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Predicting Regional Unemployment in the EU

Author

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  • Paglialunga, Elena

  • Resce, Giuliano

  • Zanoni, Angela

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.

Suggested Citation

  • Paglialunga, Elena & Resce, Giuliano & Zanoni, Angela, 2025. "Predicting Regional Unemployment in the EU," Economics & Statistics Discussion Papers esdp25101, University of Molise, Department of Economics.
    • Handle: RePEc:mol:ecsdps:esdp25101
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    Keywords

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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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