Chapter 5

Selective Schools and Education

Decisions: Evidence from Malawi1

An earlier version of this chapter appeared as Tinbergen Institute discussion paper TI

2010-041/2.

5.1 Introduction

Evidence is building on the role of the school environment (a cocktail of physical resources,

and human resources available in the school, as well as the ability and socioeconomic

background of peers) in pupil learning. The literature on peer effects indicates that peers

matter for pupil learning (e.g. Ding and Lehrer 2007; Hoxby, 2000; Lavy, Paserman,

and Schlosser, 2008; Lyle 2007; Sacerdote, 2001; Zimmerman 2003). Teachers have been

shown to be an important driver of pupil learning (e.g. Angrist and Lavy, 1999; Krueger,

1999; Rivkin, Hanushek, and Kain, 2005).2 And several recent studies show that high

quality selective schools have a modest beneficial impact on pupil learning (Clark, 2010;

Jackson, 2010; Ozier, 2011; Pop-Eleches and Urquiola, 2010).

Evidence on the impact of the school environment on school participation, however,

1Acknowledgements for Chapter 5: I thank two anonymous referees, Pierre-Andre Chiappori, IsaacMbiti, participants at the 2010 CSAE conference in Oxford, the 2010 EEA meetings, the 2010 EUDNconference, and participants in seminars at the Paris School of Economics, the Tinbergen Institute, theUniversity of Amsterdam, and colleagues at the VU University Amsterdam for valuable comments. I alsothank the officials at Malawi’s Ministry of Education and the Malawi National Examinations Board whokindly assisted by providing the necessary data and information for this research.

2The importance of teachers has been a topic of considerable debate (see Mishel and Rothstein,2002) and the evidence for the role of teachers in pupil learning in developing countries does not alwayscorrespond with that for developed countries (Kremer and Holla, 2009).

121

122 Chapter 5. Selective Schools and Education Decisions

is more limited. Studies investigating the impact of randomly implemented temporary

interventions that affect a single aspect of the school environment, such as textbooks

(Glewwe, Kremer, and Moulin, 2009) or teacher incentives (Glewwe, Ilias, and Kremer,

2009), typically find that these interventions hardly affect school participation.3 Older

studies that investigate more comprehensive measures of the school environment, such as

Case and Deaton (1999), are often criticized for their lack of a clean strategy to identify

causal effects. Literature reviews by Glewwe and Kremer (2006) and Kremer and Holla

(2009), therefore, conclude that there is little evidence that school quality matters for

school participation.

This study contributes by identifying the causal effect of a highly comprehensive mea-

sure of the secondary school environment in Malawi on pupils’ schooling decisions. Malawi

is particularly suitable for this type of research as premature dropout in secondary schools

is a major problem.4 To identify the causal effect of the school environment on school par-

ticipation, this chapter exploits the assignment mechanism employed by Malawi’s Ministry

of Education to select pupils into public secondary schools. Malawi’s public secondary

schools can be divided into 2 main categories: conventional schools and community day

schools. Conventional schools are universally favored by parents and pupils and disparities

between conventional schools and community day schools in terms of physical and human

resources are large. Together these two categories of public secondary schools can accom-

modate approximately 40% of the 100,000 pupils who annually successfully complete the

primary school exam.

Pupils are selected into a fixed number of places available in these 2 categories of

secondary schools based on their performance on the standardized national primary school

exam.5 The top performers on the primary school exam are selected into conventional

schools. Second tier performers on the primary school exam are selected into community

day schools. By selecting pupils into specific community day schools and conventional

schools, the Ministry of Education not only severely restricts school choice between, but

also within these school types. Third tier performers are not selected into public secondary

3Banerjee et al. (2005) is a notable exception, as they find that hiring an additional teacher in ruralIndia increased girls attendance by 50%.

4At 25%, Malawi’s net secondary enrollment rate is among the lowest in the World. Net enrollmentrate taken from the online World Bank Education Statistics Database, accessed on December 06, 2010.

5Such tracking policies can be encountered throughout Sub-Saharan Africa. Presumably, these poli-cies are in place to deal with chronic shortages in terms of physical and human resources available forsecondary education. Examples are Botswana, Chad, Ghana, Kenya, Nigeria, Senegal, and Uganda (seehttp://education.stateuniversity.com, accessed in May 2010).

Section 5.1: Introduction 123

schools and have three options: (i) they drop out of school, (ii) they retake the primary

school exam one year later to have another chance to get selected into a public school, or

(iii) they enter a private secondary school (generally poorly equipped and scantly staffed

institutions even in comparison to community day schools).

The assignment procedure results in cutoff points in the primary school exam scores

for selection into the different types of public schools. This study exploits these cutoff

scores in a regression discontinuity framework to identify the causal effect of selection

into the different school types on pupils’ schooling decisions. The analysis is based on

unique data that cover the entire cohort of students who took the primary school exam

at the end of 2004. I linked the primary school exam scores for this cohort of students to

primary and secondary school exams in subsequent years to track their progress through

the education system. Looking at an entire cohort of pupils has the advantage that the

relevance of the analysis is not reduced by external validity concerns.

The first main result of the study is that pupils who passed the primary school exam

in 2004, but not with a sufficient score to get selected into a public school, were more

likely to retake the primary school exam in 2005. Retaking the primary school exam

did not result in pupil learning, as the average exam performance of pupils who retook

the primary school exam deteriorated from 2004 to 2005. This first result shows that

the school environment (or more precisely the future schooling environment) affects the

schooling decisions of pupils.

This result also shows that tracking programs that separate pupils by ability can re-

sult in general equilibrium effects that affect students before they enter the schools in

the tracking system. In Malawi these general equilibrium effects may result in negative

spillovers to the extent that exam retaking increases class sizes in the final grade of pri-

mary school. While such general equilibrium effects are an integral outcome of tracking

programs, they cannot be observed by focusing only on pupils who enter the tracking pro-

gram. As such, this finding complements influential recent research by Duflo, Dupas and

Kremer (forthcoming), who show that tracking within schools in Kenya had a beneficial

effect on learning of pupils, regardless of whether the pupils were selected into a higher

or lower ability track.6

The second main result of this chapter is that, within the group of pupils selected into

6The effects on primary school pupils before they enter the (tracking) secondary schools describedin this chapter are not the only potential general equilibrium effects. Overall, the general equilibriumeffects may be beneficial. Competition for a place in a public school, for instance, may increase theaverage performance of pupils in primary schools.

124 Chapter 5. Selective Schools and Education Decisions

public schools, pupils selected into the elite conventional schools are approximately 25 to

30 percentage points more likely to stay in the school into which they were selected than

pupils selected into lower quality community day schools. Approximately one third of this

difference can be explained by the fact that pupils who were selected into community day

schools were more likely to switch to other schools. The other two thirds of this difference

is most likely due to a higher dropout rate among pupils selected into community day

schools.7

This second result reconfirms that, in Malawi, the school environment has a substantial

influence on pupils’ schooling decisions, as pupils who are selected into a low quality school

are more likely to switch to another school or drop out.8 9 This finding is of importance

to policy makers who have to decide how to spend the available resources for secondary

education. It suggests that, at least in the case of Malawi, there is a double dividend to

investments in school quality, as they potentially improve both pupil learning and pupils’

education decisions.

The remainder of this chapter proceeds as follows. Section 5.2 provides background on

the education sector in Malawi and discusses the procedures used by Malawi’s Ministry of

Education to select pupils into secondary schools. Section 5.3 describes the methodology

to exploit these selection procedures to obtain causal estimates. Section 5.4 describes the

data. Section 5.5 presents the main results and discusses the validity of the methodology

and section 5.6 concludes.

7Here, dropping out means either not entering secondary school or dropping out of secondary schoolbefore taking the secondary school exam.

8This finding is not likely to be the result of a school reputation effect, where pupils who enter betterschools automatically land the better jobs. Most of Malawi’s employment is either in the government, onfarms, or in small private businesses. Employment in higher level government jobs (or in the few largerfirms) almost always requires tertiary education. Malawi’s tertiary education institutions typically selectpupils on the basis of an entry exam and do not discriminate by the type of secondary school pupilsattended. Employment on farms and in small private businesses typically does not require any form ofsecondary education. Moreover, I have no evidence that employment in jobs that do require secondaryeducation favors those who attended pupils from higher quality secondary schools.

9The conclusion of this chapter discusses why the results presented in this chapter may differ fromprevious research on the relationship between the school environment and schooling decisions, includingrecent papers by Jackson, (2010) and Pop-Eleches and Urquiola (2010), which employ a methodologysimilar to the one used in this study.

Section 5.2: Background 125

5.2 Background

5.2.1 Basic Facts

Malawi is a relatively small landlocked country in Sub-Saharan Africa. In 2005, the year in

which the cohort of pupils under consideration in this chapter entered secondary school, it

had about 15 million inhabitants. Due to high birth and mortality rates a high proportion

of Malawi’s population is of primary or secondary school age. According to the Ministry

of Education (2005) there were approximately 2.5 million children of primary school age

and 1 million children of secondary school age in 2005. Basic education in Malawi consists

of 8 years of primary education (standard 1 through 8) and 4 years of secondary education

(Form 1 through 4). All primary and all secondary schools offer the same curriculum, the

contents of which are determined by the Ministry of Education.

