Measuring the Impacts of Prime-age Adult Death on Rural Households in Kenya
Bibliographic record
Abstract
Using a two-year panel of 1,422 Kenyan households surveyed in 1997 and 2000, we measure how primeage adult mortality affects rural households’ size and composition, agricultural production, asset levels, and off-farm income. First, the paper uses adult mortality rates from available data on an HIV-negative sample from neighboring Tanzania to predict the number of deaths that might have been expected in the absence of HIV, and compares this to the number of deaths actually recorded over the survey interval in the Kenyan sample. Based on this procedure, only a quarter of the prime-age female deaths in the 25-34 age range and about half of the male deaths in the 35-44 year age range age range could have been predicted on the basis of the HIV-negative Tanzanian adult mortality rates. In the Nyanza area, the discrepancies were even larger over a broader number of age/sex ranges. This provides a strong indication that AIDS accounts for a large proportion of the recorded deaths for these age/sex categories, particularly in the Nyanza area. Next, using a household fixed-effects model that controls for time-varying effects, we measure changes in outcomes between households afflicted by adult mortality vs. those not afflicted over the three-year survey period. The effects of adult death are highly sensitive to the gender and position of the deceased family member in the household. Households suffering the death of the head -of-household or spouse incurred a greater-than-one person loss in household size. The death of a male household head between 16 and 59 years is associated with a 68% reduction in the net value of the household’s crop production. However, these results are sensitive to age ranges chosen. Female head-of-household or spouse mortality causes a greater decline in cereal area cultivated, while cash crops such as coffee, tea, and sugar are most adversely affected in households incurring the death of a prime-age male head. Off-farm income is also significantly affected by the death of the male head of household, but not in the case of other adult members. The death of other prime-age family members is partially offset by an inflow of other individuals into the family. Other prime-age family members’ mortality has less dramatic effects on the households’ agricultural production, assets, and off-farm income. Lastly, there is little indication that households are able to recover quickly from the effects of prime-age head-of-household adult mortality; the effects on crop and non-farm incomes do not decay at least over the three-year survey interval. The paper concludes by discussing the implications of these findings for agricultural research and extension programs as well as for safety net programs designed to cushion the impacts of prime-age adult death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".