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Hedging Risk in Curricular Choice:

A Test of a Rational Choice Model of Education*

Manuscript's total word count (excluded tables): 6,934

Limor Gabay-Egozi, Yossi Shavit

Tel Aviv University

and

Meir Yaish

University of Haifa

Prepared for presentation at the Session on Social Stratification, at the 2007 Annual Meeting of the

American Sociological Association, New York City, and at the Montreal Meeting of RC28, 2007

* This study was supported by an Eshkol Fellowship from the Israeli Ministry of Science and Technology, and by a

fellowship from the Horowitz Institute at Tel Aviv University to the first author. Please direct correspondence to

gabaylim@post.tau.ac.il

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Abstract

Rational choice theory of education views 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. The model asserts that choice between options is affected by students' utility

considerations, failure expectations in either option, and motivation to avoid downward social

mobility. We evaluated these assumptions using data on students' choices of school majors in Tel

Aviv high schools and found that inequalities between social strata in educational choice were

not mediated by the mechanisms proposed by the model. Moreover, and importantly, many

students (the hedgers) did not choose between long-term utility and short-term risks but

combined the two. Hedgers combined school subjects that are expected to yield long-term

utility, such as the hard sciences, with those that reduce the risk of failure in the short term, such

as social sciences. The hedgers proved to be drawn disproportionately from disadvantaged

classes and were more likely to be women. These results suggest that educational systems that

allow multiple rather than binary choices enhance the attainment of working-class youth by

enabling them to combine risky options, which may enhance their mobility, with safer choices,

which assure success in the short term.

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Introduction

In recent years policy makers have advocated a degree of parental choice in education (Hirsch 1994,

Hayman et al. 1997, Plank and Sykes 2003), suggesting that choice will enhance equality of

opportunity. Aiming to equalize educational opportunities, most countries have made wide-

ranging changes in education systems, including choice policy such as de-tracking, increased

parental choice of school and the introduction of voucher systems. Nevertheless, as argued by some,

choice could be a stratifying rather than an equalizing mechanism in the educational attainment

process. For example, Lucas argued that despite the replacement of overt tracking by apparent choice

in course selection, de facto tracking persisted as did class and race-based inequality therein (Lucas

1999). Ayalon studied curricular choice in Israeli high schools which differ in the degree of choice

available to students. She found that gender and socioeconomic differences were more pronounced

where choice was abundant (Ayalon 2006). 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 constraints that determine students' educational attainment while pull factors refer to

choice. These concepts echo Boudon's (1974) distinction between primary and secondary

effects. Primary effects are factors responsible for the association between social origins and

children’s academic ability and performance, while secondary effects refer to factors that

account for educational choices.

In recent decades interest has grown in rational-choice 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 assumes that

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individuals are not only governed by push factors operating behind their backs, but are conscious

decision makers whose actions are influenced by a costs-benefit calculus (Hedström and Stern

forthcoming). This paper tests three important assumptions of the Rational Choice Model of

education.

Theoretical Considerations

Studies which focus on push factors concentrate on two sets of variables affecting educational

stratification: 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).

Choice, rather than constraint, has lately attracted increasing attention as a stratifying

mechanism in the educational attainment process. 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 for scholastic performance (cf. Erikson and

Jonsson 1996, Breen and Jonsson 2000, Need and de Jong 2001, Davies et al. 2002, Breen and

Yaish 2006). In England and Wales, for example, class differentials in educational choice

account for about 25 percent of inter-class educational inequality (Erikson et al. 2005, Jackson et

al. forthcoming).

The growing interest in secondary factors as an educational stratification mechanism has

led scholars to develop theoretical models using broader notions of rationality, aiming to explain

why children of similar abilities but different class backgrounds are observed to make different

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educational choices (Goldthorpe 1996, 1998, Erikson and Jonsson 1996, Breen and Goldthorpe

1997, Morgan 1998, 2005). Breen and Goldthorpe's (1997) Rational Action Theory is arguably

the most influential of these models.

