Economy-wide impact of TPP: new challenges to China
Bibliographic record
Abstract
The Trans-Pacific Partnership (TPP) agreement as originally announced in October 2015 was undoubtedly the largest regional trade accord in history and, if approved, could have set new terms for the nearly US $28 trillion in trade and business investment between the parties to the deal. But the deal hit a roadblock when the USA decided to withdraw from the agreement in January 2017. While some of the countries are interested in taking the TPP forward without the USA, there are others which are still looking at convincing the USA to reconsider its position on the deal. Thus, while all the debate and deliberation around the prospect of TPP continues, an important point to note is that the TPP deal does not include China, world’s largest merchandise trader, which had combined exports and imports worth US $3963.5 billion in 2015. Against this backdrop, the present paper seeks to analyze the impact of the TPP agreement on various trade and other economic variables of China both if the USA continues to be a part of TPP and if the USA withdraws from TPP using the Global Trade Analysis Project. The unique contribution of the present study lies in analyzing the trade integration scenarios among the TPP member countries involving all of tariff and non-tariff liberalization and improved market access between the countries, without the USA. The results indicate that China’s trade with TPP region, both exports and imports, will suffer post-TPP implementation. Exports which are likely to be hit are leather and leather products, motor vehicles, meat products, processed food, iron and steel. Imports of oilseeds, paper and paper products, nonmetallic minerals and machinery are also expected to suffer. More than the loss in trade, China will experience substantial welfare loss due to all of allocative inefficiency, worsening terms of trade and endowment effect. Of these, worsening terms of trade explain the largest part of welfare loss to the country. With the withdrawal of the USA from the deal, China’s trade with the region and its welfare is likely to suffer less.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".