5.2.2 Primary Education in Malawi

For most Malawians access to formal primary and especially secondary education was

limited until 1994, when primary school fees were abolished (virtually overnight) and

primary school became accessible to Malawians of all backgrounds. According to Al-

Samarrai and Zaman (2007) the abolition of primary school fees resulted in a surge in

enrollment from 1.9 million students in 1994 to an all-time high of 2.9 million students

in 1999. The expansion of access to primary schools evidently improved the equity of

Malawi’s primary education system. UNDP estimates that in 2005 Malawi had a net

primary enrollment rate of 91%.10 At the same time, expanded access inevitably posed

and continues to pose a challenge to the quality of primary education as many primary

schools have to cope with shortages of nearly all physical and human resources.

5.2.3 Secondary Education in Malawi

Education policy in Malawi has primarily focused on improved access to primary education

over the past half a century. The resulting expansion of access to primary education,

especially in the period after 1994, has put the secondary education system under pressure

as the numbers of primary school graduates wishing to attend secondary school have

soared. Compared to the primary education system, the capacity of the secondary school

10Net enrollment rate taken from the online World Bank Education Statistics Database, accessed onDecember 06, 2010.

126 Chapter 5. Selective Schools and Education Decisions

system is limited and, at 25%, the 2005 net secondary enrollment rate was among the

lowest enrollment rates in the world.11

Traditionally, the bulk of secondary education in Malawi was provided in a group of

elitist secondary schools, generally referred to as conventional schools. These conventional

schools can be subdivided into 24 national boarding schools, 41 district boarding schools,

and 52 district day schools. National boarding schools are considered to be the best

conventional schools and serve pupils from the entire country. District boarding schools

are considered to be the next best and serve only pupils attending primary schools in the

same district. District day schools serve only pupils who live within commutable distance

from the school. As the differences between the different types of conventional schools in

terms of physical and human resources are small, I treat these schools as one homogeneous

group in the remainder of this chapter.

In the 1970s, the government also started to provide secondary education in so-called

“distance education centers”. These distance education centers later came to be known

as community day schools. The government, in cooperation with various donor organi-

zations, is currently working to get the quality of community day schools up to par with

conventional schools. However, despite the government’s efforts to improve the quality

of the community day schools significant disparities with conventional schools persist, as

illustrated by Table 5.1. The table is based on government census data (discussed in

more detail below) and gives an overview of the average characteristics of nearly the en-

tire population of community day schools and conventional schools. (A similar table, but

with differences in resources between conventional day schools and conventional boarding

schools can be found in Appendix A1)

Table 5.1 shows that, on average, community day schools do somewhat better in terms

of teacher to pupil ratios, but this result is reversed when teacher education is taken

into account. In conventional schools nearly all teachers have obtained a degree beyond

secondary school. In community day schools only 1 in 6 teachers has done so. Basic

physical resources are often lacking in community day schools, while they are available

in most conventional schools. Differences in availability of libraries, toilets, electricity,

and books are particularly striking. These differences translate into higher school fees in

conventional secondary schools.

11See previous footnote.

Section 5.2: Background 127

Table 5.1: Average characteristics of community day schools and conventionalschools

Community day ConventionalSchool size (number of pupils) 178 462IncomeAnnual school fees per student (in US$) 25 65Annual school income per student (in US$) 97 161Human ResourcesTeachers per 100 pupils 5.6 4.4Educated teachers per 100 pupils 0.8 4.0Non-teaching staff per 100 pupils 0.8 2.6Physical ResourcesPercentage of schools with a library 35 88Percentage of schools with a PC room 4 46Percentage of schools with toilets 3 79Percentage of schools with tap or borehole 78 89Percentage of schools with electricity 22 82Classrooms per 100 pupils 2.9 2.4Book to pupil ratio (Chichewa) 0.5 0.8Book to pupil ratio (English) 0.5 1.0Book to pupil ratio (mathematics) 0.4 1.0Observations 451 112

Notes: Source: The 2005 and 2006 Education Management Information System(EMIS). Schools included are those community day schools and conventional schoolsfor which the Ministry of Education selected the 2005 pupils. Numbers are averagedover the years 2005 and 2006. Annual income per student in US Dollar calculatedusing an exchange rate of 140 Malawi Kwacha per US Dollar. Educated teachers arethose teachers with a degree beyond secondary school.

The government of Malawi currently primarily attempts to increase access to formal

public education by increasing the number of community day schools. The number of con-

ventional schools, on the other hand, is stagnant. Despite the efforts by the government

to expand access to public secondary education, the surge in primary school graduates in

the 1990s could not be fully absorbed by the formal public institutions for secondary ed-

ucation (community day schools and conventional schools). As a result private secondary

schools mushroomed over the past 15 years.

Private secondary schools cannot be treated as a homogeneous group, because they

exhibit vast differences in terms of quality and fees. Some private schools provide expen-

sive and high quality education to privileged inhabitants of Malawi’s cities. However, the

vast majority of private schools are poorly equipped and scantly staffed institutions even

in comparison to community day schools. They cater to the average Malawian pupil who

128 Chapter 5. Selective Schools and Education Decisions

was not selected into a community day school or conventional school, and do so at the

lowest possible cost. Many private schools are not officially registered or regulated, do

not function as exam centers (their pupils have to sit for exams as external candidates

at schools that do function as exam centers), and the government knows little about the

quality of these schools. In general, unless they can afford to attend fancy upper class

private schools, students will prefer to enroll into a community day school or conventional

school.12

5.2.4 Examination

Student performance in Malawi’s schools is assessed on the basis of three exams. The first

exam is the Primary School Leaving Certificate Examination, which pupils take at the end

of primary school. The primary school exam tests pupils on 5 subjects: Chichewa (the

national language), English, mathematics, science, and social studies. The second exam

is the Junior Certificate Examination (henceforth the junior secondary school exam), an

exam taken by pupils after the first 2 years in secondary school. The third exam is the

Malawi School Certificate Examination (henceforth the senior secondary school exam),

which pupils take at the end of secondary school. In both the junior secondary school

exam and the senior secondary school exam students are tested on at least 8 out of 24

course subjects selected by the pupils.13

The primary school exam, junior secondary school exam, and senior secondary school

exam are all standardized national exams set and marked by the Ministry of Education

and the Malawi National Examinations Board. These exams are compulsory regardless

of the type of school (conventional, community day, or private) pupils attend.14 Besides

the grades for individual courses, the Examinations Board also awards students an overall

“pass” or “fail” based on the aggregate score of the course subjects examined.

12Because private schools are not part of Malawi’s official school system most of them do not appearin the Ministry of Education Data. Hence, I cannot provide an overview of the average characteristics ofthese schools. The assertion that “the vast majority of private schools are poorly equipped and scantlystaffed institutions even in comparison to community day schools” is based on personal observationsduring dozens of school visits in and around Zomba, a district in the South of Malawi.

13Secondary schools in Malawi offer 24 course subjects (not all 24 course subjects are offered by allschools). Pupils choose at least 8 of these course subjects and are examined in the subjects of their choicein the junior secondary school exam and senior secondary school exam.

14Compulsory meaning that within public schools pupils cannot progress to the next grade withoutpassing this exam. Within private schools exam participation is also compulsory, but it is unclear howstrictly private schools enforce these rules.

Section 5.2: Background 129

5.2.5 Selection

5.2.5.1 Selection Procedures

The Malawi National Examinations Board exam data (also discussed in more detail below)

show that in 2004 there were 150,748 pupils who sat for the primary school exam out of

whom 94,789 passed. The Ministry of Education was able to provide 39,090 of the pupils

who passed the primary school exam a spot in one of the public secondary schools for the

2005 school year: 11,900 in conventional schools and 27,190 in community day schools.

Because the number of primary school graduates surpasses the number of available spots

in public secondary schools the Ministry of Education employs a merit based selection

system that uses performance on the primary school exam as a selection criterion.

Selection into grade 1 of secondary school is conducted by a team of officials from

the Ministry of Education and the Division Education Offices (henceforth the selection

team). On each of the five course subjects examined in the primary school exam pupils can

score a total of 100 points maximum. The selection team generates an aggregate primary

school exam score that is the sum of a pupil’s scores on his/her 4 best subjects. Based

on this aggregate primary school exam score, the selection team then selects pupils into a

national boarding school, district boarding school, district day school, or into a community

day school. Here, I describe the exact procedures according to which the selection team

selects pupils into the fixed number of places available in each school type.15 Appendix

A2 provides additional details on the selection procedures.

National Boarding Schools For selection into Form 1 of national boarding schools,

pupils are stratified by gender and then selected based on merit. In 2005 the national

boarding schools were able to accommodate 718 male and 773 female pupils. The Ministry

of Education ranked all male and female students according to their aggregate primary

school exam scores and then selected the top 718 male students and the top 773 female

students into national boarding school.

The selection team also decides which specific national boarding school each of the

selected pupils can enter. This decision is based on the distance of each pupil’s primary

school to the national boarding schools. As much as possible, the selection team selects

pupils into the national boarding school closest to their primary school.

15The details of the selection procedures were kindly provided by members of the selection team.

130 Chapter 5. Selective Schools and Education Decisions

District Boarding Schools For organizational purposes the Ministry of Education

divides Malawi into 33 education districts. Of the 33 districts, 29 have district boarding

schools, which are only accessible to students who took the primary school exam in the

district under consideration. Similar to the selection into national boarding schools, pupils

in districts with one or more district boarding schools are stratified by gender and then

selected into the district boarding schools based on merit.

If there are multiple district boarding schools in a district the selection team also

decides which specific district boarding school a pupil can attend. To do so, pupils are

ranked according to aggregate performance within their gender group and then distributed

across the district boarding schools in groups of three in descending order.16 This pro-

cedure ensures that pupil performance on the primary school exam is balanced across

district boarding schools.

District Day Schools District day schools do not provide boarding facilities and pupils

have to commute (usually walk) to these schools on a daily basis. It is therefore important

that pupils are selected into district day schools located within a reasonable distance from

their home village.

