Schooling Matters: A study of secondary school

dropouts among low-income youth of Bangladesh

by

Raged Mohamed Anwar

M.A. (Economics), Simon Fraser University, 2010 B.S. (Business Administration), University of Texas at Dallas, 2007

Research Project Submitted in Partial Fulfillment

of the Requirements for the Degree of

Master of Arts

in the

School for International Studies

Faculty of Arts and Social Sciences

Raged Mohamed Anwar 2012

SIMON FRASER UNIVERSITY

Spring 2012

All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced, without authorization, under the conditions for “Fair Dealing.” Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance

with the law, particularly if cited appropriately.

ii

Approval

Name: Raged Mohamed Anwar

Degree: Master of Arts (International Studies)

Title of Research Project:

Schooling Matters: A study of secondary school dropouts among low-income youth of Bangladesh

Supervisory Committee:

Chair:

Dr. John Harriss Professor

Morten Jerven Senior Supervisor Assistant Professor

John Richards Supervisor Professor of Public Policy

Date Approved: 19 April 2012

iii

Partial Copyright License

iv

Abstract

Past research has established a link connecting higher levels of education and

development. Nonetheless, high incidence of dropout behavior persists in developing

nations, and various organizations are focusing on reducing poor academic outcomes in

both the primary and secondary levels. The present study employs a previously unused

dataset—one that offers a higher level of homogeneity of household income and past

student performance by considering the low-income youth of Bangladesh—to assess

whether individual, household, or school characteristics are better indicators of student

performance on exams. The results reveal that both household and school

characteristics affect performance and the two most important factors appear to be

whether a student resides in an urban or rural area and the school he/she attends.

Keywords: education and development; school quality; schooling outcome; dropout behavior; socioeconomic factors and exam performance; empirical econometric analysis; urban and rural students; Bangladesh

v

Acknowledgements

I would first like to thank Professor Morten Jerven for his guidance in transforming an

institutional quantitative analysis into an empirical study in development. Additionally, I

would like to thank him, as well as the other faculty members at the School for

International Studies and my amazing cohort, for providing me with a deeper and more

holistic understanding of development than I previously had. I would also like to offer my

gratitude to Ellen Yap and Dorris Tai for their dedicated and prompt assistance with the

numerous administrative issues I have faced over the past two years.

Next, I must give thanks to Professor John Richards for his continued support, advice,

and guidance from start to finish of this study. Professor Richards helped me improve

academically, professionally, and personally in ways that I simply could not have

otherwise.

I would also like to thank Erum Mariam for giving me the opportunity to work alongside

the SCOPE team at the BRAC Institute of Educational Development, and the SCOPE

team members for welcoming me into their group during my stay in Dhaka.

I must also show sincere appreciation to my dear friend and colleague, Tenzin Yindok

for her countless hours of proof reading, criticisms, and general feedback through two

master’s degrees. Finally, I would like to thank my sister for leading by example, and my

parents for the numerous sacrifices they made – ones that I would have been unable to

make – so my sister and I could be where we are today; I really cannot thank you two

enough.

vi

Table of Contents

Approval .......................................................................................................................... ii Partial Copyright License ............................................................................................... iii Abstract .......................................................................................................................... iv Acknowledgements ......................................................................................................... v Table of Contents ........................................................................................................... vi List of Tables and Figures ............................................................................................. vii

1. Introduction .......................................................................................................... 1

2. Literature Review .................................................................................................. 6

3. SCOPE, Data, and Exam Scores ........................................................................ 15

4. Bivariate Analysis ............................................................................................... 23

5. Multivariate Analysis .......................................................................................... 30

6. Discussion of Major Findings and Policy Recommednations ......................... 45

7. Conclusion .......................................................................................................... 51

References ................................................................................................................... 52

vii

List of Tables and Figures

Table 1. Contemporary Education Statistics for Bangladesh and Relevant Groups ........................................................................................................... 2

Table 2. Descriptive Statistics .................................................................................... 18

Table 3. Exam Scores by Subject and Gender .......................................................... 20

Table 4. Exam Scores by Centre ............................................................................... 21

Table 5. Bivariate Table ............................................................................................. 25

Table 6. Regression Results ...................................................................................... 35

Figure 1. Class Size and Exam Scores ....................................................................... 22

1

1. Introduction

Any discussion regarding education in Bangladesh begins, and often ends, with

impressive statistics and stories about how female stipend programs and other initiatives

helped close the education gender gap over the last two decades. Indeed, in 1990 the

ratio of female-to-male enrollment in primary schools was just below 84 percent. While

this was above average in South Asia, it was slightly below the relevant global ratios,

and even below other low- to middle-income economies1. The female-to-male

enrollment ratio in secondary schools, 50 percent, was downright shameful; it was 10

percentage points lower than the South Asian average, and 33 percentage points below

global standards. Over the next 20 years, however, Bangladesh made wonderful strides

that helped it surpass not only its economic and geographic neighbors, but also the

world ratio. As of 2008, this ratio had increased to 107 percent in primary schools and

112 percent in secondary schools; to put it into context, the world ratios were 96 percent

in each category (World Development Indicators).

Unfortunately, gender parity is one of the few bright spots in the primary and

secondary Bangladeshi education system when put in the global context. In looking at

1 The female to male ratios for South Asian and low to middle income countries, as well as the

world, are from 1991. This is the closest year to 1990 in which these measures are available for the mentioned aggregate groups.

2

other measures of educational outcomes and participation, Bangladesh ranks behind not

only the world average, but also low- to middle-income economies and its South Asian

neighbors. The adult literacy rate is below 56 percent, three out of five children drop out

of primary school, and nearly 60 percent of children were not enrolled in secondary

schools in 2008 (Table 1).

Table 1: Contemporary Education Statistics for Bangladesh and Relevant Groups

Bangladesh South Asia

Low/Mid Income World

Adult Literacy Rate (% of people 15 yrs old and up, 2009): 55.90 61.10 70.61 83.68

Primary Completion Rate (% of relevant age group, 2009): 60.51 85.51 88.16 88.48

Secondary School Enrollment (% gross, 2008):

All 42.29 54.59 57.93 67.69

Female 44.79 51.02 54.79 66.38

Male 39.88 57.90 60.88 68.92

Source: World Development Indicators, World Bank

However, the state of education in Bangladesh is improving. In fact, most

measures of education fare much better now than two to three decades earlier.

According to the Campaign for Popular Education (CAMPE), an education advocacy

collaborative in Bangladesh, secondary school enrollment has tripled since 1980 and the

number of schools has doubled (Ahmed et al. 2006). Since 1990, adult and youth

literacy rates have increased from 35- and 46 percent to 56- and 75 percent

respectively. Likewise, primary enrollment has increased from 72- to 95 percent, and

secondary enrollment has more than doubled from 18- to 42 percent (World

Development Indicators).

3

However, it appears the growth in demand for and provision of education

increased at a quicker pace than the Bangladeshi education system was prepared for.

While the numbers of enrolled students, teachers, and facilities have increased, outcome

measures have begun to falter; more so at the secondary level than the primary level.

The fraction of children enrolling in and completing primary school consistently rose in

the 1990s, but the resulting increase in enrollment in secondary schools has not been

matched with as high outcome measures as desired. Between 1998 and 2007,

secondary school repetition rates increased by 70 percent, and among those who enroll

in secondary schools, 49 percent of girls and 64 percent of boys failed to complete the

full cycle (Multiple Cluster Indicator Survey 2006, World Bank). CAMPE provides higher

dropout estimates and contends that over 80 percent of those who enroll in secondary

school fail to obtain their secondary school certificate (awarded at grade 10) (Ahmed et

al. 2006).

In order to reduce the high number of secondary school dropouts, BRAC, a non-

governmental development organization in Bangladesh expanded its education wing to

include a pilot project called SCOPE that aims to create a model that reintegrates former

dropouts to the secondary schooling system. BRAC is a world-renowned development

organization, often identified as the world’s largest NGO, and was created in Bangladesh

in 1972, immediately following the nation’s independence. According to its website, it

reaches over 110 million people in countries throughout South Asia, the Middle-east,

and Africa. Its services range from education – it operates over 30,000 primary schools

in Bangladesh – and microcredit to social advocacy and corporate endeavors such as

commercial banking and the retail industry.

4

In the paper that follows, data obtained from the SCOPE program are employed

to identify individual, household, and school-specific factors that affect academic

performance among secondary school dropouts from economically disadvantaged

backgrounds. All students in the SCOPE data set live in households with very low

income and are former dropouts. This homogeneity of income and past academic

performance allows for more precise inspection of other factors that may affect present

academic performance, albeit at the expense of the ability to generalize results to the

population at large.

Prior to analyzing the data, section two of this paper presents an overview of the

relevant literature that surveys both the macroeconomic effects of education on

development and the microeconomic factors that affect education acquisition decisions.

Following the literature survey, a description of the SCOPE program, the data that will be

employed for the analysis, and an overview of exam scores are presented in section

three. Section four presents a bivariate analysis employing a dichotomous dependent

variable and includes analysis of socioeconomic factors that include personal,

household, and SCOPE centre characteristics. Section five employs multivariate

analysis in an attempt to disentangle the effects of overlapping characteristics, and

section six provides a discussion of the major findings, contextualizes the study, and

notes the policy implications. Finally, the concluding remarks are presented in section

seven.

