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Prying the Private School Effect:
An Empirical Analysis of Learning Outcomes of Public and Private Schools in
Pakistan
Kiran Javaid, Tareena Musaddiq and Atiyab Sultan1
As the preference for private school education becomes more widespread in Pakistan, the
debate on the relative merits of public and private education has gained increasing relevance and
importance. To assess the differences in the educational outcomes of the students in the two
streams, it is necessary to isolate the pure effect of school choice (private versus public). Using
ASER Pakistan data for the years 2010 and 2011 we employ various techniques to analyse the
effect of private schooling. In particular, the Oaxaca decomposition is applied to assess
achievement differences between public and private school students, while Fixed Effects
estimation is used to study province, district, village and household level differences. An in-
depth study of the learning outcomes of the private school students as opposed to those enrolled
in government schools is enabled by a pooled cross-sectional analysis using data from both
years, as the level of fixed effects is made increasingly strict (from province to village and then
household level). Private school advantage is significant at each of these levels, whereas gender
discrimination present at only the village and household level. Oaxaca decomposition shows that
only five percent of the achievement differential can be attributed to the endowment differences
between the two groups.
JEL Classifications: I20, I21
Keywords: Learning outcome differentials, Private vs. Public Schooling, Pakistan,
1 Kiran Javaid, Teaching Fellow, Department of Economics, Lahore University of Management Sciences (LUMS),
Pakistan; [email protected]; Tareena Mussadiq, Teaching Fellow, Department of Economics, Lahore University of Management Sciences (LUMS), Pakistan; [email protected]; Atiyab Sultan, Teaching Fellow, Department of Economics, Lahore University of Management Sciences (LUMS), Pakistan; [email protected]. Kiran Javaid, Teaching Fellow, Department of Economics, Lahore University of Management Sciences (LUMS), Pakistan
1
1. Introduction
The debate on public versus private education has gained increasing importance in recent
years. The issue is of special importance for developing countries, many of which are home to
widespread networks of private schools. The existence of such networks questions the role and
capacity of the government in providing education for all. It also underlines the need to build
state capacity in providing education, more so since many individuals are unable to afford
private schools. In some countries, the acknowledgment of dysfunctional government schools
has given rise to public-private partnerships in an attempt to increase literacy and access to
schools. However, a key question remains regarding the quality of education in private versus
public schools. To evaluate their performance, many researchers have used tests that have been
specially designed to gauge the quality of public versus private education. This paper adds to this
body of knowledge by comparing public and private schools in 85 districts of Pakistan using the
results of a test that evaluated the linguistic and mathematical skills of the students.
Education in Pakistan has assumed increasing importance recently with the country still
far from attaining literacy for all. The country is lagging behind in the pursuit of the Millennium
Development Goals, especially in education. This is perhaps both a result of a resource constraint
as well as a lack of political will to devote a larger budget share towards education. Over the
years, the state-run system of schools has become an inferior or second-grade mode of education
in popular perception, with the concomitant rise of a vast network of private schools. Private
schools in Pakistan in 1983 numbered 3,343 which rose astronomically to 36,096 by
20002.Interestingly, not all private schools are expensive or elite schools and there is a wide
variation in the fees and quality of education being provided by these schools. The situation is
2FBS(2001)
2
also complicated by the different streams of education in the country: the government
examination boards conduct the Matriculation and Intermediate /F Sc. exams, whereas many
private schools instead enable students to take the British GCE O and A Levels exams. A few
schools are also conducting Baccalaureate examinations and religious seminaries (madressahs)
also have their own exams. The dominant tracks are the Matric/FSc and the O/A Levels schemes.
A couple of studies have analyzed learning outcomes in Pakistani schools by comparing
public and private schools.3 However, since previous datasets mostly studied primary schools,
these studies only analyze the differential in outcomes at this level. This paper uses the latest
data (for the years 2010 and 2011) from ASER (Annual Status of Education Report) which spans
over the entire country and collects data on all levels of schooling including primary, middle and
secondary schools. The paper uses this data to evaluate the differential in learning outcomes of
public and private schools, thereby enabling an informed and insightful comparison of the
quality of education being provided by these schools.
The paper is structured as follows: Section 2 contains a literature review, Section 3
describes the dataset in greater detail, while Section 4 discusses the methodology employed.
Section 5 contains descriptive statistics and also discusses the results of the study and finally
Section 6 concludes.
