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Learning Achievements in India: A Study of
Primary Education in Rajasthan
Sangeeta Goyal
South Asia Human Development
The World Bank
Human Development Unit South Asia Region May 2007 Document of The World Bank
Preliminary version: for comments
Abstract
This paper presents findings from a study of learning outcomes in grades IV and V of government, private aided and private unaided schools in Rajasthan. Approximately 6000 students were tested in 200 schools in three tests – two language tests (Reading Comprehension and Word Meaning) and one test in mathematics. The survey also collected information on student, family background and school characteristics. The survey results showed that overall learning levels were low absolutely and relatively in government schools. The average percentage correct scores in government schools ranged from 40-50 percentage points, a quarter to a fifth below the average scores in private schools.
The analysis of determinants of learning outcomes provided a number of
important insights. Firstly, the school attended by the child has the most substantive impact on the quality of learning. School fixed effects account for more than half the variation in test scores. Once we take school fixed effects into account, the type of school management and other school related characteristics lose all explanatory power. Secondly, private schools, whether aided or unaided, outperform public schools. Thirdly, there is large variation in the performance of public schools - a section of public schools has better test scores than the representative private school.
Findings from the study provide directions for policy interventions and for future
research for more evidence-based policy making. The variation in public school performance and the dominance of school specific factors in explaining test scores imply the importance of raising quality of schools in the public sector. Moreover, not only are the learning outcomes low, learning gains from one grade to another are flat with nearly constant and large dispersion of scores around the mean in both grades. Therefore, to achieve any given learning target, improving school quality would require increasing the amount of incremental learning that takes place in each grade. Government schools also do not use their resources effectively leading to inefficient allocation of public resources to provide education. Teachers in private schools get much lower salaries than teachers in government schools and private schools are 1.5-2 times more cost-effective than government schools in terms of learning gains per rupee. This indicates there is much room for improving the cost-effectiveness of public sector education provision. Among other determinants, we find that social group, household wealth and mother’s literacy have significant but small impacts on test scores. Note: The survey work for this study was funded by the EPDF Trust Fund (Project ID: P0554559-SPN-TF054642).
2
Preliminary version: for comments
1. Introduction
Countries seeking to increase the level and pace of economic growth, and to raise the
productivity and earnings of their citizens, have increasingly focused on increasing the
quantity and quality of their people’s educational attainment. Consequently, growth in
school enrollment has been phenomenal across the world in the last four to five decades.
However, even as the quantity of education has increased over time, the quality of
education, especially primary education, remains a cause for serious concern. The
experience of many developing countries including India is that children do not master
basic literacy and numeracy even after four and five years of schooling.1
In this paper we examine the determinants of learning achievements of students of
grades IV and V in language and math in government, private aided and private unaided
schools in Rajasthan. In India as in most developing countries, the public sector is the
dominant provider of primary education. Government managed and financed primary
schools are in principle ‘freely’ accessible by any child of school going age. According to
official data, nearly ninety percent of all primary school going children in India attend
government schools. Alongside free public education, there is a growing sector of fee-
charging private unaided schools. These schools are managed and financed privately,
often along profit-making principles. Children, even from poor families, are attending
these schools in large and increasing numbers. There also exists a hybrid variety of
schools in India known as government aided/private aided primary schools. These
schools are managed by the private sector but largely financed by the government.2
3
1 The Sarva Shiksha Abhiyan (SSA) or Universalization of Elementary Education Mission, which is the flagship mission of the Government of India in the education sector, was introduced in 2001 to ensure all eligible children go through eight years of schooling. In the first few years of the program, the focus was on improving access to schools, increasing enrollments and reducing drop-out of children from elementary grades. Increasingly, SSA is now focusing on quality of education in schools, including teacher presence and activity in class-rooms, teacher training and assessment systems. 2 All the schools that are a part of this study have ‘recognition’ from the government. There is also a fast growing sector of fee-charging private unaided unrecognized schools.
Preliminary version: for comments
According to the Census of India 2001, the western state of Rajasthan had a
population of 56.5 million with a literacy rate of 60.4% as compared to the national
average of 64.1%. Of the 9.96 million children in the age-group 6-14 years enrolled in
school in 2004-05, nearly 22% went to private schools. Historically, Rajasthan has had
lower than average economic growth and human development indicators.
This study is based on primary data collected in the state in early 2006. We use
percentage of correctly answered questions in language and mathematics tests as proxies
for cognitive competencies in literacy and numeracy, the true underlying learning
objectives. Because we use percentage scored in any particular test and not acquisition of
particular competencies to rank performance, the data is better suited for drawing
inferences about the relative effectiveness of different determinants of learning
achievements. In Section 2, we review the theoretical and empirical literature on the
determinants of learning outcomes pertinent to the Indian context. In Section 3, we
describe the sampling methodology, the data and the analytical framework used in this
study. In Section 4, we report the unadjusted average learning levels across school types,
genders, social groups and rural-urban locations. Section 5 provides the results from our
empirical analyses of the determinants of learning outcomes. In Section 6, we take a look
at the labor market for teachers in Rajasthan. Section 7 provides some policy implications
based on the findings of this study and concludes. We will use the terms government
school and public school interchangeably in this paper.
2. Background and Previous Literature
The question of how to improve the quality of educational attainment in schools has
become one of utmost importance to policy-makers. It is generating a large body of
research, previously in developed, but now also in developing countries. Most empirical
studies of determinants of learning achievement relate measurable school characteristics
and student and family background characteristics to learning outcomes.
4
Preliminary version: for comments
A number of studies show that school attended (school fixed effects) explains a large
amount of the variation in learning outcomes. Das et al (2005) in their study of primary
schools in Pakistan find that nearly 50% of all the variation in test scores in Pakistan can
be attributed to school fixed effects. A study similar to this one for the state of Orissa also
shows that between 50-60% of the variation in test scores is determined by school fixed
effects (World Bank, forthcoming)
School fixed effect plausibly captures (observable and unobservable) dimensions
of school quality. Standard proxies for school quality used in the literature are school
inputs such as pupil teacher ratio, the use of multi-grade classes, quantity and quality of
school infrastructure, teacher numbers and characteristics, provision of mid-day-meals
etc. The relation between observable schooling inputs and student outcomes however is
not consistent and in general weak in most studies. Empirical evidence from developed
countries generally does not find any effect of pupil-teacher ratio. Lavy and Angrist
(1999) for Israel and Urqiola (2006) for rural Bolivia, however find that smaller class-
sizes benefits students learning attainment. Regarding the use of multi-grade classrooms,
the general belief is that they are detrimental to learning though research from outside
India is non-conclusive (Miller, 1990). There are few studies that include the share of
graduate teachers and the share of non-regular teachers as controlling characteristics for
schools. It is difficult to predict the direction of the net effects of these characteristics.
