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Free trade and the burden of domestic policy

2008· article· en· W2164112045 on OpenAlexaffvenue
Sumeet Gulati

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAccessionIncentiveCommercial policyFree tradeEconomicsTrade barrierWelfareInternational economicsInternational tradeCustoms unionBusinessPublic economicsEuropean unionMarket economy

Abstract

fetched live from OpenAlex

Abstract. Consider a small economy facing accession to a exogenously defined trade agreement. Before accession, the government controls trade and pollution policy. After accession, it retains control over pollution policy, but must allow free trade in all goods. This is a choice many governments face while joining trade agreements today. They decide whether greater market access to other members is more valuable than control over trade policy. I ask two questions. All else being equal what happens to environmental policy after accession? Second, what affects the choice of accession and how does this choice impact aggregate welfare? I show that a loss in control over trade policy alters the political incentives determining environmental policy. Before accession, producers can transfer a portion of their burden of environmental regulation to consumers through price increases. After accession the same regulation is borne entirely by producers. Owing to the change in burden, there exist plausible conditions under which the adoption of free trade can lead to more stringent environmental regulation, a reduction in the preferential treatment of special interest groups, and an increase in aggregate welfare.

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)0.013
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 score0.995
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0220.001

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 head0.199
GPT teacher head0.197
Teacher spread0.002 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicClimate Change Policy and EconomicsFrench-language works237,207