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The determinants of missed funding: Predicting the paradox of increased need and reduced allocation

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  • Di Stefano, Roberta
  • Resce, Giuliano

Abstract

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.

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  • Di Stefano, Roberta & Resce, Giuliano, 2025. "The determinants of missed funding: Predicting the paradox of increased need and reduced allocation," Journal of Economic Behavior & Organization, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:jeborg:v:231:y:2025:i:c:s0167268125000307
    DOI: 10.1016/j.jebo.2025.106910
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    Cited by:

    1. Marco Di Cataldo & Elena Renzullo & Andrés Rodríguez-Pose, 2025. "Cohesion or collusion? EU funds in places with corrupt local institutions," Papers in Evolutionary Economic Geography (PEEG) 2510, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised May 2025.
    2. Caravaggio, Nicola & Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2025. "Predicting policy funding allocation with Machine Learning," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).

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    More about this item

    Keywords

    Competitive funding; Cohesion; Place-based policies; Predictive modelling; Machine learning;
    All these keywords.

    JEL classification:

    • H5 - Public Economics - - National Government Expenditures and Related Policies
    • H7 - Public Economics - - State and Local Government; Intergovernmental Relations
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • J1 - Labor and Demographic Economics - - Demographic Economics
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis

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