Minority Population Concentration and Earnings: Evidence From Fixed-Effects Models
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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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 | 0.031 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".