Identification and Inference in First-Price Auctions with Collusion
Why this work is in the frame
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Bibliographic record
Abstract
This dissertation develops a method to detect collusion and estimate its effect on the seller's revenue in first-price auctions with independent, private valuations. The challenge is that collusion may be difficult to detect because colluders can use a simple and costless strategy to make their bids appear competitive. If the econometrician observes an exogenous shifter of the level of competition in the auction in addition to the winning bids, a statistical test for collusion by a given bidder can be formulated as a test of independence between the exogenous shifter and the valuations that rationalize its bids under the null hypothesis that it is not colluding. Simulations confirm this test performs well even when colluders attempt to disguise their behavior. I then adopt a multiple hypothesis testing framework to simultaneously test for collusion bidder by bidder. By controlling the probability of making one or more type I errors, the set of rejected hypotheses serves as a lower confidence bound on the set of colluders. To produce a lower confidence bound on the cost of collusion, I use consistent estimates of the bidders' valuation distributions to numerically solve for the seller's expected revenues in auctions with and without collusion. To provide an example of this identification strategy, I use exogenous variation in the reserve prices at British Columbia's timber auctions to estimate the extent of collusion in the years preceding a lumber trade dispute between the United States and Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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