Informational Advantage and Information Structure: An Analysis of Canadian Treasury Auctions ∗
Bibliographic record
Abstract
Several important auction settings, including treasury auctions in Canada and the U.S., have the feature that some bidders (dealers) observe the bids of a subset of other bidders (customers). Quantifying the economic advantage that informationally advantaged bidders derive from this institutional feature requires that we empirically distinguish between private vs. interdependent values paradigms. Bidders with private values who obtain information about rivals’ bids use this information to update their beliefs about the distribution of residual supply. With interdependent values, bidders also update their beliefs about the value of the good being auctioned. We use these differential updating effects to construct formal hypothesis tests of the presence of private vs. interdependent values. Using data from Canadian treasury auctions, we cannot reject the null hypothesis of private values in auctions of 3- and 12-month treasury bills. We also do not find evidence supporting the alternative hypothesis of interdependent values. We use the estimated model to quantify the value of observing customer bids to a dealer. We find that the extra information contained in customers’ bids leads on average to an increase in payoff equal to 13 ? 35% of the expected surplus of dealers.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".