Introduction
‘In today’s global economy, inequalities weaken the potential of the nations it exists in, be it a developing economy or a developed one. Inequalities widen the gap between the rich and the poor, it makes the rich richer and the poor poorer, it becomes a threat to poverty eradication, hinders economic development, holds the stability of a nation hostage and weakens financial and economic development.
Income inequality could also lead to social unrest if the rich keep getting richer and the poor stop benefitting as much from economic growth as the rich. If income inequality transpired in a manner with which it affects the rich and the poor in the same way, then it would not be such a popular issue but it does not. Income inequality becomes a policy issue when nations are unable to achieve an equal environment for everyone in the nation and cannot answer the question of how to solve the issue of income inequality. Income inequality in sub-Saharan Africa is very common and widespread, making it one of the poorest regions in the world. This was concurred by the African Progress Panel [APP] (2013),which stated “rising income inequality seems to be the main cause of the declining overall record of poverty reduction in sub-Saharan Africa”.
Income inequalities can trace its beginnings to the recession and oil shocks of 1974 which affected the global economy not just sub-Saharan Africa. However, sub-Saharan Africa was severely affected by the recession which plagued many countries for years with some haven not recovered from it till date. Countries like Nigeria and Angola experienced a drop in revenue because oil prices dropped, Botswana was forced to call for international financial aid to assist its weakening economy, South Africa also faced a recession (Arieff et al., 2010).
The 1960s and 1970s saw sub-Saharan Africa embrace import substitution as an alternative to complete international trade with the years following this seeing trade barriers being dropped. The Heckscher Ohlin model is one that describes the relationship between income inequality and international trade, this model stresses the need for countries to operate at a lower cost so as to raise its revenue (Leamer, 1995).
Africa in recent years has been known for its emerging economies that have high probability for growth that could benefit the rest of the world. Existing literature have highlighted the other factors that could lead to income inequality in sub-Saharan Africa, but have failed to highlight the extent at which these factors actually influence income inequality and economic growth.
Also, most papers have focused on income inequality in developed economies and the growth of these economies but have failed to highlight exclusively how international trade affects economic growth in developing economies which is what this dissertation aims to do. Development indicators from the world bank will also be used to analyse this topic.
To accomplish this aim, this paper will start with a literature review examining past research income inequality. Secondly, other determinants of income inequality excluding international trade will be discussed next with each’s relationship with income inequality and international trade discussed. Furthermore, international trade as a cause of income inequality in sub-Saharan Africa will be discussed while exploring the literature available and different economic theories on the subject matter. The literature review will continue with the trends of income inequality in sub-Saharan Africa and other factors which affect economic growth and globalisation.
Chapter 3 continues with the methodology used in this dissertation with section 3.1 explaining the different variables used in constructing the data and regression model including their sources. The next section will explain the model specifications and the equations used in the regression model. Section 3.3 will then explain the regression results gotten from the data, concluding with the highlights of the limitations that arose in this dissertation.
Chapter 2
Literature review
This chapter delves into existing literature to answer the aim of the dissertation, demonstrating how the literature has been developed and extended in recent years. Income inequality and how it affects economic growth will be highlighted in the first section. The second section will highlight some factors causing income inequality in sub-Saharan Africa. Subsequently, international trade as a cause of income inequality in sub-Saharan Africa will be discussed. Then the trends in income inequality in sub- Saharan Africa will be explored.
2.1 income Inequality
Income is absolutely essential in any economy as it is a measure that highlights whether an economy has grown or not. In recent years, many countries have seen a rise in economic growth, however, this is not fully translated into the people living within, as the poverty rates is still at a high-level irrespective of the high GDP per capita (Chen and Ravallion, 2007).
Income redistribution leading to equal distribution could lead a fall in economic growth according to Knowles (2001). He said, redistribution would affect [reduce] investment thus economic growth because the rich save more than the poor. Also, progressive taxes could be introduced which would also affect savings and investment thus economic growth.
Some economists and researchers claim income inequality can be beneficial to an economy and is unavoidable while others say otherwise, Cingano (2014) was one of such that claimed income inequality reduces economic growth. He said the implementation of political mechanisms that redistribute income through taxes may affect the productive capacity of firms and their willingness to invest. Stunted business growth in any economy would have everyone affected from the workers to the families living within leading to a decrease in economic growth, political instability and social unrest.
Income inequality could also leave the poor unable to make specific investments. For instance, a low income household may be unable to afford the hospital when ill thus leading to a decline of investment in that economy. When the majority [the poor] of an economy does not invest in an industry, total output falls for said industry, thus stunting economic growth (Cingano, 2014).