2. Literature Review
A wealth of theoretical and empirical research exists on the effectiveness of private
versus public schools. The results of these studies present a varied and granulated picture in
which private schools can sometimes outperform public schools, and in other locations lag
3 See Alderman, Orazem,andPaterno 2001; Arif and Saqib 2003; Das, Pandey, and Zajonc 2006 ).
3
behind them. In Pakistan, private schooling is generally considered to be of superior quality as
opposed to state-run government schools. In a paper exploring the extent and limitations of
private schooling in Pakistan, Andrabi, Das and Khawaja (2006) provide a comprehensive
treatment of the subject concluding that a key reason for the proliferation of private schools is
their low fees. The limits to their growth are interestingly defined in terms of horizontal and
vertical constraints with the former referring to the fact that private schools can only exist in a
community where there is already a pool of potential teachers (often women). The vertical
constraint identified by the authors refers to the fact that since most teachers are only educated
up to the secondary school level, providing education beyond that level is not possible for the
schools. Overall, private schools are found to have been quite instrumental in increasing
education at the primary school level and also in reducing gender discrimination.
Apart from these resource constraints however, a richer understanding of the private
school phenomena involves analyzing the service they provide and comparing it concretely with
the alternative(s), especially public schools. Academic research has focussed on disentangling
the private school ‘premium’ that might explain the difference in outcomes from the two types of
schools in two distinct ways: comparing the wages accruing to the graduates of public versus
private schools, or comparing the learning outcomes from the two types of schools.
Some of the research in this area focuses on why this differential exists i.e. can it be
explained in terms of a higher quality of education imparted in private schools or due to non-
academic gains that result from the environment of private schools: such as networks, resources
and other opportunities that have a payoff in the labour market.4 Other considerations in studying
4Brown and Belfield (2001) and Asadullah (2005)
4
the demand for private schools relate to the level of fees charged e.g. Alderman et al (2001) find
that the demand for private schooling in Pakistan increases as the school fees charged by these
schools fall. The increased demand comes from both students switching from public schools to
private schools and from students that were not previously enrolled. They also find that the
increased demand is consistent with superior results on both mathematics and language tests by
private schools. While their analysis is conducted by simulating elasticities and changes in the
probability of enrollment in a school against different variables, Andrabi et al.(2002) provide a
nuanced account of the clientele of private schools in their paper ‘The Rise of Private Schooling
in Pakistan: Catering to the Urban Elite or Educating the Rural Poor?’ It is seen that even low
income households opt for private schooling in urban as well as rural areas. Private schools have
lower student-teacher ratios, encourage co-education and employ locally resident teachers, all of
which contribute to lowering the costs of the provision of education.
A more cynical view of private school education is provided by Brunello and Rocco (2008) in
their discussion of educational standards in public and private schools using data from Italy and
the United States. By modeling pre-set educational standards and the expectations from public
and private schools, they show that multiple equlibria are possible. Dispelling with the simplistic
notion that private schools are always of better quality, they consider the possibility that private
schools may be charging higher fees for other services e.g. leisure, access to certain networks or
religious groups, etc. They show that distinct equilibria are possible, one with better quality
education provided by private versus public schools as in the US, and one where private schools
are worse off as in Italy. This contextual awareness of the effectiveness of private schools is
pertinent in our study and we investigate the issue in great vertical depth by looking at district,
household, village, etc. effects.
5
Wage differentials between public and private schools
In general, research has focussed on two different measures of estimating this premium.
The first relates to the wage differentials between students from public and private schools. In
this vein of research, the overwhelming thrust has been in the favour of private school graduates
with these earning higher salaries than their state-schooled counterparts e.g. A detailed study by
Bedi et al (2000) using data from Indonesia presents evidence that students who were enrolled in
private schools at the secondary school level ended up with higher salaries than those educated in
public schools.
Learning Outcomes in public and private schools
The other measure of estimating the private school premium has been through a
comparison of learning outcomes in private and public schools. A wealth of scholarship has
emerged in this regard, and it is progressively getting more sophisticated in the estimation and
explanation of this premium. The key point here is to disentangle the ‘private school effect’ from
other factors that may be influencing learning outcomes. Since often the results of these studies
are based on a comparison of test scores, the performance of students on these tests needs to be
evaluated in closer detail to disengage those factors that affect academic achievement. These
include family background, the age of the child, family income, parents’ education and so on. In
addition, other variables like the number of siblings or birth order can also have an effect.