These models share certain properties. First, the educational attainment process is

viewed as a sequence of transition points at which students (and their families) decide whether to

drop out or to continue in any of the available educational options to the next level. Each

available option is evaluated and ranked on the basis of its utility and risk. Utility is defined as the

degree to which it enhances occupational and economic attainment. Risk is defined as the odds of

failing to complete a prescribed course of study successfully due to the perceived difficulty and

the effort required. It is assumed that options associated with high utility are also associated with

high risk of failure, so decision makers face an inherent dilemma between maximizing the

former and minimizing the latter. To anticipate, our own results reveal that many students hedge

short-term risks and long-term utility. They choose both high-utility and low-risk options, hoping

thereby to reap the long-term benefits of the former while insuring themselves against short-term

failure. Hedging is possible in educational systems that allow multiple rather than binary choices.

Second, although students from different social classes rank all options equally in terms

of their associated utility and risk, their own subjective probability of success is formed on the

basis of past performance at school.

Finally, Breen and Goldthorpe's (1997) Relative Risk Aversion mechanism assumes that

students from all social classes alike aim to maintain their parental class position and choose

educational options which, they believe, will minimize the risk of downward mobility (Breen and

Goldthorpe 1997: 283-5). Since students of all origins wish to maintain their class position, the

implications of the relative risk aversion mechanism differ by class background: for middle-class

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children it implies choosing the risky option because this alone will lead them to the upper

classes. Conversely, working-class children tend to choose less risky options because they are

content with attaining working-class occupations.1

In this paper we test these assumptions. More specifically, we test the following three

hypotheses:

Hypothesis I: Students' educational choices are affected by their beliefs about the likely returns

to the various educational credentials.

Hypothesis II: Young students' educational choices are driven by their beliefs about their own odds

of success or failure in the alternative educational trajectories.

Hypothesis III: Educational decisions are motivated by individuals' apprehension of downward

class mobility (i.e., the relative risk aversion motivation).

The Setting

We tested these assumptions with data on the choice of major subjects by secondary school

students in Tel Aviv, Israel. Our sample consisted of ninth and tenth 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

twelve, after a year in pre-school and six 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 ten through twelve, where students are prepared for the matriculation

1 When weighing the pros and cons of each educational option, students (and parents) are assumed to take into account,

among other things, the direct and indirect costs of education. Although perceived cost is an important variable in

rational-choice models of education, it is not measured in this study. In Israel, high school majors hardly differ in

their expected costs, so this element is disregarded in both the theoretical and empirical parts of the paper.

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examinations leading to the award of the matriculation certificate (bagrut), which is required for

higher education.2

The matriculation exams are offered on a unit basis: they can be taken at the basic level

(3 units) or the advanced levels (4 or 5 units).3 To earn the certificate students must take exams

in subjects totaling at least 20 units. Seven subjects are compulsory, two of them being English

and mathematics. They must also take an elective major at the advanced level. Research shows

that students from advantaged class origins tend to specialize in sciences, while those from lower

classes and ethnic groups tend to specialize in the humanities and the social sciences (Ayalon 1994,

1995, Ayalon and Yogev 1997). The major is 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).

Admission into higher education predicates a matriculation certificate, and the more

prestigious departments and institutions of higher education set several additional requirements:

(1) advanced English (at least 4 units); (2) a high matriculation grade point average; and (3)

some departments specifically require applicants to have passed advanced matriculation exams

in math and sciences. Moreover, the complicated formula employed by universities tends to

favor applicants who have passed more than one advanced level examination.

In recent cohorts, over 70 percent sat for one matriculation examination or more, but only

half of those were eligible for the certificate (Ayalon and Shavit 2004). Math and English are the

2 Upper secondary education consists of two main tracks: academic and vocational. Until recently the vocational

track trained most students in a vocation 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). 3 The number of units refers to the time devoted to the subject (one unit is equal to one hour a week for three years,

or three hours a week for one year), which corresponds to the subject's level and degree of difficulty (Ayalon, 1994)

.

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major stumbling blocks: only about 35 and 40 percent, respectively, of recent birth cohorts

passed them, compared, for example, with 50 percent who passed Hebrew language. In an

attempt to raise students' success rates in math and English, schools actively determine their unit

level as early as ninth grade. Once the level has been determined, a student is tracked and has

little further choice in this regard.

