Fiscal Decentralization and the Challenge of Hard Budget Constraints
Bibliographic record
Abstract
A multi-country study of the conditions under which decentralized countries might ensure fiscal discipline. In many parts of the world, lower levels of government are taking over responsibilities from national authorities. This often leads to difficulty in maintaining fiscal discipline. So-called soft budget constraints allow these subnational governments to expand expenditures without facing the full cost. Until now, however, there has been little understanding of how decentralization leads to large fiscal deficits and macroeconomic instability. This book, based on a research project at the World Bank, develops an analytical framework for considering the issues related to soft budget constraints, including the institutions, history, and policies that drive expectations for bailouts among subnational governments. It examines fiscal, financial, political, and land market mechanisms for subnational discipline in Argentina, Brazil, Canada, China, Germany, Hungary, India, Norway, South Africa, Ukraine, and the United States. The book concludes that the dichotomy between market and hierarchical mechanisms is false. Most countries—and virtually all developing countries—must rely on market mechanisms as well as hierarchical constraints to maintain fiscal discipline. When bailouts cannot be avoided, they present important opportunities to reform underlying institutions. Successful market discipline—where voluntary lenders perform important monitoring functions—is most likely to emerge from a gradual process that begins with carefully crafted rules and oversight.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".