DISCUSSION PAPER SERIESIZA DP No. 16167

Impact of Early Childhood School
Intervention on Enrollment and Learning
Outcomes: Evaluation of a Public Programin India
Nilanjan Bhattacharya
Debayan Pakrashi
Sarani Saha
Soham Sahoo

MAY 2023

DISCUSSION PAPER SERIESIZA DP No. 16167

Impact of Early Childhood School
Intervention on Enrollment and Learning
Outcomes: Evaluation of a Public Programin India
Nilanjan Bhattacharya

Sarani Saha

Debayan Pakrashi

Soham Sahoo

Indian Institute of Technology Kanpur

Indian Institute of Technology Kanpur

Indian Institute of Technology Kanpur andIndian Statistical Institute Kolkata

Indian Institute of Management Bangaloreand IZA

MAY 2023

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IZA DP No. 16167

MAY 2023

ABSTRACT
Impact of Early Childhood School
Intervention on Enrollment and Learning
Outcomes: Evaluation of a Public Programin India*
We evaluate the impact of introducing a pre-primary schooling program in government
schools in the Indian state of West Bengal in 2013 on children’s early enrolment in schools
and subsequent test scores. Using double difference, triple difference, and synthetic
control methodologies, we find that the program significantly increased enrolment in the
pre-primary sections of the government schools. However, the rise in enrolment did nottranslate into improved performance of the students. Analyzing the test scores, we findthat after the program’s introduction, both math and reading scores of treated children did
not improve compared to the control group. We attribute this result to the deteriorating
physical and learning infrastructure in the state government schools, captured via a decline
in the availability of classrooms and teachers.
JEL Classification:

I21, I25, I28

Keywords:

pre-primary education, learning outcomes, enrollment

Corresponding author:
Soham Sahoo
Indian Institute of Management Bangalore
Bannerghatta Road
Bengaluru 560076
India
E-mail: soham.sahoo@iimb.ac.in* For helpful comments, we thank Abhiroop Mukhopadhyay, Sourabh B Paul, and participants of Annual
Conference on Economic Growth and Development 2022 at Indian Statistical Institute, Delhi, IGIDR Biennial
Conference 2022, Asian and Australasian Society of Labour Economics Conference 2022, Contemporary Issues in
Development Economics Conference 2022 at Jadavpur University, and Indian Public Policy Network Conference 2022
at IIM Ahmedabad. We also thank the ASER Centre for sharing the data with us.

1. Introduction
Good quality early childhood education (ECE) is important in ensuring fair education
opportunities for everyone. ECE can lead to better learning outcomes (Berlinski et al., 2009;
Britto et al., 2017), equitable learning opportunities for marginalized groups (Berlinski et al.,2008; Elango et al., 2015; Heckman, 2013), improved health outcomes (Elango et al., 2015),
and even long-term economic benefits extending to adulthood (Gertler et al., 2017; Garcia etal., 2014; Heckman et al., 2010). These findings have given rise to the concept of dynamic
complementarity in skill formation. Children exposed to early childhood programs may benefit
more from later human capital investments (Cunha & Heckman, 2007). Given the benefits, it
is obvious that there has been a rapid increase in ECE programs globally in the recent past
(Behrman & Urzúa, 2013; Cascio, 2015; Nores & Barnett, 2010; Sayre et al., 2015; Wotipka
et al., 2017). Providing decent early childhood care, and pre-primary education by 2030, as
envisioned in target 4.2 of the Sustainable Development Goal 4 (UN, 2015), also echoes its
importance.
Despite the high return to early childhood investment in education, an estimated 175 million,
which corresponds to almost fifty percent of the world’s children between 3 and 6 years (preprimary age group), are deprived of pre-primary schooling (UNICEF, 2019). Only one in every
five children has been exposed to pre-primary education in developing and low-income
countries (UNICEF, 2019). This deprivation is an obstacle to achieving the children’sdevelopmental potential. The analysis of an ECE intervention in a low and middle-income
context is essential as these countries account for most of the children in the world.
In this paper, we evaluate the causal impact of a government-run free pre-primary program
implemented in 2013 on children’s enrolment and test scores in the Indian state of West Bengal.
The effect of such a program is more relevant in the context of India, where quality ECE
programs are not available to the young masses, particularly children coming fromunderprivileged families. To explore the pathway, we also analyze if schools' infrastructure has
any role that ultimately affects the children’s learning outcomes. Our study also aligns with
India's recently announced, ‘National Education Policy 2020’ (NEP, 2020). In India's existing
10+2 education structure, children in the age group of 3-6 are not included. Presently, a childis admitted to class 1 at the age of 6. The NEP 2020 proposes a 5+3+3+4 framework, coveringchildren from age three. It sets a strong foundation for Early Childhood Education (ECE) aimed
at the holistic development of the child. Focussing on a government-run free pre-primary2

program, we analyze the effectiveness of universal provisioning of quality early childhood
development as envisioned in the NEP 2020.
In particular, we analyze the causal impact of a government-run, pre-primary program onenrolment and learning outcomes by exploiting the exogenous variation in exposure to preprimary schooling brought about by implementing the program in 2013. We employ doubledifference and triple-difference methodologies to estimate the effects of the program onenrolment and test scores. We have also used the synthetic control methodology to rule out any
bias in results that can occur due to the ad-hoc nature of the states selected for the tripledifference regressions.
Our main findings suggest that the program has succeeded in increasing enrolment but has no
impact on learning outcomes captured via math and reading scores. Results from thedifference-in-differences show that the program increased the number of government schools
with a dedicated pre-primary section compared to the private schools in West Bengal andgovernment schools in the control states of Bihar, Jharkhand, and Orissa. A similar result for
the enrolment of pre-primary students has been found. The change in pre-primary enrolment
in the government schools in West Bengal, therefore, showed a massive increase after the
program was introduced in 2013 compared to their private counterparts and the control states.
The success story of enrolment, however, did not translate into better learning outcomes. A
comparison of pre-primary children between the treated and control cohort shows that treatedcohort children from government schools in West Bengal do not achieve any better learning
skills when compared to the control cohort.
We attribute this result to the worsening infrastructure of the state government schools in West
Bengal. Comparing the number of pre-primary teachers and total teachers between thegovernment schools in West Bengal and the government schools in its neighboring states, wefind that number of pre-primary teachers increased. In contrast, the percentage change in thenumber of total teachers reported a decline in the government schools of West Bengal. The
number of pre-primary teachers increased since the existing pool of teachers was assigned toteach at the pre-primary level. Therefore, even though the reported number of pre-primaryteachers was high in government schools in West Bengal, the school infrastructure did notcomplement the higher enrolment in pre-primary by recruiting more teachers, leaving theschools with more students but fewer teachers.

