European
Sociological
Review
Advance Access published June 5, 2009
European
Sociological
Review
VOLUME 00 NUMBER 00 2009 1–17
1
DOI:10.1093/esr/jcp031, available online at www.esr.oxfordjournals.org
Curricular Choice: A Test
of a Rational Choice Model
of Education
Limor Gabay-Egozi, Yossi Shavit and Meir Yaish
Rational choice theories of education view student’s educational decision as a sequence
of binary choices between options that entail long-term utility and options that reduce
short-term risk of failure. One of the best articulated models of educational choice asserts
that choice between alternative options is affected by students’ utility considerations, their
expectations regarding the odds of success or failure in alternative educational options,
and their motivation to avoid downward social mobility. We evaluated these propositions
using data on students’ curricular choices in Tel Aviv-Jaffa high schools. We found that
educational choice was affected by subjective utility and failure expectations, but not by
class maintenance motivations. Just as important, and contrary to the model’s main
assertion, educational inequality between social strata was not mediated by any of these
choice mechanisms. Finally, and importantly, about a fifth of the students in Tel Aviv-Jaffa
did not choose between long-term utility and short-term risks, but combined the two.
These students, the hedgers, combined the riskier scientific subjects that are expected to
yield long-term utility with social sciences and the humanities that reduce the risk of failure
in the short term, but are not expected to yield large long-term utilities. The hedgers,
moreover, were shown to be disproportionately female and drawn from disadvantaged
social strata. These results suggest that educational systems that allow multiple rather
than alternative choices may enhance the attainment of working-class youth because
they enable them to opt for long term utility while providing a safety-net in the form
of additional safer subjects.
Introduction
In recent years policy makers have advocated choice
in education as a means to enhance equality of
educational opportunity (Hayman et al., 1997; Plank
and Sykes, 2003). But as some argue, choice could
be a stratifying rather than an equalizing mechanism
in the educational attainment process. For example,
Ayalon (2006), who studied students in Israeli high
schools differing in the degree of choice available to
students, found that gender and socioeconomic
differences among them were more pronounced
where choice was prevalent. The implications of
educational choice for educational stratification therefore merit study.
One of the main debates in the literature on
educational stratification is encapsulated in the title
of Gambetta’s (1987) book: Were They Pushed or Did
They Jump? Push factors refer to the various structural
and social constraints that determine students’ educational attainment, while pull factors refer to choice.
These concepts echo Boudon’s (1974) distinction
ß The Author 2009. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: [email protected]
2
GABAY-EGOZI, SHAVIT AND YAISH
between primary and secondary effects. The former are
factors responsible for the association between social
origins and children’s scholastic ability and performance; the latter are factors that account
for educational choices controlling for class differences
in ability.
In recent years interest has grown in choice-related
explanations of educational stratification (e.g. Boudon,
1974; Gambetta, 1987; Goldthorpe, 1996, 1998; Erikson
and Jonsson, 1996; Breen and Goldthorpe, 1997;
Morgan, 1998, 2005). Rational-choice theory, for
example, assumes that individuals are conscious decision makers whose choices are influenced by a costsbenefit calculus (Hedström and Stern, 2008). The
growing interest in choice as an educational stratification mechanism has led scholars to develop theoretical
models to explain why children of similar abilities
but different class backgrounds are observed to make
different educational choices (Goldthorpe, 1996, 1998;
Erikson and Jonsson, 1996; Breen and Goldthorpe,
1997; Morgan, 1998, 2005). One of the most influential of these models is Breen and Goldthorpe’s
(henceforth BG) model of educational decision
making. Although this model was motivated by, and
developed to explain persistent inequality in class
based educational opportunity (cf. Blossfeld and
Shavit, 1993), it is ultimately a model of educational
choice (cf. Breen and Yaish, 2006; Stocké, 2007; Van
de Werfhorst and Hofstede, 2007), and we approach
it as such. Importantly, the BG model is theoretically
well developed, and a number of empirical studies
have found support for its predictions regarding
the statistical association between class and educational attainment patterns (Need and De Jong, 2000;
Davies et al., 2002; Breen and Yaish, 2006). Yet, the
model’s assumptions have rarely been tested directly
(cf. Stocké, 2007; Van de Werfhorst and Hofstede,
2007).
Choice and Educational
Stratification
Most studies on the determinants of inequality of
educational opportunity focus on two main sets of
variables: familial resources and characteristics of the
educational system. Among them are families’ economic resources (Duncan et al., 1998), cultural
resources (De Graaf et al., 2000), students’ track
placement (Shavit, 1984, 1990) and the curriculum
offered in schools (Apple, 1990; Oakes, 1990; Ayalon,
1995, 2002; Burkam et al., 1997). The privileged classes
benefit from a variety of material, cultural, and
cognitive assets which they mobilize to gain a persistent edge in the competition for desired credentials,
and the educational systems are often structured in
ways that benefit them further (e.g. Halsey et al., 1980;
Gamoran and Mare, 1989).
More recently, choice, rather than constraint, has
attracted increasing attention as a stratifying mechanism in the educational attainment process. Some
scholars have noted that despite the attenuation of
structural constraints on processes of educational stratification, inequality between social strata in educational
attainment persists. For example, Lucas argued that
despite the replacement of overt tracking by apparent
choice in course selection in America, de facto tracking
persisted, as did class and race-based inequality therein
(Lucas, 1999). Scholars interested in pull factors argue
that inequality is also due to processes involving choice
(e.g. Goldthorpe, 1996; Breen and Goldthorpe, 1997).
Several studies have shown that class differentials
in educational attainment persist even after controlling for push factors and scholastic performance (cf.
Erikson and Jonsson, 1996; Breen and Jonsson, 2000;
Need and De Jong, 2000; Davies et al., 2002; Breen
and Yaish, 2006). In England and Wales, for example,
class differentials in educational choice account for
about 25 per cent of inter-class educational inequality
(Erikson et al., 2005; Jackson et al., 2007).
The publication of BG’s paper (1997) stimulated
considerable research on rational choice processes
in education. Following Mare (1981), BG view the
educational attainment process as a sequence of
transition points at which students (and their families)
decide whether to drop out or to continue to the next
level in any of the available educational options.
At each transition point students evaluate each available option in terms of the expected utility to be
gained by successfully completing it, and in terms
of the risk of failing to do so.1 Utility is defined as
the degree to which completion would enhance the
student’s future occupational and economic attainment. Risk is defined as the odds of failing to complete
the prescribed course of study.
Importantly, the model implies that subjective utility
and risk are positively correlated. This is because
educational trajectories that are associated with high
utility expectations are also associated with high failure
expectations. Thus, decision makers face an inherent
dilemma between maximizing the former and minimizing the latter.
The theoretical centerpiece of BG’s model is the
so called Relative Risk Aversion mechanism (RRA).
Accordingly, there exists a threshold that determines
a student’s minimum acceptable level of educational
CURRICULAR CHOICE
attainment, which will guarantee entry to a class
position at least as good as that of their parents.
In other words, students set their threshold at a level
that will minimize the risk of downward mobility
(Breen and Goldthorpe, 1997, pp. 283–285). The
model further assumes that students from all social
classes are equally motivated to maintain their parental class position. The implications of the RRA
mechanism for educational choice, however, differ by
class background. For upper class children it implies
choosing the risky option because this alone will lead
them to the upper class. Conversely, working-class
children tend to choose less risky options because
they suffice for the attainment of working-class
occupations. When individuals reach an educational
threshold which they believe will gain them entry
into the same social class position as their parents’,
the costs of pursuing any further education (in terms
of real costs, earnings forgone, and the risk of failure
to complete) outweigh the utility of doing so (Breen
and Yaish, 2006). Hence, net of scholastic ability, the
RRA mechanism raises the class differentials in educational choice, and reproduces inequality of educational
attainment between classes.
