lnEX P_{i (t - 1)} \\ + \sum_{i = 1}^{n} γ_{3 it} lnIN F_{i (t - 1)} + \sum_{i = 1}^{n} γ_{4 it} lnFD I_{i (t - 1)} \\ + \sum_{i = 1}^{n} γ_{5 it} lnGC F_{i (t - 1)} + μ_{it} \end{matrix}

(10)

\begin{matrix} lnIC G_{it} = α_{2 i} + \sum_{i = 1}^{n} γ_{1 it} lnFDIN Q_{i (t - 1)} + \sum_{i = 1}^{n} γ_{2 it} lnEX P_{i (t - 1)} \\ + \sum_{i = 1}^{n} γ_{3 it} lnIN F_{i (t - 1)} + \sum_{i = 1}^{n} γ_{4 it} lnFD I_{i (t - 1)} \\ + \sum_{i = 1}^{n} γ_{5 it} lnGC F_{i (t - 1)} + μ_{it} \end{matrix}

(11)

The study examined causality by assuming an augmented autoregressive model that ensures the exponential distribution of the test statistic independent of the factors’ assimilation or the presence of a cointegration relationship (Kirikkaleli, 2016; Liu et al., 2021). The Dumitrescu and Hurlin (2012) panel causality test there is no causal relationship between the variables in the panel under the null hypothesis. This test utilizes two statistics: Wbar and Zbar. To explain, Zbar statistics denote the conventional normal distribution, while Wbar statistics indicate the test means (Kirikkaleli et al., 2021; Yilanci & Gorus, 2020).

Empirical Results

Cross-Sectional Dependence Tests and Slope Homogeneity Test

The results of the Pesaran Scaled LM, Breusch-Pagan LM, and Pesaran-CD tests are shown in Table 5. For all test data for all country income levels and Africa as a whole, the null hypothesis of no cross-sectional dependency in variables was strongly rejected at the 1% level of significance. The hypothesis that the slope values were homogeneous was dismissed by both the income class groups and the all countries panel. This indicates that the model’s slope heterogeneity is present in all three classes.

Table 5.

Cross-Sectional Dependency Test and Pesaran-Yamagata Slope Homogeneity Test.

Test	All countries			LIC			MIC
Test	Statistic	d.f.	Prob.	Statistic	d.f.	Prob.	Statistic	d.f.	Prob.
CD-Test
Breusch-Pagan LM	4016.87^a	1,128	.000	400.8985^a	210	.000	1115.243^a	351	.000
Pesaran scaled LM	60.8216^a		.000	9.314892^a		.000	28.84451^a		.000
Pesaran CD	18.0946^a		.000	4.152083^a		.004	4.596687^a		.000
Slope Homogeneity Test
Delta tilde ( $\tilde{δ}$ )	4.915^a		.002	4.356^a		.000	2.188^b		.021
Adjusted Delta tilde ( $\tilde{δ}$ _adj)	8.974^a		.000	7.952^a		.000	3.994^a		.002

Note. LIC is low income countries, MIC is middle-income countries. Prob.is Probability. The superscript “a,”“b”, and “c” denotes significance at 1%, 5%, and 10% respectively.

Heteroscedasticity Test

The null hypothesis of no heteroscedasticity among the model’s residuals could not be rejected, as seen in Table 6. This shows that the proposed models and estimation techniques are trustworthy and precise.

Table 6.

The Breusch-Pagan Heteroscedasticity Test.

Models	All countries		LIC		MIC
Models	Chi² ( $χ^{2})$	Prob	Chi² ( $χ^{2})$	Prob	Chi² ( $χ^{2})$	Prob
FDCOC	0.45	.5017	2.55	.1103	1.34	.2992
FDGVE	1.44	.2299	0.15	.7602	2.33	.1325
FDPSV	0.10	.7496	2.12	.1772	1.92	.1606
FDRQT	0.66	.4175	0.65	.4187	0.62	.5318
FDROL	1.59	.2078	0.11	.7346	0.98	.4223
FDVAA	0.42	.5152	0.34	.5590	0.48	.7529
FDINQ	2.36	.1067	2.19	.1393	2.36	.1369

Note. LIC is low income countries, MIC is middle-income countries. Prob. is Probability.

Panel Unit Root Test

The CADF and CIPS unit root tests that are robust to CD were used to determine the order of integration of the variables due to the presence of dependencies within the cross-section. Appendix C displays the test results; the variables were non-stationary for all of the groups in the study at levels [I(0)], but they were stationary after being transformed to first difference [I(1)].

Panel Cointegration Test

For all of the groups, the Westerlund (2007) cointegration test was used. The null hypothesis of no cointegration was dismissed the three groups, as seen in Table 7. As a result, for all three county category classifications, the findings typically confirm a long-run association between the variables.

Table 7.

Cointegration Test of FDINQ and ICG.

