Factors that Contribute to Income Inequality

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Abstract

This paper aims to take a deep, analytical dive into the factors that contribute to income inequality. Inequality has been a major issue across the world for centuries and has continued to puzzle economists with some reaching the conclusion that it is an inevitable by-product of a free market. Growing inequality in the last few decades has fueled various policy debates around possible solutions to this issue affecting millions of people.

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The Gini is a reliable measure of inequality developed by Italian statistician Corrado Gini and it ranges between both extremes represented by 0 and 1; with 0 being perfect equality and 1 being total inequality. With a score of 0.41, the United States, for example, is considered to be moderately unequal compared to the rest of the world. Several factors are thought to cause inequality in a particular economy such as tax policies and public education; but this paper seeks to understand the correlation between income inequality and the average life expectancy of a population. The purpose of this research is to be able to identify policy directions that countries can take in order to reduce income inequality.

Introduction

All governments are faced with the challenge of distributing income in an equitable way; as a means to reduce inequality. Virtually every country has an issue with income disparity, with some being more adversely affected than others. Some have argued that inequality is not entirely bad as it increases welfare for most citizens, however, the consensus seems to be that less inequality is the ideal state of any economy. The question of income inequality is more than just a moral one with several studies pointing to economic benefits brought about by higher wages for working class people. The argument is that high wages for the low and middle class families has a positive effect on consumption and aggregate demand in an economy. There is a positive direct correlation between reduced inequality and GDP growth which and this gives governments all the more reason to find an optimal strategy for income redistribution. Progressive taxes, for example, ensure that wealthy individuals pay a higher proportion of their income in taxes which means that less of the burden is placed on poor and working class families. Some have however cited the economic benefits of reducing taxes for the wealthy, arguing that it stimulates job growth by creating an enabling environment for business to thrive. Capital, they argue, is attracted by this low tax environment, which means employers are able to create more business opportunities and thus hire more people. A good example of this is the debate on minimum wage that has had some of the largest corporations facing criticism over their hiring practices. Some large corporations have even threatened to replace some of this labor using technology, claiming that higher wages are punitive to business.

This paper, however, focuses on the relationship between age and inequality in an economy. The hypothesis makes the assumption that a higher life expectancy means less inequality for any given country holding all other factors constant. This allows for solutions around health, education and family planning, which could go a long way into reducing inequality for some of the worst hit countries.

Literature review

Most of the literature around the subject of inequality seeks to find the root causes and focuses mostly on the quantifiable economic components of the problem.

Data

The Gini coefficient is one of the key variables used for this research as well as factors affecting life expectancy data. The initial data set, as shown below, has been gotten from the CIA website which breaks down several countries income statistics.

