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Record W3122948869 · doi:10.3386/w21591

Immigrant Employment and Earnings Growth in Canada and the U.S.: Evidence from Longitudinal Data

2015· article· en· W3122948869 on OpenAlexafffundabout
Neeraj Kaushal, Yao Lu, Nicole Denier, Julia Shu‐Huah Wang, Stephen J. Trejo

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentFonds de Recherche du Québec-Société et CultureNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchRussell Sage FoundationColumbia Population Research CenterSage FoundationNational Science Foundation
KeywordsImmigrationWage growthEarnings growthEarningsLongitudinal dataDemographic economicsWageEconomicsLongitudinal studyLabour economicsDemographyGeographySociologyMedicine

Abstract

fetched live from OpenAlex

We study the short-term trajectories of employment, hours worked, and real wages of immigrants in Canada and the U.S. using nationally representative longitudinal datasets covering 1996-2008.Models with person fixed effects show that on average immigrant men in Canada do not experience any relative growth in these three outcomes compared to men born in Canada.Immigrant men in the U.S., on the other hand, experience positive annual growth in all three domains relative to U.S. born men.This difference is largely on account of low-educated immigrant men, who experience faster or longer periods of relative growth in employment and wages in the U.S. than in Canada.We further compare longitudinal and cross-sectional trajectories and find that the latter over-estimate wage growth of earlier arrivals, presumably reflecting selective return migration.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.012
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.008
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.353
GPT teacher head0.480
Teacher spread0.126 · 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

Citations0
Published2015
Admission routes3
Has abstractyes

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