Arguments supporting income inequality leading to economic growth claim that it incentivises the masses to work more and harder, invest more, trade more and generally take more risks. Individuals are also encouraged to save more when there is income inequality, promoting capital accumulation and raising investment.
2.2 Determinants of Income Inequality
The key factor to be discussed in this dissertation is international trade, however, that is just one of the many factors that causes income inequality. Two other factors will be considered here: Gross Domestic Savings and Infrastructure.
Gross Domestic Savings when measured is the total GDP of a country minus the total consumption of the country (World Bank, 2016). Savings is generally associated with economic growth as most developed countries have a high marginal propensity to save, it also correlates with an increase in financial development. Financial development in any country would correspond with a rise in savings, prompting the “acquisition of financial investments by businesses and other institutions” (Ghirmay, 2004).
Savings will always have a positive effect on income inequality. Every economy will always figure out a way for the allocation of capital expenditures and substantial assets within each and every industry irrespective of whether citizens of a country are encouraged to increase their savings and investment or not (Goldsmith, 1969). However, savings would be better boosted if individuals and businesses are compelled to save more due to an assurance of returns on investment, which would raise their income levels and in the long run could lead the country towards economic growth. Any drop in savings has the ability to stunt economic growth (Shaw, 1973). Theoretically, the above statements detail the relationship between economic growth and income inequality, however, there has not been much research done on this topic, so anything is possible when relating the two.
Infrastructure has the ability to break or make an economy not just affect income inequality. Generally, developing economies are supposed to converge to their developed counterparts, as they do not have to spend time developing new inventions to aid work, making it a lot cheaper for them to replicate these technologies. Hence, they are able to vault over whatever technological obstacles may be in their path thus giving them the ‘advantage of backwardness’ (Todaro and Smith, 2015).
However, it seems countries in Sub-Saharan Africa did not get the memo as the development of infrastructure in Sub-Saharan Africa is one of the lowest in the world, as the electricity in most of these countries is low, and there is also a problem of inaccessible roads. Although, the telecommunication network in these countries has improved significantly within the past couple of years (Aker and Mbiti, 2010).
Most of these countries have been able to converge with their developed counterparts based on this as they have been able to jump from the landline stage straight to the use of mobile phones. Mobile phone usage has increased from over 15 million to around 375 million as at 2008 and it represents the fastest growing infrastructure when compared to the other forms of infrastructure (Aker and Mbiti, 2010).
Telephones in Sub-Saharan Africa has had a very positive effect on the growth of businesses, low income ones especially. It has aided communication between the farmer in the village who wants to know what the people in the urban areas want and the amount they are willing to pay for it. Hence, under the right condition infrastructure could have a positive impact on the income of these farmers (Lopez, 2013).
Alternatively, not much research has gone into the negative impacts of infrastructure development, but it should be known that technologies such as the telephone could be an instrument of bad news telling the farmer for instance, that, the public do not desire his produce or are not willing to pay a high price for it (Adeyemi, 2015).
2.3 International Trade in Sub-Saharan Africa
International trade in Sub-Saharan Africa is a complex topic, the World Bank and International Monetary Fund [IMF] credit the low economic growth in Africa to ‘slow trade policies and trade marginalisation’. Concurring with the IMF and the World Bank was Collier and Gunning (1999) arguing that the low economic growth rate in 1990s Africa was due to the outrageous control placed on trade and development strategies.
Africa as a whole has been referred to in many publications as one of the most closed up regions in the world’s economy due to trade restrictions (Hammouda, Achterbosch, Osakwe and Tongeren, 2004). The benefits of eradicating the barriers to trade has been a highly discussed topic among economists in the past and present, as even Easterly and Levine (2001) argued that Africa as a whole has seen a 0.4%/year decline in economic growth due to trade restrictions.
Import substitution was one of the many strategies adopted by countries in Sub-Saharan Africa to boost economic growth and avoid international trade. This practice was also carried out to eradicate the reliance on developed countries for certain goods and creating a diversified market for the manufacturing industries in these countries (Bruton, 1998).
These countries justified engaging in import substitution as a means of growing the infant industries, promoting economic growth and build a unified economic system. These infant industries were thus invested in to help them gain the relevant technological know how to grow, produce efficiently and enable the country in avoiding exploitation from developed countries (Bruton, 1998).
