Non-Tariff Measures in Indian Context and the European Union
Bibliographic record
Abstract
Indian economy is one of the fastest growing economies in the world today. In line with global trade trends, Indian export sector has been growing and contributing significantly to the economy. Given its exports structure, India is well positioned to benefit from the structural changes in technology and emerging forces of globalization. Indian economy has shown remarkable progress in terms of foreign trade after the introduction of economic reforms in 1991. The European Union (EU) is a very important trading partner of India. The trade volumes between India and EU have shown remarkable improvement in last one and a half decade. After starting out at a relatively low level in the 1990’s, the trade volumes, both with respect to Indian exports to the EU as well as with respect to Indian imports from the EU, started to increase most noticeably after the year 2001.Use of non-tariff measures (NTMs) as means of protection has captured a lot of focus after reduction of tariffs in the world trade. India even after being a strategic partner for European Union (EU) has to face lot of NTMs on its exports. Based on studies in the past, link between the incidence of NTMs imposed by the home country and the income level of the foreign country has been established. The interplay of incidence of NTMs and the GDP remains largely unexplored in the context of India-EU trade relationship. This paper tries to fill this gap and show the importance of the study in policy decisions. Authors have used UNCTAD’s NTM data and Spearman’s correlation coefficient to measure the strength and direction of the relationship between incidence of NTM with per capita GDP of the exporting country (India). The authors have used different permutations of data from the main data set (1994-95 to 2016-17) for analysis and have concluded that incidence of NTMs on Indian exports to EU is positively co-related to the per capita GDP of India.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".