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Mergers to Monopoly

2002· article· en· W2109006647 on OpenAlexaff
Mihkel M. Tombak

Bibliographic record

VenueJournal of Economics & Management Strategy · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsMonopolyConsolidation (business)Industrial organizationMarket powerBusinessMergers and acquisitionsWelfareBargaining powerProduct marketMicroeconomicsEconomicsMonetary economicsMarket economyIncentiveFinance

Abstract

fetched live from OpenAlex

Horizontal mergers between firms that have different costs are examined. Owners can transfer technology to an acquired firm and decide whether to consolidate or operate their firms as separate entities in the product market. Thus mergers can exhibit both efficiencies and a market‐power effect. The prices of target firms are determined via a bargaining game. An equilibrium sequence of mergers entails the largest firm targeting the next largest rival firm. Initially, this sequence of mergers with technology transfers involves no consolidations and improves welfare. Ultimately, the acquisitions lead to consolidation and may decrease total welfare.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0420.004

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 head0.047
GPT teacher head0.219
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2002
Admission routes1
Has abstractyes

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