Estimating the Costs of Electronic Retail Payment Networks: A Cross-Country Meta Analysis
Bibliographic record
Abstract
As economies across the world continue to digitize, debates around the design and efficiency of national infrastructures for electronic payments have gained added relevance. Central to these debates is the question of how many electronic funds transfer (EFT) systems can viably coexist within a jurisdiction while achieving scale economies to ensure that average cost is minimized, a threshold that largely depends on the shape of the cost function. In this paper, we conduct a cross-country meta-analysis using data from 13 social cost studies across 9 jurisdictions between 2001 and 2016. We quantitatively estimate a cost function relating the total transaction volume to the per-transaction cost and interpret its parameters in terms of fixed and variable costs. We find a rapidly decreasing, convex cost curve that plateaus quickly at around one billion annual transactions. Additionally, we estimate the marginal cost of an EFT to be approximately $0.55 per transaction, expressed in 2025 Canadian dollars, and the total fixed cost to be approximately $83 million per year.
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 | 0.052 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".