Public Debt and Economic Growth in the European Union: Threshold Effects and Debt Sustainability in a Dynamic Panel Framework
Bibliographic record
Abstract
This study investigates the nonlinear relationship between public debt and economic growth within the European Union (EU), emphasizing threshold effects beyond which debt accumulation adversely impacts growth. Using an unbalanced panel of 27 EU member states from 1995 to 2023, we apply a dynamic panel estimation technique based on the two-step System Generalized Method of Moments (GMM) to address endogeneity, reverse causality, and the persistence of debt and growth dynamics. Additionally, a panel threshold regression model is employed to estimate the critical debt-to-GDP ratio where the marginal effect of debt becomes significantly negative. The findings confirm a nonlinear debt-growth relationship. Moderate debt levels are generally neutral or mildly supportive of growth, particularly in countries with strong institutions and sound fiscal frameworks. However, once public debt exceeds the estimated threshold of 84–90% of GDP, its marginal impact turns significantly negative, especially in countries with weaker governance and elevated sovereign risk. The results remain robust across various specifications, subsample analyses, and when excluding financial crisis years. These results have direct implications for EU fiscal policy. They support revising fiscal rules to reflect country-specific institutional and macroeconomic conditions, allowing more flexibility in low-debt contexts and stricter constraints in high-debt, institutionally weak environments.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".