Is the Quality of Governance a Precursor for Regional Disparity or Prosperity?

doi:10.21203/rs.3.rs-3150235/v1

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Is the Quality of Governance a Precursor for Regional Disparity or Prosperity?

https://doi.org/10.21203/rs.3.rs-3150235/v1

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This study looks at the impetus of quality of state governance in supporting regional inclusiveness akin to developing the Sub-Saharan African region. Whereas the intrinsic value of state governance quality as anecdotes of better development in unique ways is universally accepted and portrays the notion of an inclusive society, its fundamental significance as a path toward equal income distribution and inclusive economic performance is still scantily understood despite the widely emerging literature. This paper's main aim is twofold: review the rising literature and, in a unique way, take a deeper look at the link between the quality of governance and regional disparity or prosperity. Secondly, the paper evaluates where the developing Sub-Saharan Africa stands in various measures of quality of governance and how it influences income inequality. The study utilizes balanced panel data from 22 Sub-Saharan African countries from 2000 to 2021. Further, the study models the relationships between the variables using Panel Fixed Effects (Panel FE-IV) and generalized system method of moments (SsyGMM). The findings show a significant positive link between government effectiveness and income inequality. Also, the results show the non-significant role of FDI in explaining increasing income inequality in SSA, paving the way for future research to interrogate the essence of FDI in promoting economic prosperity, primarily when the SSA region is heavily relying upon FDI inflows for development.

Governance Quality

Regional Disparity

Panel Data

System GMM

SSA

Recently, there have been rising concerns about the increasing income disparities, within and across regions, both in developed and developing regions (Ciani & Torrini, 2019; Nchofoung et al., 2022; Nchofoung & Asongu, 2022). The cause of this increasing disparity in recent decades has also gained massive academic and political attention. One of the critical hypotheses carried forward by scholars associates disparity with the quality of governance (Nchofoung et al., 2021). Specifically, the disparity has been seen as an outcome of sudden policy switches, failed democratization processes, institutional breakdowns, and violation of laws and tax policies (Sarkhosh-Sara et al., 2020; Ezcurra, 2019). Such cases are predominantly worst in Sub-Saharan Africa, with the region categorized as the least developed, characterized by wide income disparities (Nchofoung et al., 2022; Bratton, 2012; Erk, 2015).

Several attributable reasons have been put forth as the genesis of the rising income disparities moving from low infrastructural endowments to poor institutional frameworks to weaker government effectiveness in dealing with perennial insecurities and high-level corruption in SSA (Achuo et al., 2021). Despite vigorous attempts and recent economic reforms carried out by Sub-Saharan African states, the majority of them have continued to encounter retarded economic growth, compounded by political disturbances, high unemployment rates, biting poverty, social turbulences, and wider income gaps attributable to institutional failures (Ngouhouo et al., 2021). For instance, the recent Anglophone crisis in Cameroon, Ivory Coast, Mali, the Democratic Republic of Congo, and the Central Republic of Africa, to mention a few. The overall consequences of such social unrest and high political instabilities are continued income disparities due to the low quality of government (Vachuiden & Ngouhouo, 2022).

The link between the quality of government and income inequality has emerged from new institutional economics initiated by North (1991). Consequently, several studies have focused on the link between the two variables. For instance, Blancheton and Chhorn (2021) focused on the association between the quality of government measured by the institutional quality index and income disparity measured by the Gini Index in Asian and Pacific regions. The authors observed the negative effect of institutional quality on income inequality. Similarly, Ferrara and Nisticò (2019), in their analysis of disparity trends within Italian sub-national regions, observe that the quality of institutional set-ups has a significant role in determining the occurrences of disparities within sub-national regions. Ezcurra (2019), considering various measures of regional disparity such as the Gini Index and coefficient of variation, observes that nations or regions with better-quality governments have lower regional disparity levels. Focusing on economic freedom as a quality of government indicator, Sarkhosh-Sara et al. (2020) conclude that economic freedom significantly impacts income disparity in developed economies.