The findings of this study suggest that both supply and demand side factors

matter for a child’s academic progress, but household characteristics are less important

than one might expect. Specifically, traditional factors thought to impact student

performance like gender and parent’s education level appear to matter very little, while

5

factors like student-teacher ratios and the school a student attends impact exam scores

a great deal. Additionally, this study finds that students in urban areas outperform their

rural counterparts, but the reason for this is left unexplained.

6

2. Literature Review

Consistent with the objective of this study, the following literature review

illustrates the impact of education on economic development by surveying past studies,

theories, and the ideological debate surrounding educational attainment and the

development community. The review begins with a description of how the link between

education and economic development has been researched and established since the

1950s. Following this macroeconomic perspective, theory and empirical evidence reveal

how households make decisions regarding education acquisition and whether the

development community can affect these decisions.

A Macro Perspective on What Matters

In 1956, Robert Solow presented a model of economic growth that has gone on

to become arguably the most important piece of literature in modern development. In his

article, he presents a theory that implies that conditional on the savings rate and the

growth rates of population and technology of an economy, developing countries may

converge economically with developed ones (Solow 1956, Mankiw et al. 1992). Six

years later, Theodore Schultz observed that national productivity gains in the world could

not be fully explained by increases in land, man-hours, and physical capital. He

suggested that much of this unaccounted productivity could be credited to improvements

in the quality of human effort. Specifically, he stated that gains in knowledge, skill,

7

health, and internal migration—or human capital, as he called the combination of these

four—are essential for increasing the quality of productive capacity an individual

contributes to a nation’s productivity. He further contended that investment in human

capital is essential for the growth of productivity of an economy and that, to date,

developed nations were characterized by larger stocks of human capital than developing

nations (Schultz 1961).

Research thereafter has established that much of the unexplained growth can be

accounted for by technological innovation, but sufficient evidence supports the notion

that labour productivity can indeed be improved by investment in human capital through

avenues such as education. For instance, Becker et al. (1994) state that even though a

considerable amount of economic growth can be attributed to technological innovation,

nations with higher levels of human capital tend to spur on more technological

innovations. In their theoretical model, by assuming increasing returns to education, the

authors find a possibility of multiple steady states. Notably, one steady state may be

characterized by a low stock of human capital and low returns to investment in human

capital, while a second state may be characterized by a high stock of human capital and

high returns to investment in education; these two states can be associated with

developing and developed nations, respectively. They suggest that nations may move

from one steady state to another if a large shock hits the country, such as war or

technological progress.

The transition from developing to developed has historically been measured in

terms of the per capita gross domestic product (GDP) growth rate of a nation. This

certainly makes sense given that a developing country may catch up, economically, to its

developed counterparts if the rate at which such an economy grows is significantly larger

8

than growth rates of developed nations for an extended period of time. If this is the

case, then why have developing countries not converged to developed ones yet? To

answer this question, Barro (1991) uses data from 98 countries spanning 1960 to 1985

to show that initial per capita GDP has negligible effects on the subsequent growth rate

of a nation’s per capita GDP based on a simple correlation. This contradicts the view

that developing economies have a tendency to converge with developed ones, but is

consistent with the observation that economic inequality persists in the world today. He

goes on to employ primary and secondary school enrollment rates as proxies for human

capital and shows that, conditional on the level of human capital, countries with lower

levels of per capita GDP do exhibit a tendency to grow quicker than those with higher

levels of initial per capita GDP. Thus, it would appear that developing countries may

indeed be able to catch up to developed nations, but only if the appropriate level – given

the stage of development – of human capital is in place. Similarly, Mankiw et al. (1992)

augment the Solow model by including investment in education as a measure of human

capital in addition to the traditional inputs, labour and capital, and corroborates Barro’s

results.

Pritchett (2001), on the other hand, notes that the worldwide increases in

enrollment rates in developing countries since the 1960s imply negative externalities of

education on growth rates. Pritchett attempts to explain this apparent paradox by

exploring three possibilities:

1) Educational attainment has remained profitable on a micro level, but

aggregate education levels have exceeded efficient levels as a share of

overall factor inputs

2) The return to education has decreased at the micro level

9

3) The type or quality of education obtained, in the countries and over the

time considered, does not lead to higher levels of productivity

Using the augmented Solow Model presented by Mankiw et al. (1992) and data

obtained from Barro and Lee (1993) and Nehru et al. (1995), Pritchett shows that an

increase in the rate of growth of the national stock of education is associated with a

reduction in the total factor productivity (TFP) growth rate. In fact, his results imply that

the macroeconomic impact of education acquisition is smaller than microeconomic data

suggest.

He goes on to present a review of a large body of empirical evidence that

supports his claim that the growth rate of the national stock of education is negatively

related to economic growth rates in developing nations, and may even lead to negative

growth rates if the quality of education provided fails to prepare individuals in a socially

value-adding manner. He provides further evidence that the impact of education varies

from country to country. However it appears there is reason to believe that in many

countries the educated benefit through socially non-optimal employment; education is

characterized by varying economic returns dependent upon sectoral and policy priorities;

and schooling quality determines the level of economic productivity obtained by

students. He, nonetheless, concludes that the lack of large economic returns should not

be the only factor that determines education policy since education has non-economic

returns, and indirect returns to population health (as measured in statistics such as

under-five mortality, total fertility and maternal mortality rates) (Pritchett 2001).

10

A Micro Perspective on What Matters

Thus far, the focus has been on the impact and desirability of education

regarding the overall economic growth of a country. This section will review the literature

on the factors that affect the educational decisions made by households. This is relevant

to the research question of this paper because we need an understanding of what has

been found to be important determinants of schooling outcomes at the micro level.

Lloyd (1974, 1978) notes that achievement, socioeconomic status, family

characteristics, non-promotion, and absenteeism in sixth grade students from California

(United States) could be employed to predict high school dropout behavior. He

extended his study to assess whether longitudinal data from third grade could be

employed as effectively as sixth grade data. His findings suggest that four out of the five

characteristics identified in Lloyd (1974) – achievement, socioeconomic status, family

characteristics, and non-promotion – may be used to correctly identify 70 percent of high

school dropouts as early as in the third grade; however, these predictors incorrectly

identified high school graduates as potential dropouts in 25 percent of the observations

(Lloyd 1978).

Ensminger and Slusarcick (1992) suggest socioeconomic factors such as

maternal education and family poverty levels in the first grade, in addition to student

performance, are good determinants of later dropout behavior. In another longitudinal

study, Jimerson et al. (2000), participants were identified prior to birth and factors such

as parental involvement, socioeconomic status, home environment, and various other

characteristics were measured throughout the life of children beginning six months after

birth and ending at 19 years of age. Their findings suggest that the dropout process

11

may begin prior to students enrolling in school. Taken together, these studies support

the notion that the post-primary dropout phenomenon is the conclusion of a

developmental process; relevant factors can be identified prior to a student dropping out;

and socioeconomic factors play a significant role in this process. Further evidence of

these conclusions has been offered by Garnier et al. (1997) and Rumberger et al.

(1990).

Heyneman and Loxley (1983) note that, prior to their study, the majority of

research pertaining to school quality—measured in terms of teacher quality, physical

infrastructure, management quality, and various other factors—concluded that family

characteristics and socioeconomic factors played a more deterministic role in student

outcomes. They point out that the vast majority of these studies employed data

regarding students and schools in North America, Europe, and Japan. As a result, they

contend, these conclusions are inappropriate for application to developing nations.

Using data from 18 countries in South America, Asia, North America, and Africa, they

find that teacher and school quality matter in developing countries, more so than in

developed ones; other studies report similar conclusions (Lloyd et al. 2000, Hanushek et

al. 2006).

Hanushek (1995) provides summary results from 96 studies that focus on

educational inputs and the resulting impact on student performance. The inputs of

interest include the student-teacher ratio, teachers’ education, teachers’ education and

experience, teachers’ salary, per student expenditure, and the quality of facilities, while

the outputs are measured in terms of performance on standardized exams, attendance

rates, and continuation or dropout rates. His findings indicate that an equal number of

studies find statistically significant positive and negative relationships for higher student-

12

teacher ratios and student performance, but many also find no significant relationship

between the two. Teachers’ education and experience levels are positively related to

student performance, but higher teacher salaries do not necessarily indicate higher

student performance. Six studies indicated a significant relationship to student

performance, while another six revealed no significant relationship. Finally, in 22 out of

34 studies that measured the relationship between school facilities and student

performance, a significant positive relationship was found in 22, and no relationship was

found in nine. However, Hanushek notes that facility measures employed in the studies

under review varied considerably, thus the measures may not be comparable. The

lesson here could be that beyond a point, increasing some schooling inputs do not lead

to major improvements.

Lee and Barro (2001) employ a cross-country data set (constructed by Hanushek

and Kimko, 2000) to estimate the effect of family characteristics and school resources on

educational outcomes, as measured by test scores, dropout rates and repetition. They

also added schooling input information found from other publications. They report that

parental education and income, and smaller class sizes lead to better outcomes.

The literature surveyed suggests that higher socioeconomic factors and higher

school quality tend to be correlated positively with future educational outcomes like

performance on exams, grade repetition, and dropout behavior. Further, a body of

literature contends that discontinuation of studies is a process that begins early in a

child’s life, and progresses until the point that student drops out. Thus, socioeconomic

indicators and school characteristics may, if identified early in the dropping out process,

be used to identify at-risk students, and intervention may help reduce the number of

students who discontinue studies prematurely.

