A data-driven approach for the European Gender Equality Index

Gender equality is a fundamental human right and an objective in the United Nations Agenda 2030 for sustainable development. Assessing the gender gap usually relies on composite indicators: tailored statistical tools that are effective in summarizing a set of indices in a single number. However, the availability of regional microdata can open the way to statistical learning tools that leverage the information contained in big structured datasets to allow deeper analyses. In this work we employ an Object-Oriented Bayesian Network to measure the gender gap on Italian province level data. The model is consistent with the European Gender Equality Index, while enabling the investigation of multivariate interactions and the simulation of scenarios. The proposed approach shows how statistical learning can enrich traditional composite indicator analysis and shed light on the determinants of gender inequality.