Precautionary Balances and the Velocity of Circulation of Money
Bibliographic record
Abstract
Inflation, as a tax on money, gives buyers an incentive to reduce their money balances. Sellers are aware of this incentive and try to attract buyers by announcing price offers that induce buyers to spend a larger fraction of their money. We examine the effect of inflation on equilibrium price offers and associated trades in a competitive search environment where buyers experience preference shocks after they are matched with a seller. With full information,equilibrium price offers consist of a flat fee applied equally to all buyers independently of the quantities they purchase. If buyers'preferences are private information, sellers must charge more to buyers who purchase larger quantities due to incentive compatibility restrictions. In this case, equilibrium price offers consist of a non-linear price schedule. However, as inflation rises, price schedules become relatively flat. This implies that buyers with a low desire to consume purchase higher quantities and spend their cash more rapidly. Buyers with a high desire to consume purchase lower quantities because, as their money balances fall, they become liquidity constrained. This is in contrast with the full information benchmark where inflation reduces the quantities purchased by all buyers. The equilibrium is efficient at the Friedman rule and inflation reduces welfare both with full and private information.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".