Negative interest rate policy and bank risk‐taking: Search for yield or de‐leverage?
Bibliographic record
Abstract
Abstract Since 2012, many central banks have implemented negative interest rate policies (NIRPs). While two opposing hypotheses about the effectiveness of NIRPs have emerged in the academic: the “de‐leverage effect” and the “search‐for‐yield effect.” The long‐term use of NIRPs provides a rare and important setting to re‐examine the relationship between interest rates and bank risk‐taking. We conduct an empirical analysis by using commercial banks' data from 2007 to 2020 for 23 countries (19 eurozone countries plus Japan, Denmark, Sweden, and Switzerland), which had adopted NIRPs. It indicates that 1% reduction in the policy rate would reduce bank risk‐taking by 4.9%. This result is stronger after the NIRPs implemented. Our results support the “de‐leverage effect” under NIRPs. We next show that the “de‐leverage effect” is greater for banks with more diversified income, smaller size or under more competitive environment. The findings help to make the debates around NIRPs effectiveness clearer as well as support for the central banks to make more effective monetary policy decisions in different economic situations.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".