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Record W2144613789 · doi:10.1111/caje.12189

Immigrant job search assimilation in Canada

2016· article· en· W2144613789 on OpenAlexaffvenueabout
Audra J. Bowlus, Masashi Miyairi, Chris Robinson

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of OttawaWestern University
Fundersnot available
KeywordsImmigrationEarningsAssimilation (phonology)Earnings growthHuman capitalDemographic economicsEconomicsLabour economicsGeographyEconomic growth

Abstract

fetched live from OpenAlex

Abstract Immigrant assimilation is a major issue in many countries. While most of the literature studies assimilation through a human capital framework, we examine the role of job search assimilation. To do so, we estimate an equilibrium search model of immigrants operating in the same labour market as natives, where newly arrived immigrants have lower job offer arrival rates than natives but can acquire the same arrival rates according to a stochastic process. Using Canadian panel data, we find substantial differences in job offer arrival and destruction rates between natives and immigrants that are able to account for three quarters of the observed earnings gap. The estimates imply that immigrants take on average 13 years to acquire the native search parameters. Due to immigrants facing much lower on‐the‐job offer arrival rates than natives, the model predicts that earnings growth through job search is minimal for immigrants prior to their job search assimilation.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.095
GPT teacher head0.205
Teacher spread0.110 · 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 designTheoretical or conceptual
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

Citations14
Published2016
Admission routes3
Has abstractyes

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