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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".