Teachers with higher educational qualifications and more secure employment can be
expected to be more motivated to perform. There is also evidence that they are also more
prone to be more absent from schools (Chaudhury et al, 2004).
The type of school management, i.e. whether the school is a government, private
aided or private unaided school, has also been found to be a significant predictor of
educational outcomes in the Indian context. Access to schools is a necessary but not a
sufficient condition for ensuring the development of cognitive competencies. According
to the empirical evidence, private unaided schools in general outperform public schools
5
Preliminary version: for comments
(Kingdon, 1996; Smith et al, 2005; Tooley and Dixon, 2006). Few systematic studies
compare private aided schools quality with other types.
That individual student and family background characteristics influence school
outcomes even after controlling for school related factors is undisputed, even though the
research does not provide conclusive evidence regarding effects. Some studies find that
boys and children belonging to the upper castes perform better (Dreze and Kingdon,
2001; Aggarwal, 2000; Filmer et al, 1997). Household wealth and parents’ education
also have positive correlations with children’s educational outcomes (Pritchett and
Filmer, 1998).
3. Data Description and Empirical Methodology
Primary data was collected for the study in the three months from February to April
2006 by A C Nielsen ORG MARG on behalf the World Bank and in cooperation with the
Government of Rajasthan (GoR). Eight districts out of the 32 districts in Rajasthan were
chosen for the study, in discussion with government officials, to be representative of
accepted stratification of the state. Two blocks were randomly chosen from each district
and 25 schools were randomly chosen from each block – 12 schools from one block and
13 schools from the other. The 12-13 schools were distributed across the categories of
government, private aided and private unaided schools in the ratio 6:2:2. Where adequate
number of private aided and unaided schools was not available, the remaining schools
were replaced by government schools. Private unaided schools were restricted to those
that have government recognition. The schools were distributed such that there were 8
rural schools for every 2 urban schools.
A maximum of 30 students from grades IV and V were tested in each school, 15
students being randomly chosen from each grade. If more than one section of a grade was
available, then first a section was chosen randomly, and then students were randomly
selected from it. If fifteen or fewer children were present in a grade, then all of them were
included in the sample.
6
Preliminary version: for comments
Both grades IV and V students were administered the same tests in three subjects –
two language tests and one mathematics test. The two language tests were a reading
comprehension test and a word meaning test used by the State Council of Educational
Research and Training (SCERT) to test students in grade IV. The SCERT tests were the
same tests used by the National Council of Educational Research and Training (NCERT).
The mathematics test was a sub-sample of curriculum consistent questions selected from
the TIMMS 2003 Mathematics test for grade IV.
Tables 1 – 4 in the Annex provide descriptive statistics of the data used in this study
based on a number of other questionnaires that were also administered to collect
correlative information. These included:
(a) School Questionnaire: This questionnaire collected information on school and teacher
characteristics.
(b) Student Questionnaire: This questionnaire collected information on student and
family background characteristics.
Analytical Framework
We use a two-pronged empirical strategy to analyze the determinants of learning
achievement.
(1) In the first case, we use a ‘panel’ approach whereby we model the achievement of
student i in school j as a function of individual and family background
characteristics , a school fixed effect term and a random error term
ijY
ijX jz ijε . We
are able to do this because we have multiple observations from the same school.
ijjijij zXY εβα +++= ; where [A] ),0(~ 2σε Nij
This ‘panel’ strategy should in principle give us unbiased and consistent estimates
of individual and family characteristics.
7
Preliminary version: for comments
(1) In the second case, we model the achievement of student i in school j as a
function of individual and family background characteristics , a vector of
schooling resources which is constant across students from the same school,
and a random error term
ijY
ijX
jS
ijε such that
ijjijij SXY ελβα +++= ; where [B] ),0(~ 2σε Nij
We cannot have the school fixed effect and the observable school characteristics in
the same equation because in that case there is likely to be ‘perfect-collinearity’.
4. Learning Levels: Differences in educational attainment by School Type,
Gender, Social Group and Rural-Urban Location
In this section, we provide the unadjusted learning levels of students in grades IV and
V along a number of dimensions: school type, gender, social group and rural-urban
location of schools.
Table 4.1 below shows the means and standard deviations of the percentage scores for
the three tests for grades IV and V. Students in grade IV score below 50% in Reading
Comprehension and Math tests. Students in grade V have higher scores in all three tests
but the difference between the subject mean scores for the two grades is always less than
10 percentage points. The average gain in grade V is highest in reading and lowest in
word comprehension.3 The dispersion in test scores, measured by the standard
deviations, is very high – almost 20 percentage points – and is greatest for reading and
lowest for the word meaning test in both grades IV and V.
8
3 This gain is not true gain as the set of students whose scores constitute the average are different in the two grades.
Preliminary version: for comments
Table 4.1: Mean Scores in Tests for grades IV and V
Read Word Math Percentage (%)
Mean SD Mean SD Mean SD Grade IV 45.65 23.5 55.71 18.9 44.28 19.99 Grade V 53.31 23.28 61.76 18.53 51.12 19.97
School Type Differences: Table 4.2 shows the average percentage scores in reading
comprehension, word meaning and mathematics test by grade for each of the three school
types. Government school students have lower average scores than private unaided and
aided school students in both grades, ranging between 42-59 percentage points.
Government school students score between 2 and 6 percentage points less than private
unaided school students and between 9 and 16 percentage points lower than private
unaided school students in grade IV. In grade V, government school students score 8-9
percentage points lower than private aided schools in all three tests, and between 8-9
percentage points lower than unaided schools in the two language tests, and about 4
percentage points lower in mathematics. Private unaided schools catch up much more
with private aided schools in grade V compared to government schools.
The table also shows the standard deviations of subject scores (in parentheses).
Compared to the mean, the standard deviations are large across school types and across
grades. For government schools, large standard deviations combined with a small mean
imply that very little learning takes place for those children who are even one standard
deviation below the mean. For private schools, a large standard deviation combined with
a relatively large mean implies that students who score a standard deviation or more
above the mean score have very high scores. Later in the paper, we will see that school
specific differences account for most of the variation in test scores, and only a few
percentage score points remain unexplained due to unobserved within school differences,
after taking into account students background and school characteristics. This is true of
variation in scores for schools within a school type and across school types.
9
Preliminary version: for comments
Table 4.2: Mean Percentage Scores by School Type
Mean Percentage Score (S.D.)