These countries were also following in the footsteps of developed nations like the US and France who had at that point in the 1960s and 1970s were engaged in high levels of protectionism to boost economic growth. This created distress for these developing countries because they believed that if these big nations did not want to take part in trade chances are they would be exploited if they engaged them and they wanted to avoid that and boost their developmental chances (Baldwin, 2004).
Trying to build these new industries without the help of developed nations led to the home grown products being more expensive than the imported ones, due to a rise in the costs of production. Even with the high prices trade was encouraged and import substitution became a major strategy in development for these countries. This caused a number of industries such as the steel, textile, brewery and bakery industry to grow faster. This strategy enables these countries achieve on average a 5.5% rise in industrial GDP growth rate between the 1970s and 1980s (WTO,2003).
After a long period of this strategy being implemented it began failing, the low skill intensive markets were not being developed as they were only effective in internal markets. A reason for this failure could be the inability of the economy to create opportunities for the newer markets as the rural markets were ignored for solemnly the upper middle class markets and up (Bruton, 1998). This caused agriculture to remain stagnant, which is a bad thing as agriculture is still one of the main origins of income to these countries and a large percentage of the population is still very reliant on agriculture. Therefore, limiting the growth of agriculture was damaging their chances of survival, thus starting the period of income gap many economists believe, as only the urban economies grew (Damiond, 1999).
Lack of finance to maintain the growth of these economies may have been another reason this strategy failed. The growth of the urban economies and the stagnation of the rural economies meant whatever money was made form the urban economies were put into the rural ones to fight off competition, however, constantly doing this without actively trying to grow these rural economies meant an increased number of debt for the nations. Hence, the 1980s were notable for an increase in debt in the economy, rise in poverty rates due to the increasing population, market failures as well health shocks (Bird, Handley, Higgins, Sharma and Cammack, 2009).
The period after the 1980s is very notable as the period where international trade became very common and prominent, a lot of the trade barriers were dropped and countries were encouraged to trade with one another. World trade as a ratio between import and export grew more than its initial value since the 1980s, and its share of the world GDP rose from 36% to 55%. Many emerging markets such as Sub-Saharan Africa arose during this period, increasing their income, and building relationships with developed countries. These countries were also able to gain specialisation and engage in division of labour which booster economic growth (Papageorgiou, Jaumotte and Lall, 2013).
The Heckscker-Ohlin (HO) Model was developed in the 1990s as well, to aid in understanding the link between international trade and income inequality. The model states that a country should export commodities that effectively utilises the factor it has exhaustive resources in causing the country to operate at a low cost and increasing its return on that factor (Leafmer, 1995). According to the model, inequality increases in richer (developed) countries and decreases in poorer (developing) countries; inequality should be higher in countries abundant in highly skilled economies (Harrison, McLaren and McMillan, 2011). However as highlighted by Hammouda, Achterbosch, Osakwe and Tongeren (2004) developing countries have also seen a steady rise in inequality, especially during the rapid globalisation period.
Inequality in trade could also be caused by relative factor returns, this occurs when the price of the abundant resource of a country increases due to a rise in demand for the good it produces causing the owner of said factor to make a higher profit and increase his income share. The widening of employment and wages and contraction of production due to the availability of the abundant resource factor could also lead to increased international trade. The author Anderson (2005) suggests a point that distributional policies introduced for specific sector of these nations will suffer losses from redistributing income. However, Spilimbergo, Londono and Szekely (1999) proposes something different saying these countries will not allow such policies because they operate in a liberal government that chooses not to redistribute income.
Trade and the other factors mentioned above are not the only ways to go about developing an economy, the countries in Sub-Saharan Africa should hence not focus on just these, but they should aspire to build the rest of the economy. If, for instance, one sector in an economy is facing constant growth with each passing year then it is important the the country’s government help develop the other sectors to achieve the same. Doing this will help eradicate income inequality.
2.4 Trends in Income Inequality: Sub-Saharan Africa
Pinvovskly (2012) in his study of income inequality in sub-Saharan Africa stated that after estimating the income distributions of these countries, comparing them to poverty and inequality rates the popular belief that “inequality and poverty rates are reducing” is right. Alternatively, a research paper using the United Nations University World Institute for Development Economics Research (UNU-WIDER) dataset (2008) using the Gini indexes for 43 sub-Saharan African countries over the years from 1960-2006 showed that income inequality has generally fallen in African countries. According to the research, inequality values have fallen from 0.66 in the 1970s to 1990s to 0.5 in the 2000s. Thus signalling a steady decline in inequality however, this research has faced numerous criticism due to the limited data used to make a general conclusion for the entire sub-Saharan African region.