3This paper contributes to the growing literature on early childhood education in the context ofdeveloping countries in several important ways. First, existing studies have found impactful
results by focusing on well-executed ECE programs in developed countries. However, ECE
programs in low- and middle-income countries significantly differ from the developed ones
(Behrman & Urzúa, 2013). In our paper, we find the evidence by focussing on a governmentrun free pre-primary program in a lower-middle income developing country context. Second,
the targeted populations come from disadvantaged backgrounds, which throws the question of
the generalizability of the evidence (Baker, 2011). In our paper, the program has a free anduniversal rollout aimed at the general population. Third, existing evidence mostly comes froma few randomized-control trials that face small sample size issues and therefore suffer from the
problem of generalizations (Heckman, 2011). We eliminate this limitation by using thegovernment-administered DISE (District Information System for Education) and ASER
(Annual Status for Education Report) data which are nationally representative databases withlarge sample sizes. Therefore, the significant contribution of this paper lies in evaluating thegovernment’s pre-primary program, which is the first study to causally evaluate a specific earlychildhood government intervention in the context of India, to the best of our knowledge, andsuggest appropriate policy reforms.
The rest of the paper is organized as follows. Section 2 describes the program in detail, followed
by a discussion on the datasets and some descriptive statistics in Section 3. The empiricalstrategy used in the paper is discussed in detail in Section 4. Next, we discuss the results ofthe paper estimated using the specification mentioned earlier in Section 5. In Section 6, we
further analyze the possible reasons for the obtained results and finally conclude the paper with
Section 7.
2. Description of the Program
India has one of the world’s largest education systems, with the number of schools as high as
1.5 million. It also includes 8.5 million teachers, and 250 million students (UNICEF, 2018).
However, there is a downside to the story when it comes to learning proficiency, as almost halfof the primary school children, estimated to be nearly 50 million – could not achieve gradelevel standards (NCERT, 2017). For example, the percentage of grade 3 children who could
read a grade 2 level text was only 27.2% in 2018 (ASER, 2018). This learning crisis is mainlycommon in the initial years of schooling. Most children enter primary school without any priorpreparation. In addition, there is widespread inequity in the education system of India due to4

various socio-economic factors. In rural India, there exists a learning gap between first
generation learners with low family income and children from wealthier families with educatedparents, by the time they attain the age of seven (Alcott & Rose, 2017). A quality earlychildhood education program can close this learning gap.
Recognizing its importance, section 11 of the ‘The Right of Children to Free and Compulsory
Education Act, 2009’ of India states that “to prepare children above the age of three years forelementary education and to provide early childhood care and education for all children until
they complete the age of six years, the appropriate government may make necessary
arrangement for providing free pre-school education for such children”. The national legal
framework provided a guarantee through this act, and the government of West Bengal (WB)
introduced a free one-year pre-primary education in government schools in the academic
session of 2013.1 According to the new rules, a student aged between 5 and 6 years on the firstday of the academic session (i.e., on the 1st of January 2013) would be eligible to take admission
in the pre-primary section. Apart from the pre-primary section, the age criteria for admission
to different classes were also revised. Before 2013, five-year-old children could get admission
in grade 1. However, starting in 2013, only six-year-old students could enroll in grade 1 of thegovernment schools.2
The program guidelines also mentioned that separate seating arrangements should be madeavailable to the pre-primary students as far as possible. If, due to the unavailability of space,schools cannot accommodate them in a separate classroom, they could sit with grade 1 students.
The existing teachers in government schools should continue teaching until new teachers arerecruited. The pre-primary students are also entitled to benefits under the mid-day meal
scheme. After having the mid-day meal, pre-primary students are allowed to leave the school.

3. Data & Descriptive Statistics
We use data from the Annual Status of Education Report (ASER), a yearly survey conducted
to assess the education status among children in almost all the rural districts of India from 2009
to 2018. The survey covers a random sample of about 20 households from each of the 30

1

This policy was implemented in both government owned and government aided schools. We combine thesetwo categories and refer to them as ‘government’ or ‘public’ schools throughout the paper. Further details of thepolicy can be found at: https://wbxpress.com/files/2012/11/Admission_Age.pdf
2
https://wbxpress.com/files/2012/07/Age_Admission.pdf
5villages selected from the rural districts of India. From each household selected, all children in
the age group 5 to 16 are surveyed and administered with the learning test module.
The survey administers basic arithmetic and reading tests and uses the same tools for all
children across the states. These test scores have been extensively used in the literature(Chakraborty & Jayaraman, 2019; Das & Sarkhel, 2023; Lahoti & Sahoo, 2020). In the ASER
data, the assessment of reading skills has ordinal ranking with an increased level of difficulty—
recognition of letters, reading of words, reading a short paragraph (a grade 1 level text), andreading a short story (a grade 2 level text). On the other hand, the arithmetic test is also basedon similar levels—recognition of single-digit numbers, recognition of double-digit numbers,
subtraction of two-digit numbers with a borrowing, and division of a three-digit number by one
digit.
In our analysis, we have considered ASER scores as a measure of learning outcomes.Since
these scores are independently collected by ASER, they are standardized and uniform acrossthe schools; hence they have better comparability than any test conducted by the schools
themselves. The ASER survey also collects child, household, and village-level information that
we use as control variables. Household level characteristics include possession of a cemented
house; presence of electricity; possession of a toilet; possession of a television; and the total
number of family members. Child-level characteristics include the child’s age, and gender.
Village-level factors include presence of a private school, a private health clinic, a bank, or acemented road in the village.
However, ASER does not include any information on whether the child in question is enrolled
in the pre-primary section of a school, so it is impossible to identify the children's pre-primaryeducation status. To analyze the program's effect on enrolment at the pre-primary level, we
have used District Information System for Education (DISE) database. The DISE dataset
gathers detailed information on different school-level characteristics including school
infrastructure, facilities, enrolment, and teachers for all the districts. In our analysis, we used
DISE data from 2009 to 2017. The sample in our study consists of 541,801 government schools
and 75,637 private schools from West Bengal. We also include 1,447,223 government schools
and 42,871 private schools from the control states. For learning outcomes, our estimation
sample includes 6,550 children in 5-6 years cohort in government schools and 1,755 childrenof the same cohort in private schools of West Bengal. For the control states, 55,863 children in6

government schools in the 5-6 years age cohort and 8,108 children in private schools have been
considered. The summary statistics are reported in Table 1.
To begin, we use the enrolment and infrastructure data from DISE to see if the program’s
implementation in 2013 increased the number of government schools with a pre-primarysection and the number of students enrolled in the pre-primary section in West Bengal. See
Figure 1.
[Insert Figure 1]
Interestingly, we notice an immediate jump in the number of government schools with a preprimary section and the number of students enrolled in pre-primary for 2013. While the numberof government schools with pre-primary drastically increased to almost 60,000, the total
number of students enrolled in the pre-primary sections of these government schools recorded
a figure of nearly 800,000 in 2013. Next, we compare the number of government and private
schools with pre-primary section in West Bengal and pre-primary students enrolled in theseschools to the neighboring states of Bihar, Jharkhand, and Orissa to check if these states also
reported any similar trends. We consider this group of states as a comparison group to West
Bengal since they share their geographical boundary with West Bengal and have many
similarities in the social, economic and cultural domain. Figure 1 suggests that the huge
expansion of pre-primary education in government schools post-2013 took place only in thestate of West Bengal and not in the neighboring states. We did not observe such a massivechange in private schools, although there is an increasing trend of pre-primary education in
private schools. We also presented child-level learning outcomes using math and reading scoresfrom ASER data in Figure 2. However, we find nothing similar to the pattern in Figure 1.
[Insert Figure 2]
Figure 1 further motivates us to dig deeper into estimating the causal impact of the pre-primary
program.
4. Empirical Methodology
We exploit the exogenous age restriction related to the admission criteria in pre-primary thatmakes the identification of the causal impact feasible3. In particular, we use a difference-in3