In sum, BG’s model postulates that students’
educational choices are affected by: their beliefs about
the relative utility of available alternatives; their beliefs
about the relative odds of success or failure in each
alternative, and their motivation to avoid downward
mobility. Although BG recognize class differences in
scholastic ability and economic resources (Breen and
Goldthorpe, 1997, p. 283), they further assume that
net of ability, classes do not differ in relative risk
aversion motivation; future utility attributed to educational options, and success expectations within different educational trajectories. This study tests the
motivational mechanisms underlying the BG model
in the context of curricular choice of secondary
school students in Tel Aviv-Jaffa in Israel.
BG’s objective was theoretical and they did not
test their propositions empirically. In recent years
others have attempted to do so (Need and De Jong,
2000; Davies et al., 2002; Holm and Jaeger, 2005;
Breen and Yaish, 2006; Van de Werfhorst and
Hofstede, 2007; Stocké, 2007), with mixed results.
Most of these studies examine the model’s behavioural
predictions rather than its motivational mechanisms
directly. For example, Need and De Jong (2000)
analysed data on Dutch secondary school students.
They evaluated the RRA proposition by testing
whether students wish to attain an educational level
at least as high as their parents’. They did not explore
whether students’ level of educational aspiration was
3
motivated by a fear of class demotion, as assumed by
the Breen and Goldthorpe model. Similarly, in their
Danish study Davies et al. (2002) noted that if RRA
is correct, when students reach their parents’ level of
education, they gain no additional utility from any
further education. They argue that this means that
the impact of parental education should be strongest
on students’ transitions up to the educational level
attained by their parents themselves, and would decline
thereafter. Again, this is an indirect derivative of
RRA. Finally, Breen and Yaish (2006) tested behavioural predictions of the BG model in England and
Wales but did not measure directly any of the
motivational variables suggested by the model.
Only two studies tested the actual motivational
processes underlying the model. Using survey data
taken from secondary school pupils in Amsterdam,
Van de Werfhorst and Hofstede (2007) tested whether,
and to what extent, family cultural capital and relative
risk aversion explained the educational performance and ambitions of secondary school students in
Amsterdam. Unlike previous studies, they measured
RRA directly by asking students to indicate how closely
they agreed with a series of six statements such as
‘I find it important to achieve a better job than my
parents’ (p. 399). The reliability of the composite
measure was 0.77. They found that the effects of social
origin on school performance were largely mediated
by cultural capital rather than by RRA, while RRA
rather than cultural capital affected educational ambitions. Hence, cultural reproduction theory provides
an important explanation for class inequalities in
early school performance, whereas ambitions are
affected by concerns with mobility, as suggested by
Breen and Goldthorpe. By measuring RRA directly,
Van de Werfhorst and Hofstede advanced research
on rational choice in education substantially, but
they did not measure the model’s other motivational components. Specifically they did not measure
the subjective utility and the failure expectations
that students associate with the alternative options
they face.
Utilizing data on parents of fourth graders in
Rhineland–Palatinate in Germany, Stocké (2007), provided the most comprehensive test to date of the
motivational mechanisms proposed by the BG model:
subjective costs, success probabilities, and the status
maintenance motivation (RRA). He found that net of
ability, parents’ subjective beliefs about their childrens’
odds of success, as well as RRA (but see below),
strongly affected their educational choices. However,
contrary to BG’s expectation, he did not find that these
4
GABAY-EGOZI, SHAVIT AND YAISH
mechanisms mediated class inequality in secondary
school choice.
Stocké measured RRA using two questionnaire
items: (i) ‘For many parents, the occupational
future of their children is particularly important.
Would you please tell me how strongly it would
bother you if your child reached a less prestigious
occupation than yourself?’ (ii) ‘Please think about
what your child will be able to reach in future with
different educational degrees. As how likely do you
regard it that your child, endowed with the different
educational degrees, will be able to reach occupationally at least what you reached?’ (asked for all
three degrees) (verbatim, Stocké, 2007, p. 517 notes 4
and 5).
In our view, the first question measures RRA and
is consistent with the measure employed by Van de
Werfhorst and Hofstede (2007). The second question
would appear to measure the labor market returns
that parents attribute to various educational credentials. In other words, it measures utility expectations
rather than RRA. We note in Stocké’s findings that
while the measure of subjective utility affected educational choices RRA did not.
The aim of our study is to provide a comprehensive and direct test of the motivational mechanisms
underlying the BG model. Like Stocké, we simultaneously test whether subjective utility, failure expectations and RRA affect educational choices. However,
we employ Van de Werfhorst and Hofstede’s measure
of RRA. We test the following hypotheses:
Hypothesis I: Students’ educational choices are
affected by their subjective utility, namely their beliefs
as to the likely returns on the various educational
options.
Hypothesis II: Students’ educational choices are driven by
their beliefs as to their own odds of success or failure in
the alternative educational options.
Hypothesis III: Educational decisions are motivated
by individuals’ apprehension of downward class mobility
(i.e. the relative risk aversion).
Hypothesis IV: The three motivational mechanisms
discussed above mediate socio-economic inequality in
educational choices.
Hypothesis V: Students of different socio-economic backgrounds (e.g. class) do not differ in any of the following:
(i) their relative risk aversion; (ii) their subjective future
utility attributed to educational options; and (iii) net of
ability, their success expectations in the different
educational options.
The Setting, Data, and
Variables
The Setting
We tested these hypotheses with data on the choice
of major subjects by secondary school students in Tel
Aviv-Jaffa in Israel. Our sample was ninth and 10th
graders who were about to choose a major for their
matriculation examinations. The choice of advanced
courses and examinations is an important junction in
the socioeconomic life course of young Israelis. At age
12, after a year in pre-school and 6 years in primary
school, Israeli children enter middle schools where
they spend grades seven, eight and nine. This spell
is followed by upper secondary school in grades 10
through 12, where students are prepared for the
matriculation examinations leading to the award of
the matriculation certificate (bagrut), which is required
for higher education.2
The secondary school curriculum is composed of
compulsory and elective subjects, such that students
can choose from a range of advanced academic courses
in different subjects. The various subjects are offered
at different levels, ranging from one to five units, and
students must pass exams in subjects totaling at least
21 units to earn the matriculation certificate.3
Seven subjects are compulsory (English, Mathematics, Civic Studies, Bible Studies, History, Literature,
and Hebrew language—in the Hebrew school sector),
and must be taken at either a basic (1–3 units) or an
advanced (4 or 5 units) level. Students must also
take at least one elective major at an advanced level.
Advanced majors are usually chosen during the spring
term of tenth grade (although in one of the schools
we studied students made the choice as early as ninth
grade).
The elective majors are not stratified formally, and
they should in principle all open meaningful educational opportunities. Nevertheless, informal stratification of these majors does exist, such that students
from an advantaged social background tend to specialize in the sciences, while those from a lesser social
background tend to specialize in the humanities and
the social sciences (Ayalon, 1994, 1995; Ayalon and
Yogev, 1997).
Admission into higher education predicates a
matriculation certificate, and the more prestigious
departments and institutions of higher education set
several additional requirements: (i) advanced English
(at least 4 units); (ii) a high matriculation grade
point average; (iii) some technical and prestigious
CURRICULAR CHOICE
departments (e.g. engineering and the sciences) and
institutions (e.g. the Technion) specifically require
higher matriculation grades in advanced math and
sciences. The elaborate formula employed by universities also tends to favour applicants who have passed
more than one advanced level examination.
In recent cohorts, 54 per cent of students obtained
the matriculation diploma (Ministry of Education,
2007). Of those who sat for matriculation examinations, about 65 per cent passed all the examinations necessary for the diploma. Mathematics and
English are generally regarded difficult examinations,
and unlike other difficult subjects (e.g. Physics,
Chemistry, and Computer Science) they are compulsory. In an effort to raise students’ success rates in
Mathematics schools evaluate their performance early
on and assign them to unit levels as early as ninth
grade. Once a student has been placed on a level he
or she has little further choice in the matter except
to opt for a lower level.
