Local Public Finance Dynamics and Hurricane Shocks
Bibliographic record
Abstract
Since 1980, over 2,000 local governments in US Atlantic and Gulf states have been hit by a hurricane. Such natural disasters can exert severe budgetary pressure on local governments' ability to provide critical infrastructure, goods, and services. We study local government revenue, expenditure, and borrowing dynamics in the aftermath of hurricanes. These shocks reduce tax revenues and expenditures, and increase the cost of debt in the decade following exposure. Major hurricanes have much larger effects than minor hurricanes. Our results reveal how hurricanes create collateral fiscal damage for local governments by increasing the cost of debt at critical moments after a hurricane strike. Municipalities with a racial minority composition 1 standard deviation above the sample mean suffer expenditure losses more than 2 times larger and debt default risk 8 times larger than municipalities with average racial composition in the decade following a hurricane strike. These results suggest that climate change can exacerbate environmental justice challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".