Economy Wide Impact of the Trade Integration between Japan and India: A GTAP Analysis
Bibliographic record
Abstract
Japan and India signed the much-awaited Comprehensive Economic Partnership Agreement on 16 February 2011. The EPA will eliminate tariff on goods that account for 94% of their two way trade over ten years. This is a strategic move between two countries which will boost bilateral trade and investment. Indian exports which were subject to rigid standards will find it easier to enter Japanese markets. On the other hand, reduction of tariffs would help Japanese exports to exploit the growing Indian market. Overall, India-Japan Comprehensive Economic Partnership Agreement (CEPA) is a major step for harmonious economic rise of Asia. In this background, the study evaluates the economy wide impact of the proposed CEPA between India and Japan at 2020. The study has used a widely recognized global CGE model. Result shows a marginal increase in output growth for India and Japan in 2020 after tariff reduction compared to BAU. A marginal export growth is expected for both the countries compared to BAU 2020. A fair amount of trade creation within these two countries is expected to occur. India would likely to increase its export to Japan by I8.25%, while for Japan it will be only 4.65% by 2020. The proposed FTA will also improve the welfare of both the countries at 2020. An important finding of the study is that in spite of tariff liberalization in agriculture sector which is protected through stringent tariff and non-tariff barriers, Japan will witness considerable welfare gain. On the whole, it reflects that compared to Japan, India is expected to gain more during the 2010-2020 from the successful implementation of CEPA. The recent triple disasters (earthquake, Tsunami and radiation leaks) in Japan in March 2011 will have shortterm negative impacts on the economy but is unlikely to affect the expected gains from CEPA between India and Japan. According to ADB’s Asian Development Outlook 2011, short negative impact may be significant but in the long-run Japanese economy will expand through reconstruction efforts and return to productive activities. On the other hand, this can also lead towards enhancing economic cooperation between the two countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".