To ensure that pupils are only selected into nearby district day schools, the selection

team selects pupils for each district day school only from so-called feeder schools. Feeder

schools are primary schools within commutable distance from the district day school.

Pupils who took their primary school exam in a feeder school belonging to a district day

school are again stratified by gender and then selected into the corresponding district

boarding schools based on merit.

Community Day Secondary Schools Finally, the selection team selects pupils into

community day schools. Similar to conventional schools, the number of male and female

pupils who can enter community day schools is fixed. The procedure used to select pupils

into community day schools is equivalent to the procedure used for district day secondary

schools.

16Suppose, for instance, that there are three District Boarding Schools: A, B, and C. The selectionteam then selects the first three pupils on the list to go to District Boarding School A, the next threepupils to go to District Boarding School B, the next three pupils to go to District Boarding School C,the next three pupils to go to District Boarding School A etc.

Section 5.2: Background 131

5.2.5.2 Selection in Practice

The number of available places in each school type and the performance of the pupils on

the primary school exam together implicitly determine cutoff points in the primary school

exam (which differ for boys and girls) to make it into each school type:

1. A national cutoff point to make it into national boarding school;

2. A cutoff point that differs per district to make it into district boarding school;

3. A cutoff point that differs per district day school to make it into district day school;

4. A cutoff point that differs per community day school to make it into community

day school.

The selection procedures result in pronounced jumps in the probability of getting

selected into the different school types at the relevant cutoff points. This chapter exploits

these jumps in the probability of getting selected to estimate the causal effect of selection

into the different school types on pupils’ schooling decisions. Specifically, I focus on the

jump in the probability of getting selected for two groups of school types. First, I focus

on the jump in the probability of getting selected into any public school, where I treat all

public schools (community day and conventional) as one group. Then, within the group

of pupils selected into public schools, I focus on the jump in the probability of getting

selected into conventional schools, where I treat all conventional schools (national, district

boarding, and district day) as one group.

Before discussing exactly how these jumps in the probability of getting selected can

be exploited to estimate causal effects, this subsection first investigates if such jumps

can indeed be observed for these two groups of schools. In order to do so, I reran the

secondary school selection exactly as described above.17 This exercise allows me to recover

the (implicit) cutoff scores that would have been used by the selection team if they had

precisely followed the described selection procedures. I then normalize the primary school

exam scores of all pupils such that they take the value zero at the relevant reconstructed

cutoff scores, which allows me to combine the data for pupils and evaluate the probability

of getting selected across the different cutoff scores.

17In order to determine which primary schools serve as feeder schools, I assume that the studentsactually selected into individual district and community day schools by the selection team cover all theprimary schools that serve as feeder schools.

132 Chapter 5. Selective Schools and Education Decisions

Figures 5.1a and 5.1b investigate the probability of getting selected into a public school

for boys and girls respectively. These graphs thus pool pupils across the different cutoff

points they face for selection into public schools. The horizontal axes of the graphs depict

the distance of pupils’ aggregate primary school exam scores to the relevant reconstructed

cutoff points for selection into a public school. Negative scores indicate the extent to

which aggregate primary school exam scores fall short of this cutoff point (in standard

deviations of the original aggregate primary exam scores) and vice versa for positive scores.

The vertical axis depicts the fraction of primary school exam takers actually selected by

the selection team to enter a public secondary school. Dots depict the fraction of pupils

selected into a public school at each integer distance from the cutoff point.18 The fitted

lines above and below the cutoff score are quadratic regressions.19 (The data used in these

graphs are described below.)

Two conclusions can be drawn from Figures 5.1a and 5.1b. First, the probability

of being selected into a public school indeed exhibits a pronounced discontinuity at the

reconstructed cutoff points for both boys and girls. Second, while the selection team

executes the selection procedures with a fair degree of precision, the actual selection does

not concur one for one with the reconstructed selection. This finding indicates that the

selection team did not precisely follow the described selection procedures. If the team

had precisely followed these procedures, the fraction of pupils selected into public schools

would have been 0 at all values below the reconstructed cutoff point (the negative scores

on the horizontal axis) and 1 at all values above the reconstructed cutoff point. The

difference between the actual selection results and the reconstructed selection results is

likely to be caused by imprecise technical execution of the selection procedure on the part

of the selection team.20

Next, Figures 5.2a and 5.2b investigate the probability of getting selected into a con-

ventional school within the group of all pupils who were selected into a public school. The

horizontal axes now depict the distance of pupils’ aggregate primary school exam scores to

the relevant reconstructed cutoff points for selection into a conventional school. All pupils

with a primary school exam score below the reconstructed cutoff point should have been

selected into a community school if the selection team had precisely executed its selection

18These integers can in principle take the values -400 to 400, but in the graphs they are normalizedby dividing them through the standard deviation.

19Similar figures were used by Lee (2008) and I follow his example.20One can think of other potential reasons for this disparity. However, at this point I have no com-

pelling evidence for any other explanation.

Section 5.2: Background 133

Figure 5.1: Probability of getting selected into a public school as a function of the distanceof pupils’ exam scores to the reconstructed cutoff score. The graphs combine all pupilsacross the different cutoff points they face for selection into public schools. Local averageswere calculated at (normalized) integer distances to the cutoff score, the smallest possiblebin size.

134 Chapter 5. Selective Schools and Education Decisions

Figure 5.2: Probability of getting selected into a conventional school as a function of thedistance of pupils’ exam scores to the reconstructed cutoff score. The graphs combine allpupils who were selected into public schools across the different cutoff points they facefor selection into conventional schools. Local averages were calculated at (normalized)integer distances to the cutoff score, the smallest possible bin size.

Section 5.3: Methodology 135

procedures. All pupils with a primary school exam score above the reconstructed cutoff

point should have been selected into a conventional school.

Again, the figures suggest that the actual selection results do not correspond one for

one with the reconstructed selection results. There is also once again a strong visible

increase in the probability of getting selected around the cutoff point. This increase,

however, is now S-shaped and not discontinuous at the cutoff point. In the following

section I describe how both the discontinuous increase in the probability of getting selected

(observed in Figures 5.1a and 5.1b) and the S-shaped increase in the probability of getting

selected (observed in Figures 5.2a and 5.2b) can be exploited to estimate the causal effect

of the different school types on education outcomes.

5.3 Methodology

5.3.1 Discontinuous Increase in Selection Probability

This chapter exploits the jump in the probability of getting selected into a public school

(observed in Figures 5.1a and 5.1b) to recover the causal effect of selection into public

schools on the rate at which pupils retake the primary school exam. This analysis gives

an insight into potential general equilibrium effects that are the results of Malawi’s large

scale selective tracking program. I recover this causal effect using the regression disconti-

nuity design, which was first introduced by Thistlethwaite and Campbell (1960) and later

formalized by Hahn et al. (2001). The essential idea behind this estimation strategy is

that pupils with a primary school exam score right below the cutoff point for selection

into a public school should in principle be comparable to pupils with a primary school

exam score right above the cutoff point in terms of unobserved characteristics. Pupils

with a primary school exam score right below the cutoff point therefore serve as a valid

counterfactual.

I limit this section to a brief discussion of the way in which I implement the regression

discontinuity design. Detailed overviews of the use of the regression discontinuity design

are provided by Imbens and Lemieux (2008) and Lee and Lemieux (forthcoming). The

estimation procedure I employ is a combination of the parametric and non-parametric

fuzzy regression discontinuity procedures discussed in those papers. Specifically, I estimate

the following regression equation:

136 Chapter 5. Selective Schools and Education Decisions

Yi = α + βTi +∑k≥1

γk(Xi − c)k +∑k≥1

δkDi(Xi − c)k + εi, (5.1)

where Yi is the outcome of interest (here retaking the primary school exam in 2005) for

pupil i. Ti is a dummy that takes the value 1 if pupil i was selected to enter a public school

in 2005. I use Di, a dummy that takes the value 1 if a pupil’s exam score exceeds the

reconstructed cutoff point c, as an instrument for the treatment indicator Ti. The term∑k≥1 γk(Xi − c)k is a polynomial of order k that approximates the relationship between

the outcome of interest and the distance of a pupil’s 2004 primary school exam score Xi to

the cutoff score c. The term∑

k≥1 δkDi(Xi − c)k includes the reconstructed indicator for

selection Di and thus allows for a different functional form of the polynomial above and

below the cutoff score. The error term εi captures all other determinants of the schooling

outcome of interest. The estimated coefficient β gives the intention-to-treat effect - the

effect of being selected into a conventional secondary school.

This estimation procedure yields consistent parametric estimates of the intention-to-

treat effect β if the specified polynomial correctly approximates the first stage relationship

between the distance to the cutoff scores (Xi − c) and selection into a public school Ti,

as well as the second stage relationship between the distance to the cutoff scores (Xi− c)and schooling outcome Yi. Misspecification becomes more likely when data further from

the cutoff point are used. I therefore check for the robustness of the estimated results

within multiple bandwidths around the cutoff scores. Following Imbens and Lemieux

(2008), I use the same bandwidth in the first and second stage regressions, which means

that regular 2SLS standard errors can be used. I estimate the preferred bandwidth (h)

in both the first and second stage using the following cross-validation criterion proposed

by Imbens and Lemieux (2008):

CVy(h) =1

n

n∑i=1

(Yi − Y (Xi)

)2

,

where the preferred bandwidth h is given by:

hoptCV = argminCVy(h).

