The Review of Economics and Statistics

VOL. CIV

MAY 2022

NUMBER 3

DELIVERING EDUCATION TO THE UNDERSERVED THROUGH
A PUBLIC-PRIVATE PARTNERSHIP PROGRAM IN PAKISTAN

Felipe Barrera-Osorio, David S. Blakeslee, Matthew Hoover,
Leigh Linden, Dhushyanth Raju, and Stephen P. Ryan*

Abstract—We evaluate a program that recruited local entrepreneurs to open
and operate new schools in 200 underserved villages in Sindh, Pakistan.
School operators received a per student subsidy to provide tuition-free pri-
mary education, and half the villages received a higher subsidy for fe-
males. The program increased enrollment by 32 percentage points and test
scores by 0.63 standard deviations, with no difference across the two sub-
sidy schemes. Estimating a structural model of the demand and supply for
school inputs, we ﬁnd that program schools selected inputs similar to those
of a social planner who internalizes all the education beneﬁts to society.

Introduction

LOW- and middle-income countries continue to struggle

with problems of low enrollment rates and low student
achievement (World Bank, 2018). Because public education
is generally seen to be failing in these countries, governments
have increasingly experimented with models giving a greater

Received for publication February 25, 2019. Revision accepted for pub-

lication July 8, 2020. Editor: Brian A. Jacob.

∗Barrera-Osorio: Vanderbilt University; Blakeslee: New York University–
Abu Dhabi; Hoover: Gallup; Linden: University of Texas at Austin,
BREAD, J-PAL, IPA, IZA, and NBER; Raju: World Bank; Ryan: Wash-
ington University in St. Louis, CESifo, and NBER.

This study is dedicated to the respectful memory of the late Anita Ghu-
lam Ali, former managing director of the Sindh Education Foundation. The
Government of Sindh’s Education Sector Reform Program, which includes
the intervention evaluated in this study, received ﬁnancial and technical as-
sistance from the World Bank and the European Commission. We thank
the following people and organizations: the Government of Sindh’s Plan-
ning and Development, Finance, and Education and Literacy Departments;
the Sindh Education Sector Reform Program Support Unit; and SEF for
partnering with the evaluation team, in particular with M. Abdullah Ab-
basi, Naheed Abbasi, Ambreena Ahmed, the late Anita Ghulam Ali, Imam
Bux Arisar, Sadaf Bhojani, Mukhtiar Chandio, Sana Haidry, Abdul Fateh
Jhokio, Aziz Kabani, Tauseef Latif, Adnan Mobin, Dilshad Pirzado, Shukri
Rehman, Shahpara Rizvi, Rustam Samejo, Noman Siddique, and Sadaf
Junaid Zuberi. Second, we thank the following World Bank and Euro-
pean Commission staff for their support of the design, implementation,
and evaluation of the intervention: Umbreen Arif, Salman Asim, Siddique
Bhatti, Reema Nayar, Quynh Nguyen, Peter Portier, Uzma Sadaf, Benjamin
Safran, and Soﬁa Shakil. Third, we thank Mariam Adil and Aarij Bashir
for their ﬁeld-based support. We beneﬁted from comments from Richard
Murnane, Emmerich Davies, and seminar participants at Harvard Univer-
sity, the World Bank, RISE Conference, NBER Education, and IZA Labor
Conference. The study received ﬁnancial support from the Australian De-
partment of Foreign Affairs and Trade and the World Bank. The experimen-
tal project has IRB approval number AAAF4126, Columbia University and
registration at the American Economic Association RCT registry repository,
number AEARCTR-0002407.

A supplemental appendix is available online at https://doi.org/10.1162/

rest_a_01002.

role to private education providers. Research on the effec-
tiveness of this approach has largely focused on programs in
which governments subsidize enrollment in existing private
schools (Patrinos, Barrera-Osorio, & Guáqueta, 2009). Be-
cause many of the most educationally deprived areas often
lack preexisting private schools with which to partner, gov-
ernments have also experimented with policies involving the
creation of new private schools. Whether such an approach
can be successful, however, is far less certain, as the absence
of preexisting private schools may be driven by unfavorable
local conditions.1

We evaluate the Promoting Low-Cost Private Schooling in
Rural Sindh (PPRS) program, which was implemented in the
Sindh province of Pakistan. In this program, publicly subsi-
dized private schools were randomly assigned to education-
ally underserved villages, with private entrepreneurs given
responsibility for creating and managing these schools, and
compensated according to enrollment on a per child basis. A
second treatment arm incentivized girls’ enrollment by pro-
viding entrepreneurs with a subsidy premium. Entrepreneurs
exercised wide latitude in the inputs they provided, including
the ability to hire teachers with lower formal qualiﬁcations
than required for government teachers.

A lengthy literature has argued that private schools have
advantages over public schools due to their stronger incen-
tives to reduce costs and innovate and that they more closely
tailor school inputs to the preferences and needs of their stu-
dents (Friedman, 1955; Shleifer, 1998).2 A number of papers
have tested this thesis empirically using experiments with
vouchers and have generally found either that private schools
deliver better educational outcomes than government schools
or that they produce similar educational outcomes but at a sig-
niﬁcantly lower cost (Kim et al., 1999; Angrist et al., 2002;
Alderman, Orazem, & Paterno, 2001; Alderman et al., 2003;

1To the best of our knowledge, Alderman, Kim, and Orazem (2003) is the
only other paper to evaluate such a program. That paper evaluates a similar
program conducted in the Balochistan province of Pakistan in the 1980’s.
The program was largely unsuccessful in rural areas, due in part to the low
supply of qualiﬁed teachers. In contrast, the PPRS program was able to tap
into a fairly large supply of educated women due to recent advances in rural
education.

2In turn, programs based on private schools, such as vouchers, may induce

higher competition and general equilibrium effects (see Hoxby, 2003).

The Review of Economics and Statistics, May 2022, 104(3): 399–416
© 2020 The President and Fellows of Harvard College and the Massachusetts Institute of Technology
https://doi.org/10.1162/rest_a_01002

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400

THE REVIEW OF ECONOMICS AND STATISTICS

Barrera-Osorio & Raju, 2015; Barrera-Osorio et al., 2020;
Muralidharan & Sundararaman, 2015; Romero et al., 2020).3
In Pakistan, an inﬂuential literature has shown that condi-
tional on child characteristics, children enrolled in low-cost
private schools have higher test scores than government-
enrolled children, though this ﬁnding is not based on experi-
mental variation (Andrabi et al., 2011, 2020).

The purported advantages of private education, coupled
with often limited state capacity, has led developing coun-
try governments to increasingly make use of public private
partnerships (PPPs) in order to meet their education objec-
tives (Patrinos et al., 2009). Among the most common types of
PPPs are schemes in which governments provide funding for
children to enroll in existing private schools.4 To mitigate the
possibility that privately operated schools will pursue objec-
tives different from those of the government, PPPs generally
include extensive contractual obligations for the provision of
speciﬁc services or may stipulate some level of school qual-
ity in order to participate (Patrinos et al., 2009). However,
even where such contracts are in place, the focus of private
entrepreneurs on proﬁts may lead to the underprovision of
socially valuable but noncontractible aspects of education
(Hart, Shleifer, & Vishny, 1997).

