Bibliographic record
Abstract
Antimicrobial medicines are difficult to access for the poor people in many parts of the world, mainly because of their costs and lack of local availability. While it is not necessary that these medicines may be produced across the world, it is possible to import them from countries that have enormous production capacities. For this to happen, the countries that lack these medicines should have trade policies in place that facilitate their cheap imports. However, trade policies typically do not take this aspect into account when they are formulated. The policy determinants of high import tariffs are industrial policy and protectionism-related concerns on one hand and revenue considerations on the other hand. In this paper, we take a close look at the global trade and tariffs in various countries in several antimicrobial medicines and medicaments, to come up with inferences on how countries that import a lot of them may do better by reducing tariffs. Especially, the article deals with anibiotics trade. The export and import drug potentials are investigated. The largest export countries proved to be China, the United Kingdom, India, Canada, Germany, Switzerland and Italy. The import leaders are India, Chile, Austria, the USA, Switzerland. A major policy implication emerging from this study is that countries ought to take a deeper look at the trends in trade and tariffs on antimicrobial drugs on a priority basis, since this has to do with lives of real people. Unnecessary blanket tariffs meant for tariff revenue should be avoided, as we find this in many countries that have hardly any production capacity for these drugs (such as the Bahamas, Djibouti, Bermuda, the Comoros, etc.). The bigger players in the sector, both in terms of imports and exports, have relatively lower tariffs, but there is still a lot of scope of reducing these tariffs to ensure that these drugs are available at affordable prices to people at large. Industrial policy motivations to levy tariffs in order to protect the domestic industry against import competition may also need to be done in a measured manner, because this is about health and safety of people and not just another industry. Having said that, for health security purposes, it makes sense to develop domestic production capacity and supply chains. That can be done based on international partnerships, R&D, domestic tax and other policy incentives like the Production Linked Incentives (PLI) scheme in India (rather than tariffs).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".