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This section discusses the cross-country empirical literature analyzing the relationship between growth, poverty and income distribution.
In his seminal paper, Kuznets (1955) found evidence of an inverted-U relationship between the level of development and income inequality. As economies develop, inequality increases initially because growth tends to benefit a small segment of the population. Overtime, inequality declines subsequently as a larger part of the population finds employment in the high-income sector. However, existing empirical evidence of the Kuznets curve is at best mixed. Deininger and Squire (1998) found no evidence of an inverted-U relationship between per capita income and inequality. The authors show that high growth was associated with declining inequality as often as it was related to increasing inequality, or no changes at all. Ravallion and Chen (1997) highlighted that changes in inequality and polarization were uncorrelated using household surveys for 67 developing and transnational economies over the period 1981-1994. The authors showed that income distribution improved as often as it worsened in growing economies, and negative growth was often more unfavorable to distribution than positive growth. Goudie and Ladd (1999) also found little evidence that growth systematically changes income distribution.
Empirical evidence on the reverse link, which is the impact of inequality on growth, is similarly mixed. For instance, Forbes (2000) showed that an increase in income inequality has a significant positive effect on economic growth in short and medium term. Alesina and Rodrik (1994) illustrated in a political economy context that when inequality is high, the poor have less voice and accountability. In such a context, the median voter will push for distortionary taxes, which will have discouraging effects on savings and hamper growth. Berg and Ostry (2011) found that lower income inequality is associated with sustained growth spells.
Few other studies have analyzed the impact of inequality on poverty. Deininger and Squire (1998) examined how initial inequality and concomitant changes in inequality impact poverty. They found that the poorest 20 percent suffer the most from growth decreasing effects of inequality. Initial inequality also hurts the poor via credit rationing and powerlessness to invest. Ravallion (2001) also shows that the poor might gain more from redistribution but suffer more than the rich from economic shrinkage.
Governance and Pro-poor Growth
A large number of studies have investigated the role of good governance for economic development and poverty reduction. Kaufmann and Aart (2002) identified a strong positive correlation between per capita income and the quality of governance across countries. The authors also highlighted a strong positive causal effect running from better governance to higher per capita income. However, they found a weak, even negative, causal effect running from per capita income to governance, not supporting a possible “virtuous circle”, in which higher income leads to further improvement in governance.
Dollar and Kraay (2002) found that a greater rule of law is associated with a larger share of growth dividend accruing to the poorest 20 percent of the population. Kraay (2004) found similar results. Resnick and Regina (2006) developed a conceptual framework specifying the relationship between different aspects of governance and pro-poor growth. Using this framework, the paper reviewed a range of quantitative cross-country studies analyzing pro-poor growth and including indicators of governance as independent variables. The review indicated that governance indicators, such as political stability and rule of law are associated with higher growth but provided mixed results regarding poverty reduction. However, governance indicators related to transparency, such as civil liberties and political freedom, tend to conduce to poverty reduction but the evidence is rather mixed when it comes to the relationship between these variables and growth. Providing a different perspective, Lopez (2004) assessed whether policies that are pro-growth are also pro-poor. He found that policies tend to be poverty reducing in the long run rather than the short run. The author also argues that political economy constraints could prevent these policies from staying in place long enough to be able to reduce poverty. Kraay (2004) found that better rule of law and enhanced accountability are both positively correlated with higher growth. White and Anderson (2001) argued that civil liberties and political freedom are pro-poor, with political freedom having a much larger impact.
This section describes the main empirical framework underlying our analysis. The analysis covers 112 developed and developing countries.3 Following various empirical studies on economic growth, the chapter relies on 10 non-overlapping 4-year periods to control for business cycle fluctuations during the sample period (1975-2012).
The following equation forms the basis of our empirical strategy: capturing poverty and where is a vector of our three distinct dependent variables = + + + + + + (2.1).
inclusiveness for each country i during period t: (i) the income of the poorest 20 percent in the Following Ravallion and Chen (1997), ); and (iii) the income distribution ( ); (ii) the poverty headcount ratio at $2 a day PPP ( poorest 20 percent ( . is the logarithm of GDP per capita. income share of the ) the chapter also controls for the logarithm of the Gini.
