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Background of the Study
A major issue facing the global economies today is income inequality. According to Oxfam (2016), the wealth owned by the richest one percent in the world exceeds that which the rest of the world owns. The extraordinarily high level of inequality is highlighted by the global income distribution. The UNDP (2005), reports that 80% of the world’s population had an income less than the average world income. The top 20% of the world’s population have an average income that is approximately 50 times the average income of the bottom 20%. The richest 20% of the world population hold about three-quarters of total world income at the top, while poorest 20% hold just 1.5% of world income. The top 20% is dominated by rich countries. Sub-Saharan Africa accounts for a rising share of the poorest 20% and the share has more than doubled from 15% to 36% since 1980 (UNDP,2005).

Several studies have shown that high level of income inequality have persisted in several African countries over the past decades (Canagaragah, Ngwafon, and Thomas,1997, Milanovic ,2003, Bigsten, 2014). Africa is not only the world’s second most unequal continent next to Latin America, it is also the poorest region in the world (UNDESA ,2009). Africa has made the smallest progress in terms of improvement in the standard of living when compared with other developing regions in the world. Of the seventeen Sustainable Development Goals (SDG), implemented by United nations, the tenth focuses on reducing inequality. Addressing the problem inequality is vital because inequality will affect progress towards the SDGs and poverty reduction negatively, and also has other adverse effects.

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The effect of macroeconomic factors on income distribution has been a concern to economist for a long time (Kuznets, 1955; Bourguignon, 2003). Major attention has been on the growth-inequality nexus. The influential inverted U shaped relationship between economic development and income inequality developed by Simon Kuznet (1955) predicts that, inequality tends to rise in the early stages of economic development, as an economy transits from a rural economy to an urban economy, reaches maximum, and declines in later stages.
Though, most studies have supported the kuznet curve hypothesis (Williamson ;1965, Barnejee and Duflo;2003, Dahan and Tsiddon ;1998), it has also been criticized. Lempert (1987), showed that kuznet curve comes as a result of historical differences between countries. He argued that kuznet, in his data set, mainly considered the middle income countries from Latin America, a region historically characterized by high income inequality. When controlling for this variables, the U-shaped of the curve tend to disappear.

Fields (2001), criticized the Kuznet hypothesis stating that inequality is not from growth itself, but from the nature of economic growth. According to Fields, the factors that characterizes the economic environment determines income inequality. These factors include: the structure of employment, the structure of output, the distribution of land, the level of economic dualism, the overall level of human capital, and the operations of the capital market. Studies like Li et al. (1998), Bourguignon (2003) among others do not support the existence of Kuznets hypothesis mostly on country specific factors and some found no relationship between growth and inequality.

Most studies like Alesina and Perotti (1996), Ravallion (1995), Benabou (1996), Forbes (2000) have concentrated on the reverse causation between growth and income inequality, but the relationship is not clear. Different mediums that can lead from inequality to growth are highlighted. They include political instability, social tensions, a poor median voter, imperfect capital markets and distorting taxation.

Other income distribution theories stresses that there are certain factors that can affect income distribution. These include but not limited to inflation, international trade, interest rate, investment, and the labor market. During periods of inflation, wages and prices do not rise proportionately. Bagus (2014) argued that wealth tends to flow from the lower class to the upper class during periods of high inflation. This is because a major proportion of the wealth of the poor and the lower class are spent on consumables, and hence when prices for consumables rises, most of their wealth goes to producers who are the upper class. Cowell and Jenkins (1995), believed that inequality results from the determination of wages in modern market economies.
Most African countries have undertaken a series of Structural adjustment programmes (SAP) in the 1980’s and 1990’s, and various policy reforms with the aim of reducing income inequality and poverty. For instance, the 2010 National Development Plan (NDP) in Uganda, the National Economic Empowerment and Development Strategy launched in mid-2004 in Nigeria, the 1993 Reconstruction and Development Program(RDP) to the current National Development Plan (NDP): vision 2030 in South Africa. Following this reforms, it was expected that the policies will reduce income inequality and poverty in these countries, but empirical research have shown that income inequality still remains a challenge to developmental efforts. The persistence of high levels of inequality calls for an assessment of the contribution of the macroeconomic factors on income distribution. According to Blank et al (1993), the macroeconomic environment is relevant for the distribution of income.

Statement of the Problem
Income inequality has been acclaimed to be the most challenging issue confronting the developing world especially in the region of Africa. In Africa, there exist some set of people who are very rich, with relatively high living standards. Such people have access to the basic needs of life such as good food, clothing, convenient shelter and basic infrastructure. While there are some that are very poor and struggle to live with less than a dollar per day. Such people lack the basic needs of life, and are characterized by poor health, unemployment, low wage, and poverty. High inequalities in Africa seems to have persisted over time with no visible sign of declining (Bigsten 2014).

The most equal countries globally as identified by 2016 world bank estimate based on Gini index include Ukraine, Iceland, Slovenia, Czech Republic, Slovac Republic and Kazakhstan. The most unequal countries globally are South Africa, Namibia, Botswana, Suriname, Central African Republic, and Lesotho. This report shows that most of the unequal countries are Africa countries
Figure 1 demonstrates the extent of the challenge of income inequality in selected African countries.

Source: Author’s presentation from Standardized World Income Inequality Database SWIIDv6.2 (2016)
From the figure above, Sao Tome and Principle, Algeria, and Ethiopia are the least unequal African country with Gini index of 31.2, 32.3, and 32.5 respectively in 2016. South Africa, Namibia, and Botswana have the highest income inequality with a Gini coefficient of 68.7, 64.6, and 64.4 respectively in 2016. Egypt, Morocco, Cameroon, Uganda, Ghana, Nigeria, Madagascar, and Malawi had Gini index around 0.40 and 0.50 while other countries had Gini index between 0.30 to 0.40 except for Zambia, Central African Republic, and Rwanda, with Gini coefficient of 58.8, 54.8, and 53.3 respectively in 2016.

Not only is inequality high in Africa, it is also accompanied by a high rate of unemployment and under employment, low wage, poor working conditions and persistent inflation and poverty. For instance, from figure 2, the unemployment level on the average increased from 10.3 to 12.9 in Egypt, 14.1 to 15.4 in Tunisia, 4.7 to 5.2 in Zimbabwe, 26.5 to 35.8 in Botswana, 24.1 to 25.3 in South Africa between 2000-2006 and 2001-2016.


Source: Author’s presentation from World bank data (2016).


Source: Author’s presentation from World bank data (2016).

Figure 3 reveal shows the average GDP growth rate in selected Africa countries. Apart from Central African Republic that experienced a decline in average growth between 2013-2016, the economic growth level has been remarkable over the past decades. For instance, the average growth in Cameroon, Tanzania, Ethiopia, Senegal, Malawi rose from 4.2% to 5.3%, 6.7% to 7.1%, 7.3% to 9.6%, 4.3 % to 5.1%, 2.6 % to 4.0% respectively between 2001-2006 and 2013-2016.

In spite of the economic growth in Africa, the state of inequality has been worsening. Economic growth has not proportionately trickled down to the population in an equitable pattern. As a result, the rich becomes richer and the poor poorer which creates inequality among the people (Prabhakar,2003).
According to Melamed (2011), rising income disparity implies that some individuals and groups in the society are systematically denied of their right, therefore high income disparity is ethically unacceptable. Lo (2012), argue that the rising income disparities globally have increased the likelihood of global crises compared to the past years. High income inequality has resulted in suboptimal allocation of human resources, concentrating political and decision making power in the hands of a few (Rajan 2010).

According to Adams (1991), comparative analysis by World Bank, UNDP, IMF and UNICEF shows that inequality is harmful to development and sustainable growth, thereby having a long run effect on poverty. Thus, understanding how macroeconomic factors affect the distribution of income inequality in Africa is fundamental to devising policy measures that can allow the rising prosperity of recent decades to be shared more equally than has been evident so far.

1.3 Objectives of the Study
The broad objective of the study is to examine the impact of macroeconomic factors on income inequality levels in Africa. The specific objectives are:
To examine the effect of GDP on the income inequality in selected African countries.

To determine the influence of unemployment on income inequality in selected African countries.
To estimate the effect of inflation of income inequality in selected African countries.
To examine the direction of causality between the macroeconomic factors and income inequality in selected African countries.

