Bibliographic record
Abstract
Currently, there are two main types of payment instruments: cash and deposit-based electronic payments. Cash is a liability of the central bank and is perceived to be very safe. Deposits are liabilities of commercial banks; they are normally safe but are subject to default risk in times of crisis. As the economy becomes increasingly cashless, can we rely on the private sector to invest in the optimal level of safety in the electronic payment system? We answer this question by modelling private incentives to invest in safety in a deposit-based payment system. Depositors can mitigate the risk in the system through monitoring to reduce the risk of default before it happens (ex ante) or through monitoring for timely detection of defaults after they’ve occurred (ex post). In addition, they can also set up a separate, safe account as a backup to receive funds from the risky account in a crisis situation. We find that because depositors do not internalize the externalities in the payment system, the private sector can over- or under-adopt the use of safe accounts. However, governments could use taxes or subsidies to correct private incentives and restore the optimal adoption of safe accounts.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.047 |
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".