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Record W4221117645 · doi:10.1111/roie.12612

Tariffs, R&D, and two merger policies

2022· article· en· W4221117645 on OpenAlexaff
Mehdi Arzandeh, Hikmet Günay

Bibliographic record

VenueReview of International Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of ManitobaLakehead University
Fundersnot available
KeywordsMonopolyEconomicsTariffOligopolyEconomic surplusMicroeconomicsWelfareRevenueMonopolistic competitionDeadweight lossInternational economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract We compare welfare‐increasing and consumer‐surplus‐increasing merger policies in an oligopoly when merging firms face endogenous trade policies, and engage in cost‐reducing R&D activity. As R&D becomes less efficient, the equilibrium market structures (EMS) become less concentrated under both merger policies. When R&D is very efficient, monopoly becomes the EMS under the welfare‐increasing merger policy. This occurs as the absence of tariff and efficient R&D under monopoly limit the price increase and the gain in profits outweighs the loss in consumer surplus and tariff revenue. The results suggest that trade policies should take into account merger policies and industries' R&D efficiency. The results also show that global welfare maximization requires global merger policy coordination.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0120.000

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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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