2004): “Inferring Treatment Status when Treatment Assignment is Unknown: with an Application to Collusive Bidding Behavior in Canadian Softwood Timber Auctions,” Working Paper
Bibliographic record
Abstract
In recent years an influential line of research estimating treatment effects of various programs has evolved. In this framework, the task confronting the econometrician is to estimate the effect of treatment conditional on treatment assignment using non-experimental data. This paper considers a twist on this approach: we provide a framework for detecting corruption (treatment assignment) given data on market outcomes. Our approach clarifies many issues of importance in the collusion literature; it rejects the validity of a number of existing approaches and highlights new approaches not previously considered. We demonstrate our approach by exploring whether bidder collusion was evident in a unique data set that includes nearly 3,000 auctions (over 10,000 individual bids) for cutting rights of standing timber in British Columbia from 1996-2000. We find mixed statistical evidence when we examine whether the data are consistent with a model of collusive behavior.
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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 | 0.066 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".