Unmet payment needs and a central bank digital currency
Bibliographic record
Abstract
This paper analyses the payment habits of Canadians both in the current payment environment and in a hypothetical cashless environment. The paper also considers whether a central bank digital currency (CBDC) would address unmet payment needs in a cashless society. Most adult Canadians do not experience gaps in their access to a range of payment methods, and this would probably continue to be the case in a cashless environment. Some people could, however, face difficulties making payments if merchants no longer generally accepted cash as a method of payment. For a payment-oriented CBDC to successfully address unmet payment needs, the main consumer groups — who already have access to a range of payment options — would have to widely adopt the CBDC and use it at scale. This is necessary to encourage widespread merchant acceptance of CBDC, which would, in turn, encourage further consumer adoption and use. Most consumers, however, face few payment gaps or frictions and therefore might have relatively weak incentives to adopt and — especially — to use CBDC at scale. If that were the case, widespread merchant acceptance would also be unlikely. This suggests that addressing unmet payment needs for a minority of consumers by issuing a CBDC could be challenging under the conditions explored in this paper. The minority of consumers with unmet payment needs will only be able to benefit from a CBDC if the majority of consumers experience material benefits and therefore drive its use. Adoption by the majority may have added policy implications that are beyond the scope of this paper.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".