Lobbying on Regulatory Enforcement Actions: Evidence from U.S. Commercial and Savings Banks
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
This paper analyzes the relationship between bank lobbying and supervisory decisions of regulators and documents its moral hazard implications. Exploiting bank-level information on the universe of commercial and savings banks in the United States, I find that regulators are 44.7% less likely to initiate enforcement actions against lobbying banks. This result is robust across measures of lobbying and accounts for endogeneity concerns by employing instrumental variables strategies. In addition, I show that lobbying banks are riskier and reliably underperform their nonlobbying peers. Overall, these results appear rather inconsistent with an information-based explanation of bank lobbying, but consistent with the theory of regulatory capture. This paper was accepted by Amit Seru, finance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it