Cost Efficiency Determinants: Evidence from the Canadian Banking Industry
Bibliographic record
Abstract
This study examines the cost efficiency of the banking industry in Canada. Utilizing 12 years of data (i.e., 2006 to 2017), and a two-stage data envelopment analysis (DEA), it provides insight on the determinants of the industry’s cost efficiency. It finds that the industry is cost inefficient, and that it could reduce costs by 11.52 percent. The cost inefficiency is due to technical and allocative inefficiencies, with technical inefficiency playing a dominant role. The technical efficiency decomposition shows that pure technical efficiency improved, but the scale efficiency deteriorated. The analysis of the determinants of cost efficiency reveals that deposit conversion into loans, high capitalization, and managerial tolerance for increase in administrative expense drive cost efficiency. On the other hand, market power and diversification diminish cost efficiency. In addition, the impact of profitability and credit risk are inconsequential to cost efficiency. This study contributes to literature by providing insights unique to Canada. Managers in the industry, policy makers, and regulators can point to these findings as empirical evidence supporting measures aimed at increasing the industry’s competitiveness and resilience.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".