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Record W3157505563 · doi:10.3390/agronomy11050916

Consumer Welfare of Country-of-Origin Labelling and Traceability Policies

2021· article· en· W3157505563 on OpenAlexaff
J Bruneau, Albert I. Ugochukwu

Bibliographic record

VenueAgronomy · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTraceabilityWelfareBusinessCountry of originProduction (economics)CommerceDistribution (mathematics)International tradeIndustrial organizationEconomicsMarketingMicroeconomicsMarket economyComputer science

Abstract

fetched live from OpenAlex

Traceability regulations are a way to protect consumers by forcing firms to identify and track products step-by-step through all stages of production, processing, and distribution. Traceability is often used in conjunction with country-of-origin labelling where products explicitly identify where production takes place. However, such country-of-origin regulations can conflict with WTO provisions. This paper analyzes the impact on consumer welfare of traceability and country-of-origin in an international trading regime to assess whether such regulations actually improve consumer welfare. The paper constructs a theoretical model that highlights the potential market failure that arises from traceability. The paper then introduces a simple international trade regime to identify impacts on consumer surplus. The paper compares outcomes with, and without, traceability and country-of-origin regulations. Given the inherent free-rider problem, the paper shows that, as long as costs associated with traceability are low enough, mandatory regulations are welfare improving. Free trade, in the absence of foreign traceability, can lower consumer welfare so provides a rationale for country-of-origin rules. However, mandatory country-of-origin rules need not be welfare enhancing. We show that country-of-origin rules are similar to import barriers and so are third-best solutions. The better solution is international adoption and recognition of traceability rules which would make country-of-origin rules moot.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.495

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)0.0000.000

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 head0.018
GPT teacher head0.228
Teacher spread0.209 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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