Prof. Daniel Ruiz Romera (p), Dr. Filippo di Pietro (p), Prof. Belén Martín Castro

Purpose: This systematic review of the literature examines the effectiveness of public financing programs for innovative small and medium-sized enterprises (SMEs) during the post-pandemic period, identifying key research gaps and future research directions.
Methodology: Following the PRISMA 2020 guidelines, we conducted a systematic review by AI-assisted active learning (ASReview) to refine the screening process and minimize selection bias. The final analysis includes 42 peer-reviewed articles from Web of Science and Scopus. The search focused on public financing instruments including grants, tax incentives, loan guarantees, and equity/quasi-equity mechanisms. Studies were systematically selected using predefined inclusion/exclusion criteria and methodological quality was assessed.
Key Findings: Evidence strongly supports that public financing generates positive input additionality, stimulating private R&D investment without crowding out private funds. Effectiveness is highly heterogeneous, with the greatest impact observed in younger, smaller and financially constrained firms. The certification effect—whereby public funding signals firm quality to private investors—emerges as a critical mechanism. However, research gaps persist, including insufficient granular firm-level data, under exploration of non-financial support mechanisms, limited understanding of different innovation modes (particularly DUI—Doing, Using, Interacting), and contradictory findings regarding optimal instrument design. Emerging research areas include inclusive digital finance, green innovation financing, and Europe’s “scale-up gap.”
Implications: Policymakers should prioritise information dissemination over simply increasing fund availability, tailor instruments to firm heterogeneity, and simplify administrative processes. Future research must develop integrated longitudinal datasets, employ more experimental designs, and address the “black box” of non-financial support.

Dr. Anna Kyosova (p)

This study uses a town-level spatial panel data set to analyze how exposed companies are to European Union funded project activities and the spillovers of these activities in Bulgaria during 2014 – 2026. The study uses project entry data from all EU funded projects (total of N = 73,783) including geographic coordinates and harmonized town identifier for all EU funded projects in Bulgaria (a total of 2,302 towns). We compiled an administrative database of all EU supported initiatives in Bulgaria during that time frame; it contains program codes, details regarding the beneficiaries (such as public/private status), financial data, and geographic information for each initiative. After standardizing the place name and geocoding to lat/long, we aggregated the data into a balanced panel of towns and years (covering 2302 towns and 13 start year cohorts).
The descriptive results show that EU funded projects are highly concentrated geographically and temporally, with many EU funded projects initiated at the same time, such as when large programmatic cycles occur or in response to crises. There is also significant primacy of capital cities with Sofia having an order of magnitude more project starts and grant volume than most other towns, therefore the authors include logarithmic transformations in their visualizations and diagnostic tests to account for this. Visualizations of the project starts and spillover exposures show positive co-movements between project starts in localities and spillover exposures to nearby localities, indicating that there is a relationship between project allocations and/or localized implementation systems for EU funded projects.
In addition, the research provides a reproducible R code to build town-year panels and spillover indicators, thereby providing a methodological contribution to the regional science literature studying EU cohesion policy. The EU funded project activity panel data set and spillover indicators developed in this study can be used as input variables for downstream causal analyses (such as fixed effect models, event studies, and spatial econometric models) to investigate whether the impact of EU funded projects are limited to treated localities, and whether EU funded projects have different types of spatial diffusion depending upon whether the project is implemented by a government entity (public) or a business entity (private).

Dr. Adriana Carolina Pinate (p), Prof. Claudio Di Berardino, prof. Martina Dal Molin, prof. Giulia Urso

This study investigates whether regional institutional quality influences individuals' subjective perception of inequality in Italy. While research typically focuses on objective economic measures, this paper addresses the gap in understanding how governance affects psychosocial experiences of disparity. Using a novel dataset of 1,300 respondents merged with the Institutional Quality Index (IQI), the authors employ a multilevel OLS approach to analyze perceptions at local, national, and global levels. The results reveal that higher institutional quality—particularly corruption control and government effectiveness—is significantly associated with lower perceived inequality, but only at the local level. Interestingly, objective income and national Gini indices fail to explain perceptions at broader scales, highlighting the "parochial" nature of institutional influence. These findings suggest that local governance reforms are essential for mitigating perceived social disparities and fostering social cohesion.