Commercial dynamics of mexican tomato in the framework of the USMCA: an analysis of trade with the united states using the gravity model
Bibliographic record
Abstract
Objective: Within the framework of the treaty between Mexico, the United States and Canada (USMCA), the objective of this study is to provide a description through econometric methods of the variables that inf luence tomato trade, in addition to describing the commercial dynamics of the sector in both Mexico and the United States.Design/Methodology/Approach: A gravity model was applied to investigate and evaluate the role of some of the main economic and geographic variables as determinants of Mexican trade f lows.Results: The results show that the most important variables are the US gross national income per capita (GNIPC), as well as the US per capita production and consumption volumes calculated from apparent national consumption (ANC). It was also found that the variable GNIPC is better to determine the model than the gross domestic product per capita (GDPPC), due to the qualities of the variables.Limitations/Implications: Statistical records for the period 1994 to 2020 were taken into account, considering all varieties of tomato produced and exported.Findings/Conclusions: Regarding income, the variable with the best fit in the model was in GNIPC, which was adopted in the World Bank’s current way of classifying countries by income, variables such as consumption and production behaved in a typical way increasing and decreasing the volume traded. Tomato (Lycopersicon esculentum Mill.) is one of the most competitive and profitable agricultural products in Mexico.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".