This cross-validation criterion minimizes the mean squared differences between actual

and estimated outcomes. In doing so, the cross-validation criterion balances the precision

Section 5.3: Methodology 137

of the estimates (which increases with the bandwidth) against the bias that may result

from using too large a bandwidth. Following Lee (2008), I determine the optimal order

of the polynomial based on the Akaike information criterion (AIC). All regression results

pool pupils across different cutoff scores and accommodate for fixed effects among groups

of pupils competing for places in the same public schools (i.e. pupils facing the same

cutoff score). Standard errors are clustered at the level of primary schools - the level at

which I investigate treatment effects.21

5.3.2 Continuous Increase in Selection Probability

The probability of getting selected into a conventional secondary school is not discon-

tinuous at the reconstructed cutoff score. Instead, Figures 5.2a and 5.2b show that this

probability increases rapidly, but by and large continuously around the reconstructed cut-

off scores. Clark (2010) observes a similar pattern for the selection of pupils into selective

secondary schools in the UK. He proposes to use the predicted probability of treatment

as a function of primary school exam scores as an instrument for treatment to recover the

causal effect of selection into a selective secondary school on secondary school outcomes.

I follow the same procedure to estimate the impact of getting selected into a conven-

tional secondary school on subsequent participation in secondary school exams. I estimate

the predicted probability of treatment using a probit specification including third order

polynomial terms of the distance to the cutoff scores (Xi − c). Otherwise, this estima-

tion procedure is identical to the regression discontinuity procedure outlined above and

amounts to estimating equation (1) using the predicted probability of treatment instead

of the reconstructed indicator for selection as an instrument. The only difference is that

I now look at outcomes at the level of secondary schools and thus cluster standard errors

at the level of these secondary schools.

5.3.3 Multiple Cutoff Points

A limitation of the standard regression discontinuity design with only 1 cutoff point is

that it provides at best a local estimate of the treatment effect. An unusual feature of

the procedure used to select pupils into community day schools and conventional schools

21In some groups of feeder schools all pupils who pass the primary school exam get selected into apublic school (district or community day). I always exclude pupils attending these groups of feeder schoolsfrom the analysis, as the pupils attending these schools do not face a discontinuity in the probability ofgetting selected.

138 Chapter 5. Selective Schools and Education Decisions

is that there are geographic differences in the cutoff points. For boys the cutoff scores

for selection into a public secondary school range from primary school exam Z-cores of

-1.4 to 1.6 and for selection into a conventional school from -1.2 to 2.5. For girls the

cutoff scores for selection into a public secondary school range from primary school exam

Z-cores of -1.2 to 1.9 and for selection into a conventional school from -1.2 to 2.6. This

situation differs from the standard regression discontinuity design and provides a unique

opportunity to estimate an average treatment effect over a range of the primary school

exam scores. Before presenting these treatment effects, the next section first discusses the

data used in this study.

5.4 Data

This chapter is based on various administrative education databases.22 I linked these

databases such that they can be used to track the progress of the cohort of students who

took the primary school exam in 2004. Linking these databases, a labor intensive process,

is a central contribution of this chapter in itself. It shows that administrative databases,

even from the world’s poorest countries, can be used as an informative complement to

survey based studies. Advantages of the use of administrative databases include the

fact that these databases typically cover a much larger fraction of the population under

consideration, thus reducing external validity concerns, and that they are much cheaper

to acquire.23

A database containing the exam scores for all 150,748 pupils who took the primary

school exam in 2004 forms the starting point. This database includes pupils’ scores on

individual course subjects, an aggregate score on the basis of which pupils are selected into

public secondary schools, and an overall pass/fail. I linked this 2004 primary school exam

database to a database containing the official 2004 selection records, which tells whether

pupils who took the 2004 primary school exam were selected into a public school and, if

so, into which specific school.24 Together these two datasets can be used to evaluate the

precision with which the selection team executes its selection procedures (see Figures 5.1,

and 5.2 above).

22The data were kindly provided by Malawi’s Ministry of Education and the Malawi National Exam-inations Board.

23Which is not to say that these databases are easy to acquire, as governments may be hesitant toshare these databases.

24These records can be linked directly using a unique student ID as an identifying variable.

Section 5.3: Methodology 139

I then linked the pupils in the 2004 primary school exam data to a database containing

the 2005 primary school exam scores. These linked primary school exam databases can

be used to examine the extent to which pupils retake the primary school exam and the

impact of selection into public schools on the exam retaking rate. I also linked the pupils

in the 2004 primary school exam data to the 2006 and 2007 junior secondary school exam

databases. Similar to the primary school exam databases, the junior secondary school

exam databases contain scores on course subjects and an overall pass/fail. The linked

2004 primary school exam and 2006/2007 junior secondary school exam data allow for the

estimation of the impact of selection into conventional schools on subsequent participation

and performance in secondary school exams.

Linking the exam results for individual students across different time periods requires

matching on pupil names and gender using an approximate string matching procedure.25

A description of this matching procedure can be found in appendix A3. I matched pupils

at the level of schools. For example, when I match the 2004 and 2005 primary school exam

data to see if pupils retake the primary school exam, I match pupils within the schools in

which they took the 2004 primary school exam. Matching within schools has advantages

and disadvantages. An important advantage is that matching within schools lowers the

false positive rate (i.e. the number of incorrectly accepted matches). The obvious flip-side

is that it results in a higher false negative rate. In one occasion the higher false negative

rate is cause for concern and, as I describe below, I therefore match at the level of districts

rather than schools.

Finally, I linked the exam and selection data to Malawi’s “Education Management

Information System” (EMIS). The EMIS is a census compiled on an annual basis by

Malawi’s Ministry of Education and contains basic information on the physical and human

resources available in all public schools. 26

5.5 Results

This section reports the main results of this chapter. The first subsection compares the

rate at which pupils who were and were not selected into public schools retake the primary

school exam. This analysis shows that Malawi’s large scale tracking program results in

25Because names are sometimes spelled differently across different time periods they cannot always bematched exactly.

26Table 1 described above used this EMIS data to describe the differences between community dayschools and conventional schools.

140 Chapter 5. Selective Schools and Education Decisions

unintended detrimental general equilibrium effects in the form of increased exam retaking

by pupils who do not get selected into a public school. The second subsection compares

the rates at which pupils who were selected into community day schools and conventional

schools participate in the junior secondary school exam. This second part of the analysis

suggests that getting selected into one of Malawi’s elite conventional schools affects pupils

schooling decisions and results in pronounced improvements in school participation.

5.5.1 General Equilibrium Effects

Pupils who do not get selected into a public school when they pass the primary school

exam have three options: (i) they drop out of school, (ii) they enter a private secondary

school, or (iii) they retake the primary school exam in the next year in order to have

another chance to get selected into a public secondary school. This subsection exploits

the previously described jump in the probability of getting selected into a public school to

investigate how not getting selected into a public school affects the primary school exam

retaking rate. Evidence on this issue matters, because it gives an insight into potential

unintended general equilibrium effects that result from the large scale implementation of

tracking programs.

Figures 5.3a and 5.3b graphically show how selection into public schools affects the

primary school exam retaking rate for boys and girls respectively. The horizontal axes

of the graphs again depict the distance of pupils’ aggregate primary school exam scores

to the relevant reconstructed cutoff points for selection into a public school. The vertical

axes depict the fraction of pupils retaking the primary school exam in 2005. The figures

indicate that there is a fairly pronounced drop in the probability of retaking the primary

school exam in 2005 at the cutoff score.

In order to estimate the size of this drop, I employ the regression discontinuity proce-

dure outlined above. Table 5.2 shows the first stage regression results (i.e. the estimated

size of the jump in the probability of getting selected into a public school observed in

Figures 5.1a and 5.1b). The table combines data for pupils across the different cutoff

scores for selection into public schools. For both boys and girls the table then provides

estimates for 3 different orders of the polynomial that approximates the relationship with

the distance to the reconstructed cutoff scores. The table checks for the robustness of the

estimates using three different bandwidths around the reconstructed cutoff score ranging

from 0.1 SD to 0.5 SD of the primary school exam scores. The results confirm that there is

Section 5.3: Methodology 141

Figure 5.3: Probability that pupils who passed the primary school exam in 2004 retookthe primary school exam in 2005. The graphs combine all pupils across the differentcutoff points they face for selection into public schools. Local averages were calculated at(normalized) integer distances to the cutoff score, the smallest possible bin size.

142 Chapter 5. Selective Schools and Education Decisions

a pronounced and robust increase in the probability of getting selected into a public school

at the reconstructed cutoff scores. At the optimal bandwidth the estimated jump in the

probability of getting selected ranges from 48 to 73 percentage points for boys and from

43 to 68 percentage points for girls across the different orders of the polynomial. Except

for the third order polynomial at the minimum bandwidth, the first stage F-statistics of

the instrument well exceed the minimum Staiger and Stock (1997) rule of thumb value of

10.

Table 5.2: First stage selection into public schools (OLS). Dependent variableis a dummy taking the value 1 if a pupil was selected into a public school.

Bandwidth (in SD): Optimal bandwidth0.1 0.2 0.5

Panel A: malePolynomial order: 1 0.463*** 0.574*** 0.734*** 0.5

(0.024) (0.018) (0.012)F=375.3 F=1012.6 F=3518.3

Polynomial order: 2 0.298*** 0.416*** 0.584*** 0.5(0.039) (0.025) (0.018)F=57.8 F=284.9 F=1098.7

Polynomial order: 3 0.150* 0.323*** 0.475*** 0.5(0.083) (0.034) (0.022)F=3.26 F=89.7 F=474.4

Panel B: femalePolynomial order: 1 0.460*** 0.538*** 0.681*** 0.5

(0.030) (0.021) (0.014)F=242.8 F=654.7 F=2231.2

Polynomial order: 2 0.278*** 0.397*** 0.535*** 0.5(0.048) (0.029) (0.020)F=33.3 F=187.4 F=705.5

Polynomial order: 3 0.153 0.302*** 0.429*** 0.5(0.099) (0.040) (0.026)F=2.4 F=55.8 F=283.2

Observations male 4019 8186 19694Observations female 2886 5982 13719Optimal order (AIC) male 3 3 3Optimal order (AIC) female 2 3 3

Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors (in parentheses) areclustered at the level of primary schools. The table combines all pupils acrossthe different cutoff points they face for selection into public schools. Regressionsinclude fixed effects for the different cutoff points faced by pupils. The outcomevariable is a binary indicator taking the value 1 if a pupil was selected into a publicsecondary school.