	All countries		LIC		MIC
Statistic	Value	Robust p-value	Value	Robust p-value	Value	Robust p-value
Gt	−3.428^c	.060	-3.890^c	.055	−3.068	.120
Ga	−5.219^c	.080	−6.446^b	.025	−6.744^a	.002
Pt	−20.090^a	.000	−15.029^a	.005	−15.415^b	.020
Pa	−5.715^a	.000	−6.373^a	.010	−7.856^a	.000

Note. The superscript “a,”“b”, and “c” denotes significance at 1%, 5%, and 10% respectively. LIC is low income countries, MIC is middle-income countries.

Panel Model Estimation

Tables 8 to 10 show the effects of the quantile regression estimates. Year dummies were used in the MMQREG estimations to minimize year shocks that could affect the outcomes; the bootstrap was also used to achieve stable performance. All of the interaction variables show a positive relationship with inclusive growth along the various quantiles, according to the tables. The relationship between inclusive growth and the interaction variables is also inelastic.

Table 8.

Quantile and POLS Regression with Bootstrap for All Countries.

ICG	FDCOC		FDGVE		FDPSV		FDRQT		FDROL		FDVAA		FDINQ
ICG	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats
25th Quantile
Intvar	0.664^c	2.100	0.943	3.010	0.669^a	2.850	0.249^c	3.540	0.215^a	6.680	0.354^a	4.010	0.644^a	8.760
EXP	0.074	1.110	0.055^b	0.800	0.049	0.720	0.044	0.660	0.060	0.830	0.043	0.650	0.067	1.020
INF	−0.057	−1.470	−0.062^c	−1.770	−0.046	−1.340	−0.056^c	−1.670	−0.054	−1.490	−0.047	−1.310	−0.073	−2.120
FDI	−0.033^c	−1.840	−0.016	−0.900	−0.061^a	−3.280	−0.020	−1.010	−0.034^c	−1.850	−0.029	−1.520	−0.007	−0.410
GCF	0.338	13.300	0.284^a	10.980	0.390^a	13.170	0.303^a	11.870	0.326^a	12.300	0.318^a	12.410	0.215^a	8.160
cons	1.599^a	3.600	2.506^a	5.100	1.102^b	2.200	2.064^a	4.340	2.012^a	4.230	1.904^a	4.110	3.347^a	7.230
Adj R²	0.542		0.728		0.641		0.510		0.423		0.526		0.687
Obs	960		960		960		960		960		960		960
50th Quantile
Intvar	0.584	4.320	0.684	3.260	0.600^a	5.490	0.874	2.610	0.287^a	8.380	0.198^a	4.330	0.396^a	5.290
EXP	0.024	0.390	0.007	0.110	0.003	0.040	−0.001	−0.010	0.004	0.060	0.010	0.150	−0.004	−0.070
INF	−0.043	−1.210	−0.046	−1.390	−0.038	−1.160	−0.042	−1.300	0.036	1.090	−0.042	−1.220	−0.048	−1.440
FDI	−0.017	−0.820	−0.007	−0.330	−0.044^b	−2.250	−0.011	−0.490	−0.017	−0.810	−0.017	−0.770	−0.005	−0.250
GCF	0.318^a	13.660	0.274^a	11.820	0.380^a	15.460	0.288^a	12.040	0.306^a	12.930	0.303^a	12.210	0.226^a	9.710
cons	2.434^a	6.110	3.198^a	7.580	1.739^a	4.340	2.956^a	6.800	2.834^a	7.230	2.641^a	6.350	3.926^a	9.550
Adj R²	0.521		0.601		0.506		0.561		0.611		0.748		0.653
Obs	960		960		960		960		960		960		960
75th Quantile
Intvar	0.494	5.210	0.352	3.090	0.538^a	7.930	0.658^c	3.940	0.745^a	5.040	0.737^a	4.310	0.105^a	5.840
EXP	−0.032	−0.430	−0.054	−0.680	−0.039	−0.560	−0.056	−0.650	−0.056	−0.800	−0.037	−0.420	−0.089	−1.120
INF	−0.027	−0.670	−0.025	−0.630	−0.032	−0.790	−0.024	−0.590	−0.018	−0.470	−0.035	−0.790	−0.018	−0.430
FDI	0.001	0.050	0.005	0.180	−0.029	−1.140	0.000	0.010	0.000	0.010	−0.001	−0.020	−0.003	−0.090
GCF	0.295^a	10.580	0.262^a	9.370	0.371^a	13.870	0.270^a	8.830	0.285^a	10.570	0.281^a	8.570	0.240^a	8.330
cons	3.376^a	8.040	4.085^a	9.030	2.309^a	5.670	4.053^a	8.000	3.706^a	9.950	3.683^a	7.540	4.607^a	9.930
Adj R²	0.408		0.520		0.745		0.653		0.628		0.541		0.647
Obs	960		960		960		960		960		960		960
POLS
ICG	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats
Intvar	0.574	4.460	0.637	3.150	0.597^a	5.600	0.846	2.640	0.287^a	8.130	0.197^a	4.390	0.392^a	5.710
EXP	0.027	0.360	0.007	0.120	0.003	0.040	−0.002	−0.010	0.004	0.060	0.011	0.153	−0.004	−0.071
INF	−0.042	−1.210	−0.045	−1.391	−0.038	−1.162	−0.041	−1.310	0.035	1.092	−0.041	−1.221	−0.041	−1.439
FDI	−0.016	−0.829	−0.008	−0.331	−0.044^c	−2.249	−0.013	−0.489	−0.016	−0.810	−0.017	−0.760	−0.004	−0.250
GCF	0.318^a	13.650	0.273^a	11.820	0.379^a	15.460	0.286^a	12.040	0.305^a	12.930	0.302^a	12.210	0.226^a	9.711
cons	2.435^a	6.111	3.197^a	7.580	1.738^a	4.350	2.957^a	6.810	2.833^a	7.231	2.642^a	6.351	3.927^a	9.549
Adj R²	0.655		0.763		0.813		0.730		0.676		0.815		0.899
Obs	960		960		960		960		960		960		960