Prior to implementing the pre-primary schooling program, the entry age for government school students wasfive years for grade I. After the program’s introduction, the entry age for children in schools remained the same.
7

differences (DD) method to causally estimate the program’s impact on pre-primary enrolment
and learning outcomes. We use DISE data to assess the impact program on enrolment at theschool level and rely on ASER data while focusing on the learning outcomes at the individuallevel.
The identification of the causal impact of the pre-primary program is challenging due toselection bias. A situation might arise where only motivated parents send their children to preprimary schooling. In such a case, children who attend pre-primary school might possesscharacteristics that promote better school performance, which could lead to a spurious positive
correlation between pre-primary attainment and learning outcomes. However, as the programin discussion over here has a universal roll-out at the state level and the age at entry remains
the same, the question of motivated parents sending their children for pre-primary education
does not arise. The chance of positive selection bias is, therefore, ruled out. We hypothesizethat this one year of extra schooling in the form of pre-primary education for government
schools can have an impact on these children’s academic achievement, measured using ASER
administered reading and math scores.
4.1. Difference-in-differences
The government schools in West Bengal are considered as the treated group as the program
was implemented in these schools only. The control group may comprise of the government
schools in the neighbouring states that are not exposed to the program. Alternatively, theprivate schools from West Bengal may also form the control group as the policy was
implemented only for the government schools in the state. Accordingly, we have two different
specifications for the double difference regression. The specifications are the same for bothsets of regressions for enrolment and learning outcomes. The only difference is that thedependent variable for enrolment includes the availability of a pre-primary section andenrolment in pre-primary, which are measured at the school level, whereas the test scores for
5-6 years old individual children are considered as learning outcomes.
In the first specification, the difference between the government schools of West Bengal andthe control states after the implementation of the program (i.e., the year 2013) is compared to

However, these five-year-old children were eligible to be enrolled in the pre-primary section of the schools only.
They would enjoy a year of pre-primary education before they are promoted to grade I at the age of six.
8the same types of schools before the program was implemented. The corresponding differencein-differences (DD) estimating equation is as follows:(1) 𝑦𝑖𝑑𝑠𝑡 = 𝛽0 + 𝛽1 . 𝑝𝑜𝑠𝑡𝑡 + 𝛽2 . (𝑝𝑜𝑠𝑡𝑡 × 𝑤𝑏𝑠 ) + 𝜗𝑋𝑖𝑑𝑠𝑡 + 𝜕𝑑𝑠 + 𝛾𝑡 + (𝜕𝑑𝑠 × 𝑡) + 𝜖𝑖𝑑𝑠𝑡
Here, 𝑌𝑖 𝑑𝑠𝑡 is the outcome variable of interest (availability of pre-primary section in the schooland number of students enrolled in pre-primary sections captured via logarithm of total numberof pre-primary students) for the 𝑖-th school from district d of state s measured at time 𝑡. For
learning outcomes, 𝑌𝑖 𝑑𝑠𝑡 corresponds to the test score (math and reading score) of the i-thchild from district d of state s at time t. The first outcome variable for enrolment is theavailability of pre-primary section. It is a dummy that takes the value 0 if the 𝑖-th school does
not have a pre-primary section and the value 1 if it has a pre-primary section. The second
variable represents the number of pre-primary students enrolled in 𝑖-th school.
For learning outcomes, 𝑌𝑖 𝑑𝑠𝑡 takes integer values from 0 to 4, where 0 means no learning skills
and 4 implies the highest level of learning. ‘𝑤𝑏𝑠 ’ is a dummy that takes the value 1 if the i-th
observation comes from West Bengal. All the standard errors are clustered at the district level.
Even though the policy was implemented at the state level, the number of clusters is too small
if we cluster the standard errors at the state level. To overcome this difficulty, we have also
used the wild cluster bootstrap method, and the corresponding p values and standard errors arereported within parentheses in all the regression tables.
The ‘𝑝𝑜𝑠𝑡𝑡 ’ dummy compares the outcomes for the post-program years (2013 or after) when
it assumes the value 1 to the same before 2013 (for ‘𝑝𝑜𝑠𝑡𝑡 ’=0). The coefficient of interest is
𝛽2 , which captures the intent-to-treat (ITT) estimate of the impact of the program. 𝑋𝑖𝑑𝑠𝑡
includes all the child-, household-, school- and village-level characteristics. For school level
regression on enrolment, 𝑋𝑖𝑑𝑠𝑡 includes the total number of classrooms, the total number ofteachers, and availability of electricity and playground in the school.
For child level regression on learning outcomes, 𝑋𝑖𝑑𝑠𝑡 controls for the household variables forthe i-th child that include the number of household members, presence of pucca house; presenceof electricity; possession of a TV, and presence of a toilet. 𝑋𝑖𝑑𝑠𝑡 also controls for child-level
characteristics, including the child’s age, whether the mother went to school, and village-level
characteristics, such as presence of a government primary school, private school in the village,
village post office, bank, and pucca road. We also include district (𝜕𝑑𝑠 ), year (𝛾𝑡 ) fixed effects
and district-specific linear trend (𝜕𝑑𝑠 × 𝑡).

9In the previous regression, the double difference compares the difference across thegovernment schools in West Bengal and the control states before and after the policy. However,another way of constructing the DD specification is to compare changes between the treatedgovernment schools and the untreated private schools in West Bengal. To consider thisalternative model, we introduce a ‘ 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 ’ dummy variable and estimate the following
regression restricting the sample to only the schools of West Bengal.
(2) 𝑦𝑖 𝑑𝑡 = 𝛽0 + 𝛽1 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 + 𝛽2 𝑝𝑜𝑠𝑡𝑡 + 𝛽3 (𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 × 𝑝𝑜𝑠𝑡𝑡 ) + 𝜗𝑋𝑖𝑑𝑡 + 𝛾𝑡
+ 𝜕𝑑 + (𝜕𝑑 × 𝑡) + 𝜖𝑖 𝑑𝑡
The dummy ‘ 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 ’ takes the value 1 if the i-th school is a public school run by thegovernment and a value 0 if it is a private school. Similarly, for regressions on learning
outcomes, ‘ 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 ’ takes the value 1 if the i-th child from district d goes to agovernment school and 0 otherwise.
The validity of the DD estimator relies on the parallel-trend assumption, which says that theoutcome variable should move parallelly between the treated and control group in the absenceof the treatment (Angrist and Pischke, 2009). In the context of our study, if the trends of theoutcome variables between the treated and control schools (children) evolved similarly before
the program, they would continue to do so in the post-periods in the absence of the pre-primary
program. To test the parallel trend assumption, we conducted year wise event study.
Essentially, the equations remain the same except that the ‘𝑝𝑜𝑠𝑡𝑡 ’ dummy is now replaced bycontinuous years. The event study graphs are presented in the appendix section.
4.2 Triple difference
Even though we get an estimate of the program using the DD method, certain broader trends
can still affect the DD estimate. Therefore, a triple difference or difference-in-difference-indifferences approach (DDD) can be adopted to refine our results and eliminate any other trends
that might be present. Similar to Muralidharan & Prakash (2017), we make use of a triple
Difference (DDD) regression by comparing the DD estimates from West Bengal to a group ofneighboring states, including Bihar, Orissa, and Jharkhand.
Once we have appropriately chosen the control states, we can estimate the program's impact
by comparing the changes in outcomes between pre- and post-periods across government and10private schools in West Bengal to the above-mentioned control states. We estimate thefollowing triple-difference (DDD) model:(3) 𝑦𝑖𝑑𝑠𝑡 = 𝛽0 + 𝛽1 . 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑𝑠 + 𝛽2 . 𝑝𝑜𝑠𝑡𝑡 + 𝛽3 . (𝑤𝑏𝑠 × 𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑𝑠 × 𝑝𝑜𝑠𝑡𝑡 )+ 𝛽4 . (𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑𝑠 × 𝑝𝑜𝑠𝑡𝑡 ) + 𝛽5 . ( 𝑝𝑜𝑠𝑡𝑡 × 𝑤𝑏𝑠 )+ 𝛽6 . (𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑𝑠 × 𝑤𝑏𝑠 ) + 𝜗𝑋𝑖𝑑𝑠𝑡 + 𝛾𝑡 + 𝜕𝑑𝑠 + (𝜕𝑑𝑠 × 𝑡) + 𝜖𝑖 𝑑𝑠𝑡
Here, the ‘𝑤𝑏𝑠 ’ dummy takes the value 1 if the school in question is in the state of West Bengal
and 0 otherwise. The rest of the variables used in equation (3) has already been discussed while
discussing the DD model. In this model, 𝛽3, the ITT estimate is our coefficient of interest.
4.3 Synthetic control
In our DD and DDD specifications, we selected the control states on the basis of geographicalproximity and similarities in cultural and socioeconomic characteristics. However, there could
still be pre-existing differences between these groups of states, making the comparison
inaccurate. To rule out any such possibility, we use the synthetic control method (SCM)
following the recent literature (Abadie & Gardeazabal, 2003; Abadie et al, 2010; Peri &Yesenov, 2019). In our context, SCM can potentially be a superior method than the classic
difference-in-differences because it takes a data-driven approach to obtain a linear combinationof states to form a suitable control group for WB.4 We used the ASER and DISE databases
from 2009 to 2018 for this purpose. Using the ASER data, we calculated the state-level annual
estimates of learning outcomes among government school children aged 5-6 years.
To obtain the synthetic control estimates for the pre-primary enrolment, we have used statelevel annual estimates of the proportion of schools with pre-primary sections and the total
number of students in pre-primary sections by using DISE data. The predictors of the preprimary enrolment are the state-wise average number of classrooms in government schools, theproportion of government schools with electricity, and playgrounds. For learning outcomes,
predictor variables for test scores are the proportion of households that are fully cemented,
average household size, proportion of households having a TV, proportion of households withelectricity, proportion of households with toilet, proportion of mothers who went to school atsome point of time, proportion of villages with the pucca road, the proportion of villages with

