Do Geographical Indications Really Increase Trade? A Conceptual Framework and Empirics
Bibliographic record
Abstract
Abstract Production location matters to many consumers and regulators and policymakers are pressed to statue on labels about country of origin, local foods and geographical indications (GIs). This paper investigates the incidence of the EU policy on GIs on bilateral trade flows. We develop ttheoretical arguments and provide empirical evidence to analyze heterogeneity in consumer preferences regarding country of origin ( domestic versus foreign ) and the implicit quality signals from GI logos. The objective of the paper is to investigate whether producing GIs really boots bilateral trade, assuming heterogeneity in consumers’ preference. We first develop an analytical framework of a simple partial equilibrium two-country model through a Cobb-Douglas utility structure to assess the impact of GIs on trade. In addition, we empirically corroborate the analytical findings with a unique data on protected GIs by product and European country. Our main findings indicate that GI-products have ambiguous effect on international trade. Indeed, their trade-impact depends on the importance of product for consumers (i. e., the intensity and the reputation of GI-product considered as deterministic weight in consumers’ preference). As expected, a heterogeneity in consumers’ preference – due to home bias about local or foreign varieties – can increase or decrease trade, despite the presence of GI-products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".