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Record W2608997193 · doi:10.1017/s002081831700011x

The Distributional Consequences of Preferential Trade Liberalization: Firm-Level Evidence

2017· article· en· W2608997193 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Organization · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultinational corporationForeign direct investmentInternational economicsLiberalizationInternational tradeFree tradeTariffEconomicsInvestment (military)Trade barrierBusinessSubsidiaryPoliticsMarket economy

Abstract

fetched live from OpenAlex

Abstract While increasing trade and foreign direct investment, international trade agreements create winners and losers. Our paper examines the distributional consequences of preferential trade agreements (PTAs) at the firm level. We contend that PTAs expand trade among the largest and most productive multinationals by lowering preferential tariffs. We examine data covering the near universe of US foreign direct investment and disaggregated tariff data from PTAs signed by the United States. Our results indicate that US preferential tariffs increase sales to the United States from the most competitive subsidiaries of multinational corporations operating in partner countries. We also find increases in market concentration in partner countries following preferential liberalization with the United States. By demonstrating that the gains from preferential liberalization are unevenly distributed across firms, we shed new light on the firm-level, economic sources of political mobilization over international trade and investment policies.

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.

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)0.001
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 score0.657
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.145
GPT teacher head0.265
Teacher spread0.120 · 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