Description
presenting the paper, you should spend at least 5 minutes describing a research or policy
idea related to the paper.
You have to use slides (my recommendation: around 15 slides)
Guiding questions (list not exhaustive!)
Every presentation will be different since they will be based on different papers, but there
are some common guidelines to adhere to, and some questions that you should make sure
that you answer in the presentation:
1. Which questions do the authors try to answer?
2. Which empirical strategy and which methods are they using?
3. Which results do they document? How?
4. Would you regard these results as trustworthy? As well identified? Why / why not?
5. How would you relate this piece of research to the topics that we have discussed in class?
Where does this fit in?
6. What potential policy implications may these results have?
7. What new research would the paper inspire you to conduct?
What does ”well identified” mean?
”Well identified” refers to whether any causal relations that the authors are claiming are
actually causal as opposed to correlational.
– How are the authors trying to get at causality?
– Which assumptions may they be relying on?
– Do you think that the result that they document is causal or correlational?
A few tips!
Since you have only 25 minutes time, you will obviously not have time to tell the
audience everything that is in the paper
An important part of this task is hence to tease out the most important things from
the paper, and convey these to the rest of us
You will most likely have to read other papers than just the one assigned (e.g. work
cited in the paper that you will be presenting, or work otherwise related) – to understand
where in the literature this paper fits in, methods, etc
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GENDER, COMPETITIVENESS, AND CAREER CHOICES*
Thomas Buser
Muriel Niederle
Hessel Oosterbeek
I. Introduction
A recently emerging literature documents large gender differences in competitiveness based on laboratory experiments (see
Croson and Gneezy 2009; Niederle and Vesterlund 2011). While
women shy away from competition, men often compete too much.
It has been hypothesized that these gender differences in competitiveness may help explain gender differences in actual education and labor market outcomes. Evidence supporting this
hypothesis is, however, thin. Bertrand (2011) attributes this to
the rather new research agenda and the difficulty in finding databases that combine a good measure of competitiveness with real
outcomes. This article aims to fill this gap. To assess the relevance of competitiveness for education outcomes—and gender
differences therein—we link a standard experimental measure
of competitiveness with the later important choice of academic
*We thank the staff of the four schools that allowed us to collect the data from
their pupils that we use in this article. We thank Lawrence Katz and four anonymous referees for their comments. We also thank Gerrit Bloothooft for classifying students into socioeconomic groups on the basis of their first names.
Nadine Ketel and Boris van Leeuwen provided excellent research assistance.
Niederle thanks the NSF for financial support. We gratefully acknowledge financial support from the University of Amsterdam through the Speerpunt
Behavioural Economics.
! The Author(s) 2014. Published by Oxford University Press, on behalf of President and
Fellows of Harvard College. All rights reserved. For Permissions, please email: journals
[email protected]
The Quarterly Journal of Economics (2014), 1409–1447. doi:10.1093/qje/qju009.
Advance Access publication on May 8, 2014.
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Gender differences in competitiveness have been hypothesized as a potential explanation for gender differences in education and labor market outcomes.
We examine the predictive power of a standard laboratory experimental measure of competitiveness for the later important choice of academic track of secondary school students in the Netherlands. Although boys and girls display
similar levels of academic ability, boys choose substantially more prestigious
academic tracks, where more prestigious tracks are more math- and scienceintensive. Our experimental measure shows that boys are also substantially
more competitive than girls. We find that competitiveness is strongly positively
correlated with choosing more prestigious academic tracks even conditional on
academic ability. Most important, we find that the gender difference in competitiveness accounts for a substantial portion (about 20%) of the gender difference in track choice. JEL Codes: C9, I20, J24, J16.
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1. We will show that in the Netherlands boys are significantly more likely to
take math classes in secondary school than are girls. In France, like in the Netherlands, secondary school children decide on which sets of classes to enroll in, and
girls are less likely to choose the math- and science-heavy options (see http://www.
insee.fr/fr/themes/tableau.asp?ref_id=eduop709®_id=19). The same is true
for Denmark (Schroter Joensen and Skyt Nielsen 2011), Switzerland (http://
www.ibe.uzh.ch/publikationen/SGH2003_d.pdf), and Germany (Roeder and
Gruehn 1997).
2. See http://nces.ed.gov/pubs2009/2009161.pdf.
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track of secondary school students in the Netherlands. The tracks
from which these students can choose vary considerably in their
prestige and their math and science intensity.
