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Record W3125281174

How are the Children of Visible Minority Immigrants Doing in the Canadian Labour Market

2011· preprint· en· W3125281174 on OpenAlexaboutno aff
Patrick Grady

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2011
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFirst generationCensusDemographic economicsPolitical scienceEconomicsSociologyPopulationDemography
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the performance of the children of immigrants (2nd generation immigrants) to Canada using data from the 2006 Census. As the composition of immigration inflows has shifted after 1980 from the traditional European source countries to the Third World, the analysis focuses on the labour market performance of 2nd generation visible minority immigrants of whom there were 398 thousand aged15 and over who reported employment income in the Census. An encouraging fact revealed by the data is that 2nd generation visible minority immigrants are becoming more highly educated than 2nd generation non-visible minority immigrants and than non-immigrants – 46.2 per cent of 2nd generation visible minority between 25 and 44 earning employment had earned university certificates or degrees compared to 31 per cent of non-visible minority 2nd generation immigrants and 24 per cent of non-immigrants in the same age groups. But, while 2nd generation visible minority immigrants obtained more education than 2nd generation non-visible minority immigrants and non-immigrants, their performance as a group did not measure up in the labour market. In the 25 to 44 age group, accounting for the largest number of 2nd generation visible minority immigrants, they only earned on average $39, 814, whereas 2nd generation non-visible minority immigrants earned $45, 352 and non-immigrants 40, 358. The labour market performance varies significantly among different visible minority groups. 2nd generation Chinese immigrants in the 25 to 44 age group actually earned $48, 098, which was actually more than 2nd generation non-visible minority immigrants and non-immigrants. Because of the large number of Chinese included as 2nd generation immigrants, this buoyed up the overall average and masked the unfortunate fact that many other visible minority groups are doing much worse than average overall and falling short of non-immigrants. A troubling aspect of the performance of 2nd generation immigrants, except for Chinese and Japanese, is the extent to which they earn substantially less than non-immigrants and especially non-visible minority immigrants for any given level of education. The paper thus provides no grounds for complacency that the children of the recent, particularly non-Asian visible minority, immigrants who are performing so poorly in Canada’s labour market will catch up with non-immigrant groups, particularly given that their parents are currently performing much worse than earlier visible minority immigrants in the labour market. And it is unlikely that 2nd generation visible minority immigrants as a group will earn enough to make up for the current earnings shortfall experienced by their parents in recent cohorts of underperforming immigrants. Furthermore, the lower earnings of many visible minority groups for any given level of education are likely to continue be used as justification for more affirmative action programs. This will adversely affect the non-visible minority and non-immigrant population, and could become a source of increasing social tension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.023
GPT teacher head0.223
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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