U.S. PROCESSED FOOD EXPORTS AND FOREIGN DIRECT INVESTMENT IN THE WESTERN HEMISPHERE
Bibliographic record
Abstract
U.S. exports of processed food products and sales by foreign affiliates of U.S. companies in the industry have been growing rapidly. Canada and Mexico are the United States' two major trading partners in the Western Hemisphere, while small quantities of processed food products are exported to a number of other countries in the hemisphere. U.S. Foreign Direct Investment (FDI), like exports, is also largest in Canada and Mexico, but there is also significant FDI in the processed food industry in South American countries such as Brazil and Argentina. U.S. FDI, measured as sales by foreign affiliates, is significantly greater than U.S. processed food exports. The relationship between FDI and trade is subject to much debate and analysis. An econometric model is developed and estimated to determine the factors affecting U.S. processed food exports and sales by affiliates in eight Western Hemisphere countries, as well as the relationship between exports and FDI. Results suggest that U.S. FDI and exports are complements. U.S. exports are also positively influenced by real GDP in the importing country and are negatively influenced by tariffs. There are also large regional differences in U.S. exports after economic variables are accounted for. U.S. exports are higher to Canada and, to a lesser extent, Mexico and are lower to Brazil and Argentina. U.S. FDI is positively influenced by real GDP in the host country, which indicates that U.S. firms invest in countries with greater market opportunities. FDI is negatively influenced by exchange rate volatility, which indicates that U.S. firms try to avoid unstable economies. Free trade agreements with Canada and Mexico also have significant positive effects on U.S. FDI. Results indicate that taking advantage of lower labor costs is not a motivating factor for U.S. firms, and that the real exchange rate does not have a significant effect on either FDI or exports.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".