Quantifying the economic benefits of payments modernization: the case of Canada’s large-value payment system
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it