Math and science intensity is one of the most significant dimensions of gender differences in educational choices. In many
Organisation for Economic Co-operation and Development countries, girls are less likely than boys to choose math- and scienceheavy courses in secondary education.1 In the United States, a
gender difference in math and sciences manifests itself at the
college level, where women are significantly less likely than
men to graduate with a major in science, technology, engineering,
or mathematics.2 While in U.S. high schools girls take as many
advanced math and science classes as boys and perform at similar
levels on average (Goldin, Katz, and Kuziemko 2006), girls are
still underrepresented among extremely high-achieving high
school students in math (Ellison and Swanson 2010). The main
reason to be concerned about gender differences in math and sciences is that the choice of math and science classes is a good
predictor of college attendance and completion (Goldin, Katz,
and Kuziemko 2006). Performance in mathematics also predicts
future earnings (for evidence and discussion, see Paglin and
Rufolo 1990; Grogger and Eide 1995; Brown and Corcoran 1997;
Altonji and Blank 1999; Weinberger 1999, 2001; Murnane et al.
2000; Schroter Joensen and Skyt Nielsen 2009; Bertrand, Goldin,
and Katz 2010).
It has been suggested that gender differences in math are
explained by gender differences in ability. However, Ellison and
Swanson (2010) provide compelling evidence that the gender imbalance among high achieving math students in the United
States is not driven by differences in mathematical ability
alone. Moreover, among equally gifted students, males are
much more likely to choose a math-heavy college major (see
LeFevre, Kulak, and Heymans 1992; Weinberger 2005).
GENDER, COMPETITIVENESS, AND CAREER CHOICES
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3. Experiments have shown that for women both the performance in as well as
the willingness to enter competitive environments is reduced when the competition
group includes males (Gneezy, Niederle, and Rustichini 2003; Balafoutas and
Sutter, 2012; Niederle, Segal, and Vesterlund 2013). Similarly, Huguet and
Regner (2007) show that girls underperform in mixed-sex groups (but not in all
female groups) in a test they were led to believe measures mathematical ability.
Furthermore, some studies find that the strong gender differences in competitiveness found in mathematical tasks are sometimes but not always attenuated when
assessed in verbal tasks (e.g., Kamas and Preston 2010; Dreber, von Essen, and
Ranehill 2014; Wozniak, Harbaugh, and Mayr 2014; see Niederle and Vesterlund
2011 for an overview).
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Intuitively, it seems plausible that competitiveness could be
a relevant trait to explain entry into fields such as sciences and
mathematics, which are male dominated and viewed as competitive.3 There is, for example, evidence of a low tolerance for competition among women who drop out of math-intensive college
majors and engineering (see Felder et al. 1995; Goodman
Research Group 2002). This evidence is fairly casual, however,
and may suffer from reverse causality. Women who drop out of
science and engineering may search for explanations such as the
negative aspect of a competitive environment.
In the Netherlands, secondary school students at the preuniversity level choose at the end of the third year of their sixyear program (at age 15) between four study tracks: a science
track, a health track, a social sciences track, and a humanities
track. These four tracks are clearly ranked in terms of math intensity and academic prestige as follows (in descending order):
science, health, social sciences, and humanities. This choice of
academic track strongly correlates with the choice of major in
tertiary education later in life.
We use the measure of competitiveness introduced by
Niederle and Vesterlund (2007). Participants perform a real
effort task first under a noncompetitive piece rate incentive
scheme and then a competitive tournament scheme. In the tournament, a participant competes against three group members,
and only the subject with the highest performance receives a payment. Participants do not receive any information about the performance of others, including whether they won the tournament.
For their final task, participants choose between the competitive
tournament scheme and the noncompetitive piece rate scheme.
This choice serves as a measure of the participants’ willingness to
compete. The typical result is that controlling for performance,
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males are more likely to enter the tournament than females.
Niederle and Vesterlund (2007) show that about one third of
the gender gap in tournament entry can be accounted for by
gender differences in confidence. Risk attitudes are often shown
to only play a minor role. Since both confidence and risk aversion
may not only account for tournament entry but also influence
students’ academic track choice, we administered incentivized
measures of students’ confidence and risk attitudes. Because we
are concerned with the choice of prestigious science tracks typically favored by males, we measure the competitiveness of students in a stereotypical male task, arithmetics, which is also
the task used in Niederle and Vesterlund (2007) and most of
the resulting literature. This measure of competitiveness has
proven to be robust across different settings and subject pools
(Niederle and Vesterlund 2011).
