Conclusion and Recommendations
Exploring determinants of income inequality by showing the importance of each component is the challenge in the pursuit to create well-founded policies against income inequality.
In recent years, many approaches have been used to deal with income inequality problem and tried to measure it with different measures. Most of these approaches aren't concerned with the overlapped Data and the others realized this problem but can't overcome it.
The core goal of this thesis is to estimate those findings showed in chapter 5 from table 7 up to table 17 that are important for Egypt today.
If income inequality is effectively high, the government of Egypt should be called to prioritize redistribution policies. But if inequality is low, this may simply be a sign of widespread hardship, low and stagnant income opportunities, low labor demand and ineffective markets. In this case, the focus of the government should be better placed on investments, inclusive growth measures, improving jobs and income opportunities, and better allocation of existing resources rather than a simple redistribution of government revenues.
So, the thesis findings suggests that income inequality in Egypt is more likely to characterize this second scenario.
Therefore, this thesis interested in the proposed approaches presented in this thesis give insight into the structure of income inequality throughout concluding the following advantages for the proposed approaches:
By reviewing the concepts of poverty, social welfare and specially income inequality. Through, describing several of income inequality measures, poverty measures and social welfare measures.
By discussing axioms of income inequality especially decomposability which illustrated deeply in chapter4 for Gini coefficient, schutz coefficient and the Generalized Entropy Family coefficient. Moreover, these coefficients are very critical income inequality measures.
The approaches were discussed and applied on real dataset which the Egypt's HIECS 2013 and 2015 rounds. All of them applied with exact decomposition that overcomes the overlapped data problem. This is the most important benefit of the proposed approaches for the income distribution especially S Gini coefficient approach.
Three measures have been used various techniques to overcome overlapping Data problem. Deeply, S Gini coefficient has been used in developing two levels the group level then population level, while Schutz has been used in eliminating a residual term by splitting up it into within group term and between group term. Finally Generalized Entropy coefficients can be deal with overlapping Data directly.
Deeply, by comparing three coefficient with each other which The S Gini, The Schutz and Generalized Entropy coefficients concluding the following:
The S Gini coefficient for between inequality term greater than within inequality term showed in table 8 and table 9 for 2013 and 2015 rounds respectively since it depends on actual householder income directly.
The Schutz coefficient for within inequality term greater than between inequality term showed in table 10 and table 11 for 2013 and 2015 rounds respectively since it depends on mean income of householder not actual householder income directly.
The Generalized Entropy coefficients have been described as follows:
I_0 The Mean logarithmic deviation coefficient for between inequality term greater than within inequality term showed in table 12 and table 15 for 2013 and 2015 rounds respectively since it depends on actual householder income directly.
I_1 The Theil's T coefficient for within inequality term greater than between inequality term showed in table 13 and table 16 for 2013 and 2015 rounds respectively since it depends on mean income of householder not actual householder income directly.
I_2 The one half the squared coefficient of variation for within inequality term greater than between inequality term showed in table 14 and table 17 for 2013 and 2015 rounds respectively since it depends on mean income of householder not actual householder income directly.
Therefore, the researcher concludes the single parameter of Gini coefficient (S Gini coefficient) is the optimum coefficient for income inequality measurement since by applying it for the Egyptian HIECS 2013 and 2015 rounds, the researcher find between inequality term greater than within inequality term by a weighted and acceptable ratio. These findings are more reasonable, logical and realistic one.
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