Bibliographic record
Abstract
This paper examines the role of money when private information about the quality of the goods is present. In the private information environment, barter exchange for high-quality goods is rare since people have incentive to produce low-quality goods and attempt to cheat uninformed trading partners. This environment gives money a role in mitigating informational frictions. I consider two environments, one where traders can signal their quality of goods and one where they cannot, and two types of informational problems -- adverse selection and moral hazard -- in a search-theoretic framework. Both environments support the notion that money reduces the adverse selection problem and increases welfare. However, with moral hazard, money is less effective in overcoming informational frictions. Because low-quality goods producers can still consume as long as they hold money even when their products are recognized as low quality, agents have incentive to produce low-quality goods. I conduct several policy analyses, and find that the role of money is very sensitive to the inflation rate. While the Friedman rule is the optimal monetary policy in my environment, it cannot generate a first-best allocation unless traders are able to signal their quality of goods.
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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 | 0.018 |
| 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 | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".