Immigrant Labour Market Outcomes in Canada: Accounting for Age at Arrival, Country of Origin and Gender
Bibliographic record
Abstract
Inunigrants make up a considerable proportion ofthe Canadian population, and are needed to support the Canadian economy. Immigrants appear to be having difficulty entering the labour market, even when achieving educationalsuccess. Answersastowhyareincompleteandtendnottotake immigrant diversity (especially in terms of country of origin and gender) into account. Toexplorelabourmarketoutcomesofworkers,thisstudyuseddata from the Ethnic Diversity Survey, and sought to examine the impact of country of birth, gender, education and age at arrival on immigrant labour market outcomes. The survey sampled 41,695 men and women and asked them about their country of origin, gender, education, age at arrival and labour market outcomes. The analyses conducted revealed several significant findings. Firstly, the study confirmed that men and women from different places of birth did experience different labour market outcomes, and this was particularly true for men and women from the Caribbean, Central and South America. Secondly, it was found that age at arrival impacts female immigrants more substantially than males, with younger ages at arrival having a beneficial impact on labour market outcomes. Thirdly, age at immigration had no impact on educational attainment for females. Males, however, reported earning a university degree of higher when they arrived in Canada after the age of 15. Next, it was found that other foreign-born men and women had different occupational outcomes as compared to the native-born\npopulation, and this was particularly true for those earning less than a university degree. Finally, the present study determined that both females and males with\niii\nless than a university education, arriving at 14 years of age or younger seem to be moreadvantagedintermsoftheiroccupationthanthosewhenolder. Theseissues are important to examine as they not only impact our Canadian immigration policies, but they continue to shape our economy and society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".