Defining and Measuring Governance
The concept of Governance is widely discussed among scholars and policymakers. It means different things to different people and there is yet no consensus around its definition. Consequently, there are varying definitions of Governance. Theoretically, governance can be defined as “the rule of the rulers”, typically within a given set of rules. In the context of economic growth and poverty reduction, governance refers to essential parts of the wide-ranging cluster of institutions. The United Nations Development Program (UNDP, 1997) defines governance as “the exercise of economic, political, and administrative authority to manage a country’s affairs at all levels. It comprises mechanisms, processes, and institutions through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations, and mediate their differences.”
According to the World Bank (1993), governance is the process through which power is exercised in the management of a country’s political, social and economic institutions for development. Kaufmann, Kraay and Zoido-Lobaton (1999) explain that “the fundamental aspects of governance” are graft, rule of law, and government effectiveness. Other dimensions are: voice and accountability, political instability and violence, and regulatory burden”. Within this notion of governance, the evident interrogation is: what is good governance? This chapter associates the quality of governance with democracy and transparency, with the rule of law and good civil rights, and with efficient public services. Also, the quality of governance is determined by the impact of this exercise of power on the quality of life enjoyed by the citizens.
Main explanatory variables
This section discusses the theoretical and expected impact of the main explanatory variables included in equation (2.1):
•Income per capita measured by the logarithm of per capita GDP and its squared term to capture a potential Kuznets curve hypothesis. The Kuznets curve hypothesis predicts that inequality will increase with rising incomes in initial stage of development and decrease at higher levels of development. Yet, the existing evidence for the Kuznets curve hypothesis is mixed (Kanbur, 2000; Barro, 2008; Woo et al., 2017).
• Human capital captured by the ratio of the gross enrolment in secondary schooling. Studies found that improvements in human endowments, through increases in education are strongly associated with poverty reduction and economic growth (Barro and Sala-i-Martin, 2004; Mankiw et al., 1992; among others). Human capital can reduce poverty in three main ways (Berg, 2008): (i) higher educational attainments lead to higher earnings, (ii) better quality and higher levels of education are associated with economic growth which subsequently increases economic opportunities, (iii) higher levels of education are correlated with higher social benefits, improving the healthcare of the poor. In addition, in empirical studies low educational attainments are often identified as a source of income inequality. Education expansion can help reduce income inequality (Corak, 2013; De Gregorio and Lee, 2002). However, the link between human capital accumulation and income inequality can be ambiguous (Knight and Sabot, 1983).
• Trade openness measured by the sum of exports and imports in percent of GDP. The theoretical relationship between trade openness and poverty is ambiguous (see Le Goff and Jan Singh, 2014). This ambiguity is also present in the empirical literature. While some studies found that trade openness do not impact poverty (Beck et al., 2007, Kpodar and Singh, 2011), others suggested a positive relationship between trade openness and poverty (Guillaumont-Jeanneney and Kpodar, 2011, Singh and Huang, 2011). In addition, Agenor (2004) found an inverted U-shaped link between globalization and poverty that globalization. Globalization leads to decreases in poverty above a certain level of globalization. Regarding the relationship between trade openness and inequality, the literature has been inconclusive, overall (Krugman 2008; Meschi and Vivarelli 2007) even though any studies show that trade openness is associated with lower income inequality (IMF, 2007, Woo and others, 2017).
• Public spending captured by respectively public spending on education and health in percent of GDP. The empirical literature suggests that higher spending on education and health is associated with reduced income inequality and poverty.
• Basic needs measured by the percentage of population with access to improved sanitation. The poorest people tend to be the ones with no or limited access to basic services. Better access to improved sanitation is expected to reduce poverty.
• Inflation measured by the change of consumer price index. Inflation tends to worsen poverty (Powers, 1995; Agenor, 1998 among others). It also tends to disproportionally hurt the poor and increase inequality (Albanesi, 2007; Fischer and Modigliani, 1978).