Section 5.3: Methodology 143

Table 5.3 investigates whether this estimated jump in the probability of getting se-

lected translates into a jump in the probability of retaking the primary school exam.27

The regression discontinuity estimates of the jump in the probability of retaking the pri-

mary school exam for boys range from an insignificant 0 percentage points to a significant

4 percentage points across the different bandwidths. However, at the optimal order of the

polynomial these results are never significant. For girls the regression discontinuity esti-

mates provide more robust evidence for a statistically significant jump in the probability

of retaking the primary school exam. This jump ranges from 6 to 10 percentage points

at the optimal order of the polynomial across the different bandwidths. For comparison,

Table 3 also provides OLS estimates of the jump in the probability of retaking the pri-

mary school exam.28 The OLS estimates range from 4 to 5 percentage points for boys to

6 percentage points for girls are always statistically significant and show little variation

with respect to the order of the polynomial and the bandwidth used. The relatively small

differences between the OLS and regression discontinuity estimates suggest that selection

on unobservables is not a major source of bias in the OLS results.

The observed increase in the probability of retaking the primary school exam when

pupils are not selected into a public school, which is especially robust for girls, is an

unintended side effect of Malawi’s large scale tracking program. To the extent that pupils

attend class during the year before they retake the primary school exam, this general

equilibrium effect will lead to negative spillovers on other pupils as it increases class sizes

in the final grade of primary school. However, it can be argued that even in the presence

of these negative spillover effects, retaking the primary school exam could be socially

desirable if it increased learning among pupils who retake the primary school exam.29

27Some of the pupils who retook the primary school exam in 2005 may have done so in another schoolthan the one in which they took the primary school exam in 2004. As I matched pupils in the 2004and 2005 primary school exam data at the level of primary schools I do not track these pupils in theabove analysis. I assume that the impact of this issue on the estimated results is minimal. First, primaryschools in Malawi are relatively homogeneous in terms of school characteristics as they are financed bythe government and free of charge. Pupils therefore have less reason to prefer one primary school overthe next than in the case of secondary schools. Moreover, to the extent that retaking the primary schoolexam in other schools does play a role, it most likely implies that the estimates provided above are anunderestimate and thus still serve as a relevant example to illustrate the potential unexpected generalequilibrium effects that can result from the large scale implementation of tracking programs.

28The OLS estimates are calculated in the same way as the RD estimates, but they do not use Di,the dummy that takes the value 1 if a pupil’s exam score exceeds the reconstructed cutoff point c, as aninstrument for the treatment indicator Ti.

29Retaking the primary school exam would also be socially desirable if retakers are eager students whoserve as a positive example to their fellow students.

144 Chapter 5. Selective Schools and Education Decisions

Tab

le5.

3:O

LS

and

Fuzz

yR

Des

tim

ates

ofth

eim

pac

tof

sele

ctio

nin

topublic

school

onre

takin

gpri

mar

ysc

hool

exam

.D

epen

den

tva

riab

leis

adum

my

takin

gth

eva

lue

1if

pupils

reta

keth

epri

mar

ysc

hool

exam

.

Ban

dw

idth

(in

SD

):O

pti

mal

ban

dw

idth

0.1

0.2

0.5

OL

SR

DO

LS

RD

OL

SR

DO

LS

RD

Panel

A:male

Pol

yn

omia

lor

der

:1

-0.0

47**

*-0

.003

-0.0

43***

-0.0

19

-0.0

43***

-0.0

39**

*0.1

0.1

(0.0

09)

(0.0

23)

(0.0

06)

(0.0

13)

(0.0

04)

(0.0

07)

Pol

yn

omia

lor

der

:2

-0.0

55**

*-0

.017

-0.0

48***

-0.0

08

-0.0

41***

-0.0

25**

0.1

0.1

(0.0

10)

(0.0

55)

(0.0

07)

(0.0

25)

(0.0

05)

(0.0

12)

Pol

yn

omia

lor

der

:3

-0.0

55**

*0.0

82

-0.0

51***

-0.0

14

-0.0

41***

-0.0

09

0.1

0.1

(0.0

10)

(0.2

43)

(0.0

07)

(0.0

44)

(0.0

05)

(0.0

18)

Panel

B:female

Pol

yn

omia

lor

der

:1

-0.0

63**

*-0

.100***

-0.0

63***

-0.0

73***

-0.0

58***

-0.0

60**

*0.1

0.1

(0.0

10)

(0.0

27)

(0.0

08)

(0.0

16)

(0.0

05)

(0.0

08)

Pol

yn

omia

lor

der

:2

-0.0

59**

*-0

.138**

-0.0

63***

-0.0

97***

-0.0

57***

-0.0

56**

*0.1

0.1

(0.0

10)

(0.0

68)

(0.0

08)

(0.0

30)

(0.0

06)

(0.0

15)

Pol

yn

omia

lor

der

:3

-0.0

58**

*-0

.216

-0.0

60***

-0.0

72

-0.0

61***

-0.0

85**

*0.1

0.1

(0.0

10)

(0.2

33)

(0.0

08)

(0.0

51)

(0.0

06)

(0.0

23)

Ob

serv

atio

ns

mal

e40

194019

8186

8186

19694

19694

Ob

serv

atio

ns

fem

ale

2886

2886

5982

5982

13719

13719

Op

tim

alor

der

(AIC

)m

ale

33

11

13

Op

tim

alor

der

(AIC

)fe

mal

e1

11

12

2

***

p<

0.01

,**

p<

0.05

,*

p<

0.1.

Sta

nd

ard

erro

rs(i

np

aren

thes

es)

are

clu

ster

edat

the

leve

lof

pri

mary

sch

ools

.T

he

tab

leco

mb

ines

all

pu

pil

sac

ross

the

diff

eren

tcu

toff

poi

nts

they

face

for

sele

ctio

nin

top

ub

lic

sch

ool

s.R

egre

ssio

ns

incl

ud

efi

xed

effec

tsfo

rth

ed

iffer

ent

cuto

ffp

oints

face

dby

pu

pil

s.T

he

outc

ome

vari

able

isa

bin

ary

ind

icat

orta

kin

gth

eva

lue

1if

ap

up

ilp

arti

cip

ated

inth

e20

05p

rim

ary

sch

ool

exam

(i.e

.if

the

pu

pil

reto

okth

ep

rim

ary

sch

ool

exam

inth

esc

hool

wh

ere

he

or

she

took

the

2004

pri

mar

ysc

hool

exam

).

Section 5.3: Methodology 145

Table 5.4 investigates this issue by looking at the exam performance of the pupils who

passed the primary school exam in 2004, but decided to retake the primary school exam in

2005. The first row of the table displays the 2005 passing rate. In total, 13% of the boys

and 19% of the girls who retook the primary school exam failed. Given that the overall

pass rate on the primary school exam had actually increased from 63% in 2004 to 67%

in 2005, this result suggests that pupil learning as a result of retaking the primary school

exam is minimal. The second row displays changes in exam Z-scores and confirms that

retaking the exam does not result in pupil learning. From 2004 to 2005, the exam scores

of boys and girls show a significant deterioration by 0.24 and 0.28 standard deviations

respectively.30 These results thus suggest that increased retaking of the primary school

exam is a detrimental side effect of Malawi’s large scale tracking program. On average, it

does not result in private benefits in the form of increased learning by those who retake

the exam, but it does result in potential social costs as it increases class sizes by roughly

4.5 percent in the final grade of primary school.31 32

Table 5.4: Performance of pupils who retake the primaryschool exam

Male Female

Passing percentage in 2005 87.2 81.1

Change in performance Z-score -0.239 -0.279(0.033)*** (0.041)***

Observations 1805 955

Notes:*** p<0.01, ** p<0.05, * p<0.1. Standard errors (inparentheses) are clustered at the level of primary schools. Thetable includes all pupils who passed the primary school exam in2004 and retook the exam in 2005.

30There is no reason to assume that the 2005 cohort of students is on average a group of higher abilitythan the 2004 cohort of students.

31Increase in class sizes calculated as follows. Roughly 44% of the students in the final grade of primaryschool is female. At the optimal bandwidth, the optimal order of the polynomial suggests an impact onmale students of 0 percentage points and on female students of 10 percentage points. Combining thesepercentage gives an average impact of 4.4%.

32The increased primary school exam retaking is a negative outcome from an education perspective.However, staying in school potentially has other beneficial effects. Baird et al. (2010) for instance showthat improved school participation in Malawi results in a host of beneficial effects on young women aboveand beyond learning in school. It is therefore not obvious that the exam retaking is a bad outcome froma social welfare perspective.

