Consumer Welfare of Country-of-Origin Labelling and Traceability Policies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".