Grade IV School Type Read Word Math
Government 42.8
(22.86)53.66
(23.84)42.34
(22.53)
Private Aided 58.34
(18.36)62.72
(19.87)54.72
(19.17)
Private Unaided 49.51
(19.91)59.43
(20.39)44.64
(17.44) Grade V
Government 50.63
(22.99)59.38
(22.12)49.25
(22.54)
Private Aided 59.71
(17.76)67.17
(17.92)58.17
(19.29)
Private Unaided 59.37
(19.81)67.25
(21.72)52.85
(17.10)
Gender Differences: There are virtually no gender differences in performance in all
tests in both grades as can be seen from table 4.3 below. In fact, the unadjusted scores of
girls are better than that of boys, albeit marginally. Figures 2 and 3 in the Annex also
show the unadjusted scores of boys and girls by school type. In math test in grade V, girls
in private aided and unaided schools score at least 5 percentage points more than boys.
What is also clear from the figures is that differences in scores between school types
outweigh differences in scores between boys and girls within any school type. This is true
of all tests in both grades.
Table 4.3: Mean Percentage Scores by Gender
Mean Percentage Score Grade IV
Read Word Math Boys 45.46 55.7 44.28 Girls 45.86 55.72 44.29 Grade V Boys 53.23 61.92 50.5Girls 53.41 61.58 51.84
Social Group Differences: There are relatively larger differences in performance
between children belonging to SC and ST on the one hand, and those belonging to
10
Preliminary version: for comments
General and OBC categories on the other. This is true for all three tests in both grades. In
grade IV, SC and ST score on the average 6-8 percentage points lower than
General/OBC. In grade V, the gaps narrow between SC and others, but widen for ST. The
score gaps between SC and others ranges from 3-6 percentage points and between 8-12
percentage points between ST and others. The mean scores for the different social groups
by test and grade is provided in table 4.4 below.
Table 4.4: Mean Percentage Scores by Social Group
Mean Percentage Score Grade IV Read Word Math SC 42.61 54.25 42.28ST 40.05 53.75 38.6OBC 48.14 56.46 46.24General 48.24 57.35 46.86 Grade V SC 51.68 60.95 49.46ST 46.91 59.5 43.49OBC 54.71 62.28 52.77General 57.51 63.44 55.72
Figures 4 and 5 in the annex also show the average percentage scores in the three tests
in the two grades by social group and school type. The figures confirm the following:
• OBC and General category children outperform SC and ST children in both
grades IV and V, and in all three tests. The performance of SC and ST children
are similar to each other and the performance of OBC and General children are
similar to each other.
• The gaps in test scores are narrower for SC and others in grade V.
• For social groups, differences between school types are larger than differences
between social groups in general, except in the case of mathematics scores in
grade V. All students, irrespective of their social group perform worse in
government schools and do better in private aided schools.
11
Preliminary version: for comments
Rural-Urban Differences: Average scores are lower in rural schools in all three tests
and in both grades, though the differences narrow in grade V. Rural-urban differences are
highest for reading comprehension test and lowest for math. Figures 6 and 7 in the annex
show the performance of schools by type and rural-urban location. From the figures, we
can observe that:
• The greater difference is between government and private unaided schools on the
one hand, and private aided schools in rural areas, on the other. Private aided
schools score 12-15 percentage points higher on the average than government
schools and private unaided schools in rural areas.
• The differences in the performance of the three types of schools are smaller in
urban areas.
5. Variations between Schools
In Section 4, we described the unadjusted learning achievement levels and gaps by
grade, gender, social group, rural-urban location and school type. We can use the method
of variance-decomposition of test scores to disaggregate the total explained variation by
source. The remaining which is unexplained variation can be attributed to omitted
variables and noise in the data. Using this method, we can also identify the adjusted
effects of particular characteristics such as school type, gender, social group etc. In a
multiple regression model, the adjusted effect is the coefficient on a particular attribute,
after taking into account all other characteristics.
Many studies find that cognitive achievement in schools can be predicted to a large
extent by the school attended. The effect of school attended can be measured by
including an indicator variable for the school, i.e. by accounting for school fixed effects,
using model A in Section 3 above. To determine the between and within school
variations, we regress test scores on an indicator variable for the school attended. The R-
square for the regression is the between variation, and the remaining is within school
variation. The between and within school variation for the two grades and the three tests
are shown in Figure 5.1 below. For Rajasthan, school fixed effects explain between 45%
12
Preliminary version: for comments
and 72% of the variation in test scores in the two grades. For both grades IV and V,
school fixed effects explains more than 70% of the test scores in mathematics and around
60% of the test scores in reading comprehension; for word meaning, school fixed effects
explain between 45% - 47% of the variation.
Figure 5.1: Between and Within Schools Variation by Test and Grade
Between and Within Schools Variation
0102030405060708090
100
Read Word Math Read Word Math
Class IV Class V
Per
cent
age
BetweenWithin
District as a source of variation in test scores by itself explains only 8-10%. Type
of school management – whether government, private aided or private-unaided – explains
between 3-4%; and the explanatory power of the grade the child attends – whether grade
IV or V – is only 2-3%. Also, district, school type and grade effects are stronger for grade
IV test scores compared to grade V. Once we take school fixed effects into account,
however, district, school type and grade lose any explanatory power.
13
We repeat the variance-decomposition exercise within each school type to compare
the variations in school quality across types and between each type and the overall
variation in test scores. We find that school quality within each type varies different
across the tests making the interpretation of results difficult. Nevertheless, we can draw
the following conclusions:
Preliminary version: for comments
• In general, government schools are the most variable, and private aided schools
are the least variable in quality. However, this does not hold for mathematics
where between 74 – 81% of the variation in test scores is explained by school
attended within the group of private aided schools, more than the other two school
types.
• The variation in school quality shrinks for grade V in private aided and unaided
schools but becomes only marginally lower for government schools.
Impact of School Characteristics
A critical issue in analyzing educational outcomes is how schools affect learning
attainment. School related factors that improve the quality of learning achievement can
provide education policy options. To unpack school quality, we replace school attended
in our OLS regressions by some standard measures of school quality used in empirical
research. This is the second of our two-pronged empirical strategy – model B in Section 3
above. We also include as a determinant the school type by management – i.e. whether
the school is a government, private aided or unaided school. The results are set out in
columns (3) and (6) of Regression tables I, II and III in the annex for grade IV and grade
V for the three tests results respectively. These results control for child and household
characteristics but not for school attended. We find:
• In all the regressions, the coefficients on school type are in the expected
direction. Private aided and unaided schools perform better than government
schools. Even though, the coefficient on private unaided school is significant
in only one regression – word meaning test for grade V – the robust t
statistics are small in all the regressions. The substantive impact of school
type on academic performance is not negligible, and the adjusted gap between
government school and private aided and unaided schools ranges between 5-7
percentage points.
• Anecdotal and more systematic empirical evidence generally document that
schools using multi-grade classrooms for teaching generally perform worse
14
Preliminary version: for comments
than schools that don’t. In the case of our data, we do not find any difference
between schools on the basis of this characteristic.