146 Chapter 5. Selective Schools and Education Decisions

5.5.2 School Participation

The evidence presented in the previous subsection indicates that not getting selected

into a public school has a substantial influence on the schooling decisions of pupils (as it

markedly increases repetition rates in the final grade of primary school). By comparing the

exam participation of pupils selected into community day schools to that of pupils selected

into conventional schools, this subsection further explores the effect of school quality on

schooling decisions. As Kremer and Holla (2009) discuss, the available literature suggests

that school quality does not play an important role in school participation. In light of

this conclusion we should not expect selection into a high quality conventional school to

affect school participation. Most of the evidence discussed by Kremer and Holla (2009),

however, comes from temporary interventions that focus on only one specific part of

school quality (such as teacher absence). A comparison of community day schools and

conventional schools circumvents the problem of focusing only on a narrow part of school

quality, as these schools differ widely in terms of virtually all aspects of school quality

(pupil ability, physical resources, and human resources).

I first graphically show that among the pupils who were selected into public schools,

the marginal pupils (pupils with primary school exam scores close to the cutoff scores for

selection into a conventional school) indeed experience a substantial increase in school

quality around the cutoff for selection into a conventional school. Figures 5.4a and 5.4b

plot the number of educated teachers per 100 pupils (i.e. teachers with a degree beyond

secondary school). In accordance with the information in Table 5.1, the average number

of educated teachers per 100 pupils increases rapidly from roughly 1 to 4 around the cutoff

score. Similar to the increase in the probability of getting selected into a conventional

school, this increase in the number of educated teachers per pupil is S-shaped.

Figures 5.5a and 5.5b examine the change in the average ability of a pupil’s fellow

students around the cutoff score. The vertical axes now display the primary school exam

Z-scores of a pupil’s peers (i.e. of pupils selected into the same school). For boys the

average normalized primary school exam score of peers increases by about three quarters

of a standard deviation around the cutoff score. The increase for girls is less pronounced,

but still amounts to about half a standard deviation.33 This increase again follows the

33The upward sloping relationship below the cutoff score is a result of the different cutoff scores forselection into community day schools. When this cutoff score for a community day school is comparativelylow, average primary exam scores in this school will be lower and at the same time the average pupil willbe further from the cutoff point for selection into a conventional school. The upward sloping relationshipabove the cutoff score is the result of the different cutoff scores used for selection into the different types

Section 5.3: Methodology 147

Figure 5.4: Average number of educated teachers per 100 pupils. Educated teachers arethose teachers with a degree beyond secondary school. The graphs combine all pupils whowere selected into public schools across the different cutoff points they face for selectioninto conventional schools. Local averages were calculated at (normalized) integer distancesto the cutoff score, the smallest possible bin size.

148 Chapter 5. Selective Schools and Education Decisions

Figure 5.5: Average normalized primary school exam scores of pupils selected into thesame school (an indicator of the academic ability of peers). The graphs combine allpupils who were selected into public schools across the different cutoff points they facefor selection into conventional schools. Local averages were calculated at (normalized)integer distances to the cutoff score, the smallest possible bin size.

Section 5.3: Methodology 149

S-shape observed for selection into conventional schools.

Figures 5.6a and 5.6b investigate whether the observed increases in peer and school

quality at the cutoff points are accompanied by increased participation in the junior

secondary school exam in the schools into which pupils were selected. For both boys and

girls we indeed observe a strong and by now familiar S-shaped increase in the participation

in the junior secondary school exam at the cutoff point. To investigate the size of this

increase I follow the instrumental variables estimation procedure described above.

Table 5.5 shows that there is a close first stage relationship between the instrument34

and the probability of getting selected. The estimated regression coefficient is close to 1

and significant at the 1% level for all orders of the polynomial and across all bandwidths

ranging from 0.2 to 1.0 standard deviations.35 F-statistics are again well in excess of

minimum Staiger and Stock (1997) values.

Table 5.6 then confirms that the increase in the probability of getting selected into

a conventional school results in strong increases in the probability of taking the junior

secondary school exam in the schools into which pupils were selected. The IV estimates are

fairly robust to different orders of the polynomial and different bandwidths. The estimates

indicate that, at the optimal bandwidth and polynomial, selection into a conventional

school increases the probability of taking the junior secondary exam in the school into

which a pupil was selected by roughly 30 percentage points for boys and girls. The OLS

estimates displayed in this table are also robust and close to the IV estimates, suggesting

that deviations from the described selection procedure are not the result of selection on

unobservables related to exam taking. These findings suggest that the quality of the

schools pupils can attend affects their education decisions, as they are more likely to stay

in a school into which they were selected when the quality of the school is higher.

of conventional schools. For instance, pupils who get selected into a national boarding school have highscores on average, often much higher than the minimum score they would need to get selected into anyconventional school.

34Fitted values based on probit estimates of the relationship between primary school exam scores andthe probability of getting selected into a conventional school.

35I use a larger range of bandwidths than for the regression discontinuity estimates, because the IVestimates do not focus on changes at the reconstructed cutoff, but rather on changes around the cutoffscore.

150 Chapter 5. Selective Schools and Education Decisions

Figure 5.6: Probability of taking the junior secondary exam in the school a pupil wasselected into. The graphs combine all pupils who were selected into public schools acrossthe different cutoff points they face for selection into conventional schools. Local averageswere calculated at (normalized) integer distances to the cutoff score, the smallest possiblebin size.

Section 5.3: Methodology 151

Table 5.5: First stage selection into conventional schools (OLS). Dependentvariable is a dummy taking the value 1 if a pupil was selected into a conventionalschool.

Bandwidth (in SD): Optimal bandwidth0.2 0.5 1.0

Panel A: malePolynomial order: 1 1.039*** 1.069*** 1.039*** 0.2

(0.008) (0.016) (0.013)F=15764.0 F=4497.1 F=6188.2

Polynomial order: 2 1.048*** 1.184*** 1.103*** 0.2(0.012) (0.037) (0.027)

F=7288.1 F=1036.9 F=1611.9

Polynomial order: 3 1.048*** 1.291*** 1.208*** 0.2(0.013) (0.062) (0.055)

F=6805.7 F=435.5 F=477.3

Panel B: femalePolynomial order: 1 1.280*** 1.161*** 1.064*** 1.0

(0.080) (0.044) (0.025)F=253.8 F=688.8 F=1801.0

Polynomial order: 2 1.311*** 1.358*** 1.165*** 1.0(0.089) (0.094) (0.053)

F=217.3 F=206.7 F=483.4

Polynomial order: 3 1.311*** 1.419*** 1.280*** 1.0(0.089) (0.115) (0.099)

F=217.1 F=151.9 F=168.5

Observations male 3030 7254 12960Observations female 2101 5061 9340Optimal order (AIC) male 1 3 3Optimal order (AIC) female 2 3 3

Notes: *** p<0.01, ** p<0.05, * p<0.1. Standard errors (in parentheses) are clus-tered at the level of secondary schools. The table combines all pupils selected intopublic schools across the different cutoff points they face for selection into conven-tional schools. Regressions include fixed effects for the different cutoff points facedby pupils. The outcome variable is a binary indicator taking the value 1 if a pupilwas selected into a conventional secondary school.

152 Chapter 5. Selective Schools and Education Decisions

Tab

le5.

6:O

LS

and

IVes

tim

ates

ofth

eim

pac

tof

sele

ctio

nin

toco

nve

nti

onal

school

onta

kin

gth

ese

condar

ysc

hool

exam

inse

lect

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0.2

69***

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0.2

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(0.0

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0.2

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86***

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83***

0.2

0.2

(0.0

37)

(0.0

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(0.0

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0.3

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0.2

16***

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0.2

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0.2

0.2

(0.0

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(0.0

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into

.

Section 5.3: Methodology 153

I now proceed to explore what can explain the higher probability of taking the junior

secondary exam in conventional schools. In order to do so, I investigate three alternative

reasons for the lower rate of participation in the junior secondary exam among pupils who

were selected into a community day school: (i) retaking the primary school exam in order

to have a chance to get selected into a conventional school, (ii) slower progression to the

junior secondary school exam, and (iii) participation in the junior secondary school exam

in another school than the one into which pupils were selected.

Figures 5.7a, 5.7b, 5.8a, 5.8b, 5.9a, and 5.9b investigate each of these issues. First,

Figures 5.7a and 5.7b show that the rate at which pupils who were selected into public

schools retake the primary school exam is low. Moreover, there is only a minimal decrease

in the probability of retaking the primary school exam at the cutoff score.36 Similarly,

Figures 5.8a and 5.8b show that the overall fraction of students taking the junior secondary

school exam one year later than expected is low and not visibly higher for pupils below

the cutoff score for selection into a conventional school. Neither retaking the primary

school exam nor slower progression through the secondary school system thus seems to

explain the comparatively high probability that pupils stay in the conventional schools

into which they were selected. Finally, figures 5.9a and 5.9b show that the rate at which

pupils switch to other schools than the one into which they were selected plays a more

important role. This probability is markedly higher for pupils with an exam score below

the cutoff point for selection into a conventional school.

Table 5.7 quantifies these graphical findings. The first pair of OLS and IV columns

investigates whether pupils who are selected into community day schools are more likely

to take the secondary school exam in another school than the one into which they were

selected. The second pair investigates whether pupils who are selected into community

day schools are more likely to retake the primary school exam. The third pair investigates

whether pupils who are selected into community day schools are more likely to take the

junior secondary school exam in 2007 (i.e. if they are likely to progress more slowly

through the secondary school system). The table only shows estimates at the intermediate

bandwidth.37 The first stage is equivalent to that shown in Table 5.5.

36Pupils who were selected into a conventional school may still wish to retake the primary school exam,if they think it gives them the chance to get selected into a better conventional school (e.g. a nationalboarding school instead of a district day school.

37The results are robust to different bandwidths. These robustness results are available upon request.

