Effects of environmental regulations on South Asian food and agricultural exports : a gravity analysis
Bibliographic record
Abstract
Regardless of the occasional dissenting voices, free trade is now being embraced by many of the nations of the world. South Asian countries joined the global consensus for frictionless trade by forming regional trade blocs under the banner of the South Asian Association for Regional Cooperation (SAARC). However, intra- and interregional trade in SAARC has not yet reached the desired stage, and a range of empirical studies have therefore been carried out with the objective of determining the causes. This current study is also motivated by the poor performance of the South Asian countries in world trade and it investigates the effects of environmental regulation on the food and agricultural trade of four South Asian nations, i.e., Bangladesh, India, Pakistan and Sri Lanka. For this study, the Gravity Model for international trade analysis was used with country- and time-specific fixed effects followed by Heckman sample selection model to avoid possible biases that are widely cited in the gravity literature. Trade data were retrieved from Trade Map while data for other gravity variables were retrieved from relevant recognized data sources. The Environmental Performance Index (EPI) was utilized as a proxy measure for the environmental regulation of the four SAARC nations and their trade partners to denote environmental regulation of reporting and partner countries. The results of the coefficient estimates revealed that even though there appears to be a relationship between stringent regulations and foreign trade without these specific effects, its significance fades as soon as both the importing and exporting country-specific effects are taken into consideration
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".