• Schools with a higher pupil teacher ratio (PTR) perform worse in all three
tests in grade IV, but no differently in tests in grade V. A 10 percentage point
increase in the PTR would reduce average percentage scores by 1-2
percentage points in reading comprehension, word meaning and mathematics
test scores respectively in grade IV.
• The average number of days of teachers absent from school in the previous
academic year has a small – a third to a quarter of a percentage point per day –
negative and significant impact, especially in grade V.
• A higher share of graduate teachers in the school has a small positive but
non-significant effect on test scores. A higher share of teachers who are non-
regular has a very small negative and non-significant effect on test scores.
Overall however, the standard measures of observable school characteristics used
in our analysis explain very little of the variation in test scores, and are unimportant once
we take school fixed effects into account.
Comparing the Distribution of Public and Private Unaided Schools Performance
So far we have compared mean scores of students in different school types. Even
though the typical government school performs poorly in comparison to the typical
private school, there is a lot of variation in performance within the category of any
particular school type. As we saw in section 5 above, most of the variation in test scores
is explained by school fixed effects. Figure 10 in the annex, shows the kernel density
distribution of the average school scores for reading comprehension and mathematics for
government and private unaided schools. We compare only these two school types
because they are ‘pure’ types. The average scores for the schools have been computed by
averaging individual student scores adjusted for child and family background
characteristics. Apart from the density distributions, the panels also show the location of
the median (the left vertical line) and the best (the right vertical line) private school. The
15
Preliminary version: for comments
kernel density distributions show that not all government schools perform badly, and that
there is also a substantial area of overlap between government and private unaided
schools. After adjusting for student and family background characteristics, 44.44% and
60.42% of public schools perform as well or better than the median private unaided
school in reading comprehension and mathematics in grade IV respectively; and 37.76%
and 44.06% in grade V respectively. This is especially true of distribution of schools in
grade V.
From the point of view of policy, the pertinent question to ask from a policy
perspective is how are good public schools different from bad public schools? For our
purposes, we are simply taking the good public schools to be those that have the same or
higher adjusted average scores than the median private unaided school. A comparison of
the mean characteristics of the two reveals that:
• In grade IV, for both language and mathematics, the better public schools
have a lower percentage of non-regular teachers.
• In grade V, for both language and mathematics, the better public schools
have a higher percentage of graduate teachers.
6. The Impact of Child, Family and Social Group Characteristics
As we have seen above, school attended has the maximum impact on test scores
on students. However, even after controlling for school attended, observable student,
family background and social group characteristics have significant, albeit relatively
small effect on test scores.
The regression results that identify child, family and social group characteristics
effects on test scores are provided in columns (2) and (5) of Regression Tables I, II and
III in the annex. In these regressions, apart from the school attended by the child, we
include as determinants the child’s age, gender, the child’s mother’s and father’s literacy,
whether the child lives in a rural or urban area, the social group of the child (general, SC,
ST, OBC), the number of days the child was absent in the week before the interview, and
16
Preliminary version: for comments
an asset index for the household the child belongs to (the construction of the asset index
is described in the annex).
The results of the regressions allow us to compare the unadjusted and adjusted
gaps in test scores for the relevant attribute under scrutiny of the child. To reiterate, the
unadjusted gap is simply the difference in average scores across an attribute such as
gender or caste, whereas the adjusted gap is the coefficient of that attribute in the
regression. For Rajasthan, the findings from these regressions are largely consistent with
expectations a priori and with findings from other studies. We find:
• Age of the child in general has no impact on test scores and has a small
negative impact in two of the regressions: for reading comprehension and for
mathematics in grade 5, age reduces test scores between ½ and a sixth of a
percentage point.
• Gender of the child has no or a very small and insignificant impact on test
scores. Unadjusted gaps are in favor of girls in general. The adjusted gaps for
girls are larger but still insignificant. This is shown in figure 7 in the annex.
• Social group does not seem to have a very strong impact on test scores in the
case of Rajasthan, though the differences across social groups are in the right
direction. Children belonging to the SC category in general score more than
those belonging to ST category and less than OBC and general category
children. SC children score on the average between 1 and 6 percentage points
more than ST children, and between 3 and 6 percentage points less than OBC
and general category children. Figures 8 and 9 in the annex show the
unadjusted and adjusted gaps between the test scores of SC students and other
social groups. Adjusted gaps between SC and ST children disappear and
become insignificant. The adjusted gaps between SC and OBC and general
categories also become much smaller and in most cases become insignificant.
Only in the case of the reading comprehension test in grade IV, the adjusted
gap is nearly half the adjusted gaps between test scores of SC and OBC and
general category students and is significant.
17
Preliminary version: for comments
• We do not find any consistent impact of mother’s and father’s literacy on test
scores. Mother’s literacy matters only for reading comprehension in grade 4.
Children of literate mothers on an average score 1.46 percentage points higher
in the reading comprehension test in grade IV than children of illiterate
mothers. Father’s literacy has an impact only on mathematics score for
children in grade IV. Children of literate fathers on an average score 1.33
percentage points higher in mathematics in grade 4 compared to children of
illiterate fathers.
• The asset index of the household to which the child belongs to has either a
very small, a quarter to a sixth of a percentage point, or no impact on test
scores.
7. The Labor Market for Teachers
Few studies analyze the characteristics of the labor market for teachers in India. In
this market, the government has a near monopoly in providing qualifications and is nearly
a monopsonist buyer since most teachers find employment in public sector schools.
Public sector (and private aided school) teacher salaries and other benefits are set by the
state – through pay commissions and other political processes – using considerations
other than qualifications or productivity.4 Salaries paid to teachers in private unaided
schools are a fraction of those paid to government school teachers, plausibly reflecting
local labor market conditions. Figure 7.1 shows the average salaries paid to regular
government school teachers and teachers in private aided and unaided schools. In
Rajasthan, private aided and unaided school teachers have similar average salaries, which
is approximately a third less than the average salary of a regular government school
teacher.
18
4 The variation in the salaries of regular government school teachers can largely be explained by seniority.
Preliminary version: for comments
Figure 7.1: Average Salary of Teachers by School Type
Average Salary of Regular Teachers
0100020003000400050006000700080009000
10000
Government Private Aided PrivateUnaided
School Type
Rupe
es
Salary (Rupees)
How do these salaries relate to differences in teacher characteristics across school
types? The data shows that the distribution of teacher educational qualifications is similar
between government and private unaided schools in Rajasthan. In both nearly 26% of
regular teachers are non-graduates and the remaining are graduates or above. On the other
hand, the share of graduates among teachers in private aided schools is on the average 7
percentage points greater.