Note. The superscript “a,”“b”, and “c” denotes significance at 1%, 5%, and 10% respectively. Coef is coefficient, Stats is statistics. Intvar is interaction variable, cons is constant, Adj R² is Adjusted R-squared. Obs is observations.

Table 9.

Quantile and POLS Regression with Bootstrap for Low-Income Countries.

ICG	FDCOC		FDGVE		FDPSV		FDRQT		FDROL		FDVAA		FDINQ
ICG	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats
25th Quantile
Intvar	0.367	0.920	−0.098^c	−0.270	0.961	3.240	0.150^b	2.450	0.762^c	1.660	0.241^a	2.870	0.193	0.670
EXP	0.092^c	1.810	0.097^c	1.820	0.090	1.610	0.088^c	1.690	0.090^c	1.750	0.091^c	1.880	0.097^c	1.900
INF	−0.041	−1.490	−0.040	−1.490	−0.048^c	−1.770	0.043	1.480	−0.042	−1.620	−0.042	−1.500	−0.043	−1.440
FDI	0.044^a	4.270	0.044^a	4.060	0.029^a	2.840	0.043^a	3.970	0.043^a	4.080	0.031^a	2.790	0.044^a	4.020
GCF	−0.009	−0.430	−0.010	−0.440	0.062	0.020	−0.016	−0.720	−0.013	−0.640	0.038	1.442	−0.015	−0.670
cons	6.841^a	18.040	6.825^a	16.660	6.945^a	19.380	7.058^a	18.190	6.986^a	18.380	6.957^a	19.550	6.929^a	17.540
Adj R²	0.643		0.665		0.578		0.667		0.642		0.701		0.616
Obs	420		420		420		420		420		420		420
50th Quantile
Intvar	0.445	1.120	−0.639	−1.830	0.326	2.880	0.635^a	2.290	0.288^a	2.920	0.337^a	2.420	0.809	2.850
EXP	0.073^b	1.430	0.072	1.390	0.055	1.090	0.058	1.160	0.064	1.270	0.058	1.150	0.073	1.500
INF	−0.045	−1.670	−0.047^c	−1.820	−0.049^c	−1.940	0.046^c	1.690	−0.048^c	−1.870	−0.039	−1.480	−0.050^c	−1.810
FDI	0.031^b	2.030	0.032^b	2.200	0.018	1.450	0.032^b	2.300	0.029^b	2.040	0.024^c	1.660	0.033^b	2.390
GCF	−0.009^b	−0.390	−0.013	−0.600	0.009	0.490	−0.018	−0.850	−0.014	−0.700	−0.004	−0.200	−0.024	−1.100
cons	7.383^a	19.850	7.464^a	19.280	7.357^a	22.430	7.677^a	20.600	7.607^a	20.500	7.543^a	21.300	7.617^a	20.280
Adj R²	0.524		0.727		0.637		0.537		0.740		0.625		0.533
Obs	420		420		420		420		420		420		420
75th Quantile
Intvar	0.549	1.030	−0.712	−2.640	0.768	4.590	0.286^a	2.470	0.203^a	3.690	0.215^a	2.700	0.763	1.500
EXP	0.047^c	0.740	0.036	0.590	0.013	0.230	0.017	0.290	0.028	0.450	0.009	0.140	0.037	0.610
INF	−0.050	−1.540	−0.056^c	−1.810	−0.050^c	−1.730	0.049	1.580	−0.055^c	−1.760	−0.034	−1.140	−0.061^c	−1.940
FDI	0.013^c	0.540	0.015	0.720	0.005	0.250	0.017	0.820	0.011	0.510	0.013	0.610	0.017	0.830
GCF	−0.008	−0.290	−0.019	−0.680	0.019	0.900	−0.021	−0.830	−0.016	−0.630	−0.010	−0.380	−0.038	−1.470
cons	8.105^c	17.950	8.393^a	18.540	7.857^a	21.830	8.508^a	19.820	8.452^a	18.990	8.398^a	19.740	8.681^a	19.770
Adj R²	0.613		0.523		0.680		0.738		0.528		0.681		0.531
Obs	420		420		420		420		420		420		420
POLS
ICG	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats
Intvar	0.447	1.121	−0.637	−1.832	0.325	2.881	0.634^a	2.289	0.289^a	2.930	0.336^a	2.421	0.810	2.851
EXP	0.074^b	1.430	0.072	1.391	0.054	1.089	0.059	1.159	0.063	1.271	0.059	1.152	0.074	1.498
INF	−0.046	−1.670	−0.048^c	−1.821	−0.049^c	−1.939	0.046^c	1.692	−0.048^c	−1.872	−0.038	−1.482	−0.051^b	−1.811
FDI	0.032^b	2.032	0.030^b	2.190	0.017	1.448	0.031^b	2.298	0.028^b	2.041	0.022^c	1.663	0.031^b	2.380
GCF	−0.009^b	−0.391	−0.013	−0.601	0.010	0.492	−0.018	−0.850	−0.014	−0.700	−0.005	−0.201	−0.025	−1.102
cons	7.382^a	19.870	7.466^a	19.230	7.352^a	22.440	7.676^a	20.604	7.609^a	20.505	7.544^a	21.303	7.619^a	20.283
Adj R²	0.718		0.823		0.817		0.849		0.736		0.637		0.736
Obs	420		420		420		420		420		420		420

