African trade of mangoes to OECD countries: disentangling the effects of compliance with maximum residue limits on production, export supply and import demand
Bibliographic record
Abstract
Abstract This article theoretically and empirically disentangles the effects of maximum residue limits (MRLs) for pesticides on production, export supply and import demand. We adopt a modelling approach based on the costs and benefits associated with food safety standards and use our theoretical framework to assess the empirical net effects of MRLs for pesticides on African mango production and trade with Organisation for Economic Co-operation and Development (OECD) member countries. On the one hand, we theoretically highlight that for a given production technology and a level of elasticity of production costs with respect to the MRL gap, producers will likely (probability and quantity) produce standard-compliant products if they are able to completely pass through the standard-compliance costs to the unit price they receive from exporters; otherwise, they will exit standard-compliant products market. On the other hand, we theoretically show that the net effects of the MRL gap on bilateral trade can be positive, zero or negative depending on the effects of consumers’ perceived quality (positive), trade costs (negative) and standard-compliant production cost (negative). We use a cross-sectional data set for 12 African countries that produced and exported MRL-compliant mangoes to 31 OECD countries in 2016. On the one hand, we find that the net effect of MRLs is positive for the level of standard-compliant mango production and negative for the probability of producing. On the other hand, they are positive in mango trade between African and OECD member countries. Our results highlight that the tightening or imposition of strict MRLs for pesticides in developed countries may be trade promoting, while they severely impede production in African countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".