Family Background, School Choice, andStudents’ Academic Performance: Evidence from
Sri Lanka
Harsha Aturupane
Tomokazu Nomura
Mari Shojo
March 2018
Discussion Paper No.1811

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANFamily Background, School Choice, and Students’ Academic
Performance: Evidence from Sri Lanka1Harsha Aturupane
The World Bank
Tomokazu Nomura 2
Aichi Gakuin University and Kobe University
Mari Shojo 3
The World Bank

Abstract
Sri Lanka has made great strides in increasing access to schooling. The country stands out as the only country
in South Asia that has attained universal primary completion. Despite this past progress, Sri Lankan students
still display weak performance. The key challenge now is to enhance the quality of education and improvestudent academic performance. This paper investigates how the student- and school-level factors are related tothe academic performance of Sri Lankan grade 8 students in public schools. It also analyzes the factors related
to school choice and how the school choice affects the students’ performance. The results of the study suggestthat there are large dispersion of average test score among the schools. Looking at the school type, Type 1AB
schools outperforms the other types of schools. Students who come from a family with high socioeconomic
status are more likely to attend Type 1AB school, and treatment effect of attending Type 1AB school on
academic performance is considerably large. Socioeconomic status also explains a significant part of dispersionof academic performance within a school. However, the result does not clearly show the relation between theobservable characteristics of the teachers and academic performance of the students.
Keywords: education, academic performance, school choice, socioeconomic status
JEL classification: I25, O15

1

We are grateful to Sri Lanka Ministry of Education and National Education Research and Evaluation Centre at the
University of Colombo for extensive collaboration with and support to us. We thank the World Bank staff who also
supported this study, in particular Amit Dar, Keiko Miwa, Upul Sonnadara, Visaka Dissanayake, Pius Fernando, Alejandro
Welch and Anita Lakshmi Fernando. Financial support for the study from the Australian Department of Foreign Affairs
and Trade (DFAT) and the World Bank is gratefully acknowledged. Its findings and conclusions are the authors’ alone, anddo not represent the World Bank.
2 Nomura is grateful to Graduate School of Economics, Kobe University for accepting him as a research fellow
and providing research resources.
3
Correspondence: Mari Shojo, Education Global Practice, The World Bank, 1818 H Street, NW, Washington DC
20433, USA; Email: mshojo@worldbank.org.

1

1

Introduction
Sri Lanka has made a great deal of effort to improve its education system and achieve education goals suchas education Millennium Development Goals (MDGs). As a result, net enrollment ratio for primary education
has reached 99 percent, while the secondary school enrollment ratio also improved from 70 percent in 2006 to
80 percent in 2012. Gender parity is also high in primary and junior secondary education enrollment (WorldBank 2015).
Despite these achievements, however, some recent reports show that Sri Lankan students still display weakacademic performance when compared to their international peers (World Bank 2012). Increasing the numberof children enrolled is crucial but not sufficient; it is also important that expanded access to schooling be
accompanied by commensurate improvement in quality of education and learning outcomes. The key challenge
for Sri Lanka is how to make progress in these latter areas.
The nature of quality of education and its association with good learning outcomes have been of greatinterest to educators and researchers in recent decades. However, there is no consensus concerning what factors
influence students’ academic performance. Many studies have attempted to estimate the impact of thecharacteristics of schools including teachers and individuals including families on student achievement. The
findings of such studies, however, are rather mixed and inconclusive.
This paper investigates student and school factors affecting learning outcomes for Mathematics, Scienceand English represented by the scores of achievement test among grade 8 students (aged 12-13) in Sri Lanka. It
also analyzes the factors related to school choice and how the school choice affects the students’ performance.
It contributes unique and important information to understanding these factors, as it is still unclear whatcharacteristics of students and schools affect student performance at the secondary education in Sri Lanka.
The reminder of this paper is organized as follows. Section 2 provides general information about education
system in Sri Lanka. Section 3 reviews the literature on factors affecting students’ learning outcomes. Section
4 describes the source of data we use in the empirical analysis. Section 5 presents the descriptive features of the2

test score distributions. Section 6 examines the relation between students’ family background and school choice,and estimates the treatment effects of attending a Type 1AB school (see Section 2 for school type) on learning
outcomes. Section 7 analyzes the association between characteristics of students, and schools and students’ testscores. Section 8 discusses the nature and implications of the relations between the student/teacher/school
characteristics and the test scores. Section 8 summarizes the findings and concludes the paper.

2

The education system in Sri Lanka
After the end of a long period of civil conflict in 2009 with the government’s defeat of the Liberation Tigers

of Tamil Eelam (LTTE) and with Sri Lanka’s concurrent overcoming of the effects global recession that began
in 2008, the national economy has grown at an average of over 7 percent annually over the past several years.
The country is now classified as a lower middle income country, with per capita gross national income (GNI)of US$3,400 in 2014, and is on target to achieve most of the 2015 MDGs; in general, human development
indicators are impressive by regional and lower middle income standards.
The education system in Sri Lanka is organized into three cycles: primary education (grades 1–5), juniorsecondary education (grades 6–9), and senior secondary education (grades 10–13). Primary schoolingcommences at age 5 or 6 years. The net enrollment rate in primary education for both boys and girls is 99 percent,and at junior secondary level, 85 percent for boys and 84 percent for girls. There is thus a high degree of gender
parity at these levels, which, however, declines somewhat at senior secondary level, with 82 percent of boys
and 88 percent of girls surviving from grade 1 to grade 11. 4
The government (public) school system in Sri Lanka is well developed and widely accessible around thecounty. Private schools are rare, accounting for less than 5 percent of total enrollment. Government schools areclassified into four functional types: Type 1AB, Type 1C, Type 2, and Type 3. Type 1AB schools have classes

4
There are a few possible explanations for the lower survival rates for boys than for girls. First, some boys drop out ofschool and take up various jobs involving physical labor (World Bank 2011a). Another reason could be that somehouseholds appear to invest additional resources in girls’ education (Himaz 2010).

3

from grades 1–13 or grades 6–13, and offer all three curriculum streams of the General Certificate of
Examination Advanced Level (GCE A/L) courses (arts, commerce, and science). Type 1C also provides classes
up to grade 13, but with only two streams (arts and commerce). Type 2 schools offer classes only up to grade
11, and Type 3, only up to grade 5 or 8. Since 1985, some 1AB schools have been designated “National” schools,
funded and administered by the national Ministry of Education. The rest are “Provincial” schools, run byprovincial councils. At the end of primary education, the majority of children sit the grade 5 scholarshipexamination, which was originally intended to be a basis for allocation of financial support for able but poor
students and to facilitate access to high-quality schools for them. The scholarship examination is supposed towiden the school choice of students and increase the competition. Some research, however, indicates that theexamination is now predominantly used by parents as a tool to gain entry for their children into popular national
schools in urban areas (e.g., Little, Aturupane and Shojo 2013).
There are several demand- and supply-side policies in effect in Sri Lanka to promote school enrollment andattendance. With regard to the former, education up to grade 11 is compulsory, and all students from grades 1
to 11 receive free textbooks and uniforms. Education is provided free of tuition costs in all government schools.
Students are entitled to subsidized transport in buses and trains. Free school meals are provided for primary
students in disadvantaged areas. Supply-side policies complementing and supplementing the above-mentioned
demand-side policies to promote participation and retention in schools include the existence of a comprehensive
network of primary and secondary schools, with access to primary education available within two kilometersfrom home and to secondary education within five kilometers from home for all children. There is automatic
progression through the education system up to grade 11. Special education programs are available for children
with special education needs, and non-formal education programs are also available for adolescents who either
never enrolled in school or dropped out at a young age (World Bank 2011a).

4

3

Literature Review
There is broad agreement, backed by international research findings, that education is a powerful driver of

improved quality of skills, and is one of the significant instruments for increased individual earnings, laborproductivity and economic growth. High-quality education (that is, fostering high learning achievement)enhances people’s ability to control their fertility rate and family health. It also facilitates gender equality, peace,and stability (World Bank 2011b; UNESCO 2014). In this context, enhancing quality of education and thereby
improving educational achievement can have significant implications for development and people’s lives inmany countries.
Learning is a product of the combination of formal schooling and factors related to students’ families,
communities, and peers (Rothstein 2000). Numerous attempts have been made by researchers to investigate thedeterminants of student achievement; however, consensus has yet to be achieved concerning factors influencing
student academic performance, and the findings of these numerous studies are mixed and inconclusive. For
instance, Coleman et al. (1966) asserted the importance of family characteristics to explain variation in student
achievement and the relatively small impact of school-level characteristics on student achievement. This
“Coleman Report” generated a flurry of research and debate on student achievement. Based on data from bothdeveloped and developing countries, Heyneman and Loxley (1983) concluded that in low income countries, theimpact of school characteristics on student achievement is comparatively greater than in higher income countries.
Student-level characteristics that have been identified in the literature as potentially contributing todifference in student achievement include gender, socioeconomic status, family size, parental education level,
attendance at private lessons/tuition, self-confidence, presence of books at home, and doing homework at home.
School-level characteristics such as school resources, school type, location, class size, teachers’ years ofexperience, and teachers’ training were also found to influence student achievement. While some research has
shown that both student- and school-level factors have a strong impact on student performance, some studies
have found further that some specific factors have less impact or a negative impact (for literature review, see

5for instance, Hanushek 1995; Glewwe, Hanushek, Humpage and Ravina 2011). Debates continue regarding
factors influencing student performance in general.
Sri Lanka conducts national assessments annually for grades 4 and 8. Aturupane, Glewwe and Wisniewski(2013), investigating the determinants of academic performance as measured by achievement tests conducted
in 2004 for grade 4 students, claimed that among student-level variables, educated parents, better nutrition,
frequent attendance, enrollment in private tutoring classes, access to exercise books, electric lighting at home,and children’s books at home positively influence the academic performance of grade 4 students. Among schoollevel variables, principals’ and teachers’ years of experience, collaboration with other schools in a “school
family,” and frequent meetings between parents and teachers have positive impacts on grade 4 test scores. Since
then there has been no analysis of the determinants of students’ performance in Sri Lanka.
In the present study, we examine the determinants of academic performance among grade 8 students using
recent data from the Sri Lankan National Assessment of Achievement conducted in 2012. This was the firstassessment that used new instruments to test students’ cognitive skills in ways keeping with the new curriculum
and the only one in recent years to collected detailed information on characteristics of students, their families,classrooms, teachers, principals, and schools in general. The 2012 National Assessment was intended to serve
as a baseline for monitoring the level and distribution of learning outcomes over time. The findings have wide
implications for future programs and policies to enhance the quality of education and improve learning
outcomes in Sri Lanka.