4

The control states for the synthetic control method are Himachal Pradesh, Punjab, Uttarakhand, Haryana,
Rajasthan, Uttar Pradesh, Bihar, Sikkim, Nagaland, Manipur, Mizoram, Tripura, Meghalaya, Assam, Jharkhand,Orissa, Chhattisgarh, Madhya Pradesh, Gujarat, Maharashtra, Andhra Pradesh, Karnataka, Kerala and Tamil
Nadu.
11

a bank, proportion of villages with post office, the proportion of villages with private schooland proportion of villages with government primary school. The outcome variables from 2009
to 2012 are also used as predictors.5
5. Results
We present the results related to the impact of the pre-primary program on enrolment in Section
5.1 and then learning outcomes in Section 5.2.
5.1 The impact on enrolment
We begin this section with the results from the regression Equation 1 in Panel A of Table 2.
The coefficient 𝛽3 associated with the interaction term between ‘𝑤𝑏𝑠 ’ and ‘𝑝𝑜𝑠𝑡𝑡 ’ is positiveand highly significant for both the dependent variables considered, with a 91 percentage points
increase in the probability of a government school in WB having a pre-primary section after
2013 and (e2.06-1) × 100 ≈ 684 percent increase in the difference in pre-primary enrolment
between government schools in WB and its neighboring states after 2013.
[Insert Table 2 here]
Next, we investigated if the government schools in West Bengal are significantly different fromthe private schools in the state in terms of availability of pre-primary sections and enrolment
in them. We estimated Equation 2 and presented the results in Panel B of Table 2. The
coefficient 𝛽2 associated with the interaction term between ‘𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 ’ and ‘𝑝𝑜𝑠𝑡𝑡 ’dummy is positive and highly significant for both the outcome variables. The result shows thatthe probability of having a pre-primary section in government schools vis-à-vis private schools
in West Bengal after 2013 increased by more than 58 percentage points. On the other hand, thedifference in pre-primary enrolment between government and private schools in the state shows(e1.41-1) × 100 ≈ 309 percent increase after the program was introduced.
Finally, we employed the triple difference method by estimating Equation 3, and the findings
from Panel C of Table 3 confirm a positive significant impact of the program on both theavailability of and enrollment in the pre-primary section of government schools. The differencein the probability of having a pre-primary section in a government school vis-à-vis a private
school after 2013 between West Bengal and its neighbouring states increased by 72 percentage
points compared to the same difference before 2013. Similarly, the impact on pre-primary5

The weights for the individual states and predictor balance between the treated and the synthetic group arereported in Appendix Tables 1-4 for both enrolment and learning outcomes.
12enrollment is estimated to be (e1.77-1) × 100 ≈ 487 percent. So, the DD and DDD regression
results suggest that the pre-primary program successfully increased enrolment and availabilityof pre-primary education in government schools.
5.2 The impact on learning outcomesFirst, we consider children in the pre-primary age group in government schools of West Bengal
and compare them to the children of the same age group from government schools in the control
states. In doing so, we estimate equation 1 for learning outcomes. The results presented incolumns 2 and 4 of Panel A in Table 3 suggest that the coefficient associated with theinteraction term between ‘𝑤𝑏𝑠 ’ and ‘𝑝𝑜𝑠𝑡𝑡 ’ is negative and significant for both the test scores.
However, the event study graphs in Panels A and B in Figure A2 show that trends for 2011 and2012 drive the decline in test scores. This implies that reading and math scores of government
school pre-primary students in West Bengal were significantly higher than the same cohort ofchildren in control states for 2011 and 2012 only. We cannot attribute the negative andsignificant coefficient to the impact of the program only.
[Insert Table 3 here]
Next, we bring in the private schools of West Bengal and investigate if the test scores ofgovernment school pre-primary children in the state are significantly different from thoseprivate school children. The coefficients associated with the interaction term between ‘𝑝𝑜𝑠𝑡𝑡 ’and ‘𝑔𝑜𝑣𝑡𝑠𝑐ℎ𝑜𝑜𝑙𝑖𝑑 ’ are found to be negative and statistically insignificant for both raw testscores in Panel B of Table 3. So, government school students of WB in the pre-primary age
group do not perform significantly better in math and reading tests than children from the same
category in private schools.
Finally, we consider the DDD model presented by equation 3. Panel C of Table 3 shows thatthe coefficient of the triple interaction term is statistically not significant. Thus, the major
findings from the regressions on learning outcomes indicate that both math and reading scores
did not improve among the government school children after the program's implementation.
5.3 Results from synthetic control analysis
In the synthetic control method (SCM), we compare the government schools in WB to those inits synthetic control counterpart. The synthetic control methodology results reaffirm ourfindings from the previous analyses using the first DD regression specification. First, we plot13the trajectory of the dependent variables for both WB and its synthetic counterpart. Next, we
take the difference in the outcome variable between WB and the synthetic control states andtrace the path of this gap over the years before and after the program.
We also adopted a bias-corrected approach of the SCM as there can be bias in the estimatedmarginal treatment effects because of discrepancies between the values of the predictor
variable in each treated unit and its synthetic control donors (Wiltshire, 2021). Similar to theapproach in Abadie and Imbens (2011) to address inexact matching on predictor variables, abias-correction method has been proposed in the recent literature (Abadie & L’Hour, 2021;
Ben-Michael et al., 2021). We call this later one the ‘Bias-corrected’ estimates and the original
one ‘Classic’ estimates (Abadie et al., 2010). We present the ‘gaps’ or the effect graphs withthe ‘Classic’ and ‘Bias-corrected’ estimates.6[Insert Figure 3 here]
Panels A and C of Figure 3 present the trends in the percentage of government schools withpre-primary and the number of pre-primary students in government schools, respectively.
Panels B and D of Figure 3 show the corresponding gaps in the outcome variable between West
Bengal and the synthetic control state. Our estimate of the effect of the pre-primary programon enrolment and learning outcomes is the difference between the outcome variables (bothenrolment and learning scores) in WB and its synthetic version after the pre-primary programin 2013. Immediately after the implementation, the two lines of pre-primary availability andpre-primary enrolment diverge noticeably. We see a significant increase in pre-primaryenrolment (both availability of the pre-primary section and number of pre-primary students)
following the program's implementation in WB compared to the synthetic state.
For reading and mathematics scores, we used the same set of predictors to generate a similar
figure as above. Panels A and C of Figure 4 shows that both math and reading scores did notchange significantly in WB after the program's implementation.
[Insert Figure 4 here]
The dotted line corresponds to the synthetic state. As we can see the lines of math and readingscore for WB do not significantly diverge from the synthetic state, The gap between the twolines, essentially gives us the effect of the program. Panels B and D in Figure 4 demonstrate

