Having it Easy: Consumer Discrimination and Specialization in the Workplace
Bibliographic record
Abstract
Most studies analyzing the adjustments of workers to discrimination focus on sorting decisions, such as occupations workers pursue. We instead analyze on-the-job adjustments, focusing on the e ffects of discrimination by consumers. Speci fically, using extraordinary data from a large-scale restaurant, we investigate the eff ects of an out-ward yet immutable physical trait - symmetry of the facial attributes of workers - on trade off s workers make, and the extent to which the trade off s are shaped by consumer preference for the trait. A large scale restaurant is well-suited for studying these issues because, as with many jobs in the services sector, workers must trade o ff quality of service for the quantity of consumers they serve. Using a combination of observational data and data generated by a field experiment, we fi nd consumers have a preference for the trait and that preferred workers deliver lower service quality. Instead they specialize in serving more consumers. The fi ndings imply that when outward physical traits substitute for service quality in consumer preferences, preferred workers specialize in tasks having no services component because consumers punish them less for poor performance. We conclude that consumer discrimination shapes comparative advantage and, in doing so, generates earnings inequality in the workplace.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".