Reassessing the Insurance Effect: A Qualitative Analysis of Fertility Behavior in Senegal and Zimbabwe
Bibliographic record
Abstract
A number of prominent demographers have recently reiterated the argument that a lasting mortality decline is a key determinant of the fertility transition. Of the main hypothesized pathways linking fertility to mortality, the one least studied is the insurance hypothesis: the notion that, in high‐mortality contexts, people decide to have more children in order to anticipate possible future child deaths and lessen the risks of having too few surviving offspring. In‐depth interviews and focus groups from Zimbabwe and Senegal are used to examine this hypothesis and to extend it into a broader theory of reproductive decision making under uncertainty. Whereas insurance strategies are frequent in Zimbabwe and occur in urban Senegal, in the higher‐mortality settings—the rural Senegalese site and the recent past described by respondents in Zimbabwe and urban Senegal—deliberate fertility‐limitation strategies are rare. The data depict fundamental changes in attitudes, strategies, and behaviors concerning family size over time and, in Senegal, over space. Important reproductive goals and risks extend far beyond numbers of children and mortality. Parents seek to have healthy, successful children for many reasons including companionship, descendants, and old‐age support. Diverse investments in child quality (their education, health, etc.) and quantity (numbers of births) are the main means to attain these goals and, less recognized by demographers, are also important ways for parents to manage uncertainty in family‐building outcomes; the “classic” insurance mechanism is only one, often minor, aspect of the quantity option.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".