Immigrant–native pay gap driven by lack of access to high-paying jobs
Bibliographic record
Abstract
Immigrants to high-income countries often face considerable and persistent difficulties in the labour market1–6, whereas their native-born children typically experience economic progress6–9. However, little is known about the extent to which these immigrant–native earnings differences stem from unequal pay when doing the same work for the same employer versus labour market processes that sort immigrants into lower-paid jobs. Here, using data from nine European and North American countries, we show that the segregation of workers with immigrant backgrounds into lower-paying jobs accounts for about three-quarters of overall immigrant–native earnings differences. Although within-job pay inequality remains notable for immigrants in several countries, our results demonstrate that unequal access to higher-paying jobs is the primary driver of the immigrant–native pay gap across a range of institutionally and demographically diverse contexts. These findings highlight the importance of policies aimed at reducing between-job segregation, such as language training10–13, job training13–15, job search assistance programmes13,15, improving access to domestic education13,16,17, recognizing foreign qualifications18,19, and settlement programmes aimed at enhancing access to job-relevant information and networks13,20,21. Policies that target employer bias in hiring and promotion decisions are also likely to be effective, whereas measures aimed at ensuring equal pay for equal work may have more limited scope for further progress in closing the immigrant–native pay gap22–28. Data from nine European and North American countries reveal that the disparity in earnings between immigrants and natives is largely a result of segregation of immigrant workers into lower-paying jobs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".