Cost Efficiency of Thrift Banks in the Philippines: A Data Envelopment Approach
Bibliographic record
Abstract
The aim of this study is to determine the cost efficiency of thrift banks in the Philippines. Data were taken from the financial statements of thrift banks in the Central Bank of the Philippines from 2013 to 2016. In deriving cost efficiency, the inputs used were total liability, net of real and other property acquired and net of bank premises, furniture and fixture, and equipment, while outputs were represented in terms of total loan portfolio (TLP) and other financial assets. With the input and output variables employed, evident in the outcomes of data envelopment analysis that variable return-to-scale (VRS) assumption resulted to greater number of cost efficient banks as compared under constant return-to-scale (CRS) assumption. Although findings had revealed that most thrift banks cost efficiency percentages lie below 50 percent, there were still identified thrift banks that has been cost efficient in terms of inputs and outputs used in the study. Hence, majority of the thrift banks in the Philippines were not cost-efficient with respect to the inputs and outputs. Additionally, the number of thrift banks found to be cost-efficient are significantly low in both CRS and VRS assumptions, although VRS reflected greater number than CRS assumption. Implications were discussed in this paper based on the findings gleaned from the econometric analysis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| 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".