Enhancing Implementation SuccessinCohesion Policy. A Machine Learning Approach

Mara Giua, Francesca Micocci and Giulia Valeria Sonzogno

No 289, Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre

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.

Keywords: CohesionPolicy; EuropeanUnion; Policyimplementation; MachineLearning (search for similar items in EconPapers)
JEL-codes: C53 C55 O18 R11 R58 (search for similar items in EconPapers)
Pages: 45
Date: 2026-03
New Economics Papers: this item is included in nep-cmp
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://economia.uniroma3.it/wp-content/uploads/si ... 2026/03/WP_289-1.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rtr:wpaper:0289

Access Statistics for this paper

More papers in Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre Via Silvio d'Amico 77, - 00145 Rome Italy. Contact information at EDIRC.
Bibliographic data for series maintained by Telephone for information ().