Let the data speak about the cut-off values for multidimensional index: Classification of human development index with machine learning

• Published in: Socio-economic planning sciences Vol. 87; p. 101523
• Main Authors: Wang, Hanjie, Feil, Jan-Henning, Yu, Xiaohua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Cut-off values; Human development; K-means clustering; PAM; Unsupervised machine learning; Unsupervised machine learning; O15; I31; C14; Human development; PAM; K-means clustering; Cut-off values
• ISSN: 0038-0121; 1873-6041
• DOI: 10.1016/j.seps.2023.101523

The Human Development Index (HDI) classification is essential as it relates to international aid policies and business strategies. Although the existing literature has criticized the arbitrariness of cut-off values of the HDI, few proposed an ideal approach to overcome this drawback. This paper first employs the unsupervised machine learning techniques, the K-means clustering and Partitioning Around Medoids algorithms, to cluster the HDI and offers more reasonable cut-off values for classifying countries in combination with the current HDI calculation method. The results indicate that we can group the countries worldwide into three clusters, given the 2018 HDI dataset. We suggest cut-off values of 0.65 and 0.85 to classify low, medium, and high human development countries. This paper provides a new perspective to classifying the HDI based on the similarity of countries’ development but not subjective judgments. •HDI is vital as it relates to international development and aid policies.•Classification of HDI in the literature is subjective and subject to critiques.•We employ machine learning to classify the HDI into three clusters.•We suggest cut-off values at 0.65 and 0.85 to classify low, medium, and high HDIs.•Machine learning to classify HDI is driven by data with less subjective judgments.