154 Chapter 5. Selective Schools and Education Decisions

Figure 5.7: Probability that pupils who passed the primary school exam in 2004 retookthe primary school exam in 2005. The graphs combine all pupils who were selected intopublic schools across the different cutoff points they face for selection into conventionalschools. Local averages were calculated at (normalized) integer distances to the cutoffscore, the smallest possible bin size.

Section 5.3: Methodology 155

Figure 5.8: Probability that pupils took the junior secondary exam in 2007 instead of2006 (an indication of delayed exam taking). The graphs combine all pupils who wereselected into public schools across the different cutoff points they face for selection intoconventional schools. Local averages were calculated at (normalized) integer distances tothe cutoff score, the smallest possible bin size.

156 Chapter 5. Selective Schools and Education Decisions

Figure 5.9: Probability that pupils took the junior secondary exam in 2006 in anotherschool than the one into which they were selected. The graphs pool all pupils who wereselected into public schools across the different cutoff points they face for selection intoconventional schools. Local averages were calculated at (normalized) integer distances tothe cutoff score, the smallest possible bin size.

Section 5.3: Methodology 157

Tab

le5.

7:O

LS

and

IVes

tim

ates

ofth

eim

pac

tof

sele

ctio

nin

toco

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nti

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onsu

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kin

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ther

)

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le=

1D

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tva

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1D

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den

tva

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le=

1if

pu

pil

took

seco

nd

ary

ifp

up

ilre

took

pri

mary

ifp

up

ilto

ok

seco

nd

ary

sch

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exam

else

wh

ere

sch

ool

exam

in2005

sch

ool

exam

in2007

OL

SIV

OL

SIV

OL

SIV

Panel

A:male

Pol

yn

omia

lor

der

:1

-0.0

82***

-0.0

95***

-0.0

08***

-0.0

09***

-0.0

02

-0.0

03

(0.0

22)

(0.0

26)

(0.0

03)

(0.0

04)

(0.0

04)

(0.0

05)

Pol

yn

omia

lor

der

:2

-0.0

85***

-0.1

21***

-0.0

07*

-0.0

08

0.0

01

0.0

02

(0.0

26)

(0.0

39)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

07)

Pol

yn

omia

lor

der

:3

-0.0

70***

-0.0

96**

-0.0

08**

-0.0

11

-0.0

03

-0.0

09

(0.0

25)

(0.0

42)

(0.0

03)

(0.0

07)

(0.0

06)

(0.0

10)

Panel

B:female

Pol

yn

omia

lor

der

:1

-0.0

99***

-0.1

15***

-0.0

04

-0.0

00

-0.0

02

0.0

05

(0.0

19)

(0.0

30)

(0.0

03)

(0.0

05)

(0.0

05)

(0.0

07)

Pol

yn

omia

lor

der

:2

-0.0

98***

-0.1

26***

-0.0

04

0.0

01

-0.0

04

-0.0

00

(0.0

20)

(0.0

38)

(0.0

04)

(0.0

06)

(0.0

06)

(0.0

09)

Pol

yn

omia

lor

der

:3

-0.0

97***

-0.1

24***

-0.0

04

0.0

01

-0.0

05

-0.0

05

(0.0

20)

(0.0

40)

(0.0

04)

(0.0

06)

(0.0

06)

(0.0

10)

Ob

serv

atio

ns

mal

e7254

7254

7254

7254

7254

7254

Ob

serv

atio

ns

fem

ale

5061

5061

5061

5061

5061

5061

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(AIC

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12

11

11

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(AIC

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0.01

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p<

0.05

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p<

0.1.

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are

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ubli

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diff

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face

for

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ctio

nin

toco

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tion

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sch

ools

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xed

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iffer

ent

cuto

ffp

oints

face

dby

pu

pil

s.

158 Chapter 5. Selective Schools and Education Decisions

The estimates confirm that differences between community day schools and conven-

tional schools in the rate of retaking the primary school exam or in the speed at which

pupils progress through the secondary school system are minimal. The rate at which

pupils take the junior secondary school exam in other schools than the ones into which

they were selected, however, does indeed appear to be part of the explanation for the pre-

viously estimated difference in the probability of taking the junior exam. At the optimal

order of the polynomial this rate is 12 percentage points higher for both boys and girls in

community day schools than for boys and girls in conventional schools. The higher rate

of switching to other schools reconfirms that pupils in Malawi attach high value to the

quality of the schools they attend: when the quality of the school into which they are

selected is high, they are less likely to switch schools.

The three investigated reasons cover the most important potential causes of the com-

paratively low rate of participation in the junior secondary exam among pupils who were

selected into a community day school.38 The results show that the difference in the rate

at which pupils switch between schools plays an important role. However, this difference

cannot explain the full difference in the rate of participation in the junior secondary exam

between pupils who were selected into community day schools and pupils who were se-

lected into conventional schools. Because it is unlikely that other factors can fully explain

the difference in the rate of participation in the junior secondary exam I conclude that

pupils who get selected into a community day school are more likely to drop out than

pupils who get selected into a higher quality school.39 This conclusion implies that, con-

trary to evidence presented in previous studies, school quality does play an important role

in school participation in Malawi.40

38They do not cover all potential causes. First, as explained above, I linked pupils the 2004 primaryschool exam database to the 2005 primary school exam database and the 2007 junior secondary schoolexam database at the school level. I therefore do not track pupils who retook the primary school examin another school than their original primary school. The same holds for pupils who took the 2007junior secondary school exam in another school than the one into which they were selected. However,it seems reasonable to assume that the number of pupils in these categories is much smaller than thenumber of pupils who can be matched at the school level and therefore that these categories are only ofsecond order importance. Second, I do not track pupils who took the junior secondary school exam inanother school than the one into which they were selected, when this other school is located in anotherdistrict. However, given that most pupils in Malawi live with their parents or guardians and come fromimpoverished backgrounds, their ability to attend schools that are not located at walking distance fromtheir homes are limited and thus also by presumption of second order importance. These issues aretherefore unlikely to explain the remaining difference in the probability of staying in the school pupilswere selected into.

39Where dropping out can mean either not entering secondary school or dropping out of secondaryschool before the junior secondary school exam.

40An obvious follow-up question is whether selection into high-quality conventional schools also results

Section 5.3: Methodology 159

5.5.3 Validity

Lee and Lemieux (forthcoming) discuss that ability of potential program participants to

sort around the cutoff point for selection into the program would invalidate the regression

discontinuity approach. Such endogenous sorting can occur when program participants

are able to precisely manipulate the forcing variable (the variable on the basis of which

participation in the program is determined). Pupils taking the primary school exam in

Malawi are a prime example of agents who do not have precise control over the forcing

variable. First, the pupils do not know their own exam score when they are taking

the exam (i.e. they do not have precise control over the forcing variable). Second, the

cutoff point is only determined after all exams have been administered and corrected.

As a result, even the people hired to correct the exams do not know the cutoff score,

which makes manipulation of the exam results more complicated. In addition, exams

are marked by Malawi’s Examination Board and not by the pupils’ own teachers. This

procedure strongly limits the probability that the people who mark the exams have a

motive to tamper with aggregate exam scores.

McCrary (2008) proposed to test directly for any manipulation of the forcing variable

by inspecting the density of the forcing variable for any discontinuities around the cutoff

point. In case of endogenous sorting, we would expect the density of the forcing variable

to be discontinuous around the cutoff score: a disproportionately large number of pupils

would have a primary school exam score just above the cutoff points for selection into

the different school types. Figures 8a and 8b provide an eyeball version of the McCrary

test. These histograms show that pupils are not clustered at the exam scores right above

the cutoff point for selection into a public school. Similarly, figures 9a and 9b show that

pupils are not clustered right above the cutoff point for selection into conventional schools.

Together these graphs show that endogenous sorting is not likely to be a source of bias

in this study. Regression tests for endogenous sorting (not shown here) confirm these

graphical results.

in increased learning. I investigated this issue by looking at the probability of passing the junior secondaryexam, conditional on participating in this exam. The analysis showed that, at the cutoff score for selectioninto conventional school, male pupils who participate in the exam have an equal probability of passingabove and below the cutoff point. The conditional passing probability of female pupils just above thecutoff point is approximately 10 percentage points higher. However, the conditional exam performanceanalysis suffers from a selection problem, as it compares a relatively high number of pupils who stay inschool above the cutoff point to a relatively low number of pupils who stay in school below the cutoffpoint. It is therefore not entirely clear how to interpret these outcomes.

160 Chapter 5. Selective Schools and Education Decisions

Figure 5.10: Number of pupils at each (normalized) integer distance to the cutoff scorefor selection into a public school. The graphs combine all pupils across the different cutoffpoints they face for selection into public schools.

Section 5.3: Methodology 161

Figure 5.11: Number of pupils at each (normalized) integer distance to the cutoff score forselection into a conventional school. The graphs combine all pupils who were selected intopublic schools across the different cutoff points they face for selection into conventionalschools.

162 Chapter 5. Selective Schools and Education Decisions

5.6 Conclusion

Malawi’s public secondary education sector faces a chronic capacity constraint and can

absorb only about 40% of the pupils who successfully finish primary school. In order to

regulate entry into the available places in public secondary schools, Malawi’s Ministry

of Education employs a merit based selection procedure. This procedure assigns the

top performers on a national primary school exam to a group of elite schools (so-called

conventional schools). Second tier performers are selected into the remaining lower quality

public schools (called community day schools). Third tier performers are not assigned to

public schools. The assignment procedure generates exogenous variation in the probability

that pupils can attend the different types of public secondary schools. This chapter

exploits this exogenous variation in a regression discontinuity framework to estimate the

causal effect of selection into the different school types on pupils’ schooling decisions.

