Fish and Fishery Products Trade by India: Trends, Competitiveness, and Comparative Advantage
Bibliographic record
Abstract
This study analyzes the trends and determines the comparative advantage and competitiveness of India’s fish and fishery products trade in the world market and of India’s exports to 10 major destinations over the period of 2000–2021. We use the revealed symmetric comparative advantage (RSCA) index to quantify India’s comparative advantage in exporting fish and fishery products and the Vollrath index to measure the revealed competitiveness of the country’s fish and fishery products trade. We collected relevant data at Harmonized System (HS) four-digit level from the UN Commodity Trade (UN Comtrade) database. Our analysis shows that India has a revealed comparative advantage (RCA) in exporting fish and fishery products to the world market. Specifically, India has a comparative advantage in exporting frozen fish, crustaceans, and mollusks; but it has a comparative disadvantage in exporting live fish, fresh and chilled fish, fish fillets and other fish meat, and dried/salted/in-brine and smoked fish to the world market. In terms of individual destinations, India has RCA in exporting live fish to Hong Kong; fresh and chilled fish to UAE (in recent years); frozen fish to China, Hong Kong, Thailand (recent years), and Vietnam (recent years); fish fillets and other fish meat to Japan (recent years); dried fish to Hong Kong; crustaceans to Japan, the US, and Canada (recent years); mollusks to the EU, Thailand (recent years), and Vietnam (recent years); and other aquatic invertebrates to Vietnam. India has a comparative disadvantage (RCD) in exporting fresh and chilled fish to the EU, Japan, the US, and Vietnam, and fish fillet and other fish meat to the US, Canada, and Vietnam. The COVID-19 pandemic has negatively affected India’s export, comparative advantage, and trade competitiveness of fish and fishery products. India’s RCA and competitiveness in exporting fish and fishery products decreased in 2018–2020, but the RCA and competitiveness increased by 2021.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".