Data
In the spring term of 2006 we collected data in
four public secondary schools in Tel Aviv-Jaffa. With
a population of nearly 400,000, Tel Aviv-Jaffa is
Israel’s largest city and is the core of the Tel Aviv
Metropolitan area with its population of about three
million (Tel Aviv-Jaffa, 2007). Tel Aviv-Jaffa has nine
administrative districts, which are fairly homogeneous
socio-economically (Tel Aviv-Jaffa, 2006). Southern
districts are typically working class while northern ones
are middle class.
We restricted our sample to Hebrew academic
secular high schools in Tel Aviv-Jaffa. We excluded
vocational, religious, Arab and two magnet schools
because they differ in their curricular menu and
emphasis. Vocational schools offer a limited menu
of academic subjects with an abundance of technical
ones, Jewish religious schools emphasize religious
studies and encourage their stronger students to take
these rather than the sciences, and Arab schools
offer a somewhat different curriculum than Hebrew
schools. Specifically, they teach different languages
(Hebrew and English as foreign languages rather than
English, Arabic and French), put less emphasis on
Bible studies, and teach some Koran instead. One
of the two magnet schools specializes in the arts
and the other caters primarily to Russian immigrant
children and offers a mix of Russian and Hebrew
curricula. In the interest of simplification, we chose to
restrict the sample to schools which share a common
curriculum.
5
Of the remaining seven schools in the city we
sampled two in working-class districts and two in
middle-class districts. Fortunately, all four school
principals cooperated with the study and allowed
us access to their schools. The four schools offer
students similar core curricula, as well as the option
to choose among the following advanced subjects:
Physics, Chemistry, Computer Science, Biology,
Economics, Social Sciences, History, Literature, and
Communication.
We distributed self-administered questionnaires
among 10th graders attending three of the schools
and among both tenth and ninth graders in the fourth
(because students at that school where encouraged to
choose an advanced major as of ninth grade). In total,
we collected data in 28 classes and obtained 683
completed student questionnaires. Of these, 18 per
cent were incomplete and were excluded from the
analysis, which accordingly covered 563 cases.4
Like Stocké’s (2007) and Van de Werfhorst and
Hofstede’s (2007) studies before us, our sample too
is local rather than nationally representative. However,
our data include good measurements of the central
theoretical concepts under investigation, including
relative risk aversion, utility and failure expectations,
as well as social origins and other control variables
(see below). While acknowledging the limited generalizability of our findings beyond Tel Aviv-Jaffa, we
believe that our data are suitable to the task at hand.
If the BG model holds as a general theory, it should
help explain socio-economic differences in educational
attainment in Tel Aviv and in other stratified communities. To anticipate, the results of our study
together with those of Stocké (2007) and Van de
Werfhorst and Hofstede (2007), add up to a rather
consistent message with respect to the BG model.
The Dependent Variables
We study the determinants of two types of curricular
choices that students make in anticipation of their
matriculation examinations. Respondents were presented with a list of electives that were available in
their school, and were asked to indicate whether or
not they intend to take each of them at an advanced
level. The distribution of choices shown in Table 1
indicates that boys tended to choose Physics, Computers, Economics and other Social Sciences, while
girls are more inclined to take the Social Sciences,
Communications, Economics, and Biology.
Our first dependent variable distinguishes between
those who chose to take English at an advanced
(4 or 5 units), rather than at a basic level (3 units).
6
GABAY-EGOZI, SHAVIT AND YAISH
Table 1 Per cent distributions of subject choice by
gender
Advanced subjects
English
Physics
Chemistry
Computers
Biology
Economics
Social Science
History
Literature
Communications
Boys
Girls
Total
93
33
22
28
19
30
30
16
12
14
92
12
17
13
20
25
52
15
14
24
93
23
19
20
20
27
41
16
13
19
Recall that admission into higher education predicates
a matriculation certificate with advanced English.
Thus, the choice of advanced English has very
clear stratifying consequences. Given the importance
of advanced English for university enrolment, it is not
surprising to see in Table 1 that 93 per cent of our
respondents intended to take English at that level.5
Our second dependent variable concerns the
choices that students make among the elective subjects.
Following common practice in the literature on school
curriculum (Apple, 1990; Kliebard, 1992; Kamens
and Benavot, 1992) we form the nine elective subjects
into two groups: the sciences (Physics, Chemistry,
Computers, and Biology) and the humanities and
social sciences (Economics, Social Sciences, History,
Literature, and Communications). The sciences (hereafter hard subjects) are generally regarded as more
prestigious than the humanities and social sciences
(hereafter soft subjects) and are considered more
demanding (Ayalon and Yogev, 1997).
In Table 2 we compare the three curricular choices
in terms of perceived utility and risk of failure. We
measured perceived utility by asking respondents
the following question: ‘For each subject listed below,
in your opinion, if a student succeeds in this subject
at the five-unit level, what are his or her chances
of admission to a university?’ [scale: 1 (‘not high at
all’) to 5 (‘very good’)]. Perception of risk is defined
as students’ subjective belief that they would gain a
low grade (64 or less on a 100-point scale) in a subject
were they to take it at an advanced level. Students’
perception of risk in the hard or the soft subjects is
then refers to the proportion of those who thought
that their grade, in each subject within the hard and
the soft subjects, would be low. As seen in Table 2,
on average, (across subjects) students perceived the
hard electives as associated with both higher utility and
Table 2 Means (and standard deviations) of
perceived utility and perceived risk in advanced
English, and in hard and soft advanced Electives
Advanced Hard
Soft
English subjects subjects
Perceived utility in
university admission
Perceived risk of failure
4.44
(0.79)
0.03
(0.16)
4.09
(0.78)
0.15
(0.26)
3.50
(0.80)
0.06
(0.15)
Note: Asterisks indicate significant (P50.05) differences between the
means of the hard and the soft clusters.
a higher risk of failure than the soft electives.6 The
utility that students associate with advanced English
is higher than the mean utility associated with either
the hard or the soft electives. In addition, the risk of
failure they associate with advanced English is comparable to that associated with the soft subjects which
are perceived as relatively safe.
As indicated earlier, although students are only
required to take one advanced subject to be eligible for
a matriculation certificate, there are strong incentives
for taking more because students can then discount
the advanced subject in which they scored lowest. The
great majority (96 per cent) of students in our sample
did indicate that they intended to take two or more
advanced electives. Although most students tended
to pick subjects within the two clusters of electives,
some intended to take a mix of both hard and soft
subjects. Thus, our second dependent variable consists
of a three-category classification representing choice
of advanced subjects: (i) hard subjects (Physics,
Chemistry, Computer Science, and Biology); (ii) soft
subjects (Economics, Social Sciences, History,
Literature and Communication); and, (iii) a mixture
of both. As seen in Table 3, 36 per cent of the students
in our sample intended to take advanced subjects in
the sciences alone, 42 per cent intended to take only
soft subjects, and 22 per cent intended to take a mix
of the two.
Thus, about a third of students who intended to take
a hard elective seemed to hedge their risk of failure
by taking a soft elective too. This strategy seems quite
reasonable given that students are only required to
complete a single advanced elective course and can discount an extra course in which they might do poorly.
Independent Variables
As noted, the rational choice model of education refers
to three motivational variables (i.e. secondary factors)
CURRICULAR CHOICE
Table 3 Per cent distribution of elective choice
clusters
Choice categories
Hard subjects only
Physics, chemistry, computers
and biology
Soft subjects only
Social science, economics,
history, literature and
communications
Mix of hard and soft subjects
Total %
(N)
Percentage
36
42
22
100
(563)
affecting educational choice: relative risk aversion,
and, for each educational option, beliefs about future
utilities and beliefs about failure expectation.
We measured Relative Risk Aversion with the following questions: ‘to what extent do you agree with
each of the following statements? [scale: 1 (‘strongly
disagree’) to 5 (‘strongly agree’)]:
(a) It is important for me to work in an occupation that is better than the occupations of my
parents.
