Trade between symmetric countries, heterogeneous firms, and the skill premium. Canadian Journal of Economics/Revue canadienne d’économique 44
Bibliographic record
Abstract
This paper examines the effects of trade liberalisation between symmetric countries on the skill wage premium. I use a model of monopolistic competition with heterogeneous firms and two factors of production: skilled and unskilled labour. I introduce a correlation between productivity and skill intensity in the production process, which generates the empirically observed link between firm size, export status, wages and skill intensity. The entry and exit of firms following trade liberalisation has non-trivial effects on the demand for both types of labour, and therefore on their wages. I show that the impact of trade liberalisation on the skill premium depends on the type of trade costs considered, and on their initial size. While a decrease in the fixed costs of trade has a potentially non-monotonic effect, a drop in the variable trade costs yields an unambiguous and substantial increase in the skill premium. The calibration of the model to the U.S. economy shows that a reduction of the iceberg costs of trade from 1.5 to 1.1 can account for an increase in the skill premium of more than 10 percentage points, which is about a fourth of the observed rise in the 1980s and 1990s.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".