Unemployment in Rural Europe: A Machine Learning Perspective

Abstract

This paper aims to provide policy-relevant findings that can contribute to the resilience of rural regions by discovering the main individual-level factors related to unemployment in those areas through the use of a set of machine learning techniques. Unemployment status is predicted using tree-based classification models: namely, classification tree, bootstrap aggregation, random forest, gradient boosting, and stochastic gradient boosting. The results are further analyzed using inferential techniques such as SHAP value analysis. Results suggest that access to training programmes can mitigate the labor market inequalities caused by differences in education levels, gender, age, alongside with parental education levels. The results also show how such inequalities are even larger for various subgroups detected by the employed algorithms.