Table 7.1: Distribution of Education: Regular Teachers (%)
Highest Education
Level
Government Private
Aided
Private
Unaided
Elementary 0.56 0 1.57
Secondary 2.66 2.56 3.15
Higher Secondary 16.50 7.69 14.17
Diploma/Certificate 5.87 6.41 6.30
Graduate 33.71 43.50 34.65
Post-Graduate 40.14 39.74 40.16
Other 0.56 0 0
Total 100 100 100
19
Preliminary version: for comments
Compared to government school teachers, private aided school teachers are
overwhelmingly not trained. More than 70% of regular teachers in private aided schools
have not received any pre-service training compared to 44% in private unaided schools
and only 30% in government schools. If we look at all the teachers (regular and non-
regular), then 38% in government schools, 77% in private aided schools and 57% in
private unaided schools have not received any pre-service training as non-regular
teachers are predominantly untrained
A small set of covariates in Mincerian type wage equations – years of experience, the
square of years of experience, age, gender, highest educational qualification, rank, status
(whether regular or otherwise) and rural-urban location explain nearly 60% of the
variation in teacher salaries in each of the school type. However, the set of predictors
vary across the school types. For teachers in government schools experience, status and
teacher rank are the significant predictors of salary. More experienced teachers earn
nearly Rupees 146 more per additional year of experience. Non-regular teachers earn
nearly Rupees 5534 less than regular teachers and teachers who are not head-masters or
their deputies earn Rupees 713 less than those who are. Thus experience, seniority and
status are strongly correlated with teacher pay in government schools. Rural-urban
location made no difference to teacher salary in government schools but in private aided
schools, teachers in rural schools earn Rupees 2050 less than their urban counterparts. In
private unaided schools, female teachers earn Rupees 1075 more than male teachers and
non-regular teachers earn Rupees 2960 less than regular teachers. Other observable
teacher characteristics were not significant predictors of salary variations in these two
school types.
Further analysis of teacher demographics in the various school types shows that while
only a third of teachers in government and private unaided schools are females, they
constitute nearly half the teaching force in private aided schools.
20
Preliminary version: for comments
Teacher Incentives
From the above it is clear that the representative regular teacher in a government
school is relatively older, more experienced and trained compared to his or her private
unaided school counterpart. Then what can account for the better performance of students
in private schools over and above personal and family characteristics, which by
themselves explain only very little? It is generally accepted that teacher incentives are
relatively weak in government schools that leads to poor teacher performance which in
turn results in poor student performance; better teacher performance in private schools,
even at much lower pay, is due to the stronger structure of incentives: private school
teachers can be penalized and even be fired for poor performance by school management
who are in turn accountable to fee-paying parents.
One aspect of teacher performance is teacher presence in schools. There is evidence
of wide-spread teacher absence in government schools in India (Chaudhury et al 2004).
We do not have data in our dataset that allows us to compute true absence rates for
teachers. Absence information in the data-set was reported by the school respondent on a
one time visit basis. The respondent was either the head teacher or a senior teacher in the
school, and the data refers to the number of days in the previous school year the teacher
was absent from school. On the basis of this information, teacher absence behavior was
worse in private aided and unaided schools, and better in government schools. In the
latter the average number of days absent (averaging by summing over all teachers) for
regular teachers was 12 days compared to 24 days for private aided schools and 16 for
private unaided schools. Contract teachers had similar absence behavior in government
schools but non-regular teachers in private aided and unaided schools had half the
number of days of absence as their regular counterparts.
8. Policy Perspective and Concluding Remarks
The analysis of determinants of learning achievement in grades IV and V in
Rajasthan provides important insights for the currently on-going debate on how to
improve the quality of public primary education. Firstly, the school attended by the child
21
Preliminary version: for comments
has the most substantive impact on the quality of learning. School fixed effects account
for more than half the variation in test scores. Once we take school fixed effects into
account, the type of school management loses all explanatory power. Secondly, private
schools, whether aided or unaided, outperform public schools. Thirdly, there is large
variation in the performance of public schools. Nearly a third of the public schools have
average performance better than the median private unaided school. From the point of
policy, this variation in public school performance provides the space for reforms that
will enable the public schools at the bottom of the distribution to perform. Future
research should explore the differences that separate the ‘good’ from the ‘bad’
government schools.
Learning Profiles and Learning Gains
What stands out from Table 4.1 above is that learning profiles are very flat: the
average gain in learning in terms of percentage points from grade IV to grade V for all
the students in the sample are: 7.66 in Reading Comprehension, 6.05 in Word Meaning
and 6.84 in Mathematics respectively. Even if we separate out the learning gains by
school type, gains are still very flat across all, particularly private aided schools where
scores increase on the average by 1-4 percentage points. Average gains in government
schools are between 6-8 percentage points and in private unaided schools are between 7-
10 percentage points. The standard deviations of achievement scores are very high in
both grades IV and V, relative to the mean. Given low mean scores, the implication is
that the students who are located even one standard deviation below the mean are
learning little. Moreover, there is little narrowing of the distribution of scores around the
respective means in the two grades implying that the incremental learning in the higher
grade is nearly constant over the entire distribution of scores.
The location and shape of the distribution of test scores has implications for
policy interventions aimed at improving the quality of education. Learning outcomes can
be improved in at least three ways: (a) better students, (b) more school years, (c) and
more learning in each grade.
22
Preliminary version: for comments
(a) Better students: We can expect three types of sorting taking place that can impact on
learning outcomes – sorting within schools where the better ability students progress
through grades, sorting across schools within a particular school type, and sorting across
school types. One way to deal with the issue of selection into schools is to offer school
choice by way of say school vouchers. Findings from this study show also that student
characteristics as we have seen above explain little of the variation in scores, most of
which is driven by school specific factors.
(b) More school years: The dispersion of scores in Table 4.2 above, in each grade and
relative to learning gains, is very high. If we assume a linear learning profile, then
everything else remaining constant, a child in the fourth grade of a government school
who is one standard deviation below the mean will take approximately six more years to
reach one standard deviation above the mean in each test (Read: 46/8 ≈ 6; Word: 38/6 ≈
6; Math: 40/7 ≈ 6).
(c) More learning in each grade: Currently, the amount of incremental learning taking
place in each grade is very low. If the ideal situation is one where students on reaching
grade V have mastered the intended curriculum for grade IV, then based on the findings
of this study, the average shortfall (=100-Mean Score) of 50 percentage points declines
only by 12-16% for the three tests in government school in the higher grade.
There is little education policy can do to improve the social background of
students – in the long run, economic development may be the best input into the
production of the quality of learning. Ensuring more school years is an untenable policy
intervention because with a given quality, it will take an infeasible number of years to
achieve any learning outcome target. The best option for policy makers is to steep-en the
currently flat learning profile – so that learning profiles in each grade approximate more
closely the shape of curves in Panel (B) below.
