Assessing the impacts of equivalency agreements in international organic trade
Bibliographic record
Abstract
Abstract This study employs Berry, Levinsohn, and Pakes' (1995; hereafter BLP) model to estimate the impacts of organic equivalency agreements (OEAs) on the market share of exporting countries that shipped organic agrifood products to the markets of the U.S., Canada, and Denmark from 2011 to 2019. The BLP model accounts for variations in the trade impacts of OEAs by considering unobserved, product‐specific, agro‐ecological comparative advantages and bilateral trade costs. The BLP estimation offers a more realistic trade pattern, showing that exporters producing and selling close substitutes for organic agrifood products with the competitors in the market would be more sensitive to the establishment of OEAs between the competitors and the market. Results indicate that OEA partners of the importer would achieve a higher share in this market than non‐OEA partners. The simulation results suggest that Peru would have captured 23.8% of the 2019 U.S. market share if Peru had signed an OEA with the U.S. in 2017. Additionally, Mexico and Turkey would have secured 35.1% and 1.8% of the 2019 Canadian and Danish markets, respectively, had the Mexico–Canada and Turkey–Denmark OEAs been in effect since 2017. These findings, along with changes in the market shares of other exporters under a hypothetically established OEA, provide new insights into organic trade patterns and highlight the potential for further development of OEAs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".