The most frequently used indicator to measure income inequality is the “Gini coefficient”, because it is one that is most responsive to income changes in the middle of the economy (middle class) and not the extreme ends and beginnings of the distribution (Bastalgi, Coady and Gupta, 2012). In comparison, the Palma developed by Gabriel Palma (2011) is sensitive to the income changes of the poorest 40% and richest 10%. This measure could very possibly be better to measure income inequality than the Gini coefficient, however it has been recommended by economists that both measures be used interchangeably and are a close fit (Cobham and Summer, 2013).
The growth of the financial industry is very important when discussing income inequality as this also has as much of an important role to play in income inequality as trade does. This industry has grown in leaps and bounds over the past three decades even when compared to that of the advanced economies. Delechat et al. (2009) made the statement that sub-Saharan Africa accounted for $76 billion in “capital net flows in 2007, and private capital flows contributed to 10% of the region’s GDP”. Even with his, the income gap has continued getting wider in Africa through financial development schemes such as pension.
Technology is another factor capable of affecting income inequality, technological growth has seen African economies move from industrial sectors to service sectors. It produces skilled workers capable of doing so much more, this in turn raises the value of these workers causing their income to rise in most cases. Engaging in technology has seen developing countries enjoy a 5% yearly rise in per capita income which their counterparts who did not engage in this failed to enjoy, they however were subject to a 1.3% increase in income (Winters, 2004).
As beneficial as technological progress is to any economy, it mostly benefits highly skilled workers thus leading to an income inequality as skill bias becomes prominent. Income inequality due to technological progress is very prominent in developed sectors of labour markets. The belief is that technology will replace labour intensive workers and this may stunt economic growth as individuals in the economy may be left without a source of income (Acemoglu, 2002). Papageorgiou et al. (2008) believe income inequality as a result of technological advancement could be the foundation for globalisation. However, they believe it could also have different impacts depending on where and how it is applied and may not always have a positive result. For instance, technology has aided the production of mechanised goods and services while alternatively leading people to be on their computers all the time promoting idleness and could lead to unproductivity.
Corruption as a factor that influences income inequality is very common in sub-Saharan Africa. Many of the tax systems introduced by the government tend to favour the rich in the economy thus leading to an unequal distribution of assets. It also affects efficiency of investments in these economies stunting economic growth. Many studies have not focused on the role corruption plays but ignoring it would be ignoring a major facet of income inequality (Gupta et al, 1998).
Education in any economy is a good thing as it is a strong determinant of an individual’s future in the country. Education links people with the skills to make themselves better and increase their chances of finding a high quality, good paying job. Education leads to a more equalised income distribution (Papageorgiou et al 2008). Income inequality is very popular among higher than secondary school certificate earners, due to the industries they decide to be a part of and the number of people in that industry that have the same qualification level they do. If these situations hold, then income inequality is inevitable, unemployment here will also be on the rise. Thus many economists encourage individuals to partake in higher education as the chances of inequality occurring on a tertiary level are slimmer than on a secondary school level. Many of these researches have focused on education for 15 and over leaving the 25 and over age group, study into these groups could help in better understanding income inequality (Fournier and Koske, 2012).
The measures used to understand income inequality are many and wide and no restrictions exist however, the Gini and the Palma are the most commonly used measures hence why they were heavily discussed. Also, the paper by Papageorgiou et al. (2008) focused on the effect technology, financial development, trade and education have on income inequality but this dissertation will focus solely on international trade and other variables to show their effects on it. The following chapter introduces the data used, the empirical model and analysis of the results.
Chapter 3
Methodology
This chapter is divided into three sections. The first addresses the data set used, the second the empirical model and the third section discusses the limitation of this study.
3.1 Data Set
A huge part of this dissertation banks on pre-existing research and literature carried out on this topic. There was a lot of inconsistency and missing data regarding sub-Saharan Africa hence, the task of selecting the highest quality data arose (Jerven, 2009). The data used was a reflection of factors believed to affect income inequality in sub-Saharan Africa. Scarcity of data meant this research was limited to 40 countries, as well as a sample of 115 observations from 1980 to 2015, this data is taken yearly with a majority of data coming from the World Bank Povcal Database designed by Chen and Ravallion (2007) and the World Bank Development Indicators (2015).
