A Study of Comparative Advantage and Intra-Industry Trade in the Pharmaceutical Industry of Iran
Bibliographic record
Abstract
BACKGROUND: Drug costs in Iran accounts for about 30% of the total health care expenditure. Moreover, pharmaceutical business lies among the world's greatest businesses. The aim of this study was to analyze Iran's comparative advantage and intra-industry trade in pharmaceuticals so that suitable policies can be developed and implemented in order to boost Iran's trade in this field. METHODS: To identify Iran's comparative advantage in pharmaceuticals, trade specialization, export propensity, import penetration and Balassa and Vollrath indexes were calculated and the results were compared with other pharmaceutical exporting countries. The extent and growth of Iran's intra-industry trade in pharmaceuticals were measured and evaluated using the Grubel-Lloyd and Menon-Dixon indexes. The required data was obtained from Iran's Customs Administration, Iran's pharmaceutical Statistics, World Bank and International Trade Center. RESULTS: The results showed that among pharmaceutical exporting countries, Iran has a high level of comparative disadvantage in pharmaceutical products because it holds a small share in world's total pharmaceutical exports. Also, the low extent of bilateral intra-industry trade between Iran and its trading partners in pharmaceuticals shows the trading model of Iran's pharmaceutical industry is mostly inter-industry trade rather than intra-industry trade. In addition, the growth of Iran's intra-industry trade in pharmaceuticals is due to its shares of imports from pharmaceutical exporting countries to Iran and exports from Iran to its neighboring countries. CONCLUSIONS: The results of the analysis can play a valuable role in helping pharmaceutical companies and policy makers to boost pharmaceutical trade.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".