Data

In the spring term of 2006 we distributed self-administered questionnaires among tenth graders

attending four Hebrew secular, non-vocational public schools in Tel Aviv-Jaffa.4 At one school

we administered the questionnaire to ninth graders also. Tel Aviv-Jaffa consists of nine

administrative districts which are fairly homogeneous socio-economically (Tel Aviv-Jaffa 2006).

Tel Aviv-Jaffa has nine secular, non-vocational secondary schools, of which two are atypical

and were not included in our target population: one specializes in the arts and the other caters

primarily to Russian immigrant children. Of the remaining seven schools we sampled two in

working-class districts and two in middle-class districts. Fortunately, all four school principals

cooperated and gave us access to their students. We handed out the questionnaires in 28

4 We restricted our sample to academic and secular high schools because the three sectors – academic, vocational

and religious schools – differ in the curricular choices on offer and in the relative utility and prestige that students

attribute to subjects. Vocational schools offer a more limited menu of academic subjects along with an abundance of

technical ones. In religious schools strong students tend to take religious studies rather than sciences, as is the case

in secular schools. For simplicity, we preferred to limit the study to secular academic schools, where curricular

options are relatively similar.

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classrooms and obtained 683 completed forms, of which 17 percent were excluded from the

analysis due to missing values.5

The Dependent Variable

Our dependent variable is a three-category classification representing choice of advanced

subjects. The categories are: hard subjects (physics, chemistry and computer science), soft

subjects (biology, economics, social sciences, history, literature and communication), and a

mixture of both. Next we explain and justify the classification.

Respondents were presented with a list of subjects available at their school and were

asked to indicate whether or not they planned to take each subject at an advanced level. The

distribution of choices is shown in Table 1.6 As seen, boys tended to choose physics, computers,

economics and other social sciences, while girls tended to choose the social sciences,

communications, economics and biology.

[TABLE 1 HERE]

From a rational-choice perspective, the subjects should be distinguished on the basis of

students' beliefs about their utility and according to students' perception of the risk of failure

associated with each subject. We measured the former by asking respondents the following

5 Students' sampling probabilities were computed as the product of the school's sampling productivity (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 in

the school (ninth and tenth graders in one of the schools). In the analysis the cases were weighted inversely to their

sampling probabilities. 6 Recall that students' placement in English and math is largely determined by teachers, and that students have little

choice in the matter. Therefore, we did not count these subjects among the available options from which curricular

choice is made.

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questions: "In your opinion, if a student succeeds in this subject at the 5-unit level, what are his

or her chances of admission to a university?" [scale: 1 ('not high at all') to 5 ('very good')].

The mean responses for each subject are shown in column 1 of Table 2. Students

perceived physics, chemistry, and computers to be of most utility with respect to university

admission, while the social sciences, history, literature and communications were perceived to be

of less utility. Economics and biology occupied intermediate positions.

[TABLE 2 HERE]

Students' perception of risk was measured as the proportion who thought that their grade

in a subject would be low (64 or less on a 100-point scale) if they took it. These proportions are

shown in column 2 of Table 2. Physics was perceived as the most risky, followed by chemistry

and computers. The social sciences, history, literature and communications were perceived as

the least risky, while economics and biology occupied intermediate positions. Thus, the hard

subjects – physics, chemistry and computers – were perceived to be of most utility but also the

most risky. We refer to all other subjects, including economics and biology, as soft subjects.

[TABLE 3 HERE]

As indicated earlier, although for a matriculation certificate students are required to take

just one subject at an advanced level, incentives exist for them to take more. In fact, 96 percent

of the students in our sample intended to take two or more advanced subjects. As seen in Table 3,

21 percent of the sample intended to take advanced subjects in the hard sciences alone, 52

percent intended to take soft subjects only and 24 percent intended to take a mix. Thus, over half

the students who intended to take a hard subject seem to hedge their risk of failure by taking a

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soft one too. This strategy seems quite reasonable given that students are only required to

complete a single advanced course and can discard an extra course in which they might do

poorly.

