The Optimum Quantity of Money Revisited: Distortionary Taxation in a Search Model
Bibliographic record
Abstract
This paper incorporates a distortionary tax into the microfoundations of money framework and revisits the optimum quantity of money. An optimal policy may consist of both a positive tax rate and a positive nominal interest rate: if the buyer’s surplus share is inefficiently small, the intensive margin is distorted and the constrained optimal policy combines a sales tax with a money growth rate above that prescribed by the Friedman rule. Monetary, but not fiscal, policy alters the agent’s bargaining position, leaving a special role for a deviation from the Friedman rule. Under similar conditions, this conclusion carries over to competitive pricing. ∗I am thankful to Shouyong Shi for his support and guidance. This paper has benefited from comments by and discussions with Andres Erosa and Miquel Faig. I also received valuable comments from seminar participants at the University of Toronto. All remaining errors and shortcomings are my own.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".