Isolating the contribution of a private school education to the test score thus requires careful
analysis with many control variables to prevent the conflation of effects of different factors.
Studies focussing on the disentanglement of the true private school effect have mostly
taken one of two forms: either an econometric analysis of pooled data from private and public
6
schools with a dummy variable representing the respective school type, or separate estimation for
private and public schools using an equation for each type. The results are mixed however with
some studies finding that private school students perform better than public school students and
vice versa in other cases.
In many studies, private school students have been seen to outperform students enrolled
in public schools e.g. Jimenez, Lockheed and Paqueo (1991) use data from Colombia, the
Dominican Republic, the Philippines, Tanzania and Thailand and find that at the secondary
school level, private school students obtained higher scores on standardized mathematics and
language tests even after controlling for the fact that on average, private school students in these
countries hail from more advantaged backgrounds. The study also finds that the unit costs of
private schools are lower than public schools. In another study, Jimenez, Lockeed and
Wattanawaha (1988) use a mathematics test for students in eighth grade in Thailand in public
and private schools. Again, private school students are seen to do better and the difference in
outcomes is explained in terms of the smaller size of private schools and their location in
wealthier neighbourhoods (leading to access to better resources and peers) even though there
were fewer certified teachers in private schools than in public schools.
Some studies have employed a more experimental set-up e.g. one discussed by Rouse
(1998) on the Milwaukee Parental Choice Programme. By randomly assigning students as
treatment (private schooling) or control groups through a voucher programme, the study finds
that private school students performed on a mathematics test better but there was mixed evidence
on the reading test. Similarly, Kim, Alderman et al (1999) use the Quetta Urban Fellowship
Program in Pakistan to conduct a random experiment by looking at enrollment in private schools
in a poor neighbourhood of the city. Enrollment for both boys and girls witnessed an increase
7
with the setting up of a private school suggesting that subsidizing private school education can
increase demand for it even while holding other features of the school constant.
However, other studies have detected no clear advantage for private schooling. Using a
randomized lottery, Cullen, Jacob and Levitt (2006) study the impact of school choice on
academic attainment and finds that there is no systematic gain found in academic measures of
measuring performance. However, students are seen to benefit in a number of non-traditional
measures e.g. discipline. Other papers have refined the comparison still further e.g. by only
focusing on the teaching of Economics at the secondary school level, Grimes (1994) shows that
students in public schools outperform their counterparts in private schools in the United States.
Other papers have also reported better performance by public school students e.g. Newhouse and
Beegle (2006) use data from Indonesia to show that at the junior secondary school level, public
school students obtain better results than madressahs or private schools.5
A number of studies have also focused on Pakistan. Das, Pandey and Zajonc (2006) use a
test at the end of third grade and find that a large differential exists between schools as opposed
to differentials between students from varying backgrounds; e.g. the gap in the scores for the
English language test between government and private schools was found to be twelve times the
gap between children from rich and poor families. By conducting a village level analysis, they
also find that good quality and bad quality schools co-exist in every village and therefore the
differences are a result of the quality of schools, not differences across villages.
Monazza Aslam (2009) also studies public and private schools in Pakistan and finds that
boys are more likely to be enrolled in private schools than girls within the household. She also
5However they find that secular private schools perform on the same level as public schools.
8
finds that private schools are of better quality and more effective in imparting quantitative and
linguistic skills. However she finds that gender significantly determines the learning
opportunities that accrue to a child with girls getting less educational expenditure within the
household and also attaining education of poorer quality.
We explore the same issue in greater detail and by using the latest survey data to obtain a
fine-grained understanding of private versus public school learning outcomes at different levels.
The next sections detail the data and methodology we employ.
3. Data
Data for Annual Status of Education Report (ASER) for the years 2010 and 2011 for
Pakistan is used for the purpose of this study. For 2010, the data was collected from 33 districts
of Pakistan and the sample size employed for the analysis is of 24,018 students. For the year
2011, the data collection was expanded to 85 districts and the sample size analyzed consists of
80,310 observations. Therefore, for the pooled analysis the sample size totaled up to 104,328
observations. The data has been divided into different groups for the various FE estimations. The
divisions at different levels comprise of eight provinces/administrative areas, 85 districts, 2692
villages and 49,244 households.
In terms of sample selection ASER uses the Probability Proportional to Size (PPS) technique
so that villages with higher population have a higher chance of being selected into the sample.
