Does Employer Prosociality Reduce or Augment Wage Inequality?
Bibliographic record
Abstract
A growing body of research demonstrates that some workers are willing to accept lower wages from employers that engage in prosocial activity. This symposium brings together a range of scholars who are examining how variation across workers in this propensity to trade wages for prosociality may exacerbate or ameliorate wage inequality. On one hand, some work suggests that higher-earning workers (including those that are more educated and more productive) are more willing to give up wages for prosociality. From this perspective, employers will more successfully substitute prosociality for wages when recruiting relatively higher-earning workers and thus reduce income inequality. On the other hand, other work suggests that groups that already tend to earn less (including women and workers from lower social classes) may be more inclined to trade prosociality for wages. From this contrasting perspective, employers will more successfully trade prosociality for wages when recruiting relatively lower-earning workers and thus augment wage inequality. A fundamental question underpinning these emergent perspectives is whether and to what extent employers recognize and respond to variation in preferences for prosociality in terms of the wages they offer. This symposium will provide a venue for scholars at the forefront of these questions to share their research with the AOM community, benefit from audience and discussant comments, and chart avenues for future research. Prosocial Claims and the College Wage Gap Author: Nathan Wilmers; Massachusetts Institute of Technology Gender Differences in Preferences for Meaning at Work Author: Vanessa Burbano; Columbia Business School Values and Inequality Revisited: How Prosocial Claims Augment the Gender Wage Gap Author: Mariana Oseguera; U. of Toronto, Rotman School of Management Prosocial Occupations, Work Autonomy, and the Origins of the Social Class Pay Gap Author: Ray Fang; Boise State U.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".