Are Canadian Banks Efficient? A Canada–U.S. Comparison
Bibliographic record
Abstract
The authors compare the efficiency of Canada's largest banks with U.S. commercial banks over the past 20 years. Efficiency is measured in three ways. First, the authors study key performance ratios, and find that Canadian banks are as productive as U.S. banks. Second, they investigate whether there are economies of scale in the production functions of Canadian banks and broadly comparable U.S. bank-holding companies (BHCs). They find larger economies of scale for Canadian banks than for the U.S. BHCs, which suggests that Canadian banks are less efficient in terms of scale, and have more to gain in terms of efficiency benefits from becoming larger. Third, the authors measure cost-inefficiency in Canadian banks and in U.S. BHCs relative to the domestic efficient frontier in each country (the domestic best-practice institution). They find that Canadian banks are closer to the domestic efficient frontier than are the U.S. BHCs. Canadian banks have also moved closer to the domestic efficient frontier than have the U.S. BHCs over time. Finally, the authors examine the dispersion in cost-inefficiency found in Canadian banks and attribute some of the dispersion to differences in information and communication technology investment. Comparisons are made with the U.S. BHC experience.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".