Pullback Management: State Budgeting Under Fiscal Stress
Bibliographic record
Abstract
Abstract This article analyzes the challenge of state budgeting during periods of dramatic and unplanned declines in state revenue collections. Most public finance textbooks describe the budget process as an orderly cycle of preparation, approval, execution, and evaluation. When governments are experiencing a steady growth or only marginal declines in revenues such as during the past two decades (1989–2009), it's easy to see how state budget officials can become accustomed to a predictable, “regular rhythm.” Thus, when the Great Recession hit, a series of month-after-month and quarter-after-quarter revenue shortfalls affected nearly all state budgets. The result was that with little warning and preparation, policymakers faced midyear budget deficits. This article takes on the topic of managing midyear budget adjustments. Beginning with a review of the institutional tools available to governors and legislatures (e.g., allotments, apportionment, budgeting, impoundments, special fund transfers, fund withholdings, furloughs), the article carries out a study of how five states went about balancing their budgets in 2009–2010 and how the lessons from these state responses can inform those dealing with the next fiscal crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".