Bank competition, stability, and intervention quality
Bibliographic record
Abstract
Abstract We use a flexible semi‐parametric estimation approach and a sample of 7,227 U.S., U.K., and Canadian banks for 2009–2015 to provide evidence that banking stability is non‐linearly determined by competition. We show that stability is not monotonic against competition, and may increase and decrease at high competition, has a mixed behaviour at medium competition, and increases at low competition. This non‐monotonic stability behaviour at different competition levels is attributed to the intervention quality, which is found to be an important determinant of the competition–stability relation. It is non‐linearly related to and being revised at different competition levels. As intervention is a policy variable, its level can be adjusted to reduce the competition effects on stability. We illustrate that for the U.S. banking sector, the intervention quality has to hedge these competition effects. Regulators should treat intervention quality as a “hedging instrument” against the destabilizing competition effects.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".