4

DataThis study uses the 2012 National Assessment of Achievement for grade 8 students, funded by the national

Ministry of Education and administered by the National Education Research and Evaluation Centre (NEREC)
at the University of Colombo. To assess the achievement level of students completing grade 8, NEREC
constructed tests in mathematics, science and English based on the competency-based curriculum introduced

6

nationwide in 2009. The National Assessment covered the entire country; a multi-stage sampling approach wasused to enable analysis by province, type of school, student gender, and linguistic medium of instruction(Sinhala or Tamil). In the first stage, sample schools were selected within strata with probability proportional tosize, without replacements. In the second stage, a group of students were selected from the sampled schools
using a cluster sampling approach. In sample selection, the province was taken as the main stratum (explicit
stratum). The final sample consisted of 12,821 grade 8 students in 438 public schools. In addition to the tests,
information on characteristics of students, their families, classrooms, teachers, principals, and the schools ingeneral was also collected through questionnaires administered to students, parents/guardians, teachers, andprincipals. Data collected through achievement tests were analyzed on a national and provincial basis, and were
weighted in order to minimize the effect of the discrepancy between the expected and the achieved sample(NEREC 2013).
An overview of our dataset is presented in Tables 1 and 2. Table 1 shows representative statistics for testscores in mathematics, science, and English, while Table 2 provides descriptive statistics for the student
variables, in panel (a), teacher and principal variables, in panel (b), and school characteristics, in panel (c). Test
scores are measured out of 100 points. The outcome variables used for this study were student test scores inmathematics, science, and English. Based on both theoretical considerations and findings from previous
empirical studies, several student- and school-level variables were selected to determine their associations withstudent learning achievement. At the student level, we include the gender of the student, number of siblings,
distance from home to school, whether the student has an undisturbed learning environment at home, whether
the student uses English for communication at home, days absent from school over a two-month period, andtime utilization for studying at home. We also include the family backgrounds of the students: educational
attainment of the parents, family income, number of books available for the student to read at home, and tuition
fees spent on the student. The school-level variables consist of characteristics of the teacher of each subject, theprincipal of the school, and the school as an institution. The information considered on the teachers includes
gender, years of experience as a teacher, educational attainment, and whether they provide remedial teaching.

7

The information on the principal includes gender, years of experience as a principal, and educational attainment.
The school characteristics include location, school type, whether the school is managed by the national
government or a provincial government, linguistic medium of instruction, index of school facilities, 5 numberof students in the class, number of students in grade 8 in the school, proportion of students who have had theirproperty stolen in the classroom, and proportion of students who have experienced violence in the classroom.

[Table 1 is inserted around here][Table 2 is inserted around here]

5

Descriptive features of the test score distribution
Figure 1 below shows the estimated kernel densities of test scores, both for individual students and school

averages, in each subject―mathematics, science, and English. Figure 1 considered together with Table 1
suggests that the academic performance of students in Sri Lanka as a whole is quite poor. Mean scores are higher
than the medians for all three subjects, and the distributions are considerably skewed to the right. The
distributions of school average scores are similar in shape to the distributions of scores for individual students,suggesting that a substantial proportion of test score variance is due to variation between the schools.

[Figure 1 is inserted around here]

Among three focal subjects, achievement in English is particularly poor, with a mode of distribution of justa little over 20 points. Since the questions are multiple-choice, this means that the majority of students achieved

5

The questionnaire for the principals includes a question about the availability of various school facilities andmaterials (10 types of teaching aids, 5 additional facilities, and 21 physical facilities). Principals were asked to choose
answers for each facility from the following options. 1: adequate in number and all in good condition and functioning; 2:adequate in number but not all in good condition/functioning; 3: not adequate and not all functioning; 4: not available. We
constructed an index of school facilities for each school by counting facilities for which the principal chose 1 or 2.

8

no more than the score that could be got by randomly choosing the answers. Mathematics and science show
slightly better scores, which are also less skewed and show considerably higher densities in the right tails of the
distributions.
It is worth noting that the distributions of test scores show multiple modes, especially for mathematics andEnglish. The distribution of test scores in mathematics seems to have peaks at around 60–80 points and at around40 points. The distribution of test scores in English has a peak at around 90 points and another peak at around
20 points. The existence of multiple modes in the distributions implies that the samples possibly represent
multiple distinct populations.
To investigate the source of this multimodality, we divided the whole sample into sub-samples according tocharacteristics of school (province, location, type of school, whether the school is managed by the national orprovincial government, and linguistic medium of instruction). Figure 2 shows the distributions of test score forthe sub-samples: (a) location; (b) school type; (c) school management; and (d) linguistic medium of instruction.

[Figure 2 is inserted around here]

To test differences in means of scores by school characteristics, we regressed the test scores on the dummy
variables for province, location, type of school, national or provincial government management, and linguistic
medium of instruction.
Table 3 presents the result of OLS regression for each subject. In Panels (a), (b) and (c) of Column (1) in
Table 3, we find some mean differences in test scores among provinces. The students in the Western andSouthern provinces perform relatively well for all three subjects, while, the students in the Eastern, Northern,North Central and Uva provinces perform relatively poorly. However, the dispersion of test scores among theprovinces is not very large. Mean scores diverge significantly from the Western province, which is the bestperforming province, only in North Central and Uva for mathematics, Northern and Uva for science, and Eastern,
Northern and North Central for English.

9[Table 3 is inserted around here]

In Panels (a), (b) and (c) of Column (2) in Table 3, we see that the dispersion of student achievement is larger
by location than by province. In our dataset, schools are categorized into three groups according to location:municipal council, urban council, and Pradeshiya Sabha (divisional councils). The results suggest that theschools located in areas administered by municipal councils have higher scores in all three subjects than in thoseadministered by urban councils or Pradeshiya Sabha. For all three subjects, schools in urban councils perform
slightly worse than schools in municipal councils—indeed, the difference is not statistically significant formathematics—whereas schools in Pradeshiya Sabha perform significantly worse than schools in municipal or
urban councils: Average test scores in Pradeshiya Sabha are about 16 points less than those in municipal
councils for all three subjects.
Going back to Figure 2, Panel (a) shows estimated kernel densities of test score distributions by location ofschool for each subject. The distributions are clearly multimodal in municipal and urban councils, for all threesubjects. This suggests that the academic achievements of students in municipal and urban councils are polarized
into two groups. On the one hand, there are a considerable number of students in municipal and urban councils
who perform quite well; on the other hand, there is also a low-performing group in municipal and urban councils
that shows a similar peak to the one in Pradeshiya Sabha.
As seen in Panels (a), (b) and (c) of Column (3) in Table 3, the largest dispersion of student achievement is
the one by school types. As discussed earlier, junior secondary schools in Sri Lanka are categorized into three
types: Type 1AB, Type 1C, and Type 2. Mean scores in Type 1C and Type 2 schools are roughly 20 points lower
than those in Type 1AB schools for all three subjects. Panel (b) of Figure 2 shows estimated kernel densities oftest score distributions by school type. The distributions in Type 1C and Type 2 schools are similar and not soskewed, although performance is poor as a whole, whereas the distributions of Type 1AB schools are
significantly different from the other types, with higher mean scores and wider-spread distributions. In

10mathematics, the mode of the distribution in Type 1AB schools is around 80 points and the distribution is skewed
to the left, suggesting that the majority of the students in Type 1AB schools perform very well in mathematics.
However, the density is also high around the modes of the distributions for the other two types of school,suggesting that a substantial minority of students in Type 1AB schools perform only as well as the majority inthe other types of schools. In science and English, however, students in Type 1AB schools perform much better
than those in the other types of schools.
Another important consideration is whether the school is managed by the national government or theprovincial government. Column (4) of Table 3 shows that the mean scores in national schools are 20 points
higher than those in provincial schools for all subjects. Panel (c) of Figure 2 shows estimated kernel densities
of test score distributions by school administration type. Since most of the national schools are Type 1AB, thisfigure looks at the difference between national and provincial schools among Type 1AB schools only; a large
difference is found even among these schools.
Finally, we compare academic performance by linguistic medium of instruction, Sinhala and Tamil.
Column (5) of Table 3 shows that mean scores for education in Tamil are 4–7 points lower across subjects. Thisis a statistically significant difference, but not a very large one. As can be seen in panel (d) of Figure 2, thedistributions of test scores are similar between Sinhala and Tamil, for all subjects.
We now consider all dummy variables together (see Column (6) of Table 3). After controlling for otherfactors, significant effects remain for school type, location of Pradeshiya Sabha, and school management(national or provincial), although the coefficients have attenuated. On the other hand, the coefficients forlinguistic medium of education and province turn out to be insignificant.
Figure 3 shows breakdown of students into school types by province and location. The number of students
in Type 1AB schools can be seen to vary by province and location, suggesting that a substantial part of thedifferences in test scores among provinces and locations can be explained by school type.

