Predicting Regional Unemployment in the EU

Elena Paglialunga (), Giuliano Resce () and Angela Zanoni ()

Economics & Statistics Discussion Papers from University of Molise, Department of Economics

Abstract: This paper predicts regional unemployment in the European Union by applying machine learning techniques to a dataset covering 198 NUTS-2 regions, 2000 to 2019. Tree-based models substantially outperform traditional regression approaches for this task, while accommodating reinforcement effects and spatial spillovers as determinants of regional labor market outcomes. Inflation—particularly energy-related—emerges as a critical predictor, highlighting vulnerabilities to energy shocks and green transition policies. Environmental policy stringency and eco-innovation capacity also prove significant. Our findings demonstrate the potential of machine learning to support proactive, place-sensitive interventions, aiming to predict and mitigate the uneven socioeconomic impacts of structural change across regions.

Keywords: Regional unemployment; Inflation; Environmental policy; Spatial spillovers; Machine learning. (search for similar items in EconPapers)
JEL-codes: E24 J64 Q52 R23 (search for similar items in EconPapers)
Pages: 30
Date: 2025-10-15
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Persistent link: https://EconPapers.repec.org/RePEc:mol:ecsdps:esdp25101

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