Performance of bank in Indonesia: A comparison between community development banks, government bank and private banks
Bibliographic record
Abstract
A unique characteristic of Indonesian banking system is the existence of community development banks, which is owned by local governments. This study examines the performance of this type of banks compared to private and federal government banks. The sample of this study consists of 15 community development banks, 56 private banks, and 3 federal government banks from 1995 to 2006. Using panel data methodologies, we find that community development banks perform at least as good as the other types of banks. There are two possible explanations for this finding. First, the survival of local government depends on the performance of local banks. Mismanagement of banks might indicate the incompetence of local elected officials. Thus the officials have more incentives to monitor local banks. Second, since community development banks only serve one province, they have specialized knowledge about that province. Third, loans are given out only to civil servants. Since it is very difficult to terminate the employment contracts of civil servants, these loans represent low risk investments to the banks. To our knowledge, this is the first study that looks at the performance of community development bank in comparison with other types of banks in Indonesia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".