Inconsistent Regulators: Evidence from Banking*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We find that regulators can implement identical rules inconsistently due to differences in their institutional design and incentives, and this behavior may adversely impact the effectiveness with which regulation is implemented. We study supervisory decisions of U.S. banking regulators and exploit a legally determined rotation policy that assigns federal and state supervisors to the same bank at exogenously set time intervals. Comparing federal and state regulator supervisory ratings within the same bank, we find that federal regulators are systematically tougher, downgrading supervisory ratings almost twice as frequently as do state supervisors. State regulators counteract these downgrades to some degree by upgrading more frequently. Under federal regulators, banks report worse asset quality, higher regulatory capital ratios, and lower return on assets. Leniency of state regulators relative to their federal counterparts is related to costly outcomes, such as higher failure rates and lower repayment rates of government assistance funds. The discrepancy in regulator behavior is related to different weights given by regulators to local economic conditions and, to some extent, differences in regulatory resources. We find no support for regulator self-interest, which includes “revolving doors” as a reason for leniency of state regulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it