Do the living arrangements of older people matter for the family transfers they receive? Evidence from Senegal
Bibliographic record
Abstract
Abstract In the absence of broad-based formal health insurance and social protection systems in much of sub-Saharan Africa, the family acts as the key provider of support to older people. This paper furthers our understanding of family support mechanisms in the context of low-income countries by focusing on support from outside the household, which has been less studied so far. By using the data of 3,114 people aged ≥50 from the second round of the Senegalese Poverty and Family Structure Survey, the paper examines how the living arrangements of older people are associated with receiving transfers from non-coresident kin. Our findings highlight a net advantage of women receiving net positive family transfers compared to men for some living arrangements. Results also indicate that living without a husband or an adult significantly increases the likelihood of older women receiving support from non-resident family members compared to those who live with both spouse and a younger adult child. However, these differences are not significant among older men. These results suggest that in constrained settings, decision-makers should consider older people's living arrangements and potential external family support when designing public policies towards them, so as to optimise the impact of policy and interventions on their welfare.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".