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Towards Inclusive Human Development in Sub-Saharan Africa

Introduction

The notion of inclusive human development cannot be overemphasized in developing and emerging markets in general given the prevailing inequalities that continue to persist in lower to middle income countries globally. Specifically, there are four main factors in scholarly and policymaking circles that motivate this paper on the poverty tragedy in sub-Saharan Africa (SSA), notably: (1) the growing exclusive development in the sub-region; (2) evolving literature on the relevance of the middle class in sustainable development outcomes; (3) paradigms shifts in the conception of governance in light of contemporary dominant models of economic development; and (4) gaps in the literature.

To be sure, these factors, which articulate the fact that “Output may be growing, and yet the mass of the people maybe becoming poorer” (Lewis,1955), are expanded in chronological order. First, in the post-2015 development era, one of the most challenging policy syndromes to Africa’s development is exclusive development. Accordingly, the reduction of inequality is central to most sustainable development goals (SDGs). This concern about poverty is even more relevant to SSA because approximately half of the countries in the subregion did not achieve the Millennium Development Goal (MDG) extreme poverty target. It is important to emphasize that the number of people living in extreme poverty consistently increased across the sub-region despite more than two decades of economic growth resurgence. The poverty tragedy is therefore traceable to exclusive development because the response of poverty to economic growth is a decreasing function of inequality.

The importance of promoting shared prosperity in the post-2015development agenda in SSA is supported by the conclusions of Bicaba et al.(2017) who articulate that if poverty is to be reduced to a threshold of below 3% by the year 2030, governments of countries in the sub-region will have to pay particular attention to inclusive development.

Secondly, the relevance of middle income status and the middle class in economic development has been articulated in a number of scholarly fronts, notably: Historical views establishing that the middle class is crucial for the economic development of technically advanced countries in Europe and North America (AdelmanandMorris,1967;Landes,1998). Contemporary scholarly perspectives have documented the importance of the middle class in, inter alia: alleviating poverty (Easterly, 2001); ameliorating social evolutions (Sridharan, 2004); consolidating institutions (Birdsall, 2007a), entrepreneurship and innovation activities (Banerjee and Dufflo, 2008); institutional reforms (Loayza et al., 2011); promoting democracy (Kodila-Tedika et al., 2016); and boosting inclusive development(Birdsall,2010).

Thirdly, consistent with Asongu and Le Roux (2019), the middle class is crucial in the understanding of the two dominant contemporary models of development, namely, the Washington Consensus and the Beijing Model. The latter is defined as “state capitalism, deemphasized democracy and priority in economic rights” whereas the former is defined as “private capitalism, liberal democracy and priority in political rights” (Asongu, 2016a). The literature is in accordance with the position that a sustained middle class is crucial for political governance to be sustainably demanded by the population. Hence, for political governance (i.e. a priority of the Washington Consensus) to be sustainably achieved, economic governance (i.e. priority of the Beijing Model) should take precedence in policymaking. China has produced a burgeoning middle class within a historically short period of time (Asongu and Ssozi, 2016). In summary, the narrative supports the view that political governance should be a longer-term goal for African countries compared to economic governance which should be a short-term goal to build the middle class necessary for a sustainable demand for political governance. This study extends the underlying strand of literature within the framework of inclusive human development by attempting to answer the following research question:

RQ1: How do low-income and middle-income countries complement political and economic governance in influencing inclusive human development in SSA?

In order not to bore readers with information that may not necessarily be easy to digest, I will make a concerted effort to organize the rest of the paper as follows. Section 2 briefly discusses the data and methodology while the empirical results are covered in Section 3 capturing only the key takeaways. Section 4 concludes with implications.

  1. Data and methodology

2.1Data

The paper examined a panel of 49 countries in SSA for the period 2000-2012 with data from 5 sources, notably, the: (1) World Governance Indicators of the World Bank for governance indicators; (2) World Development Indicators of the World Bank for income levels and control variables; (3) Financial Development and Structure Database of the World Bank for some control variables; (4) United Nations Development Programme for the inclusive development variable; and (5) principal component analysis(PCA)for composite governance indicators.

The temporal and geographical scopes of the study are constrained by data availability. Considering recent African development literature and the motivation of this study, the inequality-adjusted human development index (IHDI) is used as the outcome variable. The six governance indicators from Kaufmann et. al. (2010) are bundled with PCA for composite indicators, notably:

  • political governance (proxied by political stability and “voice and accountability”), which is the election and replacement of political leaders.
  • economic governance (measured with government effectiveness and regulation quality) understood as the formulation and implementation of policies that deliver public commodities; and
  • Institutional governance (proxied with corruption-control and the rule of law), which is defined as the formulation and implementation of policies that deliver public commodities.

