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 working-age adult mortality affects rural households= size and composition, crop production, asset levels, and off-farm income. First, the paper uses adult mortality rates from available data on an HIV-negative sample to predict the proportion of deaths observed between 1997 and 2000 due to AIDS. Next, using a difference-in-differences estimation, 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 mortality 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 head-of-household between 16 and 59 years is associated with a 68% reduction in the net value of the household=s crop production. 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 male head-of-household. 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 working-age family members is partially offset by an inflow of other individuals into the family and has less dramatic effects on the households= agricultural production, assets, and off-farm income. The effects of adult mortality are also sensitive to the household=s initial asset levels. Lastly, there is little indication that households are able to recover quickly from the effects of working-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.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".