European Structural and Investment Funds (ESIFs) and Regional Development across the European Union (EU)
Bibliographic record
Abstract
This scoping review synthesizes the evidence from eleven key studies to assess the impact of European Structural and Investment Funds (ESIFs) on regional development across the European Union (EU), focusing on fund efficiency, regional disparities and convergence, governance quality, economic freedom, and fund management. A systematic search was conducted across multiple databases to identify the relevant literature published up to 2023. Eleven studies were selected based on the date published and their focus on ESIFs’ role in regional development, employing a range of methodological approaches including Data Envelopment Analysis (DEA), spatial econometrics, and multivariate analyses. The thematic analysis identified four main categories: Methodological Approaches in Evaluating Fund Efficiency, Regional Disparities and Convergence, The Interconnection between Governance Quality, Economic Freedom, and the Efficiency of Structural Fund Management, and The Absorption Capacity and Fund Management. The review highlights the importance of sophisticated analytical tools in evaluating fund efficiency, with DEA and spatial econometrics providing critical insights into fund management efficiency. Studies underscored the nuanced efficacy of ESIFs in reducing regional disparities, albeit pointing to the need for more targeted fund allocation. Governance quality and economic freedom emerged as pivotal factors enhancing fund management efficiency, suggesting the potential of governance reforms in optimizing ESIF allocation and utilization. Challenges related to fund absorption and management were illuminated, advocating for enhanced institutional management capabilities and the development of innovative performance indicators. The findings of this scoping review contribute to a deeper understanding of the complexities surrounding ESIFs’ impact on regional development within the EU. They underscore the critical importance of governance quality, economic freedom, methodological rigor, and strategic fund allocation in enhancing the effectiveness of ESIFs. The review calls for tailored policy interventions and the integration of national and European funding strategies to maximize the impact of these programs on regional development and SME support. Future research should continue to refine these methodological approaches and explore the causal effects of funding, to enhance our understanding of ESIFs’ efficiency in promoting regional development and convergence within the European Union.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".