Bank Capital Buffer and Economic Growth: New Insights from the US Banking Sector
Bibliographic record
Abstract
This research intends to explore the relationship between capital buffer, nominator effect, denominator effect, and economic growth for large insured commercial banks of the USA. The study applied a two-step system Generalized Method of Moment (GMM) framework by taking the unique and comprehensive dataset over the period extending from 2002 to 2018. The research found a countercyclical relationship between a capital buffer and economic growth. In the case of well-capitalized banks, this relationship is more critical than adequately capitalized banks. In the case of low-liquid banks, counter-cyclicality is more significant than high-liquid banks. The results also suggest the pro-cyclical relationship between nominator, denominator, and economic growth. The results remain consistent and robust with the use of the tier-one capital buffer ratio. The findings have implications for regulators to incorporate the counter-cyclicality between the capital buffer and economic growth, while formulating the policies for capital requirements in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".