Beauty, Job Tasks, and Wages: A New Conclusion About Employer Taste-Based Discrimination
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 attribute—those that require substantial amounts of interpersonal interaction—a large beauty premium exists. In contrast, in jobs where attractiveness seems unlikely to truly enhance productivity—jobs 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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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".