An empirical study of auction revenue rankings: the case of municipal bonds
Why this work is in the frame
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Bibliographic record
Abstract
Using a novel dataset of 386 first-price municipal bond auctions held in California, I perform counterfactual revenue comparisons, based on the theoretical result of Milgrom and Weber (1982). I show that the revenue in the second-price auction is nonparametrically identified, and the counterfactual revenue in the English auction can be bounded in an informative way. These results form a basis for nonparametric estimation of counterfactual revenue differences. I find that the revenue gain from using the English auction would be in the range of 11%—19% of the gross underwriting spread, and that most of it would already be captured by using the second-price auction. The recent explosive growth of Internet English auctions, administered by Grant Street Group, provides external support to the claim that auction design matters in this market.
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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 | 0.005 | 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 | 0.001 | 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 it