Why Do Skilled Immigrants Struggle in the Labor Market? A Field Experiment with Six Thousand Resumes
Bibliographic record
Abstract
Thousands of resumes were sent in response to online job postings across multiple occupations in Toronto to investigate why Canadian immigrants, allowed in based on skill, struggle in the labor market. Resumes were constructed to plausibly represent recent immigrants under the point system from the three largest countries of origin (China, India, and Pakistan) and Britain, as well as non-immigrants with and without ethnic-sounding names. In addition to names, I randomized where applicants received their undergraduate degree, whether their job experience was gained in Toronto or Mumbai (or another foreign city), whether they listed being fluent in multiple languages (including French). The study produced four main findings: 1) Interview request rates for English-named applicants with Canadian education and experience were more than three times higher compared to resumes with Chinese, Indian, or Pakistani names with foreign education and experience (5 percent versus 16 percent), but were no different compared to foreign applicants from Britain. 2) Employers valued experience acquired in Canada much more than if acquired in a foreign country. Changing foreign resumes to include only experience from Canada raised callback rates to 11 percent. 3) Among resumes listing 4 to 6 years of Canadian experience, whether an applicant's degree was from Canada or not, or whether the applicant obtained additional Canadian education or not had no impact on the chances for an interview request. 4) Canadian applicants that differed only by name had substantially different callback rates: Those with English-sounding names received interview requests 40 percent more often than applicants with Chinese, Indian, or Pakistani names (16 percent versus 11 percent). Overall, the results suggest considerable employer discrimination against applicants with ethnic names or with experience from foreign firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".