Economic surplus implications of Mexico’s decision to phaseout genetically modified maize imports
Bibliographic record
Abstract
The Mexican government has decided to ban imports of genetically modified (GM) maize, to rely on agroecology for maize production to satisfy domestic yellow maize requirements. No economic impact assessment of this policy decision was made public, and the implications of this decision for users of yellow maize and consumers are significant. This article measures the economic surplus generated from Mexican GM yellow maize imports and domestic conventional yellow maize production over the last 20 years, and projects the economic surplus generated over five years from adopting agroecology for yellow maize production. We explore three likely scenarios and find that in all of them, yellow maize processors lose almost twice as much economic surplus as producers. In the most conservative loss estimate (Scenario 1), the surplus loss in five years is equivalent to 35% of the economic surplus generated over the last 21 years from GM maize imports and domestic Mexican conventional production. In all simulated Scenarios, between 2024 and 2025 the price of a metric ton of yellow maize will increase 81percent because of the change in production systems (from conventional to agroecology). These financial losses will ultimately factor into the prices consumers pay for poultry and red meat products, resulting in higher domestic retail food prices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".