learning achievement in primary education in...

Learning Achievements in India: A Study of

Primary Education in Orissa

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

Executive Summary

This paper presents findings from a study of learning outcomes in grades IV and V of government, private aided and private unaided schools in Orissa. 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 30 to 40 percentage points, half or a third below the average scores in private unaided 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 3-4 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


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. Access to

schools is a necessary but not a sufficient condition for ensuring the development of

cognitive competencies.

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 Orissa. 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, more than three quarters 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 designated as government aided/private aided primary schools. These

schools are managed by the private sector but largely financed by the government.1

The state of Orissa is situated in the eastern part of India and shares borders with

Jharkhand, West Bengal and Andhra Pradesh. According to the Census of India, 2001,

the state had a population of 36.7 million with an overall literacy rate of 63.1% compared

1 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 that is not covered in this study.

3


to the national average of 64%. Of the 5.81 million school-going children in the state in

2004-05, 94.7% attended government schools, and the remaining were in private schools

(DISE 2004-05). Historically, Orissa has been a backward state in terms of 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 Orissa. Section 7 provides policy implications based on

the findings of this study and concludes. An annex collects most of the tables and graphs

used in this study. We 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.

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

4


be attributed to school fixed effects. A study similar to this one for the state of Rajasthan

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 a smaller class-

sizes benefits students learning attainment. Regarding the use of multi-grade classrooms,

the general belief is that they are detrimental to learning. 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. According to existing empirical evidence,

private unaided schools in general outperform public schools (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,

5


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 between February and April 2006 by A C

Nielsen ORG MARG on behalf the World Bank in cooperation with the Government of

Orissa (GoO). Eight sample districts were chosen from the thirty districts in Orissa, in

discussion with government officials, to represent accepted stratification of the state. Two

blocks were randomly chosen from each district and twenty-five schools were randomly

chosen from each block – twelve schools from one and thirteen schools from the other.

The twelve-thirteen schools were distributed across the categories of government, private

aided and private unaided schools in the ratio 6:2:2. This division was done to get a

minimum sample size of each school type for meaningful estimates. Where adequate

number of private aided and unaided schools was not available, government schools were

chosen to complete the sample. Private unaided schools were restricted to those with

government recognition. The schools were distributed such that there were eight rural

schools for every two urban schools.

A maximum of thirty students from grades IV and V were tested in each school,

fifteen 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.

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).

6


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. The respondent was the head-teacher or the acting head in case the former

was not available.

(b) Student Questionnaire: This questionnaire collected information on student and

family background characteristics. The respondent was the student. Wherever necessary,

the school register and the teacher were consulted to get complete information.

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.

(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

ijY

ijX

7


schooling resources which is constant across students from the same school,

and a random error term

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 and Gaps: Differences in educational attainment by School

Type, Gender, Social Group and Rural-Urban Location

In this section, we provide the unadjusted learning levels of and gaps between

students in grades IV and V along a number of dimensions: school type, gender, social

group and rural-urban location of schools. Unadjusted scores are simple raw means of

percentage correct scores.

Table 4.1 below shows the means and standard deviations of scores (percentage

correct answers) for all three tests for all the students in the sample in grades IV and V.

Table 4.1: Mean Percentage Scores in tests for Grades IV and V

Read Word Math Percentage Points

Mean SD Mean SD Mean SD Grade IV 37.02 21.92 42.75 26.2 32.76 22.11 Grade V 46.63 23.67 50.63 25.7 41.07 21.11

A number of points can be noted from the table. The mean percentage scores are

very low in all three subjects in both grades. In grade IV, mean percentage scores are

below 40 percent in Reading Comprehension and Math tests, and only 42.75 percent in

the Word Meaning test. Even in grade V, scores though higher are below 50 percent in

Reading Comprehension and Math tests and just 50 percent in the Word Meaning test.

Standard deviations of the scores are very large – above 20 percentage points in all three

tests and in both classes. There is little narrowing of the distribution of scores in grade V.

8


School Type Differences: Levels of student achievement found in different school

types in this survey are consistent with findings from other studies in India and

elsewhere. Students in government schools perform both absolutely and relatively poorly

compared to students attending private aided and unaided schools. As can be seen from

Table 4.2 below, the mean percentage scores in private unaided schools in all tests in

both grades are almost one and a half times greater than government schools. The mean

percentage score in government schools is below 40 percent for all tests in all classes,

with grade IV students averaging below 30 percent in mathematics. On the other hand,

the average percentage scores of students in private unaided schools are between 50-60

percent. Private aided schools perform better than government schools but worse than

private unaided schools. The difference in the performance of private aided schools and

government schools is smaller than their difference with private unaided schools.