While centralized control may facilitate the implementa-
tion of contractual terms speciﬁed by the government, de-
centralization has the potential to make schools more respon-
sive to local demand. Recent research from Liberia studies
the effects of a program in which the management of failing
government schools was handed over to large companies op-
erating chains of private schools, in which decision making
was highly centralized (Romero et al., 2020). The authors
ﬁnd that these schools were successful in improving educa-
tional outcomes, though follow-up research has somewhat
tempered these ﬁndings (Romero & Sandefur, 2021).

The program studied in this paper extends the existing re-
search in two important ways. First, the management of these
schools was highly decentralized, with schools being oper-
ated by local entrepreneurs who exercised wide discretion
in the inputs they provided. Second, the PPRS program in-
volved the establishment of new, privately operated schools.
In contrast, most previously studied programs have examined
schools that had already existed for some time, so that inclu-
sion in such programs implicitly selects on the prior success
of participating schools.

The PPRS program was designed and administered by the
Sindh Education Foundation (SEF), a semiautonomous or-
ganization in the Sindh provincial government. The program
offered local entrepreneurs a set of beneﬁts to establish and

3Angrist et al. (2002) show that voucher winners in Colombia had higher
test scores and school progression. Muralidharan and Sundararaman (2015)
use a voucher scheme to show that private schools in Andhra Pradesh,
India, deliver similar levels of instruction in most subjects as public schools,
though at a fraction of the cost and time and have a large, positive impact
on Hindi (a non-local language) skills.

4See Patrinos et al. (2009) for a comprehensive survey of the types of

PPPs.

run tuition-free, coeducational primary schools in education-
ally underserved villages. The beneﬁts included a per student
subsidy, school leadership and teacher training, and teaching
and learning materials. The per student subsidy amount was
ﬁxed at less than one-half the per student cost for public
primary and secondary education in the province. The pro-
gram was randomly assigned to 200 out of 263 qualifying
villages in eight districts selected for their poor education
outcomes. To address the large gender disparity in school en-
rollment prevalent in rural Sindh, half of the program villages
were randomly assigned to a gender-differentiated subsidy
scheme, under which school operators received a higher per
student subsidy for girls than for boys.

For the purpose of assessing the performance of program
schools, we explore three counterfactuals. First, we com-
pare educational outcomes of children in treatment villages
to those in control villages in order to determine the effect
of gaining access to program schools on village-wide edu-
cational outcomes. Second, we compare the test scores of
children enrolled in program schools to those in government
schools (in control villages) in order to assess whether pro-
gram schools yield the quality advantages often ascribed to
private provision. Finally, we undertake an absolute assess-
ment of the efﬁciency of program schools (given local re-
sources) by comparing program school inputs to those of a
social planner who maximizes social surplus.

The program was highly effective. Comparing treatment
and control villages nearly two years after the schools were
opened, the program increased school enrollment for children
aged 5 to 10, the program’s stated target age group, by 32
percentage points. The program also raised total test scores
by 0.63 standard deviations, with mean test scores increasing
from 46.9% correctly answered questions in control villages
to 66.7% in treatment villages. For children induced by the
program to enroll in school, the increase in test scores was
nearly 2 standard deviations. The overall treatment effect was
the same for boys and girls, and the gender-differentiated
subsidy treatment had similar impacts on girls’ enrollment
and test scores as the gender-uniform one.

Improvements in educational outcomes were primarily
driven by making schools available in villages where they had
previously been absent. However, program schools yielded
additional gains by increasing enrollment in villages where
government schools were present, as well as through the
higher quality of program schools relative to government
schools. Evidence for the quality of program schools can be
seen in the fact that virtually all government-enrolled children
in treatment villages switch to program schools, as well as
the higher test score received by children enrolled in program
schools. Though the latter ﬁnding is not based on experimen-
tal variation, we show that it is unlikely to be due to selection
effects.

Finally, we examine the efﬁciency of input choices in pro-
gram schools vis-à-vis the social planner’s solution based on
structural model estimations of schooling demand and educa-
tion production. The experimental design provides a unique

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DELIVERING EDUCATION THROUGH PUBLIC-PRIVATE PARTNERSHIPS

401

opportunity for conducting this analysis in a credible man-
ner. In nonexperimental settings, one would be concerned
that there are correlated unobservables (such as village-level
preferences for education) that are driving both the educa-
tional outcomes of interest and the presence of schools and
the inputs they select.

Using a structural estimation of the supply and demand for
school inputs, we compute the optimal set of school inputs
that a social planner would have chosen for each village,
taking into account the input costs, the deadweight loss from
taxes, the surplus accruing to students, and the social beneﬁt
of education. We ﬁnd that SEF and program-school operators
captured approximately 94% of the total amount of potential
social surplus. The principal difference between the social
planner and program school operators is that the latter hire
teachers who attract slightly fewer students but are cheaper
and increase proﬁts (e.g., female teachers, teachers with less
experience, and teachers with higher rates of absenteeism).
The results of this study indicate that government sup-
port for local private providers may be a viable alternative to
purely public provision. The challenging context in which the
program was implemented suggests the potential for such an
approach to be effective in many other parts of the developing
world.5

II. Background

Schooling in Pakistan

School enrollment is low in Pakistan, even in comparison
to countries with a similar income level (Andrabi et al., 2008).
At the time the PPRS program was initiated in 2008/2009, the
primary school net enrollment rate (NER) for children aged 6
to 10 in Pakistan was 67% (72% for boys and 62% for girls)
(Government of Pakistan, 2011). In rural Sindh, where the
PPRS program was implemented, the primary-school NER
was 65% for boys and 46% for girls (Government of Pakistan,
2011).

