Are Gender Differences in Performance Innate or Socially Mediated?
Bibliographic record
Abstract
To explain persistent gender gaps in market outcomes, a lab experimental literature explores whether women and men have innate differences in ability (or attitudes or preferences), and a separate field-based literature studies discrimination against women in market settings. This paper posits that even if women have comparable innate ability, their relative performance may suffer in the market if the task requires them to interact with others in society, and they are subject to discrimination in those interactions. The paper tests these ideas using a large-scale field experiment in 142 Malawian villages where men or women were randomly assigned the task of learning about a new agricultural technology, and then communicating it to others to convince them to adopt it. Although female communicators learn and retain the new information just as well, and those taught by women experience higher farm yields, the women are not as successful at teaching or convincing others to adopt the new technology. Micro-data on individual interactions from 4,000 farmers in these villages suggest that other farmers perceive female communicators to be less able, and are less receptive to the women's messages. Relatively small incentives for rewards undo the disparity in performance by encouraging added interactions, improving farmers' accuracy about female communicators' relative skill.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".