Table 4.2: Mean Percentage Scores by School Type

Government Private Aided

Private Unaided

Mean Percentage Scores Grade IV

Read 30.62 38.27 58.43 Word 34.55 48 68.67 Math 26.96 31.89 53.16

Grade V Read 40.98 45.13 68.32 Word 43.84 51.09 75.4 Math 36.4 38.29 59.85

Gender Differences: Test scores by gender are provided in Table 4.3. As can be seen

from this table, unadjusted gaps in learning levels between boys and girls are small, with

girls scoring on the average between 2 to 4 percentage-points lower than boys. The gaps

between boys and girls are maintained in grade V, though both score higher. Figures 1-3

in the annex also show these scores by school type. In any particular school type, gender

differences are small and the larger gaps are across school types.

9


Table 4.3: Mean Percentage Score by Gender

Mean Percentage Score Read Word Math Grade IV Boys 37.89 44.45 34.5Girls 36.05 40.85 30.8 Grade V Boys 48.01 52.92 42.47Girls 45.18 48.24 39.6

Social Group Differences: In table 4.4, the mean scores by social group for the three

tests and for the two grades are provided. From the table it is clear that social group

differences in performance are substantial. The unadjusted average gaps between general

category and SC and ST students in grades IV and V are between 12 and 14 percentage

points; gaps between general category and OBC students are smaller – between 5 and 8

percentage points. Table 4.4: Mean Percentage Scores by Social Group

Mean Percentage Score Read Word Math Grade IV General 44.51 51.19 39.48SC 29.78 36.07 27.29ST 32.073 39.05 26.32OBC 37.91 42.02 34.21 Grade V General 54.09 58.83 47.43SC 41.99 43.91 36.29ST 41.5 47.52 35.72OBC 46.79 50.03 42.176

Figures 2, 3 and 4 in the annex show the unadjusted scores by social group in the

different school types. The data reveals the following interesting patterns which are

similar across tests and grades:

• All students, irrespective of the social group they belong to substantially under-

perform in government schools compared to private unaided schools.

• Children categorized as belonging to OBC households do as well as general

category students in all school types and better in private aided schools.

10


• The average percentage scores of SC and ST students in private aided schools are

similar to those in government schools, whereas the average educational

attainment of general category students and OBC students in this school type is

substantially better compared to their counterparts in government schools.

• All students, irrespective of the social group they belong to do better in private

unaided schools.

Rural-Urban Differences: Differences in the test results of schools located in rural

and urban areas also reveal interesting patterns in the performance of different school

types. These can be seen in figures 5 and 6 in the annex. The figures show that:

• Government schools in both rural and urban areas under-perform, whereas private

unaided schools in both rural and urban areas have better performance.

• Private aided schools in rural areas perform worse than their counterparts in urban

areas. In rural areas, private aided schools do only a little better than government

schools, and in urban areas do a little worse than private unaided schools.

5. Variations between Schools

In Section 4, we described the unadjusted learning achievement levels and gaps by

grade, school type, gender, social group, and rural-urban location. 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.

As noted above, many studies, especially for developing countries, find that cognitive

achievement in schools can be predicted to a large extent by the school attended. This

effect can be measured by 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 explained sum of squares is the between schools variation, and the

11


remaining is variation within school. For Orissa, schools explain 50 percent or more of all

variation in test scores in all three subjects and in both grades. This can be seen in figures

5.1 and 5.2 below. School effects are strongest for Mathematics and weakest for Word

Meaning in both grades. There is some narrowing of differences in quality between

schools in grade V compared to grade IV. Figure 5.1: Between and Within School Variation, Grade IV Figure 5.2: Between and Within School Variation, Grade V

57 5164

43 4936

0102030405060708090

100

Read Word Math

Perc

enta

ge (%

)

WithinBetween

52 4657

48 5443

0102030405060708090

100

Read Word Math

Per

cent

age

(%)

WithinBetween

We also included school type – whether government, private aided or unaided, and

district in the same regression.2 School-type explains between 18-25 percent of the

variation. The district in which the school is located explains very little, only between 2-7

percent of the variation in test scores. District, school-type and school fixed effects are

stronger in grade IV compared to grade V. Once school attended is controlled for, district

and school-type lose any explanatory power.

We repeated the variance-decomposition exercise within each school type. We can

use the results of this exercise to make observations regarding the distribution of quality

within the category of government, private aided and private unaided schools. The

results show an interesting pattern. The variance explained by school attended becomes

smaller for government and private unaided schools but substantially larger for private

aided schools, especially for reading comprehension and mathematics. School attended

explains nearly 77% of the variation in Mathematics test scores for private aided schools

in grade IV and 61% in grade V. For both government and private unaided schools,

2 Results with school type and district are not shown in the paper but available upon request.

12


school attended explains between 30-40% of the variation in reading comprehension and

word meaning test scores and around 47% in mathematics. Overall, we observe the

following:

• The variation in quality of government schools and private unaided schools is

lower than the variation in quality of all schools.

• An average government school is of “low” quality and an average private unaided

school is of “high” quality.

• Private aided schools’ quality is more variable than that of the sample implying

that the group is a mix of some “low” quality schools and some “high” quality

schools.

