An Assessment of Post-COVID-19 EU Recovery Funds and the Distribution of Them among Member States
Bibliographic record
Abstract
The European Commission has launched numerous recovery plans for Member States to try to mitigate the damage caused by COVID-19. The most important element of this program is the Recovery and Resilience Facility (RRF), which is worth EUR 672.5 billion in loans and grants. Seventy per cent of the RRF grants will be distributed between 2021 and 2022, with the remaining 30 per cent in 2023. The allocation of grants for the period 2021–2022 has been made according to different socioeconomic criteria. In this context, the aim of our work is to assess the recovery policies jointly developed by EU countries and to analyze which of the criteria adopted for the allocation of the grants included in the RRF for the period 2021–2022 has been most decisive in the distribution of these funds. In addition, we also examine whether other health indicators directly related to the pandemic can also be related to the amount of funding that EU countries will receive in this period by carrying out regression analysis. Our results show that the countries that will receive more RRF grants are those with larger populations, Gross Domestic Product (GDP) per capita and higher unemployment rates. Furthermore, it is noted that health criteria, as well as those of a socioeconomic nature, may be relevant in the allocation of recovery funds. In this way, our results can be the start of a debate in the literature on whether the socioeconomic criteria adopted in the distribution of these funds have been appropriate. or whether other criteria, such as those of a health nature, should have been taken into account.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".