Immigrant Category of Admission and the Earnings of Adults and Children: How far does the Apple Fall?
Bibliographic record
Abstract
Immigrants in many Western countries have experienced poor economic outcomes. This has led to a lack of integration of child immigrants (the 1.5 generation) and the second generation in some countries. However, in Canada, child immigrants and the second generation have on average integrated very well economically. We examine the importance of Canada's admission classes to determine if there is an earnings benefit of the selection under the Economic Classes to: 1) the Adult Arrival immigrants and 2) the Child Arrival immigrants (1.5 generation) once old enough to enter the labour market. We employ unique administrative data on landing records matched with subsequent income tax records that also allows for the linking of the records of Adult Arrival parents and their Child Arrival children. We find, relative to the Family Class, the Adult Arrivals in the Skilled Worker category have earnings that are 29% higher for men and 38% higher for women. These differences persist even after controlling for detailed personal characteristics such as education and language fluency at 21% for men and 27% for women. Child Arrival immigrants landing in the Skilled Worker Class have earnings advantages (as adults) over their Family Class counterparts of 17% for men and 21% for women. These Child Arrival Skilled Worker advantages remain at 9% for men and 14% for women after controlling for child characteristics, the Principal Applicant parent's characteristics and the parent's subsequent income in Canada.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".