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Record W3177376132 · doi:10.1111/ecin.13099

International sourcing, complementary inputs, and the structure of trade agreements: Deep, shallow, narrow, and wide

2022· article· en· W3177376132 on OpenAlex
Richard Chisik, Sara Rohany Tabatabai

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

VenueEconomic Inquiry · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDeep integrationInternational tradeRules of originTrade agreementFrontierInternational economicsEconomicsWelfareCommercial policyFree tradePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract We analyze Preferential Trade Agreement (PTA) formation among a subset of members of a multilateral agreement when imported inputs are complementary to one another. A shallow (focused only on border policies) multilateral agreement does not place countries on the efficiency frontier. Furthermore, no subset of countries will form a shallow PTA. Alternatively, a deep PTA that addresses behind‐the‐border policies increases each country's welfare. This result suggests that the recent proliferation of PTA formation is driven by a need for deep integration. Although these deep PTAs increase welfare over a shallow multilateral agreement the efficiency frontier can only be reached by a deep multilateral agreement that covers both border and behind‐the‐border policies. Whether a deep PTA can generate consensus approval for further multilateral‐deep integration depends on the structure of the PTA and the success of the multilateral‐shallow agreement in lowering tariffs.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.999

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)0.0020.000

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 head0.037
GPT teacher head0.222
Teacher spread0.185 · 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