(b) It is important for me that my salary be at
least equal to the level of my parents’ salary.
(c) My parents would not be satisfied if I were to
work at a lower occupation than theirs.
(d) I would like to attain a social class on a level
at least equal to my parents’.
(e) I am concerned that I might reach a social
class that is lower than the social class of my
parents.
(f) It is important for me to attain a higher level
of education than the level of education of my
parents’.
This measure is based on Van de Werfhorst and
Hofstede’s six-scale items measurement of Relative
Risk Aversion (2007). Items (ii) to (v) represent
students’ concern with downward mobility whereas
items (i) and (vi) represent their attitude to upward
mobility. Consistent with Van de Werfhorst and
Hofstede’s measurement, factor analysis of these six
items found them to load on a single factor, which
we interpret to represent the class maintenance motivation (i.e. relative risk aversion). This factor
accounted for 63 per cent of the total variance in
the test items, and the factor loadings of five out of the
7
six items were above 0.66 (item e ¼ 0.47). The
relatively low loading of item e is probably related
to the translation of the original statement, and was
therefore removed from the analysis due to translation
issue.7 The five-item factor accounted for 72 per cent
of the total variance, and its reliability was identical
to that obtained by Werfhorst and Hofstede (0.78).
Beliefs about future utilities were measured by the
question cited earlier concerning the subjective odds
that a student (i.e. any student) who succeeds in
each subject will enter university. For each student we
computed the mean perception of success associated
with the hard subjects and the soft subjects. The ratio
of the former to the latter was then our measure of
the relative utility that students attribute to the hard
subjects as against the soft subjects.
Relative Failure Expectation was measured as the
difference between the proportion of hard and soft
subjects respectively, in which the student expected to
obtain a low grade (64 or less). High values represent
apprehension of the hard subjects.
Control Variables
Following well documented gender differences in
curricular choices (e.g. Ayalon, 1995; Bradley, 2000),
we include a dummy variable for gender. Against the
relatively constancy of class differentials in educational
attainment, in nearly all advanced societies gender
differentials in levels of educational attainment
(favouring males over females) have declined sharply
and even reversed since the 1970s. BG attribute the
decline to perceptions of rising occupational and
economic returns to women’s education (Breen and
Goldthorpe, 1997, pp. 296–297). In a footnote, they
also refer to gender differences in subject choice (Breen
and Goldthorpe, 1997, p. 303, note 10) and suggest
that since women expect their careers to be interrupted
by family obligations, they are prefer subjects which
lead to occupations that afford flexible working
arrangements. To anticipate, and consistent with
other studies on gender differences in curricular
choices (e.g. Ayalon, 1995; Bradley, 2000), we find
boys were more likely than girls to take hard rather
than soft electives.
We indicate social origin by two variables: parental
education and family economic resources. We began
our analysis with class as an indicator of social origin
but soon realized that student reports of father’s
occupation and of his employment status (which are
the building blocks of the class schema) are highly
unreliable, therefore, we prefer to represent social
origins with parental education and their economic
8
GABAY-EGOZI, SHAVIT AND YAISH
resources. Parental education was measured as the
highest qualification obtained by either father or
mother. It was measured by a six-category classification which maintains a clear hierarchical order. In
addition, students were also asked to provide information on the availability of a variety of durable goods
in the home (such as air-conditioning, computer,
dishwasher, car, etc.). Economic resources were measured as the sum of available items in the parental
household.8
Students’ scholastic performance was indicated by
their grades, measured as the mean of self-reported
grades in Hebrew, English, and Mathematics on
the most recent report card. Grouping was the level
at which a student considered himself or herself in
10-grade Mathematics. It was coded as a dummy
variable indicating advanced placement. As noted
earlier, group placement is largely determined by
students’ prior performance in Mathematics and once
placement has been made it is an evident push factor
which affects subsequent curricular choice. Students
placed in low level Mathematics are unlikely to choose
advanced hard subjects.
Finally, we controlled for the number of subjects
the student intended to take at an advanced level,
because the odds of taking a mixture of both hard
and soft subject should increase, the more subjects’
students take. The inclusion of this variable in our
models did not substantially alter the effects of any
of the other variables.
Empirical Findings
Descriptive Analysis
Table 4 presents descriptive statistics of the independent variables by curricular choices: the choice of
advanced and basic English, and the choice among
the three categories of advanced matriculation electives. Consistent with previous studies in Israel (Ayalon
and Yogev, 1997; Ayalon, 2003; Katz-Gerro and Yaish,
2003) and elsewhere (Ma and Willms, 1999; Bradley,
2000), we found that boys were more likely than girls
to take hard rather than soft subjects.
As expected, we also see that students who chose
advanced English and the more demanding and
rewarding sciences were disproportionately drawn
from a more advantaged social background than
those who chose the less demanding subjects (cf.
Oakes et al., 1992; for Israel see Ayalon, 1994, 1995;
Ayalon and Yogev, 1997). More importantly, on
average, students who chose a mix of hard and
soft subjects (the hedgers) came from lower strata
than those who selected either pure category of
subjects (hard or soft ones). This is an interesting
result that we shall return to later.
Regarding scholastic performance and grouping,
the entries in Table 4 reveal that students who chose
hard subjects won higher grades than those who
selected the mixed or the soft subjects. Similarly, those
who chose hard subjects were more likely to have
been placed in an advanced group in math. The same
apply for the differences between those who choose
advanced English compared with their counterparts
who intended to take the subject at a basic level.
Turning to the motivational factors, we see that
those who selected advanced English evinced more
confidence in their ability to succeed in the subject
than those who chose it at a basic level. There are
no significant differences in Relative Risk Aversion
and in utility expectations between those who chose
advanced and basic English. As for the three clusters
of electives, we see that students who chose mixed
subjects exhibited the highest degree of relative risk
aversion, suggesting that those who are concerned with
class maintenance hedge their risk by adopting a mixed
strategy. The hard (and risky) subjects increase the
expected long-term utility of the mix and if one does
poorly in these, success in the soft subjects averts
short-term failure. Thus, choosing a mix of electives
could minimize exposure to unwanted short-term
risks, while allowing the possibility of long-term
success in higher education and in the labour market.
Expectedly, those who selected hard subjects hold
stronger beliefs about their positive utility (i.e. admission to university) than those who chose the other
combinations, and those who selected soft subjects
showed the lowest confidence in their ability to gain
academic success in the hard subjects. Also expectedly,
the more advanced subjects a student chose, the
more likely he or she was to choose a combination
of mixed subjects.
