The Impact of the Negative Deposit Facility Rate on the Banking System
Bibliographic record
Abstract
The main aim of the study is to investigate the repercussions of the monetary policy of negative interest rates conducted by the European Central Bank as a response to defective performance levels across the banking system during a time of economic trough afflicting European countries. The assumption under negative interest rates is that this should make monetary institutions more likely to issue credit, thus fighting loan contraction and creating a solid ground for proper money circulation and economic expansion. Simultaneously, this policy entails, for financial institutions, an extra payment due to their liquidity holdings at the ECB, in the form of deposits or current accounts. Nonetheless, it should be kept in mind, that the primary objective of the ECB is to seek price stability, and for this reason, in comparison, the lucrative purpose of the banking sector is a problem to put on the back burner. Under these circumstances, the amount of total payments carried out by the banking system of each 19 countries of the Monetary Union has been the object of the study to understand which countries are more or less sensitive to the policy. Furthermore, those figures are compared to the due forecasted payments to be done by each country's banking system on aggregate level after the recent implementation of a two-tier system for the liquidity held in the current accounts. The results show that a great disproportion exists in the affliction of negative interest rates across the banking system in different eurozone countries and that each of them will be differently affected by the two-tier system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".