We will revisit these results later in the paper when we compare the distribution of

average school scores for government and private unaided schools.

Impact of School Characteristics

We have seen above that schools matter for educational attainment. A critical issue in

education is how schools affect educational attainment, i.e. which factors determine the

effectiveness of schools and teachers and therefore are suggestive for providing policy

levers. To determine this, we substitute for school quality in our regressions by school

characteristics including school type (government, private aided and private unaided),

whether schools teach in a multi-grade context, the pupil-teacher ratio for the school, the

percentage of graduate teachers, the percentage of non-regular teachers, the average

teacher salary, and the average years of teacher experience. After controlling for child

and family characteristics, we find that in grade IV:

• Differences across school types, especially government and private unaided

schools, are very large. This is so even with the inclusion of controls for a

number of school characteristics such as PTR etc. Students in private unaided

schools score 14-24 percentage points higher on the average than those in

government schools. (Read - 13.65%, Word - 23.9%, Math - 14.4%). Students

in private aided schools score no differently except in the word meaning test

(9.21%).

13


• Schools that have multi-grade classrooms score have a negative impact on

student scores. However, the difference (4.5%) is significant only for the

Reading Comprehension test.

• Schools with higher pupil-teacher ratios have a small but significant negative

impact on test scores. A one-standard deviation increase in PTR reduces

scores by 1.67 percentage points for Reading Comprehension and 1.4

percentage points for Mathematics.

• The higher the percentage of teachers whose academic qualifications are

graduation and above, the better the test scores in those schools.

We find similar effects for some school characteristics in Grade V:

• School type effects are smaller but still very large. Private unaided schools’

students score on the average 11-20 percentage points more than government

school students. There is no difference between private aided and government

schools.

• The effect of percentage graduate teachers becomes larger.

• Multi-grade classrooms do not seem to have any effect on grade V scores.

• The effect of pupil-teacher ratio is no longer significant. It becomes smaller

for reading comprehension and math tests and larger for word meaning.

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 11 in the annex, shows the kernel density

distribution of the average school scores 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

14


panels also show the location of the median (the left vertical line) and the best (the right

vertical line) private school. The kernel density distributions show that:

• 10% of government schools have average adjusted scores similar to or better than

the median private unaided school in reading comprehension in grade IV. 4% of

government schools are better than the best. In grade V, 14% are similar to or

better than the median though none are better than the best, in reading

comprehension.

• In word meaning, 8% government schools are as good as or better than the

median private unaided school in grade IV, though none are better than the best.

In grade V, the former share increases to 10%.

• In mathematics, 12% government schools are as good as or better than the median

private unaided school and 4% are better than the best. In grade V, though none is

better than the best 18% are as good as or better than the median private unaided

school.

Thus, this variation in the quality of government schools (adjusting for student and

family background characteristics) provides room for policy to intervene to improve the

quality of government schools that are to the left of the distribution.

Are schools differentially effective for different types of students?

One of the major determinants of socio-economic status in India is social group or

caste. Caste is systematically related to one’s opportunities in life and well-being

outcomes in the Indian context.3 The assignment of students to schools is not random

and parents (on behalf of the students) choose the school their children attend. The

reasons they choose a particular school could be due to perceived school quality, attitude

towards schooling, resource constraints (direct and indirect costs of schooling including

distance to the school), and community norms and expectations such as those associated

with different social groups. A question then arises as to how do different social groups

3 A logit model using attendance in a particular type of school as the binary dependent variable shows that SC and OBC3 students are less likely to go to private aided and unaided schools controlling for other characteristics. SC students are 65-70% less likely than general students to go to private unaided schools and OBC students are 40-44% less likely to do so.

15


perform in different school types? The answer to this question is important as it can tell

us whether schools can override some of the disadvantages typically associated with

belonging to socio-economic groups such as SC and ST.

To answer the above question, we look at the determinants of performance within

each social group and see how each group performs across school types, controlling for

other characteristics. This approach also partly controls for the selection effect into

different school types. We find that schools are not differentially effective for students

belonging to different social groups, conditional on enrollment. All social groups perform

relatively well in good schools, and all perform relatively poorly in less well performing

schools. Tables 5.1 and 5.2 below show the average percentage score differences of the

various social groups in private aided and unaided schools compared to government

schools, for grades IV and V respectively, and in the three different subjects. These tables

show that:

• There are large positive significant differences in the performance of all social

groups attending private unaided schools. This is true of scores in all three

subjects.

• The largest differences are for SC children in grade IV.

• The differences narrow in grade V but are still large in magnitude and remain

significant.

• In grade V, the largest differences are for General followed by OBC.

• The differences between government schools and private aided schools are not

large and mostly not significant.