To ensure there is no ambiguity and in line with other scholarly works, the income level classification is consistent with the World Bank income groups. These are: high income, $12,276 or more; upper-middle income, $3,976- $12,275; lower-middle income, $1,006-$3,975; and low income, $1,005 or less. Four control variables are adopted to account for variable omission bias, namely, gross domestic product (GDP)per capita growth, private domestic credit, remittances, and foreign direct investment (FDI) inflows.

2.2. Methodology

2.2.1 Principal component analysis: PCA is a technique that is used in empirical literature to reduce highly correlated variables into a set of smaller uncorrelated PCs. The procedure for adopting the main PCs is the Kaiser (1974) criterion, which suggests that PCs with an eigen value greater than one and reflecting about 70% of the total variation should be selected.

In summary, political governance has an eigen value of 1.671 and reflects a total variability of 83.50%. Hence, 85.50% of information contained in “voice and accountability” and political stability is captured by the composite political governance indicator. In the same vein, economic governance reflects 93.90% of common information in government effectiveness and regulation quality and has an eigenvalue of 1.878. The institutional governance composite indicator is informational and not used in the empirical analysis in light of the focus of the study on economic governance and political governance. The PC-derived composite indicators can provide robust estimates.

Table 1: Principal Component Analysis (PCA) for Governance (Gov)

Principal Components Component Matrix (Loadings) Proportion Cumulative

Proportion

Eigen Value
  VA PS RQ GE RL CC      
                   
First PC (Polgov) 0.707 0.707 0.835 0.835 1.671
Second PC -0.707 0.707 0.164 1.000 0.328
                   
First PC (Ecogov) 0.707 0.707 0.939 0.939 1.878
Second PC -0.707 0.707 0.060 1.000 0.121
                   
First PC (Instgov) 0.707 0.707 0.930 0.930 1.861
Second PC -0.707 0.707 0.069 1.000 0.138
                   

P.C: Principal Component. VA: Voice & Accountability. RL: Rule of Law. R.Q: Regulation Quality. GE: Government Effectiveness. PS: Political Stability. CC: Control of Corruption. Polgov (Political Governance): First PC of VA & PS. Ecogov (Economic Governance): First PC of RQ & GE. Instgov (Institutional Governance): First PC of RL & CC.

 

 

  1. Empirical Results

Table 2 presents the empirical results. While Panel A shows how low-income levels modulate governance to influence inclusive development, Panel B discloses findings on how middle-income levels modulate governance to affect the same outcome variable. The left hand-side and right hand-side of both panels focus on respectively, political governance and economic governance. In order to assess the overall impact of the relevance of income levels in moderating governance for inclusive development, net effects are computed from the unconditional effect of governance and the conditional impact resulting from the interaction between income levels and the corresponding governance dynamic.

 

For instance, in the first column of Table 2, the net effect of low-income levels in modulating political governance for inclusive human development is 0.021 ([-0.031× 0.632] + [0.041]). In the computation, the mean value of low-income countries is 0.632, the unconditional effect of political governance is 0.041 while the conditional impact from the interaction between low income and political governance is -0.031. In the same vein, in the last column of Panel A in Table 2, the net impact of low income in modulating economic governance for inclusive development is 0.022 ([-0.063× 0.632] + [0.062]).  In the computation, the mean value of low-income countries is 0.632, the unconditional effect of economic governance is 0.062 while the conditional impact from the interaction between low income and economic governance is -0.063.

 