Within each village, for the purpose of selecting households the village is divided into four
hamlets such that the population of the village is divided into four equal parts. Next, a household
is picked from the centre of each of these households and interviewed. Every fifth household
9
from the left of the first one selected is chosen until five households have been selected from
each hamlet to yield an overall sample of 20 households from each village.
The head of the household answers the information concerning the characteristic of the
household such as the asset base of the house etc. The children belonging to the household
available at the time of the survey are asked to sit for the learning assessments which have three
components namely English, Mathematics and Urdu (the national language).
4. Methodology
The aim of the study is to determine whether there are significant differences in the
outcomes of private vs. public schools. Traditionally, this can be done in two ways. Since the
primary benefit of schooling is thought to be the wage premium derived from higher or better
quality education, one way to assess the difference between public and private schooling is to
examine the premium of private schooling in labor market earnings accruing to graduates of
various schools, both private and public.6 The other method is to look at the differences in
students learning achievements at the school level. This approach treats learning outcomes as
output from an education production function with various types of inputs contributing to the
outcome. The inputs considered can be categorized at the individual level (students’ age,
gender), household level (parenting choices, parents education, number of siblings) and school
level (facilities at school, option of tuition etc). The output is determined by a score of students’
performance on a test.
6See Nasir (1999) and Asadullah (2005)
10
Within the second approach, the analysis can be conducted by either pooling the data for
students belonging to both public and private schools and adding aprivate enrollment dummy
variable to ascertain the effect of private schooling on the educational attainment of an
individual, or separate education production functions for public and private students can be set
up.
This study employs the first approach and estimates the education function using the
ordinary least squares approach as:
Where Y is the test score and X is a vector encompassing the child, household and village
level control variables as discussed earlier while D is a dummy variable taking the value of one if
the student is from a private school and zero otherwise. Table 1 below lists the independent
variables and provides a brief description of each.
Table 1: Descriptive Statistics
Variable Description
Private Dummy variable: Takes a value of one if the student goes to a private
school and zero otherwise
Female Dummy variable: Takes a value of one if the student is a female and zero
otherwise Child Age Age of the student in years
Tuition Dummy variable: Takes a value of one if the student takes outside tuition
zero otherwise Number of Siblings Number of siblings of the student
Father’s Age Age of the students father in years
Father’s Age Squared Age squared of the students father in years
Father attended school Dummy variable: Takes a value of one if the students father attended
school and zero otherwise Mother’s age Age of the students mother in years
Mother’s age squared Age squared of the students mother in years
Mother’s attended school Dummy variable: Takes a value of one if the students mother attended
school and zero otherwise Birth order of child Number of elder siblings in the house
Wealth Wealth index
11
At the individual level the gender and age of the student are expected to impact the
learning outcome. Age of the child should have a positive relationship with the test score, since
higher age should most likely correspond to a higher grade and a better performance on the test.
The gender dummy will help assess any gender differences in learning outcomes that might be
there. At the household level father and mothers age and age squared are added as explanatory
variables. Apriori it is expected that the relation of parents’ ages with child’s education should be
positive initially and should eventually become negative. The coefficients on both mothers and
fathers age are hence expected to be positive while that of the age squared should be negative.
Also if the parents of the student are educated it is more likely that s/he would perform better at
school. The reasoning is twofold; not only will the parents be able to better motivate the child but
would also be able to help the child with any course work. Likewise the birth order and number
of siblings are also expected to impact the child’s performance. On the one hand the lower down
the child is in the order of birth in the house and greater the number of siblings that the parents
need to support, less will be spent on the child’s education. On the other hand however theory
also suggests that having elder siblings could also mean additional support in learning and might
motivate the children to learn/study more effectively. Additional support may also come from
tuition that the child may be taking outside of school which is expected to impact the learning
outcome positively and increase the score.
Wealth of the family is also an important determinant when it comes to educational
attainment. It is indicative of a households financial position and hence the ability to afford
quality education. For the purpose of this study a wealth index is constructed which takes into
account various assets that would directly and indirectly contribute in the well being of the child
and ease access to different resources which might result in better learning achievement. The
12
indicators used in construction of the index include conditions of the house the family lives in
(kind of dwelling and toilet facility), ownership of assets pertaining to information and
communication technology (mobile phones, electricity) and assets that ease transportation (car,
cycle, motorbike).