Pakistan has witnessed a dramatic growth in private
schools in the last three decades, much of which has occurred
in villages and poorer urban neighborhoods (Andrabi, Das,
& Khwaja, 2008). These schools have succeeded in terms of
both cost and quality. At less than $20 per annum in 2000, the cost of private primary school fees represented about 2% of mean total household spending (Andrabi, Das, & Khwaja, 2008). Low-cost private schools are nonetheless found to pro- duce higher test scores than government schools in rural Pun- jab province (Andrabi et al., 2011, 2020). The affordability of these schools has been made possible by low ﬁxed costs and low operational costs, driven primar- ily by the low wages paid to teachers. Low wages are in turn made possible by the generally lower educational qualiﬁca- tions of private school teachers, as well as the fact that many teachers are women, for whom there are fewer alternative la- bor market opportunities. Teachers in government schools, in contrast, are part of the civil service and are required to have certain minimal educational qualiﬁcations, and their salaries are determined by seniority and formal educational qualiﬁ- cations.6 As a consequence, teacher salaries in government schools are ﬁve times higher than those in private schools and constitute 80% of expenditures in public institutions (Bau & Das, 2020). Another advantage of private schools is the autonomy they enjoy in the design of their curriculum. This contrasts sharply with public schools, where the curriculum is set by the central government, with little room for variation. Only 5% of primary school students in rural Sindh were enrolled in private schools during the 2008–2009 term (Gov- ernment of Pakistan, 2011). One of the most important con- straints on the presence of low-cost private schools appears to be the supply of local women with secondary education, as this labor force is crucial to the cost structure that makes these schools viable (Andrabi, Das, & Khwaja, 2013). The location of government schools, in contrast, depends primar- ily on budget constraints. While the “typical” village has one or two public schools, villages in remote areas often do not have a government school or the a school has insufﬁcient staff and high rates of teacher absenteeism. B. PPRS Program In 2007, the provincial government initiated the Sindh Edu- cation Sector Reform Program (SERP), a multifaceted reform of public spending and provision in primary and secondary education. A key component of SERP was the use of PPPs, entailing public ﬁnancing and private provision, with the ob- jective of simultaneously increasing access to schooling and the quality of education for socioeconomically disadvantaged children. Funded by the provincial government, the Promoting Pri- vate Schooling in Rural Sindh (PPRS) program was designed and administered by the Sindh Education Foundation (SEF), a semiautonomous organization established in 1992. The prin- cipal objectives of the PPRS program were to increase access to schooling in marginalized areas, reduce the gender dispar- ity in school enrollment, and increase student learning, in a cost-effective manner. We evaluate the ﬁrst phase of this program, which was implemented in 8 (out of, at that time, 23) districts in the province. SEF selected the districts based on the size of the out-of-school child population, the gender disparity in school enrollment, and the percentage of households located at least ﬁfteen minutes away from the nearest primary school. The 5Indeed, since its inception, the PPRS program and the related SEF As- sisted Schools (SAS) program have been expanded to cover more than 550,000 students across more than 2,000 schools, speaking to the impor- tance and potential of this model. 6Teachers in the public sector must have, at a minimum, a primary teacher’s certiﬁcate or certiﬁcate of teaching. The previous requirement that they have a BA or BsC has been phased out. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e – p d f / / / / 1 0 4 3 3 9 9 2 0 2 2 0 0 6 / r e s t _ a _ 0 1 0 0 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 402 THE REVIEW OF ECONOMICS AND STATISTICS eight lowest-ranked districts were selected, excluding those that were experiencing heightened law-and-order concerns. Based on a budgetary assessment, SEF approved the cre- ation of primary schools in 200 villages. These schools were to be established and operated by private providers and were required to admit all children within the village free of charge. Program-school operators received a per student cash sub- sidy; free school leadership and teacher training; and free textbooks, other teaching and learning materials, stationery, and bookbags. Two types of subsidies were provided: a gender-uniform subsidy, in which entrepreneurs received 350 rupees per stu- dent per month (approximately $5 in annualized 2008 U.S.
dollars); and a gender-differentiated subsidy, in which en-
trepreneurs received an additional 100 rupees per month for
each female student (450 rupees). One hundred villages were
assigned to each of the two subsidy treatments. The subsidy
amounts were set at less than one-half the per student cost
of public primary and secondary government schools in the
province. The subsidies were provided to entrepreneurs on a
quarterly basis and were based on a formula that multiplied
the number of children in attendance by 1.25 to reﬂect an ex-
pected 20% absence rate. Attendance was assessed by SEF
during periodic, unannounced monitoring visits.

Local private entrepreneurs were invited to apply to the
program through an open call in newspapers and to propose
educationally underserved villages in the selected districts
to establish and operate schools. SEF vetted the applications
(ultimately, through visits to shortlisted villages) based on
several criteria, including written assent from the parents of
at least 75 children of primary school age that they intended
to enroll their children in the school should it be established;
an available building in the village that was located at least
1.5 kilometers from the nearest school and of sufﬁcient size;
and the identiﬁcation of potential teachers with a minimum
of eight years of schooling (middle school completion), with
at least two being women.

Once in the program, school operators would continue to
receive the subsidy and other beneﬁts as long as they ad-
hered to certain basic conditions. The SEF strictly enforced
the condition that families not be charged for enrollment but
was more lenient in enforcing the school infrastructural fea-
tures and environmental conditions. In addition, the contract
stipulated that compensation would be based on a formula
using veriﬁed attendance, as described above. No contract
was terminated in any of the sample schools due to contract
breach.

III. Data

SEF administered a vetting survey to determine whether
proposed villages qualiﬁed for the program. This survey,
which we refer to as the baseline survey, was conducted in
February 2009. Next, the 263 qualifying villages were ran-
domly assigned to the two subsidy treatments and the control.
After random assignment, the original evaluation sample was

reduced to 199 villages through the exclusion of sites that
were situated in large towns with numerous existing schools.
The effective evaluation sample consisted of 82 villages under
the gender-uniform subsidy treatment, 79 under the gender-
differentiated treatment, and 38 in the control group.

Schools were established in summer 2009. Because the
new school year normally commences in the spring, program-
school students had an abbreviated ﬁrst school year. An initial
follow-up survey was conducted nearly one year after the
program started (May–June 2010), during which a full census
of the village was taken. A second follow-up survey was
conducted in April and May 2011, after the conclusion of the
second school year under the program.

The baseline survey included a household survey of 12
households randomly selected from the list (submitted by
the entrepreneur) of 75 households that had agreed to send
their children to the proposed program school should it be
established. The household survey collected information on
the household, the household head, and on each child aged 5
to 9. There was also a survey of the entrepreneur and proposed
teachers, as well as physical checks of the proposed school
site and building.

The ﬁrst follow-up survey was implemented as a full vil-
lage census and included only a small number of questions on
household, household head, and child characteristics. For this
activity, enumerators had a prominent member of the com-
munity guide them through the village and indicate every
household that belonged to the village. The full list of house-
holds was then used as the sampling frame for the second
follow-up survey. The second follow-up survey was longer
and more comprehensive than the ﬁrst, but included only a
subset of households.

It is important to note that the sampling frame used for the
follow-up surveys may differ from that used for the baseline
survey, as the latter was based on the entrepreneur’s assess-
ment of which children belonged to the village. For the same
reason, the catchment area from which children were admit-
ted into the program schools was likely also different from
the village boundaries used for the follow-up survey. This can
be seen most clearly in the enrollment ﬁgures from the school
surveys, which often exceeded the total number of children
within the village. This is likely due to entrepreneurs’ ad-
mitting children from outside the village and ambiguities in
the deﬁnition of village boundaries. Reassuringly, where con-
trol and treatment villages were located close to one another
(within 1 to 2 kilometers), there is no evidence that children
in the control villages enrolled in nearby program schools.7
The second follow-up survey, conducted nearly 2 years
after the start of the program, and with schools having been
in operation for 1.5 years, consisted of three instruments: a
school survey; a household survey; and a child survey, which

7In addition, the distribution of the distances of households from program
school sites (proposed and actual), as well as visual inspections of GIS maps
of villages, indicates that the village boundaries determined by the census
included all households within the primary clusters of settlements.

