Winner and Loser in Terms of the FTAs and the Trade War: Case Study of the Japanese Market
Bibliographic record
Abstract
Abstract The US raised trade war issues under protecting national security against China in July 2018. Likewise, the trade war has spread out across other regions such as India, the EU, Canada, Mexico, Russia, and Turkey through an additional tariff on products such as steel and aluminum. Clearly, the uncertainty has shown an increase since this friction created a pessimistic environment for the future world economy and did hurt economic development. Therefore, it has had negative effects for welfare -especially those (low-income consumers) who prefer to buy cheap imported goods. Contrary to protectionism, Japan has signed new FTAs with the EU and the US. In that context, this paper quantitatively examines the Japanese new FTAs under the trade war. It employs the general equilibrium approaches to not only investigate the economic structure of each country trade flow but also address the FTAs and the impacts of the welfare and sectoral value chains of the trade war. Essentially, the paper scenarios depend on the official list of the FTAs and the trade war-related goods. As a result of the FTAs under the trade war, the new Japanese trade agreements have provided some opportunities for its market as well as targeted countries. For instance, the Japanese benefit from the EU-Japan FTA would be $4.11 billion U.S.D. and the EU would gain $768 million U.S.D. within the 15-year. Moreover, the US not only would get a huge advance but also could get back its export market share from Pacific island nations in Japan when Japan would eliminate the tariff on concerned sectors for the US goods. For example, the US and Japan would improve their welfare by $4.09 billion U.S.D. and $398 million U.S.D., respectively through the limited USA-Japan FTA. That is, the US market would comparatively earn much more than Japan. Lastly, those who participate in the FTAs would boost their GDP, welfare, and value-added (productivity). For example, not only would Japan provide some opportunities for its market and then enhance its welfare and GDP, but also the EU and the US would boost their macro variables. However, from the perspective of the other regions/countries, those regions/countries which are not in the trade deal could lose their export market share in Japan, the US, and the UE and would, therefore, have a negative impact on their GDP and welfare.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".