Public Debt and Economic Growth after Covid-19 in Europe: Challenges and Policy Implications
Bibliographic record
Abstract
The Covid-19 pandemic has had a profound impact on European economies, leading to a significant increase in public debt levels. This paper examines the challenges and policy implications of managing public debt and fostering economic growth in Europe post-Covid-19. It provides a conceptual framework by defining and measuring public debt, exploring the relationship between public debt and economic growth, and highlighting the role of public debt in times of crisis. The paper analyzes the impact of Covid-19 on public debt in Europe, including the fiscal response, debt accumulation, and reasons for increased debt levels. It further discusses the challenges posed by high public debt, such as debt sustainability, crowding out private investment, financial stability risks, and constraints on future fiscal policy. The study then presents policy implications for balancing public debt and economic growth, including fiscal consolidation measures, long-term debt management strategies, prioritizing public investments, implementing structural reforms, considering monetary policy, and fostering international cooperation. Additionally, the paper provides case studies of selected European countries, evaluating their approaches, assessing policy effectiveness, and drawing key lessons and best practices. Finally, the paper concludes with a summary of key findings, policy recommendations for European governments, and suggestions for future research directions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".