The Gini coefficient was used as the dependent variable in the regression model as it is the commonest measure of income inequality used and results are indicated on a 0 to 100 scale representing perfect equality and perfect inequality respectively. Existing literature was used to determine the independent variables, with these variables being ‘demographic’, ‘developmental’ and ‘macroeconomic’. The following sub sections go into detail on why they were chosen and how they fit into the model.
3.1.1 Demographic Variables
According to Gallo (2002), swift urban activity growth as opposed to agriculture has led to a rise in income inequality. Income inequality is defined by the differences between the rich and the poor and increased urbanisation only highlights these differences more causing income inequality to rise hence, why it is included in the study. Kuznets (1955) claims that the per capita average income experienced in the urban areas is higher than those in the rural areas. Also, when comparing income percentages in the rural area compared to the urban area, it is usually narrower.
3.1.2 Developmental Variables
Existing literature on income inequality has highlighted the role Information and Communications Technology [ICT] has played on this topic. A large portion of past research has also used ICT as an independent developmental variable. Technology has had both a positive and negative impact in the development of developing and developed countries and may be responsible for the changes in income inequality. The number of mobile phone lines in Sub-Saharan Africa is used in this research as it shows the level of infrastructure in communication (Acemorglu, 2002).
3.1.3 Macroeconomic Variables
These variables study the economy as a whole. Macroeconomics and trade have a heavily webbed relationship because the government uses the latter’s tools to protect infant industries, as well as erect certain barriers to trade. Macroeconomics helps in showing the performance of an economy; showing how much it has grown and how stable it is (World Trade Organisation, 2004 p.91).
Determining if international trade is one of the causes of income inequality means looking into imports and exports of the countries in question which is part of the demand and supply of a nation. Income is also used as a macroeconomic variable in this model as well as export volume index and import volume index. Per capita GDP is also included in this model as an independent variable as well as gross domestic savings as a percentage of GDP.
3.2 The Model Specification
The regression model taken for this model is similar to that used in Jaumotte (2008) and it is:
〖Gini〗_it= α_i+ β_1 GDPPC_it+β_2 GDPPC_it^2+β_3 GDS_it+β_4 Exports_it+β_5 Imports_it+β_6 Urban_it+β_7 Infrastructure_it+u_it
Where:
Gini = level on income equality or inequality at level “it”
GDPPC = GDP per capita in constant 2005 US dollars
GDS = Gross Domestic Savings as a percentage of GDP
Exports = Export Volume Index
Imports = Import Volume Index
Urban = % of population in urban areas
Infrastructure = mobile phone lines per 100 people (Fulsang, 2013).
Note: To show control for the unobserved heterogeneity at the country level country dummies (i) are introduced and uit illustrates the random outlier. Also, all independent variables are lagged by one period because the values are going to be more effective than if the present values are used.
3.3 Limitations of the study
Few limitations arose when researching this paper, one of them being missing data which was briefly explained in the methodology. This problem came about because most of these countries did not gain independence until the late 1980s to 1990s causing some of these data to be missing. True for countries such such as Somalia, South Sudan and Eritea.
Missing data also affected the number of independent variables that could be added to the regression analysis, one of them being education. The research paper by Papageorgious et al (2008) tried to show how effective educated people in the society were at making better economic choices however, incomplete date existed for the age group 15 and over who had completed their education. Of the 40 countries looked at 14 had missing data regarding education so the variable had to be excluded.
Future research into this topic has to include various datasets and methods also, other causes of income inequality has to be investigated to be used as literature in the future.
Chapter 4
Econometric Results and Interpretation
Empirical results are explained in the chapter. The first table describes the dependent and independent variable.
Table 1: Descriptive Statistics
Observations Mean SD Min Max
Gini Index 118 45.56 8.86 28.90 74.33
Per Capita Income in Constant US Dollars 118 1412.62 2104.48 195.18 13158.59
Gross Domestic Savings as % of GDP 115 14.61 10.95 0.05 58.35
Telephone Lines Per 100 people 117 2.65 5.50 0.02 29.37
Urban Population 118 36.68 13.74 5.06 83.42
Export Volume Index 118 138.55 162.99 17.57 1651.13
Import Volume Index 118 158.67 111.92 34.22 648.06
The Gini index varies between 28.90 and 74.33 when measuring the income distribution with the mean being 45.56. This shows a wide variance in income distribution between these countries. Per capita income among these countries also shows a huge variance as it ranges from a very low minimum of $195.18 to a high maximum of $13158.59. the standard deviation of 2104.48 concurs that income values are not close to being uniform among these countries. The export volume index which range from 17.57% to 1651.13% of GDP shows that these countries are willing to trade with other countries. Furthermore, communication variable [telephone lines] which has a minimum value of 0.02 and a maximum value of 29.37 shows a smaller variation. The presence of the standard deviation being 2.65 shows that some of these countries are unwilling to improve their infrastructure via telephone. The urban population with a mean value of 36.68 ranging from 5-06 as its minimum and 83.42 as its maximum shows a wide variance in the data.