[Figure 3 is inserted around here]11

6

Family backgrounds and school choice
As discussed in the previous section, the academic performance of the students varies by school type:Type 1AB schools perform much better than the other types. If these differences come from the quality ofeducation provided by schools, parents who care about children’s education might want to send their children
to Type 1AB schools (which are indeed apparently known as better schools). In this section, we analyze therelationship between the family backgrounds of students and their (families’) school choices. If we find that
only parents who have better educational backgrounds or higher income send their children to better schools,
this will imply that there is very limited opportunity to access good education for students with low
socioeconomic status, a situation of concern that will require specific policy interventions.
We employ the probit model to analyze factors related to school choice. Let y be a dummy variable thattakes the value of 1 if the student is in a Type 1AB school, and 0 otherwise. The model is specified as thefollowing equation.
Pr(y = 1|xi) = Φ(xiγ + ui),(1)

where xi is the vector of family background variables of student i, Φ() is the cumulative distribution function of
the standard normal distribution, γ is the vector of parameters to be estimated, and ui is the error term.
Theoretically, the explanatory variables of school choice should represent the family backgroundcharacteristics of the students at the time they enter school. Since these students are in grade 8, the school choice
was made much earlier before the survey. However, most of the variables used here might be considered not tochange frequently, and to be relatively persistent. For example, the educational attainment of the parents will
not change frequently, and although family income and the other variables could change, their present value
should be closely correlated with their value at the time school choice was made. Thus, we assume that thepresent values of these variables work as a reasonable proxy for their values at time of school choice.
The results of the estimation for equation (1) are shown in Table 4. The explanatory variables used are genderof the student, mother’s educational attainment, father’s educational attainment, family income, number of12books available for the student to read at home, amount of tuition fees spent on the student, and number ofsiblings the student has. Geometrical conditions (province and location) are also controlled for.

[Table 4 is inserted around here]

The results suggest that the family backgrounds of the children indeed affect their school choice. Students
whose parents have higher educational background, particularly GCE O/L level and higher, are more likely toattend Type 1AB schools. It is noteworthy that the coefficient for father’s education is larger than that for themother.
Family income also affects school choice, even after controlling for the parents’ education. Students fromfamilies with higher income are more likely to be in Type 1AB schools than other students.
The number of books available to the student at home and the amount of tuition fees spent on the student
both also have significant effects. These are considered to be proxies for how much attention and importanceare given by parents to children’s education, implying that those who pay more attention to the education of
their child have a greater tendency to send their child to Type 1AB schools.
The number of siblings has a negative effect on choice of Type 1AB schools. This is likely because resources
spent on a child decrease when the family has many children. These results suggest that the opportunity toacquire a good education is constrained by the resource available for each child.
The most important question here is whether school choice affects the student’s academic performance.
Since school choice is not random, the difference in test scores between students in Type 1AB schools and inother schools cannot be interpreted as a treatment effect. Thus, to identify treatment effects, we apply thepropensity score matching method, which estimates the average treatment effect of attending a Type 1AB schoolby comparing test scores of students with the same propensity scores across school types.
The estimated average treatment effect for each subject is reported in Table 5. It is suggested that attending
a Type 1AB school makes students’ test scores roughly 10 points higher than attending other types of school.

13

[Table 5 is inserted around here]

7

Factors affecting test scores
7.1

The model and methods of estimation

We now analyze how the student- and school-level variables are related to the learning performance of thestudents. The empirical model we use is represented by the following equation,
sij = α + θj + zijβ1 + qjβ2 + uij,

(2)

in which sij represents the test score of student i in school j, α is an intercept, θj is the school-specific effect ofschool j, zij is the vector of the characteristics of student i in school j, which could affect the test score of thestudent, qj is the vector of the characteristics of school j, uij is the idiosyncratic error term, and β1, β2, are thevectors of parameters we intend to estimate.
The method employed to estimate the parameters depends on the assumption regarding the school-specificeffect, θj. If we assume that θj is non-random in the sense that it is correlated with the explanatory variables, wewill employ the fixed-effect model, whereas if we assume that θ is random in the sense that it is not correlatedwith the explanatory variables, we can employ the random-effect model.
Using the fixed-effect model, we can estimate the effects of the characteristics of the students and theirfamilies on the test scores, controlling for the effects of the school each individual belongs to. The advantage ofusing the fixed-effect model is that it can control for any school characteristics affecting the student learning
performance, including unobservable ones. However, we are not able to include school-level variables using the
fixed-effect model, because the effect of school characteristics cannot be identified from θij by this model. Thus,
to identify the effect of school characteristics, we regress the average score of each school on the school
characteristics controlling for the mean value of student characteristics. Finally, we employ the mixed-effectmodel to estimate the effect of the student- and school-level variables together.

14

7.2

Fixed-effect model

We now estimate the fixed-effect model. As discussed in the previous subsection, this model estimates theassociation of student-level variables with intra-school variation in learning performance. The variables we use
to represent the characteristics of the student are gender, number of siblings, distance from home to school,
whether the student has an undisturbed learning environment at home, whether the student uses English forcommunication at home, number of days absent from school, educational attainment of the parents, family
income, number of books available for the student’s reading at home, and amount of private tuition fees spenton the student.
In addition, information about the student’s amount of time spent learning at home—on homework,
receiving additional private instruction, self-study, etc.—is available for analysis. It should be noted, however,that using such information reduces the sample size by more than 30 percent due to the low response rate onthese questions. Thus we estimate the model without information on the student’s time learning at home (model1), and with it (model 2).
Table 6 shows the results for the fixed-effect estimation. In Sri Lanka, girls outperform boys on all threesubjects, and the differences are statistically significant when we do not control for the student’s time spentlearning at home. However once time spent learning at home is controlled for, the differences are not significant.
This suggests that the girls study at home more than the boys do, and this is what explains differences inacademic performance by gender.

[Table 6 is inserted around here]

Number of siblings correlates negatively with academic performance for all three subjects. We estimated thecoefficients of the number of elder siblings and the number of younger siblings separately, and found that thecoefficients of the younger siblings are larger than those of the elder siblings. The coefficients are even larger
in model 2, which controls for the student’s time used for learning at home, than in model 1, suggesting that15

students who have many siblings perform worse than for some other reason because they do not have enoughtime to study at home.
Distance from home to school does not correlate with scores in mathematics and science, but it does
correlate with scores in English. The negative coefficients of home distance in English may suggest that students
living in remote areas do not have many opportunities to use English and do not perform well in English.
Students who have an undisturbed learning environment at home perform significantly better. The effect isrelatively large. Thus, it seems important to provide students with an undisturbed learning environment at homein order to improve their academic performance.
Students who speak English at home perform better not only in English but also in mathematics and science.
This may reflect the generally high socioeconomic status of families using English, beyond what is alreadycaptured by family income, parental education, and so on.
The number of days absent from school does not decrease scores, and even increases them in some models.
We cannot give a reasonable explanation for this.
The coefficients of family income are mostly statistically insignificant. This is because family income isclosely correlated to parents’ education; when we exclude the parents’ education, the coefficients of family
income variables became significant. Nevertheless, students from families with very high incomes perform welleven after controlling for parents’ education.
The coefficients of parents’ educational attainment are mostly significant, even after controlling for income.
Students whose parents have higher educational background are more likely to perform well.
The number of books available to the student at home and the amount of private tuition fees spent on thestudent both have significant coefficients, as expected. These variables can be viewed as measuring thesocioeconomic status of the family and how much the parents care about their children’s education. The results
suggest that the amount of resource spent on education by parents plays an important role in children’s academic
performance.

16

The time spent on homework also significantly affects students’ academic performance. Students who spend
15 to 30 minutes on homework daily perform better than who spend no time on homework, and students who
spend 30 minutes to 1 hour perform even better. However, students who spend more than 1 hour on homework
perform only as well as those who spend 15–30 minutes. This suggests that efficient time use on homework isimportant for the better academic performance. The coefficients are relatively large in science and mathematics,
but small in English.
The time spent on private tuition has a significant effect only if it is more than 1 hour. Combined with theinsignificant coefficient for days of absence from school and the significant coefficient of tuition fees, this result
suggests that private tuition works as a supplement to public school and plays an important role in the academic
performance of the students. It should be also noted that time spent on self-learning has a significant effect onlyin science.

7.3

School-level variables and test scores

In this section, we estimate the effects of school characteristics on the academic performance of students. To doso, we first regress the mean scores by school on the mean values of the explanatory variables employed in the
fixed-effect model; then, we add the school-level variables to the set of explanatory variables. Finally, we apply
the mixed-effect model.
Table 7 shows the results of the regressions on the school mean. In model 1, we use as a set of explanatory
variables the means of the variables used in the fixed-effect model, with some variables that turned out to be
insignificant in the fixed-effect model omitted. The results are mostly the same as in the fixed-effect model,suggesting that the factors explaining the within-school variation of test scores also explain between-school
variation. The important difference is that the coefficients of mean log value for days of absence become
negative (and significant in mathematics), suggesting that students in schools where many students are
frequently absent perform not so well, although absence does not affect the individual absent student’s test score.

