Productive Safety Net Programme and Children’s Time Use between Work and Schooling in Ethiopia
Bibliographic record
Abstract
Government, non-government and donor organisations have developed a social assistance programme known as the Productive Safety Net Programme (PSNP) which has two subprogrammes, namely the Public Work Programme (PWP) and Direct Support Programme (DSP). PSNP is designed to reduce the vulnerability of poor people to drought. It targets households in most cases without considering ex ante the issue of intra-household resource distribution. This paper assesses, using Young Lives survey data, the impacts of PSNP and Agricultural Extension Programme (AEP) on time use between work and schooling, as well as the highest grade completed by 12-year-old children in rural and urban Ethiopia. Empirically the study used propensity score matching techniques to estimate the impact of PSNP and AEP on child welfare measured by time use in various types of work, schooling and studying. We found that PWP in rural areas increases child work for pay; reduces children’s time spent on child care, household chores and total hours spent on all kind of work combined; and increases girls spending on studying. The DSP in rural and urban areas reduces time children spent on paid and unpaid work, and increases the highest grade completed by boys in urban areas. On the other hand, AEP in rural areas was effective in reducing child work for pay and total work, increasing time girls spent on schooling and the highest grade completed by girls.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".