Ebola, jobs and economic activity in Liberia
Bibliographic record
Abstract
BACKGROUND: The 2014 Ebola virus disease (EVD) outbreak in the neighbouring West African countries of Guinea, Liberia and Sierra Leone represents the most significant setback to the region's development in over a decade. This study provides evidence on the extent to which economic activity declined and jobs disappeared in Liberia during the outbreak. METHODS: To estimate how the level of activity and number of jobs in a given set of firms changed during the outbreak, we use a unique panel data set of registered firms surveyed by the business-development non-profit organisation, Building Markets. We also compare the change in economic activity during the outbreak, across regions of the country that had more versus fewer Ebola cases in a difference-in-differences approach. FINDINGS: We find a large decrease in economic activity and jobs in all of Liberia during the Ebola outbreak, and an especially large decline in Monrovia. Outside of Monrovia, the restaurants, and food and beverages sectors have suffered the most among the surveyed sectors, and in Monrovia, the construction and restaurant sectors have shed the most employees, while the food and beverages sectors experienced the largest drop in new contracts. We find little association between the incidence of Ebola cases and declines in economic activity outside of Monrovia. CONCLUSIONS: If the large decline in economic activity that occurred during the Ebola outbreak persists, a focus on economic recovery may need to be added to the efforts to rebuild and support the healthcare system in order for Liberia to regain its footing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".