Canadian Banking Industry Profitability: Exploring the Relevance of Two Competing Hypotheses
Bibliographic record
Abstract
This study investigates the Canadian banking industry profitability and seeks to determine if there is evidence of market power hypothesis (MPH) and/or efficiency structure hypothesis (ESH) in the industry. Using GMM and data from 2006 to 2018, it finds no support for the structure-conduct-performance (SCP) component of MPH. However, evidence of relative market power (RMP) in the industry reflects partial support for MPH, implying that banks that offer differentiated products are able to exercise market power, increase market share, and achieve better profitability. The lack of support for X-efficiency (ESX) and scale efficiency (ESS), which are components of ESH, indicates no support for the ESH. The finding that QLH holds in the industry suggests the level of competition is inadequate and that managers in the industry exhibit suboptimal behaviour. Therefore, increasing the level of competition in the industry will stimulate managerial effectiveness. Findings relating to the control variables show that spread impedes profitability, whereas capitalization and the joint influence of spread and liquidity risk have facilitating effects. Credit risk is immaterial to profitability however, the effect of economic growth can be positive. This study provides a better understanding of the industry, which is important to managers, regulators, and policy makers. The robustness checks affirm the consistency of the findings and policy implications.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".