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Record W2958452995 · doi:10.2478/izajodm-2019-0002

Asymmetric Information and the Discount on Foreign-Acquired Degrees in Canada

2019· article· en· W2958452995 on OpenAlex
Yigit Aydede, Atul Dar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIZA Journal of Development and Migration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsWageImmigrationLabour economicsEconomicsMatching (statistics)Quality (philosophy)Risk aversion (psychology)Wage inequalityHuman capitalDemographic economicsExpected utility hypothesisEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Abstract A growing wage gap between immigrant and native-born workers is well documented and is a fundamental policy issue in Canada. It is quite possible that wage differences, commonly attributed to the lower quality of foreign credentials or the deficiency in the accreditation of these credentials, merely reflect lower wage offers that immigrant workers receive due to risk aversion among local firms facing an elevated degree of asymmetric information. Using the 2006 and 2011 population censuses, this paper empirically investigates the effects of wage bargaining in labor markets on the wage gap between foreign- and Canadian-educated workers. Our results imply that a significant part of the wage gap between foreign-educated and Canadian-educated immigrant (and native-born) workers is not driven by the employers’ risk aversion but by differences in human capital endowments and occupational matching quality.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.888

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 communication0.0000.001
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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