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Record W1534605718

Predatory Pricing, the Theory of the Firm and the Recoupment Test: An Examination of Recent Developments in Canadian Predatory Pricing Law

2006· article· en· W1534605718 on OpenAlexaffabout
Edward Iacobucci

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPredatory pricingEconomicsTest (biology)MicroeconomicsBusinessMonopolyEcology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.192
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes2
Has abstractyes

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