International Mobility of Skilled Labour: Analytical and Empirical Issues, and Research Priorities
Bibliographic record
Abstract
The international mobility of skilled labour has become a key component of the global knowledge-based economy. Rising levels of foreign direct investment (FDI), international trade, research and development (R&D), technological advances and increased demand for skilled workers seem to have all contributed to an increase in the international mobility of skilled labour. Internationally mobile individuals are often found participating in industries that are largely knowledge-based and global in scope. As a result, it has become increasingly important that the economic policy discussion surrounding the international mobility of skilled labour must take into consideration the wide variety of ways the migration of skilled labour affects the economy. Numerous drivers, policy and non-policy induced, are at work. Attention must now turn towards the links between these movements and the institutions regulating them; the performance in the trade of goods and services; FDI; human capital formation and multinational enterprises location; and income convergence among countries. This paper provides an overview of the literature on four key issues surrounding the international mobility of skilled workers, while identifying potential directions for future research. First, global trends of recent international skilled migratory flows – magnitude and their composition in terms of underlying skills/education of migrants with a focus on Canada-US migratory flows. Second, fundamental (non-policy) drivers of the increased skilled migratory flows, especially among advanced countries. Third, economic costs and benefits associated with cross-country movement of skilled labour and the main factors conditioning these costs and benefits. Fourth, how policy has adjusted or should adjust to increased skilled labour mobility in the global economy?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".