Bibliographic record
Abstract
In a world where the means of exchange is convertible into the numeraire consumption good at a fixed rate, no one wants to hold money over time – and due to convertibility there is no means by which the Friedman rule can generate deflation. This is the environment we study in this paper in order to demonstrate that there is still a way to reach the first-best: institutionalize the naked shorting of the unit of account, or in other words establish a banking system. To motivate the benefits of a banking system, the environment has real productivity shocks that are constantly changing the optimal level of economic activity, so the optimal quantity of money is inherently stochastic. Efficiency in such an environment requires the capacity to expand the money supply on an “as needed” basis. We show how a debt-based payments system that relies on banks to certify the individual debtors’ IOUs addresses the monetary problem. This model explains (i) central bank monetary policy as a means of stabilizing the banking system and (ii) usury laws as means of promoting equilibria that favor non-banks over those that favor banks. Furthermore, by modeling a commercial bank-based monetary system as an efficient solution to a payments problem this paper develops a theoretic framework that may be used to evaluate central bank digital currency proposals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".