The Impact of the COVID-19 Pandemic on Household Welfare in Ethiopia: Evidence from a Microsimulation Exercise
Bibliographic record
Abstract
Various studies have shown the detrimental effects the COVID-19 pandemic has had on the world economy. We examine the pandemic’s effects on Ethiopian households’ welfare using a microsimulation exercise and data from the 2018/19 Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) survey. We also evaluate the role of the Productive Safety Net Program (PSNP) in cushioning the adverse impact of the pandemic. Our results suggest that the pandemic induced an increase of between 2 and 4 percentage points in the poverty rate in the first three months, which translates to between 2.38 and 4.12 million people slipping into poverty. This is a substantial loss in the poverty reduction gains Ethiopia recently made. Most of the pandemic’s effects are driven by changes in direct income and food prices. The pandemic has had different impacts on rural and urban as well as male- and female-headed households. The study reveals how the pandemic’s impact on inequality varies by socio-economic category. We also find that the PSNP prevented about 0.8 million people from sliding into poverty. Policy implications include the need to carefully design and target social protection programs to mitigate the pandemic’s adverse impacts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".