Conditional Cash Transfers and Education Quality in the Presence of Credit Constraints
Bibliographic record
Abstract
We investigate the relative merits of unconditional cash transfers (UCT), conditional cash transfers (CCT), and improvements in education quality on efficiency and welfare. In our setting some parents under-invest in their children's education because capital market imperfections prevent them from borrowing. When credit constrained households can be perfectly targeted by the government, we show that CCT are more effective than UCT in enhancing efficiency and equivalent in terms of welfare. When public education quality is very low, raising quality is welfare improving, but is never efficiency enhancing. If the government cannot target constrained households, UCT may be the best policy both in terms of efficiency and welfare. / Nous étudions les avantages relatifs des transferts monétaires inconditionnels (UCT), des transferts monétaires conditionnels (CCT) et de l’amélioration de la qualité de l'éducation sur l'efficacité et le bien-être. Dans notre modèle, certains parents sous-investissent dans l'éducation de leurs enfants parce que des imperfections du marché de crédit les empêchent d'emprunter. Lorsque les ménages confrontés à des restrictions du crédit peuvent être parfaitement ciblés par le gouvernement, nous montrons que les CCT ont un impact plus important en termes d’efficacité que les UCT et sont équivalents en termes de bien-être. Lorsque la qualité de l'éducation est très faible, l'amélioration de la qualité augmente le bien-être mais ne peut pas améliorer l'efficacité. Si le gouvernement ne peut pas cibler les ménages contraints monétairement, les UCT peuvent être la meilleure politique à la fois en termes d'efficacité et de bien-être.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".