Underemployed and Penalized: Education–Occupation Mismatch and Racial/Ethnic Inequality among Highly Educated Workers
Bibliographic record
Abstract
How does education–occupation mismatch shape racial/ethnic labor market inequality among highly educated workers? Bridging the literatures on racial/ethnic discrimination and labor market signaling, we propose a new concept, “racialized signaling,” to explain inequality in the college-to-work transition, operationalized through education–occupation mismatch. We then use longitudinal data to examine the labor market consequences of racialized signaling, analyzing vertical and horizontal dimensions of mismatch. We find that Black and Hispanic graduates experience the negative consequences of mismatch most strongly at the point of occupational allocation relative to their White peers, whereas Asian graduates experience the greatest negative consequences of mismatch regarding wage penalties. Advanced degrees, STEM degrees, and degrees from more selective institutions have some moderating effects, but they do not fully level the playing field for minority graduates. Overall, our findings suggest education–occupation mismatch is a powerful, although heterogeneous, mechanism reproducing racial/ethnic inequality among the most educated segment of the U.S. population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".