Table 2 and 3 reports the regression and in the first table, the square of per capita GDP constant in 2005 US dollars is not included as one of the independent variables.
Table 2: Regression Results with Per Capita Income
Coefficient S.E. t-statistics p-value
Per Capita Income in Constant US Dollars 0.005 0.025 1.866 0.057*
Gross Domestic Savings as % of GDP 0.163 0.105 1.450 0.151
Export Volume Index -0.021 0.013 -1.690 0.096*
Import Volume Index -0.020 0.008 -2.350 0.022**
Urban Population 0.437 0.178 2.460 0.016**
Telephone Lines per 100 people -2.238 0.923 -2.420 0.018**
Constant 34.358 7.414 4.630 0.000***
No. of Observations 114
R-Squared 0.790
Adjusted R-Squared 0.665
Table 2 shows the positive relationship between per capita GDP and inequality. Export and import volume index is shown to reduce inequality, urban population here is seen to have a positive relationship with income inequality. Also, infrastructure and inequality have a negative relationship. The next table shows how squaring the per capita GDP changes the results. R-squared and adjusted r-squared are different because of the size of the model.
Table 3: Regression results with Square of Per Capita Income
Coefficient S.E. t-statistics p-value
Per Capita Income in Constant US Dollars -0.006 0.005 -1.290 0.202
Square of Per Capita Income in Constant US Dollars 0.000 0.000 2.100 0.039**
Gross Domestic Savings as % of GDP 0.165 0.103 1.600 0.113
Export Volume Index -0.020 0.012 -1.670 0.100*
Import Volume Index -0.015 0.009 -1.720 0.090*
Urban Population 0.419 0.174 2.410 0.018**
Telephone Lines per 100 people -1.865 0.919 -2.030 0.046**
Constant 48.144 9.775 4.930 0.000***
No. of Observations 112
R-Squared 0.804
Adjusted R-Squared 0.672
Note: * 10% significant level, ** 5% and *** 1% [FOR ALL TABLES].
Table 3’s result is not surprising; the new variable did alter the adjusted r-squared value from 66.6% to 68.2% highlighting how good it is at predicting income inequality. The table above also shows the unbiasedness of the regression, the positive t-statistic value (2.1) shows that income has a positive and important relationship with income inequality. International trade is negatively related with income inequality as noted by the negative values of trade globalisation variables. Also, the population could help determine income inequality as the urban population variables are positive. From the above tables, it can be concluded that international trade is negatively related to the income inequality in sub-Saharan Africa.
4.1 Summary of Results
The question highlighted in the aim has been partially answered by the regression model introduced which shows that international trade is negatively related with income inequality which is supported but many papers. The question then arises ‘should further literature be taken into consideration?’, ‘is income inequality truly inevitable?’
However, taking into consideration the work done in this dissertation it is safe to say that as a country continues to grow, income inequalities will definitely occur. The task of most countries now is to ensure these inequalities do not widen to a point where it becomes the norm and there is social unrest in the nation. Every country has to develop and policies have to be put in place to combat the negative side effects of development’ (as cited in Adeyemi, 2015).
Conclusion
International trade may seem like a new phenomenon but it is not. It had just evolved from trade by barter which was the exchange of ‘goods and services’ for ‘goods and services’. This evolution is as a result of improved technology and new ideas from individuals and businesses which has been very beneficial to almost all countries but has led to some costs such as income inequality.
This dissertation aimed at deciphering if international trade was a cause of income inequality in sub-Saharan Africa. This was done by using relevant theory and and existing literature to serve as a foundation for the regression results. Different opinions arose when going through literature on this topic. Most economists agree that international trade has caused a drop in economic growth. Since international trade is a by product of development, a similar conclusion could be drawn that international trade has a negative relationship with income inequality and with accordance to Papageorgiou et al. 2008’s research it is inevitable when development occurs.
The positive results yielded from the regression analysis using the other independent variables shows that international trade is not the only thing causing income inequality in sub-Saharan Africa. The task for new researchers is to analyse the effect other variables such as corruption and education have on income inequality. Future research also needs to discuss whether income inequality is actually inevitable and something that needs to be planned for.