A CGE-ML Approach to Analysing India’s Free Trade Agreements
Bibliographic record
Abstract
India has set an ambitious export target of US$0.5 trillion by 2025 and US$1 trillion by 2030 from US$291 billion in 2021 as part of its Atmanirbhar Bharat campaign. Since India opened up its economy in 1991, India has concluded several bilateral and regional free trade agreements. India signed a Comprehensive Economic Partnership Agreement (CEPA) with the United Arab Emirates in February 2022 and Economic Cooperation and Trade Agreement (ECTA) with Australia in April 2022. India is in the process of concluding trade agreements with the UK, the European Union, Canada, Israel and GCC countries. This article estimates the impact of all the above mentioned FTAs on India’s GDP and its components with an increased emphasis on its exports using a computable general equilibrium framework and machine learning techniques. The analysis estimates that the FTAs will boost India’s GDP by 4.10% to add US$109.096 billion in 2030 and the exports increase by 16.73% or US$46.08 billion. The exports from India to UAE, Australia, UK, European Union, Canada, Israel and GCC countries are estimated to increase by US$67.312 billion by 2030. This increase is relatively higher than the increase in aggregate exports of India suggesting a trade diversion from countries that are not part of the FTAs toward the seven countries with which India is anticipated to sign an FTA.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".