In the rest of the analysis we therefore employ a multiple choice model which

distinguishes three choices: risky but rewarding hard subjects, less risky and less rewarding soft

subjects, and a mix which hedges risk. Our expectation is that, net of scholastic performance, any

remaining class differences in the choice among these alternatives will be explained by

secondary factors.

Secondary Factors

The rational choice model of education refers to three secondary factors affecting educational

choice: relative risk aversion, beliefs about future utilities, and failure expectation.

Relative risk aversion was measured by the following battery of 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 that my salary be at least equal to the level of my parents' salary.

b. My parents would not be satisfied if I were to work at a lower occupation than theirs.

c. I would like to attain a social class on a level at least equal to my parents'.

We factor-analyzed these items and found them to load on a single factor, which we interpret to

represent the class maintenance motivation (i.e., relative risk aversion).

Beliefs about future utilities was measured by the question shown earlier about the

subjective odds that a 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

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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 by students' assessment of their expected

performance in each subject were they to take it at an advanced level. Within each cluster of pure

subjects – the hard and the soft – we calculated for each student the proportion of those in which

he or she expected to earn a low grade (64 or less). We then computed the difference between the

former and latter proportion. High values represent apprehension of the hard subjects.

Primary Factors, Scholastic Performance and Grouping

Two variables indicate social origins: parental education and family's economic resources.

Parental education is measured as the highest qualification obtained by either father or mother. It

is measured on a six-category classification which maintains a clear hierarchical order. 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 are

measured as the sum of available items in the parental household.7

Students' scholastic performance is indicated by their grades, measured as the mean self-

reported grade in Hebrew, English and math on the most recent report card.

Grouping is the level at which students considered themselves placed in tenth-grade

math. Originally grouping was measured on a three-point scale corresponding to levels 3

(regular), 4, and 5 (advanced). In the following analyses grouping is measured as a dummy

variable indicating advanced level. As noted earlier, teachers assign students to math levels, 7 In unreported analyses we also controlled for father's occupational SEI, number of siblings, and students' reading

habits, but 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.

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usually in ninth grade. Group placement is largely determined by students’ prior performance in

math, but once placement has been made group is an evident push factor. It reflects students'

earlier achievements, but it also shapes their self-esteem and confidence in math and constrains

their future choices. Students placed at regular-level math are unlikely to choose advanced hard

subjects. Finally, we also control for sex, with a dummy variable indicating boys. Having made

these preliminary qualifications we can embark on the analysis.

Analysis

Table 4 presents descriptive statistics of the independent variables by our three categories of

advanced matriculation subjects. The results in Table 4 indicate significant gender differences in

the choice of matriculation subjects. Boys are over-represented in the hard subjects and in the

mixed category (which includes at least one hard subject), and are under-represented in the soft

subjects. This gender difference in the selection of school majors at high school, and later in

higher education, is 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).

With regard to primary factors, Table 4 shows, as expected, that students who choose the

more demanding, yet more rewarding, matriculation subjects (i.e., hard subjects) are from a more

advantaged social background than those who choose the less demanding subjects (cf. Oakes et

al. 1992; for Israel see Ayalon 1994, 1995, Ayalon and Yogev 1997). However, students who

choose the least demanding matriculation subjects (i.e., soft subjects) are found to be from a

relatively more advanced social background than those who select a combination of hard and soft

subjects. 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 choose hard subjects display academic excellence, while those who select the

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mixed or the soft subjects tend to achieve lower grades. Similarly, those who choose hard

subjects are more likely to have been placed in advanced grouping in math.

[TABLE 4 HERE]

As for secondary factors, Table 4 shows that students who choose mixed subjects exhibit

the highest degree of relative risk aversion. Students who are highly concerned with class

maintenance apparently hedge their risk by adopting a mixed strategy. The hard subjects increase

the expected long-term utility of the mix (i.e., higher education and labor market utilities), while

the soft subjects cushion the consequence of possible failure in the short term (i.e., failing to

obtain a matriculation certificate). If one does poorly in the hard (and risky) subjects, success in

the soft ones averts short-term failure. Choosing a mix of subjects can be seen as a hedging

strategy designed to minimize exposure to unwanted short-term risks, while allowing the

possibility of long-term success in higher education and in the labor market.

