The Differing Nature of Black-White Wage Inequality Across Employment Sectors
Bibliographic record
Abstract
This paper argues that the underlying causes of racial wage inequality may dif- fer across labor market sectors. In particular, because employers hiring for jobs in the more highly skill-intensive sector have a greater incentive to accurately as- sess worker skill than employers hiring for jobs in the less skill-intensive sector, these more skill-intensive employers also have incentives to invest more in skill re- vealing technology, and thereby obtain more precise information regarding worker skill, than less skill-intensive employers. Under some technologies, these cross sec- tor information differences will lead to several implications regarding racial wage inequality. Most notably, (i) after controlling for worker skill, very little racial wage inequality should remain in the highly skill-intensive sector, yet substantial racial wage inequality may remain in the less skill-intensive sector, and (ii) workers from the relatively worse paid group should be more likely than similarly skilled workers from the better paid group to work in the highly skill-intensive sector. Using data from the NLSY, I find empirical support for these implications. Specifically, after controlling for pre-market academic skills, the entire racial wage gap disappears in the highly skill-intensive sector, but almost half of the unconditional gap remains in the less skill-intensive sector. Furthermore, I find that black workers are roughly 25 percent more likely than similarly skilled white workers to work in the highly skill-intensive sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".