Work

Predicting policy funding allocation with Machine Learning

Year: 2025

Type: article

Abstract: Allocating funds through competitive opportunities is a core tool of place-based development policies, and it can generate economic benefits and support the revitalisation of left-behind territories. By relying on Machine Learning techniques, this paper investigates the predictability of actors expected to benefit from EU development funding over the 2014-2020 period in Italy. The results show that it is possible to make out-of-sample predictions and diagnose the precise factors influencing fund allocation, such as territorial attributes, economic dimensions, and production specialization. Knowing in advance potential winners of the calls can help design tailored territorial, and even sectorial, policies to address the obstacles to local development and green transition, and to efficiently distribute resources within the policy framework. This evidence contributes to the reflection launched by the Commission’s on the future of the competitiveness of the EU.

Source: Socio-Economic Planning Sciences

DOI: 10.1016/j.seps.2025.102175

Authors: Nicola Caravaggio, Giuliano Resce, Cristina Vaquero-Piñeiro

Institutions: University of Molise, Roma Tre University

Language: English

Topic: Regional Development and Policy

Subfield: Political Science and International Relations

Field: Social Sciences

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Estimated APC paid: $3,550