Socioeconomic variables have been studied in many different contexts. Considering several socioeconomic variables as well as using the standard series clustering technique and the Ward’s algorithm, we rank the countries in the world and evaluate the similarity and inequality between geographic areas. Various relationships between variables are also identified. Additionally, since the Gini coefficient is one of the most frequently used metrics to measure economic inequality, with a global scope, we model this coefficient utilising machine learning techniques. 16 exploratory variables are utilised, which pertain to the health (9), economic (2), social labour protection (4) and gender (1) fields. International repositories that include time series of variables referred to these domains as well as education and labour market fields are used.

社会经济变量已在许多不同的背景下进行了研究。考虑到几个社会经济变量以及使用标准系列聚类技术和沃德算法，我们对世界各国进行排名并评估地理区域之间的相似性和不平等性。还确定了变量之间的各种关系。此外，由于基尼系数是衡量全球范围内经济不平等最常用的指标之一，因此我们利用机器学习技术对该系数进行建模。使用了 16 个探索性变量，涉及健康 (9)、经济 (2)、社会劳动保护 (4) 和性别 (1) 领域。使用的国际存储库包括涉及这些领域以及教育和劳动力市场领域的变量时间序列。