Buying Anonymity: An Investigation of Petroleum and Natural Gas Lease Auctions
Bibliographic record
Abstract
This paper examines how concealing the existence of private information affects winning bids in a large, well-functioning auction environment. Standard auction theory suggests firms should wish to advertise the existence of private information in order to reduce the bids of their competitors (Milgrom and Weber, 1982). There are a limited number of empirical studies on how concealing the existence of private information affects bids. Instead, most articles test for the presence of private information, rather than the effect of revealing or concealing its existence.\nAn institutional feature of the auctions for petroleum and natural gas leases in Alberta is that firms can hire a broker to bid on their behalf, thereby hiding their identity. Anecdotal evidence suggests rms use brokers to conceal information from their competitors. In order to\ntest the predictions of standard theory, I develop a model of bidding behaviour incorporating the choice to use a broker. The model provides an explicit relationship between broker usage, firms' private information, and equilibrium bids. Using a newly constructed dataset, I estimate\nthis relationship. I find results consistent with standard theory: bids are higher when brokers are used to hide the existence of some private information.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".