Quantifying the economic benefits of payments modernization: the case of Canada’s large-value payment system
Bibliographic record
Abstract
In this paper, we develop a discrete-choice framework to quantify the economic benefits of Canada’s large-value payment system modernization (ie, the replacement of Canada’s large-value transfer system (LVTS) with Lynx, the new large-value payments system in Canada). We first estimate participants’ preferences for liquidity cost, payment safety and the network effect by exploiting intraday variations in the relative choice probabilities of the two substitutable subsystems (tranches 1 and 2) in the LVTS. Then, with the estimated model, we calculate the changes in participants’ welfare when the LVTS is replaced by Lynx. First, compared with the LVTS, Lynx has higher liquidity costs but is more secure. Second, when over 90% of current LVTS payments migrate to Lynx, there is an overall welfare gain. Third, accounting for equilibrium adjustment after the replacement of the LVTS with Lynx, about a 75% improvement in service quality level is needed to generate overall net economic benefits to participants. Among other things, adopting a liquidity-saving mechanism and reducing risks in the new large-value payments system could help achieve this improvement. Finally, the welfare changes are fairly heterogeneous across participants, especially between large and small participants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".