Research Questions
What is the effect of GDP on the level of income inequality in Africa?
How does unemployment impact on the level of income inequality in Africa?
What is the effect of inflation on the level of income inequality in Africa?
What is the direction of causality between the macroeconomic factors and income inequality in Africa?
Research Hypotheses
In this study, the following hypotheses shall be tested:
H01:GDP has no significant effect on the income inequality level in Africa.

H02:Unemployment has no significant effect on the level of income inequality in Africa.

H03: The level of Inflation has no significant effect on the income inequality level in Africa.

H04:There is no significant causal relationship between the macroeconomic factors and income inequality.

Significance of the Study
A study of this capacity especially in Africa cannot be overemphasized. The study will provide valuable knowledge on the tenth Sustainable Development Goals that seeks to reduce inequalities.
The findings from the study will guide the government and policy makers on relevant policies to make and implement that seeks to address income inequality. It will also will contribute to the improved welfare of the public.

Furthermore, the outcome of this study will also be of benefit to those in the academia. It will serve as a point of reference to students, scholars, and researchers on the subject matter. More importantly, the study will add to existing literature on macroeconomic factors and their role on income inequality.

Scope of the Study
The purpose of this study is to investigate the effect of macroeconomic variables on the income distribution of Africa. There are about fifty-three (53) countries in Africa. For the purpose of this study, twenty-eight (28) African countries will be selected. Countries are selected to capture every region of Africa: Southern, Eastern, Western, Northern and Central region of Africa. At least two countries from each of the regions have been included in the sample despite data constraints. They include Algeria, Egypt, Mauritania, Morocco, Tunisia, Cameroon, Central African Republic, Burundi, Ethiopia, Rwanda, Uganda, Tanzania, Madagascar, Burkina Faso, Ghana, Niger, Nigeria, Senegal, Botswana, Cabo Verde, Cote d’ivore, Guinea, Sierra Leone, South Africa, Malawi, Namibia, Zambia, Zimbabwe. The period 2001 to 2016 will be covered. The selection of countries and the choice of this period is informed by data availability. The variables of interest include Income inequality obtained from the Standardized World Income Inequality Database (SWIID), while the macroeconomic variables- Gross Domestic Product, Inflation, and Unemployment are obtained from World Bank database.

2.1 Conceptual Framework
2.1.1 Income Distribution/ Inequality
Income distribution refers to the allocation of income among owners the factors of production. It can be seen as how the total/income of an economy is distributed among its population.

Income distribution can be viewed in two principal ways: the functional distribution of income and the personal or size distribution of income. The functional distribution of income deals with the distribution of income among the factors of production like land, labour and capital. It shows how much income each factor of production receives. Personal or size distribution of income is the distribution of income among persons or groups. It shows how much income individuals or households receive from all sources. The personal distribution in a country is greatly influenced by its functional distributional of income. If factor rewards are equitable, the personal distribution of income will also be equitable. On the other hand, if the rewards to the factors of production are unjust and based on the exploitation of the factors of production, the personal distribution of income will also be unjust and inequitable.

Income inequality is a state in an economy in which the share of the wealth earned by the rich and poor are highly unequal. Todaro and Smith (2009) defined income inequality as unequal distribution of income among households, whereby the share going to the richer individuals is far greater than that going to the poorer individuals. Income inequality is associated with differences in the measures of economic well-being amongst individuals in a group, among groups in a population, or among countries (Hunt and Lautzenheiser,2014). World Bank (2013) assert that inequalities are the disproportionate redistribution of properties to the segments of the population and the unsustainable progress from generation to generation. Economic inequality occurs when one person/group is given some resources or material choices and the same thing is withheld from another (Ray,1998).
From various definitions, we can bring to a conclusion that Income inequality is the unequal distribution or allocation of income among the members of a particular population. Countries that have more income inequality are disadvantaged in that the richer segments of that population hold a greater share of a country’s wealth in comparison to the poorer segments of that population (Todaro and Smith, 2009). Income inequality can be measured using Gini coefficient, Lorenz curve, Atkinson index, the Generalized Entropy index, among others. The Gini coefficient will be used in this study. The Gini coefficient is an income distribution measurement that takes a value between 0 and 1, where 0 represents perfect equality and 1 represents perfect inequality.

2.1.2 Macroeconomic Variables
Macroeconomic variables are factors that influence the aggregate behavior and performance of the whole economy. Macroeconomic factors are indicators used to measure health of an economy and they serve as a signal of the level of economic activity in economy. These indicators include gross domestic product (GDP), inflation, unemployment, international trade, among others. For the purpose of this study, three key macroeconomic variables will be assessed. These are Gross domestic product (GDP), inflation and unemployment.

Gross Domestic Product is the monetary value of all final goods and services produced in a country within a specified period, usually a year. It measures the annual productivity of the property of and labor of all citizens and foreign residents within the geographical borders of a country on annual basis. GDP is the value of products and services within the political boundaries of a country (Ibrahim and Dost,1997). Mwangi (2013) established that the growth rate of GDP reflects the state of the economic cycle. Economic growth is measured in relation to GDP. Economic growth is “the process whereby the real per capita gross domestic product increases over a long period of time (Jhingan, 2010). It reflects increase in the goods and services produced in an economy.

Inflation is the persistent rise in the general price in an economy. Jhingan (2002), view inflation is an appreciable increase in the general level of prices. According to Thomas (2006), inflation by whatever name it is given, refers to increase in the general price level whether, “demand pull” which occurs when aggregate demand is greater than the available supply or “cost-push inflation which occurs when the aggregate supply suddenly decreases, owing to an increase in the price/cost of the commodity or production where no suitable alternatives exist. Inflation rate therefore, measures changes in the average price level on the basis of a price index. These indices are used internationally to compensate economic agents for losses in purchasing power over time. The most commonly used measurement for inflation is the consumer price index.
According to Mankiw (2010), inflation acts as a disguised taxation by transferring income from those whose propensity to save is lower to those whose saving propensity is higher. Cardoso (1992) established that inflation shifts the wage profile. High inflation makes the income spent on essentials by low wage earners relatively higher than that used by high income earners.

In general terms, Unemployment can be defined as a situation in which those who are willing and are able to work at the prevailing wage do not find job. Unemployment is the difference between the number of labour at current wage rate and working conditions and the number of labour not hired at these levels (Briggs ,1973). It is a situation which occurs when an individual is who is actively searching for work is unable to find a job. According to Nicholas (2000), an individual is unemployed if he or she is eligible for work but does not have a job.
Unemployment can also be seen as a situation in which the number of persons in the population who are willing, and offer themselves for employment but could not be employed because of lack of vacancies for them (Aguene ,1991). Fajana (2000), and Standing (1983) described unemployment as the state of wordlessness experienced by individuals who are members of the labour force who perceived themselves and are perceived by others as capable of work. Haralambos (2004) stated that the unemployed represent those who were without work in a particular week, who were available to start work in the next two weeks and who were waiting to start a job or had looked for work in the previous month. The Bureau of Labour statistics view unemployment as a situation in which an individual has no job, having actively looked for job in the past four weeks and is readily available for work. International Labour Organization (ILO) states that only who belong to the age bracket 15 to 65 years should be included in the labour force of a country. Unemployment show unused factor of labour and deny individuals the right to earn income to support their basic needs.
In this study, Unemployment is the total labor share that is without work, but is available and looking for work. The most frequently used measurement of unemployment is the unemployment rate. Levernier, et al (1995) argued that, when unemployment rates rise, it affects more people in the lower tail of the personal income distribution by lowering average per capita income. Garcia, Prieto-Alaiz and Simon (2013), noted that rising unemployment indicates that there will a reduction in the share of income going to the lower and middle bands of the population, thereby increasing income inequality.
2.2 Theoretical Literature
The theoretical literature about income distribution have focused on the functional and size distribution of income.

2.2.1 Functional Theory of Income Distribution
The theories based on functional distribution of income relate to the distribution of the rewards (rent, wages, interest, and profit) for the services of the factors of production (land, labour, capital and organization). The Classical Theory of Income Distribution
The earliest form of this theory is found in the works of David Ricardo (1817), which he sorts to determine the laws which regulate the distribution between rent, profit and wages. The idea in Ricardian thought is that, the whole produce of the earth is divided among the factors of production in which rent is paid first, then the distribution among profits and wages are then made. Differential rent is made when the less productive acres of land are put in use, resulting in a rise in the price of goods. Consequentially, the owners of the more productive acres of land receive a higher rent.