Although several recent studies confirm the existence of a link between the quality of government and regional disparity, the majority of these studies have paid much attention to the developed regions such as Europe and parts of Asia (Charron et al., 2014; Crescenzi et al., 2016; Rodríguez–Pose & Zhang, 2019; Sarkhosh-Sara et al.,2020)—the remaining part of the world-specially the developing regions such as SSA—remains a mysterious black box. Specifically, while there is a general theoretical acceptance of the intrinsic value of quality of government as ends to equal income distribution and better economic performance, how this is translated into short-to-long-term policy priorities, especially for institutionally fragile and low-income regions, is still not reasonably understood, despite the considerable emerging and still rising literature strand (Rodrik, 2004; Erk, 2015; Bratton, 2012).

The novelty of this study is threefold. One, the study pays a closer look at the SSA region, where a dearth of studies has examined the association between quality of government and income disparity and how this shapes the overall economic performance of Africa. This has been classified as a deliberate research omission for two key reasons; first, sub-national government institutions have traditionally played a significant role in SSA countries (Iddawela, Lee & Rodríguez-Pose, 2021). Second, over the recent two decades, several SSA countries have embraced devolution or regional system of governance, which have increasingly raised concerns that performance variations of sub-national governments can exacerbate significant corruption, and continued income disparities (Wilfart, 2018; Erk, 2015; Iddawela, Lee & Rodríguez-Pose, 2021). Additionally, the study recognizes that different measures of quality of government do not associate with income disparity in the same way, leaving room for the need to investigate the association between the two variables from a different angle—computing composite quality of government index using the quality indices provided for by World Bank (Worldwide Government Index) (Ngouhouo et al., 2021). Lastly, the study uses balanced panel data for the two recent decades, 2000 to 2021, from 22 countries to model the link between the quality of government and income disparity, estimated using System-GMM and Panel Fixed Effect-Instrument Variable (Panel FE-IV) techniques.

The remaining part of this paper is structured as follows: Section 2 presents background literature detailing theoretical underpinnings and provides empirical evidence from recent studies. Section 3 entails variable description, data description, data sources, the estimation strategy, and estimation techniques used in modeling the link between the quality of government and income disparity. Section 4 presents the statistical results and discussions. Section 5 summarises the study findings, makes conclusive remarks, and professes policy recommendations.

The ongoing disquiet in the economics literature regarding the role of quality of governance and institutions can be seen as part of the endless search for deep determinants of economic prosperity and a relatively equal society. Essentially, this can be traced to the mounting displeasure dating back to the 1980s with what was, until then, the dominant neoclassical growth model established in the 1950s by Solow and Swan (1956). The typical neoclassical growth model identified capital accumulation as the critical factor in explaining levels of economic prosperity. However, successive attempts to empirically test the neoclassical model turned unclear (Zhuang, de Dios & Martin, 2010). This prompted further model advancements to a new strand of economics literature, known as the new institutional economics (NIE), following the preliminary work of North (1990, 1993, & 2008). NIE tries to extend neoclassical economics by including institutional analyses, focusing on the role of government institutions in explaining short-to-long-term economic prosperity in terms of income distribution (North, 2008). The author defines government institutions as the “rules of the game,” that is, the human-devised informal and formal regulations that shape the interrelationships of human beings. Subtly, formal government institutions primarily refer to the statutes, constitutions, and explicit government rules and protocols organized and enforced by objective mechanisms —most significantly, the state with its strong-arm powers and organization. On the other hand, informal institutions comprise undocumented rules such as traditions, customs, codes and norms of conduct, taboo, and other frameworks vested in and enforced through interactions and interpersonal ties (North, 2008).

Alongside these theoretical advancement assertions, accrued experience among the global development agencies point out that structural change and economic stability policies based on external aid routinely fail or are thwarted by overriding political and institutional factors. This has pushed for efforts to interrogate the political atmosphere and institutional processes that affect policy inceptions and implementation beyond the contents and structure of the policy itself (Burnside & Dollar, 2004). The greater involvement of enormous structures in determining policies, their performance, and results has resulted in the modern-day quality of governance—which refers to the ruling or the entire system of political institutions and use of political powers to run an economy (Burnside & Dollar, 2004). Thus, according to this definition, good state governance quality aids in solidifying democracies, and social cohesion, reducing poverty, ensuring economic prosperity, and deepening trust in governance and public administration (Turnovsky, 2011). In line with this definition, Kaufmann, Kraay, and Mastruzzi (2007) advanced the working description of governance to what is currently highly used as a set of government indicators popularly known as Worldwide Governance Indicators produced by World Bank; i) voice and accountability index, ii) political instability index, iii) government effectiveness index, iv) control of corruption index, v) regulatory quality index, and vi) the rule of law index (Kaufmann, Kraay, & Mastruzzi, 2009). For every economy, these component indicators are aggregated and rescaled using an unobserved-components technique to produce a value centered at zero and run from negative to positive. Large positive values represent a high quality of governance, and the converse is also true, as discussed further in section 3 of this paper.

