Brain Drain, Educational Quality and Immigration Policy: Impact on Productive Human Capital
Bibliographic record
Abstract
With the 1967 reform, Canada’s immigration policy changed from a country-preference system to a points system. The latter provides points according to applicants’ education level but abstracts from the quality of their education. This paper considers the points system (h), the country-preference system (p), as well as a system that includes both educational quantity and quality (termed the “q2 points system”). It focuses on their impact on the host country’s average productive human capital – the product of educational quality and quantity – or skill level S, on that of the immigrants, and of high (low) education-quality source countries 1 (2). It shows, among others, that i) S is greater under the q2 system than under the points system (Sq > Sh) ; ii) S is greater under the country-preference system than under the points system (Sp > Sh) ; iii) whether S is greater under the q2 or the country-preference system is ambiguous, with Sq > (l) Sp if the quality of education in Country 1 relative to Country 2 is higher (lower) than the degree of preference for migrants from Country 1 relative to Country 2 ; iv) an increase in education quality in the high- (low-) quality source country has a positive (ambiguous) impact on S under all three policies, and the impact is larger under the q2 than under the points system ; and v) a switch from a points system to a q2 system results in a human capital gain or net brain gain for Country 1 and a loss or net brain drain for Country 2.Classification JEL : F22, I25, I26, J24.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".