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DELIVERING EDUCATION THROUGH PUBLIC-PRIVATE PARTNERSHIPS

403

TABLE 1.—EVALUATION SAMPLE SIZES

Control
(1)

Treatment
(2)

Number Villages
Full Census

Number Households w/Young Children
Number Young Children

Follow-up Sample

Number Households w/Young Children
Share Census Households w/Young Children

Number Young Children
Share Census Young Children

1,451
4,567

1,069
0.74
3,121
0.68

161

6,634
20,395

4,857
0.73
14,647
0.72

Uniform
(3)

3,532
11,036

2,554
0.72
7,669
0.69

Differentiated
(4)

3,102
9,359

2,303
0.74
6,978
0.75

Total
(5)

199

8,085
24,962

5,926
0.73
17,768
0.71

This table reports sample sizes by treatment status. Treatment denotes pooled treatment; Uniform, the gender-uniform subsidy treatment; and Differentiated, the gender-differentiated subsidy treatment.

included a test administered and supervised by the surveyors.
The household survey was administered to households with
at least one child aged 5 to 9 (at the time of the ﬁrst follow-up
survey).8 A child survey was administered to each child aged
5 to 10, which included a test on language (either Urdu or
Sindhi, as preferred) and mathematics. The household and
child surveys were administered at the child’s home.

The school survey was conducted for all schools located
within the village. The survey included interviews of head
teachers and all other teachers and visual inspections by enu-
merators of school infrastructural and environmental con-
ditions. GPS data were gathered from all surveyed house-
holds and schools. Where possible, we also surveyed schools
located outside the village, but within 3 kilometers, using an
abbreviated school survey.

Table 1 reports sample sizes of the baseline and follow-up
surveys by treatment status. The census conducted during the
ﬁrst follow-up survey indicates that there were 8,085 house-
holds with children aged 5 to 10, and 24,962 children in this
age range, in the 199 sample villages. The second follow-up
survey included 5,926 households and 17,768 young chil-
dren, constituting 73% and 71% of the total census popula-
tions, respectively.

IV. Empirical Strategy

We assess the effectiveness of program schools along two
dimensions. First, we ask how successfully program schools
meet their objective of increasing enrollment and test scores
in treatment villages relative to control villages. Second, we
seek to assess the efﬁciency with which program schools meet
this objective.

To answer the ﬁrst question, we estimate the intention-to-
treat effect of program schools comparing child enrollment
and test scores across control and treatment villages. We also
test whether the gender-differentiated treatment differentially
affected enrollment of girls and test scores. In addition, we
seek to disentangle the mechanisms driving treatment effects
by assessing the respective roles played by school proximity
and school quality, using existing government schools as the
counterfactual.

To address the second question, we pose and estimate a
structural model, which we use to assess how closely the
school inputs selected by private entrepreneurs aligned with
those of a benevolent social planner. We use a discrete choice
model to estimate demand for schooling and a revealed pref-
erence approach to infer input costs. Combining these ele-
ments with estimates from the literature on the social value
of education, we explore how demand, cost, and the social
value of education would change with different school inputs
in each treatment village.

The validity of our results depends on the comparability
of populations across the experimental groups. Because the
program was randomly assigned across villages, treatment
status should be orthogonal to household and child charac-
teristics that might be correlated with the outcomes. Insofar
as this holds, it will be sufﬁcient to compare outcomes across
the treatment and control groups to evaluate the reduced-form
impacts of the program.

To assess comparability, we estimate the differences in
mean household and child characteristics between program
and control villages at baseline and follow-up. In table 2,
columns 1 and 3 report mean characteristics in control vil-
lages at baseline and follow-up, respectively. Columns 2 and
4 report the differences in mean characteristics between pro-
gram and control villages at baseline and follow-up, based on
a regression of the indicated variable on an indicator variable
for treatment status. Differences across control and treatment
villages were small and statistically insigniﬁcant for virtually
all household and child characteristics, with the exception of
gender. A joint signiﬁcance test gives an F -statistic of 0.390,
indicating that the samples are balanced. Appendix table
A1 reports differences in household and child characteristics
across villages under the gender-uniform and -differentiated
subsidy treatments.

V. Program Impacts

The ITT effect of the program is based on the following

speciﬁcation:

Yi j = β0 + β1Tj + β2Xi + δi + εi j,

(1)

8In large villages, up to 42 randomly sampled households (with qualify-
ing children) in the village were interviewed; in villages with fewer than
42 qualifying households, the majority, all households in the village were
interviewed.

where Yi j is the outcome of interest for child i in village j, Tj
is an indicator variable indicating whether village j was as-
signed a program school, Xi is a vector of child and household

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THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 2.—BALANCE ACROSS PROGRAM AND CONTROL VILLAGES

Baseline

Follow-up

Child Age

Child Female

Child Enrolled at Baseline

Child of HH Head

Household Size

Number Children

HH Head Years Education

HH Head Farmer

Total Land

Brick House

Semi-Brick House

Mud House

Thatched Hut

Number Goats

Sect: Sunni

Language: Urdu

Language: Sindhi

Joint Signiﬁcance:

Control
(1)

6.890

0.367

0.229

9.542

3.044

2.347

0.724

Treatment –
Control
(2)
−0.075
(0.083)
0.053**
(0.025)
0.031
(0.050)

−0.592
(0.529)
−0.259
(0.176)
0.340
(0.458)
−0.021
(0.057)

F -stat
p-value

0.675
0.693

Control
(5)

Treatment –
Control
(6)

7.354

0.424

0.284

0.856

7.221

4.755

2.631

0.562

4.229

0.056

0.192

0.510

0.242

3.915

0.877

0.114

0.662

0.081
(0.054)
0.030*
(0.016)
−0.027
(0.083)
0.022
(0.026)
−0.097
(0.288)
−0.140
(0.188)
0.127
(0.316)
−0.016
(0.067)
0.898
(1.113)
−0.004
(0.023)
−0.016
(0.065)
0.085
(0.075)
−0.065
(0.072)
−0.035
(0.789)
0.034
(0.060)
0.046
(0.043)
0.064
(0.071)
0.390
0.983

This table reports balance in characteristics across program and control villages (for children aged 5–9 at baseline and 5–10 at follow-up). Columns 1 and 3 report mean child and household characteristics in control
villages at baseline and follow-up, respectively. Columns 2 and 4 report differences in mean child and household characteristics in program villages at baseline and follow-up, respectively. Treatment denotes pooled
treatment. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

characteristics, and δi are district ﬁxed effects. Household
characteristics include the education of the household head,
whether the household head is a farmer, total land holdings,
the number of children, and the number of adults. Child char-
acteristics are child age and child gender. In other speciﬁca-
tions, we examine the differential impacts of the program by
gender, by the two subsidy treatments, and by the two sub-
sidy treatments interacted with gender. Standard errors are
clustered at the village level, j. Observations are weighted
by the inverse probability of their being sampled from the
census for inclusion in the survey.

A. Enrollment

The ﬁrst outcome we measure is the effect of the treatment
on enrollment for children aged 5 to 10. Enrollment is based
on the respondent-reported enrollment status of the child in
the just-concluded school term. We also estimate the effect
of the treatment on the highest grade attained. Because these

measures may be subject to misreporting, we also adminis-
tered tests to the children to gain a better assessment of the
true educational outcomes. As we show subsequently, the
treatment effects for test scores are consistent with those for
self-reported enrollment.

