Black and Latinx workers reap lower rewards than White workers from years spent working in big cities
Bibliographic record
Abstract
The large labor markets of big cities offer greater possibilities for workers to gain skills and experience through successively better employment opportunities. This "experience effect" contributes to the higher average wages that are found in big cities compared to the economy as a whole. Racial wage inequality is also higher in bigger cities than in the economy on average. We offer an explanation for this pattern, demonstrating that there is substantial racial inequality in the economic returns to work experience acquired in big cities. Using data from the National Longitudinal Survey of Youth, 1979 we find that each year of work experience in a big city increases Black and Latinx workers' wages by about one quarter to half as much as White workers' wages. A substantial amount of this inequality can be explained by further racial disparities in the benefits of high-skill work experience. This research identifies a heretofore unknown source of inequality that is distinctly urban in nature, and expands our knowledge of the challenges to reaching interracial wage equality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".