Table 10.

Quantile and POLS Regression with Bootstrap for Middle-Income Countries.

ICG	FDCOC		FDGVE		FDPSV		FDRQT		FDROL		FDVAA		FDINQ
ICG	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats	Coef.	z-stats
25th Quantile
Intvar	0.119^a	7.790	0.550	4.860	0.243^a	5.410	0.058^a	3.160	0.545^a	8.000	0.571^a	3.870	0.113^a	6.840
EXP	0.024	0.260	−0.006	−0.060	0.046	0.490	−0.019	−0.200	0.020	0.220	−0.018	−0.190	−0.028	−0.300
INF	−0.010	−0.290	−0.017	−0.470	0.012	−0.340	−0.018	−0.480	−0.008	−0.220	0.021	0.540	−0.019	−0.580
FDI	−0.055^b	−1.990	−0.035	−1.270	−0.070^b	−2.400	−0.039	−1.280	−0.045^c	−1.650	−0.050^c	−1.730	−0.036	−1.370
GCF	0.289^a	11.190	0.220^a	7.380	0.332^a	10.280	0.212^a	6.120	0.264^a	9.620	0.228^a	7.100	0.171^a	5.500
cons	4.009^a	7.620	5.218^a	9.820	3.301^a	5.510	5.461^a	9.580	4.471^a	8.310	5.182^a	8.990	6.042^a	10.520
Adj R²	0.676		0.651		0.549		0.426		0.450		0.629		0.522
Obs	540		540		540		540		540		540		540
50th Quantile
Intvar	0.268^a	3.380	0.762	4.090	0.866^a	6.030	0.435^a	4.830	0.173^a	7.640	0.383^a	3.420	0.748^a	8.090
EXP	0.005	0.060	−0.015	−0.200	−0.003	−0.040	−0.021	−0.260	−0.006	−0.080	−0.012	−0.150	−0.033	−0.440
INF	−0.019	−0.540	0.020^b	0.570	−0.016	−0.460	−0.021	−0.580	−0.012	−0.360	0.025	0.660	−0.022	−0.650
FDI	−0.024^c	−0.890	−0.013	−0.490	−0.043	−1.610	−0.016	−0.540	−0.021	−0.790	−0.019	−0.670	−0.017	−0.620
GCF	0.218^a	7.890	0.171^a	5.690	0.274^a	9.530	0.170^a	5.230	0.207^a	7.420	0.175^a	5.630	0.142^a	4.830
cons	5.395^a	11.800	6.275^a	14.380	4.611^a	9.830	6.340^a	13.650	5.667^a	12.800	6.213^a	13.420	6.845^a	15.450
Adj R²	0.621		0.428		0.503		0.538		0.741		0.516		0.611
Obs	540		540		540		540		540		540		540
75th Quantile
Intvar	0.168^a	2.700	0.633	2.560	0.521^a	5.720	0.527^b	2.460	0.180^a	3.880	0.104	0.800	0.292^a	2.700
EXP	−0.024	−0.270	−0.029	−0.330	−0.048	−0.580	−0.024	−0.290	−0.043	−0.490	−0.005	−0.050	−0.040	−0.460
INF	−0.031	−0.670	0.025	0.550	−0.021	−0.460	−0.025	−0.550	−0.019	−0.420	0.030	0.630	−0.025	−0.540
FDI	0.021	0.520	0.018	0.490	−0.018	−0.540	0.017	0.430	0.012	0.310	0.019	0.480	0.007	0.170
GCF	0.113^b	2.490	0.101^b	2.410	0.221^a	5.430	0.110^b	2.590	0.126^a	2.790	0.110^a	2.780	0.105^b	2.450
cons	7.425^a	13.060	7.791^a	15.340	5.807^a	10.880	7.620^a	15.220	7.360^a	13.680	7.486^a	14.900	7.847^a	15.120
Adj R²	0.568		0.535		0.828		0.492		0.610		0.393		0.508
Obs	540		540		540		540		540		540		540
POLS
ICG	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats	Coef.	t-stats
Intvar	0.267^a	3.379	0.764	4.091	0.867^a	6.031	0.434^a	4.831	0.172^a	7.641	0.382^a	3.410	0.749^a	8.091
EXP	0.005	0.061	−0.014	−0.202	−0.003	−0.041	−0.021	−0.261	−0.007	−0.081	−0.011	−0.149	−0.034	−0.442
INF	−0.020	−0.540	0.021^b	0.571	−0.017	−0.459	−0.022	−0.581	−0.013	−0.359	0.026	0.661	−0.022	−0.650
FDI	−0.024^c	−0.890	−0.013	−0.490	−0.043	−1.610	−0.016	−0.540	−0.022	−0.790	−0.019	−0.670	−0.017	−0.625
GCF	0.219^a	7.891	0.171^a	5.691	0.275^a	9.531	0.172^a	5.231	0.208^a	7.421	0.176^a	5.631	0.141^a	4.830
cons	5.370^a	11.810	6.283^a	14.390	4.627^a	9.810	6.317^a	13.690	5.646^a	12.880	6.276^a	13.470	6.885^a	15.480
Adj R²	0.849		0.753		0.824		0.631		0.782		0.797		0.863
Obs	540		540		540		540		540		540		540

