Bibliographic record
Abstract
This paper compares the new uniform-price U.S. Treasury auctions to the traditional discriminatory mechanism and examines the extent to which the auction mechanisms are responsible for underpricing. Empirically, I find that even for the newer uniform-price auctions, the average price received by the Treasury is less than the price of the same securities in the concurrent secondary market, although this underpricing is reduced by half relative to the older mechanism. The auctions are modeled in a multi-unit common-value setting with a winner’s curse problem. Underpricing results in equilibrium for both auction formats, although to a greater degree for the discriminatory auction. In the context of the model, the equilibrium level of underpricing in an individual auction can be predicted from the summary statistics released by the Treasury after each auction. Empirical results show that the magnitude of underpricing in the auctions, and the cross-sectional variation in underpricing, is consistent with the model.
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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 | 0.008 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".