Child Work and Schooling: The Role of Household Asset Profiles and Poverty in Rural Ethiopia
Bibliographic record
Abstract
Child labour is commonly associated with poverty. This is consistent with the expectation that the supply of child labour will fall as incomes increase. However, the empirical evidence for this link is weak. We thus seek to extend the theoretical and empirical framework to better address demand determinants for child labour. We argue that this demand is household-specific given that in Ethiopia, as in most other developing countries, child labour is overwhelmingly performed for the child's own household in the absence of a smoothly functioning child labour market. A simple agricultural household model with a missing labour market shows that household asset portfolios and household composition are the principal determinants of child labour demand. Multinomial logit, mixed logit and simultaneous equation models are used to analyze child time use decisions in the context of rural Ethiopia while addressing issues of income endogeneity and the failure of the independence of irrelevant alternations hypothesis. Our results suggest that the demand for child labour plays a major role in child time-use decisions and that this demand varies substantially between households according to their asset profiles and household composition. In addition, by adequately addressing the demand side, we actually find support for a poverty-child labour link. These results imply that in pursuing asset accumulation-based poverty alleviation policies, attention should be paid to the possibility that this will encourage households to withdraw their children from school in order to take advantage of the increased returns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".