The Differences of Bank Efficiency, Risk, And Performance Before and During the Covid-19 In Indonesia
Bibliographic record
Abstract
This study aims to analyze the differences in efficiency, risk, and performance between small and big banks before and during the Covid-19 pandemic. Commercial banks included in KBMI 1 are classified as small banks, and commercial banks included in KBMI 2, 3, and 4 are classified as big banks. This study used a sample of 23 small banks and 23 big banks with research periods from the second quarter of 2018 to the first quarter of 2020 (before the Covid-19 pandemic) and the second quarter of 2020 to the first quarter of 2022 (during the Covid-19 pandemic). BOPO proxy as bank operational efficiency, LDR proxy as liquidity risk, and Gross NPL proxy as credit risk. In addition to banking performance indicators, the authors use ROA, ROE, and NIM proxies. Data analysis methods used descriptive statistical analysis and non-parametric tests with the Mann Whitney-U test method with a significance level of 5% because the normality and homogeneity tests were not fulfilled. The results showed significant differences in the variables BOPO, Gross NPL, LDR, ROA, ROE, and NIM between big and small banks before and during the Covid-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".