Bibliographic record
Abstract
"How is the stability of the financial sector affected by competition in the deposit market and by banks’ choices about the level of transparency? We propose a model in which both elements interact and influence investors’ withdrawal decisions and banks’ level of distress (that is, the probability banks will default on their debt). The model also shows how measures regulating bank competition and bank transparency affect the stability of the financial sector. Banks face a trade-off when choosing how transparent they should be—that is, how much information they should provide about their investment portfolios. On the one hand, greater transparency reduces costly investor withdrawals when the bank is solvent because investors have better information about bank returns. On the other hand, greater transparency improves the information of competitors, who are then more likely to enter the market and reduce the value of future bank profits. We show that policies that aim to increase bank competition lead to higher bank deposit rates, increasing both withdrawal incentives and instability. Policies that aim to increase transparency in the banking sector can also increase instability. The reason is that when banks are more transparent, they have incentives to raise their deposit rates—which leads to larger withdrawal incentives and higher levels of distress."
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".