Several recent empirical studies have used the listed governance indicators with regional income disparity differently. The first strand of literature has focused on individual governance measures and income inequality—for instance, Sarkhosh-Sara et al. (2020) use economic freedom as a proxy for governance quality indicators in explaining the Italian regional income inequalities. The study findings indicate that economic freedom is significantly related to income inequality. Similarly, Li et al. (2000) use the corruption control index as a measure of governance quality to link governance and income inequality and observe an inverted U-shaped association: very high corruption levels are linked with low-income inequality levels. Contrary, Dobson and Ramlogan-Dobson (2012), analyzing the informal sector in Latin America, document evidence of an association between corruption and income disparity. They further argued that where weaker government institutions (compounded by large informal sector), it might be significant to allow corruption to thrive.

Regarding regulatory governance quality, Ciani and Torrini (2019) observed that poorly constructed tax-benefit systems contribute to Italy’s income inequality. Focusing on the quality of local institutions, Lasagni et al. (2015) observed that the better the quality of local institutions, the higher the creation of income and firm productivity in Italy. Along the same line, Sonora (2019) analysed the effect of the rule of law on income inequality in Latin American nations. The author observed that quality of property protection via effective control of corruption and effective legal regime narrows the income gap between the poor and the rich. Focusing on 36 Asian economies, Huynh (2021) examined the influence of institutional quality on the relationship between foreign direct investment and income distribution. The study concluded that institutional quality moderates the effect of foreign direct investment on income inequality.

The second strand of literature increasingly attracting much attention focuses on developing regions, specifically SSA. Using four governance indicators; government effectiveness, the rule of law, corruption, and political rights, Adeleye et al. (2017) examined the interactive effect of government institutions in Sub-Saharan Africa and deduced that ability to control corruption reduces income inequality and increases economic growth efficiency. Likewise, Hossain and Rahman (2017) observed that the quality of governance increases the inflows of FDI, hence decreasing the income inequality in SSA. Ajide, Adeniyi, and Raheem (2017) indicated that political stability, government effectiveness, and control of corruption influence income inequality by diverting resources meant for economic development. Similarly, focusing on the SSA region, Lahouij (2017) affirms that the quality of governance is significantly linked to economic growth regardless of income level. The study indicated that the rule of law, political stability, voice, and accountability influence the per capita income. More recently, Nyiwul (2021) used fractional regression and opined that there is a significant negative relationship between the quality of climate policies and social inequality in Africa. Sarkodie and Adams (2020) analyzed the impact of corruption control and distribution income on electricity access in South Africa. The authors observed a long-run positive relationship between income level attributed to the control of corruption and the quality of electricity access. Similarly, Nguyen et al. (2021) observed that effective governance or state institutions and educational expenditure decrease income inequality in developing and developed nations. The author observes that economic growth widens income inequality and narrows foreign direct investment in developing countries.

