Polygyny and Fertility: Continuity or Change in Sub-Saharan Africa
Bibliographic record
Abstract
This study revisits the polygyny‒fertility relationship in sub-Saharan Africa amid significant sociodemographic transformations, including declines in both fertility rates and the prevalence of polygyny. Using data from multiple rounds of the Demographic and Health Surveys across 23 African countries, we examine the contribution of polygyny to reductions in the total fertility rate (TFR), explore how the polygyny‒fertility relationship has evolved over time, and assess changes in the total number of children ever born, number of recent births, ideal fertility, and the desire for another child by polygyny status. Our findings show that the decline in polygyny has substantially contributed to reductions in TFR. While realized fertility-measured by children ever born and recent births-has declined for all married women, reductions have been greater among women in monogamous unions. Fertility preferences, including ideal fertility and the desire for another child, have decreased only among women in monogamous unions, while remaining stable for those in polygynous unions. Additionally, except for children ever born, we find minimal variation in fertility outcomes by wife's rank within polygynous unions. Taken together, these results underscore the complex influence of marriage systems on fertility and highlight the distinct fertility patterns of women in monogamous versus polygynous unions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".