Beauty, Polygyny, and Fertility: Theory and Evidence
Bibliographic record
Abstract
We propose a simple model of a mating economy in both monogamous and polygynous cultures, and derive implications for how polygyny affects individual and aggregate fertility. We find that an attractive woman is more likely to find a high-status husband. However, when polygyny is allowed, high-status husbands naturally attract other women; this implies that female beauty increases the likelihood of entering into a polygynous relationship. A woman in a polygynous relationship produces fewer children than a woman in a monogamous \nrelationship as long as the preference for reproduction relative to consumption is not too strong. However, the societal practice of polygyny increases aggregate fertility through two distinct channels: (1) by increasing the number of marriages; and (2) by triggering fertility contagion: a woman, whether involved in a monogamous or polygynous relationship, produces more children as polygyny becomes more prevalent in her neighborhood. We empirically validate each of the model's key predictions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".