A Novel Look at Socio-Economic Inequalities using Machine Learning Techniques and Integrated Data Sources (INEQUALITREES)

This research project investigates the levels and main drivers of two key manifestations of socio-economic inequality: poverty and inequality of opportunity (IOp, hereafter), by adopting a multidimensional, interdisciplinary and cross-national approach. A key innovative feature of our project consists in the application of machine learning techniques to integrate large-scale and heterogeneous datasets from various sources, including more standard survey data, less common administrative and register data, as well as data extracted from satellite images. The project is multidimensional since we will analyse IOp and poverty with respect to three key individual outcomes: education, income and health. We propose a cross-national view by focusing on Bolivia, Germany, India and Italy.