We administered our experiment in four schools in and
around Amsterdam, measuring students’ competitiveness a few
months before they chose their study track. To avoid problems of
reverse causality, it is important to measure competitiveness
before students have different and potentially influential experiences resulting from their choices. After the school year, the
schools provided us with the track choices of their students as
well as with their grades. Because grades may not be the most
accurate predictor of ability, we asked the students for their own
perceptions of their mathematical ability.
The students in our sample exhibit the expected gender differences. Although the academic performance of girls (including
math grades) is at least as good as that of boys, boys choose substantially more prestigious academic tracks than girls. Also,
while the performance of boys and girls on the experimental
task is very similar, boys are twice as likely as girls to choose
the competitive payment scheme. Our first finding is that the
choice of the tournament scheme in the experiment is significantly positively correlated with the prestige and math and science intensity of the chosen academic track. Being competitive
bridges around 20% of the distance between choosing the lowest
and the highest ranked track. The effect of competitiveness is
comparable in size to the effect of being male. Our main finding
is that gender differences in competitiveness can account for 20%
of the gender gap in the prestige and math and science intensity
of the chosen academic track, controlling for grades and perceived
mathematical ability. We show that the effect of competitiveness
GENDER, COMPETITIVENESS, AND CAREER CHOICES
1413
II. Study Design
II.A. Data Collection
We invited secondary schools in and around Amsterdam to
participate in a research project investigating the determinants
of study track choices of students in the pre-university level of
secondary school. We demanded one class hour (45 or 50 minutes)
of all grade 9 classes at the pre-university level. The invitation
letter stated that students would participate in an in-class experiment and be paid depending on their choices. For detailed instructions, see the Online Appendix. We describe the Dutch
school system and the study track choice in Section III.
Four schools gave us access to their students, one in the city
of Amsterdam and three in cities close to Amsterdam. In each
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is not driven by a possible effect of confidence or risk attitudes on
academic track choice. When we control for the experimental
measures of confidence and risk attitudes, the decision to enter
the tournament still closes the gender gap in the prestige of
chosen tracks by a significant 16%. These results not only demonstrate the external relevance of the concept of competitiveness
but also validate the specific measure of competitiveness provided
by Niederle and Vesterlund (2007).
The remainder of the article proceeds as follows. The next
section describes the collection of the data and the variables.
Section III provides more details of the academic tracks from
which the students have to choose. We then present the results
from the study in three stages. First, in Section IV we document
significant gender differences in the prestige (and math intensity)
of the track choices made by the students in our sample and show
that they cannot be explained by gender differences in ability.
Second, in Section V we document significant gender differences
in competitiveness and assess the extent to which these differences can be attributed to gender differences in confidence and
risk attitudes. Finally, in Section VI we examine whether competitiveness correlates with track choice and assess to what
extent gender differences in competitiveness can account for
gender differences in the prestige and math and science intensity
of chosen tracks. Section VII discusses alternative interpretations of the tournament entry measure, and Section VIII
concludes.
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4. For the students for whom we have both the definite track choice and the
intention stated in the questionnaire, the questionnaire answer accurately predicts
the final choice in 93% of the cases.
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school, we captured all students in grade 9 of the pre-university
level for a total of 397 students in 16 classes. Because the schools
are geographically dispersed, we do not worry that students
received information about the experiment from students in
other schools. For any given school, experiments in different
classes were administered on the same day, often at the same
time. The data collection in the schools took place in March,
April, and May 2011. The participants were paid a week after
the experiment through sealed envelopes. They earned an average of E5.55, with a minimum of zero and a maximum of E25.
There was no fixed participation fee.
After the end of the school year, the schools provided us with
the students’ final grades in grade 9 and their track choices for
the last three years of high school. For 35 students we do not have
such a track choice. For 20 of these students, we can use information about their expected track choice obtained through the
short questionnaire at the end of the experiment.4 We drop the
remaining 15 students for whom we have neither a definite choice
nor a clear answer from the questionnaire. We have to drop an
additional four students from the analysis because they showed
up late to class and missed part of the experiment, two students
because their questionnaires were incomplete and they therefore
lack key control variables, and 14 students because we did not
obtain their grades. This leaves us with a sample of 362 subjects.
