U.S. Demand for Fresh Fruit Imports
Bibliographic record
Abstract
Over the last three decades, U.S. imports of fresh fruits have been constantly increasing at an annual average growth rate of 7% (USITC, 2016). Fresh fruits make up 9% of the total U.S. food imports (UN Database, 2016) with the top seven fruits accounting for 82% of the value of the U.S. fresh fruit imports and Canada and Mexico (NAFTA countries) as the most important trade partners (USITC, 2016). This study analyzes the main U.S. markets and supply sources of the top imported fresh fruits and estimates a Source-Differentiated Almost Ideal Demand System model (SDAIDS) using time-series data, with North American Free Trade Agreement (NAFTA) countries and the rest of the world (ROW) as import sources. Our results suggest that source of origin is an intrinsic quality attribute for most of the fresh fruits analyzed. More specifically, the study found that most uncompensated own-price elasticities are inelastic, most cross-price elasticities are positives indicating that the fruits imported from given sources are net substitutes, and that statistically significant expenditure elasticities are positive implying that the quantity imported of all the fresh fruit analyzed increases as real expenditure for those fruits rises. The results of this study will be useful to policy-makers in regulating the international market of fresh fruits, setting optimal import taxes and price floors, and predicting likely scenarios of imports from Canada and 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".