A CGE-ML Approach to Analysing India’s Free Trade Agreements
Why this work is in the frame
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it