Public-Private Partnership Schools in Punjab: Is there a Learning Premium that can be Explained by Management Practices?
Notice bibliographique
Résumé
The rapid rise of low-cost private schools (LCPS) in developing countries has sparked fierce debate. Proponents argue these schools promote access in areas underserved by government schools, as well as benefit from improved autonomy in decision-making and accountability to parents to produce superior learning outcomes. Critics note that they fail to reach the poor, and any learning premium is driven by selection effects. Despite the ongoing debate, in the face of growing demand for schooling, public-private partnerships (PPPs) have become a key feature of the global education landscape. This trend is especially pronounced in the Punjab province of Pakistan, where many private schools participate in public financing schemes through subsidy, charter and voucher programs. With increasing recognition of the global learning crisis, the performance of PPP schools, as well as the drivers of any achievement gap, are of great interest to policy makers. Recent scholarship has foregrounded school management practices – including accountability to clients – as a key factor in school effectiveness. Drawing on data from the Service Delivery Indicator (SDI) Survey from Punjab, this dissertation contributes to these debates: first by asking if there is an achievement gap between PPP and government schools in Punjab (as well as fee-based private schools), and second, assessing the role of school management quality in explaining the achievement gap. I hypothesize that certain management best practices – focusing on the domains of people management, operations, leadership, target setting and monitoring, as measured by the SDI – are driven by increased autonomy for private schools, and should account for part of the hypothesized ‘PPP school premium’. The results suggest that while the achievement gap between PPP and public schools is small after accounting for student background, it is statistically significant and relatively consistent across models. In addition, when comparing the PPP approaches, voucher schools have the strongest achievement followed by subsidy and then charter schools. Second, I find that the index for management practices is positively associated with learning outcomes. However, despite having more autonomy in decision making, fee-based private schools and PPP schools do not appear better managed than government schools, and as a result, management practices do not explain the achievement gap.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».