Bibliographic record
Abstract
Is ability drain (AD) economically significant? That immigrants or their children founded over 40% of the Fortune 500 US companies suggests it is. Moreover, brain drain (BD) induces a brain gain (BG). This cannot occur with ability. Nonetheless, while BD has been studied extensively, AD drain has not. I examine migration's impact on ability (a), education (h), and productive human capital or 'skill' (s) – which includes both a and h – for source country residents and migrants, under the points system (PS), 'vetting' system (VS), which accounts for s (e.g., US H1-B visa), and 'new' points system (NS), which combines PS and VS (e.g., Canada, 2015+). I find that i) Migration reduces (raises) source country residents' (migrants') average ability and has an ambiguous (positive) impact on their average education and skill, with a net skill drain more likely than a net BD; ii) AD is greater than BD; iii) the effects increase with ability's inequality or variance V(a); iv) the policies in turn raise V(a), V(h) and V(s), with V(a) > V(h); v) effects in i) - iv) are larger under VS than PS; vi) residents' (migrants') consumption is lower (higher) under either policy than under a closed economy; vii) consumption falls with ability's inequality; viii) contrary to the situation with education and skill, consumption inequality is lower under VS than PS; viii) ability, education and skill (consumption) under NS are identical (is larger than) the combined values under PS and VS. Orders of magnitude, empirical research plans, and policy implications are provided.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.009 |
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".