Table 5.1: Performance Difference of Social Groups between Government and Other School

Types for Grade IV

Read Word Math Private

Aided Private Unaided

Private Aided

Private Unaided

Private Aided

Private Unaided

General 10.27 18.79* 12.74* 25.32* 6.18 17.39* SC 4.4 26.38* 5.98 31.89* 3.21 22.72* ST -2.24 15.72* 5.84** 23.15* -2.99 14.65* OBC 20.52* 19.18* 20.32* 23.29* 13.34* 17.34*

Note: *5% level of significance; **10% level of significance

16


Table 5.2: Performance Difference of Social Groups between Government and Other School

Types for Class V

Read Word Math Private

Aided Private Unaided

Private Aided

Private Unaided

Private Aided

Private Unaided

General 3.81 21.09* 5.2 26.31* 2.75 18.93* SC 5.98 18.82* 7.99 24.09* 0.51 13.42* ST 1.95 16.78* 4.4 16.72* -0.61 6.52 OBC 9.65 16.94* 7.99 22.31* 8.87 16.12*

Note: *5% level of significance; **10% level of significance

If schools are differentially effective, disadvantages caused by family and social

circumstances can be either mitigated or exacerbated.

6. Educational Attainment: The Impact of Child, Family and Social Group

Characteristics

As discussed above, school attended has the maximum impact on test scores of

students. Nevertheless, even after controlling for school effects, observable students and

family background and social group characteristics have significant, albeit relatively

small, impact on the learning achievement of students.

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,

father’s occupation, whether the child lives in a rural or urban area, the social group of

the child (general, scheduled caste (SC), scheduled tribe (ST), other backward classes

(OBC)), the number of days the child was absent in the week before the interview, and an

asset index for the household the child belongs to (the construction of the asset index is

described in the annex).

We can use the results of the regressions to compare the unadjusted and adjusted gaps

in test scores for the relevant attribute of the child, under scrutiny. The unadjusted gap is

17


simply the difference in average scores across an attribute such as gender or caste, the

adjusted gap is the coefficient on that attribute in the regression. For Orissa, the findings

regarding the impact of child, family and social group characteristics are largely

consistent with expectations a priori and with findings from other studies.

• Age and gender of the child has no impact on test scores. The unadjusted gap in

test scores between boys and girls were small – between 2-4.5 percentage points.

Figures 7 and 8 in the annex show the unadjusted and adjusted gaps between the

scores of boys and girls in the three tests in the two grades. The adjusted gap

between genders is negligible and not significant.

• The largest differences in test scores are with respect to the social group of the

child. The unadjusted gaps between the scores of general category and SC

students and ST students were substantial, as we have seen in Section 3. Figures 9

and 10 in the annex compare the unadjusted and adjusted gaps between the scores

of general category and students belonging to other social groups. The adjusted

gaps for SC and ST are still a quarter to a third of the unadjusted gaps, and are

significant. On the other hand, the adjusted gaps between general category and

OBC students become small and insignificant..

• Children whose mothers are literate have higher test scores, whereas father’s

literate status has no impact. This is consistent with findings from many other

studies.

• Whether a child belongs to a rich or a poor household has a significant impact on

the child’s performance in schools for many reasons. Richer households can

afford to spend more on education enhancing resources. The parents of children

belonging to richer households may be more educated and may also be able to

send children to better quality schools. Moreover, richer households are better

able to withstand shocks and may not use the children as insurance. We find that a

one point increase in the household asset index increases test scores by 1-2

percentage points.

• The higher the number of days a child was absent from school in the week before

the interview, the worse a child’s test scores. Here we make the assumption that

the number of days a child was absent in the week before is a proxy for a long

18


term tendency of the child to be absent from the school, rather than an exact linear

relationship. Absence from school reduces a child’s test scores by 0.5 – 2

percentage points and this effect is significant.

7. The Teacher Labor Market

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

therefore determining supply) and is almost a monopsonist buyer for most teachers find

employment in public sector schools. The salaries and benefits of public sector (and

private aided school) teachers are set by the state – through pay commissions and other

political processes – using considerations other than qualifications and/or productivity.4

Salaries paid to teachers in private unaided schools are only a fraction of those paid to

government school teachers, plausibly reflecting local labor market conditions. Figure 7.1

below shows the average salaries paid to regular government school teachers and teachers

in private aided and unaided schools. While government school teachers get paid around

Rupees 8000, a typical private aided school teacher gets about half of that, and a typical

private unaided school teacher gets only a third.

Figure 7.1: Average Salary of Regular Teachers by School Type

Average Salary

0100020003000400050006000700080009000

Government PrivateAided

PrivateUnaided

School Type

Rup

ees

Average Salary

4 The variation in salaries of government school teachers can to a large extent be explained by seniority.

19


How do these salaries relate to differences in teacher characteristics across school

types? If we look at the distribution of educational qualifications of teachers across

school types in the data, a striking finding is that whereas only 40% of all teachers in

government schools are graduates or above, nearly 80% of teachers in private unaided

schools have a graduate or a higher degree. The share of teachers in private aided schools

who are graduates or above is also high at 60%. If we remove non-regular teachers from

the sample, the share of graduate teachers increases marginally for government and

private aided schools whereas for private unaided schools it increases by a further 10

percentage points. Thus, private unaided school teachers overwhelmingly have higher

educational qualifications compared to government school teachers.

