Bilateral trade flows between U.S TPP countries: Country Pair Analysis
Bibliographic record
Abstract
The Trans-Pacific Partnership (TPP) is a proposed regional free trade agreement (FTA) among 12 countries: Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, the United States, and Vietnam. The TPP by eliminating more than 18, 000 taxes and other trade barriers on American products across the 11 other countries is expected make it easier for American entrepreneurs, farmers, and small business owners to sell Made-In-America products abroad. This paper attempts to examine the factors that affects trade creation and trade diversion between the US and TPP countries using the gravity model by applying both panel pooled data from 1991 to 2015 to four gravity equations (agricultural related products, bulk agricultural products, consumer oriented agricultural products, and intermediate agricultural products) in each case. The factors include traditional trade variables GDP of US (exporting country), GDP of importing countries, FTA’s, border, language, real exchange rate, arable land and population for U.S. Three models (One-way random effect, the two-way random effect and pooled) were applied to each of the four products. In all, the pooled model showed the highest predictive power and with consistent parameters. Similarly, considering the specific products, consumer oriented and intermediate products are the most sensitive to these factors while bulk agricultural products are the least.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".