Although a sample of four schools can hardly be representative for the total of over 500 schools in the Netherlands that offer
the pre-university level, the four schools appear to be average on
several dimensions. First, as we will discuss in more detail later,
the track choices by gender in the four schools are close to the
national averages. Second, with 51%, the proportion of girls in
our sample is close to the national average of 53% at the preuniversity level. Third, the numbers of students in the four
schools are (around) 700, 800, 1,500, and 2,000. The average secondary school size in the Netherlands is close to 1,500 (CBS 2012,
p. 80). Fourth, the average grades on the nationwide exams in the
final year in the four schools are 6.2, 6.2, 6.3, and 6.8, where the
national average for the pre-university level equals 6.2. Finally,
the pass rates on the final exam in the four schools are 0.87, 0.87,
GENDER, COMPETITIVENESS, AND CAREER CHOICES
1415
0.91, and 0.95, where the national average for the pre-university
level equals 0.88.
II.B. Experimental Variables and Student Characteristics
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1. Competitiveness. Our measure of competitiveness is taken
from Niederle and Vesterlund (2007). Participants perform a real
task in three rounds, first under a noncompetitive piece rate
scheme and then under a competitive tournament scheme.
Participants then choose which of the two payment schemes to
apply to their third and final performance. This choice serves as
our measure of their competitiveness.
The task of the experiment is to add up sets of four two-digit
numbers for three minutes. The performance in each round corresponds to the number of correctly solved problems. In each
round participants received envelopes that contained a sheet of
26 problems. There were always three versions of the 26 addition
problems to prevent copying from neighbors. After having read
out the instructions that were on top of the envelopes and answering questions (if any), the experimenter gave the signal that subjects could open the envelopes and start the addition problems.
Participants were not allowed to use calculators but could use
scratch paper. Once the three minutes of solving problems were
over, subjects had to drop the pen and stand up.
Participants were informed at the start of the experiment
that they would perform in three rounds, one of which would be
randomly chosen for payment at the end of the experiment
through the roll of a die in front of the classroom. Participants
received detailed instructions on each round only immediately
before performing in the task in that round. Participants did
not receive any information about their own performance or the
performance of others at any point during the experiment. Only a
week later, when participants were paid, could they make inferences about their relative performance.
In round 1, participants were paid for their performance according to a non-competitive piece rate of 25 euro-cents per correctly solved problem. In round 2, they performed in a
tournament against three competitors. The competitors were randomly selected by computer among students from the same class
after the end of the experiment. The person with the largest
number of correctly solved problems received E1 per correct
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2. Confidence. The decision whether to enter the tournament
in round 3 may depend on the students’ beliefs about their relative performance in their group of four competitors. We therefore
elicited those beliefs. Specifically, we asked the students to guess
their rank in the round 2 tournament, from 1 (best) to 4 (worst) of
their group of four. If their guess was correct, they received E1.6
3. Risk attitudes. The decision to enter the tournament in
round 3 may also depend on the students’ risk attitudes. We elicited risk attitudes using two separate measures from the experimental literature. First, following Eckel and Grossman (2002),
subjects picked one option among a sure payoff of E2 and four
50/50 lotteries with increasing riskiness and expected payoffs: 3
or 1.5; 4 or 1; 5 or 0.5; 6 or 0. The outcome of the lottery was
determined by the roll of a die at the end of the experiment.
Second, we asked subjects ‘‘How do you see yourself: Are you
generally a person who is fully prepared to take risks or do you
5. There are several advantages to having participants compete in round 3
against the previous round 2 tournament performance. First, the performance of a
subject who chose the tournament is evaluated against the performance of other
subjects in a tournament. Second, the choice of compensation scheme of a subject
should not depend on the choices of other players. Third, the participant causes no
externality to another subject. Hence motives such as altruism or fear of interfering
with someone else’s payoff play no role.
6. When two subjects have the same number of correctly solved additions, they
receive the same rank. For example, if two subjects are tied for first place, they are
both ranked first and receive E1 if their guessed rank is equal to 1. The next best
subject is ranked third.
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problem and the others received no payment. In case of a tie, the
winner was randomly determined.
