Decision tree insights into spatial and temporal patterns of convergence in EU labour markets

This study aims to enhance our understanding of convergence patterns within the European Union by analyzing labour market efficiency (LME) indicators across 258 NUTS2 level regions from 25 EU countries. Utilizing advanced statistical methods based on decision trees, we investigate whether the data reveal groups of NUTS2 regions that exhibit shared patterns of convergence, divergence or structural similarities. Our methodological innovation lies in the application of a decision tree model to create detailed maps of European regions. This innovative approach allows us to assess the performance of regions based on multiple labour market indicators, identifying nodes that we translate into regional clusters. This novel method provides a more salient and empirically driven evaluation of convergence patterns in European labour markets and enables us to assess changes in these patterns over time. Our findings highlight the persistence of certain labour market characteristics and uncover regions that deviate from expected trends, revealing a heterogeneous and dynamic labour market landscape across Europe. These findings have the potential to inform more nuanced and region-specific economic policies that cater to the distinct labour market conditions across Europe, fostering more tailored approaches to regional development and convergence.