In sum, while the reviewed studies confirm the significant link between the quality of governance and income inequality in developed and developing regions, it is essential to highlight how the quality of governance affects income inequality. First, the quality of governance indicated by political instability, government ineffectiveness, and outright corruption limits economic growth and reduces the FDI, foreign aid, and cross-border trade, widening the income gap between the rich and the poor (Halder & Sethi, 2021). On the other hand, economies with robust institutions increase the FDI-economic growth-led development, but weaker institutions promote FDI volatility which influences economic growth negatively (Boateng et al., 2021). In this study, we include FDI and economic growth as the control variables on the link between the quality of governance and income inequality. Indeed, nations with stronger institutional quality encourage donor-funding economies and neighboring trade partners to engage and derive economic lessons from the economies with high-quality governance and institutions (Nketia et al., 2022). Agreeably, in economies with fragile institutional policies and quality of governance, poverty responds less to redistributive policies and income growth; thus, correcting inequality through quality governance would be pivotal for poverty reduction (Posel & Casale, 2020). Empirically, studies (Alam et al., 2017; Azam & Emirulah, 2014; Jiandang et al., 2018) have confirmed a significant positive connection between the quality of governance and economic growth in developing nations in Asia, Pacific, and African regions. We extend this literature by determining whether a significant link exists between the quality of governance and income inequality and, second, by ascertaining the influence of FDI and economic growth on the connection between governance and income inequality in SSA.

To examine the link between the quality of governance and income inequality, four indicators of quality of governance; control of corruption, government effectiveness, institutional quality, and regulatory quality are used. The data for these four indicators are drawn from the Worldwide Governance Index compiled by World Bank (Kaufmann, Kraay, & Mastruzzi, 2009). The control of corruption indicator reflects the citizen’s perception of the extent to which the state exerts its power to gain the private sector and state capture by elites and private interest. A low index indicates an increased cost of production which deters better investments, hence distorting the efficient allocation of resources in an economy (Hamid, 2017). Government effectiveness is another governance quality indicator that captures the perception of public service provision, quality of service and extent of independence from the political influence, and quality of implementation and credibility of government’s policies (Nifo & Vecchione, 2014; Engjell, 2015). Regulatory quality is the third indicator that reflects perceptions of the government’s ability to initiate, formulate and implement favorable policies and regulations that enable and spur private sector development (Engjell, 2015). Political stability is the fourth quality of governance indicator which reflects the perceptions of probability that the government will be overthrown or destabilized by violence and unconstitutional means. This is key for economic prosperity as it enables a viable investment environment (Bassan, 2013). The data on income inequality measured by the Gini Index calculated using household income levels are obtained from the Standardized Income Inequality Database (SWIID) (Jenkins, 2015).

The study focuses on the Sub-Saharan African context by utilizing balanced panel data for 22 countries from 2000 to 2021. The analysis controls foreign direct investment (FDI) and economic growth as these variables point out how the government aligns its policies with the economy’s development. FDI is measured as a percentage of GDP, showing that low-income countries rely on foreign capital to supplement their budget. This spurs elasticity of supply, which translates to higher income levels (Alam et al., 2017). On the other hand, economic growth measured by GDP per capita indicates the extent to which individuals contribute to wealth creation through capital formation, influencing income levels (World Bank, 2015).

To analyze the link between the quality of governance and income inequality, we rely on the application of the ordinary least squares (OLS) model specified as follows:

$${Gin}_{i,t}={\beta }_{0}+{\beta }_{1}{Gin}_{i,t-1}+{\beta }_{2}{(GOV\_QUA)}_{it-1}+{\theta }_{k}{\sum }_{k=1}^{k}{\left(\text{C}\text{O}\text{N}\text{T}\text{R}\text{O}\text{L}\text{S}\right)}_{it}+{\phi }_{i}+{\tau }_{i}+{\epsilon }_{it} \left(1\right)$$

where ${Gin}_{it}$ is the Gini Index a proxy measure of regional inequality, and $GOV\_QUA$ is the quality of governance denotation as suggested by Nifo and Vecchione (2014). A dynamic structure for income inequality and quality of governance indices has been included, as the variables enter our model specification with the first lag. This choice is fundamentally pushed because governance quality changes occur over time before influencing structural economic prosperity measures such as income inequality. Additionally, the decision to include the dynamic rather than static link between the quality of governance and income inequality is informed by the fact that the two variables exhibit slow time changes (Barra &Ruggiero, 2022). CONTROLS is the vector of the control variables—FDI (% GDP) and economic growth (GDP per capita) —that have been observed o impact income inequality as informed by earlier studies such as Dobson and Ramlogan-Dobson (2012), Alam et al., (2017), and Vachuiden and Ngouhouo (2022). Furthermore, our model specification includes the first lag of the Gini Index to capture the probable persistence of income inequality level. Lastly, $\phi$ is an unobserved region-specific effect, $\tau$ is the time dummies used to control for time-specific impacts, $\epsilon$ is the error term, and subscripts $i$ and $t$ represent regions and period (years) correspondingly.

