The Role of Competition and Size on Zombie Rural Banks in Indonesia
Bibliographic record
Abstract
This study aims to identify rural banks categorized as "zombies" and explore how competitiveness level and bank size affect their likelihood of becoming zombies.This study uses data from rural banks spanning 2015 to 2022 and applies logistic regression analysis on balanced panel data.Findings indicate that increasing competitiveness among rural banks reduces their likelihood of becoming zombies, while larger bank size increases this risk.Specifically, a 1 percent increase in competitiveness decreases the probability of rural banks becoming zombies by 15-17 percent, whereas a 1 percent increase in bank size can increase this probability by 29-88 percent.Province-level analysis also identifies specific provinces that strongly influence zombie information.Robustness tests using the Panzar-Rosse H-statistic confirm the negative competition effect, although statistical significance is concentrated in one zombie definition, thereby validating the main results.Instrumental variable estimates that use provincial banking density as an instrument indicate that potential endogeneity is limited and does not materially bias the conclusions.Moreover, the competition effect is markedly stronger on the island of Java, and Banten emerges as a provincial hotspot for zombie rural banks, underscoring geographic heterogeneity in the phenomenon.This study expands existing literature by considering regional differences and exploring the impact of competition and bank size on rural banks within and outside the island of Java.The findings of this study suggest that policymakers and regulators need to carefully monitor rural banks and enhance regulation and supervision to mitigate the risk of zombie bank formation.The insights provided can also be implemented to improve the stability and sustainability of the banking sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".