Ambient Influences on Municipal Net Assets: Evidence from Panel Data
Bibliographic record
Abstract
Abstract Governments’ net assets balances are viewed as a measure of fiscal health and have been linked to municipal credit ratings. This study explores the extent to which ambient socioeconomic factors are captured in aggregated restricted and unrestricted net assets balances (termed “liquid net assets”) to understand why such balances are relevant to credit analysts and others. We model liquid net assets balances using observable nonaccounting factors (e.g., unemployment rates) to learn whether they reflect such influences. We use panel data for fiscal years 2007–2011 so our results comprehend effects of recent economic fluctuations. We find that liquid net assets balances impound a rich array of influences, bearing a positive association with the mayor‐council form of government, community wealth, the incidence of property crimes, and increases in governments’ business‐type net assets. Liquid net assets balances bear a negative association with liabilities for postemployment benefits, unemployment, and violent crime. The results indicate that net assets balances capture noteworthy debt burden, administrative, and socioeconomic influences and, as such, have meaning beyond their basic accounting interpretation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".