Analyzing Structural Changes and Trade Impacts in the Tomato Industry
Bibliographic record
Abstract
Almost half of the tomatoes consumed in the U.S. are imported. In 2014, Mexico accounted for more than 80 percent of the tomato imports and Canada for around 10 percent, being the two largest importers of fresh tomato. The accelerated increase of Mexican exports of tomato into the United States has resulted in trade disputes with domestic growers. Under this perspective, the role played by agricultural and economic policy to cope with these matters is studied. Tests for endogenous breakpoints provide information about any policy or economic intervention that could have caused a structural change in the tomato industry from 1970 until 2014. The empirical analysis uses a Vector Autoregressive Model (VAR) model in which the innovation accounting method and Directed Acyclic Graphs (DAGs) are used to show causal flow of information between the variables of interest in contemporaneous time. Results show breakpoints for imports from Canada, imports from Mexico and imports from the rest of the world. This suggests that NAFTA and pricing policies might have caused structural changes especially in the tomato importing industry. DAGs also reveal that these factors have important implications in the tomato industry changing causal relations among variables of interest. Therefore, this study indicates that agricultural policies do affect the underlying causal structure of the U.S. tomato industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".