The outcome or the dependent variable is a test score which in the case of this study
consist of an overall score based on assessment of student’s learning/skills in English, Urdu and
Mathematics. For each of the three subjects scores are assigned depending on the students’
ability e.g. whether a student can recognize alphabets only, form a word, has counting skills or
whether s/he can add or subtract. A higher score indicates a better performance. The score of
each child is standardized by converting them into Z-scores i.e. subtracting the mean and
dividing by the standard deviation. This enables an easier interpretation of the results.
The analysis, using the OLS technique as described above, is performed at various levels
namely fixed effects at the household, village, district and provincial levels. Conducting the
analysis in this manner allows us to control for differences between the units at the various
levels. The fixed effects technique helps in mitigating the endogeneity bias inherent in a simple
OLS estimation.
Oaxaca Decomposition:
Once the estimates of the regression analysis are available it is worthwhile to apply the
Oaxaca decomposition7 to the results to determine the proportions of the differential in z-scores
explained by the endowment differences between the students in the two types of schools and the
quality of education provided. Although the Oaxaca decomposition has traditionally been
7 See Oxaca (1973)
13
employed to explain wage differentials in the labor markets and evaluate differentials due to
discrimination, the approach has also been applied for differentials in education.8
This approach decomposes the mean difference in learning outcomes, based on the OLS
estimates, into the characteristic (explained portion) and coefficient effect (the unexplained part).
The unexplained portion in this case will possibly indicate the better educational services
provided by the private schools, amongst other unobservables.
The procedure involves the use of the OLS coefficients in conjunction with mean of the
variables in the two sectors to estimate the endowment and discrimination effects. The difference
in the z scores is given by
Zprivate
– Zpublic
= Xprivate
βprivate
– Xpublic
βpublic
where βprivate
represents OLS estimates for the private school children which is considered the
advantaged group and βpublic
represents the OLS estimates for public sector (disadvantaged
group). Xprivate is
a vector representing the means of the independent variables for the private
while Xpublic is
for the children enrolled in public school.
The difference between the coefficients of the two sectors is given by:
Δβ = βprivate
– βpublic
implying that βpublic
= βprivate
- Δβ
If this second relationship is substituted in the first one, the following equation results:
Zprivate
– Zpublic
= βprivate
(Xprivate
– Xpublic
) + Xpublic
Δβ
This equation will be used to determine the proportion explained by endowment differences and
the unexplained portion.
8 See Thapa (2011) and Desai et al (2008)
14
5. Results
Descriptive Statistics
Before a discussion of the regression results, it is pertinent to look at some summary
statistics from the dataset. Table 2 shows the mean values for each variable used in the
estimation for both public and private schools. The last column shows the result of a t-test
conducted to determine whether or not the difference between the values for public and private
schools is significant. The average z-score of a public school student is significantly less than
that for a private school student. However, it is important to note that other variables which
might also have an effect on the score are also significantly different between the two categories,
hinting towards the existence of a selection bias.
Of particular interest are variables that explain the differing characteristics of students in
the two types of schools. On average 24.5% of private school students take tuitions compared to
only 7.8% of students enrolled in public schools. This indicates that taking tuitions might be an
important determinant of the differential in learning outcomes between public and private
schools. Similarly, a higher proportion of parents with privately enrolled children attended
school compared to parents of children enrolled in public schools. While 71.6% of fathers and
41.6% of mothers of private schools students were educated, the figures are much lower for
public schools (52.1% and 20.3% respectively.) These figures can imply that educated parents
might prefer private schooling and their children might also have the advantage of more help and
guidance from their parents in their schoolwork. Lastly, there is a significant difference in the
wealth index, indicating that richer parents can afford private schooling for their children and
prefer private schools to public schools.
