The effects of trade liberalization on skill acquisition: a systematic review
Bibliographic record
Abstract
Purpose The benefits of trade liberalization on upskilling and skill-based wage premiums for high-skilled workers have recently been questioned in policy circles, in part because of rising income inequality and populist movements in developed economies such as the USA. The purpose of this paper is to determine the effects of trade liberalization on the relative supply and demand for skills. Design/methodology/approach Through the systematic review of the literature on trade and skill acquisition, this paper isolates a total of 25 articles published over the past two decades. Findings Key findings demonstrate the importance of the relative development of the trading partner, with more developed countries experiencing higher upskilling, while less developed countries experience deskilling. Technology, geographic level of analysis, sector and gender were also found to be important influences on human capital acquisition associated with international trade. Originality/value Overall, the authors find support for the idea that trade with developing countries places pressure on low-skill jobs in developed countries but increases the demand for educated workers. The implications of shifts in skills for public policy-making and in terms of the skill premium on wages are discussed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".