Infant Industry Argument: Theoretical Framework and Current Opportunity of Adoption
Bibliographic record
Abstract
This paper verifies the possibility of using Infant industry protection strategy to improve manufacturing competitiveness in developing countries. The main theoretical bases of this strategy are: its significant role in creating dynamic comparative advantages in manufacturing sector. This protection also gives the industry time to learn by doing and achieve its positive externalities. In addition, infant industry protection can be a virtual solution to market failure which may impedes the establishment of such industry. This study supports infant industry argument validity by showing successful experiences of some countries at various times in history. It is found that most countries used such policy to reach their industrialization. Some studies tried to refute the infant industry argument but they based their criticism mainly on the failure of some developing countries to correctly apply this policy, not on their theoretical justification. Despite current WTO restriction to use infant industry, the paper argues that the chance of adopting this strategy still exists. This can be mainly achieved by some policy space of WTO rules to adopt this policy, especially if these countries focus on technology intensive industries. In addition, developing countries can exploit the increasing number of Regional Trade Agreements (RTAs) to support their infant industries. RTAs extend the market size that may help infant industries to develop their competitiveness through achieving economies of scale, learning by doing and supporting backward and forward linkages.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".