Before carrying out any econometric estimation, a combination of several panel unit root tests such as Levin et al. (2002), otherwise knowns as (LLC), Im et al. (2003), and otherwise known as (IPS) tests, are carried out to check for non-stationarity traits of the collected data at level form (p > 1), and at the first difference (p < 1), moreover, augmented cross-sectional IPS (CIPS) by Pesaran (2007) accounts for the possibility of cross-sectional dependence in the panel data. Although some variable data may be stationary at level form, this study will consider stationarity at the first difference to enhance data reliability as the panel is detrended to remove seasonality and trends. Should the Breush-Pagan Lagrange Multiplier test confirm significant regional disparities, ordinary pooled Ordinary Least Squares (OLS) will become inappropriate. Hence, fixed effects (FE) and random effects (RE) models become the ultimate alternative methods. Again, since the choice of either FE or RE follows the Hausman test, which is not always significant, this study will prefer Panel Fixed Effect-Instrument Variable (Panel FE-IV) technique as it deals with regions that may possess unobserved time-invariant country-specific effects that can be controlled using the Panel FE-IV model.

Since our cross-sectional units (22 countries) are more significant than the time series (2000–2021), heteroscedasticity can arise. Specifically, the income inequality measure can exhibit a distinct difference as per the quality of governance across countries, thus signifying the presence of heteroskedastic residuals. Therefore, this study considers the modified Wald test for FE groupwise heteroscedasticity. If the null hypothesis of homoscedasticity for the two models is rejected, robust standard errors will be provided for all estimates. Further, the Wooldridge test for the model will be conducted to test for the serial correlation of the common errors, as it causes the results to be less efficient. Although lagged and detrended data is known to be ideal in controlling the likelihood of endogeneity, this study will consider an additional robustness check using a systematic generalized method of moment (SyGMM) with the instrumental variables and lagged Gini Index to 1 year as part of the exogenous variables to identify endogeneity possibility and check any consistency of the results from FE model.

Designed for short panels, applying the SyGMM technique allows uniformly estimating the first-order autoregressive term and overcoming potential issues related to the application of the exogenous instruments, as the estimator uses lagged levels and differences as the instruments (Roodman, 2009). Indeed, lagged levels and differences help to obtain a set of valid instruments. Specifically, while differences are not correlated with fixed effects, lagged levels are not associated with the disturbance term in case the latter is serially uncorrelated. If, nevertheless, as in the case of the present contribution, the disturbance term exhibits first-order serial correlation. Therefore, applying lags with higher order provides a set of valid instruments. Usually, the validity of the selected instruments is evaluated using the Sargan test of over-identifying restrictions. In contrast, the Arellano-Bond statistic value determines autocorrelation in the disturbance term.

Table 1 presents the descriptive statistics, where the mean of the income inequality (Gini Index) for the selected 22 Sub-Saharan African countries from 2000 to 2020 is 0.62. The quality of governance measured government effectiveness index, corruption control index, political stability index, and regulatory quality index ranging between − 2.5 and 2.5 has the mean value averaging above − 0.5, signifying a weak government performance (Kaufmann, Kraay, & Mastruzzi, 2009). Economic growth measured by GDP per capita growth shows that the annual growth in the 22 selected countries for 2000–2021 is 1.5%, and the average FDI measured by the percentage of GDP resulting from net inflows is 4.58%.