Income distribution in the Ricardian system is made as follows: the surplus over production cost make up the rent, while the remaining is shared between profit and wages. Profit is assigned the residual between the marginal product of labor at a particular time and subsistence wage. Relying on, and reformulating Malthus theory of population, Ricardo states that workers will multiply so much such that the wages will grow faster than profits. As a consequence of this, the share of profit will decrease, while rent and wage will rise in the long run. The Ricardian theory focuses on the conflict between rent and profits. Marx Theory of Income Distribution
Karl Marx (1887) concentrated on the conflict between profits and wages. He regarded the main class conflict to be between the capitalists and workers. For Marx, only two main sources of income exist: profits and wages. Although rent, benefits and interests were also recognized as sources of income, he assumed that only the capitalists received these types of income (Ferran, 1997). Marx developed this theory by distinguishing between labour and labour power. Labour is the activity of producing goods and services. Labour power is the potential a worker has to work. This theory assumes that the value of each commodity is determined by the labour contained in it measured by labour units (hours, minutes and so on). The capitalists hire a worker and makes use of his or her labour power for a certain time. Marx follows the classical paradigm by assuming an unlimited supply of labour which allows the capitalists to hold wages at subsistence level.

The key idea for Marx is that the price of hiring a worker and the value he produces are different. the difference between them is the surplus value or profit. Given that the worker can create value at a certain time, the surplus value or profit would depend on the value of the labour power. The implication here is that, the wage share in a country is determined by the value of the labour power. The capitalist can increase productivity and earn more profit by increasing working time and innovating. Marx believed that the working class would not benefit much from the productivity increases accrued by the capitalist system. The economic position of the working class can only improve when the capitalist system collapses. Cline (1975), noted that the Marxian system collapses, as a result of falling wages and intolerable poverty of workers in the face of capitalist accumulation. The Marginalist / Neoclassical Theory
Unlike the Marxian theory, the neoclassical or marginalist school believe that all factors are in scarce supply, and assume that in equilibrium, all factors are paid the value of their marginal products. No form of exploitation of workers exist: each agent is paid the amount of income which corresponds to its contribution to total output. Marginal productivity principle is generalized here as the basis for the remuneration of the factors of production, eliminating the attribution of residual income to just a single factor.
This theory is based on the microeconomic foundations traceable to the work of Leon Walras which he stated that there exists an equilibrium price for all product and factor market. Equilibrium is achieved when all factor payments equalize their corresponding marginal productivity. Thus, income distribution is considered as part of the general pricing process. Market forces determine the prices of factors and quantities. Production functions and factor substitutability form the basis of the neoclassical theory (Cline 1975, Bigsten, 1983). The production function theory which makes marginal product depend on the prices of factors gives factor demand. Factor substitutability is also assumed so that if the supply of a factor rises, its relative price decreases.

The neoclassical theory, by explaining the functional distribution of distribution also help in explaining the size distribution of income. The sum of rewards of the factors of production which an individual possesses makes up his income (Bigsten, 1983). Thus, relative changes in the functional distribution resulting in changes in the size distribution of income in the long run can be explained by changes in relative supplies of factors, elasticity of substitution between factors, and changes in the demand for products (Cline, 1975). Keynesian Income Distribution Theory
While the neoclassical school base their theory on marginal propensities of factors, the Keynesian school uses marginal propensities to save to explain income distribution. Nicholas Kaldor (1966) establishes this view stating that, the income of the society is distributed between different classes, each having its own propensity to save. For Kaldor, only two classes exist: the capitalists and the working class. He used the savings-investment equality to explain income distribution which was inspired by Keynesian idea that savings depend on investment with class differences in thriftiness. In his model, distribution between wages and profit depends on investment by acting on the price level. Investment increase profit raising prices of final output. Under full employment condition where savings and investment is equalized, the relative share of profit in income depends on the ratio of investment to output, given the wage earners and capitalist propensity to save (Cline, 1975) Kalecki Theory of Income Distribution
This theory was offered by Michel Kalechi (1951). He modeled income distribution using the concept of monopoly. The main claim he made is that, firms operate below full capacity and their variable cost are constant over the relevant output range. Firms therefore set a markup depending on their average markup. Kalecki use the Lerner measure of the degree of monopoly expressed as µ = (p m)/p, where p is product price and m is the marginal cost. If marginal revenue and marginal cost are equal, then µ is equal to the inverse of the elasticity of product demand of the firm (Kalecki, 1951).
Kalecki analysis focused on wage share. As stated by Cline (1975), Kalecki showed that the share of labour and “average” degree of monopoly power are negatively related while aggregating for a close economy. Thus, considering the relationship between the degree of monopoly and the elasticity of product demand described above, the lower the elasticity of the demand for product, the lower the wages share in value addition. The implication of this model is that economic growth that depends on a growing monopoly power in the economy would increase the gap between wage and profit shares in total income.
2.2.2 Theories of Size Income Distribution
The functional distribution of income is of limited value in analyzing the actions of government. Ahluwalia and Chenery (1983), stated that the reason why policies influencing the distribution between wages and profits mainly concern the upper end of the size distribution is because majority of those who belong to the middle income groups are wage earners. Although, the marginalist theory makes some contribution in understanding the factors that affect the size distribution of incomes, differences in factor blessing appear not to be sufficient in explaining large inequalities in developing nations. The Theory of Individual Choice.

Friedman and Savage (1948), presented this theory by considering the reaction of individuals to risk. Friedman explains that, being a risk taker, is the reason why a small number of people can assume a large proportion of total income, since, as in the lottery, the amount of money that a great number of individuals can lose is small when compared with the large amount that a few number can win (Ferran, 1997). A risk adverse individual will choose less risky choices. As a result, a society made up of risk averse individuals will generate less inequality than one composed of risk taking individuals. This theory suggests that the high income groups are risk-taking while the low income groups are risk averse individuals. The Human Capital Theory
Jacob Mincer (1958) explained personal income distribution in terms investment in human capital and expected returns from the investment. This theory focuses on the explanations based on the individual’s decision to invest in education and training, and, the pattern of the individual’s lifetime earnings. The idea is that investment in education and training involve cost, both direct cost and forgone income during the investment period. Thus, individuals who choose to invest are only those who will be compensated by higher lifetime earnings. More so, longer training periods pay higher earnings to compensate for the foregone income during training. Those who invest will likely improve on their skill and be more productive than those who do not. “Schooling raises earnings and productivity mainly by providing knowledge, skills, and a way of analyzing problems” (Becker, 1993). Therefore, individuals who invest less in human capital have lower earnings than those who invest more.

This theory focuses on job earning differentials as the main cause of income inequality and, places emphasis on the impact of schooling on earning differentials. The Job Competition Approach
The “Job Competition” model can be traced to the work of Thurow (1975). According to this model “wages are paid based on the characteristics of the job in question and workers are distributed across job opportunities based on their relative position in the labour queue” (Cline 1975). Thurow rejects the assumption that the market for labour determines wages. He argues that wages are not determined by the marginal product of the worker associated with his/her educational level, but wages are set by marginal product associated with the skills the worker acquires on the job. As a result, individuals with the same levels of education may be offered different wages.
So, according to the job competition model, educational expansion would not tend to equalize the distribution of income, but would rather increase the admission requirements for obtaining jobs. Job opportunities expansion would tend to have an equalization effect. This theory does not consider that educational level is an important factor in determining how rapid individuals can learn skills on the job.

2.3 Theoretical Explanations Relating Macroeconomic Variables and Income Inequality
This will be carried out in general terms by looking at the effect of each variable separately on income distribution.

2.3.1 Economic Growth
The relationship between economic growth and income inequality has been given much attention by development economists.

Simon kuznets (1955) investigated the character and causes of long term changes in the personal distribution of income. Kuznets contended that the income distribution within a nation was likely to change over time as it moves from a poor rural society to a rich modern society. The average per capita income of the urban population is usually higher than that of the rural population, though income distribution within the urban population is more unequal. The upper income groups in the urban population often accumulate savings and the total impacts of such savings would be the concentration of an increasing share of income yielding assets in the upper class. Therefore, as industrialization increases in the urban sector, the nation’s income distribution will tend to fall until such time as the urban division dominates. Thereafter, the distribution of income will tend to stabilize as a result of: the movement of worker away from lower income industries to higher income industries; the reduced growth of the wealthier classes’ population; and the exploitation of the opportunities for wealth creation offered by technology undertaken by those whose assets are not in established industries.

