The Missing Link in Bank Behavior: Deposit Interest Rate Setting Under a Dual-Benchmark Framework—A Literature Review
Bibliographic record
Abstract
The efficacy of monetary policy depends on an accurate model of bank behavior, yet the existing literature has a significant blind spot: the central role of deposit interest rate setting. This paper argues that the deposit rate is the primary arena where banks’ strategic and asymmetric responses to policy signals are revealed. Motivated by the unique dual-benchmark system in Indonesia, where a prudential deposit insurance rate actively competes with the central bank’s policy rate, this study addresses a conceptual problem with global relevance, namely, how monetary policy transmission functions when confronted with conflicting policy signals. To investigate this gap, this paper employs a Systematic Literature Review (SLR), combined with bibliometric analysis. By synthesizing findings from 63 articles selected via the PRISMA protocol, this review first maps the intellectual structure of the field, confirming that while themes of monetary policy and bank behavior are mature, the crucial dimension of deposit rate setting, particularly within a dual-benchmark context, remains a ‘missing link’. The primary contribution of this study is, therefore, building a conceptual framework that recenters the deposit interest rate as the fundamental indicator for assessing asymmetric bank behavior and identifying policy distortions. The findings provide a structured foundation for future empirical research and offer critical insights for regulators on the implications for monetary policy transmission and financial system stability.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".