17

[Table 7 is inserted around here]

In model 2, we include school-level variables: index of school facilities, number of students in the classroom,
number of students in grade 8 in the school, proportion of students who have ever had their property stolen in
the classroom, and proportion of students who had ever experienced violence in the classroom. Including school
characteristics does not change the coefficients of the student-level variables much, although some coefficientsare attenuated; the signs of the coefficients of the school-level variables are mostly as expected, and they arestatistically significant. However, the index of school facilities is significant only in science, plausibly because
studying science requires more facilities than studying mathematics or English. Finally, the coefficients ofnumber of students in the classroom and number of students in grade 8 are somehow mixed. Because class size
and the school size could be endogenous, we cannot interpret these coefficients simply. Regardless, overall,stealing and violence in the classroom correlate negatively with academic performance, as expected.
In model 3, we add the characteristics of the teachers of each subject and the principal of the school. Most
coefficients are not significant. This may suggest that the characteristics of teachers and principal are notassociated with student learning performance. However, it is virtually universally agreed that quality ofeducation greatly depends on quality of teachers. In this light, the insignificance of the coefficients here could
possibly have several causes. First, these students are in grade 8 and would have been taught by many teachers
in their school careers so far. Thus, the characteristics of their current teachers will carry less weight for theircurrent academic performance. Second, teachers and principals are not randomly assigned. For example,
students who do not perform well may possibly be assigned to good teachers, and principals who have got agood reputation may be sent to schools with low learning performance. Such endogeneity might affect the results.
To identify the effect of the teacher precisely, we need information about all teachers who have taught the student.
Although we have information on average characteristics of the teachers in the school (education attainment,
qualification, attendance, attitude, and so on) from the principal questionnaire, the response rate was low andthe measurement errors are problematically large. Thus, we omitted these from the analysis.

18

Table 8 shows the results of mixed-effect regressions. We estimated three models for each subject, with theunderlying assumption is that school-specific effects are not correlated with the explanatory variables. The
results are mostly consistent with those of the fixed-effect model and regression on school means (Table 6 andTable 7). However, the teacher and school characteristics are not significantly correlated with the test scores.
We discuss the results further in the conclusion.

[Table 8 is inserted around here]

8

Conclusion
In this paper, we examined students’ family background, school choice, and academic performance. The

findings can be summarized as follows. First, there is a large difference in test scores between Type 1AB schools
and other types of schools. Students from families with high socioeconomic status are more likely to be in Type
1AB schools, and the treatment effects of attending Type 1AB school on academic performance are large. These
results suggest that for students of low socioeconomic status, the opportunity to achieve better academic
performance is limited. Second, the fixed-model results suggest that the socioeconomic status of the student’s
family is also closely correlated to students’ test scores. In contrast, there is no clear evidence that teacher andschool characteristics other than type of school are associated with academic performance.
It is worth discussing why teacher and school characteristics are not associated with academic performance.
If differences in academic performance between Type 1AB and the other schools are due to differences in thequality in education provided, the characteristics of the schools should also differ in consistent and significant
ways. However, no clear effects of teacher and school characteristics on students’ academic performance wereobservable in the data, especially given the issue of measurement error mentioned above. If teachers areallocated in light of characteristics that are unobservable in the present research, it may be these qualities that
correlate with the academic performance of the students, remaining uncaptured by the data.

19

It is also important to be aware of the limitations of our dataset. Although the present survey is well designed
to assess academic performance, the measurement error is quite large. Many responses are inconsistent with one
another, which may attenuate the regression coefficients. Aturupane et al. (2013) pointed out the problem ofmeasurement error in the 2002 NEREC test score data. They argued that the teacher and school variables inparticular contain inconsistent and missing values because teachers and principals completed the questionnaire
without any assistance. Aturupane et al. (2013) addressed this problem using an additional dataset collected by
National Education Commission (NEC), providing more detailed information for a random subsample of the
NEREC respondents. Since the NEC survey was conducted by trained interviewers, the collected information
should be more accurate. Aturupane et al. (2013) used teacher and school variables from the NEC survey, but
most of them did not turn out to be significant. Therefore, the differences between Type 1AB schools and othertypes of schools remain mostly unobservable and are not captured by the survey.
In addition, since the data were obtained by the survey at specific point in time and are therefore notexperimentally sound, the coefficients estimated in the regression models might not be interpreted as causal
effects on the test scores. However, our results at least tell us what kind of students we need to pay attention toin formulating policy, that is, what kind of students are left behind. Our results suggest that students from low
socioeconomic status families who do not have enough resources for education of their children are the ones
who tend to be left behind and to need special attention and care in their education. However, we still need tofurther investigate the relevance of differences in teacher and school characteristics for difference in academic
performance between Type 1AB and other types of schools. This will be done in future research.

20

References

Aturupane, H., P. Glewwe, and S. Wisniewski. 2013. “The Impact of School Quality, Socioeconomic Factors,
and Child Health on Students’ Academic Performance: Evidence from Sri Lankan Primary Schools.”
Education Economics 21 (1): 2-37.
Coleman, J.S., E.Q. Campbell, C.J. Hobson, J. McPartland, A.M. Mood, and F.D. Weinfield. 1966. Equality of
Educational Opportunity. Washington, DC: National Centre for Educational Statistics.
Glewwe, P.W., E.A. Hanushek, S.D. Humpage, and R. Ravina. 2011. “School Resources and Educational
Outcomes in Developing Countries: A Review of the Literature from 1990 to 2010.” NEBR Working
Paper 17554. Cambridge, MA: National Bureau of Economic Research.
Hanushek, E. 1995. “Interpreting Recent Research on Schooling in Developing Countries.” World Bank
Research Observer 10: 227-246.
Heyneman, S.P. and W.A. Loxley. 1983. “The Effect of Primary-School Quality on Academic Achievement
across 29 High-Income and Low-Income Countries.” American Journal of Sociology 88 (6): 1162–1194.
Himaz, R. 2010. “Intra-Household Allocation of Education Expenditure: The Case of Sri Lanka.” Economic
Development and Cultural Change 58: 231-258.
Little, A., H., Aturupane, and M. Shojo. 2013. “Transforming Primary Education in Sri Lanka: From a ‘Subject’of Education to a ‘Stage’ of Education.” South Asia Human Development Unit Discussion Paper Series
61. Washington, DC: World Bank.
National Education Research and Evaluation Centre (NEREC). 2013. National Assessment of Achievement ofGrade 8 Students in Sri Lanka—2012. Colombo: NEREC.
Rothstein, R. 2000. Finance Fungibility: Investing Relative Impacts of Investments in Schools and Non-School
Educational Institutions to Improve Student Achievement. Washington, DC: Centre on Educational
Policy Publications.
United Nations Educational, Scientific, and Cultural Organization (UNESCO). 2014. EFA Monitoring Report
2013/14. Teaching and Learning: Achieving Quality for All. Paris: UNESCO.
World Bank. 2011a. Transforming School Education in Sri Lanka: From Cut Stones to Polished Jewels.
Washington, DC: World Bank.
————. 2011b. World Bank Group Education Strategy 2020. Washington, DC: World Bank.
————. 2015. Transforming the School Education System as the Foundation of a Knowledge Hub: P113488- Implementation Status Results Report; Sequence 08. Washington, DC: World Bank.

21

Table 1. Distributions of test scores

Mathematics
ScienceEnglishObs
12,814
12,874
12,817

Mean
51.4
41.9
40.0

Std Dev
21.0
21.4
23.3

Min
0.0
0.0
0.0

10%
25.0
16.0
16.0

25%
35.0
25.0
22.0

50%
47.5
39.0
32.0

75%
67.5
58.0
56.0

90%
82.5
81.0
80.0

Max
97.5
100.0
100.0

Table 2. Descriptive statistics(a) student―level variables
Gender(b) school-level variables

Male
FemaleNumber of elder siblings

0.910(1.165)1.272(1.596)

Number of younger siblings

Distance from school

Less than 15 min
15 ― 30 min
30 min ―1 hour
More than 1 hour

Home environment
Using English
Days of absence

Location

Municipal
Urban
Pradeshiya Sabha

0.132
0.094
0.774

School type(base=1AB)

Type 1AB
Type 1C
Type 2

0.363
0.397
0.240

School managemet

National
Provincial

0.218
0.782

Language

Sinhala
Tamil

0.671
0.329

0.483
0.517

0.309
0.350
0.233
0.108
0.081
0.617
16.977(20.122)

School facilities
Number of students in the classroom
Number of students in grade 8

Time spent on homeworkLess than 15min
15 ― 30 min
30 min ― 1 hour
More than 1 hour

0.135
0.326
0.341
0.198

Time spent for tuition

Less than 15min
15 ― 30 min
30 min ― 1 hour
More than 1 hour

0.097
0.141
0.239
0.523

Time spent for selflearning

Less than 15min
15 ― 30 min
30 min ― 1 hour
More than 1 hour

0.221
0.354
0.255
0.170

Family income< Rs.10,000
Rs.10,001― Rs.20,000
Rs.20,001― Rs.30,000
Rs.30,001― Rs.40,000
Rs.40,001― Rs.50,000
Rs.50,001―

0.405
0.298
0.149
0.066
0.039
0.043

Mother's education

No education
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course post O/L or A/L
Bachelor's Degree
Post-graduation and above

0.062
0.182
0.203
0.302
0.133
0.080
0.019
0.019

Father's education

No education
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course post O/L or A/L
Bachelor's Degree
Post-graduation and above

0.058
0.158
0.179
0.335
0.146
0.085
0.020
0.018

Tuition fees
Number of books for mathematics at home
Number of books for science at home
Number of books for English at home

