Recalculation of the Human Development Index via Multiplicative Data Envelopment Analysis

• Published in: Social indicators research Vol. 175; no. 1; pp. 217 - 245
• Main Author: Özekin, Ayça
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2024; Springer Nature B.V
• Subjects: Computation; Correlation; Correlation coefficient; Criticism; Data; Data analysis; Data envelopment analysis; Dependency; Development programs; Extraction; Human development; Human Development Index; Human Geography; Income; Indexes; Low income groups; Microeconomics; Original Research; Public Health; Quality of Life Research; Social Sciences; Sociology; Values; Multiplicative data envelopment analysis; Sub-dimensions; Human development index; MNCP model
• ISSN: 0303-8300; 1573-0921
• DOI: 10.1007/s11205-024-03441-5

The human development index (HDI) of the United Nations Development Programme (UNDP) has been subject to criticism since 1990 due to several reasons. These include the way it calculates sub-dimension indices from raw data, its method of combining these indices, and the equal weight it assigns to each sub-dimension. Data envelopment analysis (DEA) models address these criticisms by reevaluating the human development index, resolving dependency concerns, and enabling direct data extraction. Furthermore, advancements in DEA techniques have resulted in alternative models that are more appropriate for calculating HDI. The study aims to recalculate the Human Development Index of 2019 for 189 countries using the Multiplicative Non-parametric Corporate Performance Model. This model, based on multiplicative data envelopment analysis and commonly used to create composite indices from ratio data, is the basis of the study. The evaluation will assess the correlation between the MNCP model results and the values of the human development index within the framework of transforming the income level into human development. Tests of correlation between the MNCP model results and the HDI values show that the correlation coefficients are positive and statistically significant for all income-level countries, despite the observation of significant average rank differences in lower-income and higher-income countries. In line with our expectations, rearranging the weight of the sub-dimensions in the MNCP model can alter countries’ rankings, according to the findings. Nevertheless, the strong correlation values indicate that instead of focusing solely on developing a new method of calculation, it is important to underline the need to reflect on the sub-indicators used in the current HDI calculation, especially those that are open to debate, such as income.