Table 1

Descriptive Statistics
Variables:	Gini_Index	GOV_E	CON_C	POL_S	REG_Q	GDP_P	FDI
Mean	0.6200	-0.8280	-0.7741	-0.7582	-0.7120	0.1559	0.0458
Maximum	0.7465	0.6459	0.8100	0.6610	0.8196	1.9939	1.0334
Minimum	0.5231	-1.8414	-1.5747	-2.4742	-2.2015	-3.0702	-0.1892
Std. Dev.	0.0494	0.4394	0.4290	0.7309	0.5015	0.4306	0.0969
N	484	484	484	484	484	484	484
Source: Author’s Construction (2023)

Table 2 presents the panel unit root test findings of variables in level form and first difference. The null hypothesis of the presence of unit root is rejected under all tests for income inequality (Gini Index), Government Effectiveness (GOV_E), Corruption Control (CON_C), GDP per capita (GDP_P), and Foreign Direct Investment (FDI), hence integrated to order 0 (I(0). All tests confirm the rejection of the null hypothesis of the existence of unit root for Political Stability (POL_S) and Regulatory Quality (REG_Q) after carrying out the first differencing, hence integrated to order 1 (I(1). Thus, given that the variables are stationary in different forms, that’s a level form (I(0), and the first difference (I(1), it is thus essential to consider testing for the relationship between income inequality and quality of governance using Pooled OLS, GMM-System, and robustness check using Panel FE-IV models.

Table 2

Panel Unit Root Test
Variable	LLC	IPS	CIPS	Status
Gini Index (Gini_Index)	-5.368**	-4.085**	-2.160**	I(0)
Government Effectiveness (GOV_E)	-2.457**	-2.277**	-1.858**	I(0)
Corruption Control (CON_C)	-2.821**	-1.772**	-1.570**	I(0)
Political Stability (POL_S)	-9.441**	-9.963**	-4.317**	I(1)
Regulatory Quality (REG_Q)	-9.147**	-11.346**	-4.811**	I(1)
GDP per capita (GDP_P)	-2.530**	-4.358**	-3.543**	I(0)
Foreign Direct Investment (FDI)	-1.550	-2.833**	-3.111**	I(0)
Note: All variables are measured at the beginning of the period. The time spans from 2000 to 2021. The **p < 0.05 denote rejection of the null hypothesis of the non-stationarity trait and absence of cross-sectional dependence.
Source: Author’s Construction (2023)

After describing the data trends and ascertaining the existence of stationarity traits and cross-sectional dependence, we embark on empirical analysis and estimation. In particular, Eq. (1) which represents the dynamic panel data model, is estimated using balanced panel data from 22 SSA countries from 2000 to 2021. The estimation of such a model suffers from unobserved time-invariant country-specific attributes and feedback effects. However, these shortfalls can be addressed using two approaches: using System-GMM estimations and adding internal instruments. In this study, we use Pooled OLS and standard System-GMM to ensure the coherence and validity of estimates. Further, we include a robustness check using the Panel FE-IV technique. By relying on the Sys-GMM method, we ensure that for the main variables, quality of governance and income inequality, i) lagged first differences are relevant instruments, and ii) lagged levels are relevant and valid to be used as first differences. To check whether the dynamic model following System-GMM, overidentification restriction, and serial correlation tests are carried out using Hansen J-statistics, AR1, and AR2 tests. Lastly, since our focus is Sub-Saharan Africa, where endogeneity concerns are likely to occur, external instruments and panel FE-IV technique are used as the robust check technique and Pooled OLS and SysGMM models.

Table 3 presents the estimation results of Pooled OLS and Sys-GMM, with the observation size varying between 462 to 484 depending on the model applied. In columns 1 and 3, we show the Pooled OLS estimates with government effectiveness, control of corruption, and GDP per capita being significantly linked to income inequality at a 1% confidence interval. Columns 2 and 4 report findings of parsimonious and complete dynamic panel model estimates following the Sys-GMM technique, with all control variables included. The Sys-GMM findings show a significant positive relationship between governance quality (government effectiveness and regulatory quality) and income inequality at 10% (see columns 2 and 4). Further, the Sys-GMM findings show that control variables (FDI and GDP per capita) have an insignificant relationship with income inequality at 1%, 5%, and 10% (see columns 2 and 4). Further, the results show an insignificant AR (1) and AR (2), confirming the absence of Arellano serial correlation. Moreover, the finding shows an insignificant Sargan Hansen’s J-statistics, confirming the correct model specification.

