Are Transfer-Dependent Governments More Creditworthy? Reassessing the Fiscal Federal Foundations of Subnational Default Risk
Bibliographic record
Abstract
Abstract Many fiscal federal scholars argue, often implicitly, that transfer dependence generally bolsters subnational creditworthiness by signalling a higher likelihood of national bailouts for distressed governments. This article argues that dependence fails to bestow general benefits on local borrowers because it suggests an inability to generate additional revenues in the event of fiscal distress, and because this inability does not, contrary to the expectations of many, necessarily translate into higher bailout expectations. Ultimately it is the nature, not the level, of transfers that affects local creditworthiness, whether through bailout or non-bailout channels. Stable and predictable payments, including robust equalization systems, support local creditworthiness, while volatile and unpredictable transfers do not. The article supports these arguments with a review of documents issued by the major international credit rating agencies and cross-national statistical analyses of bailout probabilities and standalone credit ratings issued by Moody’s Investors Service. It also discusses the implications of the findings for work on the fiscal discipline of subnational governments.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".