Finally, and as expected, those who select hard subjects hold stronger beliefs about the

positive utility of the hard subjects than those who choose the other combinations of subjects;

those who select soft subjects have the lowest confidence in their ability to succeed in hard

subjects.

Primary Effects on Academic Performance, Grouping and Secondary Factors

Before testing our three hypotheses, we estimate in Table 5 the effect of social background on

performance and grouping, and that of social background, performance and grouping on

secondary factors.

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Consistent with previous research, social background has positive effects on both

academic performance and grouping. Further, boys and girls are seen not to differ in their

academic performance, though more girls than boys are placed in lower grouping levels in math.

In the last three columns of Table 5 we present the results of OLS regression models on

secondary factors: relative risk aversion, beliefs about future utilities, and failure expectation.

Table 5 indicates that students of a more advantaged social background are less concerned with

maintaining their parental socioeconomic positions, and tend to attribute higher future utility to

the hard rather than the soft subjects. 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 of

advantaged social origins may take their position for granted, 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.

[TABLE 5 HERE]

Referring to beliefs about utility, the effect of social origin on relative utility expectation

should be seen in the context of class-based inequality in information, whereby these beliefs are

formed. Studies have shown large class differences in the amount and the accuracy of information

acquired about education: the working class proves less informed than the advantaged classes (Hayman

2001, Ball et al. 1995, Lareau 1987, Schneider et al. 2000). Less informed people, moreover, are less

discriminating among educational alternatives (Morgan, 2005).8

8 Table 5 also indicates gender differences in secondary effect. Although this issue is of considerable importance it is

beyond the scope of this paper. We hope to treat it in a separate study.

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Primary and Secondary Effects on Choice of Advanced Subjects

Testing the three hypotheses, we ask: are students' educational choices affected by their

apprehension of downward class mobility (i.e., relative risk aversion), by their beliefs about the

likely returns on the various choices, and by their beliefs about their own odds of success or

failure in the alternative trajectories? We also ask: do these secondary factors explain away any

remaining class inequalities in educational choice? That is, is choice a stratifying mechanism?

To answer these questions we estimate a multinomial logit model on three curricular

choice clusters, consisting of (a) pure hard subjects, (b) pure soft subjects, and (c) a mix. The

model includes the following independent variables: sex, parental education, family's economic

resources, scholastic performance, grouping in math, relative risk aversion, subjective utility of

the hard as against the soft subjects, and failure expectation in the hard as against the soft

subjects. Tables 6a, 6b and 6c present the parameter estimates of each pair of contrasts among

these choices: hard subjects vis-à-vis soft subjects (Table 6a), hard subjects vis-à-vis a mix

(Table 6b), and soft subjects vis-à-vis a mix (Table 6c). For ease of comparability in and between

the models all independent variables, except the sex and grouping dummies, were standardized

to a mean of zero and standard deviation of one.

[TABLE 6a HERE]

Hard Subjects vis-à-vis Soft Subjects

Model I in Table 6a indicates that males, and students with educated parents, are more likely to

opt for hard than for soft subjects. When we add academic performance to this model (Model II)

the effects of parental education and economic resources are sharply reduced, and adding

grouping (Model III) renders the effect of parental education even weaker and statistically

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insignificant. The implication of this result is that choice between the two homogeneous clusters

of subjects is largely predetermined by performance and grouping, and that no effects of social

background remain to be explained by secondary factors. In Model IV the analysis focuses on

the gross effects of the three secondary factors. The relative risk aversion mechanism proves not

to exert its expected effect on choice between hard and soft subjects. By contrast, the other two

secondary factors – relative utility and relative failure expectation – are significant, and in the

expected direction: students who expect relatively higher utility from the hard subjects and hold

low failure expectations in them are more likely to select hard than soft subjects. When, in Model

VI, we also control for social background, academic performance and grouping the net effects of

the secondary factors remain largely unchanged, compared with Model IV.

In sum, the main determinants of this specific educational choice are: academic

performance, grouping, relative failure expectation and subjective utility, but not relative risk

aversion. Secondary factors do not seem to mediate the effects of social background.