4,438(8858)
1.924(14.589)
2.492(18.991)
2.863(1.165)

Stealing in the classroom
Violence in the classroom

15.039(9.243)33.998(8.669)112.599(93.075)
0.382(0.191)
0.372(0.162)(c) teacher and principal
Mathematics teacher
GenderMale
FemaleYears of teaching

Education

14.117(10.653)
GCE O/LGCE A/L
Bachelor's Degree
Master's Degree

Time spent for lesson planning(hours)Remedial teaching
Science teacher
GenderMale
FemaleGCE O/LGCE A/L
Bachelor's Degree
Master's Degree or higher

Male
FemaleGCE O/LGCE A/L
Bachelor's Degree
Master's Degree

0.075
0.714
0.163
0.049
1.480
(1.447)0.709

MaleFemaleYears of experience as a principal

Education

0.254
0.746
13.644(8.907)

Time spent for lesson planning(hours)Remedial teaching
Principal
Gender

0.044
0.642
0.227
0.086
1.621
(1.462)0.733

Years of teaching

Education

0.275
0.725
14.881(9.995)Time spent for lesson planning(hours)Remedial teaching
English teacher
Gender

0.083
0.614
0.235
0.068
1.767
(1.453)0.752

Years of teaching

Education

0.425(0.575)

0.855
0.145
10.885(7.331)GCE O/LGCE A/L
Bachelor's Degree
Master's Degree
Ph.D.

0.044
0.274
0.333
0.331
0.017

Table 3. Results of OLS Regression(a) Mathematics(1)
Province(base=Western)

CentralEastern
Northern
North Western
Northern Central
Sabaragamuwa
Southern
Uva

Location(base=Municipal)

Urban

(4)

(5)

(6)
-2.030(2.301)-1.067(2.815)0.238(2.837)0.353(2.415)-1.895(2.279)-0.227(2.143)-0.754(2.155)-5.852 **(2.531)0.790(2.156)-8.608 ***(1.748)-8.405 ***(1.728)-11.928 ***(1.851)
12.619 ***(1.793)-0.102(1.278)58.583 ***(2.323)
12,8140.328-2.708(3.102)-16.924 ***(2.227)1C-19.402 ***(1.320)-22.786 ***(1.426)

Type 2
School managemet(base=Provincial)
Language(base=Sinhala)(3)-3.563(3.406)-6.283(3.830)-3.973(3.686)-1.963(3.742)-7.605 **(3.313)-2.147(3.555)-0.026(3.383)-8.477 **(3.765)Pradeshiya Sabha
School type(base=1AB)(2)

National

20.991 ***(1.462)

Tamil
Constant

54.765 ***(2.302)63.398 ***(2.041)

61.121 ***(1.153)44.200 ***(0.737)-4.611 **(1.946)52.637 ***(1.061)

Observations
R-squared

12,8140.020

12,8140.125

12,8140.242

12,8140.226

12,8140.009

Note: Standard errors in parenthesis.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school-level. Sampling weights are used to obtain the coefficients and standard errors.

(b) Science(1)
Province(base=Western)

CentralEastern
Northern
North Western
Northern Central
Sabaragamuwa
Southern
Uva

Location(base=Municipal)

Urban

(4)

(5)

(6)
-0.711(2.490)1.276(2.906)0.257(2.760)0.493(2.461)
1.961(2.264)0.985(2.335)2.242(2.313)-3.972(2.610)-1.460(2.166)-8.848 ***(1.767)-7.292 ***(1.684)-10.891 ***(1.837)
12.199 ***(1.827)-2.918 **(1.344)48.376 ***(2.456)
12,8740.288-5.531 *(3.246)-16.445 ***(2.357)1C-18.078 ***(1.350)-22.052 ***(1.514)

Type 2
School managemet(base=Provincial)
Language(base=Sinhala)(3)-2.623(3.390)-5.862(3.904)-6.193 *(3.155)-1.650(3.570)-3.264(3.502)-1.222(3.480)3.386(3.329)-6.659 *(3.609)Pradeshiya Sabha
School type(base=1AB)(2)

National

20.450 ***(1.547)

Tamil
Constant

44.015 ***(2.199)53.953 ***(2.187)51.151 ***(1.203)34.865 ***(0.655)-7.594 ***(1.865)43.908 ***(1.050)Observations
R-squared

12,8740.021

12,8740.101

12,8740.209

12,8740.206

12,8740.024

Note: Standard errors in parenthesis.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school-level. Sampling weights are used to obtain the coefficients and standard errors.

(c) English(1)
Province(base=Western)

CentralEastern
Northern
North Western
Northern Central
Sabaragamuwa
Southern
Uva

Location(base=Municipal)

Urban

(4)

(5)

(6)
0.792
(3.176)-9.629 ***(3.237)-7.003 *(3.628)0.137(2.693)-4.593(2.907)0.327(2.971)-2.356(2.539)-2.689(3.322)
1.324(3.227)-14.095 ***(2.289)-10.833 ***(1.938)-13.355 ***(2.029)12.991 ***(2.342)3.100(2.543)52.160 ***(3.001)
12,8170.413-2.419(4.375)-23.333 ***(2.783)

1C-23.922 ***(1.707)-26.268 ***(1.720)Type 2
School managemet(base=Provincial)
Language(base=Sinhala)(3)-0.835(4.651)-13.601 ***(3.976)-9.173 **(4.477)-3.880(4.478)-12.700 ***(4.008)-2.807(5.016)-2.771(4.167)-6.845(5.164)Pradeshiya Sabha
School type(base=1AB)(2)

National

24.426 ***(2.124)

Tamil
Constant

45.253 ***(2.953)56.378 ***(2.583)51.678 ***(1.610)31.625 ***(0.944)-5.835 **(2.532)41.558 ***(1.346)

Observations
R-squared

12,8170.041

12,8170.200

12,8170.282

12,8170.248

12,8170.012

Note: Standard errors in parentheses.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school level. Sampling weights are used to obtain the coefficients and standard errors.

Table 4. Probit model of school choice

Dependent variable:

School type
(1: Type 1AB, 0: Type 1C and Type 2)
Log (number of books)

Male
Gender(base=female)
Mother's education Up to Grade 5(base=no education)
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree

Father's education(base=no education)

Post-graduation
and above
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree

Family income(base = < Rs.10,000)

Post-graduation
and above
Rs.10,001―
Rs.20,000
Rs.20,001―
Rs.30,000
Rs.30,001―
Rs.40,000
Rs.40,001―
Rs.50,000
Rs.50,001―

0.106(0.070)-0.175(0.126)-0.020(0.127)0.243 **
(0.120)0.525 ***
(0.125)0.482 ***
(0.132)0.446 ***
(0.163)0.529 ***
(0.179)-0.019(0.090)0.123(0.099)0.338 ***
(0.105)0.577 ***
(0.112)0.560 ***
(0.122)0.802 ***
(0.169)0.761 ***
(0.189)0.109 **
(0.049)0.310 ***
(0.061)0.343 ***
(0.084)
0.558 ***
(0.105)0.436 ***
(0.108)Log (tuition fees)
Num. of siblings
Province(base=Western)

CentralEastern
Northern
North Western
Northern Central
Sabaragamuwa
Southern
Uva

Location(base=Municipal)

Urban
Pradeshiya Sabha

Constant

Observations

0.073 ***
(0.016)0.047 ***
(0.007)-0.072 ***(0.016)
0.197(0.294)0.238(0.339)0.508 *
(0.291)
0.245(0.295)0.278(0.304)0.144(0.302)0.437(0.288)0.463(0.293)-0.411(0.299)-0.952 ***(0.215)-0.439 *(0.322)9,659

Table 5. Results of propensity score matching

Mathematics

Number ofobservations
9,535

Science

9,308

English9,545

Average treatment effect(standard error)
8.337(0.532)7.193(0.566)10.813(0.692)

Table 6. Fixed-effect model

Gender(base=female)
Number of elder siblings

Male

Number of younger siblings
Distance from school(base=less than 15 min)15 ― 30 min
30 min ― 1 hour
More than 1 hour

Home environment
Using English
Log(days of absense)
Time spent on homework(base=less than 15 min)Mathematics
model 1model 2model 1model 2model 1model 2

-1.109 **(0.436)-0.294 *(0.171)-0.600 ***(0.096)-0.196(0.415)0.290(0.533)-0.792(0.657)-4.331 ***(0.727)0.933 **
(0.416)0.281(0.206)-1.764 ***(0.477)-0.714 ***(0.185)-0.813 ***(0.112)0.356(0.453)
1.158 **
(0.584)-0.949(0.770)-5.494 ***(0.709)0.598(0.448)0.445 **
(0.219)-0.946(0.593)-0.786 ***(0.250)-0.820 ***(0.139)0.287(0.581)0.871(0.691)-1.185(0.924)-4.397 ***(1.108)0.584(0.621)0.291(0.298)3.891 ***
(0.851)5.142 ***
(0.847)4.000 ***
(0.845)0.683(1.084)0.961(0.901)2.918 ***
(0.877)1.375 **
(0.635)1.772 **
(0.716)1.430 *
(0.835)-0.428(0.601)0.424(0.768)0.864(1.078)1.850(1.280)1.452(1.370)-3.758 ***(0.452)-0.503 ***(0.167)-0.566 ***(0.105)-0.656(0.424)-1.213 **(0.553)-1.697 **(0.733)-3.067 ***(0.590)2.721 ***
(0.454)0.451 **
(0.179)-3.624 ***(0.568)-0.470 **(0.219)-0.750 ***(0.151)-1.029 *(0.533)-1.991 ***(0.689)-2.483 **(1.003)-2.178 **(0.876)2.999 ***
(0.601)
0.326(0.216)2.249 ***
(0.659)2.528 ***
(0.689)0.306(0.893)0.922(0.770)0.788(0.736)3.455 ***
(0.784)0.160(0.530)-0.408(0.627)-0.740(0.708)-0.355(0.602)-0.161(0.758)0.171(0.823)
1.222(1.126)
3.694 ***
(1.196)

