Government debt spillovers and creditworthiness in a federation
Bibliographic record
Abstract
Estimates are presented for the impact of debt accumulation by the central and subcentral governments of a federation on the creditworthiness of other federation member governments. The estimates, calculated using an ordered probit model and Canadian provincial data, indicate that debt accumulation by the central government has reduced the creditworthiness of indebted provincial governments. Interprovincial debt accumulation effects are negative but relatively small, except for the debt of the largest province, which has a strong positive effect on the creditworthiness of the other provinces. These findings may have implications for other federations and associated jurisdictions, such as the European Union. JEL Classification: H63, F36 Les effets de retombée de la dette gouvernementale et la cote de crédit dans une fédération. On calcule l'impact de l'accumulation de la dette par les gouvernements fédéral et sub‐fédéraux dans une fédération sur la cote de crédit des autres gouvernements de la fédération. Ces calibrations, à l'aide d'un modèle probit en utilisant les données provinciales canadiennes, montrent que l'accumulation de la dette par le gouvernement central a réduit la cote de crédit des gouvernements provinciaux endettés. Les effets trans‐provinciaux de l'accumulation de la dette sont négatifs mais relativement faibles, sauf dans le cas de la province la plus grande, laquelle a un fort effet sur la cote de crédit des autres provinces. Ces résultats peuvent avoir des implications pour d'autres fédérations comme l'Union Européenne.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".