Comparison of Bank Efficiencies between the US and Canada: Evidence Based on SFA and DEA
Bibliographic record
Abstract
This paper aims to achieve two targets. First, using balanced panel data from 2008 to 2017 it compares the cost efficiencies between US and Canadian commercial banks to examine whether structural differences in the two countries' banking industries create differences in efficiencies. Since efficiency is a valuable measurement to indicate the ability of an organization to utilize limited resources to produce, in this article we compare the operating competitiveness of these banks. Next, to achieve the first goal, both the Stochastic Frontier Analysis and Data Envelopment Analysis are employed to examine cost efficiencies in order to find new evidence given the mixed results in previous literature. Profit efficiency is also compared with cost efficiency based on a parametric approach. The results regarding cost and profit efficiency conforms to prior studies indicating a relatively low correlation. However, SFA and DEA produce very different and uncorrelated results, though DEA generates overall lower efficiencies, as expected. Thus, the findings suggest that methodology cross-checking along with information regarding variables selection are necessary before decision making. Essentially, there is not enough evidence to conclude that bank efficiencies are different either between the US and Canada, or between large and small banks in US. However, DEA suggests an increasing trend in average efficiencies, as this parameter is not time-adjusted. A more technical exploration of how to reliably measure efficiencies is awaited to make advancements in this area.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".