Table 3 reports the impacts of the treatment on school en-
rollment and grade attainment. Because treatment effects are
similar across the two treatment arms (as shown in subsequent
analysis), we use the pooled treatment in our baseline speci-
ﬁcation. Columns 1 through 4 report impacts on enrollment
with different sets of controls. Column 5 reports impacts on
highest grade attained with the full set of controls. Based on
the model with the full set of controls, the program increased
enrollment among young children by 31.7 percentage points
and increased grade attainment by 0.38 grades.

B. Test Scores

Table 4 reports the impact of the pooled treatment on test
scores. Test scores are standardized by subtracting the mean

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405

Treat_p

Control Mean
N
R-squared
Child Controls
HH Controls
District FEs

(1)

0.316***
(0.066)

11,717
0.086
No
No
No

TABLE 3.—PROGRAM IMPACTS ON ENROLLMENT

Enrollment

0.527

(3)

0.313***
(0.064)

11,717
0.103
Yes
Yes
No

(2)

0.316***
(0.066)

11,717
0.087
Yes
No
No

(4)

0.317***
(0.065)

11,717
0.109
Yes
Yes
Yes

Highest Grade
Attained

(5)

0.382***
(0.119)
0.800
11,211
0.225
Yes
Yes
Yes

This table reports program impacts on enrollment and highest grade attained at follow-up measurement (for children aged 5–10). Means of outcome variables in control villages are reported in the second row.

Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

Control
Mean

(1)

0.460
(0.307)
0.485
(0.341)
0.469
(0.310)

TABLE 4.—PROGRAM IMPACTS ON TEST SCORES

Treatment Effects, Z-Score

ITT

(2)

0.532***
(0.153)
0.503***
(0.168)
0.537***
(0.164)
No
No
No

(3)

0.522***
(0.156)
0.494***
(0.171)
0.527***
(0.167)
Yes
No
No

(4)

0.521***
(0.154)
0.492***
(0.168)
0.525***
(0.164)
Yes
Yes
No

(5)

0.627***
(0.123)
0.591***
(0.128)
0.631***
(0.127)
Yes
Yes
Yes

TOT

(6)

1.944***
(0.283)
1.805***
(0.228)
1.941***
(0.260)
Yes
Yes
Yes

Math Score

Language Score

Total Score

Child Controls
HH Controls
District F.E.s

This table reports program impacts on standardized test scores (for children aged 5–10). Column 1 gives the mean percent of correct answers in control villages, with the standard deviation reported in parentheses.
Columns 2 through 5 report the intention-to-treat (ITT) impacts on test scores, with various sets of controls. Test scores are standardized using the mean and standard deviation from control villages. Column 6 reports
the treatment-on-the-treated (TOT) impacts on test scores for enrolled children. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

score for all children aged 5 to 10 in control villages and divid-
ing by the standard deviation (47% and 31%, respectively).
Columns 2 to 5 report treatment effects with various sets of
controls. The outcomes are math score, language score, and
the total score. Based on the model with the full set of con-
trols, the program increased total test scores by 0.63 standard
deviations. Program impacts were similar for both subject test
scores. We also estimate the treatment-on-the-treated (TOT)
impact of enrollment on test scores (column 6), using the
treatment as an instrument for enrollment status. The pro-
gram increased total test score by 1.94 standard deviations
among children induced by the program to enroll in school,
and the effect was similar for the subjects.

In appendix table B1 we report results using as the outcome
the percent of test questions answered correctly. Columns 2
to 5 report the ITT estimates with various sets of controls,
and column 6 reports the TOT estimates with the full set of
controls. The ITT effect on total test score was a 19.8 percent-
age points increase, and the TOT effect was 60.9 percentage
points.

Figure 2 shows the full distribution of test scores across
the control and treatment groups. As is apparent, there is
a mass of students answering 0% of questions correctly in
control villages and 100% of questions in treatment villages,
which may lead us to underestimate the treatment effects.
We therefore estimate an item response theory (IRT) model,

using both MLE and Bayesian (EAP) methods.9 The results
of this analysis are given in appendix table B2. The results of
both EAP and MLE procedures are similar to those observed
using the standardized test score as the outcome, indicating
that ﬂoor and ceiling effects are not systematically biasing
our results.

We also examine program impacts on test scores by the
competency being tested (appendix table B3) and by child age
(appendix table B4). The treatment effect is generally stable
across different competencies. For child age, we report the
ITT and TOT test-score effects using as the outcome variables
both the percent of questions answered correctly (columns
4 and 6, respectively), as well as the standardized test score
measure (columns 5 and 7, respectively). The program effects
were generally similar across age groups.

C. Differential Impacts on School Enrollment and Test Scores

We also examine the impacts of the two subsidy treat-
ments disaggregated by gender (see table 5). Within control
villages, there is no gender differential in enrollment or grade

9The intuition for this method is that a latent skill parameter (θ) can
be estimated for each child based on the difﬁculty of correctly answered
questions, where the difﬁculty of a question is based on the correlation
between answering that question correctly and the overall test score (see
van der Linden & Hambleton, 2013).

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THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 5.—GENDER DIFFERENTIAL IMPACTS BY SUBSIDY TREATMENT

Uniform

Uniform × Female

Differentiated

Differentiated × Female

Female

N
R-squared
H01: Uniform = Differentiated

H02: Uniform × Female =
Differentiated × Female
H03: Uniform × Female =
–Differentiated × Female

H04: Uniform + Uniform × Female =

Differentiated + Differentiated × Female

Enrollment
(1)

0.335***
(0.066)
−0.038
(0.031)
0.316***
(0.068)
−0.001
(0.028)
0.000
(0.025)
11658
0.110
0.595
0.441
2.758
0.098
0.501
0.480
0.379
0.539

Highest Grade
Attained
(2)

0.415***
(0.135)
−0.099
(0.081)
0.375***
(0.134)
0.051
(0.063)
−0.003
(0.052)
11152
0.226
0.309
0.579
4.693
0.031
0.138
0.711
1.902
0.169

Test
Score
(3)

0.576***
(0.136)
0.087
(0.055)
0.636***
(0.137)
0.043
(0.059)
−0.086*
(0.049)
10376
0.204
0.933
0.335
1.164
0.282
1.484
0.225
0.052
0.820

F -stat
p-value
F -stat
p-value
F -stat
p-value
F -stat
p-value

This table reports gender-differential impacts on outcomes (for children aged 5–10) by subsidy treatment, with the full set of child and household controls and district ﬁxed effects. Uniform denotes the gender-uniform

subsidy treatment and Differentiated, the gender-differentiated subsidy treatment. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

attainment, though girls do score 0.086 standard deviations
lower on tests. We do not ﬁnd differential effects by subsidy
treatment, gender, or subsidy treatment and gender. How-
ever, girls experience larger improvements in test scores than
do boys, which nearly eliminates the test score differential,
though this difference is measured imprecisely (p-value =
0.225 for hypothesis test H03).