For Africa as a whole, Table 8 reveals that all things being equal, 1% increase in FDCOC causes inclusive growth to increase by 0.664%, 0.584%, and 0.494% for the low, moderate, and high quantiles, respectively. Using the moderate quantile as a yardstick, the FDCOC has no discernible effect on inclusive development. Furthermore, ceteris paribus 1% increase in FDPSV leads to a substantial increase in inclusive growth of 0.669% and 0.538% in the 25th and 75th percentiles, respectively, and 0.6% in the 50th percentile. However, ceteris paribus, in the 25th, 50th, and 75th quantiles, a 1% rise in FDROL would result in inclusive growth of 0.215%, 0.287%, and 0.745%, respectively. For the low, medium, and high quantiles, respectively, a percentage increase in FDVAA is associated with a substantial increase in inclusive growth of 0.354%, 0.198%, and 0.737%, all other things being equal. Finally, all things been equal, 1% rise in FDINQ causes inclusive growth in the 25th, 50th, and 75th quantiles to increase by 0.644%, 0.396%, and 0.105%, respectively. The outcomes POLS regression are similar to the 50th percentile results.

Again, Table 9 shows the outcomes for low-income countries. All things being equal, in the low, moderate, and high percentiles, a percentage rise in FDRQT causes inclusive growth to increase by 0.150%, 0.635%, and 0.286%, respectively. Furthermore, a 1% rise in FDROL would increase inclusive growth by 0.762%, 0.288%, and 0.203% in the 25th, 50th, and 75th quantiles, respectively, ceteris paribus. Furthermore, where all other factors are equal, a percentage rise in FDVAA is correlated with a substantial increase in inclusive growth of 0.241%, 0.337%, and 0.215% for the low, medium, and high quantiles, respectively. The outcomes POLS regression are similar to the 50th percentile results.

Table 10 shows the results of regressions involving countries with a middle-income status. All other factors being equal, a percentage rise in FDCOC would cause inclusive growth to increase by 0.119%, 0.268%, and 0.168% for the 25th, 50th, and 75th quantiles, respectively, according to the table. Furthermore, a 1% rise in FDPSV would substantially increase inclusive growth by 0.243% and 0.521% in the 25th and 75th percentiles outliners, respectively, and 0.866% in the 50th percentile, ceteris paribus. Furthermore, if all other factors remain constant, a percentage rise in FDRQT would result in inclusive growth rising by 0.249%, 0.874%, and 0.658% in the low, moderate, and high percentiles, respectively. Similarly, ceteris paribus, 1% rise in FDROL would increase inclusive growth by 0.545%, 0.173%, and 0.180% in the 25th, 50th, and 75th quantiles, respectively. Furthermore, where all other factors are constant, a percentage increase in FDVAA is associated with a substantial increase in inclusive growth of 0.571%, 0.383%, and 0.104% for the low, medium, and high quantiles, respectively. Finally, a 1% rise in FDINQ causes inclusive growth in the 25th, 50th, and 75th quantiles to increase by 0.113%, 0.748%, and 0.292%, respectively. The outcomes POLS regression are similar to the 50th percentile results.