23
Preliminary version: for comments
Figure 8.1: Learning Gains in Government Schools Panel (A): Current Learning Profile Panel (B): Ideal Learning Profile
0102030405060708090
100
Grade IV Grade V
ReadWordMath
0102030405060708090
100
Grade IV Grade V
ReadWordMath
The objectives of education policy reform needs be to (a) improve the performance of
schools and (b) to keep costs down. Therefore, any education policy reform in the Indian
context will have to involve teacher quality. Teachers are the main input into the
teaching-learning process. Private schools perform better than public schools as is
evidenced by the better performance of their students. Salaries not only constitute the
largest share of the recurrent expenditures of public schools, but private school teachers
earn a fraction of the salary of public school teachers. It is not the personal
characteristics of the teachers but the incentives that are offered by the two school
systems that plausibly determine their behavior, which in turn determines teacher quality.
Most private school teachers do not have any training unlike regular public school
teachers, most of whom have at least pre-service training. Public school teachers also
have much higher experience in the profession on an average than private school
teachers. The higher education levels of private school teachers and their choice of low-
paying employment in private schools plausibly reflects the labor market conditions.
Government schools do not make use of their resources – mainly teachers, effectively,
and this is linked to technical or allocative inefficiency in the use of given resources. The
formal condition for the optimal allocation of resources is to equalize learning gains per
rupee for all inputs. In government schools, teacher salaries constitute the largest item of
expenditure on school resources. The table below shows the average learning gain from
grade IV to grade V by school type, divided by the average teacher salary for that school
24
Preliminary version: for comments
type. For ease of interpretation, the resultant ratios (shown in columns (1), (2) and (3))
were multiplied by 1000. As can be seen in the last column in the table (column (4)),
private unaided schools were nearly twice as more cost-effective than government
schools, implying that public school teachers earn considerable rents.
Table 8.1: Cost-Effectiveness of Education Delivery by School Type
Average Learning Gain Per Rupee
GovernmentPrivate Aided
Private Unaided (3)/(1)
(1) (2) (3) (4) Read 0.82 0.20 1.31 1.60 Word 0.60 0.64 1.04 1.73 Math 0.73 0.49 1.09 1.50
Other policy implications also emerge from the study, but by way of further research.
For example, in this study we find that schools with multi-grade classrooms record lower
test scores. Teachers in government schools are not trained to teach in a multi-grade
classroom context. This disconnection between the realities of the teaching environment
and the tools provided to teachers in government schools plausibly impacts negatively on
learning outcomes. A similar case can be made regarding teaching large class-sizes which
again is a reality for many government school teachers for which they may not be trained.
Educational quality determines individual earnings, income distribution and
economic-growth of countries (Hanushek and Woessman, 2006). Public schooling will
remain the dominant provider of schooling for the majority of the population. Policy-thus
makers need to find cost-effective ways to improve quality in public schools. Improving
the performance of public schools is made difficult by the fact that measurable school
characteristics have proven to be weak proxies for school quality in the standard
education production function approach. However, there are some desirable
characteristics that any reform agenda must have:
• Education policy reforms should be based on robust empirical evidence, given the
opportunity costs of scarce public resources. Policy makers should have a fair
idea about the returns to the marginal rupee across alternative interventions, and
25
Preliminary version: for comments
should choose those interventions where the returns are the largest. This requires
accurate assessment of the costs and benefits of any intervention.
• People respond to incentives. The success of any reform initiative will therefore
also depend on which outcomes are identified for monitoring and evaluation, for
establishing accountability and for judging success and failure of the reform. If
the objective of reforms is to improve learning outcomes, then education
providers – line department officials, school principals, teachers and other
stakeholder – will have to be made accountable for achieving this goal.
.
26
Preliminary version: for comments
References
Aggarwal, Yash (2000), “Primary Education in Delhi: How Much Do The Children
Learn?” NIEPA, New Delhi.
Chaudhury, Nazmul, Jeff Hammer, Michael Kremer, Karthik Muralidharan and F. Halsey
Rogers (2004), “Teacher Absence in India,” The World Bank, Washington D.C.
Das, Jishnu, Priyanka Pandey and Tristan Zajonc (2006), “Learning Levels and Gaps in
Pakistan,” World Bank Policy Research Working Paper #4067, The World Bank,
Washington D.C.
Dreze, Jean and Geeta G. Kingdon (2001), ‘Schooling Participation in Rural India’,
Review of Development Studies, 5(1), February, pp 1-24.
Filmer, Deon, King, Elizabeth M and Lant Pritchett (1997), ‘Gender Disparity in South
Asia: Comparison Between and Within States,’ World Bank Policy Research Working
Paper No. 1867, The World Bank, Washington D.C.
Filmer, Deon and Lant Pritchett (1998), ‘Education Enrollment and Attainment in India:
Household Wealth, Gender, Village and State Effects’, South Asia Region, IDP – 97, The
World Bank.
Fuller, Bruce (1986), Raising School Quality in Developing Countries: What Investments
Boost Learning, World Bank Discussion Paper No. 2, World Bank, Washington D.C.
Goldhaber, Dan, and Dominic Brewer (1997), “Why Don’t Schools and Teachers Seem
to Matter? Assessing the Impact of Unobservables on Educational Productivity.” Journal
of Human Resources, 32(3), pp. 505-523.
27
Preliminary version: for comments
Hanushek, Eric and L. Woessman (2006), “The Role of Education Quality in Economic
Growth,” xx.
Kingdon, G (1996), “The Quality and Efficiency of Public and Private Schools: A Case
Study of Urban India”, Oxford Bulletin of Economics and Statistics, 58(1), February, pp
55-80.
Lavy, Victor and Joshua Angrist (1999), “Using Maimonides’ Rule to Estimate the Effect
of Class Size on Scholastic Achievement,” Quarterly Journal of Economics, 114(2), pp
533-575.
Muralidharan, Karthik and Michael Kremer. “Public and Private Schools in Rural India.”
Working Paper, Department of Economics, Harvard University, March 22, 2006.
Smith, F., Hardman, F., and J. Tooley (2005), ‘Classroom interaction and discourse in
private schools serving low income families in Hyderabad, India’, International
Education Journal, 6(5), pp 607-618.
Tooley, James and Pauline Dixon (2006), ‘‘De facto’ privatization of education and the
poor: implications of a study from sub-Saharan Africa and India’, Compare, 36(4), pp
443-462.
Urqiola Miguel (2006), “Identifying Class Size Effects in Developing countries:
Evidence from Rural Bolivia,” The Review of Economics and Statistics, 88(1), pp 171-
177.
