Explaining cross‐country differences in policy response to child labour
Bibliographic record
Abstract
Abstract We develop a model of child labour where poverty and inequality combine to determine policy response to child labour. If there are strategic complementarities between parents’ decisions to educate their children and firms’ technology choice, multiple school‐enrolment equilibria arise. Only rich countries and those that are not ‘too’ poor and have a low wealth inequality benefit from adopting child labour laws. This is because such laws commit an economy with either of those initial conditions to the full school‐enrolment equilibrium which Pareto‐dominates all other equilibria. Moreover, wealth redistribution is not sufficient to eliminate child labour. JEL Classification: I20, O33 Une explication des différences d’un pays à l’autre dans la réaction des politiques au phénomène du travail des enfants Les auteurs développent un modèle de travail des enfants dans lequel les niveaux de pauvreté et d’inégalité se combinent pour déterminer les politiques. Si des complémentarités stratégiques existent entre les décisions des parents d’éduquer leurs enfants et le choix de technologie des entreprises, de nombreux équilibres impliquant divers niveaux d’inscription à l’école sont possibles. Seuls les pays riches, et ceux qui ne sont pas « trop » pauvres ou qui ont un degré d’inégalité de la richesse relativement bas, tirent profit de lois réglementant le travail des enfants. C’est le cas parce que de telles lois engagent une économie qui a ces caractéristiques à se diriger vers un équilibre d’inscription totale des enfants à l’école. Dans ces cas, il s’agit d’un équilibre qui domine au sens de Pareto tous les autres équilibres. On note de plus que la redistribution de la richesse ne suffit pas pour éliminer le travail des enfants.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".