Beauty, Job Tasks, and Wages: A New Conclusion about Employer Taste-Based Discrimination
Why this work is in the frame
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Bibliographic record
Abstract
We use novel data from the Berea Panel Study to reexamine the labor market mechanisms generating the beauty wage premium. We find that the beauty premium varies widely across jobs with different task requirements. Specifically, in jobs where existing research such as Hamermesh and Biddle (1994) has posited that attractiveness is plausibly a productivity enhancing attributethose that require substantial amounts of interpersonal interaction-a large beauty premium exists. In contrast, in jobs where attractiveness seems unlikely to truly enhance productivityjobs that require working with information and data-there is no beauty premium. This stark variation in the beauty premium across jobs is inconsistent with the employer-based discrimination explanation for the beauty premium, because this theory predicts that all jobs will favor attractive workers. Our approach is made possible by unique longitudinal task data, which was collected to address the concern that measurement error in variables describing the importance of interpersonal tasks would tend to bias results towards finding a primary role for employer taste-based discrimination. As such, it is perhaps not surprising that our conclusions about the importance of employer taste-based discrimination are in stark contrast to all previous research that has utilized a similar conceptual approach.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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