13

On a broader note, it is useful to think about the general mechanisms through

which factors like socioeconomic status of the households, and schooling inputs matter.

In other words, the focus should be on why and not what. As with any economic

decision, schooling decisions depend on both the supply of and the demand for it – in

other words by costs and benefits of getting educated. Banerjee and Duflo (2011)

contend that the debate regarding intervention in primary and secondary schooling splits

policy makers, advisers, and scholars along two camps: those who believe the problem

may be fixed on the supply side, and those who contend it must be addressed on the

demand side. Supply-side proponents of intervention are typically of the opinion that

“we have to find a way to get the children into a classroom, ideally taught by a well-

trained teacher, and the rest will take care of itself” (Banerjee and Duflo 2001, P 73).

Those who feel the issue must be addressed on the demand side assert the quality of

education provided in certain regions is low because investment in education does not

have a high enough return to warrant parents schooling their children. They note that,

when a proper education sufficiently improves economic prospects, they will demand

exactly that. They argue that investment in education is similar to any other good or

service: if the expected return is high, people invest; otherwise they do not.

The rebuttal from supply-side interventionists is that children rarely have the

ability to make an informed decision about the returns to investing in education. Instead,

parents make the decision based on whether educating their children will have positive

returns for the parents. Naturally, when the interests of the children and parents diverge,

children may end up receiving lower levels of education than would be optimal for them

or society in general. Thus, supply-side proponents assert that the decision to educate

children should not be left solely in the hands of parents (Banerjee and Duflo 2011).

14

Past studies have established that personal, household, and school-related

factors have a significant impact on students’ decision to drop out of post-primary

schooling; these factors are typically measured through indicators such as age, gender,

race, socioeconomic status, parental education, and student-teacher ratios (Rumberger

1983, Ensminger and Slusarcick 1992, Rumberger 1995, Alexander et al. 1997). This is

a particularly important issue in developing countries since survival rates in education

systems appear to vary inversely with national income; high-, middle-, and low-income

countries reported 90-, 60-, and 32-percent net secondary school enrollment rates in

2008, and a similar trend exists for past years as well2 (World Development Indicators

2011). The existing literature on education in developing countries motivates the

inclusion of the main explanatory variables for the study in this paper: location, student-

teacher ratio, and parents’ education, income and occupation. A higher student-teacher

ratio, poorer and less educated parents, and being in a rural area are all expected to

reduce the educational potential and outcome of a child.

2 The relevant data are not available for all three mentioned groups beyond 2008. Additionally,

gross enrollment rates are characterized by a similar inverse relationship.

15

3. SCOPE, Data, and Exam Scores

SCOPE

In an effort to address the issue of low student survival rates, BRAC’s Institute of

Educational Development (IED) implemented a pilot program to reintegrate secondary

school dropouts, from slums and other low-income areas, into the mainstream education

system. The pilot program, Second Chance for Children of Post-Primary Education

(SCOPE), began accepting students in 2009 and provides students with the chance to

catch up in their studies and make up academic deficiencies through shortened courses

prior to enrolling them into mainstream secondary schools. According to an internal

concept note guiding the SCOPE program, the overall aim and objective is to “develop a

model that ensures the completion and achievement of competencies of post-primary

education for disadvantaged primary school graduates and [dropouts] who are unable to

attend mainstream secondary [schools].”

The SCOPE program was designed to address the needs of students who

discontinue studies beyond the primary level due to economic hardship, lack of access

to good schools, poor academic performance, and gender discrimination. All students in

the sample live in low-income areas and report substantial work outside of school. As a

result, SCOPE classes are offered in short three to four hour sessions up to four times a

week. Class schedules are often changed in order to facilitate student needs that arise

due to work demands in a given area. Each batch of students begins with a foundation

16

course that helps them make up previous academic deficiencies. They then proceed to

a condensed grade six curriculum, and have the ability to progress through grade eight

within the SCOPE program. The primary cycle is typically five years; hence, the first

year of secondary study is the sixth. Each year of study is equivalent to three

condensed modules. After completing the grade eight module, or sooner if a student is

capable, the desire is to have them return to mainstream secondary schools.

Since its inception, SCOPE has accepted two batches of students. The first

batch, 124 students, began coursework in October of 2009 in six SCOPE centres

spanning urban and rural areas. The second batch, the focus of this study, began

coursework in April of 2010 across 13 new centres, also located in both urban and rural

areas. The second batch initially consisted of 351 students, but due to late enrollments

and dropouts, socioeconomic data are available for 369 students.

Data

The unrestricted sample consists of 369 observations for which SCOPE has

socioeconomic data. Of these 369 observations, approximately 60 percent are girls, and

40 percent are boys. However, during the course of SCOPE modules, some students

dropped out reducing the exam-taking sample size to 287, with girls representing 62.7

percent. The rise in share of girls results from a greater proportion of boys dropping out

than girls.

While a study of performance in each subject would be ideal, for brevity, the

analysis will primarily consider the average score on the following six subjects: Bangla,

17

English, math, social studies, science, and religion. The two subjects excluded for much

of the analysis are home economics and agriculture. These two are excluded because

not all students are subject to examinations in these two courses; at most SCOPE

centres, girls take the home economics course and boys take the agriculture course.

Out of the 287 students who sat for the final exam of module two, 12 sat for at least one

subject exam but did not take all six that are included in the calculation of the average

exam score. Thus, the restricted sample for analysis of the average exam score is 275,

with girls accounting for 64 percent of the examinees. The average exam score of the

six subjects considered is highly positively correlated with each of the six exam scores in

the restricted general-, female-, and male-populations; the correlation coefficients range

between 0.77 and 0.88.

Table 2 presents the descriptive statistics of the variables of interest. Students’

ages range between 12 and 17 years, with children of 14 and 15 years accounting for

73-percent of the sample. Among girls, 79-percent fall within the 14 to 15 range, and the

same can be said of 63-percent of boys. Among students’ fathers, nearly half are

farmers and, unsurprisingly, the vast majority of famers, 134 out of 136, live in rural

areas; similarly 80 percent of fathers in rural areas are farmers. In the urban population,

the distribution of fathers’ occupations is more evenly spread. Small business owners

and daily labourers each account for 25 percent of the population, and rickshaw/van

pullers and factory workers represent 12 and 13 percent respectively. Tradesmen, such

as potters, blacksmiths, and barbers, account for another 14 percent, and, as noted, in

contrast to the rural areas, only two respondents note father’s occupation as farmer.

18

Table 2: Descriptive Statistics

Sex Variable Mean Std Min Max

Girls Average Score of Exams 74.22 15.12 29.50 97.67

Age 14.33 0.81 13 16

Father’s Education 2.13 2.52 0 11

St Ratio 26.20 6.54 12 33

Household Annual Income (Taka) 73554.29 23602.70 30000 138000

Boys Average Score of Exams 70.45 14.18 40.50 95.67

Age 14.67 1.11 12 17

Father’s Education 1.98 2.88 0 11

St Ratio 24.34 6.88 12 33

Household Annual Income (Taka) 79545.45 96955.31 24000 200000

Girls Boys All

Variable Category Obs Pct Obs Pct Obs Pct

Father's Farming 78 44.57 55 55.56 133 0.49

Occupation Small Business 24 13.71 14 14.14 38 0.14

Tradesman 13 7.43 2 2.02 15 0.05

Work in Shop 2 1.14 2 2.02 4 0.01

Rickshaw/Van Puller 11 6.29 5 5.05 16 0.06

Daily Labour 22 12.57 11 11.11 33 0.12

Factory Worker 12 6.86 4 4.04 16 0.06

Service in Low Post 3 1.71 1 1.01 4 0.01

Other 1 0.57 0 0.00 1 0.00

No Response 9 5.14 5 5.05 14 0.05

Location Urban 85 48.57 22 22.22 107 39.05

Rural 90 51.43 77 77.78 167 60.95

Centre Teghoria 2 1.14 13 13.13 15 5.47

Zonail 14 8.00 4 4.04 18 6.57

Gunaritola 9 5.14 7 7.07 16 5.84

Ulia 8 4.57 8 8.08 16 5.84

Sirajabad 12 6.86 12 12.12 24 8.76

Pochabohela 19 10.86 13 13.13 32 11.68

Beraid * 25 14.29 8 8.08 33 12.04

Sater Kul * 12 6.86 8 8.08 20 7.30

Noorzahan * 23 13.14 2 2.02 25 9.12

Ashrafabad * 25 14.29 4 4.04 29 10.58

Dhara 12 6.86 7 7.07 19 6.93

Baghitola 7 4.00 8 8.08 15 5.47

Char Banglia 7 4.00 5 5.05 12 4.38

Sex 175 63.87 99 36.13 - -

* denotes centre located in urban area

19

Unfortunately, the number of individuals living in the household is not available,

which prevents any analysis that requires per capita income. According to the SCOPE

team, there may be severe underreporting of income as a result of parents’ desire to

enroll students into BRAC schools, because BRAC gives preference to low-income

applicants. The average reported household annual income for the general population is

72,200 taka, and ranges between 24,000 and 200,000 taka. The reported annual

income is higher for girls’ families than for boys’, 73,400 and 70,200 taka respectively.

Incomes range between 2,400 and 200,000 taka for boys, and 2,400 to 138,000 for girls.

To put these incomes into context, even using a very conservative estimate, the highest

per capita income would be approximately $2.50 per day.