28
Preliminary version: for comments
Annex Table 1: Number Schools by Type and Location, Rajasthan
School Type Rural Urban Total Government 129 17 146
Private Aided 7 9 16
Private Unaided 25 13 38
Total 161 39 200
Table 2: Student Sample Size by Class and Gender, Rajasthan
Boys Girls Total
Class IV 1719 1508 3227
(0.53) (0.47) (1.00)
Class V 1702 1470 3172
(0.54) (0.46) (1.00)
Total 3421 2978 6399
(0.53) (0.46) (1.00)
Table 3: Mean Student Characteristics, Rajasthan
Scheduled Caste (%) 20.55
Scheduled Tribe (%) 22.71
OBC (%) 15.36
General/Other (%) 41.59
Father Literate (%) 71.95
Mother Literate (%) 31.52
Mean SD
Number of Days Absent 0.64 1.088
Household Asset Score 3.35 2.01
29
Preliminary version: for comments
Table 4: Descriptive Statistics of School Characteristics by School Type, Rajasthan
School Type Government Private Aided Private Unaided
Mean SD Mean SD Mean SD
Percentage of Graduate Teachers (%)
67.4
27.80
68.17
25.23
69.64
24.28
Percentage of Teachers with Pre-Service Training (%)
61.78
48.61
23.00
42.26
42.50
40.00
Average Teaching Experience (Years)
11.72
5.34
10.08
7.26
9.04
6.47
Average Age of Teachers (Years)
39.17 9.37 36.59 11.18 35.12 10.67
Pupil Teacher Ratio
44.33 37.51 33.68 10.32 49.58 66.59
SC/ST Share (%)
46 30 30 28 33 28
Percentage using Multi-grade Classrooms (%)
59.85
43.75
55.56
30
Preliminary version: for comments
Figure 1: Average Percentage Scores in Read, Word, Math in Grades IV and V by School Type,
Rajasthan
Average Percentage Scores
0
10
20
30
40
50
60
70
80
Read Word Math Read Word Math
Class IV Class V
Per
cent
age
Scor
e (%
)
GovtPvt AidedPvt Unaided
Figure 2: Average Percentage Scores in Read, Word, Math in Grade IV by Gender and School Type,
Rajasthan
Average Percentage Scores by Gender and School Type: Class IV
010203040506070
Read Word Math Read Word Math Read Word Math
Govt Pvt Aided Pvt Unaided
Per
cent
age
Scor
e(%
)
BoysGirls
31
Preliminary version: for comments
Figure 3: Average Percentage Scores in Read, Word, Math in Grade V by Gender and School Type,
Rajasthan
Average Percentage Scores by Gender and School Type: Class V
01020304050607080
Read Word Math Read Word Math Read Word Math
Govt Pvt Aided Pvt Unaided
Perc
enta
ge S
core
(%)
BoysGirls
Figure 4: Average Percentage Scores by Social Group and School Type, Rajasthan
Percentage Test Scores by Social Group
010203040506070
Read Word Math Read Word Math
Class IV Class V
Perc
enta
ge S
core
(%)
SCSTOBCGeneral
32
Preliminary version: for comments
Figure 5: Average Percentage Scores by Rural Urban Location for Grade IV, Rajasthan
Average Percentage Scores by Rural-Urban Location and School Type: Class IV
0
10
20
30
40
50
60
70
Read Word Math Read Word Math Read Word Math
Govt Pvt Aided Pvt Unaided
Per
cent
age
Scor
e (%
)
RuralUrban
Figure 6: Average Percentage Scores by Rural-Urban Location for Grade V, Rajasthan
Average Percentage Scores by Rural-Urban Location and School Type: Class V
01020304050607080
Read Word Math Read Word Math Read Word Math
Govt Pvt Aided Pvt Unaided
Perc
enta
ge S
core
(%)
RuralUrban
33
Preliminary version: for comments
Figure 7: Unadjusted and Adjusted Gaps between Boys and Girls Test Scores
-2.00-1.50-1.00-0.500.000.501.00
UnadjustedAdjusted
Unadjusted -0.40-0.02 -0.01-0.18 0.34 -1.34
Adjusted -1.10-0.87 -1.46-0.66 0.51 -1.50
ReadWordMathReadWordMath
Grade IV Grade V
Figure 8: Unadjusted and Adjusted Gaps between Test Scores of SC and other Social Groups, Grade IV
-8-6-4-20246
UnadjustedAdjusted
Unadjusted 2.56 -5.5 -5.6 0.5 -2.2 -3.1 3.68 -4 -4.6
Adjusted -0.5 1.1 1.21 0.59 1.05 -1.2 0.36 1.06 1.47
ST OBCGeneST OBCGeneST OBCGene
Read Word Math
34
Preliminary version: for comments
Figure 9: Unadjusted and Adjusted Gaps between Test Scores of SC and other Social Groups, Grade V
-10
-5
0
5
10
UnadjustedAdjusted
Unadjusted 4.8 -3.1 -5.8 1.5 -1.3 -2.5 6.1 -3.3 -6.3Adjusted 0.6 1.6 3.5 0.4 -0.3 -0.3 -0.2 1 0.7
ST OBCGen ST OBCGenST OBCGenRead Word Math
35
Preliminary version: for comments
Figure 10: Kernel density distribution of unadjusted and adjusted School Average Scores for
Government and Private Unaided Schools
Grade IV
0
.005
.01
.015
.02
Den
sity
-50 0 50Percentage Score
Govt Pvt Unaided
Adjusted Grade IV Reading Comprehension Scores
0
.005
.01
.015
.02
Den
sity
-40 -20 0 20 40 60Percentage Score
Govt Pvt Unaided
Adjusted Grade IV Mathematics Scores
Grade V
0
.005
.01
.015
Den
sity
-50 0 50Percentage Score
Govt Pvt Unaided
Adjusted Grade V Reading Comprehension Scores
0
.005
.01
.015
.02
Den
sity
-50 0 50Percentage Score
Govt Pvt Unaided
Adjusted Grade V Mathematics Scores
36
Preliminary version: for comments
OLS Regressions
Regression (I)
37
Dependent Variable: Percentage Score in Reading Comprehension Test
Grade IV Grade V
(1) (2) (3) (4) (5) (6)
School Yes Yes No Yes Yes No
Age -0.149 -0.772 -0.597 -0.999
-0.54 -1.2 (2.15)* -1.96
Age-Squared 0 0 0 0
-0.2 -0.08 -0.27 -0.79
Male -1.103 -0.359 -0.663 0.136
-1.57 -0.33 -0.81 -0.11
ST -0.465 -1.082 0.623 -4.839
-0.29 -0.39 -0.44 -1.74