Hard Subjects vis-à-vis a Mix of Subjects

In Table 6b we see again, as expected, that parental education and economic resources are

positively associated with the choice of hard subjects rather than a mix of subjects. Interestingly,

net of academic performance (Model II) and grouping (Model III), social background still exerts

statistically significant effects on this educational choice. Do secondary factors explain these

remaining effects of background?

[TABLE 6b HERE]

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Before answering this question we examine, in Model IV, the gross effects of secondary

factors on this specific choice. Expectedly, students who attribute relatively high utility to the

hard subjects tend to choose them. More interestingly, students with strong motivations for

status maintenance are seen to be less likely to select a pure cluster of hard subjects and seem to

prefer a mix. This suggests that those who are strongly motivated to maintain their family class

position tend to hedge risk by choosing a mix of subjects.

Hedging reduces the risk of failure in the short term while leaving open the option of

benefiting from the long-term utility afforded by the hard subjects. We know from Table 5 that

students from relatively less affluent and less educated families are more concerned with class

maintenance than those from more affluent and more educated families. This implies that

hedging in this context is associated with members of the less privileged strata in society. This

assertion is corroborated by the results of Model VI, which show positive effects of background

on the choice of hard subjects rather than a mix. Finally, a comparison of Models III and VI

shows that secondary factors do not mediate the effects of social background on this particular

choice.

Soft Subjects vis-à-vis a Mix of Subjects

As can be seen in Model I of Table 6c, girls and students from privileged social strata are more

likely to choose soft subjects than a mix. In Model III we observe that performance does not

affect the choice under investigation, while grouping in math is positively associated with a mix

of subjects. This latter result is not surprising as the mix category includes, by definition, hard

subjects, which normally require advanced math. We see further that the effect of parental

education is larger in Model III than in Model I, such that net of performance and grouping,

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students from advantaged families are more likely to choose soft subjects than a mix.9 Again,

students of less advanced origins are more likely to opt for the mixed strategy. This result is

consistent with our claim that choosing the mixed category represents hedging behavior.

[TABLE 6c HERE]

We would argue that among equally able students, the less privileged classes are more

wary of putting all their eggs in the soft-subject basket – these generally being perceived as less

rewarding than the hard subjects – for fear of failing to win admittance to university. Therefore,

they hedge this long-term risk by taking hard subjects as well, which they believe will enhance

their long-term objectives. The privileged classes, on the other hand, are less apprehensive about

their long-term life chances. They are confident in the help that their family can provide, and are

less concerned with hedging behavior in curricular choice.

The effects of social background hardly change when secondary factors are added to the

model (Model VI). That is, secondary factors do not seem to mediate the effects of social origins.

Summary and Discussion

It is generally recognized that students of a privileged social background progress farther in the

educational system than those of less advantageous origins. Rational choice models of education

attribute these inequalities to differences in educational choice made by individuals from different

social strata. This paper set out to test the main propositions of these models.

9 This change is due to the suppressor effect of Grades and Grouping on the association between parental education

and choice: parental education affects performance and grouping positively (see Table 4), and these in turn reduce

the odds of choosing soft subjects.

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According to these models, 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 demanding and risky

in the short term, but can lead to rewarding life-course outcomes in the long run. 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 against downward social mobility.

According to Breen and Goldthorpe's (1997) version of the model, 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,

rational-choice models postulate that social reproduction is mediated, in part, by differential educational

choice.

Here we put these assumptions to empirical test. More to the point, we examined the role of

utility considerations, of failure expectations in education, and of class maintenance motivation in

shaping educational choice. We examined the validity of these assumptions by analyzing 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 the validity of the rational choice assumptions, and indeed

on the entire model. We now deal with each of these assumptions in turn. Although we found that

educational choice was affected by class maintenance motivation, as well as by subjective utility

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and failure expectations, these effects did not mediate class inequalities in educational choice, which

were largely mediated by performance and prior grouping.

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 argue here that students who choose high-utility

options and low-risk ones hedge short-term risks and long-term utility. We find that hedgers

came disproportionately from less affluent and less educated families, had lower academic

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 is perhaps incomplete. It 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 present students with 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

22

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.

