【佳文推荐】让数据来定多维指数的临界值：基于机器学习的人类发展指数分类

*中文标题和摘要系简单翻译，可能存在部分错误，请以英文为准

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Hanjie Wang, Jan-Henning Feil, and Xiaohua Yu* (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, in press

摘要：

人类发展指数（HDI）的分类是至关重要的，因为它涉及到国际援助政策和商业战略。尽管现有文献批评了人类发展指数临界值的任意性，但很少有人提出理想的方法来克服这一缺点。本文首先采用了无监督的机器学习技术，即K-means聚类和Partitioning Around Medoids算法，对HDI进行聚类，并结合目前的HDI计算方法，为国家分类提供更合理的临界值。结果表明，鉴于2018年的HDI数据集，我们可以将全世界的国家分为三个集群。我们建议用0.65和0.85的临界值来划分低、中、高人类发展国家。本文为基于国家发展的相似性而非主观判断的人类发展指数的分类提供了一个新的视角。

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.

原文链接：

http://doi.org/10.1016/j.seps.2023.101523

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来源：三农学术

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