• Financial development and openness captured by M2 and the Chinn Ito index of capital account openness. The relationship between financial sector development and economic growth has been well established in the academic empirical literature (King and Levine, 1993; Levine, Loayza and Beck, 2000; Levine, 2005). Finance can positively impact growth through capital accumulation and technological progress. Financial systems produce information ex ante about possible investments, promote efficient allocation of capital, mobilize and pool savings. Empirical studies also find that financial development is associated with reductions in the growth of Gini and poverty (Beck, Demirgüç-Kunt and Levine, 2007; Honohan, 2004).
Pro-poor and Inclusive Growth: Empirical Evidence
Before analyzing regressions, a simple plotting illustrates the tight link between poverty reduction and per capita income growth. In both transformed between- and within-variables, income growth is associated with higher income among the poor (Figure 2.1).
As a starting point, the chapter examines the impact of economic growth on the income of the poorest 20 percent and the poverty headcount at $2 a day to examine the extent to which growth is pro-poor. The coefficient of interest is , which gives the impact of economic growth on logarithm terms); measures the effect of a change in the poverty reduction (the equation is in Gini index on poverty reduction.
Because the chapter defines growth as pro-poor if it reduces poverty (Ravallion and Chen 1997), the results suggest that growth is generally pro-poor using the two indicators. A 1 percent increase in real GDP per capita leads to about a 1.4 percent increase in the income of the poor (Table 2.1, column 5). A similar 1.0 percent increase in real GDP per capita leads to a decrease of about 2.3 percent in the poverty headcount (Table 2.2, column 3). The results also show that inequality increases poverty.
Table of contents :
General Introduction and Overview
1.2. Data issues and Stylized facts
1.3. Outline and main results
The Quest for Pro-poor and Inclusive Growth: The Role of Governance
2.2. Literature Review
2.2.1. Growth, Poverty, and Income Distribution
2.2.2. Governance and Pro-poor Growth
2.3. Econometric Methodology
2.4.1. Measuring Poverty and Inequality
2.4.2. Defining and Measuring Governance
2.4.3. Main explanatory variables
2.5. Pro-poor and Inclusive Growth: Empirical Evidence
2.5.1. Has growth been pro-poor and inclusive?
2.5.2. Pro-poor and inclusive growth: the role of governance
2.5.3. Other determinants of pro-poor and inclusive growth
2.6. Nonlinear and threshold estimations
2.6.1. Exogenous nonlinear estimation
2.6.2. Endogenous nonlinear estimation: Panel Smooth Transition Regression
2.7. Conclusion and discussion
Reallocating Public Spending to Reduce Income Inequality: Can it work?
3.2.1. Composition of public spending: A new dataset
3.2.2. Some Stylized Facts on Public Spending and Income Distribution
3.3. Econometric Analysis: Composition of Public Spending and Income Inequality
3.3.1. Estimated Model
3.3.2. Baseline Results
3.3.3. The Role of Conflict and Institutions
3.4. Further Robustness Checks
3.4.1. Long-Run Impact of Public Spending
3.4.2. Alternative indicators of inequality
3.4.3. Accounting for local government spending, the efficiency of public spending, and the use of debt to finance public outlays
Djeneba Doumbia | Three Essays on Inclusive Growth | 2018
Informal Sector Heterogeneity and Income Inequality: Evidence from the Democratic Republic of Congo
4.2. Informal sector heterogeneity and inequality: Literature review
4.3. Data and descriptive statistics
4.3.1. 1-2-3 survey
4.3.2. Characteristics of the informal sector in DRC
4.4. Identification strategy: Informal firms
4.4.1. Defining a top performer
4.4.2. Sample selection bias
4.4.3. Identification of the constrained gazelles and survivalists
4.5. Heterogeneity in the informal sector
4.5.1. Individual entrepreneur characteristics
4.5.2. Firm typology and the choice of sector
4.5.3. Structural and behavioral factors
4.6. Urban poverty and income inequality in the informal sector
4.7. Drivers of the performance of informal firms
4.7.1. Explaining differences in income using Blinder-Oaxaca decomposition
4.7.2. Drivers of informal firms’ performance
4.7.3. Explaining differences in performance using Blinder-Oaxaca decomposition
4.8. Conclusion and Policy Recommendations