Laws for fiscal responsibility for subnational discipline: international experience
Bibliographic record
Abstract
Fiscal responsibility laws are institutions with which multiple governments in the same economy¡ªnational and subnational¡ªcan commit to avoid irresponsible fiscal behavior that could have short-term advantages to one of them but that would be collectively damaging. Coordination failures with subnational governments in the 1980s and 90s contributed to macroeconomic instability and led several countries to adopt fiscal responsibility laws as part of the remedy. The paper analyzes the characteristics and effects of fiscal responsibility laws in seven countries¡ªArgentina, Australia, Brazil, Canada, Colombia, India, and Peru. Fiscal responsibility laws are designed to address the short time horizons of policymakers, free riders among government units, and principal-agent problems between the national and subnational governments. The paper describes how the laws differ in the specificity of quantitative targets, the strength of sanctions, the methods for increasing transparency, and the level of government passing the law. Evidence shows that fiscal responsibility laws can help coordinate and sustain commitments to fiscal prudence, but they are not a substitute for commitment and should not be viewed as ends in themselves. They can make a positive contribution by adding to the collection of other measures to shore up a coalition of states with the central government in support of fiscal prudence. The commitment of the central government to its own fiscal prudence was critical for success of fiscal responsibility laws for 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".