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Record W1881203587

Efficiencies Defences for Mergers within a Dominant Group

2000· article· en· W1881203587 on OpenAlexaboutno aff
Lin Bian, Donald G. McFetridge

Bibliographic record

VenueCarleton University's Institutional Repository (MacOdrum Library, Carleton University) · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementStatutory lawBusinessIndustrial organizationUnit (ring theory)Joint (building)International economicsMerger guidelinesInternational tradeEconomicsFinanceEngineeringLawPolitical scienceMathematicsCommission
DOInot available

Abstract

fetched live from OpenAlex

For a summary, see McFetridge (1999).Antitrust enforcement in a number of countries allows an efficiencies defence for horizontal mergers, joint ventures, specialization agreements and other horizontal arrangements. 1 These defences may be statutory, jurisprudential or administrative.They involve, in essence, a balancing of real per unit cost savings (improvements in technical efficiency) resulting from a merger or agreement against its anticompetitive effects.In an earlier paper (Bian and McFetridge, 2000), we proposed four alternative interpretations of the efficiencies defence for horizontal mergers and specialization agreements provided in Sections 96 and 85 respectively of Canada's .We called these four interpretations the total surplus standard, the price standard, the weighted surplus standard and the Hillsdown standard..In the simplest terms, the total surplus standard allows any arrangement that does not reduce total surplus.The price standard allows any arrangement that does not increase price.The weighted surplus standard typically weights consumers surplus more heavily than profits and allows any arrangement that does not reduce weighted surplus.The Hillsdown standard, suggested by Canada's Competition Tribunal in its Hillsdown decision, allows any arrangement that entails cost savings in excess of losses in consumer surplus.In our earlier paper, we derived and tabulated closed-form expressions for the respective percentage reductions in long-run marginal cost required for a merger or specialization agreement to satisfy each of the four efficiencies standards described above as well as a profitability constraint.We expressed these critical rates of cost reduction in terms of the number of firms in the relevant market prior to the merger, the pre-merger elasticity of market demand, a conduct parameter and a spillover parameter which reflects the extent to which efficiencies realized by the merged entity are replicated by competitors.We showed that the percentage reduction in marginal cost required to satisfy either the price standard (used by U.S. antitrust authorities) or the Hillsdown standard is frequently much higher than is required for a profitable, total surplus-increasing merger.The implication is that the adoption of either of these interpretations of Sections 96 and 85 would likely preclude a significant number of total surplusincreasing mergers or specialization agreements.In this paper we offer a simple but useful extension of our earlier results.In Bian and McFetridge (2000), we assumed pre-merger symmetry, Cournot behaviour (as a base case) and no threat of entry.In this paper, we allow the market to be comprised of a competitive fringe as well as an initially symmetric dominant group of Cournot oligopolists.We then find the critical rates of cost reduction required to satisfy the total surplus, price and weighted surplus standards as well as the profitability constraint for mergers within the dominant group.Specifically, we solve for the critical rates of cost reduction in terms of the number of firms in the dominant group prior to the merger, the pre-merger elasticities of market demand and fringe supply, the market In , the fringe was comprised of smaller scale and specialized renderers as 2 well as vertically integrated meat processors with small amounts of non-captive rendering business.See the Tribunal's decision, www.ct-tc.gc.ca/english/cases/hillsdow/155_a.pdf,pp.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.160
Teacher spread0.149 · 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.

Study designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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