The determinants of missed funding: Predicting the paradox of increased need and reduced allocation
利用机器学习分析欧盟下一代基金公开招标数据，预测哪些地方政府可能放弃资金，发现人口因素和制度质量是关键，需求大的地区反而处于劣势。
This research investigates how local governments overlook funding opportunities within the cohesion policies, utilizing machine learning and analysing 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 likely to abstain from invitations, by identifying key determinants. Population-related factors play an important role in predicting inertia, alongside other demand-related elements, particularly in regions with limited services. The study emphasizes the importance of local institutional quality and individual attributes of policymakers. The factors justifying fund allocation have adverse effects on participation, placing regions with greater investment needs at a competitive disadvantage. Anticipating non-participation in calls can aid in achieving policy targets and optimizing the allocation of funds across various local governments.