Evaluating Public Per-Student Subsidies to Low-Cost Private Schools : Regression-Discontinuity Evidence from Pakistan
This study estimates the causal effects of a public per-student subsidy program targeted at low-cost private schools in Pakistan on student enrollment and schooling inputs. Program entry is ultimately conditional on achieving a minimum stipulated student pass rate (cutoff) in a standardized academic test. This mechanism for treatment assignment allows the application of regression-discontinuity (RD) methods to estimate program impacts at the cutoff. Data on two rounds of entry test takers (phase 3 and phase 4) are used. Modeling the entry process of phase-4 test takers as a sharp RD design, the authors find evidence of large positive impacts on the number of students, teachers, classrooms, and blackboards. Modeling the entry process of phase-3 test takers as a partially-fuzzy RD design given treatment crossovers, they do not find evidence of significant program impacts on outcomes of interest. The latter finding is likely due to weak identification arising from a small jump in the probability of treatment at the cutoff.
Summary: | This study estimates the causal effects
of a public per-student subsidy program targeted at low-cost
private schools in Pakistan on student enrollment and
schooling inputs. Program entry is ultimately conditional on
achieving a minimum stipulated student pass rate (cutoff) in
a standardized academic test. This mechanism for treatment
assignment allows the application of
regression-discontinuity (RD) methods to estimate program
impacts at the cutoff. Data on two rounds of entry test
takers (phase 3 and phase 4) are used. Modeling the entry
process of phase-4 test takers as a sharp RD design, the
authors find evidence of large positive impacts on the
number of students, teachers, classrooms, and blackboards.
Modeling the entry process of phase-3 test takers as a
partially-fuzzy RD design given treatment crossovers, they
do not find evidence of significant program impacts on
outcomes of interest. The latter finding is likely due to
weak identification arising from a small jump in the
probability of treatment at the cutoff. |
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