Among the 13 centres under the umbrella of SCOPE, nine are located in rural

areas and four in urban areas. Of the 274 students under consideration, 107 study in

urban SCOPE centres and girls represent the majority in both rural and urban centres.

However, the male/female difference is considerably greater in urban areas with girls

accounting for 80 percent of students; in rural areas, 54 percent of students are girls.

Additionally, each class in SCOPE centres contains between 12 and 33 students.

Table 3 provides summary statistics of module 2 exam performance for students

of the second batch of SCOPE. All exam scores reported are percentages unless

otherwise stated. The data suggest that the batch performed very well on the Bangla

examination with an average of 84 percent. The standard deviation among student

performance within Bangla is smaller than in other subjects as well. Students performed

the worst in agriculture with an average of just below 63 percent and the greatest

variation in scores appears in math and science exams. Girls outperformed boys in

20

every subject and exam performance within subsets of girls and boys is consistent with

the combined population.

Table 3: Exam Scores by Subject and Gender

Girls Boys All

Subject Mean Median St Dev Obs Mean Median St Dev Obs Mean Median St Dev Obs

Bangla 84.78 86 10.43 176 82.02 84 11.63 99 83.79 86 10.94 275

English 75.30 81 19.37 176 70.41 74 18.48 99 73.54 77 19.16 275

Math 71.50 74 20.50 176 70.02 74 20.61 99 70.97 74 20.51 275

Social Studies 71.20 74 16.65 176 63.86 64 16.68 99 68.56 70 17.00 275

Science 72.41 78 22.00 176 69.73 76 22.32 99 71.44 78 22.11 275

Religion 69.98 70 18.80 176 66.65 64 16.42 99 68.78 68 18.02 275

Home Economics 67.91 64 20.45 157 54.00 54 2.83 2 67.74 64 20.38 159

Agriculture 66.22 66 13.88 27 61.92 62 19.30 98 62.85 64 18.30 125

Performance on exams varies greatly between and within rural and urban

centres (Figure 4). Students in urban centres averaged 83-percent on the exam,

compared to 67-percent average for rural students. The urban/rural difference persists

within male and female subsets, but the difference among girls (18.05-percent) is

noticeably greater than for boys (12.20-percent). This may be in part due to the variation

in class sizes among SCOPE centres. Figure 1 illustrates the relationship between class

size, as measured by the number of students who sat for exams in a given SCOPE

centre, and performance on exams. It is difficult to determine, given centre data, which

of the three red-diamond observations may be outliers; all three are included to construct

the trend line in Figure 1. The trend lines in Figure 1 suggest that centres characterized

by very small class size report lower average exam scores. Classes that are moderate

in size, 15 to 30 students, are characterized by higher exam scores. And finally, those

that have more than 30 students may have higher or lower exam scores than the

21

moderate sized ones. The trend line in the lower scatter offers similar conclusions.

However, if the three centres depicted by red diamonds are removed as potential

outliers, the trend line appears linear. At this point, no conclusion can be confidently

reached regarding the relationship between class sizes and exam performance.

However, this will be discussed further in section V of the analysis in which multivariate

analysis is employed.

Table 4: Exam Scores by Centre

All Girls Boys

Centre Mean St Dev Obs Mean St Dev Obs Mean St Dev Obs

Teghoria 70.51 9.67 15 61.33 5.19 2 71.92 9.52 13

Zonail 72.02 12.46 18 69.62 12.29 14 80.42 10.21 4

Gunaritola 76.31 7.13 17 74.00 7.17 10 79.62 6.07 7

Ulia 72.26 8.92 16 68.69 8.10 8 75.83 8.70 8

Sirajabad 68.84 12.13 24 68.29 9.43 12 69.39 14.76 12

Pochabohela 51.65 6.64 32 50.90 6.61 19 52.73 6.79 13

Beraid * 71.55 8.60 33 71.47 9.02 25 71.79 7.67 8

Sater Kul * 80.42 7.07 20 80.58 8.04 12 80.17 5.83 8

Noorzahan * 88.72 6.11 25 88.30 6.20 23 93.50 0.24 2

Ashrafabad * 92.11 4.74 29 92.61 4.34 25 89.00 6.62 4

Dhara 71.96 12.80 19 74.64 8.98 12 67.38 17.46 7

Baghitola 74.07 6.06 15 76.62 5.72 7 71.83 5.77 8

Char Banglia 48.61 9.34 12 50.40 11.11 7 46.10 6.43 5

Total 72.85 14.85 275 74.20 15.08 176 70.45 14.18 99

* Centres in shaded region of table located in urban areas

22

Figure 1: Class Size and Exam Scores

23

4. Bivariate Analysis

In this section, key variables are aggregated into fewer categories that offer more

observations leading to more reliable conclusions (see Table 5). The variables

considered in this section include a student’s gender and age; the father’s education

level and occupation; and centre characteristics that may impact student performance.

Personal Characteristics

Since the number of girls in the sample is considerably higher than boys, it is not

surprising that more girls are represented in both the top- and bottom-halves of

academic performers. However, among girls a higher fraction, seven percentage points,

is represented in the top half of students. On the other end of the gender spectrum, less

than half the boys, 46 percent, received scores above the median. Thus, it appears that

boys are likely to fare worse than their female counterparts.

In considering the age of the students, the first thing that catches the eye is that

absolutely no 12- and 17-year-olds find themselves in the top half the of academic

performers. The number of observations is certainly small, but the fact that none fall into

the top half warrants a closer look. A closer inspection of the data reveals that of the six

individuals who are 12- or 17-years-old, all are male and six live in rural areas. The one

24

boy who lives in an urban area has an average of 72, which is close to the median score

for the general sample, but roughly 12 percentage points lower than the urban median

(84 percent). The remaining six rural residents averaged 54 percent, which is

considerably lower than the rural median (68 percent), as well as the overall median.

Although the number of observations is quite small, the 12- and 17-year-olds fare much

worse than their counterparts in the sample under consideration. Within the 13- to 16-

year-olds, those who are younger appear to fare somewhat better than those who are

older. Specifically, 13- and 14-year-olds are more likely to perform above the median,

while 15- and 16-year-olds are more likely fall into the bottom half.

Household Characteristics

In considering the effect of the highest level of education of the father on student

performance, the father’s education variable has been re-categorized to: fathers who are

illiterate, those who have attended grades one through four, a grade five education

(completion of primary schooling), and those with more than primary education (grades

six and above). It appears that primary completion by the father is associated with

superior academic performance. Eighty five percent of students whose father report

exactly a fifth grade education performed in the top half of all exam takers. The children

of illiterate fathers fare the worst with only 43 percent above the median, but trail the

children of secondary school dropouts by only 2 percentage points. The children of

primary school dropouts are equally dispersed above and below the median.

25

Table 5: Bivariate Table (median score for all students, 74.33%, is used unless specified otherwise)

Variable Category Bottom Half Top Half Bottom Half Top Half Total

Sex Male 54% 46% 53 46 99

Female 47% 53% 82 93 175

Total 49% 51% 135 139 274

Age 12 100% 0% 2 0 2

13 47% 53% 18 20 38

14 43% 57% 46 60 106

15 52% 48% 49 45 94

16 53% 47% 16 14 30

17 100% 0% 4 0 4

Total 49% 51% 135 139 274

Father’s Education Illiterate 57% 43% 73 54 127

Classes 1-4 51% 49% 45 43 88

Class 5 15% 85% 6 33 39

Class 6+ 55% 45% 11 9 20

Total 49% 51% 135 139 274

Father's Occupation Other 30% 70% 16 38 54

(Urban and Rural) Farmers 65% 35% 86 47 133

Factory Workers 13% 88% 2 14 16

Small Business Owners

29% 71% 11 27 38

Daily Laborers 61% 39% 20 13 33

Total 49% 51% 135 139 274

Father's Occupation Other 51% 49% 21 20 41

(Urban only, Farmers 50% 50% 1 1 2

median = 84.00%) Factory Workers 14% 86% 2 12 14

Small Business Owners

32% 68% 8 17 25

Daily Laborers 72% 28% 18 7 25

Total 47% 53% 50 57 107

Father's Occupation Other 46% 54% 6 7 13

(Rural only, Farmers 50% 50% 66 65 131

median = 67.67%) Factory Workers 100% 0% 2 0 2

Small Business Owners

38% 62% 5 8 13

Daily Laborers 63% 38% 5 3 8

Total 50% 50% 84 83 167

Location Urban 22% 78% 24 83 107

Rural 66% 34% 111 56 167

Total 49% 51% 135 139 274

Centre Teghoria 60% 40% 9 6 15

( * ) denotes urban centre Zonail 61% 39% 11 7 18

26

Gunaritola 38% 63% 6 10 16

Ulia 50% 50% 8 8 16

Sirajabad 63% 38% 15 9 24

Pochabohela 100% 0% 32 0 32

Beraid * 58% 42% 19 14 33

Sater Kul * 25% 75% 5 15 20

Noorzahan * 0% 100% 0 25 25

Ashrafabad * 0% 100% 0 29 29

Dhara 58% 42% 11 8 19

Baghitola 47% 53% 7 8 15

Char Banglia 100% 0% 12 0 12

Total 49% 51% 135 139 274

Source: SCOPE database, BRAC Institute of Educational Development (IED)

The next characteristic of interest is father’s occupation. The categories of

interest are the three that account for the greatest fraction of fathers – farmers, small

business owners, and daily labourers – and factory workers. Factory workers will be

included as a separate variable because the descriptive statistics suggest the children of

factory workers may fare better than their counterparts. All others are aggregated into a

fifth category called ‘other’.