6

These bias-corrected graphs are created using Stata’s ‘allsynth’ command (Wiltshire, 2021).
14

that the pre-primary program did not impact math and learning scores as it is very close to thezero mark.
We also conducted a series of placebo run by iteration. In each iteration, we choose one of thecontrol states and reassign the program intervention to that state. By doing so, we shift WB to
the donor pool. We estimate the impact associated with each placebo. For each control state,
we obtain different values of the impact, eventually leading to a distribution of estimatedimpacts for the donor pool. The figures for the placebo run are given in the appendix.
6. Potential Mechanism for the Decline in Learning Outcome: School Infrastructure
Our findings show that the program does not have any impact on the test scores. We
hypothesize that the null impact may be due to structural factors like school facilities and theavailability of teachers. For instance, increased enrolment without a commensurate
improvement in the availability of facilities and teachers might result in congestion externality
and have an adverse effect on academic skills.
Using the DISE data, first, we compared indicators of school infrastructure in government
schools in WB to the same category of schools in the control states. We considered indicators
like the average number of classrooms, the average number of teachers in the pre-primarysection and the total number of teachers. Next, we compared the government schools in WB to
the private schools in the state. Finally, the differences between government and private schools
in WB are compared to the same difference in the control states.
[Insert Table 4]
Correspondingly, we used all three specifications for DD and DDD. The outcome variables
were the logarithm of the total number of classrooms, the logarithm of number of teachers in
pre-primary, and the logarithm of the total number of teachers. The result from Panel A of
Table 4 shows that the difference in the number of classrooms in government schools between
WB and the control states after the program’s introduction declined by (e.09-1) × 100 ≈ 9 percent
compared to the same difference before the program was introduced. The most important result
is the comparison between columns 2 and 3 in Panel A. Even though the difference in thenumber of pre-primary teachers in government schools between West Bengal and the control
states increased by (e.18-1) × 100 ≈ 20 percent after the introduction of the program, thedifference in the number of total teachers decreased by (e.04-1) × 100 ≈ 4 percent during thesame time. It implies that the government schools in WB used the existing pool of teachers to15

teach at pre-primary sections without recruiting additional teachers. In fact, the growth inteacher recruitment for the government schools in the state was much less than the same
category of schools in the control states. Similar stories come up from Panels B and C of Table
4. The difference in the number of total classrooms and the difference in the number of total
teachers between the government and private schools in WB declined by (e.12-1) × 100 ≈ 13
percent and (e.13-1) × 100 ≈ 14 percent, respectively after the program was introduced. The
triple difference estimate from Panel C also reports a decline of (e.47-1) × 100 ≈ 60 percent in
the difference in the number of total teachers between government and private schools in WB
and the control states after 2013, whereas the difference in the number of pre-primary teachers
across the same group increased by (e.29-1) × 100 ≈ 34 percent.
We also adopted the synthetic control methodology again to rule out any bias in our results.
Results from the synthetic control methodology are presented in Figure 5.
[Insert Figure 5]
It is evident from Figure 5 (Panel A-F) that infrastructure has deteriorated more in thegovernment schools of WB after the introduction of pre-primary. The result from synthetic
control reaffirms that although enrolment has increased, the average number of teachers ingovernment schools is less than its synthetic counterpart. The result is in tune with the groundreality where the pre-primary students sit with students from higher standards. They do notreceive any extra attention from the teachers. As a result, they fail to develop any learning skills
even though they enjoy one extra year of schooling in pre-primary before being promoted toclass 1.
7. Conclusion
Our estimates of the program indicate that it has a massive role in increasing pre-primaryenrolment. Hence, the program has successfully sent children at their nascent age to schools.
While this effect is prevalent across the state, we also observe that the program fails to have an
impact on the test scores of children.
Despite the positive influence on enrolment, we found no improvement in learning ability (bothmath as well as reading score). This phenomenon can be attributed to the declining school
infrastructure that has taken place in West Bengal over the years after the program wasannounced. The announcements made by the state have not been reflected in the improvementof school infrastructure. Extant literature shows the importance of learning environment where16

classroom overcrowding can negatively affect student achievement by hampering the teaching
and learning process (Angrist & Lavy, 1999; Case & Deaton, 1999). The decline in theavailability of classrooms and teachers potentially accounts for the non-significant impact onlearning outcomes in government schools.
Suppose the sole objective of the pre-primary program was to make children familiar with theschool environment and make it a habit for them to attend classes. Our findings strongly
suggest that the program has indeed been quite successful in increasing pre-primary enrolment.
From the labor market perspective, a pre-primary program can also frees up mothers’ time fromchildcare activities. Thus, such a program can have a positive spillover effect through a higher
female labour force participation rate (Halim et al., 2022). However, if the benefits of preprimary program in the form of better learning outcomes are to be realized, further steps suchas heavy investments in school infrastructure needs to be undertaken.

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20Figure 1: Trends in enrolment
Panel A: Availability of pre-primary in government
schools

Panel B: Pre-primary enrolment in government schools

Panel C: Availability of pre-primary in private Schools

Panel D: Pre-primary enrolment in private schools

Notes: DISE data from 2009-2017 were used for these outcomes.

21

Figure 2: Trends in learning outcomes
Panel A: Average math score of 5-6 years old government
school students

Panel B: Average reading score of 5-6 years old government
school students

Panel C: Average math score of 5-6 years old private
school students

Panel D: Average reading score of 5-6 years old government
school students

Notes: ASER data from 2009-2018 were used for learning outcomes.

22Figure 3: Synthetic control analysis for enrolment

Panel A: Availability of pre-primary in government
schools of West Bengal and synthetic control

Panel B: Gap between West Bengal and synthetic
control in the availability of pre-primary ingovernment schools

Panel C: Pre-primary enrolment in government schoolsof West Bengal and synthetic control

Panel D: Gap between West Bengal and synthetic
control in pre-primary enrolment in government
schools

Note: The synthetic control method was used to generate this figure. DISE data from 2009 to 2017 were used.
The predictor variables are the state-wise total number of pre-primary students (availability of pre-primary) in
government schools (for 2009-2012), the state-wise average number of classrooms in government schools, theproportion of government schools with electricity, and playgrounds. The difference in the outcome variable(number of government schools with pre-primary, number of students in pre-primary of government schools)
between West Bengal and its synthetic control counterpart is referred to as the ‘gap’.

