The Determinants of Missed Funding: Predicting the Paradox of Increased Need and Reduced Allocation

This research investigates how local governments overlook competitive funding opportunities within cohesion policies, utilizing machine learning and analyzing data from open calls within the European Next Generation EU funds. The focus is on predicting which local governments may face challenges in utilizing available funding, specifically examining the allocation of funds for Italian childcare services. The results demonstrate that it is possible to make out-of-sample predictions of municipalities that are likely to abstain from invitations, also identifying key determinants. Population-related factors play a pivotal role in predicting inertia, alongside other service-demand-related elements, particularly in regions with limited services. The study emphasizes the importance of local institutional quality and individual attributes of policymakers. The adverse effects on participation resulting from factors that justify fund allocation may place regions with higher investment needs at a competitive disadvantage. Anticipating potential non-participants in calls can aid in achieving policy targets and optimizing the allocation of funds across various local governments.