Table 3

Quality of Governance and Income Inequality
Variable	Pooled OLS (1)	SysGMM (2)	Pooled OLS (3)	SysGMM (4)
Gini Index(-1)		1.010*** (0.0117)	1.002*** (0.0073)	0.900*** (0.0781)
Const.	0.648*** (0.0085)	-0.0045 (0.0075)	-6.975 (0.0051)	0.066 (0.0498)
Government Effectiveness	0.046*** (0.0079)	0.002* (0.0009)	0.002 (0.0013)	0.006* (0.0033)
Corruption Control	-0.017*** (0.0080)	-0.001 (0.0008)	7.879 (0.00127)	-0.002 (0.0016)
Political Stability	0.003 (0.0107)	0.0006 (0.0010)	0.0002 (0.0016)	5.871e-5 (0.0005)
Regulatory Quality	-0.0103 (0.0238)	0.003* (0.0020)	0.005 (0.0034)	0.0018* (0.0011)
GDP per capita	-0.002*** (0.0006)	5.516e-06 (5.089e-05)	-8.672 (8.811e-05)	-2.693e-5 (3.073e-5)
Foreign Direct Investment	0.021 (0.0238)	0.0003 (0.0023)	-0.0003 (0.0036)	0.0013 (0.0020)
Year FE	YES	YES	YES	YES
Observations	484	462	484	462
Number of Countries	22	22	22	22
AR(1) p-value		0.3727		0.4291
AR(2) p-value		0.2893		0.2763
Hansen’s J-stat p-value		0.352		0.2456
Note: All variables are measured at the beginning of the period. The time spans from 2000 to 2021. Columns 2, 3, and 4 included the lagged Gini Index and time dummies. For SysGMM estimations, variables in levels lagged between 0 and 1 periods are included as instruments for the first differences. SysGMM estimations are done with the inclusion of asymptotic standard errors and time dummies. The standard errors are in parentheses.

**p < 0.05, ***p < 0.01, *p < 0.1.

Source: Author’s Construction (2023)

4.1 Robustness Checks: Panel FE-IV Estimation

Robustness check is conducted because of two main reasons. One resides in the identified positive differential impact of quality of governance, as shown in Table 3. The second reason is the methodological lacking of Pooled OLS and SysGMM. Specifically, there might still be concerns regarding the feedback effect and endogeneity from income inequality to quality of governance. As mentioned earlier, the SysGMM estimation technique is ideal for addressing endogeneity problems. Nevertheless, SysGMM estimation relies on internal instruments’ adjustments, such as lagging and variable transformations (Bazzi & Clemens, 2013). Therefore, one must consider some exogenous variables affecting income inequality as internal instruments. In this effect, we use FDI and GPD per capita as the exogenous and instrumental variables, with the government effectiveness and regulatory quality as a proxy for governance quality being identified as significant in all estimation models in Table 3.

Using these instrumental variables as part of exogenous variables allows us to control for simultaneity bias and feedback effect (effect of income inequality on selected quality of governance and controls of FDI and GDP per capita) issues directly, in addition to Pooled OLS and SysGMM. The strategy here entails regressing government effectiveness and regulatory quality (a proxy for quality of governance), FDI, and GDP per capita on income inequality using a Panel FE-IV technique. We further utilize Pooled OLS, SysGMM, and Panel FE-IV methods to estimate the model presented in Eq. (1), where government effectiveness, regulatory quality, and FDI and GDP per capita are used as internal instruments. Angrist-Pischke F and Hansen J tests are carried out to further evaluate the exogeneity and relevance of government effectiveness and regulatory quality in influencing income inequality together with FDI and GDP per capita.

Table 4 presents the panel FE-IV estimates of the Gini Index model for SSA. In alignment with the findings observed in Table 3, panel FE-IV findings show a significant positive effect of government effectiveness on income inequality (see columns 1, 2, and 3). However, the FE-IV findings show no significant relationship between regulatory quality and income inequality. Regarding the role of FDI and GDP per capita on income inequality, the Panel FE-IV findings indicate a significant negative effect of GDP per capita and insignificant influence of FDI on income inequality (see column 3), aligning with the results in Table 3. Furthermore, Hansen J test shows the absence of exogeneity and relevance of government effectiveness and regulatory quality together with FDI and GDP per capita (see columns 2 and 3).

