Skill distribution and income disparity in a North‐South trade model
Bibliographic record
Abstract
Abstract. What are the impacts of free trade agreement on the welfare of different types of workers in a developed country? What is the impact of free trade on a developed country's income disparity? What is the effect of free trade on the skill distribution of a developed country? The objective of this paper is to address the above questions in a two‐sector general‐equilibrium North‐South trade model in which both countries produce one final good and one high‐tech intermediate input. The final good is produced with the use of a high‐tech intermediate input and unskilled workers. Horizontally differentiated skilled workers produce the high‐tech intermediate input. Each country is populated by a continuum of unskilled workers with differential potential ability. Workers in the North and South can acquire skills by investment in training or education. Thus, skill distribution in the North and South is determined endogenously in the model through a self‐selection process. I characterize two different types of equilibria: a closed‐economy equilibrium without trade and a free trade equilibrium. Then, I investigate the impact of free trade, in the presence of training costs, on the skill distribution within each country, income disparity, and social welfare. JEL classification: D63, F10, J31
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".