Monetary Policy Pass-Through with Central Bank Digital Currency
Bibliographic record
Abstract
"Many central banks are considering issuing a central bank digital currency (CBDC). This would introduce a new policy tool—interest on CBDC. We investigate how this new tool would interact with traditional monetary policy tools, such as the interest on central bank reserves. We build a model in which CBDC and bank deposits are perfect substitutes as electronic payment methods. We discuss the effects (or pass-through) of two monetary policy tools: the interest on reserves and the interest on CBDC. Specifically, we examine how, in the presence of each other, these two policy tools affect the rates and quantities of deposits and loans. We find that when it is in effect, the interest on CBDC fully dictates the deposit rate and eliminates the pass-through from the interest on reserves to the deposit rate. How the interest on CBDC affects the pass-through from the interest on reserves to other economic variables depends on the market structure of the deposit market. While CBDC tends to weaken the pass-through of the interest on reserves when banks have market power, the reverse holds when the market is competitive. In turn, a high interest on reserves may weaken the pass-through of the interest on CBDC. In general, coordination between the two policy rates is needed to effectively achieve policy goals."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".