15
Table 2: Raw differences between public and private school students’ z-score
Public Private T value
Z-score 0.127 0.323 -30.05
Female 0.364 0.39 -7.9
Child age 9.501 9.04 21.04
Tuition 0.078 0.245 -78.98
Number of siblings 1.908 1.794 12.9
Father age 42.31 40.906 23.24
Father school 0.521 0.716 -59.65
Mother age 36.99 35.734 18.97
Mother school 0.203 0.416 -74.36
Birth order 1.934 1.96 -3.49
Wealth 0.236 0.401 -88.07
Note: The t-values indicate that all the variables are significant at the 5% significance level
Figure 1: Z scores for Public and Private School Students
Figure 1 above shows that the z-scores of public and private school students for the years
2010 and 2011. The difference between the achievement levels of private and government school
students is falling over the year, and the gradient for the students enrolled in public schools is
steeper than the one for private schools. This means that public schools students are catching up
with the private school students. However, this is the raw difference in z-scores and the data is
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
2010 2011
private public
16
only for two years therefore this result might not hold when a longer time series is taken into
account.9
Pooled Regression:
The results for the estimation using OLS regression with fixed effects at the provincial,
district, village and household levels for the pooled data for the years 2010 and 2011 are
presented in Table 3. Fixed effects estimation at these four levels helps in controlling for those
characteristics that have an invariant effect on the test scores at the provincial, district, village
and household levels respectively. A dummy for the year 2010 has been added to analyze the
achievement differences over the two years. Significantly negative coefficients for the entire set
of regressions shows that the students on average performed better in 2011 than in 2010.10
Fixed effects at the provincial level help control for observable and unobservable
differences between provinces/regions. The Eighteenth Constitutional Amendment devolved
education to the provinces, which makes clustering at this level all the more important. There
exist cultural differences between provinces that can also be translated into different attitudes
towards education as well as different levels of political stability, for instance the military
operation on the tribal regions of FATA. However, even after using province fixed effects the
private school advantage of 0.103 standard deviations remains.
9 Analysis of a longer time series not possible in this paper as ASER Pakistan was formed only recently, and
therefore only reports the data for the past couple of years.
10 To see the regression results for the latest ASER data (year 2011) refer to appendix 1
17
Table 3: Learning Assessment Pooled Regression using Fixed Effect Estimates
No FE Province FE District FE Village FE HH FE
Private 0.123***
0.103***
0.111***
0.0958***
0.0378***
(21.17) (5.97) (8.13) (12.04) (3.79)
Female -0.00436 -0.00848 -0.00904 -0.0100**
-0.0145***
(-1.11) (-0.55) (-1.12) (-2.20) (-3.16)
Child age 0.176***
0.178***
0.178***
0.177***
0.153***
(176.81) (24.06) (58.21) (122.61) (74.29)
Tuition 0.157***
0.146***
0.145***
0.141***
0.111***
(21.51) (3.81) (9.24) (14.77) (8.15)
Number of
siblings
0.0621***
0.0590***
0.0539***
0.0500***
(23.50) (8.21) (12.39) (14.90)
Father’s age 0.00882***
0.00404 0.00725***
0.0115***
(5.77) (1.35) (2.96) (6.50)
Father’s age sq. -0.0000655***
-0.0000325 -0.0000610***
-0.0000911***
(-4.29) (-1.44) (-2.69) (-5.71)
Father attended
school
0.0692***
0.0436* 0.0381
*** 0.0271
***
(11.76) (1.95) (2.97) (3.48)
Mother’s age 0.0159***
0.0169**
0.00546* 0.00348
*
(15.83) (2.96) (1.79) (1.68)
Mother’s age sq -0.000179***
-0.000187**
-0.0000648* -0.0000441
**
(-14.41) (-2.92) (-1.88) (-1.99)
Mother attended
school
0.124***
0.102***
0.0887***
0.0654***
(18.94) (10.00) (7.01) (9.48)
Birth-order -0.0542***
-0.0507***
-0.0501***
-0.0521***
-0.104***
(-17.00) (-9.04) (-8.54) (-12.62) (-20.06)
Wealth 0.00145***
0.00120***
0.00111***
0.000816***
(34.87) (9.63) (12.52) (14.11)
Year 2010 -0.205***
-0.220**
-0.198***
-0.185***
(-30.72) (-2.94) (-5.22) (-9.10)
Constant -2.512***
-2.323***
-2.141***
-2.119***
-1.125***
(-70.37) (-44.42) (-27.68) (-49.03) (-38.32)
Observations 104328 104328 104328 104328 104328
R2 0.4686 0.4685 0.5047 0.5629
t statistics in parentheses
*p< 0.10,
**p< 0.05,
***p< 0.01
Moving the analysis to the district level, some district are largely urban as opposed to
others, which means that they do not represent a typical rural area that one would observe in a
18
predominantly rural district. Therefore there is an inter-district variation in the extent of urban
influence on the rural area that needs to be controlled for. Furthermore, since the district is the
smallest administrative unit in our study, fixed effects at this level also take into account the
differences in resources allocation to the education sector. We find that district level fixed effects
regressions still show a private school advantage of 0.11 standard deviations.