Table 2: Income and Governance

                         
  Dependent variable: Inclusive human development
   
  Panel A: Low Income and governance
                         
  Low Income and Political Governance Low Income and Economic Governance
  OLS Q.10 Q.25 Q.50 Q.75 Q.90 OLS Q.10 Q.25 Q.50 Q.75 Q.90
Constant 0.507*** 0.404*** 0.467*** 0.493*** 0.509*** 0.602*** 0.491*** 0.406*** 0.459*** 0.480*** 0.484*** 0.541***
  (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
LI -0.115*** -0.119*** -0.139*** -0.099*** -0.077*** -0.113*** -0.102*** -0.122*** -0.126*** -0.090*** -0.066*** -0.062**
  (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.031)
PG 0.041*** -0.003 0.021*** 0.039*** 0.040*** 0.060***
  (0.000) (0.525) (0.000) (0.000) (0.000) (0.000)            
EG 0.042*** -0.004 0.035*** 0.042*** 0.040*** 0.062***
              (0.000) (0.536) (0.000) (0.000) (0.000) (0.000)
LI× PG -0.031*** 0.011 -0.005 -0.022** -0.037*** -0.070***
  (0.000) (0.159) (0.601) (0.025) (0.000) (0.000)            
LI ×EG -0.017** 0.018* -0.004 -0.0002 -0.017** -0.063**
              (0.027) (0.056) (0.734) (0.980) (0.037) (0.015)
GDPpcg 0.0008 0.0007 0.002 -0.0006 0.0009 0.002 0.0006 0.0006 0.001 -0.0008 0.00006 0.001
  (0.478) (0.548) (0.156) (0.651) (0.404) (0.264) (0.552) (0.582) (0.474) (0.476) (0.954) (0.579)
Credit 0.0009*** 0.001*** 0.0009** 0.001*** 0.001*** -0.00004 0.0007** 0.001*** 0.0006 0.001*** 0.001*** 0.0002
  (0.001) (0.000) (0.012) (0.000) (0.000) (0.913) (0.015) (0.000) (0.157) (0.000) (0.000) (0.816)
Remittances -0.002*** 0.0003 -0.001** -0.001*** -0.002*** -0.003*** -0.001*** 0.0003 -0.001 -0.001*** -0.001*** -0.002*
  (0.000) (0.520) (0.044) (0.009) (0.000) (0.000) (0.000) (0.575) (0.120) (0.003) (0.002) (0.066)
FDI 0.0004 0.0008 0.0003 -0.001 0.001* 0.002** 0.001** 0.001 0.001 0.0007 0.0003 0.002
  (0.387) (0.159) (0.655) (0.130) (0.055) (0.029) (0.024) (0.103) (0.146) (0.197) (0.554) (0.206)
                         
Net   Effects 0.021 na na 0.025 0.016 0.015 0.031 na na na 0.029 0.022
                         
Fisher 59.37***           61.34***          
Pseudo R² 0.599 0.335 0.316 0.303 0.433 0.537 0.596 0.345 0.343 0.352 0.439 0.488
Observations 310 310 310 310 310 310 310 310 310 310 310 310
                         
                         
  Panel B: Middle  Income and Governance
                         
  Middle  Income and Political Governance Middle  Income and Economic Governance
  OLS Q.10 Q.25 Q.50 Q.75 Q.90 OLS Q.10 Q.25 Q.50 Q.75 Q.90
Constant 0.392*** 0.284*** 0.327*** 0.393*** 0.432*** 0.489*** 0.389*** 0.284*** 0.332*** 0.389*** 0.417*** 0.478***
  (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
MI 0.115*** 0.119*** 0.139*** 0.099*** 0.077*** 0.113*** 0.102*** 0.122*** 0.126*** 0.090*** 0.066*** 0.062**
  (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.032)
PG 0.009* 0.007 0.015** 0.016** 0.002 -0.010
  (0.091) (0.188) (0.043) (0.028) (0.610) (0.251)            
EG 0.024*** 0.014* 0.030** 0.042*** 0.023*** -0.0007
              (0.000) (0.051) (0.020) (0.000) (0.000) (0.969)
MI× PG 0.031*** -0.011 0.005 0.022** 0.037*** 0.070***
  (0.000) (0.159) (0.601) (0.025) (0.000) (0.000)            
MI ×EG 0.017** 0.018* 0.004 0.0002 0.017** 0.063**
              (0.027) (0.056) (0.734) (0.980) (0.037) (0.015)
GDPpcg 0.0008 0.0007 0.002 -0.0006 0.0009 0.002 0.0006 0.0006 0.001 -0.0008 0.00006 0.001
  (0.478) (0.548) (0.156) (0.651) (0.404) (0.264) (0.552) (0.582) (0.474) (0.476) (0.954) (0.579)
Credit 0.0009*** 0.001*** 0.0009** 0.001*** 0.001*** -0.00004 0.0007** 0.001*** 0.0006 0.001*** 0.001*** 0.0002
  (0.001) (0.000) (0.012) (0.000) (0.000) (0.913) (0.015) (0.000) (0.157) (0.000) (0.000) (0.816)
Remittances -0.002*** 0.0003 -0.001** -0.001*** -0.002*** -0.003*** -0.001*** 0.0003*** -0.001 -0.001*** -0.001*** -0.002*
  (0.000) (0.520) (0.044) (0.009) (0.000) (0.000) (0.000) (0.575) (0.120) (0.003) (0.002) (0.066)
FDI 0.0004 0.0008 0.0003 -0.001 0.001* 0.002** 0.001** 0.001 0.001 0.0007 0.0003 0.002
  (0.387) (0.159) (0.655) (0.130) (0.055) (0.029) (0.024) (0.103) (0.146) (0.197) (0.554) (0.206)
                         