Kuznet developed a hypothesis which stipulates that inequality increases with rising per capita income over time as a country develops and becomes less dependent on low productive agriculture and more dependent on industrial sectors, and it falls after a certain average income is attained. This relationship between income inequality and per capita income is usually represented by an inverted U–shaped curve known as Kuznets curve.

181927517399000 Inequality

GDP per capita
Figure 4: Kuznet curve
The literature in the 1960s and 1970s supported the hypothesis that per capita income level and income inequality are related (Ahluwalia 1976). Most of the recent studies, however, challenged this hypothesis and several empirical studies found no significant relationship between inequality and per capita income. Barro (2000) disagree with the need for inequality to rise as a country develops. He hypothesizes a U-shaped relationship between income and growth, arguing that income inequality falls in the early stages of economic growth and rises when a certain level is attained in the economy. Li, Squire, and Zou (1998) argue that the Kuznets curve works better for a cross section of countries at a point in time than for the evolution of inequality over the time within countries.
According to Todora and Smith (2011) growth, measured in GDP per capita, can be accompanied by an improved income distribution, an unchanged income distribution, or a case where income distribution worsens.
2.3.2 Unemployment
The broad view is that low income earners are the mostly affected victims of higher unemployment. The debate is that the least experienced and the least skilled lose their job first. More so, in times of higher unemployment, the lower income earners experience a decline in their relative wages as a result of excess supply of labour. Oi (1962) and Reder (1955) offers explanations concerning this phenomenon. Oi explains in terms of human capital theory. He argues that firms incur a fixed cost in hiring workers and in training them to improve their productivity. They expect to recover these cost over the worker’s tenure with the firm. The proportion this cost to the wage rate is larger for those with greater investment in human capital. The employer thus can retain such workers or layoffs among those with little specific training during periods of declining product demand. The leads to larger increases in unemployment among the unskilled and/or widening of wage differentials assuming wage is sufficiently flexible and labour supplies are relatively inelastic.
Reder offers a different explanation. He argues that, rather than laying off workers, employers engage their underemployed skilled workers in less skilled jobs during recession. They do this because they are afraid of losing them permanently, and also avoiding higher costs of rehiring such workers in subsequent recoveries.
In general, most of the effect of unemployment can be attributed to the lower income bands. However, unemployment may have an increasing impact on higher income earners as it rises. Professionals, experts and other self-employed may also suffer income losses during periods of high unemployment. The implication here is that; high income earners may also suffer income losses as well during recession. If individuals from all parts of the distribution suffer income losses during period of high unemployment, then effect of unemployment on income inequality will depend on which part of the distribution loses the most.

2.3.3 Inflation
The most common belief of the effect of inflation on income distribution is that, it causes redistribution away from the poor to the rich. According to Fisher and Modiglini (1978), inflation increases income inequality because it hurts the poor more the rich. Garcia, Prieto-Alaiz, and Simon (2013) assert that inflation could have a negative influence on income inequality. It is believed that, during times of high inflation, investors mostly prefer investing in financial instruments that have high profitability above the inflation level. The low income earners with low investment capacity are mostly affected by rising inflation more than those with high incomes. However, inflation can have a positive influence on income disparities. Crowe (2004), concluded that higher inflation is associated with higher inequality. This is as a result from the reduction in real debt owed as low-income band. The real income is raised when the real debt is reduced, thus income inequality reduces.
Inflation can cause an increase or decrease in income inequality, it depends on the origin of the inflationary forces (Schultz ,1969). If the inflation is due to the forces of demand, income inequality will rise. This is because prices will rise ahead of costs, and this will raise the share of profits. Since profits accrue mainly to the high income groups, then their share of total income will increase. In consequence of this, income inequality will increase. On the other hand, if the inflation is from cost push forces, then the share of income that accrue to the upper income group will fall. This is as a result of a fall in the share of profits relative to wage. Thus income inequality will decrease.

2.3 Empirical Literature
Several studies have been carried out on income distribution and different macroeconomic variables.
Sarel (1997), examined the relationship between income distribution and macroeconomic factors. Relying on cross country evidence, the study found a significant negative relationship between income inequality and macroeconomic factors such as growth rate, investment, terms of trade and real depreciation (in the case of low income countries) suggesting that the less fortunate segment of the poor benefit more from economic growth. Inflation, public consumption and the level of real exchange rate had no significant effect on changes in income distribution. The author recommends growth promotion and investment to improve income distribution.

Jantti and Jenkins (2001), used data on equalized disposable household income from the United Kingdom for 1961-1999 to analyze the impact of macroeconomic factors on income distribution. Employing a parametric model approach, they find out that neither inflation nor unemployment have significant effects on income inequality. Higher growth rates, by contrast, slightly increased income inequality. Their findings suggest that there exists no clear cut relationship between macroeconomic factors and the UK income distribution during this period.

Blinder and Esaki (1978) analyzed the relationship between income inequality and macroeconomic activity for the United states economy. The authors used inflation and unemployment as explanatory variables and quintile shares of income distribution as dependent variable, to explain income inequality. The results obtained showed that unemployment significantly affected income inequality, whereas inflation have a weak effect among quintiles for the U.S economy.
Breen and Gracia-penelosa (2005), using a cross-section of developed and developing countries, found out that greater output volatility is associated with higher Gini coefficient and the share of the top 20% of income quintile of the population, while it reduces the share of the other quintile. They show that greater volatility results in redistribution from the middle class (second and third quintiles) to the wealthiest class (fifth quintile). They concluded that high income individuals are characterized by lower degree of risk aversion than workers. Greater volatility of output by increasing risks shifts income from high income individuals (mangers) to workers. Policies aimed at ensuring macroeconomic stabilization which will reduce income inequality at the same time is recommended.

Skare and Stjepanovic (2014) studied the link between income distribution of households and relevant macroeconomic variables in 200 countries using panel data. Their findings show that macroeconomic variables like exports, inflation, unemployment, labor force and population have strong impact on income distribution irrespective of the differences in the amount of income or class.

Lee, Kim and Cin (2013) examined the determinants of income inequality in Korea from 1980 to 2012. Their study does not support both Kuznets’ inverted U-shaped relationship hypothesis between income inequality and economic growth and Barro’s hypothesis of U shaped relationship. Macroeconomic variables such as the rate of GDP growth and government spending were found to be statistically insignificant in affecting income inequality. The study also revealed that an increase in investment would decrease income inequality, while trade openness would increase income inequality and the rise of aging population would deepen the income inequality. The authors recommend an active intervention towards better-targeted income support and distribution in order to offset the factors working against income inequality.

Deyshappriya (2017) analyzed the impacts of macroeconomic factors on income inequality and income distribution in 33 Asian countries. The author employed dynamic panel data analysis based on the generalized method of moments over the period 1990–2013. The study also examined the impacts of the political economy and demographic factors as well. The outcome of the analysis revealed an inverted U-shaped relationship between gross domestic product (GDP) and inequality, supporting, the Kuznets curve hypothesis. Inflation, terms of trade, unemployment and official development assistance (ODA) were found to significantly affect income distribution. While higher inflation, political risk, terms of trade, and unemployment increased inequality, Education, labor force participation, and ODA reduced inequality. The study recommends ensuring economic growth stability, and maintaining price stability and political stability for more equal income distribution.

Odedokun and Round (2004) analyzed income distribution and inequality, the effect of inequality on economic growth and the channels through which inequality affects growth within the African regional context. The study employed Quasi panel data 35 countries over the different period during the 1960s to 1990s. Their findings revealed that inflation rate has no effect on income distribution. International openness was also found to have no effect on income distribution. The study provided evidence that high inequality reduces growth.

Awe and Rufus (2012) in their research on the determinant on income distribution in Nigeria studied the relationship between different macroeconomic variables and income distribution using cointegration technique. Macroeconomic variables such as inflation, employment rate and government expenditure exhibited a positive relationship with income distribution, while variables as growth rate of output and government expenditure exhibited a negative relationship with income distribution. Policies that bring about an equal distribution and associated income earning opportunities were recommended among others.

Baker and Creedy (2009) explored macroeconomic variables and income inequality in New Zealand over the period 1985 to 1994. They employed a method based on modelling the income data on a large sample of individuals for each year using conditional mixture distribution. The method was able to isolate the precise effect of changes in the macroeconomic variables on the form of income distribution. The authors used Atkinson inequality measure to show how comparative static analyses can be performed. The results of the study observed that over the period 1987 to 1991, macroeconomic variations in growth and unemployment increased the inequality. More so, the increase in growth and reduction in unemployment reduced the inequality over the period 1993 to 1994.

