Inconsistent Regulators: Evidence From Banking
Bibliographic record
Abstract
US state chartered commercial banks are supervised alternately by state and federal regulators. Each regulator supervises a given bank for a fixed time period according to a predetermined rotation schedule. We use unique data to examine differences between federal and state regulators for these banks. Federal regulators are significantly less lenient, downgrading supervisory ratings about twice as frequently as state supervisors. Under federal regulators, banks report higher nonperforming loans, more delinquent loans, higher regulatory capital ratios, and lower ROA. There is a higher frequency of bank failures and problem-bank rates in states with more lenient supervision relative to the federal benchmark. Some states are more lenient than others. Regulatory capture by industry constituents and supervisory staff characteristics can explain some of these differences. These findings suggest that inconsistent oversight can hamper the effectiveness of regulation by delaying corrective actions and by inducing costly variability in operations of regulated entities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".