School Proximity and Educational Outcomes

We ﬁnd that treatment villages that lacked a nearby gov-
ernment school witnessed a 58 percentage point increase in
enrollment, whereas the presence of a government school
reduced the treatment effect to a 20 percentage point increase
in enrollment (results not shown). This suggests that the prin-
cipal mechanism driving improvements in educational out-
comes is the dramatic reduction in the distance to school,
which reduced the cost of enrollment.

To better understand the role of school proximity, we next
present ﬁgures displaying the relationship between school
proximity and educational outcomes. Figure 1 shows the re-
lationship between educational outcomes and the distance
to the nearest proposed program school site. The treatment
causes an upward shift in both enrollment and test score at
all distance from the proposed program school site. This
relationship is relatively similar across genders (appendix
ﬁgure A1).

In appendix ﬁgure A2, we plot the relationship between
educational outcomes and the distance to the nearest oper-
ational primary school of any type.10 Remarkably, there is

virtually no relationship between educational outcomes and
school distance in treatment villages, while in control vil-
lages, there is clear gradient between distance and both ed-
ucational outcomes. In addition, even when located within
very small distances of the nearest school, children in control
villages are less likely to be enrolled, and receive a lower test
score, than children in treatment villages.

Appendix ﬁgure A3 shows the relationship between ed-
ucational outcomes and distance to the nearest school, dis-
aggregated by village treatment status and child gender. In
treatment villages, boys and girls have virtually identical en-
rollment rates and test scores at all distances. In contrast,
in control villages, boys have better educational outcomes
than girls at distances more than 0.6 kilometer from the
nearest school. This suggests that program schools either
provide inputs (such as female teachers) more attractive to
female students than those of nonprogram schools, or that en-
trepreneurs have taken alternative measures to recruit female
students.

There are two likely explanations for the disparity across
control and treatment villages in the relationship between
distance and educational outcomes. First, because payment is
based on the number of students enrolled, entrepreneurs may
have taken measures to maximize enrollment. Alternatively,
it may be the case that program schools are perceived to
be relatively high quality and that the returns to education
therefore overwhelm the costs incurred in traveling greater
distances.

10We plot this relationship in control villages up to a distance of 1.5
kilometers and in program villages up to a distance of 1 kilometer due to
the small number of households in these villages located farther away.

We ﬁnd strong evidence that attributes other than the prox-
imity of program schools also contributed to program-school

School Quality and Educational Outcomes

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FIGURE 1.—SCHOOL PROXIMITY AND EDUCATIONAL OUTCOMES

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Panels 1.1 (1.2) plot the probability of enrollment (test score) for children aged 5 to 10 against the distance to the proposed program school site using a local polynomial regression. The sample is disaggregated by
treatment status.

enrollment and improvements in educational outcomes. As
noted above, approximately half of the villages had a nearby
government school at the time of the survey, and a smaller
number had other types of primary schools (appendix table
A3).11 However, not only do we ﬁnd a substantial increase
in enrollment even in villages with a proximate government

school, we also ﬁnd that children generally switched from
government to program schools when given the option.12

One likely reason for the preference for program schools
is their perceived quality. Indeed, a central motivation for
the use of a PPP design was the evidence found in earlier
research indicating that low-cost private schools in Pakistan
deliver better educational outcomes than government schools

11The percentages are 55% and 46% of control and treatment villages,
respectively. This difference is not statistically signiﬁcant and represents
2.5 additional villages with government schools across the entire sample of
38 control villages.

12Whereas an average of nineteen children were enrolled in government
schools in control villages, only three were enrolled in each treatment village
(appendix table A3), constituting 89% and 8% of enrolled children in the
control and treatment groups, respectively.

408

THE REVIEW OF ECONOMICS AND STATISTICS

FIGURE 2.—TREATMENT AND TEST SCORES

Figure 2 shows the distribution of test scores (for children aged 5 to 10) disaggregated by treatment status. Test scores are measured as the percentage of correct answers.

Program-
Enrolled
Children
(1)

0.723

0.717

0.744

Math Score

Language Score

Total Score

TABLE 6.—TEST SCORES BY SCHOOL TYPE

Enrolled Children

Difference:

Program-Enrolled –

Govt
(2)

0.250***
(0.088)
0.163***
(0.061)
0.221***
(0.077)

Priv
(3)

0.068
(0.272)
0.047
(0.159)
0.064
(0.230)

P-value
Govt = Priv
(4)

0.521

0.484

0.511

Govt-Enrolled
Children
Treatment Village–
Control Village
(5)

−0.015
(0.101)
0.004
(0.077)
−0.006
(0.091)

This table reports differences in mean standardized test scores (for children aged 5–10) according to the type of school children are enrolled in, with the full set of child and household controls and district ﬁxed
effects. Column 1 reports mean test scores for children enrolled in program schools. Columns 2 and 3 give the coefﬁcients for indicator variables denoting whether children are enrolled in government or private schools,
respectively. Column 4 gives the p-value for a test of equality of government and private school coefﬁcients. The sample is limited to children who either reside in a treatment village and are enrolled in a program
school, or who reside in a control village and are enrolled in either a government or private school. Column 5 limits the sample to children enrolled in a government school in either a treatment or control village and
reports the difference in test score across control and treatment villages. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

do (Andrabi et al., 2011, 2020). We therefore test whether
the advantages observed with private schools carry over to
program schools.

For this analysis, we compare mean test scores of children
enrolled in program schools to those of children enrolled in
proximate government (and private) schools in control vil-
lages. In table 6, column 1 reports mean test scores for pro-
gram schools; columns 2 and 3 report differences in mean
test scores between program schools and and government
and private schools (in control villages), respectively; and
column 4 reports p-values from tests of differences in mean
test scores between government and private schools. Children
in program schools scored 0.22 standard deviation higher on
the total test than those in government schools (0.25 standard
deviations higher on the mathematics test and 0.16 standard
deviation higher on the language test). In contrast, differences

in mean test scores between program and private schools were
small and statistically insigniﬁcant.

These comparisons do not causally identify differences in
quality between school types, as student-composition effects
may bias the estimates. For example, if program schools at-
tract students who would not otherwise have been enrolled
and if these students come from more socioeconomically dis-
advantaged backgrounds, the program-school effect on test
scores may be biased downward. In contrast, if the most
talented students in government schools switch to program
schools in treatment villages, the test scores of children in
program schools would overstate their quality.