Table 7.1: Distribution of Education among Regular Teachers by School Type (%)

Highest Education Level

Government Private Aided

Private Unaided

Elementary 3.47 0 0.44 Secondary 28.71 20.22 1.75 Higher Secondary 25.25 13.48 5.68 Diploma/Certificate 4.21 4.49 1.31 Graduate 30.94 50.56 72.05 Post-Graduate 6.93 11.24 18.78 Other 0.5 0 0 Total 100 100 100

On the other hand, compared to government school teachers, private unaided school

teachers are overwhelmingly not trained. Teacher training information in the data-set is a

little noisy, nevertheless, we find that more than 90% of teachers in private unaided

schools have not received any pre-service training and 85% report not having received

any in-service training. Government school teachers also have longer tenures than their

counterparts in the other school types. A regular teacher in a government school has

average teaching experience of 19.10 years compared to 12.56 years for teachers in

private aided schools and 7 years in private unaided schools.

Further analysis of teacher demographics in the various school types show that they

are neither overwhelmingly male nor female within any school type. The median regular

teacher in a private unaided school is nearly 12 years younger to the median regular

20


teacher in government schools (45 years in government schools versus 33 years in private

unaided schools). Regular teachers constitute on an average 75% of the teaching force of

government schools. All teachers in private unaided schools can be effectively thought of

as teachers under contract.

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; better teacher performance in private schools,

even at much lower pay, is due to the stronger structure of incentives that private school

teachers face: they be penalized and/or be fired for poor performance by school

management who are in turn accountable to fee-paying parents.

A small set of covariates (in Mincerian type wage equations) – age, age-squared,

years of experience, the square of years of experience, gender, highest educational

qualification, rank, status (whether regular or otherwise) and rural-urban location explain

nearly 89% of the variation in teacher salaries in government schools, about 53% for

private aided schools, and only 16% in private unaided schools. For teachers in

government schools, age, experience, rank and status are significant predictors of salary.

Older teachers, regular teachers and urban teachers earn more in private aided schools.

None of these factors emerge as significant predictors of private unaided school teacher

salary.

Measuring teacher quality is difficult. Many studies find that observable

characteristics of teachers that can be plausibly be expected to be correlated with quality

such as education, training and experience only weakly explain teacher performance such

as presence in school, time devoted to teaching conditional on presence, and student

learning achievements.

21


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 absence rates for teachers.

The data regarding absence 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 similar

across school types: government and private unaided school teachers were absent

between 12-13 days on average, and private aided school teachers were absent for 10

days on average in the previous school year. For government school teachers, a very

small share of absence was unscheduled, according to the survey, whereas for teachers in

the other two school types, most of the leave was unscheduled.

8. Policy Perspective and Concluding Remarks

The analysis of determinants of learning achievement in grades IV and V in Orissa

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 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 10% of the public schools have average

performance better than the median private unaided school, and 4% outperform the best

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 the learning profiles are very flat:

the average gain in learning in terms of percentage points from grade IV to grade V for

22


all the students in the sample are 9.61 in Reading Comprehension, 7.88 in Word Meaning

and 8.31 in Mathematics respectively. Even if we separate out the learning gains by

school type, gains are still flat across all – though the sharpest increase in all three tests

are for government schools and lowest for private aided schools. Learning gains are

similar for government and private unaided schools for the reading comprehension test,

but a third smaller for private unaided schools for the remaining two tests. 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 have nearly zero learning. 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

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.

(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 4.1, 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 four more years to reach one

standard deviation above the mean (44/10 = 4.4).

(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

23


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 65-75 percentage points

declines only by 13-14% for the three tests in government school.

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 learning quality. 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 most feasible option for policy makers is to

steep-en the currently flat learning profile – such that learning profiles in each grade

approximate more closely the shape of curves in Panel (B) below.

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. This study was not

designged to exploit variation in teacher incentives to identify teacher effects on learning

outcomes. However, a plausible hypothesis supported by the findings of this study is that

24


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. This is a question that should be further probed in future studies.

Private schools also generate more learning per rupee than public schools. 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 they face.

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

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 3-4 times more cost-effective than government

schools, implying that public school teachers earn large rents.

Table 8.1: Cost-Effectiveness of Education Delivery by School Type

Average Learning Gain Per Rupee

Government

Private

Aided

Private

Unaided

Relative

Cost-

Effectiveness

(3)/(1)

(1) (2) (3) (4)

Read 1.30 1.72 4.95 3.82

Word 1.16 0.77 3.37 2.90

Math 1.18 1.60 3.35 2.83

25


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

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


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.

Hanushek, Eric and L. Woessman (2006), “The Role of Education Quality in Economic

Growth,” xx.

