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A Simple Yet Robust Estimation of Binned Data: Egypt Income Distribution and Geographical Inequality

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Nehal SalahEldin Afifi
Osama
Published in
Sustainable Statistical and Data Science Methods and Practices
Publisher
Springer Nature Switzerland

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Abstract

The chapter delves into the estimation of income distribution and inequality in Egypt using binned data, focusing on the Robust Pareto Mid-Point Estimator (RPME) method. It consolidates data from five surveys conducted between 2008 and 2018, highlighting the income disparities between urban and rural areas. The RPME method is introduced as a refinement of traditional mid-point estimators, using the Pareto distribution to handle open-ended top brackets. The chapter provides a detailed analysis of the method's application, including descriptive statistics and inequality measures. The results show an increasing mean income over time and higher inequality levels in urban areas compared to rural areas, with the top income bracket averages being significantly higher in urban areas. The chapter concludes by emphasizing the efficiency and robustness of the RPME method in estimating income distribution and inequality measures.
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previous chapter A New Estimator for the GPD Parameters Under the POT Approach
next chapter Exploring Practical Applications and Python Code Snippets for Supervised Machine Learning Classification Algorithms
Title
A Simple Yet Robust Estimation of Binned Data: Egypt Income Distribution and Geographical Inequality
Authors
Nehal SalahEldin Afifi
Osama
Copyright Year
2023
Book
Sustainable Statistical and Data Science Methods and Practices

Print ISBN: 978-3-031-41351-3

Electronic ISBN: 978-3-031-41352-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-41352-0_11
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