The evidence is strongly supportive of the former hypothe-
sis. First, as previously noted, program schools attract nearly
all the children who would have otherwise been enrolled in
government schools, making it unlikely that the differential

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DELIVERING EDUCATION THROUGH PUBLIC-PRIVATE PARTNERSHIPS

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TABLE 7.—PROGRAM IMPACTS ON ASPIRATIONS

Civil servant

Doctor

Employed in Private enterprise

Engineer

Farmer

Housewife

Laborer

Landlord

Lawyer

Police/army/security

Raise livestock

Teacher

Marriage Age

Education Attainment (in years)

Control
(1)

0.139

0.124

0.014

0.009

0.060

0.149

0.012

0.008

0.009

0.108

0.011

0.318

18.751

9.023

Treatment –
Control
(2)

0.000
(0.036)
0.053**
(0.022)
0.002
(0.007)
0.023***
(0.005)
−0.043***
(0.015)
−0.067***
(0.025)
−0.003
(0.004)
0.000
(0.003)
0.004
(0.004)
−0.021
(0.017)
−0.004
(0.008)
0.048
(0.031)
0.125
(0.382)
1.380***
(0.530)

Female
(3)

−0.077**
(0.035)
−0.037**
(0.017)
−0.014*
(0.007)
0.002
(0.005)
−0.088***
(0.023)
0.289***
(0.048)
−0.007
(0.006)
−0.009*
(0.004)
−0.012**
(0.006)
−0.136***
(0.027)
0.006
(0.005)
0.103**
(0.049)
−0.988***
(0.216)
−0.603***
(0.190)

Treatment
(4)

0.005
(0.047)
0.064**
(0.025)
−0.002
(0.010)
0.031***
(0.005)
−0.074***
(0.025)
−0.011
(0.007)
−0.003
(0.005)
−0.001
(0.005)
0.002
(0.006)
−0.032
(0.029)
0.001
(0.007)
0.015
(0.028)
0.135
(0.428)
1.312**
(0.560)

Treatment ×
Female
(5)

−0.004
(0.037)
−0.021
(0.021)
0.011
(0.008)
−0.017**
(0.007)
0.071***
(0.023)
−0.140***
(0.051)
−0.001
(0.006)
0.003
(0.005)
0.004
(0.007)
0.030
(0.029)
−0.011**
(0.005)
0.065
(0.052)
0.079
(0.275)
0.190
(0.215)

This table reports program impacts on parental-reported aspirations for children aged 5–10, with the full set of child and household controls and district ﬁxed effects. Column 1 reports mean aspirations in control
villages for the indicated variables, and column 2 reports differences in mean aspirations between program and control villages. Columns 3 to 5 report coefﬁcients from regressions of the indicated variable on indicator
variables for girls, program status, and the interaction of the two. Treatment denotes pooled treatment. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%,
and ∗10%.

is due to cream skimming. As further evidence against child
sorting, in appendix table A4 (columns 4 and 8) we ﬁnd
that mean characteristics of government-school students are
largely similar across control and program villages.13

In addition, program schools have encouraged the
enrollment of socioeconomically disadvantaged students.
Appendix table A4 reports differences in household and child
characteristics across unenrolled and government-school stu-
dents in control villages (columns 1, 2, 5, and 6) and across
government-school and program-school students (columns 3
and 7). In control villages, government-school students came
from households where household heads had more years of
education (+1.6 years) and were less likely to be farmers
(−11.0 percentage points) and lived in better-quality accom-
modations (mud/thatch = −19.2 percentage points), relative
to unenrolled children. These differences are almost identical
to those between government-enrolled and program-enrolled
children, so that program-school students more closely re-
semble unenrolled children in control villages.

F. Aspirations

The program has substantial impacts on aspirations for
children. Table 7 reports impacts on aspirations for each child
aged 5–10 expressed by the adult respondent. Column 1 re-
ports means in control villages, column 2 reports the dif-
ferences in means between program and control villages,
and columns 3–5 estimate heterogeneous treatment effects
by gender.

Relative to their counterparts in control villages, program-
village households were more likely to desire that their
boys become doctors (+6.4 percentage points) and engineers
(+3.1 percentage points), and less likely to desire that they be-
come farmers (−7.4 percentage points). They were also more
likely to desire that their girls become teachers (+8.0 percent-
age points), and less likely to desire that they become house-
wives (−15.1 percentage points). Program-village house-
holds desired higher educational attainment for their boys
and girls (+1.3 and +1.5 years, respectively). There was no
effect of the treatment on the desired age of marriage.

13The principal exception is that government-school students in program
villages were slightly older (+0.3 years) than their counterparts in control
villages. This is presumably because some share of the younger children
who would have otherwise enrolled in government schools absent the pro-
gram selected program schools instead, skewing the age distribution slightly
upward.

VI. Program Cost-Effectiveness

In appendix C, we present estimates of

the cost-
effectiveness of the program under different assumptions.
Using the low and high values of annual cost per student,

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THE REVIEW OF ECONOMICS AND STATISTICS

we estimate cost-effectiveness values of 16 to 39 percent-
age points in school enrollment and 0.3 to 0.8 standard de-
viations in total test scores, both per $100 spent. Program
cost-effectiveness values associated with test scores appear
to be at the lower end of the range of similarly estimated cost-
effectiveness values for fourteen education interventions re-
ported by Evans and Popova (2016), and was superior only
to a conditional cash transfer program in Africa.

VII.

Structural Estimation of Program School Efﬁciency

We extend the analysis to assess the efﬁciency of the in-
put choices made by SEF and program-school operators by
asking whether the social planner could have improved on
the program solution, and if so, by how much and by what
means. We ﬁrst estimate the supply and demand for school
inputs and the social surplus generated by program schools.
Using the parameters of this model, we then determine the
inputs chosen by the social planner and estimate the share of
the potential social surplus captured by program schools.

The experiment provides a unique opportunity for conduct-
ing this analysis in a credible manner. In nonexperimental set-
tings, endogenous school placement could bias the estimated
parameters of the model. For example, unobservable village-
level educational preferences may be correlated with school
presence, selected inputs, and enrollment outcomes. Because
the experiment exogenously varies the placement of schools
across villages, it allows us to estimate the demand- and
supply-side parameters necessary for conducting the struc-
tural analysis.

A. Program School Inputs

Before presenting the structural estimation, we present de-
scriptive statistics of program school characteristics and com-
pare these to government schools in the study villages. This
analysis provides a preview for how program schools select
inputs for the purpose of of maximizing proﬁts. In addition,
we compare school characteristics across the gender-uniform
and gender-differentiated subsidy treatments, allowing us to
test whether entrepreneurs under the latter subsidy scheme
select inputs speciﬁcally for the purpose of attracting female
students.

In table 8, columns 1 and 3 report means and standard de-
viations for characteristics of program schools. Columns 2
and 4 report the differences in mean characteristics between
program and government schools. Differences are estimated
using a seemingly unrelated regressions (SUR) speciﬁcation
to account for within-school correlations in school character-
istics. Program schools were open 5 days per week, which
was 0.5 more days per week than government schools. Pro-
gram schools were more likely to use English as the medium
of instruction (+30.9 percentage points) and less likely to
use Sindhi (−37.0 percentage points). Physical infrastructure
was generally better in program schools than in government
schools: they were more likely to have an adequate number

of desks (+14.9 percentage points), drinking water (+30.1
percentage points), and toilets (+28.9 percentage points).

According to responses by headmasters, program schools
had a larger number of teachers than government schools
(+1 teacher) and a higher fraction of female teachers (+25.1
percentage points). There was also a higher fraction of teach-
ers with fewer than ﬁve years of teaching experience (+54.7
percentage points) and a smaller fraction with more than 10
years of teaching experience (−62.5 percentage points).