The Effects of Social Background on
Performance and the Motivational Factors
Before testing whether the three motivational variables
explain educational choices, we estimated, as shown in
Table 5, the effect of social background on performance, and that of social background and performance
on status maintenance motivation, utility considerations, and failure expectations. Consistent with previous research, social background exerted positive
effects on scholastic performance. Furthermore, boys
and girls did not differ in their performance but they
did differ significantly in the motivational factors.9
CURRICULAR CHOICE
9
Table 4 Means (and standard errors) of key variables by choice categories
Variables
Social background
Sex (boys)
Parental education
Economic resources
Scholastic performance & grouping
Scholastic performance
Grouping in Math (advanced level)
Motivational factors
Relative risk aversion
English utility
Relative utility
(hard versus soft subjects)
English failure expectation
Relative failure expectation
(hard versus soft subjects)
All
English
basic
(3)
Hard
subjects
(4)
Mixed
(1)
English
advanced
(2)
(5)
Soft
subjects
(6)
0.51
(0.50)
4.67
(1.56)
6.17
(2.21)
0.51
(0.50)
4.73
(1.53)
6.24
(2.18)
0.48
(0.51)
3.86
(1.65)
5.39
(2.23)
0.63
(0.49)
5.10
(1.31)
6.59
(1.86)
0.55
(0.50)
4.17
(1.67)
5.39
(2.23)
0.39
(0.49)
4.57
(1.60)
6.23
(2.36)
82.81
(11.12)
0.78
(0.41)
83.44
(10.95)
0.80
(0.40)
74.75
(10.10)
0.58
(0.50)
87.40
(9.35)
0.94
(0.24)
78.91
(11.82)
0.74
(0.44)
80.90
(10.82)
0.68
(0.47)
0.10
(1.00)
4.43
(0.79)
1.20
(0.28)
0.05
(0.22)
0.09
(0.23)
0.10
(1.00)
4.44
(0.79)
–
0.11
(1.03)
4.28
(0.79)
–
0.19
(1.05)
–
0.18
(0.95)
–
0.18
(0.96)
–
0.03
(0.16)
—
0.34
(0.48)
—
1.35
(0.31)
—
1.14
(0.23)
—
1.11
(0.22)
—
0.01
(0.14)
0.07
(0.21)
0.17
(0.27)
—
—
1.37
(0.62)
2.95
(1.09)
1.88
(0.74)
189
144
230
Number of chosen subjects
1.94
(0.99)
Number of cases
563
517
46
Note: Asterisks in Column 2 indicate statistically significant (P50.05) differences between the means shown in Columns 2 and 3. Asterisks in Column 4
indicate statistically significant (P50.05) differences between the means shown in Columns 4 and 5. Asterisks in Column 5 indicate statistically
significant (P50.05) differences between the means shown in Columns 5 and 6. Asterisks in Column 6 indicate statistically significant (P50.05)
differences between the means shown in Columns 4 and 6.
The last three columns of Table 5 test Hypothesis IV,
which postulated that when scholastic performance
is controlled, no socioeconomic differences should
appear in any of the three motivational factors. The
results indicate that students from advantaged social
background are less concerned with maintaining their
parental socioeconomic positions. Thus, with regard
to the Relative Risk Aversion mechanism, our finding,
consistent with that of Stocké (2007), refutes one of
the core assumptions of the BG model, namely that
all social strata are equally concerned about status
maintenance. A plausible explanation for the negative
association between relative risk aversion and social
background might be related to the degree of confidence that people of different social backgrounds
have in their social position. Students from advantaged
social strata may rely on their family to supply, if
necessary, the buffers against downward mobility, or
many of them may even take their position as given
and are therefore less concerned with status maintenance. By contrast, students from less privileged
families may suffer from status anxiety and fear of
status loss. Thus, to the extent that RRA has a positive
effect on the choice of high utility subjects, it may
actually attenuate, rather than reproduce inequality
between generations. To anticipate, we shall soon
show that RRA did not affect educational choice and
does not mediate the effects of origins on educational
choice. Contrary to BG’s assumption, but consistent
with Stocké’s (2007) results, we found significant
socio-economic differences in beliefs about relative
utility. No such differences appeared in failure
10
GABAY-EGOZI, SHAVIT AND YAISH
Table 5 OLS regression coefficients (and standard errors) on performance and motivational factors
Independent
variables
Sex (boys)
Social background
Parental education
Economic resources
Scholastic
performance
Relative
risk aversion
Relative utility
of sciences
Relative failure expectations
in sciences
0.77
(0.83)
0.29
(0.08)
0.07
(0.02)
0.07
(0.02)
1.68
(0.30)
1.05
(0.21)
0.16
(0.03)
0.08
(0.02)
0.02
(0.01)
0.01
(0.01)
0.01
(0.01)
0.01
(0.01)
68.89
(1.54)
0.14
0.00
(0.00)
0.97
(0.29)
0.14
0.004
(0.00)
0.66
(0.08)
0.09
0.003
(0.00)
0.38
(0.07)
0.05
Scholastic performance
Scholastic performance
Constant
Adjusted R2
Significance level: P50.05.
expectations, which is consist with the BG model and
Hypothesis IV.
Primary and Secondary Effects on Choice
of Advanced Subjects
Reverting to the first three hypotheses outlined at the
outset, we asked: are students’ educational choices
affected by their apprehension of downward class
mobility (i.e. relative risk aversion), by their beliefs
about the likely future returns on the various choices,
and by their beliefs about their own odds of academic
success or failure in the alternative trajectories? We
also asked whether these secondary factors mediated
the effects of social origins in educational choice, that
is: is choice a stratifying mechanism?
To answer these questions we first estimated a
binary logit model on the choice between advanced
and basic levels in English. We then examined,
by applying a multinomial logit model, the determinants of choice between the three curricular clusters
identified earlier (i.e. hard, soft, and mixed subjects).
These models included the following independent
variables: parental education, family’s economic
resources, scholastic performance, grouping in math,
relative risk aversion, subjective utility (of advanced
English or of the hard as against the soft advanced
subjects), and failure expectation (in English or in the
hard as against the soft subjects). We also controlled
for sex in our models and in the multinomial model
also for the number of chosen subjects. Table 6
presets the parameter estimates of the odds of choosing advanced English. Tables 7 and 8 present the
parameter estimates of the contrasts between hard
versus soft subjects (Table 7) and between hard
subjects and a mix of hard and soft subjects
(Table 8).10 To facilitate comparison of the effects
of the continuous independent variables we standardized them to a mean of zero and a unit standard
deviation. Dummy variables retained their original
metric.
In Model I in these tables, we regressed educational
choice on social background alone. In Models II and
III we added performance and grouping to each model
respectively. These two models were meant to assess
the extent to which performance and grouping mediate the effects of social background. In Model IV we
regressed educational choice on the three motivational
factors alone, and Model V included all variables
simultaneously.11
Advanced versus basic English
Model I in Table 6 indicates that students with
educated parents were more likely to opt for advanced
English. When we added performance to this model
(Model II) the effect of parental education was
reduced, and adding grouping in Math (Model III)
rendered the effect of parental education even weaker
and statistically insignificant. High achieving students
and those who had been assigned to an advanced
grouping in math were more likely to choose English
at advanced level. Importantly, the choice between
advanced or basic level English was largely determined
by performance and grouping, and no effects of social
background remained to be explained by secondary
factors.
CURRICULAR CHOICE
11
Table 6 The effects (and standard errors) of primary and motivational factors on the odds of choosing
English at an advanced rather than basic level
Independent variables
Sex (boys)
Social background
Parental education
Economic resources
I
II
III
IV
V
0.14
(0.32)
0.23
(0.32)
0.17
(0.33)
0.18
(0.34)
0.15
(0.35)
0.38
(0.16)
0.25
(0.18)
0.28
(0.17)
0.10
(0.19)
0.22
(0.17)
0.07
(0.19)
0.12
(0.20)
0.08
(0.20)
0.54
(0.16)
0.51
(0.16)
0.62
(0.35)
0.32
(0.17)
0.58
(0.38)
Scholastic performance & grouping
Scholastic performance
Grouping in Math (advanced level)
Motivational factors
Relative risk aversion
Utility in English
Failure expectation in English
Constant
Cox and Snell R2
2.61
(0.23)
0.022
2.58
(0.23)
0.041
2.16
(0.32)
0.045
0.09
(0.17)
0.04
(0.16)
2.90
(0.42)
2.87
(0.25)
0.069
0.23
(0.19)
0.07
(0.16)
2.22
(0.48)
2.47
(0.36)
0.083
Significance levels: P50.05; 0.055P50.09.
Table 7 The effects (and standard errors) of primary and motivational factors on the odds of choosing hard
rather than soft clusters of electives
Independent variables
Sex (boys)
Social background
Parental education
Economic resources
I
II
III
IV
V
1.23
(0.21)
1.34
(0.22)
1.31
(0.23)
1.02
(0.24)
1.14
(0.26)
0.39
(0.12)
0.07
(0.12)
0.24
(0.13)
0.20
(0.13)
0.09
(0.13)
0.19
(0.13)
0.12
(0.15)
0.22
(0.15)
0.82
(0.14)
0.76
(0.15)
1.91
(0.34)
0.55
(0.16)
1.55
(0.40)
Scholastic performance & grouping
Scholastic performance
Grouping in Math (advanced level)
Motivational factors
Relative risk aversion
Relative utility
(hard versus soft subjects)
Relative failure Expectation
(hard versus soft subjects)
Number of subjects chosen
Constant
Cox and Snell R2
Significance level: P50.05.
