Ethnic Neighbourhoods and Male Immigrant Earnings Growth: 1981 Through 1996
Bibliographic record
Abstract
This paper examines the effect of ethnic neighbourhoods on wage growth as well as other labour market outcomes of immigrant men in Canada using the 1981, 1986, 1991 and 1996 Censuses. While the primary measure of affiliation is country of birth, ethnicity, language and visible minority status are also examined to determine the robustness of the findings. Consistent with U.S. findings, ethnic neighbourhoods based on country of birth are found to have a negative impact on the ten-year wage growth of immigrants. Further, the model for wage growth is found to be robust to different lengths of time and different base years as well as the specification of language and ethnicity as the affiliation grouping. Using country of birth as the affiliation index, exposure is also found to have a negative impact on the growth of total and weekly earnings as well as the initial wages of entry cohorts. While little evidence is found on the effects of ethnic neighbourhoods on changes in employment, a negative effect of exposure is found on entry employment rates of the most recent landing cohorts. Although the overall effect of ethnic neighbourhoods on wage growth is negative, ethnic neighbourhoods are found to have a divergent effect on different landing cohorts, having a positive impact on the wage growth of the more recent cohorts and a negative impact on earlier cohorts.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".