In round 3, participants could choose which of the two payment schemes would be applied to their performance. Like in
round 1, a participant who chose the piece rate received 25
cents per correct problem. A participant who selected the tournament would win if her new round 3 performance exceeded the
round 2 performance of her three competitors. In case of a tie,
the winner was randomly determined. Therefore, just like in
Niederle and Vesterlund (2007), the choice of payment scheme
was an individual decision as a subject could not affect the payoffs
of any other participant.5
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4. Subjective ability. We collected two subjective ability measures in the postexperimental questionnaire. First, we asked the
students to rank themselves on mathematical talent compared to
other students in their year (and school) on a scale from 1 (the
best 25%) to 4 (the worst 25%).7 Second, we asked the students
how difficult they find it to pass their math class on a scale from 0
(very easy) to 10 (very hard). Although these questions may yield
a better assessment of mathematical ability than grades, they
could in addition be a measure of confidence, which in turn
could influence study track choices. Indeed, it has been found
that conditional on academic performance, boys are more confident in their relative ability than girls (Eccles 1998), a difference
that seems greatest among gifted children (Preckel et al. 2008).
5. Student characteristics. For each student, we obtained
their first name, gender, birth date, and expected track choice.
Though we did not collect any socioeconomic background data on
the students in our sample, we have their names. Bloothooft and
Onland (2011) show that in the Netherlands, first names are
strongly predictive of social class, income, and lifestyle, and
7. This was phrased as three yes/no questions: ‘‘Do you think your mathematics ability is in the top 25% of your year?’’, ‘‘. . . top 50% of your year?’’, ‘‘. . . top 75% of
your year?’’ A student who answered all three questions with no was automatically
assumed to be in the bottom 25%. We had 44 students who answered no to all
questions. A student who answers yes to one of the questions also should answer
yes to the next (if one is in the top 25%, one is also in the top 50%). Sixty-seven
students, however, switched back to no. For these students, we count the first yes as
their true answer. Clearly ‘‘wrong’’ answers consist of the yes/yes/no and yes/no/yes
patterns. All other patterns can be rationalized by (i) students truly understanding
the question or (ii) misreading the question and answering yes only to their own
quartile. There are 10 answers that follow the yes/yes/no pattern and 0 that follow
the yes/no/yes pattern.
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try to avoid taking risks?’’. The answer is on a scale from 0
(‘‘unwilling to take risks’’) to 10 (‘‘fully prepared to take risk’’).
Dohmen et al. (2011), using representative survey data from
Germany, find that this simple nonincentivized risk question predicts both incentivized choices in a lottery task as well as risk
taking across a number of contexts, including holding stocks,
being self-employed, participating in sports, and smoking.
Lonnqvist et al. (2010) find the question to be much more stable
over time than lottery measures of risk attitudes.
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III. The Study Track Choice
The students in our study are drawn from the population of
Dutch secondary school students who are enrolled at the pre-university level. In the Dutch school system, tracking first takes
place when students go from primary school (grades 1 to 6) to
secondary school, normally at age 12. There are three levels:
around 20% of students graduate from the six-year pre-university
level, 25% from the five-year general level, and 55% from the fouryear vocational level. Who enrolls at which level is to a large
extent determined by the score on a nationwide achievement
test administered at the end of primary school. Girls are somewhat more likely to go to the pre-university level, making up 53%
of the students (CBS 2012). In the first three years at the preuniversity level, students are taught in the same class of around
25 students for all subjects during the entire school year.
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they develop a classification of names into 14 categories. We
applied this classification to our sample (Table A.I in the Online
Appendix lists the 14 categories and their proportions). The
schools provided us with their grades and track choices, which
we merged by name and birth date. In the Dutch school system,
grades are expressed on a scale from 1 (worst) to 10 (best), where
6 is the first passing grade. We use the students’ grades at the end
of ninth grade to construct three objective ability measures. The
first is grade point average (GPA), calculated as the average of all
grades. The second is their grade for mathematics. Grades need
not be a perfect predictor of mathematical ability which is why we
also collected subjective ability measures. The third is each student’s relative math grade compared to the rest of her class. The
rank of each student is equal to 1 plus the number of students
with a strictly better grade. To compute this rank, we include all
students in our sample for whom we have grades, including the
students we had to drop for the final results. We then normalize
this measure by dividing by the number of students in each class.
In all our analyses, we standardize all nonbinary control
variables to have mean zero and a standard deviation of 1. This
facilitates the comparison of the magnitudes of the coefficients of
different control variables. Table A.I in the Online Appendix provides the mean and standard deviations of all our control
variables.
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Although the composition of classes may change from year to
year, this is in none of the four schools in our study based on
further ability tracking. Different subjects are typically taught
by different teachers.
Halfway through the six years of secondary school, at the end
of grade 9, students at the pre-university level have to choose one
of four study tracks:
(ES)
. the humanities-oriented track Culture & Society (CS)
Each student can select any track, though low grades in specific subjects may lead to teachers recommending other tracks.
