All that Glitters is Not Gold: Wages and Education for Us Immigrants
Bibliographic record
Abstract
Many destination countries consider implementing points-based migration systems as a way to improve migrants’ quality, but our understanding of the actual effects of selective policies is limited. We use data from the ACS 2001–2017 to analyze the overlap in the wage distribution of low- and high-educated recent migrants from different origins after controlling for other observable characteristics. When we randomly match a high- with a low-educated immigrant from the same country, more than one-quarter of time the low-educated immigrant has a higher hourly wage, notwithstanding a statistically significant difference in the mean wage of the two groups for most origins. For 98 out of 114 countries, this synthetic measure of the overlap in the two wage distributions stands above the corresponding figure for natives. We also find that at least 82% of the variance in log wages for migrants with a given number of years of schooling is due to differences within rather than across countries. This suggests that heavily relying on education to select immigrants might fail to markedly improve their quality.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".