Potential Impact of TPP Trade Agreement on US Bilateral Agricultural Trade: Trade Creation or Trade Diversion?
Bibliographic record
Abstract
Trans-Pacific Partnership (TPP) trade agreement is a trade agreement U.S is negotiating with 11 other countries in the Asia-Pacific region (Australia, Brunei Darussalam, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, and Vietnam) to reduce or eliminate tariffs on U.S. products exported to the TPP countries. With TPP, U.S expects to expand its trade with members of the partnership; resulting in GDP growth. However, there exist large concerns about the potential negative impact TPP will have on U.S. agricultural trade. Therefore, this paper examines the potential effect of TPP agreement on U.S agricultural trade using panel VAR and IRF models. A system of three VAR equations is developed for the three endogenous variables agricultural trade, real exchange rate, and the price ratio of imports to exports. In addition, the future pattern of trade is determined using the IRF curves. The lagged coefficients of agricultural trade volumes were significant in all three models implying current trade patterns are influenced by past volumes of trade. Also, the lagged price ratios have negative effect on current agricultural trade volumes as expected. Overall, the study found that a unit shock in price ratios as a result of the TPP agreement leads to a trade creation for U.S in the short run but in the long run, leads to more trade diversion than trade creation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".