The Impact of the COVID-19 Pandemic on Household Welfare in Ethiopia: Evidence from a Microsimulation Exercise
Why this work is in the frame
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Bibliographic record
Abstract
Various studies have shown the detrimental effects the COVID-19 pandemic has had on the world \neconomy. We examine the pandemic’s effects on Ethiopian households’ welfare using a \nmicrosimulation exercise and data from the 2018/19 Living Standards Measurement Study - \nIntegrated Surveys on Agriculture (LSMS-ISA) survey. We also evaluate the role of the Productive \nSafety Net Program (PSNP) in cushioning the adverse impact of the pandemic. Our results suggest \nthat the pandemic induced an increase of between 2 and 4 percentage points in the poverty rate \nin the first three months, which translates to between 2.38 and 4.12 million people slipping into \npoverty. This is a substantial loss in the poverty reduction gains Ethiopia recently made. Most of the \npandemic’s effects are driven by changes in direct income and food prices. The pandemic has had \ndifferent impacts on rural and urban as well as male- and female-headed households. The study \nreveals how the pandemic’s impact on inequality varies by socio-economic category. We also find \nthat the PSNP prevented about 0.8 million people from sliding into poverty. Policy implications include \nthe need to carefully design and target social protection programs to mitigate the pandemic’s \nadverse impacts.
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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 | 0.001 |
| 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 it