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Record W2253005534

Are Merger Control and Article 82 EC in the Same Market? - The Assessment of Mergers Which Facilitate Exclusionary Conduct under EC Merger Control

2006· article· en· W2253005534 on OpenAlexaboutno aff
Thorsten Kaeseberg

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
Fundersnot available
KeywordsMerger controlEuropean commissionCommissionCompetition (biology)Predatory pricingBusinessControl (management)IncentiveRelevant marketJurisprudenceEconomic JusticeCompetition lawLaw and economicsEuropean court of justicePolitical scienceLawEconomicsInternational tradeEuropean Union lawEuropean unionMarket economyManagement
DOInot available

Abstract

fetched live from OpenAlex

In its judgments in Tetra Laval and GE/Honeywell, the Court of Justice of the European Communities distinguished between mergers which directly lead to a significant impediment to competition (SIEC) and mergers which facilitate exclusionary conduct, thereby leading to a SIEC in the medium-term. When assessing the latter 'behavioural' type of mergers, the European Commission, according to these judgments, has to take into account the diminishing effect of two deterrents on the incentive of the merged entity to adopt the alleged exclusionary behaviour: first, an ex post use of Article 82 EC against the exclusionary conduct and, second, behavioural commitments by the merging firms not to engage in the practice, which may be attached as obligations to the merger decision. This article first analyses which merger scenarios fall into the behavioural category. It then discusses in which of these situations Article 82 EC and behavioural commitments are effective deterrents and thus substitutes for a prohibition of the merger. The article advocates that behavioural commitments are always preferable to reliance on Article 82 EC. It finally supports the jurisprudence by the Court that the same standard of proof should apply for all types of mergers.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 designObservational
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

Citations6
Published2006
Admission routes1
Has abstractyes

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