Bibliographic record
Abstract
An econometric model of Canada’s five largest banks is estimated using time series data from 1976 to 1996. The principal findings are that chartered bank technology is characterized by increasing returns to scale. Scale efficiency is sufficiently large to offset the consequences of reduced competition that might have arisen from a merger between Bank of Montreal and Royal Bank of Canada, Canadian Imperial Bank of Commerce and Toronto Dominion Bank, or both. The estimated model predicts that all the mergers proposed in 1998 would have led to slightly lower prices and, consequently, to an increase in consumer welfare. Une analyse de bien–être des fusions des banques à charte canadiennes. L’auteur calibre un modèle économétrique des cinq plus grandes banques à charte au Canada à l’aide de séries chronologiques de 1976 à 1996. Les principaux résultats montrent que la technologie des banques à charte a des rendements croissants à l’échelle. Ces rendements à l’échelle sont suffisamment importants pour compenser les effets de réduction de la concurrence qui auraient pu se produire en conséquence de la fusion de la Banque de Montréal et la Banque Royale, de la Banque Impériale de Commerce et de la Banque Toronto–Dominion, ou des deux. Le modèle suggère que toutes les fusions proposées en 1998 auraient entraîné des prix légèrement plus bas, et en conséquence un accroissement dans le niveau de bien–être des consommateurs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".