Public-Private Partnership Schools in Punjab: Is there a Learning Premium that can be Explained by Management Practices?
Bibliographic record
Abstract
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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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".