  1. Lesotho 63.2
  2. South Africa 62.5
  3. Micronesia, Federated States of 61.1
  4. Haiti 60.8
  5. Botswana 60.5
  6. Namibia 59.7
  7. Zambia 57.5
  8. Comoros 55.9
  9. Hong Kong 53.9
  10. Guatemala 53.0
  11. Paraguay 51.7
  12. Colombia 51.1
  13. Papua New Guinea 50.9
  14. Panama 50.7
  15. Chile 50.5
  16. Rwanda 50.4
  17. Eswatini 50.4
  18. Gambia, The 50.2
  19. Brazil 49.0
  20. Congo, Republic of the 48.9
  21. Nigeria 48.8
  22. Costa Rica 48.5
  23. Kenya 48.5
  24. Mexico 48.2
  25. Dominican Republic 47.1
  26. Nicaragua 47.1
  27. Honduras 47.1
  28. Bolivia 47.0
  29. China 46.5
  30. Malaysia 46.2
  31. Malawi 46.1
  32. Togo 46.0
  33. South Sudan 46.0
  34. Ecuador 45.9
  35. Singapore 45.9
  36. Saudi Arabia 45.9
  37. Mozambique 45.6
  38. Peru 45.3
  39. United States 45.0
  40. Cameroon 44.6
  41. Guyana 44.6
  42. Thailand 44.5
  43. Iran 44.5
  44. Philippines 44.4
  45. Central African Republic 43.6
  46. Chad 43.3
  47. Zimbabwe 43.2
  48. Israel 42.8
  49. Angola 42.7
  50. Burundi 42.4
  51. Ghana 42.3
  52. Gabon 42.2
  53. Congo, Democratic Republic of the 42.1
  54. Argentina 41.7
  55. Uruguay 41.6
  56. Cote d’Ivoire 41.5
  57. Russia 41.2
  58. Madagascar 41.0
  59. Morocco 40.9
  60. Djibouti 40.9
  61. Turkmenistan 40.8
  62. Senegal 40.3
  63. Turkey 40.2
  64. Bulgaria 40.2
  65. Georgia 40.1
  66. Mali 40.1
  67. Tunisia 40.0
  68. Jordan 39.7
  69. Uganda 39.5
  70. Burkina Faso 39.5
  71. Guinea 39.4
  72. Sri Lanka 39.2
  73. Venezuela 39.0
  74. Bhutan 38.8
  75. Serbia 38.7
  76. Maldives 38.4
  77. Cambodia 37.9
  78. Japan 37.9
  79. Yemen 37.9
  80. Lithuania 37.9
  81. Tanzania 37.6
  82. Mauritania 37.0
  83. Indonesia 36.8
  84. Uzbekistan 36.8
  85. Greece 36.7
  86. Laos 36.7
  87. Benin 36.5
  88. New Zealand 36.2
  89. El Salvador 36.0
  90. Falkland Islands (Islas Malvinas) 36.0
  91. Mauritius 35.9
  92. Spain 35.9
  93. Korea, South 35.7
  94. Algeria 35.3
  95. India 35.2
  96. Jamaica 35.0
  97. Macau 35.0
  98. Vietnam 34.8
  99. Estonia 34.8
  100. Cyprus 34.8
  101. West Bank 34.5
  102. Latvia 34.5
  103. Sierra Leone 34.0
  104. Niger 34.0
  105. Mongolia 34.0
  106. Greenland 33.9
  107. Portugal 33.9
  108. Bosnia and Herzegovina 33.8
  109. Azerbaijan 33.7
  110. Macedonia 33.7
  111. Taiwan 33.6
  112. Kyrgyzstan 33.4
  113. Ethiopia 33.0
  114. Nepal 32.8
  115. Tajikistan 32.6
  116. United Kingdom 32.4
  117. Canada 32.1
  118. Bangladesh 32.1
  119. Liberia 32.0
  120. Montenegro 31.9
  121. Italy 31.9
  122. Timor-Leste 31.9
  123. Egypt 31.8
  124. Armenia 31.5
  125. Ireland 31.3
  126. Poland 30.8
  127. European Union 30.8
  128. Sao Tome and Principe 30.8
  129. Croatia 30.8
  130. Pakistan 30.7
  131. Austria 30.5
  132. Luxembourg 30.4
  133. Netherlands 30.3
  134. Australia 30.3
  135. Switzerland 29.5
  136. France 29.3
  137. Denmark 29.0
  138. Albania 29.0
  139. Hungary 28.2
  140. Malta 28.1
  141. Iceland 28.0
  142. Romania 27.3
  143. Finland 27.2
  144. Germany 27.0
  145. Moldova 26.8
  146. Norway 26.8
  147. Belarus 26.5
  148. Kazakhstan 26.3
  149. Belgium 25.9
  150. Ukraine 25.5
  151. Czechia 25.0
  152. Sweden 24.9
  153. Slovenia 24.4
  154. Slovakia 23.7
  155. Kosovo 23.2
  156. Faroe Islands 22.7

Random sampling

The data used in this research qualifies as a random assumption as it uses 20 countries from the list of 157 with regards to life expectancy data.

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