1.41
(0.19)
1.39
(0.18)
0.386
1.41
(0.19)
1.55
(0.19)
0.425
1.55
(0.21)
3.15
(0.37)
0.460
0.02
(0.12)
1.19
(0.16)
1.23
(0.18)
1.70
(0.22)
1.59
(0.22)
0.507
0.14
(0.13)
1.05
(0.16)
1.06
(0.19)
1.84
(0.24)
3.07
(0.43)
0.540
12
GABAY-EGOZI, SHAVIT AND YAISH
Table 8 The effects (and standard errors) of primary and motivational factors on the odds of choosing hard
rather than mixed clusters of electives
Independent variables
Sex (boys)
Social background
Parental education
Economic resources
I
II
III
IV
V
0.81
(0.28)
0.92
(0.29)
0.92
(0.30)
0.74
(0.30)
0.87
(0.31)
0.35
(0.16)
0.10
(0.16)
0.21
(0.16)
0.03
(0.16)
0.13
(0.16)
0.01
(0.17)
0.17
(0.18)
0.05
(0.18)
0.81
(0.17)
0.77
(0.17)
1.31
(0.43)
0.60
(0.19)
1.11
(0.47)
Scholastic performance & grouping
Scholastic Performance
Grouping in Math (advanced level)
Motivational factors
Relative risk aversion
Relative utility
(hard versus soft subjects)
Relative failure Expectation
(hard versus soft subjects)
Number of subjects chosen
Constant
2.63
(0.23)
0.18
(0.23)
2.62
(0.23)
0.35
(0.24)
2.76
(0.24)
1.51
(0.45)
0.14
(0.15)
0.92
(0.18)
0.84
(0.20)
2.93
(0.25)
0.55
(0.26)
0.03
(0.17)
0.77
(0.18)
0.73
(0.22)
3.03
(0.27)
1.68
(0.50)
Significance level: P50.05.
In Model IV the analysis focused on the gross effects
of the three secondary factors. The effects of relative
risk aversion and utility consideration were very small
and statistically insignificant. By contrast, students
who hold low failure expectations in advanced English
were more likely to select advanced English. When, in
Model V, we also controlled for social background,
performance and grouping in Math, the net effects of
failure expectation was slightly reduced but maintained
its statistical power (compared with Model IV).
In sum, the main determinants of choice between
advanced and basic English were scholastic performance, grouping in math, and failure expectations
in English, but not relative risk aversion or utility
perceptions. Moreover, although the odds of choosing
advanced English are affected by students’ subjective
failure expectations, they are largely determined by
previous performance and no effects of social background remained to be explained by secondary factors.
Hard versus soft subjects
The analysis of choice between hard and soft electives
yields similar results to those obtained above. Model I
in Table 7 indicates that males, and students with
educated parents, were more likely to opt for hard
than for soft subjects. When we added performance
to this model (Model II) the effect of parental education was reduced, and adding grouping (Model III)
rendered the effect of parental education even
weaker and statistically insignificant. Strong students
and those who had been assigned to an advanced
grouping in math were more likely to choose hard
rather than soft subjects. Importantly, the choice
between the hard and soft clusters of subjects was
largely determined by performance and grouping,
and no effects of social background remained to be
explained by secondary factors.
In Model IV the analysis focused on the gross
effects of the three secondary factors. The relative risk
aversion mechanism was very small and statistically
insignificant. By contrast, the effects of both relative
utility and relative failure expectation were significant,
and in the expected directions: students who expected
relatively higher utility from the hard subjects and
hold low failure expectations in them were more likely
to select hard than soft subjects. When, in Model V,
we also controlled for social background, performance
and grouping the net effects of relative utility and
CURRICULAR CHOICE
relative failure expectation were slightly reduced but
maintained their statistical power (compared with
Model IV). Again, the coefficient for relative risk
aversion in Model V was not significant statistically.
Finally, we found that the odds of choosing hard
rather than soft subjects declined as the number of
subject selections increased. Evidently, students who
choose the hard sciences are more likely than those
who choose soft subjects to concentrate on the
relatively difficult task at hand and avoid spreading
their attention over several subjects.
Hard versus a mix of subjects
In Table 8 we present the effects of the independent
variables on the log odds of choosing hard rather than
mixed subjects. These results, too, are quite similar
to those reported in Tables 6 and 7. Boys were more
likely to choose hard subject than a mix of hard
and soft ones; the socioeconomic differences in these
odds were fully accounted for by performance and
grouping; the effects of RRA were statistically insignificant; those of perceived relative utility and relative
risk of failure were statistically significant and in the
expected directions, and the secondary factors did not
mediate socio-economic differences in choice. Finally,
the odds of taking a mix of subjects increased
substantially as the number of selections increased.
In sum, the main determinants of choice among
hard subjects, soft subjects and a mix of the two were
scholastic performance, grouping in math, relative
failure expectations, and subjective utility, but not
relative risk aversion. Just as important, the three
motivational factors posited by BG’s Rational Action
Theory did not seem to mediate the effects of social
background.
Thus far, then, our results lead to the same conclusion; secondary effects are absent with respect to
educational inequality between social strata in curricula
choices within the Israeli secondary system.
Summary and Discussion
It is generally recognized that students from privileged
social backgrounds progress farther in the educational
system than those from less advantageous origins.
Rational choice models of education, in particular
the BG model, attribute these inequalities, in part, to
differences in educational choice made by individuals
from different social strata. According to the model,
the educational process consists of a sequence of
branching points at which students are faced with
binary choices. On the one hand, the high road is
13
demanding and risky in the short term, but can lead
to rewarding life-course outcomes in the long term.
On the other hand, the low road is less demanding
and safer in the short term, but can offer only limited
long-term rewards. That is, long-term utility and
short-term risks are positively associated. It is further
assumed that on reaching educational decision points
students consider their success probability in each
alternative, its utility, defined as the probability that it
will lead to desirable occupational outcomes, and the
extent to which it will protect them from downward
social mobility. The desire to avoid downward mobility
gives rise to class differentials in educational choice.
Students from the upper strata are impelled to choose
the high road, which is necessary if they are to
maintain their social position, while those of lower
strata choose the low road, which is sufficient to
maintain their social position. Finally, the BG model
postulates that social reproduction is mediated, in part,
by differential educational choice.
In this paper we put these propositions to an empirical test. Specifically, we examined the role of utility
considerations, of failure expectations in education,
and of the class maintenance motivation in shaping
educational choice. We examined the validity of these
assumptions by analysing recently collected data from
a purpose-designed survey of Tel Aviv-Jaffa high
school students who in 2006 were about to select
school subjects at an advanced level for their matriculation examinations.
In the main, our results cast doubt on some of the
main assumptions of the BG model. Summarizing
our results concerning each assumption in turn, first
we found that educational choice was affected by the
relative utility that students attribute to the alternative
choice options. It was also affected by students’ subjective failure expectations on the alternative educational routes. However, it was not affected by the
so-called Relative Risk Aversion motivation (i.e. the
class maintenance motivation). These results are consistent with those reported by Stocké (2007) for
German parents choosing secondary educational
tracks for their children. Hence, the role of mobility
concerns at this stage of the educational career is
questionable.
Furthermore, although affected by social origin
(and two of the three also hold effect on educational
choice), secondary factors did not mediate class
inequalities in educational choice, which were largely
mediated by performance and prior grouping in Math.
