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Record W6982644788

An Investigation on Wage Penalties: The Effect of Foreign-Born Status on Employee Wages, Salaries, and Incomes in the United States and Canada

2023· article· en· W6982644788 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship @ Claremont (The Claremont Colleges) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsMicrodata (statistics)SalaryWageImmigrationEfficiency wagePublic useRegression analysis
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates whether there is a wage penalty that negatively affects foreign-born employees in the United States and Canada, addressing the following two questions: (1) Does being a foreign-born employee result in a wage penalty in the United States and Canada? And if so, (2) How does this penalty differ across the two countries over time? With data collected from the Integrated Public Use Microdata Series (IPUMs), four separate multiple linear regression models are estimated to compare the presence of wage penalties across various industries and occupations. These analyses cover the following comparisons: the United States in 1990 and 2000, Canada in 1991 and 2001, the United States in 1990 and Canada in 1991, and the United States in 2000 and Canada in 2001. For each comparison, this study finds that individuals who identify as foreign-born to the country they are employed, face a wage penalty. These findings indicate that this specific status (foreign-born) correlates to lower wages compared to the average employee and suggests changes in immigration policy in both countries over time as potential explanations. These results are important for future consideration of wage and salary incomes to individuals in both countries, regardless of foreign and domestic-born status.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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.

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
Published2023
Admission routes1
Has abstractyes

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