Robustness Check

The analysis used two types of tests to determine robustness. The POLS findings were used as an initial step in checking the quantile regression results. Tables 8 to 10 show that the POLS regression results were similar to the MMQREG median quantile (50th quantile) result. Apart from the POLS numbers, both the quantile regression estimates and the POLS figures were graphed to further confirm the findings. Figures 1 to 3, depict graphical representations of MMQREG and POLS outcomes, illustrating the effects of FDV and INQ interaction on ICG for all countries, low-income, and middle-income countries, accordingly. The regression POLS coefficients (represented by the dotted line) remain constant in the chosen points of distribution, while the quantile regression estimates (represented by the green line) and the confidence interval term (represented by the gray area) that is contained around the different coefficients vary significantly by each ICG distributional point. This illustrates that the residual effects of interaction variables on inclusive growth vary across quantiles, suggesting that quantile regression findings are more accurate.

Figure 1.

Graphical illustration of MMQREG and POLS results for all countries.

Figure 2.

Graphical illustration of MMQREG and POLS results for low-income countries.

Figure 3.

Graphical illustration of MMQREG and POLS results for middle-income countries.

Panel Causality Test

Due to the obvious benefits of the panel causality test, the study used Dumitrescu and Hurlin (2012) panel causality test. Appendix D shows the test results for all countries, low-income and middle-income countries. In comparison, Figure 4 illustrates the findings in a vibrant pictorial style. The causality results indicate that FDROL and FDVAA had bi-direction causality with inclusive growth in all countries. That is, they have an effect on inclusive growth and vice versa. Furthermore, FDCOC, FDRQT, and FDINQ display a one-way causality from inclusive growth to them, suggesting that inclusive growth affects them but not actually the other way around. FDPSV further demonstrates unidirectional causality to inclusive growth; that is, the relationship vector affects inclusive growth but not the other way around. FDGVE, on the other hand, has no causal relationship with inclusive growth; meaning FDGVE has no effect on ICG and vice versa.

Figure 4.

Causal direction of FDINQ and ICG.

Low-income countries’ causal direction indicates that FDRQT, FDROL, and FDVAA have bi-directional causality with inclusive growth; therefore, inclusive growth affects them, and they influence inclusive growth. Furthermore, FDCOC and ICG demonstrates uni-directional causality from FDCOC; therefore, FDCOC affects inclusive development, but inclusive growth does not inherently influence FDCOC. Furthermore, ICG has a one-way causality direction for FDPSV and FDINQ; therefore, inclusive growth affects FDPSV and FDINQ but not generally the other way around. Finally, FDGVE does not indicate a causal association between inclusive growth in low-income countries. FDPSV, FDRQT, FDVAA, and FDINQ demonstrate bi-direction causality with ICG in middle-income countries. As a consequence, inclusive growth affects FDPSV, FDRQT, FDVAA, and FDINQ, as well as inclusive growth. FDCOC demonstrates uni-direction causality from inclusive growth, suggesting that it is affected by inclusive growth but does not affect it. Again, FDROL has a one-way causal relationship with inclusive growth, suggesting that FDROL drives inclusive growth but not the other way around. Finally, there is no causal relationship between FDGVE and inclusive growth.

Discussion of Results

The interaction of financial development and corruption control (FDCOC), the interaction of financial development and government effectiveness (FDGVE), and the interaction of financial development and regulatory quality (FDRQT) does not necessarily promote inclusive growth in Africa as a whole, according to the findings. These findings support the assertion of Nketia and Kong (2021) who stated similar outcome. So if the financial/banking sector is paired with governance indices such as corruption prevention, public and civil service delivery, and policy and legislation implementation, it will still not help Africa to generate and increase jobs and employment. This is because the majority of the governance metrics listed are bad, and this, combined with an uncertain financial system, may make it difficult to create employment. For example, in Africa as a whole, corruption and the perceived corruption less inspiring, therefore even when it is connected to the finance sector, it does not help generate jobs through industrialization or the extension of potential employment prospects. Similarly, in Africa as a whole, public and civil services, as well as how politicians manipulate the activities in these institutions, does not help the economy. This is attributable to how people are selected to manage these services; predominantly through nepotism and favoritism by politicians, which results in unqualified or less qualified candidates securing jobs and occupying public offices, resulting in abysmal performance. As a result, even though the financial services are encouraging, the wrong individuals filling the public services would cause finance to have little to no bearing on job creation. Similarly, due to a lack of enforcement, government regulations and policy formulation in Africa as a whole are fundamentally weak. Even as the financial sector is growing, there is almost always political interference on how laws and legislation are enforced, making it difficult to improve employment or generate new jobs. However, it must be noted that generating jobs and growing employment would promote the fight against corruption and ensure that government regulations are enforced.

