Consumers—Especially Women—Avoid Buying From Firms With Higher Gender Pay Gaps
Bibliographic record
Abstract
We document a unique driver of consumer behavior: the public disclosure of a firm’s gender pay gap. Four experiments provide causal evidence that when firms are revealed to have gender pay gaps, consumers are less willing to pay for their goods, a reaction driven by consumer perceptions of unfairness. Unlike reactions to CEO‐to‐worker wage gaps, this effect varies by consumers’ gender: Compared to men, women show larger decreases in purchase intentions toward firms with gender pay gaps. Social media data, from before and after the United Kingdom legally mandated many firms to disclose their gender pay gaps, further demonstrate that gender pay gaps correlate with negative consumer reactions; once again, women are more likely than men to express negative sentiments online in response to pay gap‐related topics. Although we show that firms consumers will punish firms with their wallets, we also observe boundary conditions: When decisions incur a sufficient cost to the self—such as when needing a ride‐share when rain is very likely—the negative effects of gender gap disclosure are attenuated.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".