15 ― 30 min
30 min ― 1 hour
More than 1 hourTime spent for tuition(base=less than 15 min)15 ― 30 min
30 min ― 1 hour
More than 1 hourTime spent for selflearning(base=less than 15 min)15 ― 30 min
30 min ― 1 hour
More than 1 hour

Family income(base = < Rs.10,000)

Rs.10,001―
Rs.20,000
Rs.20,001―
Rs.30,000
Rs.30,001―
Rs.40,000
Rs.40,001―
Rs.50,000
Rs.50,001―

0.239(0.390)0.226(0.525)0.049(0.739)0.090(0.870)0.355(0.915)-0.656(0.528)-0.229(0.222)-0.691 ***(0.126)-0.458(0.526)-0.194(0.644)-1.017(0.854)-4.333 ***(1.088)0.975 *
(0.529)-0.364(0.237)3.230 ***
(0.787)4.202 ***
(0.774)2.837 ***
(0.846)-0.253(0.907)1.141(0.865)
3.573 ***
(0.878)0.058(0.560)-0.237(0.674)0.413(0.705)-0.593(0.517)-0.806(0.645)-0.878(0.860)-0.943(0.964)-0.680(1.124)

Science

0.066(0.438)0.797(0.583)0.463(0.897)2.384 *
(1.243)1.749(1.177)

English0.036(0.392)0.628(0.584)1.091(0.717)2.341 **
(1.024)3.708 ***
(1.051)

Mother's Education(base=no education)

Up to Grade 5-0.081(0.782)0.534(0.805)2.685 ***
(0.829)4.095 ***
(0.947)3.843 ***
(1.052)5.714 ***
(1.451)7.323 ***
(1.605)-0.172(0.866)0.195(0.881)1.094(0.878)3.638 ***
(0.964)3.668 ***
(1.082)
7.842 ***
(1.431)
7.370 ***
(1.570)0.701 ***
(0.055)0.486 ***
(0.178)0.708 **
(0.327)43.962 ***(1.110)-0.887(1.143)-0.822(1.231)1.811(1.238)3.423 **
(1.383)2.937 **
(1.365)4.207 **
(1.761)6.018 ***
(1.796)0.000(1.354)0.521(1.374)1.111(1.400)3.436 **
(1.431)3.590 **
(1.539)7.931 ***
(1.727)7.328 ***
(1.907)0.645 ***
(0.072)0.605 ***
(0.212)0.332(0.371)43.914 ***(2.005)-0.984(0.770)-0.834(0.796)1.266(0.813)2.939 ***
(0.924)2.804 ***
(1.066)5.887 ***
(1.823)5.765 ***
(1.773)0.748(0.895)1.489 *
(0.890)3.384 ***
(0.858)6.994 ***
(1.008)5.400 ***
(1.105)9.326 ***
(1.694)
10.587 ***(1.872)0.620 ***
(0.055)0.557 **
(0.218)1.351 ***
(0.375)33.738 ***(1.105)-0.606(1.136)-0.678(1.160)1.106(1.105)
2.139 *
(1.256)2.857 **
(1.328)4.853 **
(2.386)5.075 **
(2.159)0.611(1.247)1.181(1.245)2.545 **
(1.212)7.002 ***
(1.337)4.445 ***
(1.452)
10.098 ***(2.208)9.752 ***
(2.089)0.711 ***
(0.080)0.663 **
(0.277)0.889 *
(0.459)28.989 ***(2.022)-0.171(0.591)-0.151(0.635)1.613 **
(0.641)
3.066 ***
(0.836)3.129 ***
(0.921)5.477 ***
(1.675)7.161 ***
(1.490)0.811(0.707)1.115(0.737)1.556 **
(0.708)4.003 ***
(0.890)3.664 ***
(0.960)7.213 ***
(1.604)5.360 ***
(1.562)0.357 ***
(0.056)-0.548 ***(0.204)2.603 ***
(0.337)35.207 ***(1.039)-0.761(0.972)-0.755(1.036)0.934(0.985)
2.198 *
(1.183)2.454 **
(1.196)
3.187(1.954)6.161 ***
(1.654)0.766(1.077)0.786(1.159)1.202(1.097)3.539 ***
(1.233)2.848 **
(1.325)5.829 ***
(1.949)4.962 **
(1.981)0.435 ***
(0.072)-0.463 *(0.253)2.447 ***
(0.400)35.142 ***(1.818)

σu
σe
ρ

0.099
0.687
0.337
10.53113.904
0.365

0.106
0.591
0.319
10.67313.988
0.368

0.119
0.661
0.340
9.771
14.759
0.305

0.131
0.641
0.340
10.27515.151
0.315

0.115
0.724
0.376
13.29613.316
0.499

0.122
0.589
0.343
13.53013.825
0.489

Observations
Number of school

9,182
435

6,273
430

8,972
435

5,619
430

9,195
435

5,931
430

Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree

Father's Education(base=no education)

Post-graduation
and above
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree
Post-graduation
and above

Log (tuition fees)
Log (number of books at home)
any book
Log (number of books at home)books for the subject
Constant

R2

withinbetweenoverallNote: Standard errors in parentheses.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school level. Sampling weights are used to obtain the coefficients and standard errors.

Table 7. Regression on school mean

model 1
Gender(base=female)
Number of elder siblings

Male

Number of younger siblings
Distance from school(base=less than 15 min)15 ― 30 min
30 min ― 1 hour
More than 1 hour

Home environment
Using English
Log (days of absense)
Mother's Education(base=no education)

Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree

Father's Education(base=no education)

Post-graduation
and above
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree
Post-graduation
and above

Log (tuition fees)
Log (number of books at home)
any book
Log (number of books at home)books for the subject
School facilities
Log(number of students in the class)Log(number of students in the grade)
Stealing in the classroom
Violence in the classroom

1.763(1.403)-1.604(0.983)-2.042 ***(0.644)-7.604 ***(2.548)0.486(2.702)-6.068(3.841)-8.081 ***(3.046)1.283(1.778)-1.646 ***(0.570)-1.597(6.608)-3.175(6.422)
3.799(6.561)15.005 **(7.344)10.741(8.752)
6.504(13.169)
22.623 *(13.398)
1.245(6.259)5.361(6.710)0.497(6.163)24.799 ***(7.292)13.713(8.679)50.453 ***(13.467)
24.552 *(14.359)
2.011 ***
(0.236)-1.493(1.109)2.771(2.489)Mathematics
model 22.901 *
(1.479)-1.369(0.984)-1.970 ***(0.640)-8.089 ***(2.554)-0.741(2.689)-9.020 **(3.880)-9.712 ***(3.044)1.614(1.770)-1.584 ***(0.568)-1.566(6.559)-2.141(6.372)
2.997(6.494)12.629 *(7.305)9.694(8.668)4.584(13.080)
21.563(13.242)2.397(6.214)5.647(6.640)-0.394(6.098)23.372 ***(7.232)
12.216(8.587)47.228 ***(13.438)
26.019 *(14.299)
1.871 ***
(0.249)-1.918 *(1.109)2.765(2.472)-0.010(0.036)0.418(1.630)1.758 **
(0.757)-3.854 *(2.181)-4.538 *(2.492)model 3model 14.440 **
(1.729)-1.347(0.990)-2.010 ***(0.648)-8.820 ***(2.646)-5.018 *(2.769)-11.370 ***(4.144)-9.344 ***(3.056)-0.049(1.850)-1.731 ***(0.589)1.214(6.630)-2.450(6.461)
7.503(6.532)
11.937(7.522)
13.099(9.144)27.912 *(14.415)
18.828(13.484)
0.754(6.295)5.617(6.659)-0.930(6.160)25.271 ***(7.576)3.769(9.008)42.178 ***(15.070)
21.962(14.816)
1.962 ***
(0.255)-1.459(1.177)2.372(2.559)0.023(0.043)-0.265(1.685)1.730 **
(0.820)-2.767(2.260)-4.823 *(2.621)-0.894(1.520)-1.699(1.084)-2.447 ***(0.699)-8.032 ***(2.785)4.455(2.954)-4.348(4.149)-11.072 ***(3.358)-0.151(1.924)-0.466(0.619)-2.511(7.286)2.863(6.990)7.362(7.151)14.075 *(8.037)17.614 *(9.465)13.408(13.100)
21.291(14.685)-5.315(6.997)-3.114(7.372)-0.333(6.748)8.065(7.999)-0.834(9.324)45.395 ***(14.859)
32.768 **(16.138)
1.645 ***
(0.252)-2.937 **(1.338)7.572 ***
(2.593)