In spite of that, financial development and political stability and absence of violence/terrorism interaction (FDPSV), and financial development and rule of law interaction (FDROL), are significant to inclusive growth in Africa as a whole, it confirms the study of Gyamfi et al. (2022). As the African financial/banking sector aligns with government stability, where there is no instigated political or non-political aggression, it can help create jobs and enhance employment in Africa. With the exception of a few sporadic cases of political instability or terrorism, Africa has encountered stable governance without fear of usurpation during the study period. This has had a positive effect on generating jobs, which has marginally increased the quality of living. This lends credence to the argument that political stability aids in the prevention of capital flight and the growth of enterprises. Furthermore, for Africa as a whole, if the financial/banking sector’s activities have collaborated with the populace’s trust in the rules of the society and abide by them; especially implementation of contract laws, property rights, the general legal system of the society, and police activities, it would stimulate employment opportunities in Africa. According to the study, the degree of faith people have in the justice system, law enforcement by police, and the level of monetary and financial advances have resulted in more jobs being generated and employment expansion. In the reverse, as more jobs are produced and made available, the populace’s trust in stipulated laws increases, and as a result, they abide by them.

In addition, financial development interaction with voice and accountability (FDVAA), and financial development interaction with governance in general (FDINQ) are relevant and promotes inclusive growth in Africa as a whole. Improvement in the banking/financial sector fused with people’s right to select leaders, articulate themselves openly, enter any association of their choosing, and have access to information, contributes to job growth and increase employment opportunities in Africa as a whole. The study therefore shows that, if Africans have access to financial improvement opportunity, coupled with freedom of choice and expression, they will drastically create more jobs in Africa. Finally, for Africa as a whole, the convergence of the financial/banking sector’s activities with governance in general creates jobs and increase employment opportunities for the population. When all of Africa’s governance practices are paired with monetary policies, resource supply, loan distribution, and financial assistance, the employment situation in Africa improves; therefore, reasonably good governance and balanced monetary policies promotes job growth. The reverse is also true; if more people are employed and receive a study source of income, the financial sector and governance in general would increase for Africa as a whole.

The effect and impacts differ with the level of income of African countries. The distinction between countries with low income and middle income helped to uncover certain revelations, which are not usually available. The interaction between financial development and corruption management (FDCOC) or when financial development is interacted with political stability, and the absence of conflict and violence (FDPSV), it stimulates job creation and sustains economic growth in middle-income countries, but not to low-income African countries. The financial and banking sector will continue to create jobs and expand existing job opportunities in mid-income countries but not in low-income countries, if financial development is combined with the perception of corruption and the usage of public office to manipulate policies for personal benefit. This is because the perception of corruption and the use of public office for personal benefit by the politicians and those with political influence is strong in low-income countries to the degree that it negates good financial policies like; financial inclusion, loan disbursement and small business support. In middle-income counties, however, a great deal is being done in order to combat corruption and the impression of corruption, therefore, unethical practices have declined drastically over the years. Such intervention combined with sound financial policies has promoted employment and increased jobs.

Likewise, if the financial/banking sector in middle-income African countries is linked to government stability, the absence of terrorist activities or violence contributes to the increase in income and new job creation avenues. This however cannot be said about low-income countries. African middle-income countries have had a stable democracy since the year 2000, without the possibility or attempts of being usurped because of the kind of government selection mechanism they practice. This has had a positive effect on job creation, and has slightly elevated living standards. On the other hand, there are pockets of violence and attempts to overthrow the government in low-income African countries. The consequence of this is that while the financial sector is stable, it does not benefit employment growth and creation of new jobs. This confirms that political stability helps deter capital flight and promotes the growth of businesses, thus improving employment. Inversely, the study also shows that if citizens have a steady source of income and employment, political prosperity would be promoted in both middle and low-income countries. The interaction between financial development and regulatory quality (FDRQT) is indeed crucial to inclusive growth in low and middle-income economies. Though government regulations and policy enforcement in Africa are essentially weak, if merged with financial sector operations, it would help job growth and increase employment in the middle-income countries. The study also revealed that financial development and government effectiveness interaction (FDGVE) does not promote job creation or sustain jobs in both low and middle-income countries. The simple reason is that the effectiveness of government is low and sometimes even seen as very ineffective.

