The impact of the TPP on selected ASEAN economies
Bibliographic record
Abstract
The Trans Pacific Partnership was set to be the world's largest and most comprehensive FTA linking 12 countries on both sides of the Pacific. In a major turnaround, late January 2017, USA-one of the major trading partners to the region announced its decision to withdraw from it. Four of the ASEAN members under the TPP, namely Brunei, Malaysia, Singapore and Vietnam, have a number of existing FTAs with some major TPP members and hold a significant trade share with them. However, these countries do not have any agreements with Canada and Mexico and except Singapore, have no other trade negotiation with the USA, though the USA absorbs a significant share of the exports of the TPP-ASEAN nations, particularly of Vietnam. Given this background, withdrawal of the USA seems to be a major setback for the TPP-ASEAN countries as these economies are expected to be the largest beneficiaries of the agreement. This study investigates how far the non-participation of the USA would affect the overall growth and welfare of each of the TPP-ASEAN countries. For this purpose, the study separately evaluates the impact of the TPP on each of the TPP-ASEAN countries. As an analytical framework, the paper uses a global CGE model and attempts a number of simulations by calibrating various trade integration scenarios, such as tariff reduction and input-augmenting technological change. Results of the study show that all of the TPP-ASEAN members enjoy a welfare gain and positive growth in total output and trade when the USA is a member. Vietnam and Malaysia, in particular are the largest beneficiaries. When the USA is omitted, these countries continue to have positive growth rates but the rates fall considerably.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".