23

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29

Table 1: Percent of Boys and Girls Who Choose to Study Subjects at Advanced Levels

Subject Boys Girls Total

Physics 33 12 22

Chemistry 22 17 19

Computers 28 12 20

Biology 19 20 19

Economics 30 25 28

Social Science 30 52 41

History 16 16 16

Literature 12 15 13

Communications 15 25 20

Table 2: Subjects by Perceived Utility and Perceived Risk

Subject

Perceived Utility in

University Admission

Perceived Risk of Failure

Physics 4.35 22.2

Chemistry 4.01 15.2

Computers 4.14 12.4

Biology 3.83 9.7

Economics 3.91 9.7

Social Science 3.69 5.5

History 3.40 4.2

Literature 3.24 6.6

Communications 3.21 5.1

30

Table 3: Composition of Advanced Subjects Chosen

a 44 cases (6 percent) did not select a major and are excluded from the analysis.

Table 4: Means (and their S.E.) of Key Variables by Choice of Subjects

Variables

All (1)

Hard Subjects

(2)

Mixed

(3)

Soft Subjects

(4) Social Background Sex (Boys) 0.50 0.69 0.58* 0.38* (0.50) (0.47) (0.50) (0.49)

Parental education 4.68 5.25* 4.16* 4.63* (1.55) (1.15) (1.73) (1.55)

Economic resources 6.20 6.81* 5.48* 6.22* (2.19) (1.86) (2.18) (2.26)

Academic Performance & Grouping Academic performance 82.97 88.10* 80.68 81.46*

(10.76) (8.41) (11.74) (10.56)

Grouping in math 0.79 0.95* 0.81* 0.70* (advanced level) (0.41) (0.22) (0.40) (0.46)

Secondary Factors Relative risk aversion 0.004 -0.09* 0.19* -0.03

(0.98) (1.06) (0.95) (0.95)

Relative utility 1.20 1.34* 1.18 1.14* (hard sciences vis-à-vis soft subjects) (0.27) (0.31) (0.25) (0.24)

Relative failure expectation 0.10 0.004 0.03* 0.17* (hard sciences vis-à-vis soft subjects) (0.25) (0.15) (0.19) (0.29)

Number of Cases 569 128 150 291 NOTE: Asterisks in column 2 indicate statistically significant (p<0.05) differences between the means shown in

columns 2 and 3. Asterisks in column 3 indicate statistically significant (p<0.05) differences between the means shown in columns 3 and 4. Asterisks in column 4 indicate statistically significant (p<0.05) differences between the means shown in columns 2 and 4.

Choice Composition % Hard subjects only 21 Soft subjects only 52 Mix of hard and soft subjects 27 Total 100

31

Table 5: OLS Regression Coefficients (and their S.E.) on Academic Performance and Secondary Factors.

Independent Variables

Performance and Grouping

Secondary Factors

Academic

Performance

Group ̂

Relative Risk

Aversion

Relative Utility

Relative Failure

Expectation

Sex (Boys) -0.72 0.23* 0.25* 0.08* -0.08* (0.78) (0.06) (0.07) (0.02) (0.02)

Parental education 1.69* 0.13* -0.06* 0.02* 0.00 (0.29) (0.02) (0.03) (0.01) (0.01)

Economic resources 0.99* 0.03* -0.05* 0.01* 0.01 (0.20) (0.02) (0.02) (0.01) (0.01)

Academic performance 0.00 0.004* -0.002* (0.00) (0.00) (0.00)

Grouping in math 0.03 0.09* -0.06* (advanced level) (0.10) (0.03) (0.03)

Constant 69.29* 1.31* 0.25 0.58* 0.35* (1.49) (0.11) (0.30) (0.08) (0.08)

Adjusted R2 0.14 0.11 0.04 0.14 0.04

^ Measured on a three-point scale corresponding to levels 3 (regular), 4, and 5 (advanced). * p<0.05

32

Table 6a: Primary and Secondary Effects (and their S.E.) on the Odds of Choosing Hard rather than Soft Subjects

Hard Subjects vis-à-vis Soft Subjects Independent Variables I II III IV V VI Social Background

Sex (Boys) 1.30* 1.42* 1.37* 1.06* 1.40* 1.19* (0.21) (0.22) (0.22) (0.22) (0.22) (0.23)