Sciencemodel 2model 3model 10.851
(1.572)-1.197(1.066)-2.170 ***(0.682)-7.116 ***(2.738)2.674(2.882)-6.988 *(4.100)-12.410 ***(3.290)0.517(1.879)-0.344(0.604)-4.001(7.088)
2.184(6.795)5.516(6.945)
11.633(7.855)15.582 *(9.205)10.992(12.721)20.381(14.236)-3.128(6.811)-1.018(7.160)-0.788(6.551)
7.545(7.777)-1.317(9.050)47.301 ***(14.545)
33.403 **(15.750)
1.566 ***
(0.262)-3.793 ***(1.318)7.824 ***
(2.531)
0.069 *
(0.039)4.100 **
(1.743)0.712(0.813)-2.449(2.336)-9.551 ***(2.653)1.485(1.874)-1.358(1.124)-1.790 **(0.725)-6.831 **(2.877)0.524(3.143)-7.841 *(4.546)-12.016 ***(3.448)-0.753(2.017)-0.692(0.661)-3.274(7.710)2.677(7.416)9.578(7.500)12.230(8.454)
12.587(10.259)
51.172 ***(16.299)35.556 **(15.736)-4.590(7.201)-2.556(7.815)-3.123(7.103)5.378(8.495)-3.711(9.822)42.510 **(16.953)
10.900(17.030)
1.604 ***
(0.284)-2.125(1.440)5.109 *
(2.701)
0.075(0.049)3.593 *
(1.894)0.555(0.894)-0.469(2.568)-8.735 ***(2.822)-3.033 **(1.469)-2.638 **(1.022)-1.580 **(0.678)-9.379 ***(2.647)1.624(2.792)-4.076(3.988)-6.304 **(3.173)3.876 **
(1.855)-0.165(0.590)-4.438(6.839)-4.190(6.656)-6.073(6.790)20.113 ***(7.637)15.311 *(9.100)-0.793(13.616)
13.961(13.887)
2.675(6.534)10.944(6.955)1.960(6.369)29.693 ***(7.573)28.343 ***(9.072)55.346 ***(13.939)60.090 ***(14.844)0.918 ***
(0.247)-5.892 ***(1.246)
13.586 ***(2.381)Englishmodel 2model 3-2.362(1.564)-2.575 **(1.035)-1.560 **(0.681)-9.974 ***(2.685)1.111(2.811)-5.808(4.083)-7.376 **(3.209)4.049 **
(1.868)-0.154(0.595)-3.960(6.869)-3.118(6.686)-5.967(6.802)18.901 **(7.677)14.885(9.118)-0.333(13.691)
13.711(13.891)
3.126(6.569)10.479(6.970)1.136(6.380)28.964 ***(7.599)27.711 ***(9.077)51.918 ***(14.065)
60.796 ***(14.950)
0.862 ***
(0.263)-5.865 ***(1.258)13.158 ***(2.405)-0.045(0.038)-0.982(1.721)1.154(0.803)-3.187(2.285)-1.138(2.626)-1.341(1.855)-2.568 **(1.087)-1.158(0.706)-8.092 ***(2.862)-1.053(3.034)-5.632(4.415)-6.921 **(3.342)2.812(2.012)-0.138(0.632)-0.451(7.154)-1.251(6.924)-2.326(7.056)23.199 ***(8.166)14.437(9.782)
11.996(15.820)
16.314(14.615)
0.191(6.831)
9.934(7.176)-1.058(6.614)26.409 ***(8.095)25.886 ***(9.617)47.346 ***(16.075)
51.915 ***(16.111)0.817 ***
(0.286)-5.691 ***(1.337)11.659 ***(2.586)-0.066(0.047)-0.069(1.842)1.116(0.878)-2.408(2.470)-1.427(2.748)

Teacher variables
Gender(base=female)Years of teaching

Male

0.284
(0.731)
0.026(0.123)-0.002(0.004)0.170(1.397)0.735(1.551)0.155(1.886)

Years of teaching squared
Education(base=GCE O/L)GCE A/L
Bachelor's Degree
Master's Degree

1.304(0.808)-0.136(0.241)-0.312(0.865)-0.144(0.137)0.003(0.004)0.079(1.955)-0.326(2.123)-0.872(2.332)-12.026(8.326)1.170(0.875)0.083(0.261)0.464(0.832)0.048(0.260)-0.065(1.081)
0.261(0.173)-0.008(0.007)-0.507(1.785)-0.599(1.794)-1.401(1.805)-0.813(3.236)

1.158(1.164)0.114(0.189)-0.001(0.008)-2.512(1.961)-1.821(1.995)-1.189(1.987)0.149(3.391)-2.179 *(1.179)-0.059(0.186)0.003(0.008)-0.984(1.848)0.534(1.873)0.567(1.880)5.725 *
(3.463)

Ph.D.
Remedial Teaching
Log (time spent for lesson planning)
Principal variables
GenderMale(base=female)Years of experience as a principal
Years of experience as a principal squared
Education(base=GCE O/L)GCE A/L
Bachelor's Degree
Master's Degree
Ph.D.

-0.730(0.861)
0.033(0.157)-0.001(0.005)-0.656(1.501)-1.353(1.820)0.240(2.272)

Constant

37.106 ***(5.750)33.217 ***(7.467)32.993 ***(7.854)32.798 ***(6.437)19.657 **(8.190)20.874 **(9.510)27.120 ***(6.063)28.677 ***(7.887)27.638 ***(8.608)Observations
R2

435
0.778

435
0.787

385
0.796

435
0.726

435
0.746

378
0.761

435
0.834

435
0.836

382
0.830

Note: Standard errors in parentheses.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school-level. Sampling weights are used to obtain the coefficients and standard errors.

Table 8. Mixed effectmodel 1
Gender(base=female)
Number of elder siblings

Male

Number of younger siblings
Distance from school(base=less than 15 min)15 ― 30 min
30 min ― 1 hour
More than 1 hour

Home environment
Using English
Log (days of absense)
Mother's Education(base=no education)

Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree

Father's Education(base=no education)

Post-graduation
and above
Up to Grade 5
Up to Grade 10
GCE O/LGCE A/L
Vocational course
post O/L or A/L
Bachelor's Degree
Post-graduation
and above

Log (tuition fees)
Log (number of books at home)
any book
Log (number of books at home)books for the subject
School facilities
Log (number of students in the class)
Log (number of students in the grade)
Stealing in the classroom
Violence in the classroom-0.987 **(0.430)-0.322 *(0.170)-0.588 ***(0.094)-0.241(0.401)0.241(0.515)-0.733(0.636)-4.707 ***(0.701)1.026 **
(0.404)0.248(0.200)-0.013(0.751)0.514(0.780)2.622 ***
(0.805)4.071 ***
(0.914)
3.786 ***
(1.032)5.502 ***
(1.434)7.386 ***
(1.566)-0.014(0.852)0.486(0.855)1.443 *
(0.861)4.130 ***
(0.942)4.082 ***
(1.071)8.410 ***
(1.402)7.609 ***
(1.567)0.708 ***
(0.053)0.462 ***
(0.177)0.737 **
(0.324)0.040(0.044)-0.041(0.058)7.689 ***
(0.633)-4.636 *(2.411)-2.187(2.890)Mathematics
model 2-0.980 **(0.437)-0.317 *(0.177)-0.511 ***(0.097)-0.511(0.423)-0.055(0.548)-1.067(0.655)-4.167 ***(0.702)
1.161 ***
(0.416)0.173(0.216)0.020(0.757)0.573(0.796)2.696 ***
(0.817)
4.169 ***
(0.958)3.902 ***
(1.109)5.585 ***
(1.647)7.328 ***
(1.799)-0.275(0.881)0.413(0.884)1.291(0.893)3.764 ***
(0.995)3.818 ***
(1.162)
8.122 ***
(1.674)7.584 ***
(1.644)0.745 ***
(0.053)0.585 ***
(0.184)0.601 *
(0.335)0.088 *
(0.052)-3.222 *(1.699)7.358 ***
(0.723)-2.628(2.559)-2.131(2.884)model 3model 1

