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

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  • Wang, Hanjie
  • Feil, Jan-Henning
  • Yu, Xiaohua

Abstract

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.

Suggested Citation

  • Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2023. "Let the data speak about the cut-off values for multidimensional index: Classification of human development index with machine learning," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pa:s0038012123000162
    DOI: 10.1016/j.seps.2023.101523
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    2. Zhang, Shuyuan & Xie, Wenlan & Sun, Siying & Wu, Fan & Xue, Ying, 2024. "Nexus of green energy innovation, governance quality, and CO2 emissions in natural resource sector: The role of sustainable human development," Resources Policy, Elsevier, vol. 88(C).
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    4. Zhou, Yu & Chen, Yang & Liu, Shenyan & Kou, Gang, 2024. "Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).

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    More about this item

    Keywords

    Human development; Cut-off values; Unsupervised machine learning; K-means clustering; PAM;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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