Abstract
AbstractSocioeconomic 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.
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