In addition, financial development interaction with voice and accountability (FDVAA), and financial development interaction with rule of law (FDROL) are critical inclusive growth in low and middle income countries. If opportunities for people to vote, express themselves freely, join their party, group or association of choice, and freedom of media are linked with progress in the banking/financial industry, it will result in job creation and employment stability. When a freedom of speech is in place and the ability to have enough financial services exists, people take advantage of it to create jobs in the economy. Inversely, getting people jobs and providing a study source of income has helped the finance sector and voice and accountability to improve in low and middle-income African countries. Again, combining the financial/banking sector’s activities with the faith populace’s confidence in and obedience to societal rules, especially the enforcement of contract laws, property rights, the general legal structure of the economies, and police activities, will encourage job growth and improve employment in both low and middle-income countries.

Finally, the interaction of financial development with governance in general (FDINQ) is useful in improving inclusive growth for African middle-income countries but not for low-income countries. When all of Africa’s government activities are combined with monetary reform, liquidity access, credit allocation, and financial assistance, the middle-income country’s employment situation improves. But for low-income countries it is indifferent. However, according to the study, in both low and middle-income African countries, if more people are engaged in decent work while they earn a study stream of income, the finance sector and governance in general will also improve tremendously.

Conclusion

From 2000 to 2019, the study examined the relationship between financial development interaction with all six distinct governance indicators, as well as a composite of them, and inclusive growth in 48 African economies. Based on the World Bank’s country income classification, the countries were divided into LIC and MIC categories. The study conducted preliminary experiments, then tested for cross-sectional effect, and eventually panel cointegration to ensure the existence of long-run relationships. The models were then estimated using MMQREG estimations before being robustly estimated using POLS estimations.

According to the findings of the 50th quantile regression, all interaction variables had a positive relationship with inclusive growth. The interaction of financial development and corruption control is essential for inclusive growth in middle-income countries but not in low-income countries nor Africa as a whole. From the causality test, inclusive growth has an effect on same interactions in Africa and middle-income countries. Furthermore, the interaction of financial development and government effectiveness has no bearing on inclusive growth in any of the country groups or across Africa. The interaction of financial development with political stability and the absence of violence/terrorism, on the other hand, is relevant to inclusive growth in both Africa as a whole and middle-income countries, but not in low-income countries. In both low and middle income countries, inclusive growth affects same interaction. Again, the interaction of financial development and regulatory quality is essential to inclusive growth in low and middle-income African countries, but not for Africa as a whole. In all three country groupings, inclusive growth influences the same interaction,

However, the interaction of financial development, with voice and accountability, and the interaction of the rule of law with financial development are relevant to inclusive growth in both LIC and MIC and in Africa as a whole. Inclusive growth has an influence on both interactions in Africa as a whole, as well as in low-income countries. Finally, the interaction of financial development and governance in general is substantial to inclusive growth in Africa as a whole and middle-income countries, but not to low-income countries. On the causality, inclusive growth influences the interaction of financial development and governance in general in Africa as a whole, as well as MIC and LIC. Nonetheless, the interaction of financial development and governance in general only influences MIC. Therefore, hypotheses 1 and 2 has been addressed by this outcome; that for H1; the interaction variables has positive impact on inclusive growth in Africa and the various country income grouping, and for H2; there is the influence of inclusive growth on the interaction variables, but, it will depend on the country income grouping.

On policy implications, for Africa as a whole to achieve tremendous improvement in job creation, job sustainability, and increasing economic growth, African Union through African Development Bank must concentrate on expanding the financial horizon to cover the unbanked, and deepen the effort to ensure young people have access to capital for business start-ups. Again, the African Union must encourage its members to strengthen the various governance indicators. To ensure inclusive growth, the African Union must set targets on the six governance indicators and compel members to meet those targets with some level of punishment attached to those who fail to meet them.

The research’s weaknesses include the fact that it did not analyze specific nations, nor did it include sub-regional blocs; as a result, the study may not necessarily represent reality in particular countries or sub-regional blocs, which may provide different results. Additionally, the study recommends that further research be conducted on the financial development and the six governance indicators interactions, and their effect on inclusive growth in specific African countries as well as sub-regional blocks such as Southern, East, West, Central, and North Africa.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440231166341 – Supplemental material for An Introspective Analysis of Inclusive Growth in Africa, With an Eminence on the Influence of Governance and Financial Development Interaction

Supplemental material, sj-docx-1-sgo-10.1177_21582440231166341 for An Introspective Analysis of Inclusive Growth in Africa, With an Eminence on the Influence of Governance and Financial Development Interaction by Kaodui Li, Easmond Baah Nketia, Yusheng Kong and Michael Appiah in SAGE Open

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study is grateful to The National Natural Science Foundation of China (71973054) for the support.

ORCID iDs

Kaodui Li

Easmond Baah Nketia

Michael Appiah

Supplemental Material

Supplemental material for this article is available online.

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