27


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.

Annex

28


Table 1: Number of Schools by Type and Location, Orissa

School Type Rural Urban Total Government 122 21 143 Private Aided 14 6 20 Private Unaided 24 11 35 Total 160 38 198

Table 2: Student Sample Size by Gender and Class, Orissa

Boys Girls Total Grade IV 1495 1343 2838 (0.53) (0.47) Grade V 1419 1365 2784 (0.51) (0.49) Total 2914 2708 5622 (0.52) (0.48)

Table 3: Sample Student Characteristics, Orissa

Percentage (%) General/Other 24.99 SC 16.89 ST 21.47 OBC 36.65 Father Literate 79.5 Mother Literate 62.46 Mean SD Number of Days Absent Last Week (Days)

0.5 1.08

Household Asset Score (Point Scale) 2.98 2.11

Table 4: Descriptive Statistics of School Characteristics by School Type, Orissa

Government Private Aided Private Unaided

29


Mean SD Mean SD Mean SD Graduate Teachers (%) 33.09 30.67 42.86 35.38 84.86 29.46 Average Years of Teaching Experience

15.63 8.44 13.67 8.35 5.97 4.79

Average Age of Teachers 40.83 7.48 38.95 7.43 30.99 5.3 Pupil Teacher Ratio 43.18 27.92 27.65 13.48 20.56 11.5 Multi-Grade Classrooms (%) 83.92 - 60 - 57.14 - Non-regular Teachers (%) 25.05 25.14 - - - -

Figure 1: Average Percentage Scores for Boys and Girls by School Type (A) (B)

30


(C)

Figure 2: Mean Percentage Score in Reading Comprehension by Social Group and School Type

Reading Comprehension Score (Mean %)

57.88

30.7237.72 39.74 43.17

69.1

38.7630.52

42.22 47.14

67.7359.36

01020304050607080

Gov

t

Pvt

.A

ided

Pvt

.U

naid

ed

Gov

t

Pvt

.A

ided

Pvt

.U

naid

ed

Class IV Class V

Perc

enta

ge (%

)

BoysGirls

Word Meaning Score (Mean %)

47.14

67.52

46.1450.57

41.5649.84

75.39

52.37

75.41

35.88

48.96

33.23

0

20

40

60

80

Gov

t

Pvt.

Aide

d

Pvt.

Una

ided

Gov

t

Pvt.

Aide

d

Pvt.

Una

ided

Perc

enta

ge (%

)

BoysGirls

Class IV Class V

Math Score (Mean %)

40.67

25.9635.19

53.11

37.64

59.45

33.08

27.98

53.25

30.54

60.37

35.96

0

10

20

30

40

50

60

70

Gov

t

Pvt

.A

ided

Pvt

.U

naid

ed

Gov

t

Pvt

.A

ided

Pvt

.U

naid

ed

Class IV Class V

Perc

enta

ge (%

)

BoysGirls

31

Reading Comprehension Score (Mean %)

Private

Govt

Aided

Private

VC

lass

IV

enta

ge (%

)

OBCSTSC

c G l


Figure 3: Mean Percentage Score in Word Meaning Test by Social Group and School Type

Word Meaning Score (Mean %)

0 20 40 60 80 100

Govt

Aided

Private

Govt

Aided

Private

Cla

ss V

Cla

ss IV

Perc

enta

ge (%

)

OBCSTSCGeneral

Figure 4: Mean Percentage Score in Math by Social Group and School Type

Figure 5: Mean Percentage Scores by School Type and Rural-Urban Location, Grade IV

Rural versus Urban (Class IV)

01020304050

Per

cent

age

Poin 60

7080

ts

Math Score (Mean %)

0 20 40 60 80

GovtPvt. AidedPvt. Unaided

Govt

Aided

Private

Govt

Aided

Private

Cla

ss V

Cla

ss IV

Perc

enta

ge (%

)

OBCSTSCGeneral

32


Figure 6: Mean Percentage Scores by School Type and Rural-Urban Location, Grade V

Rural versus Urban (Class V)

