Bibliographic record
Abstract
The 2x2x2 Heckscher-Ohlin model predicts that trade openness causes the skill premium to increase in the skill abundant developed countries, and to decrease in the skill scarce developing countries, after trade openness. Empirical evidence, however, shows that the skill premium declined in some developing countries, while others experienced an increase in wage inequality. This paper develops a North-South model, where firms produce a low-skilled and a high-skilled intensive good. The production of a unit of either good involves a continuum of L-tasks and H-tasks. The L-tasks can be performed by low-skilled workers only, and the H-tasks can be performed by high-skilled workers only. The Northern firms can produce the task in their headquarters, or offshore the task to the South. The results suggest there is a threshold skill abundance level in the South, above which countries experience an increase in the skill premium after an improvement in the offshoring technology, and below which countries experience a decrease in the skill premium. In this context, the North offshores the H-tasks to countries that are relatively more abundant in high-skilled labor, and L-tasks to countries that are relatively more abundant in low-skilled labor. Therefore, countries that become the hosts of L-tasks experience a decrease in the skill premium, because there will be higher demand for their low-skilled workers, while those that become the hosts of the H-tasks will experience an increase in the skill premium, because there will be higher demand for their high-skilled workers. This accounts for the asymmetric patterns of skill premia in the South.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".