Impact of the West African Ebola Epidemic on Agricultural Production and Rural Welfare: Evidence from Liberia
Bibliographic record
Abstract
Abstract The 2014-15 Ebola epidemic took a devastating human and economic toll on three West African countries, of which Liberia was perhaps the hardest hit. The pathways through which the crisis affected economic activity in these largely agrarian societies remain poorly understood. To study these mechanisms in the context of rural Liberia, we link a geographically disaggregated indicator of Ebola disease mortality to nationally representative household survey data on agricultural production and consumption. We find that higher Ebola prevalence (as proxied by local mortality) led to greater disruption of group-labor mobilization for planting and harvest, thereby reducing rice area planted as well as rice yields. Household welfare, measured by per capita expenditures spanning two points before and after the crisis, fell by more in Ebola prevalent areas with more intensive rice-farming, precisely those areas more adversely affected by agricultural labor shortages.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".