0102030405060708090

Perc

enta

ge P

oint

s

GovtPvt. AidedPvt. Unaided

Govt 40.63 44.06 36.39 42.23 43.04 36.5

Pvt. Aided 42.26 48.6 35.44 51.8 56.88 44.9

Pvt. Unaided 67.41 73.39 57.87 70.02 79.16 63.56

Read Word Math Read Word Math

Rural Urban

Figure 7: Unadjusted and Adjusted Gaps Figure 8: Unadjusted and Adjusted Gaps

by Gender, Grade IV by Gender, Grade V

33


-1-0.5

00.5

11.5

22.5

33.5

4

Read Word Math

Unadjusted GapAdjusted Gap

00.5

11.5

22.5

33.5

44.5

5

Read Word Math

Unadjusted GapAdjusted Gap

Figure 9: Unadjusted and Adjusted Gaps in Test Scores by Social Group, Grade IV

34


02468

10121416

SCSTOBC

SC 14.73 4.501 15.12 3.921 12.19 3.765ST 12.437 3.199 12.14 4.354 13.16 4.588OBC 6.6 0.426 9.17 1.455 5.27 0.953

UnadjustedAdjusted UnadjustedAdjusted UnadjustedAdjustedRead Word Math

Figure 10: Unadjusted and Adjusted Gaps in Test Scores by Social Group, Grade V

02468

10121416

SCSTOBC

SC 12.1 2.982 14.92 4.474 11.14 3.343ST 12.59 0.306 11.31 4.313 11.71 2.791OBC 7.3 1.182 8.8 2.273 5.254 1.078

UnadjusteAdjusted UnadjusteAdjusted UnadjusteAdjustedRead Word Math

OLS Regressions

35


Regression (I) Dependent Variable: Percentage Student Score in Reading Comprehension

Grade IV Grade V

(1) (2) (3) (4) (5) (6) School Yes Yes No Yes Yes No Age -6.61 1.404 -7.389 3.861 -1.66 -0.3 -1.6 -0.65 Age-Squared 0.326 -0.08 0.295 -0.249 -1.57 -0.33 -1.36 -0.89 Male -0.561 -0.268 1.511 1.766 -0.76 -0.38 -1.97 (2.11)* SC -4.501 -1.02 -2.982 0.701 (4.29)** -0.67 (2.16)* -0.36 ST -3.199 1.014 -0.306 1.986 (2.97)** -0.57 -0.18 -0.91 OBC -0.426 1.796 -1.182 0.532 -0.44 -1.35 -1.14 -0.32 Father Literate -0.229 -0.142 1.526 1.094 -0.3 -0.14 -1.54 -0.91 Mother Literate 2.12 4.182 3.016 4.396 (2.67)** (4.06)** (2.99)** (3.70)**Household Asset Score 1.233 1.423 0.849 0.855 (5.52)** (5.04)** (3.06)** (2.47)* Days Absent Last Week -0.647 -1.253 -0.522 -0.944 (2.41)* (3.69)** -1.67 (2.61)**Rural -10.052 3.928 -24.76 1.179 (7.09)** -1.96 (13.46)** -0.59 Average Salary of Teachers in School

0 0

-0.13 -0.11 Average Years of Teacher Experience

0.067 0.215

-0.68 -1.85 Average Days Teachers Absent in the Last Academic Year 0.005 0.117 -0.04 -0.76 Percentage Graduate Teachers 0.058 0.092 -1.94 (2.87)**Percentage Non-regular Teachers -0.025 -0.009 -0.84 -0.28 Multi-grade -4.384 -0.145 (2.43)* -0.07 Mid-Day Meals 7.097 9.861 (3.11)** (4.94)**Pupil Teacher Ratio -0.05 -0.045 (2.43)* -1.62 Private Aided 1.314 -0.055 -0.42 -0.02 Private Unaided 13.386 12.476 (3.76)** (2.60)**

36


Constant 36 69.636 11.632 42.333 86.714 4.002 (2.26e+12)** (3.69)** -0.53 (1.30e+14)** (3.50)** -0.12 Observations 2828 2828 2828 2776 2776 2776 R-squared 0.57 0.58 0.35 0.52 0.53 0.31 Robust t statistics in parentheses * significant at 5%; ** significant at 1%

Regression (II)

37


Dependent Variable: Percentage Student Score in Word Meaning Grade IV Grade V

(1) (2) (3) (4) (5) (6) School Yes Yes No Yes Yes No Age 0.617 7.826 0.202 9.172 -0.13 -1.33 -0.03 -1.58 Age-Squared -0.058 -0.408 -0.08 -0.496 -0.22 -1.31 -0.28 -1.8 Male 0.05 1.028 2.592 3.385 -0.05 -1.07 (2.75)** (3.61)**SC -3.921 -1.689 -4.474 -2.255 (2.49)* -0.86 (2.59)* -1.13 ST -4.354 -0.381 -4.313 1.447 (2.55)* -0.17 (2.48)* -0.61 OBC -1.455 -0.911 -2.273 -0.923 -1.16 -0.58 -1.53 -0.53 Father Literate 0.522 -0.967 -0.82 -3.378 -0.46 -0.61 -0.76 (2.67)**Mother Literate 2.258 3.738 3.4 2.519 (2.02)* (2.75)** (3.04)** (2.01)* Household Asset Score 1.8 1.756 1.496 1.987 (5.03)** (4.52)** (3.92)** (5.26)**Days Absent Last Week -0.79 -1.668 -0.939 -1.538 (1.97)* (3.58)** (2.60)* (3.76)**Rural 21.044 2.55 13.438 1.652 (11.94)** -0.97 (7.15)** -0.75 Average Teacher Salary in School -0.001 -0.001 -0.83 -0.71 Average Teacher Experience in School