Sciencemodel 2model 3model 1Englishmodel 2model 3-0.979 **(0.437)-0.322 *(0.177)-0.517 ***(0.097)-0.481(0.423)-0.027(0.548)-1.042(0.655)-4.183 ***(0.701)
1.143 ***
(0.415)0.196(0.217)0.021(0.756)0.565(0.795)2.685 ***
(0.816)
4.142 ***
(0.957)3.878 ***
(1.107)5.556 ***
(1.647)7.292 ***
(1.801)-0.282(0.881)0.413(0.883)1.310(0.892)3.771 ***
(0.994)3.819 ***
(1.161)8.105 ***
(1.673)7.559 ***
(1.645)0.745 ***
(0.053)0.593 ***
(0.184)
0.596 *
(0.335)0.091 *
(0.048)-0.311(1.648)3.404 ***
(0.805)-6.865 ***(2.654)-4.240(2.786)-1.643 ***(0.469)-0.699 ***(0.182)-0.780 ***(0.108)0.196(0.438)0.904(0.560)-1.185(0.754)-5.740 ***(0.684)0.701(0.434)0.394 *
(0.209)-0.830(0.732)-0.725(0.759)1.406 *
(0.778)3.225 ***
(0.888)3.067 ***
(1.044)
6.114 ***
(1.771)
6.551 ***
(1.698)0.882(0.853)
1.638 *
(0.853)
3.578 ***
(0.840)7.431 ***
(0.981)5.986 ***
(1.083)10.176 ***(1.687)
11.162 ***(1.848)0.648 ***
(0.054)0.574 ***
(0.214)
1.371 ***
(0.371)0.123 ***
(0.044)0.052(0.061)5.554 ***
(0.618)-3.237(2.358)-7.255 ***(2.704)-1.944 ***(0.482)-0.795 ***(0.191)-0.790 ***(0.115)-0.105(0.467)0.765(0.577)-1.444 *(0.816)-5.437 ***(0.717)0.677(0.473)0.458 **
(0.226)-0.545(0.765)-0.661(0.799)1.544 *
(0.809)3.600 ***
(0.933)3.273 ***
(1.108)6.641 ***
(2.004)6.976 ***
(1.747)0.644(0.916)1.505(0.922)3.745 ***
(0.909)7.303 ***
(1.066)5.930 ***
(1.155)10.321 ***(1.975)
11.421 ***(1.920)0.671 ***
(0.056)
0.646 ***
(0.238)1.304 ***
(0.420)0.141 ***
(0.051)
1.142(1.870)4.962 ***
(0.708)-1.429(2.617)-5.243 *(2.794)-1.948 ***(0.482)-0.801 ***(0.191)-0.795 ***(0.115)-0.074(0.468)0.797(0.578)-1.417 *(0.814)-5.445 ***(0.716)0.658(0.472)0.477 **
(0.226)-0.539(0.764)-0.660(0.798)1.540 *
(0.808)3.577 ***
(0.932)
3.253 ***
(1.107)
6.613 ***
(2.004)6.935 ***
(1.750)0.635(0.916)1.497(0.922)3.752 ***
(0.908)7.297 ***
(1.065)5.917 ***
(1.155)10.293 ***(1.974)
11.393 ***(1.920)0.671 ***
(0.056)0.654 ***
(0.238)1.294 ***
(0.420)0.136 ***
(0.049)3.340 *
(1.835)1.177(0.831)-4.683 *(2.697)-7.206 ***(2.705)-3.722 ***(0.448)-0.474 ***(0.163)-0.544 ***(0.104)-0.680 *(0.412)-1.236 **(0.526)-1.718 **(0.714)-3.192 ***(0.583)2.767 ***
(0.449)0.465 ***
(0.174)-0.202(0.571)-0.209(0.614)1.489 **
(0.611)3.209 ***
(0.803)3.478 ***
(0.898)6.291 ***
(1.627)8.388 ***
(1.417)0.933(0.673)
1.329 *
(0.699)1.750 ***
(0.669)4.484 ***
(0.845)4.335 ***
(0.938)8.535 ***
(1.590)6.494 ***
(1.577)0.370 ***
(0.055)-0.487 **(0.196)2.586 ***
(0.332)0.025(0.061)-0.157 *(0.086)
10.785 ***(0.939)-8.141 ***(2.756)-1.603(3.422)-3.769 ***(0.473)-0.444 ***(0.170)-0.586 ***(0.111)-0.802 *(0.435)-1.299 **(0.571)-1.946 ***(0.736)-2.806 ***(0.574)2.484 ***
(0.457)0.382 **
(0.181)
0.085(0.552)-0.134(0.592)2.067 ***
(0.584)3.630 ***
(0.841)
4.248 ***
(0.957)
6.783 ***
(1.976)9.271 ***
(1.630)0.715(0.676)
1.586 **
(0.707)1.680 **
(0.663)4.286 ***
(0.880)4.199 ***
(0.974)
7.831 ***
(1.802)
6.379 ***
(1.658)0.388 ***
(0.059)-0.419 **(0.205)2.471 ***
(0.374)0.031(0.065)-6.113 **(2.496)9.383 ***
(1.012)-3.297(2.735)0.224(3.254)-3.776 ***(0.473)-0.447 ***(0.170)-0.587 ***(0.111)-0.785 *(0.434)-1.283 **(0.571)-1.936 ***(0.736)-2.820 ***(0.573)2.481 ***
(0.457)
0.396 **
(0.181)0.086(0.552)-0.137(0.592)2.060 ***
(0.584)3.618 ***
(0.840)4.235 ***
(0.955)6.764 ***
(1.975)9.241 ***
(1.631)0.710(0.677)1.580 **
(0.707)
1.691 **
(0.663)4.291 ***
(0.879)4.205 ***
(0.974)7.820 ***
(1.802)
6.367 ***
(1.658)0.388 ***
(0.059)-0.412 **(0.205)2.465 ***
(0.373)0.018(0.054)-2.473(2.138)3.628 ***
(0.984)-8.157 ***(2.733)-2.362(2.947)

Teacher variables
Gender(base=female)Years of teachingMale-1.130(0.873)0.032(0.140)-0.001(0.004)0.281(2.123)-0.760(2.368)0.558(2.564)-0.687(3.422)0.713(0.915)0.137(0.276)-0.133(1.081)
0.025(0.208)0.001(0.007)0.028(1.441)-0.809(1.857)-2.985(2.472)0.654(0.957)0.008(0.179)0.002(0.006)0.693(1.308)0.141(1.682)-1.163(2.361)0.863(0.887)-0.039(0.253)-0.542(0.962)-0.112(0.153)0.002(0.005)0.879(2.351)-0.334(2.618)0.926(2.752)7.545 **
(3.193)1.132(0.904)-0.133(0.282)1.406(1.101)-0.338(0.360)0.922(0.913)-0.439(0.299)-0.831(1.045)0.014(0.191)0.000(0.007)-2.028(1.866)-1.395(1.909)-1.542(1.971)
2.221(2.793)0.659(0.998)0.061(0.167)-0.003(0.007)-2.789 *(1.621)-2.270(1.697)-3.013 *(1.747)0.174(3.287)0.438(1.068)-0.015(0.201)
0.002(0.008)-3.632 *(2.193)-2.253(2.247)-1.354(2.324)3.467(4.063)

1.799 *
(1.012)-0.028(0.175)0.003(0.007)-4.161 *(2.167)-3.011(2.208)-2.868(2.271)
1.246(3.908)-5.133 ***(1.678)-0.095(0.240)0.003(0.010)-1.519(1.557)0.457(1.696)
1.118(1.846)12.306 ***(4.347)-2.626 *(1.437)-0.077(0.199)0.001(0.008)-2.363 *(1.330)-0.865(1.429)-1.008(1.512)9.806 **
(4.308)9.044 ***
(2.860)18.915 ***(6.356)-3.012 **(1.357)-4.083 ***(1.543)1.554(2.025)-3.843 ***(1.474)6.100 ***
(1.486)
5.116 ***
(1.061)29.798 ***(5.956)5.591 **
(2.714)5.563(6.893)-2.689 **(1.311)-3.958 ***(1.489)-1.221(1.786)-4.986 ***(1.510)5.864 ***
(1.476)3.530 ***
(1.114)19.608 ***(6.710)-9.372 ***(3.006)13.676 *(8.242)-4.089 ***(1.544)-4.583 ***(1.773)2.839(2.646)-7.698 ***(1.921)8.442 ***
(2.009)3.667 ***
(1.249)33.645 ***(7.784)

9,464

8,330

8330

9,257

8,063

8,063

9,478

8,260

8,260

Years of teaching squared
Education(base=GCE O/L)GCE A/L
Bachelor's Degree
Master's Degree

0.357(0.875)-0.085(0.154)0.001(0.004)0.732(1.617)1.301(1.818)3.353(2.516)-0.830(0.798)0.041(0.140)-0.001(0.004)1.490(1.505)1.989(1.686)2.481(2.313)

1.927 *
(1.005)-0.120(0.292)

Ph.D.
Remedial teaching
Log (time spent for lesson planning)
Principal variables
Gender(base=female)Years of experience as a principal
Years of experience as a principal squared
Education(base=GCE O/L)GCE A/L
Bachelor's Degree
Master's Degree
Ph.D.

Location(base=Municipal)

Urban
Pradeshiya Sabha

School type(base=1AB)

1C
Type 2

School managemet(base=Provincial)
Language(base=Sinhala)
Constant

Observations

National
Tamil

Note: Standard errors in parentheses.
***, **, * indicate that the coefficients are statistically significant at 1%, 5%, 10% level.
Standard errors are clustered at school-level. Sampling weights are used to obtain the coefficients and standard errors.

Figure 1. Distribution of test scores

EnglishScience

.02
.01
0

0

0

.01

.01

.02

.02

.03

.03

.03

Mathematics

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

Student level

Student level

Student level

School average

School average

School average

Note: Sample weights are used for estimation.

Figure 2. Distribution of test score for subsamples(a) Distribution of test scores by location of school

40

60

80

100

.025
.015
.01
0

.005
0

.005

.01

.015

.02
.015
.01
.005
0

20

0

English.02.025

Science.02.025

Mathematics

0

20

40

60

80

100

0

20

40

60

80

100

Municipal council

Municipal council

Municipal council

Urban council

Urban council

Urban council

Pradeshiya Sabha

Pradeshiya Sabha

Pradeshiya Sabha(b) Distribution of test scores by school typeEnglishScience

.03
.02
.01
0

0

0

.01

.01

.02

.02

.03

.03

.04

.04

.04

Mathematics

0

20

40
1AB
Type 2

60

80

100
1C

0

20

40
1AB
Type 2

60

80

100
1C

0

20

40
1AB
Type 2

60

80

100
1C

(c) Distribution of test scores by the school management (Type 1AB only)

ScienceEnglish

0

20

40

60

80 100

.02
.015
.01
.005
0

0

0

.005

.005

.01

.01

.015

.015

.02

.02

Mathematics

0

20

40

60

80 100

0

20

40

60

80 100National

National

National

Provincial

Provincial

Provincial(d) Distributions of test scores by language

20

40

60

80 100

.02
0

0

.01

.01

.01
0
0

English.03.03

Science

.02

.02

.03

Mathematics

0

20

40

60

80 100

0

20

40

60

80 100SinhalaSinhalaSinhala

Tamil

Tamil

Tamil

Note: Sample weights are used for estimation.

Figure 3. School types by province and location