Modified Human Development Index using Data Envelopment Analysis Approach
- 1 Department of Statistics, Faculty of Economics and Political Science, Cairo University, Egypt
Composite Indicator is considered themathematical aggregation which has wide usage for monitoring performances,conducting benchmarks, analyzing policies, and communicating publicly. HumanDevelopment Index (HDI) is the most popular index which measures humandevelopment through average achievement in its main dimensions: Health status,education status, and living standard but it is faced with several critiques,positive and negative. Moreover, HDI was tested to have a positive andsignificant correlation with natural resource abundance. Therefore, based onMathematical Programming approaches, previously tested for Composite Indicators’development, this research proposes a new calculated HDI using a DataEnvelopment Analysis approach based on the Goal Programming model; includingmissing values’ estimation. This new proposed HDI was validated throughSensitivity Analysis of Normalization and Weighting methods; in addition toWilcoxon Signed Rank Test. The first test shows a positive high correlationbetween the proposed HDIs and the United Nations HDI. Those tests ensure thatHDI rankings are highly correlated and that they are unchanged given thedifferent normalization and weighting techniques. Moreover, they reflect thatthe paired sample mean is not the same. This highlights the advantageousproperty of the proposed HDI; preserving both the advantages of GoalProgramming and Data Envelopment Analysis approaches, in addition to others.
Copyright: © 2022 Yasmine Refai Salama, Ramadan Hamed and Mahmoud Rashwan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Composite Indicator (CI)
- Human Development Index (HDI)
- Goal Programming (GP)
- Data Envelopment Analysis (DEA)
- Missing Values