Bibliographic record
Abstract
Abstract This paper examines whether the sensitivity of local government credit ratings to external signals about the local economy varies with the quality of the governments' financial reports. We find the credit ratings of local governments that are required to comply with GAAP are less sensitive to changes in local home values than similarly affected governments that are not required to comply with GAAP. Further, we show that GAAP's moderating role increased after Governmental Accounting Standards Board (GASB) 34 substantially improved the quality of GAAP‐compliant governments' financial reports, which helps to attribute the main findings to reporting quality. To understand the mechanism, we study positive and negative economic signals separately. The results are pronounced when the change in home values is negative, consistent with reporting quality decreasing the rating agency's uncertainty about local governments' preexisting likelihood of default. We conclude that credit rating agencies are less sensitive to local economic signals when the local government's financial reports are of higher quality.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".