Why Do Skilled Immigrants Struggle in the Labor Market? A Field Experiment with Six Thousand Resumes
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it