Predatory Pricing, the Theory of the Firm and the Recoupment Test: An Examination of Recent Developments in Canadian Predatory Pricing Law
Bibliographic record
Abstract
Under US federal antitrust law, predatory pricing requires both pricing below cost as well as a reasonable prospect of recoupment on investment. Recent changes in Canadian law have moved away from the recoupment test and this article critically examines the rationale and wisdom of such developments. In support of the move away from recoupment, it is true that even unprofitable predatory pricing, which has no potential of recoupment, still engenders deadweight loss to the society. Moreover, unprofitable predatory pricing is not necessarily self-defeating; managers of firms may rationally engage in costly price wars in order to retain their private benefits of control, and firm owners may allow such self-interest for strategic reasons.These considerations do not, however, support a move away from the recoupment test. Rather, they offer a new interpretation of it: although it is true that unprofitable predatory pricing is not captured by the recoupment test explicitly, the presence of competitive markets suggest both that recoupment is not likely and that unprofitable predatory pricing is unlikely; the recoupment test therefore implicitly accounts for unprofitable predatory pricing. Moreover, simple reliance on price-cost tests creates its own problems, including the risk of overdeterrence. It is dangerous to diminish the importance of the recoupment test in predatory pricing cases.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| 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 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".