Offshoring and Directed Technical Change
Bibliographic record
Abstract
To study the short-run and long-run implications on wage inequality, we introduce directed technical change into a Ricardian model of offshoring. A unique final good is produced by combining a skilled and an unskilled product, each produced from a continuum of intermediates (tasks). Some of these tasks can be transferred from a skill-abundant West to a skill-scarce East. Profit maximization determines both the extent of offshoring and technological progress. Offshoring induces skill-biased technical change because it increases the relative price of skill intensive products and induces technical change favoring unskilled workers because it expands the market size for technologies complementing unskilled labor. In the empirically more relevant case, starting from low levels, an increase in offshoring opportunities triggers a transition with falling real wages for unskilled workers in the West, skill-biased technical change and rising skill premia worldwide. However, when the extent of offshoring becomes sufficiently large, further increases in offshoring induce technical change now biased in favor of unskilled labor because offshoring closes the gap between unskilled wages in the West and the East, thus limiting the power of the price effect fueling skill-biased technical change. The unequalizing impact of offshoring is thus greatest at the beginning. Transitional dynamics reveal that offshoring and technical change are substitutes in the short run but complements in the long run. Finally, though offshoring improves the welfare of workers in the East, it may benefit or harm unskilled workers in the West depending on elasticities and the equilibrium growth rate.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".