Competing With Bitcoin - Some Policy Considerations for Issuing Digitalized Legal Tenders
Bibliographic record
Abstract
The proliferation of peer-to-peer virtual alternatives to traditional banknotes has raised concerns among policymakers about the future of traditional means of making payments and how it might affect monetary policy implementation and its effectiveness. This study provides a brief overview of the existing research in this area. It compares positions taken in the literature by authors on some of the key policy issues relevant for central banks when thinking about the issuance of digitalized legal tenders. We examine the implications of government issued digital alternatives to traditional currencies for monetary policy effectiveness, payments and settlements, and financial market stability. We also discuss recent advances in financial technology to improve the making of payments and settlements, which might help contribute to financial inclusion. At the same time, new technologies represent challenges for regulatory authorities, for instance related to efforts to contain anti-money laundering and prevent financing of terrorism. A number of authors argue that government issued digital currency is necessary to address the flaws in private crypto currencies, and to improve monetary policy effectiveness. Central banks have begun to analyze possible features of digitalized legal tenders, to better understand the policy considerations involved and effects these could have for interest rate transmission and financial markets, but there is no clear consensus on key modalities associated with digitalized legal tenders. Moreover, many central banks do not regard privately issued virtual currencies as a serious threat to traditional currencies. Given the ongoing debate, it is difficult to make firm predictions about the impact of central bank issued digital currencies on monetary policy transmission and financial markets at this point.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".