Bibliographic record
Abstract
Treasury bills and other near-money assets provide owners with liquidity service benefits that are reflected in prices in the form of a liquidity premium.I relate time variation in this liquidity premium to changes in the opportunity cost of money: The liquidity service benefits of near-money assets are more valuable when short-term interest rates are high and hence the opportunity cost of holding money is high.Consistent with this prediction, the liquidity premium of T-bills and other near-money assets is strongly positively correlated with the level of short-term interest rates.Once short-term interest rates are controlled for, Treasury security supply variables lose their explanatory power for the liquidity premium.I argue that an analysis of scarcity and price of near-money assets is incomplete without taking into account the substitution relationship with money and its supply by the central bank.Payment of interest on reserves (IOR) could potentially reduce liquidity premia because IOR reduces the opportunity cost of at least one type of money (reserves).In the UK and Canada, however, the introduction of IOR did not shrink liquidity premia.Apparently, the reduction in banks' opportunity cost of money did not result in a broader fall in the opportunity costs of money for non-bank market participants.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".