Net   Effects 0.020 na na 0.024 na na 0.030 0.025 na na 0.029 na
                         
Fisher 59.37***           61.34***          
Pseudo R² 0.599 0.335 0.316 0.303 0.433 0.537 0.596 0.345 0.343 0.352 0.439 0.488
Observations 310 310 310 310 310 310 310 310 310 310 310 310
                         

*,**,***: significance levels of 10%, 5% and 1% respectively. Bilaid: Bilateral aid. LI: Low Income. MI: Middle Income. PG: Political Governance. EG: Economic Governance. GDPpcg: Gross Domestic Product per capita growth. FDI: Foreign Direct Investment. OLS: Ordinary Least Squares. R² for OLS and Pseudo R² for quantile regression. Lower quantiles (e.g., Q 0.1) signify nations where inclusive human development is least. na: not applicable because at least one estimated coefficient needed for the computation of net effects is not significant. The mean value of Low Income is 0.632 while the mean value of Middle Income 0.367.

 

It is important to note that the findings of OLS and QR are distinct in terms of significance and magnitude of significance because the OLS findings vary throughout the conditional distribution of inclusive human development. This heterogeneity confirms the relevance of assessing the investigated linkages throughout the conditional distributions of inclusive human development. The following findings can be established. First, low income modulates governance (economic and political) to positively affect inclusive human development exclusively in countries with above-median levels of inclusive human development. It follows that countries with averagely higher levels of inclusive human development are more likely to benefit from the relevance of income levels in influencing governance for inclusive development.

 

Second, in Panel B, the importance of middle income in modulating political governance to positively affect inclusive human is apparent exclusively in the median while the relevance of middle income in moderating economic governance to positively influence inclusive human development apparent in the 10th and 75th quantiles.

 

Third, from the OLS results, regardless of panels, income levels modulate economic governance to affect inclusive human development at a higher magnitude, compared to political governance. This finding is logical in the light of the definition of economic governance which is conceptually more associated with inclusive development compared to political governance. Accordingly, economic governance is the formulation and implementation of policies that deliver public commodities, which include education and health amenities captured by inequality-adjusted human development.

 

Fourth, the significant control variables have the expected signs. Accordingly, except for remittances, the other variables involved in the conditioning information set positively affect inclusive human development.

 

  1. Concluding remarks and future research directions

The literature is consistent on the view that close to half of the countries in sub-Saharan Africa (SSA) did not achieve the Millennium Development Goal (MDG) extreme poverty target. Moreover, the number of people living in extreme poverty have been increasing in the sub-region since the mid-1990s. This paper complements existing literature on dominant development paradigms (i.e. the Washington Consensus versus the Beijing Model) by assessing the role of income levels (low and middle) in modulating governance (political and economic) to influence inclusive human development. The empirical evidence is based on interactive quantile regressions and forty-nine countries in SSA for the period 2000-2002.

 

The following main findings are established. First, low income modulates governance (economic and political) to positively affect inclusive human development exclusively in countries with above-median levels of inclusive human development. It follows that countries with averagely higher levels of inclusive human development are more likely to benefit from the relevance of income levels in influencing governance for inclusive development.

 

Second, the relevance of middle income in modulating political governance to positively affect inclusive human development is apparent exclusively in the median, while the importance of middle income in moderating economic governance to positively influence inclusive human development is apparent in the 10th and 75th quantiles. Third, from the OLS results, regardless of panels, income levels modulate economic governance to affect inclusive human development at a higher magnitude, compared to political governance. Policy implications are discussed considering the post-2015 agenda of sustainable development goals and contemporary development paradigms.

 

The benefit of low-income levels in modulating governance (political and economic) to positively affect inclusive human development is a positive function of inclusive human development. It confirms the hypothesis that the response of poverty to development is a decreasing function of inequality in the perspective that countries with comparatively higher levels of inclusive development will benefit more from the ability of low income countries to leverage on governance to affect inclusive human development in the post-2015 development agenda. This conclusion is in line with Bicaba et al. (2017) on the importance of reducing inequality for shared economic development if SSA is to eradicate extreme poverty by 2030.

 

Furthermore, irrespective of income levels, income modulates economic governance to affect inclusive human development at a higher magnitude than political governance is evidence of the fact that focusing on economic governance will engender more inclusive development benefits compared to political governance. Hence, prioritizing economic governance will be more beneficial for inclusive development compared to the corresponding benefits from prioritizing political governance. It is important to note that the motivation of this write up, political governance is a priority for the Washington Consensus while economic governance is a priority for the Beijing Model. Future studies can use relevant estimation approaches to assess country-specific cases to provide more targeted policy implications.

 

Prof. Joseph Nnanna

Chief Economist, Development Bank of Nigeria

 

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