The Human Capital Model of Selection and the Long-run Economic Outcomes of Immigrants
Bibliographic record
Abstract
In Canada, the selection of economic immigrants throughout the 1990s and 2000s was based largely on the human capital model of immigration. This model posits that selecting immigrants with high levels of human capital is particularly advantageous in the long run. It is argued that higher educational levels allow immigrants to both bring the skills needed in a knowledge-based economy and, perhaps more importantly, better adjust to both cyclical and structural changes in the labour market than immigrants with lower educational levels. This paper examines the trends in the earnings advantage that more highly educated immigrants hold over less educated immigrants by immigration class. The focus is on three questions. First, did the well-documented decline in entry earnings observed over the last quarter-century vary by immigrant educational level and by admission class? Second, have there been significant shifts across recent cohorts in the economic advantage that more highly educated immigrants hold over their less educated counterparts, both at entry and in the longer run? Third, and most importantly, does the relative earnings advantage of more highly educated immigrants change with time spent in Canada, that is, in the longer run?
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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 | 0.004 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".