Garcia, Prieto-Alaiz, and Simon (2013), used a parametric modelling (a Dagum distribution) approach to measure the impact of macroeconomic factors on personal income distribution in developing countries. The study showed that macroeconomic factors such as GDP growth, employment rate and real interest rate have greater influence in shaping personal income distribution in developing countries. GDP growth variable showed greatest influence on the level of inequality. Increase in the growth rate was found to reduce the level of inequality. The findings further showed that the shares of total income of the first three quartiles of the distribution increased and the share of the richest 25% of the population decreased as growth rate increased. Employment rate and real interest rates showed significant positive impact on inequality. On the contrary, according to the findings, the inflation rate did not seem to have a significant impact on inequality. These findings are consistent with Sarel (1997) and Odedokun and Round (2004).

Monnin (2014), used panel data to assess the empirical linkage between income inequality, inflation and six other variables in 10 OECD countries, over the period 1971-2010. The author measures income inequality by the income share of the top 10% earners in each country. The study finds a U shaped link between inflation and income inequality showing that low inflation is associated with higher income inequality. While economic development and unemployment were found to possessed a significant positive relationship with income inequality, Unionization possessed a negative relationship with income inequality. Business cycles had a pro-cyclical movement with income inequality, while openness to international trade and skilled biased technological change were found to have no significant link with income inequality.

Afonso, Schuknecht and Tanzi (2008), focused on income distribution determinants and public spending efficiency from a cross country perspective. They employ a nonparametric (Data Envelopment Analysis) approach to assess the efficiency of public spending in achieving more income equalization. The findings show that public policies significantly affect income distribution directly through social spending, and indirectly through high quality education and sound economic institutions. They recommend the improving education performance and strengthening institutional quality framework.
Cingano (2014), studied the impact of income inequality on economic growth in OECD countries. Drawing from econometric analysis, the author show that income inequality has significant impact on further provided evidence for human capital as a channel through which income inequality affects growth. The study showed that income disparities depresses skill development among individuals with poorer background, while individuals with rich background are not affected. The author recommends policies that do not only reduce income inequalities, but also sustains long term growth.

Ali (2014), analyzed the impact of inflation and income inequality on economic growth for the Pakistan economy for the period 1972 to 2007. Applying vector error correction method, the study found a negative association between income inequality and growth. It further showed a growth increasing impact of inflation, remittances, foreign direct investment, and manufacturing value added in Pakistan. The author makes a conclusion that higher income inequality is better for growth, as it helps trickle down the benefits of growth to the poor. Economic policies that focus on income redistribution through progressive tax system is suggested among others.

Thalassinos, Ugurlu, and Murantoglu (2012), explored the relationship between income inequality and inflation in 13 European countries over the period 2000 to 2009. Employing panel data estimation technique, the study found significant positive relationship between inflation and income inequality. in addition, macroeconomic variables such as employment rate and openness to trade showed a significant positive relationship with income inequality, while GDP showed a significant negative relationship with income distribution. They suggest that policy makers be concerned with the distribution implications of government policies.

Alavin, Siam, and Al-handi (2013), employed Johansen cointegration test and Granger causality test to examine the relationship between income distribution and economic growth in Jordon for period 1987-2010.They confirmed the existence of a long run relationship between the two variables and also found evidence that supports the kuznets inverted U- hypothesis. The analysis showed that income distribution causes and affects economic growth.
Apergis, Dincer, ; Payne (2011), used a panel data set for 50 US states to study the causality between unemployment, poverty and income inequality for the period 1980 to 2000. The findings revealed a bidirectional relationship that existed between unemployment and income inequality both in the long run and in the short run.

Huaranga (2018) investigated the relationship between income inequality and macroeconomic activity for the Peruvian economy over the period 1997-2015. Inflation, real GDP and real interest rate were taken as the independent variables. Additionally, social and financial variables were used to explain changes in inequality. Utilizing a unit root time series bootstrap technique, the study observed that the effect of variables such as inflation, real GDP, interest rates, financial access and nontraditional exports is weakened by the particular features of the Peruvian economy during the period considered. As a result, no evidence of this relationship was found. The author conclude that inequality is highly complex and, recommends that macroeconomic policy should be carefully applied in order to reduce income inequality.

Employing ARDL approach, Shahpari and Davondi (2012), study the effect of human capital on income inequality on Iran over the period 1969 to 2007. They found out that increasing human capital (mean level of schooling) can reduce income inequality, and hence improve the distribution of income. The study also revealed that unemployment, inflation and GDP can increase income inequality. They recommend increasing the accumulation of capital, and adopting policies that control inflation and unemployment at the same time.

Gonzalez and Menendez (2000) assessed the effect unemployment on labour earnings inequality in Argentina between the period 1991 and 1998. The results indicated that unemployment significantly increased in earnings inequality in Argentina. As an economy move towards full employment, inequality tends to reduce. The study by Saunders (2002) had similar findings as that by González and Menendez (2000). Saunders (2002) studied unemployment-inequality nexus in the US economy and found a strong and positive relationship between unemployment and income inequality.

Birdsall (2007), analyzed the effect of income inequality on growth and development in developing countries and concluded that inequality would negatively affect growth at low income levels. A similar study undertaken by Mnif (2015) in 59 developing countries also confirmed a negative relationship between income inequality and growth.

Farre-Olalla and Vella (2006), examined the effect of macroeconomic variables on income distribution in Spain using household data for the period 1985 to1996. Employing a semi parametric double index based procedure, they concluded that macroeconomic variables have redistributive effect on income distribution.

A Tunisian study undertaken by Wahiba and Weriemmi (2014), investigated the relationship between growth and income inequality between 1984 to 2011. The results revealed that economic growth, openness, and fertility rates possessed a significant positive relationship with income inequality.

A study in Nigeria undertaken by Nuruddeen and Ibrahim (2014), examined the relationship between poverty, income inequality and economic growth for the period 2000 to 2012. The study employed Cointegration and Granger causality test. Empirical results revealed a unidirectional relationship between growth and poverty indicating that growth trickles up the upper income group increasing income inequality.

Yue (2011), examined the relationship between income inequality, economic growth and inflation in Korea from 1980 to 2002. significant negative relationship between income inequality and economic growth. Furthermore, no empirical evidence was found to support the long run relationship between income inequality and inflation.

In a panel study involving 46 counties, Li and Zou (2002), examined the effect of inflation on income distribution and growth over the period 1952 to 1992. Empirical result revealed that inflation and population growth increases income inequality, while government spending, human capital stock and financial development reduced income inequality. In addition, the study found a negative relationship between inflation and growth.

Similarly, in a panel study involving 26 developed and 66 developing countries, Siami-Namin and Hudson (2015), explored the relationship between inflation and income inequality for the period 1990 to 2014. Findings revealed that inflation is negatively associated with income inequality. The study concluded that inflation is an important variable in explaining income inequality.

Bhat, Ganaie, and Kamaiah (2018), analyzed the relationship between macroeconomic variables and income distribution in India for period 1963 to 2007. The study used two indicators to represent income inequality: The Gini coefficient representing overall income inequality, and the share of income of top one percent of the population. Empirical result showed that growth had a negative impact on the overall inequality, and a positive effect on the income share of the top percent of the population. The results from the study further showed that inflation increases income inequality, while trade openness and government expenditure revealed a significant positive relation with income distribution in the long run.
Martinez, Ayala and Ruiz-Huerta (2011), analyzed the influence of unemployment on income inequality in OECD Countries. The study revealed that the middle and lower income bands are the mostly affected victims of unemployment indicating that unemployment increased income inequality.

Dipietro, Anoru, and Sawhey (2005), studied the “macroeconomic determinants of the income shares of the very highest income groups” in United States for the period 1961 to 1998. Employing Phillips-Hansen modified OLS technique, findings showed that unemployment and inflation were negatively associated with the income shares of the top most income groups. While interest rate and the extent of trade had positive influence on the income shares of the top most income groups.

Darma and Ali (2014), studied the effect of inequality on growth in West Africa over the period 1980 to 2011. Empirical result from the study showed that inequality negatively affects growth. The study recommends policies that redistribute income in an equal manner by ensuring that as the profit of entrepreneurs’ rises, the wages of the working class also increases.

Gabis (2005), explored the relationship between income inequality and economic growth using data set of 52 countries over the period 1965 to 2003. Empirical findings showed a positive relationship between income inequality and growth over the short and medium term.

