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Record W2109065555 · doi:10.1515/1935-1682.3026

On the Relationship between Tariff Levels and the Nature of Mergers

2012· article· en· W2109065555 on OpenAlexaff
Ayşegül Yıldız Ulus, Halis Murat Yildiz

Bibliographic record

VenueThe B E Journal of Economic Analysis & Policy · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTariffCommitInternational economicsEconomicsOligopolyProduct differentiationInternational tradeFree tradeCournot competitionIncentiveLiberalizationMicroeconomicsMarket economy

Abstract

fetched live from OpenAlex

Abstract This paper employs an endogenous merger formation approach in a two-country oligopoly model of trade to examine the international linkages between the nature of mergers and tariff levels. Firms sell differentiated products and compete in a Bertrand fashion in product markets. Two effects play key roles in determining equilibrium market structure: the tariff saving effect and the protection gain effect. The balance between these two effects implies that, when foreign country practices free trade, low home tariffs yield international mergers irrespective of the substitutability levels. By contrast, when foreign tariffs are sufficiently high and products are close substitutes, national mergers obtain in the equilibrium. Unlike this asymmetric result of unilateral trade liberalization, we find that when bilateral tariffs are sufficiently low, international mergers arise. These results fit well with the fact that global trade liberalization has been accompanied by an increase in international merger activities. From a welfare perspective, we show that international mergers are preferable to national mergers and thus social and private merger incentives become aligned together as trade gets bilaterally liberalized. Finally, if countries can commit to a trade policy, they would optimally commit to a low tariff to induce international mergers when products are close substitutes while any tariff commitment is optimal when products are sufficiently differentiated.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.102
GPT teacher head0.281
Teacher spread0.179 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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