Enabling Deep Negative Rates to Fight Recessions: A Guide
Bibliographic record
Abstract
The experience of the Great Recession and its aftermath revealed that a lower bound on interest rates can be a serious obstacle for fighting recessions. However, the zero lower bound is not a law of nature; it is a policy choice. The central message of this paper is that with readily available tools a central bank can enable deep negative rates whenever needed—thus maintaining the power of monetary policy in the future to end recessions within a short time. This paper demonstrates that a subset of these tools can have a big effect in enabling deep negative rates with administratively small actions on the part of the central bank. To that end, we (i) survey approaches to enable deep negative rates discussed in the literature and present new approaches; (ii) establish how a subset of these approaches allows enabling negative rates while remaining at a minimum distance from the current paper currency policy and minimizing the political costs; (iii) discuss why standard transmission mechanisms from interest rates to aggregate demand are likely to remain unchanged in deep negative rate territory; and (iv) present communication tools that central banks can use both now and in the event to facilitate broader political acceptance of negative interest rate policy at the onset of the next serious recession.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".