Stochastic Frontier Model for Cost and Profit Efficiency ofIslamic Online Banks
Bibliographic record
Abstract
Are Islamic online banks stable and efficient? This paper addresses this question. Parametric technique, Stochastic Frontier Analysis is used to evaluate and compare the cost and profit efficiency of the Islamic banks in Bangladesh over the period of 2001- 2010. The specification of functional forms of Translog stochastic cost and profit frontier models are developed. Translog stochastic cost and profit frontier models were found preferable than Cobb-Douglas production function. In case of cost model, other earning assets are found negative but significant and price of labor is observed positive and significant. On the other hand, price of fund with the value of (-0.421) is found significant and negative for profit model, suggest that bank can control more personnel expenses than depositor profit expenses. The year-wise average cost inefficiency and profit efficiency were observed 43.9% and 82% respectively. IBBL was recorded as the most profit efficient bank and ICB limited bank was observed as the most cost inefficient bank. IBBL, Al-Arafah and EXIM banks were more stable in terms of cost efficient than other Islamic banks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".