Prevalence and Drivers of Stalls and Reversals in Age at First Marriage in Sub-Saharan Africa
Bibliographic record
Abstract
Abstract The occurrence of stalls and reversals in age at first marriage despite ongoing education expansion is a salient and understudied demographic paradox of sub-Saharan Africa (SSA). Using Demographic Health Survey data, we contribute novel comprehensive evidence that illuminates this puzzle. First, we provide a systematic overview of stalls and reversals in age at first marriage among women born 1945–1999 in four broad SSA regions and 33 individual countries. Second, we test whether stalls and reversals in first marital age occurred because education expansion (a) dominated primary schooling, which is compatible with early marriages (compatibility hypothesis), or (b) led to changing socioeconomic conditions, which facilitated early marriages (diminishing returns hypothesis). We find that the median age at first marriage (MAFM) either stalled or reversed in 19 countries. At the regional level, MAFM stalled in Central and East Africa and reversed in Southern Africa. Stalls and reversals occurred primarily among the birth cohorts from 1965–69 through 1985–89. We find evidence for either hypothesis, yet mechanisms are context-dependent. We discuss the heterogeneity of education-marriage linkages across SSA and their implications on policies and programs aiming at eradicating early marriages in this region.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".