Within the general population, the first noteworthy finding is that nearly two thirds

of farmers’ children find themselves in the bottom half of performers. A similar ratio can

be seen for the children of daily labourers. On the other end of the spectrum, children of

factory workers, small business owners, and those categorized into ‘other’ are

overrepresented in the top half of academic achievers. These differences are quite large,

with 70 to 86 percent of children of these fathers performing above the median.

However, given the nature of work that each father reports, one must question

whether poor outcomes are a result of fathers’ occupation, or other characteristics that

27

go hand-in-hand with these jobs. Farmers are more likely to live in rural areas, and

factory workers and small business owners in urban areas. If higher scores are linked to

aspects of urban and rural areas, then it is expected that the children of farmers fare

worse than children of factory workers and small business owners. This is a very valid

concern, and is addressed – to some extent – in the multivariate analysis by controlling

for the location of the SCOPE centre a student enrolls at.

Separate cross tabulations, using different medians for urban and rural students,

reveal that among urban students, the children of factory workers and small business

owners continue to outperform their counterparts, with 86 and 68 percent of these

students in the top half of academic performers. At the other extreme, among the

children of daily labourers, only 28 percent are among the top half of students.

Additionally, the above-median performance of the students in the ‘other’ category, and

the below-median test scores of farmers’ children disappear. The implication here is

that, in urban areas, the children of factory workers and small business owners are more

likely to outperform their counterparts, and the children of daily labourers are likely to

underperform. However, the children of farmers and those in the ‘other’ category are not

likely to be affected by their fathers’ occupations. Only two observations are available

for the children of farmers in urban areas, thus this may not be a reliable finding. The

impact of being a farmers’ child should be inspected within the rural population given

that nearly all farmers’ children are enrolled in rural SCOPE centres.

The cross tabulation restricted to students who are enrolled in rural areas – using

the rural median – reaffirms the finding from the urban sample that the impact of a

father’s occupation is negligible if the father is a farmer. Additionally, the findings are

28

similar with respect to the children of small business owners, daily labourers, and those

in the ‘other’ category. However, the number of observations and absolute differentials

are quite low for the latter three categories, and as a result, may be unreliable. The rural

data also refutes the finding that the children of factory workers outperform their

counterparts, but with only two observations, this contradiction may be discarded.

The general findings, in combination with the conditional findings, suggest that

the children of farmers do not fare any better or worse than their counterparts. Instead,

it appears that this apparent effect is simply an urban-rural effect. Since nearly all

farmers’ children live in rural areas, it appears that farmer’s children fare worse; in

reality, it is just that students in rural areas fare worse than their urban counterparts.

Regardless of geographic residence, the children of factory workers and small business

owners do outperform their counterparts, and the children of daily labourers

underperform.

Centre Characteristics

Students who study in urban areas fare considerably better than their rural

counterparts. Over three quarters of urban students were in the top half of the academic

performers while two thirds of rural students were in the bottom half. But, when the

exam scores for centres are disaggregated from the urban-rural level to centre-specific

data, it seems that even among urban and rural areas, there is a considerable amount of

variation.

29

Among the four urban centres, the Noorzahan and Ashrafabad centres exhibit

the best performance with all of the students in these two centres performing above the

median. Saterkul students also performed admirably with three quarters in the top half.

However, the students of Beraid did relatively worse on the exams with nearly 60

percent in the bottom half; students from four rural centres performed at least as well as

those in Beraid.

Regarding the students in the rural centres, those in the Pochabohela and Char

Banglia centres performed the worst; none did well enough to warrant placement in the

top half. The students of Gunaritola and Baghitola were the only two groups in rural

areas that had more students in the top half than the bottom half. The remaining rural

centres had anywhere from 38 to 42 percent of their students in the top half.

It is unclear why students in urban areas perform better than their counterparts,

but it is evident that this is not a simple coincidence. All students, regardless of the

location of the SCOPE centre, are administered the same exam, and marking

instructions are streamlined to offer little room for subjectivity. This suggests that the

variation in scores among centres is at least in part a result of factors that vary between

urban and rural areas. A number of factors could contribute to this ranging from different

valuation of education among the two regions, occupational prospects, teacher and

centre quality, and quality of the cohort.

30

5. Multivariate Analysis

The preceding bivariate analysis identifies factors that may be relevant, but it

does not explain whether these characteristics are mere indicators or causes of

underperformance. For instance, it is quite possible that the urban/rural indicator, farmer

(father’s occupation), and father’s education capture the same effect since many

students exhibit similarities in these three attributes. That is the gap in knowledge I

attempt to address in this section of the study. Specifically, are selected indicators in the

bivariate sections that are highly correlated with each other separate causes of poor

academic performance, or are these different variables capturing the same effect?

Functional Form and Variables

The regression results presented in Table 6 are derived with average exam

scores modeled as a linear function of regressors:

Y = β0 + β1X1 + β2X2 + β3X3

Such that:

Y: Average Exam Score of each student

X1: Personal Characteristics

X2: Household Characteristics

X3: Centre Characteristics

31

In the regression results presented, each column represents one regression.

Regressions 1 to 6 include all 274 students discussed in this analysis. Regressions 7

and 8 include urban and rural residents respectively, and regressions 9 through 12 break

up the sample by gender. The relevant F-statistic, adjusted coefficient of determination,

and number of observations are listed below the results in each column.

In each of the regressions presented, the dependent variable is the average

percentage scored in the six subjects considered, like the bivariate section. The

personal characteristics included in the regressions are a student’s gender and age, and

the household characteristics include the highest level of education obtained by the

student’s father, and the father’s occupation. Regarding age, students are categorized

into 13- to 14-year-olds, 15- to 16-year-olds, and all others; these are the same variables

and categories presented in section IV. Father’s education levels are categorized into

illiterate, primary school dropout, primary school graduate, and those who have

education past the primary level. Father’s occupation is categorized into farmers, factory

workers, small business owners, daily labourers, and all others. All characteristics are

included as binary variables in order to allow for as much flexibility in functional form as

appropriate; while this reduces the degrees of freedom available, given the size of the

dataset, it appears justified.

In controlling for centre characteristics, regressions employ two continuous

variables for the student-teacher ratios, one binary variable that identifies whether the

centre is located in an urban or rural area, and binary variables to identify a combination

of SCOPE centres. The student-teacher ratio variables include a linear as well as a

32

quadratic regressor. Inclusion of the quadratic variable captures nonlinear effects of

student-teacher ratios on exam performance. So, for instance, if there is indeed an

inverted-u shape that guides the relationship between class sizes and student

performance, as depicted in Figure 1, the regressions will capture it.

The urban variable accounts for any variation that exists between centres,

households, and students in urban and rural areas. These characteristics may include

variations in class size, the distance a student travels from home to school, differences

in attitude regarding education between urban and rural residents, and many others.

The centre identifiers control and account for characteristics associated with individual

centres that are not captured by the student-teacher ratio, urban/rural aspects and family

characteristics; these school-specific characteristics may include class size, teacher

quality, and infrastructure among others. The primary difference between the

urban/rural indicator and centre variables is that the urban variable accounts for

differences between centres in urban and rural areas, but ignores differences between

centres within urban and rural areas. So, for instance, if students in certain rural centres

perform better than those in other rural centres due to infrastructural differences or

teaching quality, the urban variable will not account for this difference; however, the

centre variables will account for these.

33

Results

Student-Teacher Ratios, Urban-Rural Effect, and Centre Characteristics

Regressions 1 through 5 include all observations of the sample under

consideration. The first regression includes only personal and household

characteristics. The adjusted R-square in regression 1 indicates that 25 percent of the

variation in exam scores can be explained by the variables included, leaving 75 percent

of the exam score variation unexplained. The results reveal that girls, on average,

perform slightly worse than boys when the relevant controls are included. This is the

opposite of what is seen in the data prior to including controls for personal and

household characteristics. This suggests that girls perform better in the sample under

consideration because they also have personal and household characteristics

associated with higher grades; they do not outperform boys simply because of their

gender. However, this difference is statistically insignificant, so there is no reason to

believe that either boys or girls would perform better if other characteristics are

controlled for.

Additionally, the age of a student appears to be an important indicator of exam

performance. A student between 13 and 16 years of age can expect to outperform

his/her counterparts by 13 percentage points, nearly one standard deviation of the exam

score distribution. Similarly, children of fathers who report primary school completion

perform nearly 11 percentage points better than others, while the children of fathers with

some primary or secondary schooling fare as well as the children of the illiterate. With

regard to fathers’ occupations, the children of farmers perform worse than all others,

except daily labourers, by 8 to 17 percentage points. Even in comparison to the children

34

of daily labourers, farmer’s children underperform by 3.53 percentage points, but this

difference is not statistically significant.

