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Record W4410864657 · doi:10.1111/roie.12810

Do Economic Integration Agreements Increase Members' Migration? Accounting for Migration in Trade Agreements

2025· article· en· W4410864657 on OpenAlexaboutno aff
Scott L. Baier, Ward M. Reesman

Bibliographic record

VenueReview of International Economics · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsStylized factEconomic integrationGravity model of tradeEconomicsGeneral equilibrium theoryInternational economicsTrade barrierWelfareApplied general equilibriumInternational tradeBilateral tradeInternational free trade agreementTrade diversionMacroeconomicsGeography

Abstract

fetched live from OpenAlex

ABSTRACT A substantial body of literature establishes that economic integration agreements (EIAs) facilitate trade among member countries. However, their impact on migration flows remains less understood. In this paper, we construct a stylized spatial general equilibrium model to study the impact of economic integration agreements on trade and migration. The model is similar to that of Allen and Arkolakis (2014) and Redding and Rossi‐Hansberg (2017), and yields structural gravity equations for both trade and migration that allow us to estimate the direct impact of trade agreements on trade and migration flows. Given these parameter estimates, we can simulate the spatial model to highlight the general equilibrium effects of trade agreements on trade and migration. In our simulation exercise, we show that if the United States‐Mexico‐Canada Agreement (USMCA) were modified to have the trade‐creating effect of a common market, it would foster more trade within the group and encourage in‐migration from outside the trading bloc. If the USMCA were also modified to allow migration costs to fall to the level of a common market, the within‐migration impact on trade and welfare would be more modest.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.032
GPT teacher head0.275
Teacher spread0.243 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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