The Effects of English Proficiency on Earnings of U.S. Foreig-Born Immigrants: Does Gender Matter?
Why this work is in the frame
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Bibliographic record
Abstract
This paper compares the effects of English proficiency on foreign-born male and female immigrants in the U.S. by using data from the 2001 American Community Survey. The analysis demonstrates the importance of English proficiency on earnings for foreign-born immigrants. The results indicate that male immigrants suffer increasing penalties with decreasing levels of English proficiency. However, female immigrants who speak intermediate English suffer the greatest earnings penalty. Moreover, male immigrants may benefit more from well-spoken English than female immigrants. The Quantile Regression approach is adopted to examine the effects of English proficiency’s effects across the entire earnings distribution. The relative importance of English proficiency is greater at the upper tier of the earnings distribution for immigrants as a whole. A similar pattern remains for both gender groups, although slight differences exist for either group.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it