The Quantity-Quality Tradeoff: How Incentives and Monitoring Shape Gender Differences at Work
Bibliographic record
Abstract
Why do women and men approach the same work differently? Prior research across occupations shows that men tend to emphasize quantity and produce more, whereas women prioritize quality. Researchers have attributed these differences to individual-level factors, such as gender-specific preferences, caregiving responsibilities, and evaluator biases. We propose that organizational practices, specifically production incentives and quality monitoring, also influence these patterns. Using data from the U.S. Patent and Trademark Office (USPTO), we conceptualize the quantity-quality tradeoff using examiner leniency and leverage discontinuities in incentives and monitoring to assess their effects. We theorize and find that stronger production incentives lead men to be more lenient than women, prioritizing quantity. Under heightened monitoring, women are less lenient than men, emphasizing quality. Further, monitoring moderates the relationship between incentives and gender differences in leniency such that the largest gender gap occurs under strong incentives and weak monitoring. Our study demonstrates that organizational practices interact with worker gender to shape the quantity-quality tradeoff, indicating that incentive and monitoring systems—though not designed to affect gender inequality—produce distinct and unintended effects that are essential to understanding and addressing workplace disparities. Funding: We greatly appreciate funding support from the Gender & Work Initiative at the Johns Hopkins Carey Business School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17754 .
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".