23

Figure 4: Synthetic control analysis for learning outcomes
Panel A: Math score of 5-6 years old in government
schools of West Bengal and synthetic control

Panel B: Gap in math score of 5-6 years old ingovernment schools of West Bengal and synthetic
control

Panel C: Reading score of 5-6 years old in government
schools of West Bengal and synthetic control

Panel D: Gap in reading score of 5-6 years old ingovernment schools of West Bengal and synthetic
control

Note: The synthetic control method was used to generate the figures. Only children aged 5 and 6 were considered.
ASER data from 2009 to 2018 were used. The predictor variables are average learning score (math, reading)(2009-2012), the state-wise proportion of households that are fully cemented, average household size, theproportion of households having a TV, proportion of households with electricity, proportion of households withtoilet, proportion of mothers who went to school at some point of time, proportion of villages with pucca road,proportion of villages with a bank, proportion of villages with post office, proportion of villages with private
school and proportion of villages with government primary school. Difference in the outcome variable (mathscore of government school pre-primary children, reading score of government school pre-primary children)
between West Bengal and its synthetic control counterpart is referred to as ‘gap’.

24Figure 5: Synthetic control analysis for school infrastructure
Panel A: Average number of classrooms in government
schools with pre-primary

Panel B: Gap in number of classrooms in government
schools with pre-primary between West Bengal andsynthetic control

Panel C: Average number of pre-primary teachers ingovernment schools

Panel D: Gap in number of pre-primary teachers ingovernment schools between West Bengal and synthetic
control

Panel E: Average number of teachers in government
schools

Panel F: Gap in number of teachers in government
schools between West Bengal and synthetic control

Note: The synthetic control method was used to generate this figure. DISE data from 2009 to 2017 were used.
The predictor variables are state-wise total number of pre-primary students in government schools (for the years
2009-2012), state-wise average number of classrooms in government schools, proportion of government schools
with electricity, and playground.
25

Table 1: Summary Statistics
West Bengal
Government schools
Private schools

Control states
Government schools
Private schools

N

Mean

SD

N

Mean

SD

N

Mean

SD

N

Mean

SD

541,801

0.58

0.49

75,6370.64

0.48

1,447,223

0.04

0.21

42,8710.70

0.46

541,801

9.19

13.46

75,637

34.77

46.20

1,447,223

1.12

8.82

42,871

42.23

50.11

541,801

0.47

0.50

75,6370.62

0.49

1,447,223

0.16

0.36

42,8710.75

0.43

541,801

0.34

0.48

75,6370.39

0.49

1,447,223

0.29

0.45

42,8710.66

0.47

541,801

3.51

1.63

75,6375.50

3.97

1,447,223

4.08

2.98

42,871

9.46

6.38

541,801

3.58

1.84

75,637

7.24

5.13

1,447,223

4.07

3.14

42,871

10.44

9.42

541,801

0.26

0.83

75,6371.80

2.60

1,447,223

0.05

0.39

42,871

2.36

2.76

School-level
characteristics
Availability of
pre-primary
Number of
students in preprimary
Availability ofelectricity
Availability of
playgroundTotalclassrooms inthe school
Total teacher
in the school
Number of
pre-primaryteachers

Child learning outcomes (5-6 years)
Math score
Reading score
GenderHouseholdlevel
characteristics
Householdpucca or not
Household size
Availability of
toiletAvailability ofelectricity
Possession ofTV
Mother went toschool

6,550
6,550
6,550

1.03
1.10
0.50

0.91
1.06
0.50

1,755
1,755
1,755

1.52
1.60
0.55

0.94
1.09
0.50

55,86355,86355,863

0.81
0.80
0.53

0.94
1.03
0.50

8,108
8,108
8,108

1.33
1.39
0.60

1.05
1.27
0.49

6,550

0.20

0.40

1,755

0.42

0.49

55,863

0.22

0.42

8,108

0.50

0.50

6,550
6,550

5.94
0.47

2.65
0.50

1,755
1,755

6.03
0.77

2.98
0.42

55,86355,863

6.84
0.20

3.00
0.40

8,108
8,108

7.21
0.50

3.50
0.50

6,550

0.72

0.45

1,755

0.88

0.32

55,863

0.53

0.50

8,108

0.79

0.40

6,550

0.39

0.49

1,755

0.70

0.46

55,863

0.20

0.40

8,108

0.51

0.50

6,550

0.66

0.47

1,755

0.86

0.34

55,863

0.42

0.49

8,108

0.74

0.44

Village-level
characteristics
Availability of
6,550
0.58
0.49
1,755
0.74
0.46
55,863
0.67
0.47
8,108
0.79
0.40
pucca road
Availability of
6,550
0.36
0.48
1,755
0.48
0.50
55,863
0.33
0.47
8,108
0.41
0.49
post office
Availability of
6,550
0.24
0.42
1,755
0.32
0.47
55,863
0.18
0.39
8,108
0.32
0.47
bank
Availability of
6,550
0.95
0.21
1,755
0.94
0.23
55,863
0.92
0.26
8,108
0.93
0.25
government
schoolAvailability of
6,550
0.27
0.44
1,755
0.53
0.50
55,863
0.26
0.44
8,108
0.47
0.50
private school
Notes: ‘N’ denotes the number of observations in each category and ‘SD’ represents the corresponding standard deviation of that group.

26

Table 2: DD and DDD results for availability of pre-primary and enrolment in preprimary level(1)
Availability of
pre-primary schools(2)
Log (1+number of
pre-primary students)

0.91***(0.04)(p val=0.02, ci[0.54, 1.43])
Yes
Yes
Yes
Yes
1,989,024
0.75

2.06***(0.11)(p val=0.06, ci[-1.04, 4.23])
Yes
Yes
Yes
Yes
1,989,024
0.65-0.38***(0.05)
0.58***(0.05)(p val=0.00, ci [0.44, 0.70])
Yes
Yes
Yes
Yes
617,438
0.78-1.16***(0.17)
1.41***(0.18)(p val=0.00, ci [0.96, 1.80])
Yes
Yes
Yes
Yes
617,438
0.58

Panel A: DD Specification 1
West Bengal x Post

School-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Panel B: DD Specification 2
Government school
Government school x Post

School-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Panel C: DDD Specification
Government school-1.74***-0.52***(0.11)(0.02)
0.27
West Bengal x Post
0.17***(0.18)(0.05)-0.46***
Post x Government school-0.15***(0.10)(0.02)
0.38*
Government school x West Bengal
0.12**(0.21)(0.05)
1.77***
West Bengal x Government school x Post
0.72***(0.21)(0.06)(p val=0.08, ci[-1.28, 4.59])(p val=0.06, ci [-0.07, 1.10])
Yes
School level controls
Yes
Yes
Year fixed effectYes
Yes
District fixed effectYes
Yes
District-specific linear trendYes
2,107,532
No of Observations
2,107,532
0.59
R Squared
0.72
Notes: The school-level control variables include the total number of classrooms, the total number of teachers, and the availability of electricity
and playground in the school. The difference in number of observations across Panels A, B and C is due to the choice of control group. In PanelA, only the government schools of WB and the control states have been considered. In Panel B, only the government and private schools of
WB has been considered. In Panel C, the government as well as the private schools are considered for both WB and the control states. Standard
errors are clustered at the district level. ***=significant at 1% level, **=significant at 5% level, *=significant at 10% level. p-value and standard
errors using the wild cluster bootstrap method are given in the parenthesis. Source: DISE 2009–17; authors’ own calculations.

