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Record W2266277318 · doi:10.1257/app.20150360

Do Free Trade Agreements Affect Tariffs of Nonmember Countries? A Theoretical and Empirical Investigation

2018· article· en· W2266277318 on OpenAlexaff
Kamal Saggi, Andrey Stoyanov, Halis Murat Yildiz

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsInternational economicsInternational tradeTrade diversionEconomicsFree tradeTrade barrierWorld tradeFree trade agreementInternational free trade agreementRegional tradeBusiness

Abstract

fetched live from OpenAlex

We investigate the effects of free trade agreements (FTAs) on tariffs of nonmember countries. In our multi-country model, the formation of an FTA leads members to reduce their exports to the rest of the world. Such external trade diversion weakens the ability of nonmembers to manipulate their terms of trade vis-à-vis FTA members, a mechanism that induces them to lower their tariffs on FTA members. We empirically confirm this insight using industry-level trade data for 192 importing and 253 exporting countries, along with information on all FTAs formed in the world during 1989–2011. (JEL F13, F14)

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.025
GPT teacher head0.233
Teacher spread0.208 · 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 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

Citations24
Published2018
Admission routes1
Has abstractyes

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