OBC 1.099 3.18 1.611 0.811
-1.15 -1.67 (1.98)* -0.44
General 1.206 1.89 3.524 1.255
-1.05 -0.85 (3.31)** -0.57
Father Literate 0.289 2.044 0.315 1.443
-0.4 -1.63 -0.48 -1.23
Mother Literate 1.485 1.403 -0.112 -0.295
(2.12)* -0.97 -0.14 -0.2
Household Asset Score 0.36 1.464 0.605 1.313
-1.85 (3.22)** (3.29)** (2.93)**
Days Absent Last Week -0.186 -0.678 0.226 -0.363
-0.61 -1.38 -0.83 -0.76
Rural -31.744 -4.963 23.375 -2.533
(25.59)** -1.32 (15.63)** -0.65
Average Salary of Teachers in School 0 -0.001
-0.95 (2.33)*
Average Years of Teacher Experience in School -0.03 -0.017
-1.71 -0.92
Average Teacher Days Absent in the Last Academic Year -0.194 -0.388
-1.25 (2.68)**
Percentage Graduate Teachers 0.016 0.061
-0.29 -1.08
Percentage Non-regular Teachers -0.053 -0.089
-1.22 -1.88
Multi-Grade 0.296 -0.166
Preliminary version: for comments
-0.1 -0.06
Mid-Day Meals -6.336 -4.174
(2.09)* -1.4
Pupil Teacher Ratio -0.188 -0.127
-1.94 -1.26
Private Aided 5.042 1.661
-0.93 -0.35
Private Unaided 4.774 6.805
-1.34 -1.71
Constant 20.926 98.178 95.914 44.444 133.141 302.646
(9.96e+10)** -0.45 -0.24 (5.69e+11)** -0.49 -0.63
Observations 3255 3038 2386 3227 2994 2364
R-squared 0.62 0.62 0.14 0.6 0.59 0.13
Robust t statistics in paranthesis
* significant at 5%; ** significant at 1%
38
Preliminary version: for comments
Regression (II)
Dependent Variable: Percentage Score in Word Meaning
Grade IV Grade V
(1) (2) (3) (4) (5) (6)
School Yes Yes No Yes Yes No
Age -0.236 -0.247 -0.374 0.01
-0.96 -0.51 -1.42 -0.02
Age-Squared 0 0 0 0
(2.02)* -0.87 -0.87 -1.02
Male -0.868 -0.085 0.508 0.684
-1.29 -0.09 -0.7 -0.7
ST 0.586 -0.319 0.381 -0.7
-0.42 -0.18 -0.32 -0.38
OBC 1.047 -0.493 0.235 -0.588
-1.21 -0.34 -0.26 -0.39
General 0.674 0.151 1.023 -0.155
-0.66 -0.1 -0.9 -0.09
Father Literate 1.007 1.898 -0.356 0.533
-1.54 (1.98)* -0.56 -0.53
Mother Literate -0.016 -0.34 0.508 1.313
-0.02 -0.3 -0.75 -1.28
Household Asset Score 0.202 0.739 0.451 0.935
-1.01 (2.30)* (2.52)* (2.98)**
Dasy Absent Last Week 0.048 -0.546 -0.15 -0.506
-0.21 -1.5 -0.59 -1.23
Rural -11.95 -4.615 -7.47 0.868
(10.49)** -1.82 (5.18)** -0.29
Average Salary of Teachers in School -0.001 -0.001
(3.25)** -1.93
Average Years of Teacher Experience in School -0.011 -0.021
-1.23 -1.71
Average Teacher Days Absent in the Last Academic Year -0.257 -0.234
(2.53)* (2.28)*
Percentage Graduate Teachers -0.008 -0.013
-0.21 -0.31
Percentage Non-regular Teachers -0.089 -0.054
(2.82)** -1.65
Multi-Grade 2.19 -2.203
-0.95 -1.06
39
Preliminary version: for comments
Mid-Day Meals -3.495 -2.981
-1.54 -1.37
Pupil Teacher Ratio -0.157 -0.047
(2.52)* -0.78
Private Aided 1.587 3.786
-0.36 -0.99
Private Unaided 2.955 6.802
-1.27 (2.15)*
Constant 46.19 469.423 352.75 58.095 156.23 322.45
(5.31e+11)** (2.31)* -1.12 (9.90e+11)** -0.62 -0.84
Observations 3255 3038 2386 3227 2994 2364
R-squared 0.48 0.48 0.11 0.45 0.46 0.08
Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
40
Preliminary version: for comments
Regression (III)
Dependent Variable: Percentage Score in Math Test
Grade IV Grade V
(1) (2) (3) (4) (5) (6)
School Yes Yes No Yes Yes No
Age -0.19 -0.099 -0.451 -0.418
-0.93 -0.16 (2.15)* -0.72
Age-Squared 0 0 0 0
-1.11 -0.14 -1.33 -0.66
Male -1.455 -1.206 -1.496 -0.63
(2.68)** -1.2 (2.55)* -0.52
ST 0.355 -3.617 -0.249 -7.727
-0.35 -1.42 -0.24 (2.73)*
*
OBC 1.057 0.767 1.042 1.207
-1.31 -0.38 -1.4 -0.53
General 1.47 2.523 0.72 3.69
-1.53 -0.99 -0.75 -1.36
Father Literate 1.326 1.396 -0.651 -0.678
(2.39)* -1.13 -1.1 -0.52
Mother Literate -0.222 1.019 0.135 1.192
-0.44 -0.79 -0.23 -0.99
Household Asset Score 0.223 1.021 -0.056 0.618
-1.38 (2.30)* -0.39 -1.55
Days Absent Last Week -0.317 -1.731 0.157 -0.72
-1.35 (3.62)*
*
-0.82 -1.94
Rural -24.964 1.626 -22.286 4.086
(24.16)** -0.47 (19.79)*
*
-1.32
Average Salary of Teachers in School 0 0
-1.04 -0.95
Average Years of Teacher Experience in School 0.001 -0.012
-0.05 -0.95
Average Teacher Days Absent in the Last Academic Year -0.174 -0.269
-1.49 (2.08)*
Percentage Graduate Teachers -0.01 -0.016
-0.2 -0.31
Percentage Non-regular Teachers -0.055 -0.057
41
Preliminary version: for comments
-1.33 -1.21
Multi-Grade -0.572 -0.75
-0.22 -0.28
Mid-Day Meals -0.971 -0.393
-0.37 -0.15
Pupil Teacher Ratio -0.209 -0.061
(2.47)* -0.62
Private Aided 7.479 6.81
-1.23 -1
Private Unaided 2.706 4.765
-0.89 -1.56
Constant 21.549 -157.441 -0.867 47.475 300.519 255.91
(1.18e+11)** -0.85 0 (9.57e+11)*
*
-1.68 -0.54
Observations 3255 3038 2386 3227 2994 2364
R-squared 0.72 0.72 0.1 0.7 0.7 0.09
Robust t statistics in parentheses
* significant at 5%; ** significant at 1%
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Preliminary version: for comments
Construction of Household Asset Score: The household asset score has been constructed on a 12 point scale with each asset getting one point if available in the household of the student. The information was gathered through questions in the student background questionnaire.
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