A New Theory of International Trade: Capital Deepening and Market Segment Determines the Pattern of International Trade
Bibliographic record
Abstract
Through growth and investment, two countries will have the same capital-labor ratio and productivity which leads to the same factor prices in the long run if two countries have the same production function with constant return to scale. Consequently, H-O model predicts no trade in the long run as Stolper and Samuelson (1941) recognized a contradiction in the H-O model that the full equalization of factor prices, which eliminates the cost difference between two countries, will lead to no trade. Since a theory is false if we derive contradictory conclusions (e.g., trade and no trade) from assumptions (i.e., premises in the sense of logic), we should completely get rid of the H-O model even H-O model explains some trade patterns (e.g., a natural resources abundant country exports natural resource) because we can derive true conclusions from a false model. Capital deepening suggests that productivity rises when the capital-labor ratio increases. Thus, productivity determines the trade pattern. For example, capital-abundant countries can export labor-intensive goods if the capital-labor ratio (productivity) of a labor-intensive industry in a capital-abundant country is higher than its counterpart in labor-abundant countries and vice versa, e.g., British exported cotton textiles to India even British was a relatively capital-abundant country to India and cotton textiles were a labor-intensive good in the nineteenth century British. Besides, oligopoly explains intra-industry trade in the global market because the strategy of competition between firms would segment the market by quality and price hierarchies rather than monopolistic competition. Finally, Ting (2020) implied that wage rates in capital-intensive industries and capital-abundant countries (e.g., manufacturing) are higher than labor-intensive industries and labor-abundant countries (e.g., agriculture) so factor prices differentiate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".