Regression 2 includes the urban/rural indicator in addition to all the variables

included in regression 1, and the resulting adjusted R-square indicates that an additional

10 percent of the variation is explained by its inclusion. Controlling for urban/rural

residence does not impact the coefficients of the gender, age, or father’s education level

in any meaningful way. Although the magnitude of the gender coefficient increases, it

remains statistically insignificant and negative. However, it is worth noting that – with the

exception of the daily labourer identifier – each of the coefficients of fathers’ occupation

changes from statistical significance to insignificant. This indicates that the poor

performance of farmers’ children is not a result of fathers’ occupation; it is a result of

factors associated with living in rural areas. Additionally, the coefficient of the daily

labourer identifier changes sign and becomes statistically significant with a coefficient of

-7.41. This suggests a father’s occupation may indeed affect student performance if the

father is a daily labourer, although this is only the case if the quality of education

delivered in the various SCOPE centres is comparable.

Regression 3 builds upon regression 2 by adding two controls for class size. The

inclusion of these two variables increases the explanatory power of the regression by 17

percentage points to 52 percent, but has little impact on the coefficients of other

variables. All coefficients maintain the same sign and similar levels of significance.

However, class size explains four to five percentage points of the above average

performance of 13- to 16-year-olds; but they continue to outperform others by eight

percentage points.

35

Table 6: Regression Results (Part 1 of 2)

(1) (2) (3) (4) (5) (6)

Female -0.13 -2.43 -1.85 -1.83

-1.83 -1.83

13-14 yr olds 13.36 ** 14.12 *** 8.06 * 5.75

5.75 5.75

15-16 yr olds 13.43 ** 13.78 *** 8.41 * 6.08

6.08 6.08

Classes 1-4 -0.73 -0.35 -0.73 0.25

0.25 0.25

Class 5 10.80 *** 7.47 *** 7.16 *** 0.47

0.47 0.47

Class 6+ -3.49 -2.19 0.16 -2.91

-2.91 -2.91

Factory Workers 17.26 *** 4.56 2.07 1.82

1.82 1.82

Small Business Owners 11.37 *** 2.01 2.21 -1.67

-1.67 -1.67

Daily Laborers 3.53 -7.41 ** -8.41 *** -5.07 ** -5.07 ** -5.07 **

Other 8.44 *** -1.86 -3.58 -3.00

-3.00 -3.00

Urban 15.51 *** 20.13 ***

13.10 *** 21.41 ***

Student-Teacher Ratio 6.25 ***

7.10 ***

(Student-Teacher Ratio)2 -0.14 ***

-0.14 ***

Zonail 2.50

-0.39 2.50

Gunaritola 7.19 ** 10.39 *** 7.19 **

Ulia 3.63

11.18 *** 3.63

Sirajabad -0.70

-3.30 -0.70

Pochabohela -17.80 *** -10.28 *** -17.80 ***

Beraid (urban) 5.57 * mc -15.83 ***

Sater Kul (urban) 13.09 *** mc -8.32 ***

Noorzahan (urban) 21.41 *** 7.15 *** ref

Ashrafabad (urban) 24.12 *** 16.23 *** 2.72 ***

Dhara 3.55

1.53 3.55

Baghitola 5.78 * 15.93 *** 5.78 *

Char Banglia -19.71 *** mc -19.71 ***

Intercept Constant 54.12 *** 54.62 *** -2.68 65.15 *** -18.85 65.15 ***

Adj. R-Squared 0.2462 0.345 0.5152 0.6578 0.6578 0.6578

F-Stat 9.92 14.07 23.32 24.85 24.85 24.85

Observations 274 274 274 274 274 274

Notes:

The dependent variable is the average percentage score in the following exams: Bangla, English, math, social studies, science, and religion.

(***) denotes significance at 1 percent, (**) at 5 percent, and (*) at 10 percent significance level

(mc) denotes variable was dropped to prevent multicollinearity; the category becomes a component of the reference category

(ref) denotes variable is part of the reference category

Source: BRAC Institute of Educational Development Database

36

Table 6: Regression Results (Part 2 of 2)

(7) (8) (9) (10) (11) (12)

Female -0.32 -2.85 *

13-14 yr olds -1.93 7.66 -0.78 -0.14

8.52

4.12

15-16 yr olds -0.52 7.36 ref ref

8.55 * 6.61

Classes 1-4 1.47 -0.07 0.91 1.74

-4.35

-1.83

Class 5 3.01 -4.94 9.42 *** 2.12

-3.89

-3.29

Class 6+ -2.03 -3.39 1.99 -3.15

-3.38

-2.18

Factory Workers -0.44 9.04 2.04 2.49

1.39

0.77

Small Business Owners -3.91 -1.12 -1.04 -2.23

6.67

0.19

Daily Laborers -5.55 -8.29 ** -8.74 ** -2.05

-7.99

-9.16 **

Other -5.26 -2.77 -4.59 -2.74

-5.92

-5.09

Urban 21.99 ***

16.80 ***

Student-Teacher Ratio 5.49 ***

6.84 ***

(Student-Teacher Ratio)2 -0.13 ***

-0.15 ***

Zonail 2.79 8.38

8.28

Gunaritola 8.09 ** 12.14 **

9.14 *

Ulia 4.67 7.11

7.64

Sirajabad 0.03 6.47

-2.23

Pochabohela -17.33 *** -11.03 **

-18.35 ***

Beraid (urban) -15.30 *** 11.71 **

7.87

Sater Kul (urban) -7.06 *** 19.42 ***

13.25 **

Noorzahan (urban) ref 27.26 ***

21.58 **

Ashrafabad (urban) 3.20 31.16 ***

17.47 **

Dhara 3.94 13.68 **

-0.61

Baghitola 6.41 * 16.65 **

2.58

Char Banglia -19.92 *** -11.05

-23.14 ***

Intercept Constant 92.56 *** 63.96 *** 11.87 61.48 *** -6.79

67.39 ***

Adj. R-Squared 0.5899 0.4887 0.5678 0.7134 0.3975 0.5398

F-Stat 12.73 9.82 21.78 22.66 6.39 6.47

Observations 107 167 175 175 99 99

Notes:

The dependent variable is the average percentage score in the following exams: Bangla, English, math, social studies, science, and religion.

(***) denotes significance at 1 percent, (**) at 5 percent, and (*) at 10 percent significance level

(mc) denotes variable was dropped to prevent multicollinearity; the category becomes a component of the reference category

(ref) denotes variable is part of the reference category

Source: BRAC Institute of Educational Development Database

37

Similarly, students in urban areas continue to outperform their counterparts by 20

percentage points even after controlling for gender, age, father’s occupation, father’s

education level, and class size. Although centre characteristics have not been controlled

for yet, sufficient controls have been included such that one cannot brush off this finding

by suggesting those in rural areas may have access to people with less education than

their counterparts or parents who work in jobs that lead to a lower valuation of education.

The coefficients of the student-teacher ratio variables indicate that as class sizes rise,

student performance increases. But beyond a certain point – the coefficients suggest

this point is 23 students per classroom/teacher – larger class sizes are characterized by

poor performance on exams. Thus, in the scatterplots presented in figure 1, it appears

an inverted-u shape is appropriate, instead of a linear relationship correlating student-

teacher ratio to exam scores. Additionally, the coefficient of the urban/rural indicator

increased from 15.51 to 20.13 when class size was controlled for, suggesting that if

class sizes were smaller in the larger urban SCOPE centres, urban students may

perform even better than they do currently.

In regression 4, the student-teacher ratio and urban variables are excluded, and

indicators for the centres are included instead. These centre variables account for all

the variation associated with each particular centre, including variation in student-

teacher ratios and whether a centre is located in an urban or rural area. Thus,

regression 4 has considerably more information resulting in a higher R-square. The

variables in regression 4 account for 66 percent of the variation in exam scores; this is

up from 52 percent in regression 3 and 25 percent in regression 1. Regression 5 adds

the student-teacher ratio variables and whether a centre is located in an urban or rural

area in addition to the centre variables. Regression 5 does not add any more

38

information in terms of determinants of exam scores than regression 4, but it allows for a

more accurate understanding of how student-teacher ratios and living in an urban or

rural area affects student performance. Since no additional information is added,

regression 5 is run with three fewer centre controls in order to offset perfect

multicollinearity; the resulting R-square in regression 5 is the same as in regression 4.

In regressions 2 and 3, the coefficient estimates of the student-teacher ratio

variables and the urban variable may have been biased because they may have

captured other effects that vary from centre to centre. For example, the centres located

in rural areas may be characterized by students who have higher average distances of

commute from home to the SCOPE centre or lower quality teaching, each of which may

adversely affect student performance. By including the centre control variables, the

effect of studying in a rural area and different class sizes can be disentangled from these

other effects.

The most notable change from regression 3 to regressions 4 and 5 is that – with

the exception of the daily labourer indicator – all personal and household characteristics

become statistically insignificant when centre identifiers are included. This suggests that

centre characteristics are better indicators of student performance than personal and

household characteristics. The urban/rural indicator coefficient decreases considerably

with the inclusion of the centre variables as well, from 20.13 in regression 3 to 13.10 in

regression 5. At first glance, it may appear that the urban variable was capturing effects

that vary between urban and rural centres and that the true effect of living in an urban

area on exam performance appears to be approximately 13 percentage points.

However, a conclusion like this would be hasty and misleading since the reference

category has changed as a result of the inclusion of fewer centre identifiers to prevent

39

multicollinearity. Unfortunately, the coefficient of the urban/rural identifier in regression 5

is not comparable with those in regressions 2 and 3. One can conclude that students

living in urban areas continue to outperform their rural counterparts even after centre

characteristics are controlled for.

