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Record W1560490568

Explaining the Deteriorating Entry Earnings of Canada’s Immigrant

2004· article· en· W1560490568 on OpenAlexaffabout
Abdurrahman Aydemir, Mikal Skuterud

Bibliographic record

VenueLabor and Demography · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsImmigrationEarningsDemographic economicsEmpirical evidenceEconomicsLabour economicsGeography
DOInot available

Abstract

fetched live from OpenAlex

The study explores causes of the deterioration in entry earnings of Canadian immigrant cohorts by estimating an empirical specification that nests a number of competing explanations found in the Canadian literature. To do this, we use the pooled sample of Canadian-born and immigrant men employed full-year, full-time from the complete 20 percent samples of the 1981, 1986, 1991, 1996 and 2001 Canadian Censuses. Our results indicate that no more than one-third of the deterioration can be explained by compositional shifts in the knowledge of an official language, mother tongue and region of origin of recent immigrant cohorts. We also find little or no evidence that declining returns to foreign education are responsible. Roughly one-third of the deterioration appears to be due to a persistent decline in the returns to foreign labour market experience which has occurred almost exclusively among immigrants originating from non-traditional source countries. We are able to explain two-thirds of the overall decline in the entry earnings of Canada’s most recent immigrants without any reference to entry labour market conditions. When we also account for entry conditions, our results suggest that Canada’s immigrants who arrived in the 1995-1999 period would otherwise be enjoying entry earnings that were significantly higher than the entry earnings of the 1965-1969 cohort.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.538
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.228
Teacher spread0.222 · 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 teacher head, 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

Citations6
Published2004
Admission routes2
Has abstractyes

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