Exploring the Relationship Between Income Inequality and Education: An Analysis Using PISA Test Results and the Gini Coefficient

The article presents a study that explores the relationship between income inequality and education-related factors, in various countries, through the use of PISA test results as well as the Gini coefficient and k-means clustering algorithm as tools for analysis. The Program for International Student Assessment (PISA) test results represent an important and relevant tool to measure education quality throughout the globe, as the test is taken in more than 80 countries, while the Gini coefficient measures the degree of economic inequality within a population. The paper first describes the data and methods used, including the extraction, cleaning, and merging of data from multiple sources using Python libraries. K-means clustering and Spectral Clustering were then applied to the data to cluster the countries based on their Gini coefficient and PISA results for Science, Reading, and Math in 2012, 2015, and 2018. Overall, the study seeks to offer a comprehensive analysis of the relationship between income inequality and education worldwide.