Minority Population Concentration and Earnings: Evidence From Fixed-Effects Models
Why this work is in the frame
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Bibliographic record
Abstract
Consistent with the hypothesis that heightened visibility and competition lead to greater economic discrimination against minorities, countless studies have observed a negative association between minority population concentration and minority socioeconomic attainment. But minorities who reside in areas with high minority concentration are likely to differ from minorities who reside in areas with few minorities on unobserved characteristics related to economic attainment. Thus, this association may be a product of differential skills, behaviors and networks acquired during childhood or of selective migration. Applying fixed-effects models to a quarter century of panel data from the National Longitudinal Survey of Youth, we find that for Blacks and Latinos the inverse association between minority population concentration and earnings is eliminated when unobserved person-specific characteristics are controlled. The findings suggest that the negative association between Black population size and Blacks' earnings is driven largely by the selection of high-earning Blacks into labor markets with relatively small Black populations. Most of the association between Latino population concentration and earnings is attributable to the level of Latino population concentration experienced during childhood.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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