Rural–Urban Migration and Fertility Ideation in Senegal: Comparing Returned, Current, and Future Migrants to Dakar to Rural Nonmigrants
Bibliographic record
Abstract
Abstract In low‐ and middle‐income countries, significant differences in fertility beliefs between rural and urban areas arise from the differential timing and pace of fertility declines. Demographers have long hypothesized about the diffusion of these beliefs and behaviors from urban to rural areas, potentially via temporary rural–urban labor migration. In this paper, we investigate the association between temporary internal migration from rural Senegal to the capital city, Dakar, and differences in the fertility and contraceptive beliefs and preferences of migrants and nonmigrants. We test socialization, selection, and adaptation hypotheses by comparing the fertility ideation of current and returning migrants with that of nonmigrants and future migrants from their place of origin. Our results support selection effects, explaining half of the differences between nonmigrants and migrants. Once selection effects are removed, significant differences remain between nonmigrants and current or returning migrants. These differences are largely explained by two complementary measures of adaptation: years lived in Dakar and the number of ties to residents of that city. The results indicate that adaptation is as important, if not more so than selection in explaining differences between migrants and nonmigrants. This holds true even for returned migrants five years after their last migration spell. Of the two potential adaptation mechanisms explored, the time spent in Dakar generally explained adaptation better than ties to nonmigrants in Dakar. However, our complementary analyses do not rule out the importance of urban networks on fertility, as they contribute to migrant selection.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".