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Record W1487132101

Non-Horizontal Mergers: A European Perspective

2007· article· en· W1487132101 on OpenAlexaboutno aff
Carles Esteva Mosso

Bibliographic record

VenueeYLS (Yale Law School) · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionDominance (genetics)EnforcementEuropean unionMerger controlPolitical scienceMerger guidelinesEuropean commissionLaw and economicsConsumer welfareHarmBusinessHorizontal and verticalAccountingLawInternational tradeWelfareEconomics
DOInot available

Abstract

fetched live from OpenAlex

In the last few years, the assessment of non-horizontal mergers in the European Union (“EU”) has evolved considerably. There is now a consistent body of jurisprudence and administrative decisions on the assessment of vertical and conglomerate concentrations. The goal pursued is consumer welfare; the potential benefits of non-horizontal mergers are recognized and sound economic thinking is relied upon in identifying those instances where such mergers could lead to anti-competitive effects. Several developments have significantly contributed to this evolution. Two judgments of the European Courts have necessarily to be mentioned first: in 2004 the European Court of Justice (“ECJ”) established general principles on the assessment of non-horizontal mergers in the Commission v. Tetra Laval BV (“Tetra/Sidel”) judgment, which were completed and made operational by the Court of First Instance (“CFI”) one year later in its judgment in General Electric Co. v. Commission (“GE/Honeywell”). Second, an amended Merger Regulation entered into force in May 2004. This introduction of a new test, which no longer requires proof that a merger will lead to dominance before allowing intervention to prevent consumer harm, is particularly relevant for non-horizontal mergers. Third, the European Commission (“the Commission”), in November 2007, adopted guidelines on the assessment on non-horizontal mergers which, within the legal framework set up by the Courts, attempt to develop an economically sound approach for the analysis of vertical and conglomerate concentrations. Finally, during this period, the Commission has dealt with several vertical and conglomerate mergers in its enforcement decisions, which have allowed it to already apply and refine its policy in a number of individual situations. This Article describes in detail and comments on all of these developments, both from a legal and from a policy perspective.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0060.012

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 head0.016
GPT teacher head0.231
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2007
Admission routes1
Has abstractyes

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