In addition, relative risk aversion was negatively related
to social origins: students from the lower strata seemed
more concerned about downward social mobility that
14
GABAY-EGOZI, SHAVIT AND YAISH
those from the more privileged strata. Although BG’s
predictions about the determinants of educational
choices were validated in part, we agree with Stocké
(2007) and with Nash (2003, 2006) that rational choice
theory in education focuses too heavily on secondary
effects. Our findings indicate that secondary effects
on educational attainment are weaker compared with
primary effects. Research on the relative importance of
secondary effects on educational attainment indicates
that the relative importance of secondary effects
depend, in part, on the specific educational transition
in question and on the societal context in question.12
Moreover, and importantly, a large proportion of
our respondents took neither the high road nor the
low road, preferring a mixture of the two. We suggest
that students who choose both high-utility and lowrisk options hedge short-term risks with long-term
utility. We find that hedgers came disproportionately
from less affluent and less educated families, had
lower scholastic achievements, and were more concerned with class maintenance, than those who chose
either pure hard subjects or pure soft subjects. The
possibility to choose a mix of subjects is not
anticipated by rational choice models of education.
This is an important finding, indicating that the
model ignores systemic differences in the structure of
available choices. The availability of multiple rather
than binary options can affect educational choice and
class-based educational inequality by allowing students
to hedge long-term utility with short-term risk.
Comparative research across educational systems that
offer students a variety of alternatives to choose from
is in order.
Our finding that the less privileged in society
adopted a hedging strategy also calls into question
the argument that curricular choice masks transparent
tracks visible only to the more affluent parents (Lucas,
1999). We find that students from less affluent and
less educated families seemed to be aware of the risks
and utilities associated with the different subjects
available to them, and they mixed them to their
apparent advantage. The availability of options need
not work to the detriment of greater equality of
educational opportunity, as suggested by Lucas (also
by Ayalon, 2006).
Research is also called for on the relationship
between students’ stated intentions and the courses
they actually take. While actual course taking is
affected by students’ preferences, it is also affected
by the availability of courses at schools, by teachers’
recommendations, by schools’ policies of student selection, and by a host of other institutional constraints.
In a future study we hope to evaluate the role of
subjective choices and of hedging, as well as of push
factors, on the actual courses students take.
Notes
1.
2.
3.
4.
5.
When evaluating their available educational
option, students (and their parents) are also
assumed to take into account the direct and
indirect costs of education. Although perceived
cost is an important variable in rational-choice
theory it is not discussed or measured in this
study because high-school alternatives (i.e. choice
of major) in Israel do not differ in their expected
costs.
Upper secondary education consists of two main
tracks: academic and vocational. Until recently
the vocational track trained most students for a
trade and prepared them for the world of work
rather than for further study. In recent years,
however, most vocational-track students have
been able to sit for matriculation examinations,
although their success rates are lower than those
of their academic counterparts (e.g. Ayalon and
Shavit, 2004).
The number of units refers to the time devoted
to the subject. A unit equals 1 h a week for three
years, or 3 h a week for 1 year. The number of
units at which a subject is taken corresponds to
its level and degree of difficulty (Ayalon, 1994).
Students’ sampling probabilities were computed
as the product of the school’s sampling probability (0.29 and 0.66 in the middle class and
working class neighborhoods, respectively) and
the students’ sampling probability within the
school, which was computed as the ratio of the
number of respondents and the number of tenth
graders (ninth and 10th graders in one of the
schools). In the analysis, the cases were weighted
inversely to their sampling probabilities. In
unreported analyses we also controlled for
sampling districts (north, south) and obtained
substantively identical results.
Some schools track students in math early-on
in secondary school and leave them little choice
in the matter. In unreported analysis we found
that when compared to known distributions of
students by level of study in math, their stated
CURRICULAR CHOICE
6.
7.
8.
9.
10.
11.
12.
choices are highly unrealistic. Therefore, we do
not analyse the choice of advanced mathematics.
The results presented in Table 2 for the hard
and soft clusters are similar to those obtained
by inspecting the utility and failure expectation
for each of their composite subject separately,
with one exception: both the perceived utility
and failure expectations for Economics are similar to those for Biology. However, since the great
majority (96%) of students who intended to take
economics also chose other social science subjects
its allocation to the social science category seems
justified.
Specifically, the wording in Van de Werfhorst
and Hofstede’s item e was: ‘I am concerned
that I might reach a social class that is lower than
the social class of my parents’, whereas in our
Hebrew version concerned was worded as afraid.
In unreported analyses we also controlled for
number of siblings, and students’ reading habits,
but, net of parental education and economic
resources, most of their effects did not reach
statistical significance. Dropping them from the
analysis did not alter the substance of our
results in any way.
In a separate study that is currently in progress,
we attempt to account for gender differences in
the motivational factors.
The effects on a third possible contrast (soft versus
mix) are not reported and can be computed as
the differences between the respective effects in
Tables 7 and 8. Of all effects on the third contrast,
only that of relative failure expectation is significant and in the expected direction. It does
not mediate the effects of social origins.
When Models IV and V were fitted with each of
the choice mechanisms (RRA, RU and RFE) only,
each effect was similar to the effect reported here.
See, for example, the sharp difference in
the estimated relative importance of secondary
effects in England and Wales (Jackson et al.,
2007) compared with Germany (Reimer and
Schindler, 2007).
Acknowledgements
An earlier version was presented in 2007 at the Annual
meeting of the American Sociological Association
and at the Montreal meeting of RC28. We are
15
thankful to those who contributed important comments, especially to John Goldthorpe, Richard Breen
and Moshe Semyonov. Finally, we would also like to
thank the two anonymous referees for their useful
suggestions and comments on previous version of this
manuscript.
Funding
Eshkol Fellowship from the Israeli Ministry of Science
and Technology; a fellowship from the Horowitz
Institute at Tel Aviv University to the first author;
the Weinberg Chair of Social Stratification and
Inequality at Tel Aviv University. This study was also
partially supported by an Israel Science Foundation
grant #831/07.
References
Apple, M. W. (1990). Ideology and Curriculum,
2nd edition. New York: Routledge.
Ayalon, H. (1994). Monopolizing knowledge – the
ethnic-composition and curriculum of Israeli highschools. Sociology of Education, 67, 264–278.
Ayalon, H. (1995). Math as a gatekeeper: ethnic and
gender inequality in course taking of the sciences
in Israel. American Journal of Education, 104,
34–56.
Ayalon, H. (2002). Mathematics and sciences course
taking among Arab students in Israel: a case of
unexpected gender equality. Educational Evaluation
and Policy Analysis, 24, 63–80.
Ayalon, H. (2003). Women and men go to university:
mathematical background and gender differences
in choice of field in higher education. Sex Roles, 48,
277–290.
Ayalon, H. (2006). Nonhierarchical curriculum differentiation and inequality in achievement: a different
story or more of the same? Teachers College Record,
108, 1186–1213.
Ayalon, H. and Shavit, Y. (2004). Educational reforms
and inequality in Israel: The MMI hypothesis
revisited. Sociology of Education, 77, 103–120.
Ayalon, H. and Yogev, A. (1997). Students, schools,
and enrollment in science and humanity courses in
Israeli secondary education. Educational Evaluation
and Policy Analysis, 19, 339–353.
Blossfeld, H.-P. and Shavit, Y. (1993). Persisting
barriers: changes in educational opportunities
in thirteen countries. In Shavit, Y. and
Blossfeld, H.-P. (Eds.), Persistent Inequalities: a
Comparative Study of Educational Attainment in
16
GABAY-EGOZI, SHAVIT AND YAISH
Thirteen Countries. Boulder, CO: Westview Press,
pp. 1–23.
Boudon, R. (1974). Education, Opportunity, and Social
Inequality: Changing Prospects in Western Society.
New York: Wiley.
Bradley, K. (2000). The incorporation of women into
higher education: paradoxical outcomes? Sociology
of Education, 73, 1–18.
Breen, R. and Goldthorpe, J. H. (1997). Explaining
educational differentials – towards a formal
rational action theory. Rationality and Society, 9,
275–305.