Abida and Sghaier (2012), explored the growth-inequality nexus for 3 countries in North Africa, empirical results indicated that income inequality adversely affects growth. The authors recommend policies that ensure a stable macro environment conducive enough for growth.

Li (2016), studied the relationship between and growth in China over a period of three decades. The study found no support for the existence of kuznet curve relationship.
2.5 Limitations of Previous Studies
Empirical studies regarding macroeconomic factors and income distribution have not yielded a decisive conclusion on the effect of macroeconomic variables on income inequality. This is partly due to differences in the countries studied, time period covered or the models applied.
Much of research done previously that considered the effect of macroeconomic factors on income inequality, as reviewed in this study, were country specific studies like Wahiba and Weriemmi (2014), Lee, Kim and Cin (2013), Baker and Creedy (2009), and Gonzalez and Menendez (2000). Other studies like Siami-Namin and Hudson (2015), Garcia, Prieto-Alaiz, and Simon (2013) have focused on developed countries and a few developing countries and are however non-African studies. Most of these studies may not be generalizable to the African case due to the material differences between Africa and those other countries studied. Most studies used OLS, fixed effects or random effects estimation procedures. One weakness in using these estimation techniques is that they fail to address the variable endogeneity problem associated with dynamic panel data analysis.

The scanty studies done in Africa (Odedokun and Round ,2004; Abida and Sghaier (2012) Darma and Ali (2014) did not consider the effect of macroeconomic variables on income distribution, but rather focused on the reverse causation between a single macroeconomic variable (basically growth) and income distribution, and on a single region. Blank et al (1993) and Fields (2001), had noted that the macroeconomic environment determine the distribution of income. Hence, the macroeconomic factors related to income inequality need to be analyzed to have a basis for effective strategies to deal with the challenge in Africa.

This study focuses on the effect of macroeconomic variables on the distribution of income in selected African countries and also seeks to establish a long run relationship among the variables. The study employs a statistical tool known as a panel generalized method of moments which addresses the endogeneity problem, country specific heterogeneity, and the possibility of serial correlation in the data generating process, and which to the best knowledge of the researcher, has not been used for investigation into the subject matter in the African context.
The major challenge researchers encounter while working on inequality data for African countries is its availability and quality. Most researchers in the past have relied on income inequality data from Deininger and Squire (1996) or the UNU-WIDER World Income Inequality Database (WIID). However, these sources have been criticized as lacking comparability across countries and over time (Solt, 2009; Atkinson and Brandolini, 2001). Considering the issue of data availability, the Standardized World Income Inequality Database (SWIID, 2016), is identified as the best suitable database, since it maximizes comparability for the broadest possible sample of countries and years. (Solt,2016).

3.1 Theoretical Framework
The theoretical underpinning for this study is basically the empirical model of Simon Kuznet (1995). He tried to find answers to the questions; Does inequality in the distribution of income increase or decrease in the course of a country’s economic growth? What factors determine the secular level and trends of income inequalities? Kuznets presented his idea to the American Economic Association of an inverted U relationship between economic growth and inequality in the distribution of income. In Kuznets theory, an increase in per capita income leads to an initial increase in income inequality, if the workers move from the agricultural sector to the industrial sector, and then declines after a turning point (Kuznets). This describes a positive relationship between growth and income inequality in the early stages of economic growth and a negative relationship in the later stages. Other factors, like demographic factors, political factors, cultural factors, and technical progress, may also influence the evolution of inequality over time. However, combining economic, social and political, factors may forestall this process and reduce the inequality level within a country (Bahmani-Oskooee, Scott ; Harvey, 2008).

The standard Kuznets curve regression model can be stated as;
LGINIit= ?i + ?i + ?1L(GDPP)it + ?2L(GDPP)it2 + ?it……………………………………..3.1
Where, GINI is the Gini coefficient representing income inequality. The first two terms on the right hand side are the intercept parameters which vary across countries i and years t. (GDP/P) represents the real GDP per capita and (GDP/P) ² represents the square of real GDP per capita which is a measure of Kuznets Curve. ?i is the slope parameter of the model and ?t is the error term.

From the above specification 1, Kuznet curve is confirmed to exist when ?1 ; 0 and ?2 ? 0 and the turning point occurs at a point in which GDP per capita is at maximum level. This is given as;
(GDP/P)max = exp(-?1/2?2) …………………………………………………………3.2
Including other explanatory variable that are important in explaining the relationship between economic growth and inequality, the model can be rewritten as;
LGINIit = ?i + ?i + ?1L(GDPP)it + ?2L(GDPP)it2 + j=1n(Xit ) + ?it………………………3.3
Where X is a vector of the explanatory variables.

3.2 Model Specification
Model One (for Objective One, Two and Three)
The empirical works of kuznet (1955) form the bulding block for the modelling structure of this work. By referring to the works of Gabis (2005) and Deyshappriya (2017), our model may be specified as follows:
GINI = f (GDP, UNEM, INF, COV) ……………………………………………………….3.4
Where GINI- represents Gini coefficient, UNEM- represents Unemployment rate, INF- Inflation rate, COV- Control variables that affect income inequality which include Trade, Population growth, and Labour Force
Econometrically, equation 3.4 can be stated as
LGINIit = ?0 + ?1LGDPit +?2UNEMit + ?3INFit + ?4TRDit + ?5PGRit + ?6LBFit + ?i+ ?it……………………………………………………………….…………………………3.5
To test Kuznets’ curve hypothesis, we introduce the squared term of the log of real GDP per capita. Thus equation 3.5 becomes:
LGINIit = ?0 + ?1LGDPit +?2L(GDP)it2 + ?3UNEMit + ?4INFit + ?5TRDit + ?6PGRit + ?7LBFit + ?i+ ?it……………………………………………………………………..3.6
To estimate objective one, two and three, we modify equation 3.6
LGINIit = ?0 + vtLGINIit-1 ?1LGDPit +?2L(GDP)it2 + ?3UNEMit + ?4INFit + ?5TRDit + ?6PGRit + ?7LBFit + ?i+ ?it……………………………………………….……….3.7
Where, i = 1…N for each country
t = 1……T for each time period
L = a natural log
QUOTE vt, ?’s ?’s = are the parameters to be estimated
GINI = Gini coefficient
LGINIit-1 = represents the lagged value of the Gini coefficient
GDP = Gross Domestic Product Per Capita (constant local currency)
UNEM = Total Unemployment rate (% of total labor force)
INF = Inflation rate (consumer price index, annual %)
TRD = Trade (% of GDP)
PGR = Population Growth rate (annual %)
LBR = Labour Force (% of total population ages 15-64)
?i = Country specific effect across individual countries
?it = Independently distributed error term in all time periods of the countries
GINI is the Gini coefficient which represents income inequality based on households’ income before taxes and transfers. We expect at least the previous value of income inequality (that is, the first lag of the Gini coefficient) to be associated with higher levels of income inequality given the tendency of income inequality to persist over time. Therefore, we expect the coefficient QUOTE to be positive.
Economic growth is measured by gross domestic product per capita (GDP). GDP is the gross value of products produced by residents in the economy. According to kuznet hypothesis, the short-term effects of GDP per capita would increase income inequality, while long-term effects would decrease it. Thus, we expect a positive relationship between GDP and income inequality and a negative relationship between the squared term of GDP and income inequality. ?1 is expected to be positive and ?2 negative.

UNEM represent Total Unemployment rate. It is the total share of labour force that is without work, but is willing to, available for and looking for work. A rise in unemployment should increase income inequality. Therefore, the coefficient of UNEM (?3) should be positive.

INF represents Inflation measured by the consumer price index. It shows the annual percentage change in the cost to the average consumer of obtaining goods and services. Increase in inflation rate could increase or decrease income inequality. Hence the coefficient of inflation (?4) could be positive or negative.

TRD represents Trade. It is the sum of exports and imports of goods and services as a ratio of gross domestic product. The coefficient of TRD (?5) is expected to be negative.

PGR represent the Growth rate in Population. One would expect to observe a positive relationship between the population growth rate and income inequality. Hence, (?6) is expected to be positive.

LBF is the stock of labour force that exists in a country. Labour force consists of all the able manpower, ages 15 and older who supply labour to produce goods and services at a particular time. We expect an increase in the labour force to yield a positive impact on income inequality. Therefore, the coefficient of the labour force (?7) is expected to be positive.