Table I shows the subjects offered in each study track and the
number of teaching hours assigned to each subject during the last
three years of secondary school, grades 10–12. Mathematics is the
only subject taught at a different level in each track, whereby D is
the most advanced version followed by B, A, and C. The order of
math and science difficulty is therefore NT > NH > ES > CS.
There is a strong correlation between the study track a student
picks in secondary school and the choice of major in tertiary education. Most NT graduates go on to study a subject in science and
engineering, NH graduates often opt for health-related subjects,
ES graduates often choose a major in economics and business or
law, and most CS graduates choose a subject in the humanities,
social sciences, or law.8
We have two sources of information about the study track
choices of students in our sample. In the questionnaire, we
asked students which track they expected to choose. The schools
8. The tertiary education distribution by study track is as follows: Of students
in the NT track, 64% study science and engineering, 15% economics and business,
9% a subject in the humanities, and 7% health care. For NH students, 48% study in
health care, 18% in science and engineering, 9% in social sciences, and 8% in economics and business. For ES students, 46% study in economics and business, 20% in
law, 19% in social sciences, and 8% in humanities. For CS students 34% study in
social sciences, 30% in humanities, and 20% in law. For details see Table A.III in the
Online Appendix. Some studies actually restrict entry to certain tracks or courses
within tracks. For example, medical schools require NT or NH; to study math,
having taken at least Math B in high school is required. Source: http://www.cbs.
nl/nl-NL/menu/themas/onderwijs/publicaties/artikelen/archief/2007/2007-2193wm.htm (Statistics Netherlands); the data are from 2006.
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. the science-oriented track Nature & Technology (NT)
. the health-oriented track Nature & Health (NH)
. the social sciences–oriented track Economics & Society
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TABLE I
SUBJECTS AND TEACHING HOURS PER ACADEMIC TRACK
Nature & Health: NH
Mathematics B 600
Physics 480
Chemistry 440
Nature, life and technology 440
or IT 440
or biology 480
or mathematics D 440
Mathematics A 520
Biology 480
Chemistry 440
Nature, life and technology 440
or geography 440
or physics 480
Economics & Society: ES
Culture & Society: CS
Mathematics A 520
Economics 480
History 440
Management and organization 440
or geography 440
or social studies 440
or modern foreign language 480
Mathematics A or C 480
History 480
Art 480
or philosophy 480
or modern foreign language 480
or Greek or Latin 600
Geography 440
or social studies 440
or economics 480
Notes. The table lists the subjects per track and the number of teaching hours per subject during the
last three years of the pre-university level. In addition all students take the following non-track-specific
subjects: Dutch (480 hours), English (400), second foreign language, Latin or Greek (480), social studies
(120), general natural sciences (120), culture (160), sports (160). The students spend roughly half their
time on track-specific subjects and half on common subjects.
Source. Ministry of Education, Culture and Science of the Netherlands.
provided us with information about their actual choices made
several months later. Two of the four schools in our sample
allow students to pick combined tracks. Of the 173 students in
those two schools, 64 students choose the NT/NH combination
and 18 the ES/CS combination. In the NT/NH track, students
take Mathematics B but physics is not required. In the ES/CS
track, students replace one of the CS electives with the economics
course. As such, the combined tracks are somewhat in between
the pure tracks, though a little closer to NT and ES, respectively.
For the main analysis of this article we use for the students in
combined tracks the chosen track as stated in the questionnaire.9
9. All of the students who picked ES/CS chose ES or CS in the questionnaire.
All of the students who picked NT/NH chose NT or NH in the questionnaire with the
exception of one student who chose CS. We treat this student as a CS student when
using the stated track to place students that chose a combination track into ‘‘pure’’
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Nature & Technology: NT
GENDER, COMPETITIVENESS, AND CAREER CHOICES
1421
tracks. See Table A.II in the Online Appendix for the number of students who pick
NT/NH or ES/CS.
10. For these two analyses we drop an additional 20 students. These are all the
students for whom we have not received a final track choice from the schools and
used the questionnaire answer instead. The questionnaire, however, did not allow
for combination tracks.
11. Source: CBS (2010); the data are from students graduating in 2009.
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However, since one can argue that the NT/NH track is closer to
NT, and the ES/CS track closer to ES, we reestimate all regressions using this alternative definition of track choice in the
Online A