Breen, R. and Jonsson, J. O. (2000). Analyzing educational careers: a multinomial transition model.
American Sociological Review, 65, 754–772.
Breen, R. and Yaish, M. (2006). Testing the
Breen-Goldthorpe model of educational decision
making. In Stephen, L., Grusky, D. B. and
Fields, G. S. (Eds.), Mobility and Inequality:
Frontiers of Research from Sociology and
Economics. Stanford: Stanford University Press,
pp. 323–358.
Burkam, D. T., Lee, V. E. and Smerdon, B. A. (1997).
Gender and science learning early in high school:
subject matter and laboratory experience. American
Educational Research Journal, 34, 297–331.
Davies, R., Heinesen, E. and Holm, H. (2002). The
relative risk aversion hypothesis. Journal of
Population Economics, 15, 683–713.
De Graaf, N. D., De Graaf, P. M. and Kraaykamp, G.
(2000). Parental cultural capital and educational
attainment in the Netherlands: a refinement of
the cultural capital perspective. Sociology of
Education, 73, 92–11.
Duncan, G. J., Yeung, J. W., Brooks-Gunn, J. and
Smith, J. R. (1998). How much does childhood
poverty affect the life chances of children?
American Sociological Review, 63, 406–423.
Erikson, R. E., Goldthorpe, J. H., Jackson, M.,
Yaish, M. and Cox, D. R. (2005). On class
differentials in educational attainment. Proceedings
of the National Academy of Sciences of the United
States of America, 102, 9730–9733.
Erikson, R. E. and Jonsson, J. O. (1996). Can
Education Be Equalized? The Swedish Case in
Comparative Perspective. Boulder, CO: Westview.
Gambetta, D. (1987). Were They Pushed or Did They
Jump? Individual Decision Mechanisms in Education.
Cambridge: Cambridge University Press.
Gamoran, A. and Mare, R. D. (1989). Secondary school
tracking and educational inequality: compensation,
reinforcement, or neutrality? American Journal of
Sociology, 94, 1146–1183.
Goldthorpe, J. H. (1996). Class analysis and the
reorientation of class theory: the case of persisting differential in educational attainment. British
Journal of Sociology, 47, 481–505.
Goldthorpe, J. H. (1998). Rational action theory for
sociology. British Journal of Sociology, 49, 167–192.
Halsey, A. H., Heath, A. F. and Ridge, J. M. (1980).
Origins and Destinations: Family, Class and
Education in Modern Britain. Oxford: Clarendon
Press.
Hayman, F., Golan, H. and Shapira, R. (1997). School
autonomy and parental choice: steps in local
educational planning. In Shapira, R. and
Cookson, P.W. (Eds.), Autonomy and Change in
Context: An International Perspective. Oxford:
Pergamon, pp. 77–108.
Hedström, P. and Stern, C. (2008). Rational choice
and sociology. In Durlauf, S. and Blume, L. (Eds.),
The New Palgrave Dictionary of Economics,
2nd edition. New York: Palgrave Macmillan.
Holm, A. and Jaeger, M. M. (2005). Relative risk aversion
and social reproduction in intergenerational educational attainment: application of a dynamic discrete
choice model. Paper presented at the Summer
Meeting of ISA-RC28 (Social Stratification and
Mobility), Los Angeles, August 18–21.
Jackson, M., Erikson, R. E., Goldthorpe, J. H. and
Yaish, M. (2007). Primary and secondary effects
in class differentials in educational attainment: the
transition to A-level courses in England and Wales.
Acta Sociologica, 50, 211–229.
Kamens, D. H. and Benavot, A. (1992). A comparative
and historical analysis of mathematics and
science curricula, 1800–1986. In Meyer, J.W.,
Kamens, D.H. and Benavot, A. (Eds.), School
Knowledge for the Masses: World Models and
National Primary Curricular Categories in the
Twentieth Century. Washington, DC: Falmer
Press, pp. 101–123.
Katz-Gerro, T. and Yaish, M. (2003). Higher education: is more better? Gender differences in labour
market returns to tertiary education in Israel.
Oxford Review of Education, 30, 571–592.
Kliebard, H. M. (1992). The decline of humanistic
studies in the American school curriculum. In
Kliebard, H.M. (Ed.), Forging the American
Curriculum: Essays in Curriculum History and
Theory. New York: Routledge, pp. 3–26.
Lucas, S. R. (1999). Tracking Inequality: Stratification
and Mobility in American High Schools. New York:
Teachers College Press.
Ma, X. and Willms, D. J. (1999). Dropping out of
advanced mathematics: how much do students
CURRICULAR CHOICE
and school contribute to the problem? Educational
Evaluation and Policy Analysis, 21, 365–383.
Mare, R. D. (1981). Change and stability in educational stratification. American Sociological Review,
46, 72–87.
Ministry of Education, http://www.education.gov.il/
netuney_bchinot/, accessed on 26 December 2007.
Morgan, S. L. (1998). Adolescent educational expectations: rationalized, fantasized, or both? Rationality
and Society, 10, 131–162.
Morgan, S. L. (2005). On the Edge of Commitment:
Educational Attainment and Race in the United
States. Stanford: Stanford University Press.
Nash, R. (2003). Inequality difference in education: is
a real explanation of primary and secondary effects
possible? British Journal of Sociology, 54, 433–451.
Nash, R. (2006). Controlling for ‘ability’: a conceptual
and empirical study of primary and secondary
effects. British Journal of Sociology of Education, 27,
157–172.
Need, A. and De Jong, U. (2000). Educational
differentials in the Netherlands – testing rational
action theory. Rationality and Society, 13, 71–98.
Oakes, J. A. (1990). Opportunity, achievement and
choice: women and minority students in science
and mathematics. Review of Research in Education,
16, 153–222.
Oakes, J. A., Gamoran, A. and Page, R.N. (1992).
Curriculum differentiation: opportunities, outcomes and meanings. In Jackson, P.W. (Ed.),
Handbook of Research on Curriculum. New York:
Macmillan, pp. 570–608.
Plank, D. N. and Sykes, G. (2003). Choosing Choice:
School Choice in International Perspective. New
York: Teachers College Press.
Reimer, D. and Schindler, S. (2007). Primary and
Secondary Effects in Access to Higher Education
Germany. Conference of the European Research
Network EQUALSOC Network (Economic Change,
Unequal Life-Chances and Quality of Life), Dijon,
France.
17
Shavit, Y. (1984). Tracking and ethnicity in Israeli
secondary education. American Sociological Review,
49, 210–220.
Shavit, Y. (1990). Segregation, tracking and the
educational attainment of minorities: Arabs and
Oriental Jews in Israel. American Sociological
Review, 55, 115–126.
Stocké, V. (2007). Explaining educational decision and
effects of families’ social class position: an empirical test of the Breen–Goldthorpe model of educational attainment. European Sociological Review, 23,
505–519.
Van de Werfhorst, H. G. and Hofstede, S. (2007).
Cultural capital or relative risk aversion? Two
mechanisms for educational inequality compared.
The British Journal of Sociology, 58, 391–415.
Tel Aviv-Jaffa, Municipality. http://www.tel-aviv.gov.
il/Hebrew/Education/schools/Index.htm, accessed
March 2006.
Tel Aviv-Yafo. 2007. Statistical Yearbook No. 46. Tel
Aviv-Jaffa: Tel Aviv-Yafo Municipality.
Authors’ Addresses
Limor Gabay-Egozi (to whom correspondence should
be addressed), Department of Sociology and
Anthropology, Tel Aviv University, Ramat Aviv,
Tel Aviv 69978, Israel. Email: gabaylim@post.
tau.ac.il.
Yossi Shavit, Department of Sociology and
Anthropology, Tel Aviv University, Ramat Aviv,
Tel Aviv 69978, Israel. Email: [email protected].
Meir Yaish, Department of Sociology and
Anthropology, Haifa University, Haifa 31905,
Israel. Email: [email protected].
Manuscript received: April 2009