Parental education 0.49* 0.32* 0.17 0.16 0.13 (0.13) (0.14) (0.14) (0.14) (0.15)

Economic resources 0.17 0.06 0.06 0.05 0.01 (0.12) (0.13) (0.13) (0.13) (0.14)

Academic Performance & Grouping

Academic performance 0.86* 0.82* 0.82* 0.70* (0.15) (0.15) (0.15) (0.16)

Grouping in math 1.69* 1.67* 1.42* (advanced level) (0.40) (0.40) (0.41)

Secondary Factors

Relative risk aversion -0.14 -0.09 -0.11 (0.11) (0.11) (0.12)

Relative utility 0.72* 0.52* (hard sciences vis-à-vis soft subjects) (0.12) (0.13)

Relative failure expectation -0.86* -0.88* (hard sciences vis-à-vis soft subjects) (0.15) (0.18)

Constant -1.57* -1.77* -3.15* -1.47* -3.15* -2.99*

(0.17) (0.18) (0.40) (0.18) (0.40) (0.42)

Cox and Snell R2 0.14 0.19 0.23 0.22 0.23 0.31

* p<0.05

33

Table 6b: Primary and Secondary Effects (and their S.E.) on the Odds of Choosing Hard Sciences, rather than Mixed Subjects

Hard Subjects vis-à-vis a Mix of Subjects Independent Variables I II III IV V VI Social Background

Sex (Boys) 0.52* 0.62* 0.60* 0.44 0.65* 0.60* (0.25) (0.26) (0.26) (0.25) (0.26) (0.26)

Parental education 0.64* 0.49* 0.43* 0.42* 0.42* (0.15) (0.15) (0.15) (0.15) (0.16)

Economic resources 0.44* 0.34* 0.38* 0.35* 0.33* (0.14) (0.15) (0.15) (0.15) (0.15)

Academic Performance & Grouping

Academic performance 0.74* 0.74* 0.74* 0.69* (0.16) (0.16) (0.17) (0.17)

Grouping in math 0.80 0.79 0.64 (advanced level) (0.44) (0.44) (0.45)

Secondary Factors

Relative risk aversion -0.33* -0.22 -0.21 (0.13) (0.13) (0.13)

Relative utility 0.54* 0.28* (hard sciences vis-à-vis soft subjects) (0.13) (0.14)

Relative failure expectation -0.14 -0.23 (hard sciences vis-à-vis soft subjects) (0.17) (0.19)

Constant -0.32 -0.52* -1.23* -0.31 -1.24* -1.20* (0.21) (0.22) (0.45) (0.21) (0.45) (0.46) * p<0.05

34

Table 6c: Primary and Secondary Effects (and their S.E.) on the Odds of Choosing Soft rather than Mixed Subjects

Soft Subjects vis-à-vis a Mix of Subjects Independent Variables I II III IV V VI

Social Background

Sex (Boys) -0.79* -0.80* -0.77* -0.62* -0.74* -0.59* (0.21) (0.21) (0.21) (0.21) (0.21) (0.22)

Parental education 0.15 0.18 0.26* 0.25* 0.29* (0.11) (0.11) (0.11) (0.12) (0.12)

Economic resources 0.27* 0.28* 0.32* 0.30* 0.32* (0.12) (0.12) (0.12) (0.12) (0.13)

Academic Performance & Grouping

Academic performance -0.12 -0.08 -0.07 -0.02 (0.12) (0.12) (0.12) (0.12)

Grouping in math -0.89* -0.88* -0.79* (advanced level) (0.27) (0.27) (0.28)

Secondary Factors

Relative risk aversion -0.18 -0.13 -0.10 (0.11) (0.11) (0.12)

Relative utility -0.18 -0.24 (hard sciences vis-à-vis soft subjects) (0.13) (0.13)

Relative failure expectation 0.72* 0.65* (hard sciences vis-à-vis soft subjects) (0.14) (0.14)

Constant 1.25* 1.25* 1.93* 1.16* 1.91* 1.79* (0.15) (0.15) (0.26) (0.16) (0.26) (0.28) * p<0.05