Assessing the Factors Associated With Iran’s Intra-Industry Trade in Pharmaceuticals
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical industry is a sensitive and profitable industry. If this industry wants to survive, it should be able to compete well in international markets. So, study of Iran's intra-industry trade (IIT) in pharmaceuticals is essential in order to identify competitiveness potential of country and boost export capability in the global arena. METHODS: This study assessed the factors associated with Iran's intra-industry trade in pharmaceuticals with the rest of the world during the 2001-2012 periods using seasonal time series data at the four-digit SITC level. The data was collected from Iran's pharmaceutical Statistics, World Bank and International Trade Center. Finally, we discussed a number of important policy recommendations to increase Iran's IIT in pharmaceuticals. RESULTS: The findings indicated that economies of scale, market structure and degree of economic development had a significantly positive impact on Iran's intra-industry trade in pharmaceuticals and tariff trade barriers were negatively related to IIT. Product differentiation and technological advancement didn't have the expected signs. In addition, we found that Iran's IIT in pharmaceuticals have shown an increasing trend during the study period. Thus, the composition of Iran trade in pharmaceuticals has changed from inter-industry trade to intra-industry trade. CONCLUSIONS: In order to get more prepared for integration into the global economy, the development of Iran's IIT in pharmaceuticals should be given priority. Therefore, paying attention to IIT could have an important role in serving pharmaceutical companies in relation to pharmaceutical trade.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".