Based on information collected from individual teachers,
we ﬁnd that program-school teachers were more likely to
be female (+24.0 percentage points), were younger (−13.9
years), and received lower monthly salaries (−11,512 ru-
pees). In addition, they had fewer years of teaching expe-
rience (−11.4 years). And they spent a similar amount of
time engaged in various classroom activities as government-
school teachers.

It is important to note that there is substantial variation
in school inputs across program schools. This marks a key
difference from other PPP models, which generally involve
greater centralized control over the amenities offered by pub-
licly funded private schools. As we show subsequently, this
variation is key for conducting the structural analysis.

Appendix table A2 presents a comparison of school
characteristics across the gender-uniform and gender-
differentiated treatments. There are no differences between
the two, indicating that entrepreneurs receiving the higher
subsidy for girls have not selected different inputs in order to
attract additional female enrollment.

B. Program School Efﬁciency

Because program-school operators may have incentives
that are not perfectly aligned with those of the social plan-
ner, it is unclear that market incentives will lead them to
choose optimal school inputs. Consider the following model
of a program-school operator deciding which school inputs
to provide. As the operator is provided a subsidy based on
enrollment, let child demand for the school be denoted by
q(x) > 0, where x is an input and q(cid:3)(x) > 0. The cost of pro-
viding the inputs is given by a positive increasing function,
c(x). The social value of providing the inputs is given by
a positive increasing function, h(x); this function captures
both consumer surplus and broader societal beneﬁts from
children receiving an education. The ﬁrst-order condition for
the program-school operator is

pq(cid:3)(x) − c(cid:3)(x) = 0,

(2)

while the corresponding ﬁrst-order condition for the social
planner is

pq(cid:3)(x) − c(cid:3)(x) + h(cid:3)(x) = 0.

(3)

The difference between these two ﬁrst-order conditions is the
inclusion of the marginal social beneﬁt. In general, program-
school operators will fail to provide the socially optimal level

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DELIVERING EDUCATION THROUGH PUBLIC-PRIVATE PARTNERSHIPS

411

TABLE 8.—CHARACTERISTICS BY SCHOOL TYPE, GOVERNMENT

Characteristics from School Survey

Days Operational

Open Admission

Uniform Required

Tuition Required

Medium: Sindhi

Medium: English

Teacher Characteristics
Number of Teachers

Pct Female

Pct Postsecondary

Pct <5 Years Experience Pct 5–10>10 Years Experience

Average Teacher Absent ≥2 Days/Month

Amenities
Building

Number Classrooms

Sufﬁcient Desks

Drinking Water

Electricity

Toilet

Program
(1)

5.118
(1.378)
0.859
(0.348)
0.024
(0.152)
0.000
(0.000)
0.613
(0.487)
0.309
(0.462)

3.781
(1.594)
0.510
(0.412)
0.520
(0.562)
0.837
(0.247)
0.151
(0.234)
0.011
(0.057)
0.394
(0.489)

0.960
(0.195)
3.230
(1.413)
0.756
(0.430)
0.845
(0.362)
0.725
(0.447)
0.787
(0.410)

Govt –
Program
(2)

−0.505*
(0.285)
0.024
(0.050)
−0.024
(0.017)
0.000
(0.000)
0.370***
(0.050)
−0.309***
(0.045)

−0.946***
(0.339)
−0.251***
(0.069)
0.305**
(0.135)
−0.547***
(0.054)
−0.057
(0.040)
0.625***
(0.060)
0.035
(0.100)

−0.023
(0.038)
−0.468
(0.395)
−0.149*
(0.085)
−0.301***
(0.105)
−0.065
(0.071)
−0.289***
(0.108)

Students

Number Boys

Number Girls

Percent Female Students

Student-teacher Ratio

Program
(3)

88.684
(45.255)
71.343
(34.782)
0.448
(0.138)
44.274
(14.279)

Characteristics from Teacher Survey

Days Absent/Month

Female

Age

Education

Salary (1000s Rs)

Years Teaching

Years Teaching this School

Hours Teaching (per week)

Total

Teaching Whole Class

Teaching Small Group

Teaching Individual

Blackboard/Dictation

Classroom Management

Testing

Administrative

0.838
(1.121)
0.493
(0.424)
25.169
(4.165)
10.968
(0.763)
4.067
(1.258)
2.781
(1.272)
1.769
(0.848)

26.914
(6.708)
6.204
(4.671)
5.540
(4.661)
5.468
(4.832)
5.171
(4.034)
3.220
(2.631)
3.205
(2.760)
2.491
(1.892)

Govt –
Program
(4)

−18.623
(11.596)
−30.899***
(5.836)
−0.041
(0.049)
−0.283
(3.931)

0.168
(0.309)
−0.240***
(0.072)
13.878***
(1.446)
0.922***
(0.170)
11.512***
(1.007)
11.431***
(1.322)
4.933***
(0.959)

1.154
(1.852)
0.467
(0.800)
−0.366
(0.698)
0.333
(0.727)
−0.256
(0.721)
0.275
(0.459)
−0.440
(0.497)
0.345
(0.333)

This table reports differences in mean characteristics between program and government schools. The unit of observation is child-school (for children aged 5–10). Columns 1 and 3 report means and standard
deviations for program schools, and columns 2 and 4 differences in means between program and government schools. Differences are estimated using a seemingly unrelated regressions (SUR) speciﬁcation to account
for within-school correlations in school characteristics. Standard errors, reported in parentheses, are clustered at the village level. Statistically signiﬁcant at ∗∗∗1%, ∗∗5%, and ∗10%.

of inputs because they do not capture the complete rents gen-
erated by their provision.

Our analysis consists of three steps. First, we estimate
a discrete choice model of household demand for schools
(referred to as “child” demand). Second, we estimate the op-
portunity costs of providing school inputs using a simple re-
vealed preference argument. Third, we calculate the social
value of school-input conﬁgurations based on surplus accru-
ing to students, school-operator input costs, and the social
value of education.

In order to solve the model, we impose the additional
assumption that both student demand and input costs are
homogeneous across villages. The latter assumption is neces-
sary due to a lack of variation in program school characteris-
tics within a given village, as there was only a single program
school per village and their characteristics were ﬁxed during

the sample period. Therefore, while some parameters of the
demand model are identiﬁed through student-school interac-
tions within a village, all of the cost parameters are identiﬁed
only via cross-village variation. Second, we also assume that
program-school operators and the social planner both have
full information about demand and costs.

We begin by estimating the demand for schooling by chil-
dren in the villages. This allows us to estimate consumer
surplus, compute how that surplus changes with changes in
school inputs, and predict enrollments under counterfactual
school conﬁgurations. We estimate demand using a standard
logit random utility framework. Each child makes a single
choice from a set of schools, J, where the utility for child i
of choice j ∈ J is given by

ui j = Xi jβ + (cid:2)i j.

(4)

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TABLE 9.—SCHOOLING DEMAND ESTIMATES

Enrollment

Constant

Toilets and/or Drinking Water

Student Female

Student Age

Distance from Home to School

Pct Teachers with <5 Years Teaching Pct Teachers Postsecondary Pct Female Pct Time Teaching Avg Teacher Absent ≥2 Day>Download pdf