Monitoring Mechanisms and Budget Variances: Evidence from the 50 Largest US Cities
Bibliographic record
Abstract
I examine how the association between the current period’s budget variance and the subsequent period’s budget is affected by various governmental monitoring mechanisms. Specifically, I consider the following governance and monitoring mechanisms: governance structure, state/city budget-limiting regulations, and voter-initiated monitoring. I find that city budgets ratchet in the top 50 populous cities in the US. I also document evidence of asymmetric ratcheting—the current period’s favorable budget variances result in budget increases in the following year that are larger than the decreases associated with unfavorable variances of the same magnitude. Consistent with the political budget cycle hypothesis that budget pattern alters during pre-election periods, I find the asymmetric ratcheting pattern becomes invisible in times of election, particularly when an incumbent runs for re-election. Given this evidence of the opportunistic budgetary pattern, I hypothesize and find that some monitoring mechanisms mitigate the sensitivity of the subsequent period’s budget with respect to the current period’s budget variance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".