27

Table 3: DD and DDD results for learning outcomes
Math score

Reading score

(1)

(2)

(3)

(4)

-0.24***(0.05)(p val=0.64., ci [-7.68,7.16])
No
No
No
Yes
Yes
Yes
62,4130.09-0.26***(0.05)(p val=0.62, ci [-8.83,5.05])
Yes
Yes
Yes
Yes
Yes
Yes
62,4130.13-0.11(0.07)(p val=0.56, ci [-7.55,9.13])
No
No
No
Yes
Yes
Yes
62,4130.10-0.13**(0.06)(p val=0.56, ci [-10.22,10.48])
Yes
Yes
Yes
Yes
Yes
Yes
62,4130.14-0.48***(0.05)-0.04(0.04)(p val=0.40, ci [-0.12, 0.05])
No
No
No
Yes
Yes
Yes
8,305
0.13

-0.32***(0.05)-0.04(0.04)(p val=0.36, ci [-0.12, 0.05])
Yes
Yes
Yes
Yes
Yes
Yes
8,305
0.21

-0.49***(0.06)-0.10(0.06)(p val=0.13, ci [-0.23, 0.03])
No
No
No
Yes
Yes
Yes
8,305
0.10

-0.31***(0.06)-0.10(0.07)(p val=0.15, ci [-0.26, 0.05])
Yes
Yes
Yes
Yes
Yes
Yes
8,305
0.19

-0.49***(0.03)-0.24***(0.07)-0.11***(0.04)
0.02(0.06)
0.07(0.05)(p val=0.59, ci [-1.93,2.17])
No
No
No
Yes
Yes
Yes
72,2760.11-0.36***(0.03)-0.26***(0.06)-0.10***(0.03)
0.01(0.05)
0.05(0.05)(p val=0.61, ci [-1.60,1.35])
Yes
Yes
Yes
Yes
Yes
Yes
72,2760.16-0.55***(0.04)-0.10(0.09)-0.13***(0.04)
0.06(0.06)
0.03(0.07)(p val=0.50, ci [-1.66,3.13])
No
No
No
Yes
Yes
Yes
72,2760.12-0.40***(0.03)-0.13(0.09)-0.11***(0.04)
0.06(0.06)
0.01(0.08)(p val=0.45, ci [-2.06,1.70])
Yes
Yes
Yes
Yes
Yes
Yes
72,276
0.17

Panel A: DD Specification 1
West Bengal x Post

Household-level controlsChild-level controls
Village-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Panel B: DD Specification 2
Government school
Government school x Post

Household-level controlsChild-level controls
Village-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Panel C: DDD Specification
Government school
West Bengal x Post
Post x Government school
Government school x West Bengal
West Bengal x Government school x Post

Household-level controlsChild-level controls
Village-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Note: Household controls include the number of household members, whether the house was pucca or not; whether it had electricity; possession of a TV, presence of a toilet.
Child-level characteristics include the child’s age, whether the mother went to school. Village-level factors controlled for include whether the village has a government primaryschool, private school, village post office, bank, pucca road in the village. linear regression coefficients are presented with clustered standard errors at the district level given inparenthesis. Child aged 5 and 6 years have been considered for analysis. In Panel A, only the government school children of WB and the control states have been considered. In
Panel B, only the government and private school children of WB has been considered. In Panel C, the government as well as the private school children are considered for both
WB and the control states. ***=significant at 1% level, **=significant at 5% level, *=significant at 10% level. p-value and standard errors using the wild cluster bootstrap method
are given in the parenthesis. Source: ASER 2009–18; authors’ own calculations

28

Table 4: DD and DDD results for school infrastructure

Panel A: DD Specification 1
West Bengal x Post

School-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared
Panel B: DD Specification 2
Government school
Government school x Post

School-level controls
Year fixed effectDistrict fixed effectDistrict-specific linear trend
No of Observations
R Squared(1)
Log(1+Total number ofclassrooms)(2)
Log(1+number of preprimary teachers)(3)
Log(1+number of total
teachers)-0.09***(0.02)(p val=0.19, ci [-0.39,0.85])
Yes
Yes
Yes
Yes
1,989,024
0.37

0.18***(0.04)(p val=0.06., ci [-0.28,0.67])
Yes
Yes
Yes
Yes
1,989,0240.18-0.04*(0.02)(p val=0.28., ci [-0.13,0.18])
Yes
Yes
Yes
Yes
1,989,024
0.47

0.09***(0.03)-0.12***(0.02)(p val=0.00, ci [-0.17,-0.08])
Yes
Yes
Yes
Yes
617,438
0.33-0.46***(0.06)
0.07(0.07)(p val=0.37, ci [-0.08,0.24])
Yes
Yes
Yes
Yes
617,438
0.28-0.22***(0.04)-0.13***(0.03)(p val=0.68, ci [-0.02,0.03])
Yes
Yes
Yes
Yes
617,438
0.42

Panel C: DDD Specification
Government school-0.01-0.62***-0.22***(0.05)(0.03)(0.07)-0.03-0.11*
0.42***
West Bengal x Post
(0.07)(0.06)(0.13)-0.03-0.26***
0.32***
Post x Government school(0.07)(0.03)(0.12)
0.14***
0.09
0.03
Government school x West Bengal(0.05)(0.07)(0.08)-0.07
0.29***-0.47***
West Bengal x Government school x Post
(0.07)(0.07)(0.12)(p val=0.75, ci [-0.74, 1.64])(p val=0.00, ci [0.16, 0.53])(p val=0.63, ci [-3.71, 0.54])
Yes
Yes
Yes
School level controls
Yes
Yes
Yes
Year fixed effect
Yes
Yes
Yes
District fixed effect
Yes
Yes
Yes
District-specific linear trend
2,107,532
2,107,532
2,107,532
No of Observations
0.35
0.32
0.44
R Squared
Note: The school-level control variables for the second regression include the total number of classrooms, and the availability of electricity andplayground in the school. In the first regression, since the dependent variable is total number of classrooms itself, it has been excluded from thelist of control variables. The Standard errors are clustered at the district level. For difference in number of observations across Panels A, B andC, see notes to Table 2. ***=significant at 1% level, **=significant at 5% level, *=significant at 10% level. p-value and standard errors using thewild cluster bootstrap method are given in the parenthesis Source: DISE 2009–17; authors’ own calculations.