Finally, regression 6 employs all personal and household characteristics, an

urban/rural identifier, and identifiers for centres. The rationale for including this set of

variables is to identify the impact of the urban/rural effect after controlling for other centre

characteristics. However, running several regressions with different urban centre

identifiers as the reference category reveals that the urban identifier coefficient varies

considerably based on which centre is dropped to avoid multicollinearity; an issue similar

to the one discussed regarding regression 5. Since exam scores vary so much from

centre to centre within the urban and rural areas, the choice of which centre identifier is

dropped greatly increases or decreases the magnitude of the coefficient of the

urban/rural indicator. Based on this evidence, it is still safe to say that living in an urban

area yields positive effects on exam scores, but the magnitude of this effect is

determined at least in part by the centre a student attends. This is precisely what

motivates regressions 7 and 8. Additionally, because of complexities in interpreting the

coefficient on the urban/rural identifier when centre identifiers are included, regressions

from this point forward do not include the urban/rural identifier and centre characteristics

together.

40

Urban and Rural

Regressions 7 and 8 are conducted conditional on whether a student is enrolled

in an urban or rural SCOPE centre. This is particularly important given the large and

statistically significant difference in academic performance between students in the two

regions. Splitting the sample into two groups facilitates analysis of whether the effects,

and non-effects, of variables persist within urban and rural areas. Additionally, it allows

for a discussion of whether urban and rural effects change the manner in which other

variables impact student performance. An example of the latter phenomenon would be

that it is possible that the impact of having a factory worker as a father affects student

performance differently in urban areas than in rural areas.

The adjusted R-squares of the two regressions suggest that the variables under

consideration are more relevant indicators of academic performance in urban areas than

in rural areas. Comparing the results of regressions 7 and 8 to regression 4, the only

variables that change significantly are the daily labourer coefficient in the urban sample

and the gender coefficient in the rural sample. The results of these regressions confirm

that the girls’ outperformance of boys can be explained by the other characteristics

controlled for in these regressions. In fact, regression 8 reveals that girls underperform

boys in rural areas when other characteristics are controlled for.

With respect to the daily labourer variable, and bearing in mind that the base

category is farmers, these regressions suggest that the below average performance of

daily labourers persists even after controlling for other characteristics of interest in the

rural areas but this is not the case in urban areas. It appears that children of daily

labourers in urban areas perform worse than their counterparts due to other factors,

41

such as centre characteristics, household education levels, and age. However, for the

children of daily labourers in rural areas, these other characteristics cannot account for

their poor academic performance. This suggests that in the rural areas, simply having a

father who is a daily labourer — regardless of the household education levels, centre

characteristics, age, and gender — decreases a child’s exam scores by nearly 8

percentage points. While the daily labourer coefficient is statistically insignificant in

regression 7, this is likely a result of the low number of observations in the base

category; there are only two farmers in urban areas. Based on the magnitude of this

coefficient and levels of significance in the other regressions, I suspect this coefficient

would be statistically significant if a higher number of farmers lived in urban areas.

Finally, there are some small differences in the coefficient magnitudes of the

centre variables. However, these changes are very small, and appear to be an effect of

the manner in which urban/rural residence interacts with the other variables in the

regression. Although at first glance, the centre variable coefficients in regression 7

appear quite different from those in regression 4, this is a result of the fact that a

different centre is considered the reference category in each regression. In regression

4, the base category is Teghoria, and in regression 7, the base category is Noorzahan.

When the base category change is taken into consideration – compare urban centre

coefficients in regressions 6 and 7 – the differences are small enough to be considered

negligible.

42

Girls and Boys

Regressions 9 through 12 break the sample into two subcategories, boys and

girls. The rationale for doing this is similar to that provided for regressions 7 and 8; in

order to see if the magnitude, effect, and significance of the variables under

consideration remain the same within the group of boys and girls, as well as to assess

whether the variables in question affect academic performance differently between boys

and girls. Regressions 9 and 11 include controls for the student-teacher ratio and

whether a student is enrolled in an urban or rural centre; and regressions 10 and 12

include centre identifiers, which implicitly control for the impact of student-teacher ratios

and geographic location.

The adjusted R-squares are noticeably higher in the regressions for girls than for

the boys, roughly 17 percentage points. These R-square differences suggest that the

variables included in the regressions are a more complete set of indicators of academic

performance for girls than boys. For instance, the variables included in regression 10

account for 71 percent of the variation in girls’ exam scores, but these same variables

account for only 54 percent of the variation in boys’ exam scores. Thus, age, father’s

education level and occupation, and centre characteristics account for a much smaller

fraction of boys’ overall performance than girls’.

43

If comparisons are made with the total sample, regressions 9 and 11 should be

compared to regression 3, since these three include the same regressors3. Two things

worth noting in these regressions is that fathers’ education level and occupational choice

appear to matter for girls, but not for boys. Most notably, completion of primary school

by a father positively impacts a daughter’s performance by over nine percentage points

– compared to the daughters of illiterate fathers – while it decreases boys’ exam scores

by an insignificant amount. Thus, the positive impact of primary completion by fathers

observed in the general population may be attributable only to girls.

Looking at the coefficient of the daily labourer indicator, the magnitudes of the

coefficients are similar in all three regressions, but the coefficient is insignificant in

regression 11. Once again, this may be due to a low number of boys with fathers who

are daily labourers or farmers. These three regressions provide further support that the

children of daily labourers are at an academic disadvantage, as are those living in rural

areas – the urban coefficient remains significant in all three regressions and ranges

between 16.80 and 21.99. The coefficients of the student-teacher ratio variables

substantiate that among both boys and girls, there is an inverted-u shape that explains

the correlation between class size and student performance. As the class size rises,

students perform better; but student performance begins to deteriorate if classes have

more than 22 students.

3 Regression 6 is different with respect to the reference category for the age groups since there

are no female observations in the 12 and 17 year old category.

44

Regression 4 is the total sample counterpart to conditional regressions 10 and

124. Consistent with regression 4, the daily labourer variable is the only personal or

household indicator that is statistically significant in regression 12, but it is insignificant in

regression 10. This suggests that whether or not a father is a daily labourer affects girls

and boys differently. Namely, the impact of having a father who is a daily labourer is

significantly negative for boys, but not necessarily for girls. Thus sons of daily labourers

may be expected to perform worse academically than the sons of others; this nine

percentage point disadvantage cannot be explained by the characteristics controlled for.

It also appears that some centres attended by students affect girls and boys differently.

Most notably, among girls, those who attend Gunaritola, Dhara, Baghitola, and any

urban SCOPE centre outperform those in all other centres; the girls in Pochabohela –

and possibly Char Banglia – perform worse than the girls in any other centre. Among

boys, those in Gunaritola, Sater Kul, Noorzahan, and Ashrafabad outperform all others,

and the boys in Pochabohela and Char Banglia perform worse than their counterparts.

4 Regression 7 is different with respect to the reference category for the age groups since there

are not female observations in the 12 and 17 year old category.

45

6. Discussion of Major Findings and Policy

Recommendations

Major Findings

In section IV, father’s education and other variables were aggregated into

categories that appeared to be of importance. The results suggest that the following

students are more likely to perform in the lower half of exam takers: boys, students

outside of the 13- to 14-year-old range, children in of father’s who have any education

level other than primary school completion, the children of fathers who are daily

labourers, and those attending a number of underperforming SCOPE centres. On a

brighter note, it appears the children of small business owning fathers and factory

workers in urban areas fare much better than others. While bivariate analysis is a good

way to unearth indicators, it is inappropriate for assessing whether these indicators are

causes of poor academic performance. It also leaves one wondering whether two or

more indicators stand as proxies for one phenomenon. Multivariate analysis was

employed in section V in order to assess whether the indicators identified in section IV

are likely causes or mere symptoms of the causal mechanisms that lead to poor

academic performance.

The results lead one to conclude that children of daily labourers may indeed be

at risk of poor performance. At the very least, one can safely conclude that even after

46

accounting for household education levels, area of residence, and the various factors

captured by the different centres, sons of daily laboring fathers lag behind their counter

parts by 5- to 10 percentage points. Among girls, the difference is insignificant when

centre characteristics are controlled for. Once centre characteristics are controlled for,

the age of students do not appear to be a significant factor.

It also becomes apparent that centre characteristics matter a great deal for

student performance. Consistent with the literature on student-teacher ratios, the

regressions presented reveal that exam scores are highest in classes with 21 to 26

students, and performance deteriorates as the cohort gets much larger or smaller.

Additionally, students in rural areas underperform their urban counterparts. This is the

case even after controlling for centre characteristics, which capture differences in

teacher quality, student-teacher ratio, physical capital, and other factors that vary from

centre to centre.

Students at some SCOPE centres perform much better than others. These

variations in scores persist even after personal and household characteristics are

controlled for. From the present analysis, it is unclear why these grades vary by centre.

Are scores lower in some centres than others because of lower cohort quality in terms of

academia, or is it a result of inconsistent grading practices? Previously, the SCOPE

program administrators randomly selected exams and marked for consistency purposes,

but these marks were not available at the time of the study for the module under

consideration. As a result, a statistical analysis of the variation in marks among centres

was not conducted. However, anecdotal evidence from the SCOPE team suggests that

there was negligible variation among exam grades given by centre teachers and SCOPE

administrators. Additionally, a very strict set of grading guidelines are provided to all

47

teachers responsible for marking exams. Therefore, one of the primary questions

moving forward is identifying why there is so much variation in student performance

among centres.

