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Record W3123334117

Why do some employers prefer to interview Matthew but not Samir? New evidence from Toronto, Montreal and Vancouver

2012· preprint· en· W3123334117 on OpenAlexaboutno aff
Diane Dechief, Philip Oreopoulos

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsnot available
Fundersnot available
KeywordsCallbackPrejudice (legal term)ImmigrationPsychologyEthnic groupSocial psychologyAdvertisingDemographic economicsPublic relationsPolitical scienceBusinessLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

In earlier work (Oreopoulos, 2009), thousands of resumes were sent in response to online job postings across Toronto to investigate why Canadian immigrants struggle in the labor market. The findings suggested significant discrimination by name ethnicity and city of experience. This follow-up study focuses more on better understanding exactly why this type of discrimination occurs -- that is, whether this discrimination can be attributed to underlying concerns about worker productivity or simply prejudice, and whether the behaviour is likely conscious or not. We examine callback rates from sending resumes to online job postings across multiple occupations in Toronto, Montreal, and Vancouver. Substantial differences in callback rates arise again from simply changing an applicant’s name. Combining all three cities, resumes with English-sounding names are 35 percent more likely to receive callbacks than resumes with Indian or Chinese names, remarkably consistent with earlier findings from Oreopoulos (2009) for Toronto in better economic circumstances. If name-based discrimination arises from language and social skill concerns, we should expect to observe less discrimination when 1) including on the resume other attributes related to these skills, such as language proficiency and active extracurricular activities; 2) looking at occupations that depend less on these skills, like computer programming and data entry and 3); listing a name more likely of an applicant born in Canada, like a Western European name compared to a Indian or Chinese name, In all three cases, we do not find these patterns. We then asked recruiters to explain why they believed name discrimination occurs in the labour market. Overwhelmingly, they responded that employers often treat a name as a signal that an applicant may lack critical language or social skills for the job, which contradicts our conclusions from our quantitative analysis. Taken together, the contrasting findings are consistent with a model of ‘subconscious’ statistical discrimination, where employers justify name and immigrant discrimination based on language skill concerns, but incorrectly overemphasize these concerns without taking into account offsetting characteristics listed on the resume. Pressure to avoid bad hires exacerbates these effects, as does the need to review resumes quickly. Masking names when deciding who to interview, while considering better ways discern foreign language ability may help improve immigrants' chances for labour market success.

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)0.004
metaresearch head score (Gemma)0.020
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 score0.034
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.010
Science and technology studies0.0090.004
Scholarly communication0.0050.001
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.108
GPT teacher head0.391
Teacher spread0.283 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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