At the village level we then control for difference between villages in terms of school
quality and the number of educational opportunities available to the children. For example,
residents of villages closer to towns or cities have relatively better developed educational
facilities as well as the possibility of attending the schools in the nearby urban centers, as
opposed to residents of villages farther away from urban areas. The private school effects falls to
0.096 standard deviations when we use village level fixed effects, which means that differences
at the village level were critical in determining achievement levels.
At the household level, we control for household level characteristics that are uniform for
all children in a household e.g. age of the parents, the wealth level of the household and whether
or not the parents attended school. It is important to note however that the private school dummy
remains highly significant at all levels even after the strongest fixed level estimation at the
household level.
Estimation at the household level enables us to study the private school affect assuming
that all the children in a household face an identical environment. Therefore the assumption is
that unobservable characteristics like the attention and care that the parents give to the children
are uniform and that siblings in a household have the same ability. This implies that when
household fixed effects are employed, we are observing the same child attending different
19
schools. Although it is a very strong assumption, the basic premise of siblings being more like
each other than any other individual still holds. This also suggests that this narrowest
specification gives the tightest upper bound on the private school effect that is still turning out to
be a significant 0.038 standard deviations.
Private school students perform 0.12 standard deviations better than public school
students in simple OLS. The coefficients on the private school dummy have a decreasing trend as
the fixed effects estimation becomes more stringent. When we move to household level fixed
effects, this difference decreases to 0.038 standard deviations. These coefficient estimates were
significantly below the OLS estimates indicating that using fixed effects eliminates a large
fraction of the bias that remained in the simple OLS. However, from the least (provincial level)
to the strictest fixed effects (from provincial level to the household level) the private school
effect remains significantly positive, implying that though there are various factors determining
achievement levels, the effect remains consequential.
Figure 2 below shows the unadjusted (raw) difference between the achievement levels of
private and public school students versus the adjusted (after controlling for other variables –
regression estimates from Table 3) differences. The unadjusted difference is almost 0.2 standard
deviations and falls to 0.123 using simple OLS. It continues to fall as fixed effects are used at
various levels and is lowest for the household level. This means that at least 0.138 standard
deviations difference in achievement levels is explained by factors other than private school
enrollment; however private enrollment remains a significant factor.
20
Figure 2: Adjusted-Unadjusted gaps between public and private school students
Overall, factors that were intuitively expected to impact the test score positively were
seen to do so. These include the age of the child, ages of parents, wealth, father and mother’s
school attendance, and private tuitions outside school. A factor that can have an ambivalent
effect: the number of siblings of a child is seen to impact the test score positively. This suggests
that the educational benefit from having additional siblings outweighs the potential negative
effects of the same. However, the effect of birth order has a negative effect on the score: so a
child lower down in the birth order performs worse than an earlier-born child. This possibly
indicates that less investment or attention is given to younger children compared to their older
siblings.
The ages of the parents have a non-linear effect on the test score i.e. for both mothers and
fathers, test score increases positively up to an optimal age suggesting younger parents devote
more energy and resources into child rearing but the effect tapers off as the parents grow older.
0
0.05
0.1
0.15
0.2
0.25
Raw OLS Province District Village HH
Unadjusted difference Adjusted difference
21
A dummy variable captures the effect of the gender of the child on the test score11
. The negative
coefficient on this variable in all the pooled regressions suggests that if the child is female, the
test score is lower suggesting some level of gender discrimination. It is important to note that
gender discrimination is significant at the village and household fixed effects level implying that
it is a within-village and within-household phenomenon. For the household fixed effects level
boys perform 0.015 standard deviations better than girls. This suggests that the evil of gender
discrimination has to be eradicated from the household level to have a nationwide impact.
Oaxaca Decomposition
Finally the Oaxaca decomposition is applied to the regression results to disaggregate the
effects of differences in endowments and private schooling/ unobservables. Table 4 below lists
the decomposition details.
Table 4: Oaxaca decomposition
endowment
differences
Private
school
effect
sum
Female 0.000589 0.013814 0.014402 Child Age -0.08335 0.066019 -0.01733
Tuition 0.021354 -0.00707 0.014285
Number of siblings -0.00591 -0.01885 -0.02476
Father’s Age -0.0183 0.386714 0.368413
Father’s Age squared 0.013128 -0.16139 -0.14826
Father’s School
Attendance
0.015538 0.019941 0.035479
Mother’s Age -0.01077 -0.30981 -0.32058
Mother’s Age Squared 0.011749 0.113048 0.124797
Mother’s School
Attendance
0.018789 -0.0021 0.016685
Birth Order -0.00025 0.033396 0.033146
Wealth 0.041556 -0.05347 -0.01192
0.004112 0.08024 0.084352
Proportion 0.0487 0.9513
11
This assumed a value of 1 if the child is female.