Table 4

Robustness Checks using Panel FE-IV
Variable	Pooled OLS (1)	SysGMM (2)	Panel FE-IV (3)
Gini Index(-1)		0.967*** (0.0296)
Const.	0.648*** (0.011)	0.023 (0.0191)	0.649*** (0.0049)
Government Effectiveness	0.033*** (0.0051)	0.003*** (0.0009)	0.033*** (0.0050)
Regulatory Quality	-0.012 (0.0226)	0.002*** (0.0008)	-0.009 (0.0216)
GDP per capita	-0.002*** (0.0006)	-2.162e-05 (2.163e-05)	-0.002*** (0.0005)
Foreign Direct Investment	0.011 (0.0234)	0.0002 (0.0013)	0.009 (0.0223)
Year FE	YES	YES	YES
Controls	YES	YES	YES
Observations	484	462	484
Number of Countries	22	22	22
AR(1) p-value		0.2986
AR(2) p-value		0.2537
Hansen’s J-stat p-value		0.0713
Note: Controls comprise FDI and GDP per capita. The time frame spans between 2000 and 2021. Column 2 included the lagged Gini Index; all columns had time dummies. The standard errors are in parentheses.

**p < 0.05, ***p < 0.01, *p < 0.1.

Source: Authors Construction (2023)

In sum, the statistical evidence provided by development organizations such as the World Bank concerning the quality of governance in developing regions such as SSA provides the dire situation of corruption level, quality of institutions, and government effectiveness in formulating and implementing sound policies. Also, the reviewed literature of the previous studies, in general, confirms the existing link between the quality of governance and income inequality. That’s, worsened the quality of institutions and governance, resulting from government ineffectiveness in initiating and adopting development policies geared toward achieving better accessibility of social services by all people is the avenue of continued disparity and fragmented economic prosperity.

The findings from this study, in many ways, confirm these assertions. First, the results paint a considerable disparity among SSA countries by providing evidence of a high Gini Index. Second, the findings depict an increasingly deteriorating quality of governance as measured by an index that falls below the zero mark. In terms of the government’s role, the results show that SSA governments have been ineffective in incepting, implementing, and monitoring social development policies in recent decades. Regarding the connection between the quality of governance and income inequality, the findings indicate a significant positive link, implying that worsened quality of governance in terms of its ineffectiveness in developing and implementing sound policies exacerbates the disparity and inequality among citizens.

The presence of regional disparities in terms of development level across Sub-Saharan African regions has been a long-term matter of worry that has not only attracted considerable focus from both academicians and state political class. Although previous studies link different income inequality levels that are considerably wide in most SSA countries to factors such as economic growth, inequality in terms of access to economic opportunities such as employment, and dwindled FDI, little is known regarding the role played by the quality of governance in influencing the income distribution or economic prosperity in the region.

This paper focused on the link between income inequality and quality of governance measured by government effectiveness, control of corruption, regulatory quality, and political stability to address this puzzling question using balanced panel data over the 2000 to 2021 period. Different estimation techniques were used, such as Pooled OLS, SysGMM, and Panel FE-IV regressions. The paper’s contribution confirms the significant positive link between income inequality and quality of governance in terms of its ability to formulate, implement and monitor sound public policies. In particular, worsened government effectiveness in implementing policies worsens or enhances continued income inequality in the economy. This is observable even after conducting robustness checks using the panel FE-IV method.

In the Sub-Saharan African context, the findings of the study point toward a policy recommendation that improving the quality of governance in terms of its effectiveness in formulating and implementing sound public policies helps enhance socio-economic structures and the quality of public goods and services. This holds the probable potential to distribute income better and enable homogenous distribution of wealth among the people. Nevertheless, what is of essence from this study’s perspective, is the non-significant role of FDI in explaining the income inequality in SSA. This result, in our view, provides an avenue for future research and suggests that adopting other governance and institutional quality measures and increasing informal economic systems may present the causes behind widening income inequality in SSA amidst increasing FDI inflows.

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No competing interests reported.

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Is the Quality of Governance a Precursor for Regional Disparity or Prosperity?

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Abstract

1. Introduction

2. Background Literature

3. Data and Methodology

4. Empirical Analysis and Discussions

4.1 Robustness Checks: Panel FE-IV Estimation

5. Conclusion and Policy Recommendations

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