Why is the canadian banking system more resilient than the U.S. system? a lesson for european competition policy
Bibliographic record
Abstract
The work focuses on the United States and the Canadian banking systems regulatory evolutions and assesses the reasons why two so similar countries ended up with such different banking systems. The aim of the paper is to show that Canada has a greater level of stability compared to the United States because in Canada the relationship between competition policy and the financial system has always been more cautious and less procompetitive. Economic history of United States and Canada will be in part addressed to demonstrate that Canada dodging of the 2008 financial crisis was not just a one-time luck. The United States has experienced several financial crises since XIX century whereas the Canadian financial system has always remained sound and stable. Firstly, I suggest that the two major factors which explain this phenomenon are (i) the strong local lobbies which fought the implementation of a centralized banking system in the U.S. and (ii) the more cautious financial and antitrust policies implemented by Canada throughout its history. Secondly, I submit that a banking oligopoly tightly regulated can prove much more effective in maintaining financial stability than a fragmented system of small and fragile entities. The paper concludes showing that the Canadian system represents a positive model to which E.U. policy makers can look to better shape the regulatory framework of the European Banking System.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".