29

Appendix
Figure A1: Event study analysis for pre-primary enrolment
Panel A: Difference in availability of pre-primary ingovernment schools between West Bengal and control
states (DD Specification I)

Panel B: Difference in pre-primary enrolment ingovernment schools between West Bengal and control
states (DD Specification I)

Panel C: Difference in availability of pre-primary
between government and private schools in West Bengal((DD Specification II))

Panel D: Difference in pre-primary enrolment between
government and private schools in West Bengal ((DDSpecification II))

Panel E: Difference of the difference in availability of preprimary between government and private schools in West
Bengal to the same difference in the control states (DDD
Specification)

Panel F: Difference of the difference in pre-primaryenrolment between government and private schools inWest Bengal to the same difference in the control states(DDD Specification)

30

Figure A2: Event study analysis for learning outcomes of 5-6 year old children
Panel A: Difference in math score among government
school children between West Bengal and control states(DD Specification I)

Panel B: Difference in reading score among government
school children between West Bengal and control states(DD Specification I)

Panel C: Difference in math score between government
and private school children in West Bengal ((DDSpecification II))

Panel D: Difference in reading score between
government and private school children in West Bengal((DD Specification II))

Panel E: Difference of the difference of math score
between government and private school children in West
Bengal to the same difference in the control states (DDD
Specification)

Panel F: Difference of the difference of reading score
between government and private school children in West
Bengal to the same difference in the control states (DDD
Specification)

31

Figure A3: Event study analysis for school infrastructure
Panel A: Difference in number ofclassrooms in government schools
between West Bengal and control states

Panel B: Difference in number of preprimary teachers in government schools
between West Bengal and control states

Panel C: Difference in total teacher
in government schools between West
Bengal and control states

Panel D: Difference in number ofclassrooms between government andprivate schools in West Bengal

Panel E: Difference in number of preprimary teachers between government
and private schools in West Bengal

Panel F: Difference in total teacher
between government and private
schools in West Bengal

Panel G: Difference in number ofclassrooms between government andprivate schools in West Bengal to thesame difference in the control states

Panel H: Difference in number of preprimary teachers between government
and private schools in West Bengal to thesame difference in the control states

Panel I: Difference in total teacher
between government and private
schools in West Bengal to the samedifference in the control states

32

Figure A4: Synthetic control gap in pre-primary enrolment
Panel A: Gap between West Bengal and synthetic control
in the availability of pre-primary in government schools(classic)

Panel B: Gap between West Bengal and synthetic
control in the availability of pre-primary ingovernment schools (bias-corrected)

Panel C: Gap between West Bengal and synthetic control
in pre-primary enrolment in government schools (classic)

Panel D: Gap between West Bengal and synthetic
control in pre-primary enrolment in government
schools (bias-corrected)

33

Figure A5: Synthetic control gap in learning outcomes
Panel A: Gap in math score in government school
children of West Bengal and synthetic control(classic)

Panel B: Gap in math score in government school
children of West Bengal and synthetic control (biascorrected)

Panel C: Gap in reading score in government
school children of West Bengal and synthetic
control (classic)

Panel D: Gap in reading score in government school
children of West Bengal and synthetic control (biascorrected)

34

Figure A6: Synthetic control gap in school infrastructure
Panel A: Gap in number of classrooms between
government schools in West Bengal and control states(classic)

Panel B: Gap in number of classrooms between
government schools in West Bengal and control states
Bias corrected)

Panel C: Gap in number of pre-primary teachers
between government schools in West Bengal andcontrol states (classic)

Panel D: Gap in number of pre-primary teachers between
government schools in West Bengal and control states(bias corrected)

Panel E: Gap in number of teachers between
government schools in West Bengal and control states(classic)

Panel F: Gap in number of teachers between government
schools in West Bengal and control states (classic)

35

Table A1: Weights and predictor balance for availability of pre-primary
Panel A: Weights for the states
State name
Himachal Pradesh
PunjabUttarakhand
HaryanaRajasthan
Uttar Pradesh
Bihar
SikkimNagalandManipur
TripuraMeghalayaAssam
JharkhandOrissaChhattisgarh
Madhya Pradesh
Gujarat
Maharashtra
Karnataka
KeralaTamil Nadu

Weight
0
0
0
0
0
0
0.17
0
0
0
0.47
0
0
0
0.36
0
0
0
0
0
0
0

Panel B: Predictor balance
Percentage of schools with pre-primary
Percentage of government schools with electricity
Percentage of government schools with playground
Average number of pre-primary teachers in government schools
Average number of teachers in government schools
Average number of classrooms in government schools
All predictor variables are averaged over the period 2009 to 2012

36

Treated
2.20
32
36
0.04
5.55
4.34

Synthetic
2.24
14
43
0.03
5.54
4.31

Table A2: Weights and predictor balance for number of pre-primary students
Panel A: Weights for the states
State name
Himachal Pradesh
PunjabUttarakhand
HaryanaRajasthan
Uttar Pradesh
Bihar
SikkimNagalandManipur
TripuraMeghalayaAssam
JharkhandOrissaChhattisgarh
Madhya Pradesh
Gujarat
Maharashtra
Karnataka
KeralaTamilnadu

Weight
0
0
0
0
0
0
0.08
0
0
0
0.38
0
0
0
0.50
0
0
0
0
0
0.04
0

Panel B: Predictor balance
Average number of pre-primary students
Percentage of government schools with electricity
Percentage of government schools with playground
Average number of pre-primary teachers in government schools
Average number of teachers in government schools
Average number of classrooms in government schools
All predictor variables are averaged over the period 2009 to 201237

Treated
1.31
32
0.36
0.04
5.55
4.34

Synthetic
1.30
18
0.41
0.05
5.30
4.34

Table A3: Weights and predictor balance for math score
Panel A: Weights for the states

State name
Himachal Pradesh
PunjabUttarakhand
HaryanaRajasthan
Uttar Pradesh
Bihar
SikkimNagalandManipurMizoram
TripuraMeghalayaAssam
JharkhandOrissaChhattisgarh
Madhya Pradesh
Gujarat
Maharashtra
Andhra Pradesh
Karnataka
KeralaTamil Nadu

Weight
0
0
0
0
0
0
0.15
0
0.34
0
0
0
0
0.38
0
0
0
0
0
0
0
0
0.13
0

Panel B: Predictor Balance

Math score
Proportion of pucca households
Household size
Proportion of households with toilet
Proportion of household with electricity
Proportion of household with TV
Proportion of villages with pucca road
Proportion of villages with post office
Proportion of villages with bank
Proportion of villages with private school
Proportion of villages with government primary school
Proportion of household where mother went to school
All predictor variables are averaged over the period 2009 to 2012

38

Treated
1.17
0.14
6.12
0.39
0.58
0.31
0.46
0.36
0.19
0.28
0.95
0.59

Synthetic
1.17
0.14
6.10
0.55
0.65
0.34
0.49
0.36
0.19
0.41
0.95
0.68

Table A4: Weights and predictor balance for reading score
Panel A: Weights for the states

State name
Himachal Pradesh
PunjabUttarakhand
HaryanaRajasthan
Uttar Pradesh
Bihar
SikkimNagalandManipurMizoram
TripuraMeghalayaAssam
JharkhandOrissaChhattisgarh
Madhya Pradesh
Gujarat
Maharashtra
Andhra Pradesh
Karnataka
KeralaTamil Nadu

Weight
0.37
0
0
0
0
0
0
0
0.21
0.06
0
0
0
0.06
0.24
0
0
0
0
0
0
0
0.06
0

Panel B: Predictor Balance
Reading score
Proportion of pucca households
Household size
Proportion of households with toilet
Proportion of household with electricity
Proportion of household with TV
Proportion of villages with pucca road
Proportion of villages with post office
Proportion of villages with bank
Proportion of villages with private school
Proportion of villages with government primary school
Proportion of household where mother went to school
All predictor variables are averaged over the period 2009 to 201239

Treated
1.19
0.14
6.12
0.39
0.58
0.31
0.46
0.36
0.19
0.28
0.95
0.59

Synthetic
1.19
0.23
6.17
0.56
0.82
0.51
0.49
0.35
0.17
0.33
0.90
0.68