Bibliographic record
Abstract
When a central bank implements the LOLR policy in a financial crisis, bank creditors often infer a bank’s quality from whether or not it borrows from the central bank. We establish a formal model to study the optimal LOLR policy in the presence of this signaling effect, assuming that the central bank aims to encourage central bank borrowing to avoid inefficiencies caused by contagion. In our model, there are two types of banks: a high quality type with high expected asset returns and a low quality type with lower returns. Both types of banks need to roll over their short-term debts. A central bank offers to lend to both types of banks. After private creditors observe whether banks borrow from the central bank, banks try to borrow from the private market. We find that there may exist a separating equilibrium where only low quality banks borrow from the central bank; and two pooling equilibria where both types of banks do and do not borrow from the central bank. Our major results are as follows: (1) Considering the signaling effect, the central bank should set its lending rate lower than the prevailing market rate to induce both types of banks to borrow from the central bank. (2) Hiding the identity of banks borrowing from the central bank will encourage banks to borrow from the central bank. (3) The central bank may serve as a coordinator for the realization of its favored equilibrium.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".