Public debt and economic growth in G7 countries: do financial and political institutions matter?
Bibliographic record
Abstract
This research aims to examine the role of institutional factors in the public debt-economic growth (PD-EG) nexus using data from 1984 to 2019 for G7 countries, constituting approximately 55% of the global public debt stock. The findings of this study, in which current panel time-series techniques are adopted, show that the PD-EG interaction can be in the form of an inverted U (Italy and UK) or U shape (Germany and U.S.A.) and that institutional factors do not generate effects in the same direction for some countries (Canada, France, and Japan). Moreover, the findings provide robust evidence that the threshold values of the PD-EG relationship may be lower in the presence of institutional factors. In this regard, we argue that just as neither a threshold value nor an effect can be accepted as generally valid in the PD-EG relationship, a similar result is valid for modelling that includes theoretical factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".