Model Two (for Objective four)
Following the works of Hossain (2010), the study adopts a panel vector autoregression (VAR) model to examine the direction of causality between relevant macroeconomic variables and income inequality. However, Granger (1988) had noted that the short- and long-run causality cannot be captured by the standard first difference VAR model, if a set of variables are cointegrated. Thus, an inclusion of an additional variable to the VAR system such as the error correction term (ECM) is necessary help us to capture the long-run relationship. In this case, we augment the Granger causality test with the multivariate vector error correction (VECM) framework as given below:
?LGINIit ?LGDPit ?LUNEMit?LINFit = ?1 ?2?3?4 + k=1 p?11k ?12k ?13k ?14k ?21k ?22k ?23k ?24k ?31k ?32k ?33k ?34k ?41k ?42k ?43k ?44k ?LGINIit-k ?LGDPit-k ?LUNEMit-k?LINFit-k + ?1 ?2?3?4ECMit-1 + ?1it?2it?3it?4it………..………………………………………………………3.8
Where ? is the first difference of the relevant variables and L is the natural logarithm. The residuals (?’s) are assumed to be independently distributed across countries with zero mean and are white noise. ECMit-1 is the one period lagged error-correction term derived from the cointegration vector. The ECMit-1 variable will be excluded from that model if the variables are not cointegrated. In this study, we test the hypotheses that, economic growth does not Granger cause income inequality in the short run, if and only if all the coefficients ?12k ‘s k are not significantly different from zero in equation 3.8. Similarly, income inequality does not Granger cause economic growth in the short run if and only if all the coefficients ?21k ‘s k are not significantly different from zero in the equation 3.8 and so on for the other variables.
3.3 Estimation Technique
In this study, a dynamic panel data Generalised Method of Moment (GMM) estimator will be used. This method allows for controlling for both individual and time specific effects. There are two types of GMM estimator namely; difference GMM developed by Arellano and Bond (1991) and system GMM developed Arellano and Bover (1995) and Blundell and Bond (1998) by and they can be considered alternatively. The basic assumption underlining this method is that the instrument is built on the lagged values of the instrumented variable. The unobserved heterogeneity effect in panel data is eliminated by first-differencing the original model, which allows to use instrumental variables estimator.

The consistency of the GMM estimators depends on the validity of instruments. This validity of instrument is addressed through the Sargan and Hansen test, which is an over identifying restrictions test. The Hansen test has a null hypothesis which states that the instruments are valid, if the null hypothesis is rejected; we conclude that the diff-GMM estimation technique is not suitable in this study and we proceed to estimate the model using sys-GMM estimator. On the other hand, if the null hypothesis is not rejected, we conclude that the sys-GMM should be chosen, since it’s more robust and appropriate as compared to diff-GMM.

3.4 Model Justification
This study employs the use of Generalized method of moments. This method is applied to this study because it is an econometric technique it accommodates a situation where the time span (T) is smaller than cross-sectional unit(N) in order to control for dynamic panel bias. The advantages it has over other panel data estimation methods are as follows; first the GMM estimation works under the assumption that all independent variables besides the lagged dependent variable are exogenous and act as a valid instrument. So therefore it converts the problem of heteroskedasticity and creates efficient unbiased results. The GMM technique also offers instrumental viable estimation technique that attains consistency and accuracy (Halkos, 2003). As a dynamic model, it solves country specific effects and endogeneity problem. Finally, the estimation consequently reduces the error term to whole noise, thereby eliminating endogeneity due to correlation between the error term and the independent variables (Halkos, 2003).
3.5 Pre-Estimation Tests
3.5.1Panel Unit Root Test
The Augmented Dicky-Fuller (ADF), the Levine-Lin-Chu test and, the Im-Pesaran-Shin (IPS) test would be used in verifying the presence of unit root in the panel series. The essence of this is to verify if the variables could be trusted for the purpose of forecasting. These test follow the general assumption of cross-sectional independence as applicable to first generation, given the various conditions that characterize the various panel unit root tests. The LLC test imposes homogeneity for the null and alternative hypothesis, while the IPS gives room for more heterogeneous behaviour. The null hypotheses to be tested is that the panel series contain a unit root against the alternative hypothesis that the panel data series has no unit root.

3.5.2 Panel Cointegration Test
Cointegration technique is used to establish the long-run relationship among integrated variables. This approach becomes more important especially if the variables are not integrated of the same order. Panel cointegration test developed by Westerlund (2007) will be employed. This test the null hypothesis of the absence of cointegration by determining whether the error correction term in the panel error correction model equals zero. This panel co-integration technique allows heterogeneity to both the long run and short run dynamics of the error correction model as well as cross sectional dependence.

Westerlund derived four panel data co-integration test statistic which are Gt, Ga, Pt and Pa. Two test are designed to test the null hypothesis that cointegration exist for the panel as a whole while the other two test the alternative that cointegration exist for at least one unit.

Diagnostic Tests
Test for Heteroscedasticity
This test was conducted in order to know whether the variance of the error term is constant. The problem of heteroscedasticity may arise as a result of incorrect data transformation, incorrect functional form, misspecification of regression model, presence of outliers etc. Wald test will be used to examine whether the variances are constant overtime.

Test for MulticollinearityThis test will be conducted to test for linear collinearity among explanatory variables. Multicollinearity occurs when two or more explanatory variables are highly correlated. This results in unreliable estimation and lack of efficiency. Simple correlation matrix will be employed to test for multicollinerity among the explanatory variables.

The Sargan/ Hansen J-statistic Test
This test is used to check the constraints of over-identifying restrictions and the validity of instruments. Sargan test is useful when the estimation is performed considering a homoscedastic weight matrix, while the Hansen test detects over-identification in presence of an heteroscedastic matrix.

Arellano and Bond Test of Hypothesis
This test is used to examine the hypothesis of no second-order serial autocorrelation in the disturbance term. The dynamic panel data requires that the error cannot be serially correlated. Serial correlation occurs when the error associated with a particular period carry over into future periods.

3.6.5 F-test of Joint significance
This test reports that the estimated coefficients on the regressors are jointly equal to zero (P=0.000) at any conventional level of significance.
3.7 Data Sources and Econometric Package
For the purpose of this study, secondary data on income inequality is sourced from the Standardized World Income Inequality Database (SWIID), while data on Gross Domestic Product, Inflation, and Unemployment, Trade, Labour force and Population growth rate are obtained from World Bank development indicators 2017. Stata 13 is the econometric package that will be used for estimation.

Adams, R. H. (1991), The effect of International Remittances on Poverty, Inequality and Development in Rural Egypt. Research Report 86. Washington DC.

Alavin, M., Siam, A., and Al-handi, M. (2013). The relationship between economic growth and income distribution. International Management Review,9(2), 24-34.

Alesina, A., ; Perotti, R. (1996). Income distribution, political instability, and investment. European Economic Review, 40(6), 1203–1228.

Ali, S. (2014). Inflation, income inequality and economic growth in Pakistan: A cointegration analysis. International Journal of Economic Practices and Theories, 4(1), 33-42.

Anyanwu, J.C., Erhijakpor , E. O.,and Obi, E. (2016). Empirical analysis of the key drivers of income inequality in West Africa. African Development Review, 28(1), 18–38.

Apergis, N., Dincer, O., ; Payne, J. E. (2011). On the dynamics of poverty and income inequality in US states. Journal of Economic Studies, 38(2), 132 – 143.

Awe, A. Rufus, O. O. (2012). Determinants of income distribution in the Nigeria economy: 1977-2005. International Business and Management, 5(1), 126-137.

Bagus, P. (2014). How monetary inflation increases inequality. London: Institute of Economic Affairs
Bahmani-Oskooee, M., Scott, W., and Harvey (2008). Short-run and long-run determinants of income inequality: Evidence from 16 countries. Journal of Post Keynesian Economics, 30(3,) 463-484.

Baker, A. and Creedy, J. (2009). Macroeconomic variables and income inequality in New Zealand: An exploration using conditional mixture distribution. New Zealand Economic Papers ,33(2), 59-79.

Banerjee, A. and Duflo, E. (2003). Inequality and growth: What can the data say? Journal of Economic Growth. 8(3), 267-299.

Barro, R. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5, 5-32.

Benabou, R. (1996). Inequality and growth. NBER Working Paper,5568,1-50
Bhat, S., Ganaie, A., and Kamaiah, B. (2018). Macro-determinants of income inequality: An empirical analysis in case of India. Economics Bulletin, 38(1), 309-325.