Policy Recommendations

Prior to making any policy recommendations, one must put this study into

context. First of all, the sample of students under study includes only previous dropouts

or those likely to discontinue studies following primary school, and all students come

from considerably disadvantaged economic backgrounds. Thus, the results of this study

should not be generalized to the student population of Bangladesh as a whole. Instead,

it should be considered a guide for dropouts, and those likely to drop out, from low-

income areas. Additionally, the sample under consideration includes only those who sat

for the exams administered. This means that those who dropped out of the SCOPE

program after beginning the modules but before writing the exams are not included in

the analysis. This implies that the resulting study is pertinent for those who may

underperform, but are likely to persevere and not drop out of the SCOPE program.

Finally, to date, no study has been conducted to assess the relationship connecting

performance in SCOPE centres and subsequent performance in other secondary

schools. While all observables suggest the SCOPE curriculum covers similar material

and assesses students in a similar manner, one cannot be certain that good

performance in SCOPE centres will translate into success at mainstream schools;

however, it would be quite surprising if this were not the case. Nonetheless, it would be

48

unwise to generalize these results to the general population of dropouts until such a

study is conducted.

The results of this study suggest that among the dropout population of low-

income youth of Bangladesh, the location of residence and the quality of schooling are

the two most important factors driving academic success. In effect, both the supply of

and demand for education matter. But perhaps more importantly, it appears the

characteristics of the students and households matter very little.

The preceding study consistently reveals that students who reside in rural areas

fare worse than urban residents. There are fundamental, and thus far unidentified,

differences in characteristics prevalent in the two types of areas that affect student

performance in very different ways. At this point, the most pressing need is to identify

why students in rural areas perform so much worse than their urban counterparts even

when personal and household differences are taken into account. Without knowing the

causes for this phenomenon, it is difficult to categorize this as a supply or demand side

factor; cultural views about education or occupational prospects may vary between

urban and rural areas or the physical structure and quality of instruction may be of lower

quality in rural areas. Nonetheless, it appears one set of policies cannot effectively

address the needs of both the urban and rural students, and any policies aimed at

reducing the achievement gap among dropouts or improving the academic prospects of

low performers should come in sets of two: one for rural areas and one for urban areas.

With respect to supply side factors, schooling quality, the results are a bit more

difficult to interpret and make actionable. If the cohort quality is comparable across

centres, then based on the preceding results one could conclude that schooling quality is

49

indeed a strong determinant of how well students perform. However, at this time, no

measure of cohort quality is available. Thus, while it appears schooling quality is quite

important for student performance, further research is warranted prior to making this

conclusion.

From an institutional perspective, the SCOPE program may benefit from

conducting research into why the students in some centres perform so much better than

others. The variation in exam scores is troubling given that SCOPE employs a module

that is intended to address any prior academic deficiencies. This would suggest that

there are two likely causes for the differences in scores: the cohort quality may be

superior in some centres compared to others, or that some centres offer a system of

delivery that is better than that offered by others. In either case, SCOPE may have an

opportunity to address this concern if the cause is identified and reveal whether

schooling quality is as important as these results suggest. Additionally, it may be

beneficial to undertake a study that assesses how student performance on SCOPE

exams is related to performance on standardized exams and those offered in

mainstream secondary schools. Until doing so, the results of studies that assess

student performance may hold little clout in the eyes of national policymakers and donor

organizations.

Finally, the results indicate that the household characteristics matter very little in

general. This is quite intriguing because it is inconsistent with much of the past

literature, and this suggests that on the demand side, community views of education are

more important than individual views. With the exception of the daily labourer identifier,

no other personal or household factor matters for student performance on the exams

under study. This should be viewed as reason for optimism. In effect, this means that

50

regardless of whether a child lives in a household with educated or uneducated people,

and despite any shortcomings the child may have, he or she still has the ability to learn

given a community that values education and good schools.

51

7. Conclusion

The SCOPE program offered a unique data set that enabled a study of supply

and demand side factors related to education and the resulting impact on student

performance. The sample observed is characterized by a higher degree of homogeneity

in terms of student ability and household income than most data sets offer. These

homogenous characteristics resulted in a study that allowed more reliable inspection of

factors that affect student performance, such as father’s education, student age, area of

residence, and schooling variation.

The results of this study suggest that the two strongest factors that affect student

performance, as measured by exam scores, are whether a student lives in an urban or

rural area and the school he/she attends; additionally, it appears father’s occupation

matters for student achievement, but only if the father is a daily labourer. One of the

surprising findings of this study is that household factors that are traditionally though to

be important determinants of academic success have little bearing on these students’

performance on exams. Unfortunately, due to limitations of the data, firm conclusions

were not drawn regarding the importance of school quality. Instead, specific direction

regarding what type of data can be acquired to make firmer conclusions is provided in

the policy recommendation section.

52

References

Ahmed, M., & Nath, S. (2005). Education Watch Report 2003/4: Quality with Equity: The primary education agenda. Dhaka: Campaign for Popular Education (CAMPE).

Ahmed, M., Nath, S., Hossain, A., & Kalam, M. A. (2006). Education Watch Report 2005: The State of Secondary Education, Progress and Challenges. Dhaka: Campaign for Popular Education (CAMPE).

Alexander, K., Entwisle, D., & Horsey, C. (1997). From First Grade Forward: Early Foundations of High School Dropout. Sociology of Education , 87-107.

Banerjee, A., & Duflo, E. (2011). Top of the Class. In Poor Economics: A radical rethinking of the way to fight global poverty (pp. 71-101). New York: Public Affairs.

Barro, R. (1991). Growth in a Cross Section of Countries. The Quarterly Journal of Economics , 407-443.

Barro, R., & Lee, J.-W. (1993). International Comparisons of Educational Attainment. Journal of Monetary Economics , 363-394.

Becker, G., & Tomes, N. (1994). Human Capital and the Rise and Fall of Families. In G. Becker, Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education (3rd Edition) (pp. 257-298). National Bureau of Economic Research: The University of Chicago Press.

Becker, G., Murphy, K., & Tamura, R. (1994). Human Capital, Fertility, and Economic Growth. In G. Becker, Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education (3rd Edition) (pp. 323-350). National Bureau of Economic Research: The Univeristy of Chicago Press.

Ensminger, M., & Slusarcick, A. (1992). AssociationPaths to High School Graduation or Dropout: A Longitudinal Study of a First-Grade Cohort. Sociology of Education , 95-113.

Garnier, H., Stein, J., & Jacobs, J. (1997). The Process of Dropping Out of High School: A 19-Year Perspective. American Educational Research Journal , 395-419.

Hanushek, E. (1995). Interpreting Recent Research on Schooling in Developing Countries. The World Bank Research Observer , 227-246.

53

Hanushek, E., & Kimko, D. (2000). Schooling, Labor-Force Quality, and the Growth of Nations. The American Economic Review , 1184-1208.

Hanushek, E., Lavy, V., & Hitomi, K. (2006). Do Students Care about School Quality? Determinants of Dropout Behavior in Developing Countries. Working Paper12737. National Bureau of Economic Research .

Heyneman, S., & Loxley, W. (1983). The Effect of Primary-School Quality on Academic Achievement Across Twenty-nine High- and Low-Income Countries. American Journal of Sociology , 1162-1194.

Jimerson, S., Egeland, B., Sroufe, L. A., & Carlson, B. (2000). A Prospective Longitudinal Study of High School Dropouts Examining Multiple Predictors Across Development. Journal of School Psychology , 525-549.

Lee, J.-W., & Barro, R. (2001). Schooling Quality in a Cross-Section of Countries. Economica , 465-488.

Lloyd, C., Mensch, B., & Clark, W. (2000). The Effects of Primary School Quality on School Dropout among Kenyan Girls and Boys. Comparative Education Review , 113-147.

Lloyd, D. (1974). Analysis of sixth grade characteristics predicting high school dropout or graduation. JSAS Catalog of Selected Documents of Psychology , 90.

Lloyd, D. (1978). Prediction of School Failure From Third-Grade Data. Educational and Psychological Measurement , 1193-1200.

Mankiw, E. G., Romer, D., & Weil, D. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics , 407-437.

Nehru, V., Swanson, E., & Dubey, A. (1995). New Database on Human Capital Stock in Developing and Industrial Countries: Sources, Methodology, and Results. Journal of Development Economics , 379-401.

Park, K., & Kerr, P. (1990). Determinants of Academic Performance: A Multinomial Logit Approach. The Journal of Economic Education , 101-111.

Pritchett, L. (2001). Where has all the education gone? The World Bank Economic Review , 367-391.

Rumberger, R. (1983). Dropping Out of High School: The Influence of Race, Sex, and Family Background. American Educational Research Journal , 199-220.

Rumberger, R. (1995). Dropping Out of Middle School: A Multilevel Analysis of Students and Schools. American Educational Research Journal , 583-625.

Rumberger, R., Ghatak, R., Poulos, G., Ritter, P., & Dornbusch, S. (1990). Family Influences on Dropout Behavior in One California High School. Sociology of Education , 283-299.

54

Schultz, T. (1961). Investment in Human Capital. The American Economic Review , 1-17.

Schultz, T. P. (1988). Education Investments and Returns. In H. Chenery, & T. Srinivasan, Handbook of Development Economics (pp. 544-621). Amsterdam: Elsevier Science Publishers B. K.

Solow, R. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics , 65-94.