Punctuated Budgets and Governors’ Institutional Powers
Bibliographic record
Abstract
State budgets are flexible: In the same year, some budget categories dramatically rise or fall, whereas others closely follow the previous year’s appropriation. Public policy scholars label a budget that contains mainly small scale changes interspersed with dramatic fluctuations as punctuated. This research seeks to identify the determinants of these punctuated budgets in the American states. What causes both incremental and large scale budgetary change? We argue that a governor’s agenda setting and veto powers increase the extent to which state budgets are punctuated. First, institutionally strong governors can dominate budgetary agendas but are subject to heightened information costs. Second, strong governors can also block legislative alternatives, but thereby induce transactions costs that hinder fiscal policy adjustments. The article analyzes these institutional constraints at the American state level using maximum likelihood estimation on panel data from 1983 to 1999. We make two contributions to the study of American public policy. First, we offer a broad empirical analysis of the causes of punctuated change. Second, we present a method by which to measure punctuated distributions over time. This method can be applied to other areas of public policy research.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".