0.162 0.291

-1.38 (2.54)* Average Teacher Days Absent in Last Academic Year 0.131 0.159 -0.68 -1.05 Percentage Graduate Teachers 0.012 0.031 -0.33 -1.07 Percentage Non-regular Teachers -0.047 -0.006 -1.42 -0.18 Multi-Grade -2.079 0.5 -0.93 -0.27 Mid-Day Meals 3.515 7.669 -1.26 (5.33)**Pupil Teacher Ratio 0.038 0.045 -1.23 -1.74 Private Aided 8.464 3.313 (1.98)* -1.01 Private Unaided 24.035 20.139 (4.94)** (4.84)**Constant 28 26.334 -11.172 22 30.092 -17.864

38


(1.72e+11)** -1.14 -0.38 (1.72e+11)** -0.98 -0.57 Observations 2828 2828 2828 2776 2776 2776 R-squared 0.51 0.52 0.3 0.46 0.48 0.29 Robust t statistics in parentheses * significant at 5%; ** significant at 1%

Regression (III)

39


Dependent Variable: Percentage Student Score in Mathematics

Grade IV Grade V (1) (2) (3) (4) (5) (6)

School Yes Yes No Yes Yes No Age -4.087 4.667 -4.216 3.835 -1.2 -1.05 -0.89 -0.73 Age-Squared 0.214 -0.275 0.165 -0.219 -1.19 -1.18 -0.73 -0.89 Male 1.058 1.943 1.916 2.018 -1.64 (2.98)** (2.60)** (2.44)* SC -3.765 -0.073 -3.343 -0.628 (3.53)** -0.04 (2.78)** -0.34 ST -4.588 -0.257 -2.791 0.337 (3.62)** -0.12 (2.03)* -0.15 OBC -0.953 2.502 -1.078 1.381 -1.18 -1.79 -1.04 -0.81 Father Literate -0.097 -0.864 0.82 -0.85 -0.13 -0.82 -1.06 -0.71 Mother Literate 1.962 3.109 2.427 2.6 (2.95)** (3.18)** (2.95)** (2.42)* Household Asset Score 1.235 1.544 1.138 0.969 (5.75)** (4.95)** (4.03)** (2.51)* Days Absent Last Week -0.579 -1.326 -0.631 -1.305 (2.39)* (4.02)** (2.13)* (3.55)** Rural 55.888 -1.05 -13.643 0.459 (40.79)** -0.49 (7.98)** -0.22 Average Salary of Teachers in School 0 0 -0.03 -0.42 Average Years of Teacher Experience 0.107 0.077 -0.92 -0.58 Average Teacher Days Absent in the Last Academic Year -0.233 -0.046 -1.62 -0.29 Percentage Graduate Teachers 0.052 0.089 -1.72 (2.94)** Percentage Non-regular Teachers -0.022 -0.009 -0.75 -0.28 Multi-Grade -1.317 1.428 -0.67 -0.65 Mid-Day Meals 5.751 7.621 (3.01)** (4.15)** Pupil Teacher Ratio -0.038 -0.019 -1.61 -0.73 Private Aided 0.399 -0.292 -0.12 -0.09 Private Unaided 13.835 11.841 (3.50)** (2.63)** Constant 16.97 36.107 -2.403 21.212 47.077 1.21 (8.07e+10)** (2.26)* -0.12 (2.94e+11)** -1.88 -0.04

40


Observations 2828 2828 2828 2776 2776 2776 R-squared 0.64 0.66 0.37 0.57 0.59 0.28 Robust t statistics in parentheses * significant at 5%; ** significant at 1%

Figure 11: Kernel Density Distributions of Adjusted Test Scores

41


Grade IV

0

.01

.02

.03

.04

Den

sity

-20 0 20 40 60Average School Score

Govt Pvt Unaided

Adjusted Reading Comprehension Scores, Grade IV

0

.01

.02

.03

.04

Den

sity

-40 -20 0 20 40Average School Score

Govt Pvt Unaided

Adjusted Word Meaning Scores, Grade IV

0

.01

.02

.03

.04

Den

sity

-20 0 20 40Average School Score

Govt Pvt Unaided

Adjusted Math Scores, Grade IV

Grade V

0

.01

.02

.03

.04

Den

sity

-40 -20 0 20 40 60Average School Score

Govt Pvt Unaided

Adjusted Read Scores, Grade V

0

.01

.02

.03

.04

Den

sity


Govt Pvt Unaided

Adjusted Read Scores, Grade V

0

.01

.02

.03

Den

sity


Govt Pvt Unaided

Adjusted Math Scores, Grade V

Construction of Household Asset Score:

42


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. List of sample districts:

1. Debgarh 2. Dhenkanal 3. Ganjam 4. Jagatsingpur 5. Kendrapara 6. Nawarangpur 7. Sambalpur 8. Sundargarh

43

learning achievement in primary education in...

Documents