Bibliographic record
Abstract
This paper reviews the theory related to the estimation of damages arising from pricefixing. Our primary objective is to provide an overview of the major issues that arise when estimating these types of damages and we suggest how economists might reasonably proceed when undertaking to provide such estimates. We describe and critique the leading approaches to damage estimation in price-fixing cases with a particular emphasis on reduced-form econometric estimation of the price that would have obtained in the market “but for” the price-fixing. We also consider complications introduced for the estimation of both the magnitude and the distribution of the damages in cases in which the first buyer (a “direct purchaser”) of a price-fixed product resells it or incorporates it into a product which is then sold to (“indirect”) purchasers further downstream. * The authors are also both Senior Consultants with the Delta Economics Group Inc. They are grateful to the Phelps Centre for the Study of Government and Business in the Sauder School of Business at UBC and to the Social Sciences and Humanities Research Council of Canada for financial support; and to Ann-Britt Everett and Jennifer Ng for excellent research assistance. As part of their work on cases involving damage assessment, they have also benefited significantly from discussions and communications with John Beyer, J. J. Camp, John Conner, Joe Fiorante, David Jones and Charles Wright.
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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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".