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Enhancing Implementation SuccessinCohesion Policy. A Machine Learning Approach

Author

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  • Mara Giua
  • Francesca Micocci
  • Giulia Valeria Sonzogno

Abstract

We test the hypothesis that Cohesion Policy (CP) under performance, measured as projects' delays and (Output Indicators) target failures, is systematicaly driven by project-level features of the CP implementation architecture rather than by contextual conditions alone: using Italian project-level data (2014-2020) in a Machine Learning approach, we show how governance ar- rangements in terms of programme type, programmers, activation procedures and beneficiary combine with underlying contextual conditions in predicting projects' outcomes. Successful policy configurations avoiding underperformance can be adopted in an evidence-based perspective by combining some of the existing policy tools and accounting for the socio-economic context upstream.

Suggested Citation

  • Mara Giua & Francesca Micocci & Giulia Valeria Sonzogno, 2026. "Enhancing Implementation SuccessinCohesion Policy. A Machine Learning Approach," Departmental Working Papers of Economics - University 'Roma Tre' 0289, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0289
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    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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