The first main result of the chapter is that the assignment procedure has a substantial

effect on the third tier performers (i.e. the pupils who pass the primary school exam

but not with a score that is sufficiently high to get selected into a public school). A

share of these pupils (0 to 4 percent of males and 6 to 14 percent of females) retakes the

primary school exam one year later to have another chance to get selected into a public

secondary school. To the extent that pupils who retake the primary school exam attend

school (which I cannot observe) retaking the primary school exam will result in negative

externalities in the form of an increased number of pupils in the final grade of primary

school. There is no evidence that retaking the primary school exam results in increased

learning by the pupils who retake the primary school exam.

A recent series of papers shows that both tracking within schools and tracking be-

tween schools can improve pupil performance (e.g. Clark, 2010; Duflo et al., forthcoming;

Jackson, 2010). Using a methodology similar to the one used in this chapter, Pop-Eleches

and Urquiola (2010) find that pupils in Romania benefit from attending higher quality

schools. However, these authors also observe that the large scale tracking program in

Romania resulted in general equilibrium effects that reduce the benefit of attending a

better school. Pupils who just made it into high quality schools, for instance, were less

likely to receive homework related help from their parents. In a similar vein, this chapter

shows that Malawi’s large scale tracking program resulted in general equilibrium effects

that potentially have a detrimental effect on other children in the final grade of primary

school. This result does not necessarily indicate that all general equilibrium effects that

Section 5.3: Methodology 163

result from large scale tracking programs or elite schools have a detrimental influence on

the pupils who are competing for a spot in the high ability track or the elite schools. The

competition itself, for instance, may well improve pupils’ academic performance. However,

the result does show that general equilibrium effects that affect the competing students

before they enter the high ability track or the elite schools can be a key impact of such

programs and should be seen as an integral result of the intervention.

The second main result of this chapter is that pupils who are selected into a high

quality conventional school are approximately 30 percentage points more likely to stay in

this school than pupils who are selected into a lower quality community day school. This

finding in itself indicates that pupils in Malawi highly value the quality of the school they

attend. Further results show that the comparatively high probability that pupils who

were selected into a community day school leave this school can only partly be explained

by these pupils retaking the primary school exam, progressing more slowly through the

education system, or switching to other schools. A higher dropout rate among pupils who

were selected into community day schools is therefore likely to be an important explanation

of the difference in the probability that pupils who were assigned into a school stay in

this school.

This last finding indicates that school quality matters for school participation in

Malawi. This conclusion appears intuitive and confirms older research on this relation-

ship, such as that provided in Case and Deaton (1999). However, the result runs against

evidence on the relationship between school quality and school participation discussed in

reviews by Glewwe and Kremer (2006) and Kremer and Holla (2009). This conclusion is

also not in accordance with other recent studies which explore the impact of attending

high quality schools on school participation using a methodology similar to the one used

in this chapter (notably Jackson, 2010, for Trinidad and Tobago; and Pop-Eleches and

Urquiola, 2010, for Romania). There are several potential explanations for the deviating

results presented in this study. First, other studies typically investigate how a narrow as-

pect of school quality affects school participation. This study, on the other hand, focuses

on a broader measure of school quality encompassing peer ability and a broad range of

school characteristics. Second, the setting of this study differs from recent papers that

do focus on a broader measure of school quality by Jackson (2010) and Pop-Eleches and

Urquiola (2010). Those studies respectively focus on Trinidad and Tobago and Romania,

which have a better equipped secondary education sector and higher net secondary enroll-

164 Chapter 5. Selective Schools and Education Decisions

ment rates (74% in Trinidad and Tobago and 73% in Romania versus 25% in Malawi).41

High dropout rates are therefore a less pressing issue in those countries. Moreover, GNI

per capita of Romania and Trinidad and Tobago is respectively 32 and 60 times higher

than in Malawi.42 As a result, the pupils in these countries are likely to have access to

better private outside education options and are thus less likely to drop out when they

are selected into a school of insufficient quality.

A1. Average Characteristics of Conventional Day Schools

and Conventional Boarding Schools

Table A1: Average Characteristics of Conventional Day Schools and Conventional BoardingSchools

Conventional day Conventional boardingSchool size (number of pupils) 409 499IncomeAnnual school fees per student (in US$) 18 100Annual school income per student (in US$) 68 230Human ResourcesTeachers per 100 pupils 4.5 4.3Educated teachers per 100 pupils 4.0 4.0Non-teaching staff per 100 pupils 2.0 3.0Physical ResourcesPercentage of schools with a library 82 94Percentage of schools with a PC room 31 59Percentage of schools with toilets 63 91Percentage of schools with tap or borehole 90 88Percentage of schools with electricity 74 88Classrooms per 100 pupils 2.7 2.1Book to pupil ratio (Chichewa) 1.3 1.6Book to pupil ratio (English) 1.5 1.7Book to pupil ratio (mathematics) 1.3 1.5Observations 49 63

Notes: Source: The 2005 and 2006 Education Management Information System (EMIS). Schoolsincluded are those community day schools and conventional schools for which the Ministry ofEducation selected the 2005 pupils. Numbers are averaged over the years 2005 and 2006. Annualincome per student in US Dollar calculated using an exchange rate of 140 Malawi Kwacha per USDollar. Educated teachers are those teachers with a degree beyond secondary school.

41Data from the World Bank Data Catalog, accessed on 11-12-2010.42See previous footnote.

Section 5.6: Appendix 165

A2. Additional Information on Selection Procedures

This appendix provides additional information on the selection procedures. Some of this

information concerns the process by which pupils can deviate from the official selection

results. As I only have access to the official selection data, I do not observe deviations

from the official selection results. However, given that these deviations take place after

the official selection procedures have been completed, they do not affect the descriptions

of the accuracy by which the Ministry of Education executes its selection procedures or

the estimated intention-to-treat effects presented in this chapter.

Tie-Breaking Primary School Exam Scores Because entry quotas for school types

are fixed they implicitly determine the PSLCE cutoff points. There may be cases where

only a fraction of pupils at the cutoff point can be selected. As an example, consider

the 2004 national boarding school selection. A total of 718 boys were to be selected into

national boarding school and the resulting cutoff score in the aggregate primary school

exam score was 268 out of 400. Because the top 716 to 766 male pupils all had an

aggregate PSLCE score of 268 only 3 of the pupils at the cutoff point could be selected.

The selection team deals with this issue by ranking all pupils with an aggregate PSLCE

score equivalent to the cutoff score in ascending alphabetical order. The pupils are then

selected according to this order until the quota is reached, which implies that those with

a name that starts with a letter early in the alphabet have a slightly higher chance of

being selected.

What if Selected Pupils Do not Enroll? Some of the pupils who were selected into

a government secondary school will, for a number of reasons, decide not to attend the

school they have been selected into. The Ministry of Education follows a straightforward

procedure to deal with this issue. When a place is freed up in one level the top ranked

pupil from the level below is pushed up a level. There is a waiting list for community

day schools, so pupils who were originally not selected into any public secondary school

can attend community day schools if places are freed up. The filling up of free places

occurs after the official first round selection takes place. It, therefore, does not affect the

intention-to-treat results presented in this study.

166 Chapter 5. Selective Schools and Education Decisions

Switching Between Schools of the Same Level It is, in principle, possible for pupils

to apply for a transfer to another school within the same level. Pupils can hand in a request

to be transferred to another school of the same level at the Division Education Office.

The officer in charge will pass the request on to the school the pupil wants to transfer to.

The school under consideration then lets the Division Education Office know if they have

space for this additional pupil. Such within-level transfers occur only after the official first

round selection takes place. They, therefore, do not affect the intention-to-treat results

presented in this study.

Switching Between Schools of a Different Level For a variety of reasons, some of

the students will attempt to transfer to a school of a lower level. Applying to a school of

a lower level is discouraged by the District Education Offices. However, if a pupil insists

on switching to a school of a lower level the Division Education Office has the ability to

permit the transfer.

Some pupils who have been selected into district or national boarding school will

apply for a transfer to a lower level school (e.g. a non-boarding school), because they

cannot afford to pay the boarding fees. Some bursaries are available for these students

through the secondary school bursary scheme (formerly known as GABLE). Nevertheless

the District Education Office will sometimes have to grant the request to be selected into

a lower education level.

There are also pupils who will attempt to be accepted into a school of a higher level.

The Division Education Office has some discretion to grant such requests if students

provide compelling reasons for the transfer. Compelling reasons can, for instance, be

physical disabilities that hamper a pupil’s performance in the primary school exam. Such

between-level transfers occur only after the official first round selection takes place. They,

therefore, do not affect the intention-to-treat results presented in this study.

A3. Linking Administrative Databases

I used approximate string matching software to match observations for which no exact

match of names came up. The software I used (called reclink) was written for Stata by

Michael Blasnik and “combines approximate string comparators and probabilistic match-

ing algorithms to identify the best matches and assess their reliability”.

Section 5.6: Appendix 167

The matching algorithm provides a match score that indicates how closely two obser-

vations match on a scale from 0 to 1. By default, the program discards all matches with

a match score below 0.6 and I maintained this default. I manually checked all matches

and considered a close match on two constituent names to be a minimum requirement. I

discarded all combinations based on less than two close matches.

Manually checking matches is a tedious and to some extent arbitrary job. In the

process I may have discarded some matches that others would not have discarded and

vice versa. However, in the vast majority of cases the success of the matching algorithm is

fairly easily determined and not controversial. Just to give an example, most people would

agree that the names “Lazalo Christina Daisoni” and “Dayisoni Christina L” constitute

a reasonable match, whereas the names “Genda Eric Henry” and “Banda Henry C” do

not.