22
Observing the decomposition at a disaggregated level we see that, on average, parents of
children attending private school are younger and have fewer children. Table 4 shows that
around 5% of the variation in the scores between public and private schools is explained by
endowment differences between the two while 95% is owing to the private school effect or some
unobservables like innate abilities. This implies that the quality of education, school facilities, etc
offered in the private schools contribute proportionately much more to raising the score of
children as compared to the differential in endowments.
6. Conclusion
The main objective of this study was to critically analyse the differential in learning
outcomes of students enrolled in public and private schools using an econometric technique that
controlled for biases at the provincial, district, village and household levels to disaggregate the
true ‘private school effect.’ The existence of such an effect would imply that children enrolled in
private schools are able to outperform their peers in public schools despite any differences in
endowments. It also highlights the existence of a private school premium, perhaps caused by
better provision of education or school facilities that are lacking in public schools. The existence
of such a differential therefore feeds into the debate on the level and quality of education being
provided by the state.
The results of the study suggest that the private school impact is significant firstly
because the dummy for private school in the pooled regression is significant at all levels with a
positive sign. Additionally the Oaxaca decomposition shows that even when endowment
differences between children enrolled in the two kinds of schools are controlled for, a substantial
proportion of the mean difference still remain unexplained which hints towards better learning in
23
private schools. However the study does not control for endogeneity completely so it is unclear
whether the positive differential is entirely due to better quality of education provided in private
schools or some unobservables such as innate abilities of children, difference in motivation and
performance of teachers etc. Furthermore, school level factors like quality of teachers or school
facilities have not been controlled for so the differences in outcomes are not explained fully.
These suggest important directions for future research in this area, with this preliminary study
providing some interesting results that pave the way for more rigorous investigation of this issue.
24
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27
Appendix 1
2011 Learning Assessment Cross-sectional Regression using Fixed Effect Estimates
No FE Province FE District FE Village FE HH FE
Private 0.121***
0.0952***
0.104***
0.0938***
0.0582***
(18.18) (4.03) (6.55) (11.00) (5.04)
Female -0.00834* -0.0134 -0.0163
* -0.0188
*** -0.0175
***
(-1.85) (-0.81) (-1.94) (-3.85) (-3.32)
Child age 0.176***
0.177***
0.179***
0.177***
0.154***
(159.16) (28.01) (55.97) (115.41) (67.22)
Tuition 0.162***
0.143***
0.133***
0.133***
0.0942***
(18.94) (4.14) (7.58) (13.26) (5.84)
Number of siblings 0.0593***
0.0562***
0.0498***
0.0488***
(20.13) (9.00) (10.18) (15.32)
Father’s age 0.00807***
0.00415 0.00647***
0.00984***
(5.05) (1.72) (3.14) (6.77)
Father’s age sq. -0.0000546***
-0.0000312 -0.0000508**
-0.0000738***
(-3.48) (-1.67) (-2.56) (-5.27)
Father attended school 0.0563***
0.0710***
0.0578***
0.0346***
(8.56) (6.97) (4.57) (4.70)
Mother’s age 0.0159***
0.0162**
0.00581***
0.00434***
(15.59) (2.38) (3.13) (3.75)
Mother’s age sq. -0.000180***
-0.000169* -0.0000642
*** -0.0000461
***
(-14.34) (-2.23) (-2.93) (-3.39)
Mother attended school 0.106***
0.0852***
0.0723***
0.0486***
(14.24) (4.45) (5.18) (6.71)
Birth-order -0.0524***
-0.0495***
-0.0468***
-0.0500***
-0.100***
(-14.79) (-7.40) (-7.03) (-11.79) (-17.55)
Wealth 0.00127***
0.000987***
0.000967***
0.000636***
(27.41) (5.15) (9.31) (12.34)
Constant -2.443***
-2.294***
-2.117***
-2.069***
-1.103***
(-64.11) (-33.21) (-27.46) (-53.20) (-33.97)
Observations 80310 80310 80310 80310 80310
R2 0.4698 0.4697 0.5171 0.5599
t statistics in parentheses
*p< 0.10,
**p< 0.05,
***p< 0.01