Bigsten, A (2014). Dimensions of income inequality in Africa. WIDER Working Paper 2014/050.

Birdsall, N. (2014). Income distribution: effects on growth and development. Center for Global Development Working Paper, 118:1-26.

Blank, R., D. Card, F. Levy and L.J. Medo (1993). Poverty, income distribution, and growth: Are they still connected? Brooking Papers On Economic Activity, 1993(2), 285-339.

Blinder, A. and Esaki, H. (1978). Macroeconomic activity and income distribution in the postwar United States, The Review of Economics and Statistics, 60(4),604–609.

Bourguignon, F. (2003) The growth elasticity of poverty reduction: explaining heterogeneity across countries and time periods, in T. Eicher and S. Turnovsky (Eds.) Inequality and growth: Theory and policy implications, The MIT Press, Cambridge, MA.

Breen, B., and Garcia-penelosa (2005), Income inequality and macroeconomic volatility: an empirical investigation. Review of Development Economies, 9(3),380-398.

Briggs, J. E. (1973), Unemployment statistics and what they mean. Monthly Labour Bulletin, Washington DC; US Department of Labour.

Canagarajah, S., Ngwafon, J., ; Thomas, S. (1997). The evolution of poverty and welfare in Nigeria, 1985-92. The World Bank Policy Research Working Paper, 1715.

Cingano, f. (2014). Trends in income inequality and its impact on economic growth. OECD Social, Employment and Migration Working Papers, No 163, OECD publishing.

Cowell, A, F and Jenkins, P, F. (1995). How much inequality can we explain? a methodology and an application to the United States. Economics Journal, 1005:428-430.

Dahan, M. And Tsiddon, D. (1998). Demographic transition, income distribution, and economic growth. Journal of Economic Growth (3),299-352.

Deyshappriya, N. P. (2017) “Impact of macroeconomic factors on income inequality and income distribution in Asian countries”, ADBI Working Paper Series, 696, Asian Development Bank Institute (ADBI), Tokyo.

Fajana, S. (1987). “Economic recession, collective bargaining and labour market segmentation in Nigeria”, Nigerian Management Review CMD, Lagos, 2(1),9-16.

Fields, G. (2001). Distribution and development: A new look of the developing world. New York, Cambridge and London. Russel Sage Foundation and MIT Press.

Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth. American Economic Review, 90(4), 1091-1113.

García, C., Prieto-Alaiz, M., ; H. Simon, H. (2013), “The influence of macroeconomic factors on personal income distribution in developing countries: A parametric modelling approach”, Applied Economics, 45(30), 4323–4334.

González, M., ; Menendez, A. (2000). “The effect of unemployment on labor earnings inequality: Argentina in the nineties”. Princeton: Princeton University.

Granger, C.W.J. (1988).” Causality, cointegration, and control”. Journal of Economic Dynamics and Control, 12: 551-559.

Habimana, O (2014)” Inequality and Economic Growth in Sub-Saharan Africa”, International Journal of Business Management and Economic Research. 5(6),100-103.

Halkos G., (2003), Environmental kuznets curve for sulfur: Evidence using GMM estimation and random coefficient panel data models, Environment and Development Economics, 8, (04), 581-601.

Huaranga J. (2018) “Income inequality and macroeconomic activity: A study of the Peruvian case”. Open Science Journal.3(1), 1-17.

Hunt, E. K., ; Lautzenheiser, M. (2014). History of economic thought: A critical perspective. New Delhi: PHI Learning.

Jantti, Markus; Jenkins, Stephen P. (2001): Examining the impact of macroeconomic conditions on income inequality, ISER Working Paper Series, 2001-17.

Jhingan, M. L (2002), Macroeconomic Theory. 10th edition, Vrinda publication Ltd, New Delhi.

Kaldor, N. (1966), Marginal productivity and the macro-economic theories of distribution. Review of Economic Studies 33, 309–19.

Kalecki, M. (1965): Theory of economic dynamics, 2nd edition, London: George Allen and Unwin.

Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review 45 (1), 1-28.

Lee,H., Kim, J., and Cin,C. B.(2013). Empirical Analysis on the Determinants of Income Inequality in Korea. International Journal of Advanced Science and Technology,53,95-110.

Lempert, D. (1987). A demographic economic explanation of political stability: Mauritius as a Microcosm, Eastern Africa. Economic Review, 3(1).

Li, H. and Zou, H.F.(2002). Inflation, growth, and income distribution: A cross-country study. Annals of Economics and Finance, 3(1), 85-101
Li, H., Squire, L, And H. Zou. (1998). explaining international and intertemporal variations in income inequality. The Economic Journal, 108
Lo, A. W. (2012). Reading about the financial crisis: A 21-book review. Journal of Economic Literature, 24(3), 129 – 144.

Martinez, R., Ayala, L., ; Ruiz-Huerta, J. (2011). The impact of unemployment on inequality and poverty in OECD countries. Economics of Transition, 9(2), 98-109.

Melamed, C. (2011), Inequality: Why it matters and what can be done, ActionAid International, April.

Milanovic, B. (2003). Is Inequality in Africa Really Different?. Policy Research Working Paper;No. 3169. World Bank, Washington, DC.

Monnin , P (2014), Inflation and income inequality in developed economies. Council of Economic Policies Working Paper 2014/1.

Mwangi, F. K (2013). The effect of macroeconomic variables on financial performance of aviation industry in Kenya. Unpublished MSC Project, University of Nairobi, 2013.

Odedokun, M. O., and Round, I. J. (2004). Determinants of income inequality and its effects on economic growth. African Development Review, 16(2), 287-327.

Oi, w. (1962), “Labor as a Quasi-Fixed Factor”, Journal of Political Economy, 70, 538-555.

Oxfam. (2016). An Economy for the 1%. Oxfam Briefing Paper No. 210.

Prabhakar, A. C. (2003). A critical reflection on globalisation and inequality: a new approach to the development of the South. African and Asian Studies,2 (3),307-345.

Ravallion, M. (1995). Growth and poverty: Evidence for developing countries in the 1980s. Economic Letters, (48), 411-4117.

Ray, D., 1998. Development economics. Princeton University Press.

Reder, M.W. (1955), The theory of occupational wage differentials, American Economic Review, 45, 833-852
Ricardo, D. (1821): Principles of political economy and taxation, 3rd edition, London: Dent Dutton, 1965.

Sarel, M. (1997). How macroeconomic factors affect income distribution: The cross-country evidence. IMF Working Paper, No.WP/97/152.

Saunders, P. (2002). The direct and indirect effects of unemployment on poverty and inequality. The Social Policy Research Centre Discussion Paper No. 118.

Schultz, T.P. (1969), Secular trends and cyclical behaviour of income distribution in the United States: 1944-1965″, in L. Soltow (ed.) Six papers on the size distribution of income and wealth, N.B.E.R.

Shahpari, G., and Davondi, P. (2012), Effect of human capital on income inequality in Iran. 109(204) 1386-1392.

Skare, M. and Stjepanovic, S., 2014) Income distribution and inequality-international comparision. Amfiteatru Economic, XVI (37), 980-988.

Thalassinos, E., Ugurlu, E., and Murantoglu, Y. (2012). Income inequality and inflation in the EU. European Research Studies, XV (1).

Thomas, P (2006). Does the US have a handle on inflation? Street insight.

Todaro, M. P., and Smith, S. C. (2009). Economic development. Boston: Pearson Addison Wesley.

UNDESA, (2009). Rethinking Poverty– Report on the World Social Situation 2010. United Nations Department of Economic and Social Affairs.

UNDP. (2011). International Human Development Indicators .UNDP.(2005). Human Development Report, Oxford University Press, Oxford.

Wahiba, N. F.and Weriemmi, M. E. (2014). The relationship between economic growth and income inequality. International Journal of Economic and Financial Issues, 4(1),135-143.

Willliamson, G, J. (1965). Regional inequality and the process of national development: a description of the pattern. Economic Development and Cultural Change, 13(4),1-84
World Bank (2003). 2002 Development Indicators. Washington Dc: World Bank.

World Bank (2015). Global Monitoring Report 2014/2015: Ending Poverty and Sharing Prosperity. Washington: World Bank.

World Bank, (2013). World Bank Development Report : Risks and Opportunity- Managing Risks For Development .Washington, DC, World Bank
Yue, H.Y. (2011). Income inequality, economic growth and inflation: A study on Korea. International Journal of Economics and Research, 2(5), 14-21.


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