Paying for Mirrors or Windows? Consumer Discrimination and Hollywood Films
Bibliographic record
Abstract
Is employment discrimination driven by consumer bias rather than employer bias? One explanation for the persistence of employment discrimination, despite considerable legal and social pressure, is that unbiased employers are penalized by biased customers. An equitable employer is therefore a less profitable one, and apparent employer bias is more accurately described as reflected consumer antipathy. The empirical challenge of relating consumer behavior to employee composition has limited prior tests of this hypothesis and focused attention largely on employer behavior or structural factors. We provide a rare direct test of the claim that consumers respond to employee composition by evaluating the commercial and artistic performance of films released theatrically within the United States between 2011-2015 as a function of the racial diversity of their cast. We find that films are not penalized for the diversity of their casts; instead employing multiple black actors in the principal cast achieves significantly higher domestic box-office revenues than films with no black actors. Moreover, we find that international audiences do not exhibit evidence of bias against diverse casts, and that the net returns to diversity remain positive when worldwide box-office revenues are considered. We confirm the robustness of these results in a survey and experimental setting that controls for film-level differences, and through an analysis of a novel dataset capturing the social media activity (on Twitter) for each film by users of different races. Our findings advance an alternative interpretation of the consumer bias thesis, where consumers prefer employers reflect their world or values, rather than their traits.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".