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Record W4376604617 · doi:10.3386/w31253

Two-Sided Market Power in Firm-to-Firm Trade

2023· report· en· W4376604617 on OpenAlexaff
Vanessa Alviarez, Michele Fioretti, Ken Kikkawa, Monica Morlacco

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMarket powerTariffOligopolyLeverage (statistics)EconomicsBargaining powerNegotiationMicroeconomicsMarket shareMonetary economicsInternational economicsCournot competitionMonopoly

Abstract

fetched live from OpenAlex

This paper develops a theory of bargaining in firm-to-firm trade with two-sided market power.The framework accommodates flexible market structures, yielding analytical expressions for pairspecific markups and pass-through elasticities.In U.S. import data, we estimate strong importer bargaining power and an upward-sloping export supply curve, consistent with oligopsony power.Pass-through of the 2018 tari!s in firm-to-firm relationships is incomplete, in contrast to productlevel studies, primarily due to exporter cost reductions driven by falling demand from dominant buyers.Our study highlights how bargaining and network rigidities shape price outcomes, with implications for markup dispersion and shock propagation in global value chains.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0050.009
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.002

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 head0.550
GPT teacher head0.482
Teacher spread0.068 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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