16 D. Rochat, J.-L. Demeulemeester Economics of Education Review 20 2001 15–26
and at the same time participating in higher technical and vocational orientations Oosterbeek Webbink, 1997.
The orientations most demanded by the economy including also the engineering and computing sciences
orientations at the university are less frequently chosen than human and social sciences, although the latter do
not offer many occupational opportunities see the recent concerns expressed by the Report of the “Groupe de
re´flexion sur l’e´ducation et la formation” to the European Commission in December 1996, p. 26. Therefore higher
education systems are criticized for performing badly in providing the economy with the skills it needs Glytsos,
1989, both quantitatively and qualitatively.
The latter element leads us to question the degree of economic rationality embodied in student choices, and
therefore the suitability of a laisser faire system. If some authors are doubtful vis-a`-vis the rationality of students
expectation formation mechanisms see Manski, 1995, others point out to the necessity of integrating equity and
efficiency considerations in order to properly evaluate the students behaviors. The inefficiencies observed on
the
educational and
subsequently labour
markets mismatches between the skill supply and the market
needs should not be so hastily attributed to the irration- ality of the students. Indeed, the students might at the
same time be perfectly rational and nevertheless decide to choose apparently low-return orientations as Humani-
ties, Arts or Education in continental Europe.
2
The weight of the social background of the students, in other
words equity considerations, can partly explain appar- ently such “bad” orientation choices. In a pioneering
paper, Mingat and Eicher 1982, drawing from the insights of the CAPM financial theory, assumed that stu-
dents operate a trade-off between the risk and return components of the orientation choice. If one assumes that
orientations with a higher rate of return i.e. which are in demand on the labour market are also more difficult,
and that students coming from poorer socio-economic background Mingat Eicher, 1982 are also more risk-
averse i.e. they give a heavier weight to the risk compo- nent in their computations than wealthier students, than
one should observe that less privilegiated students will choose less risky i.e. less difficult or shorter and there-
fore also less remunerative orientations.
3
The inef-
2
One should keep in mind that employers in some countries UK, Japan rely much more on the relative reputation of insti-
tutions rather than on the precise subject chosen by the student when evaluating prospective applicants for a job. In such a con-
text, it might be better to get a degree in arts from a well-known institution than an engineering degree from a second-class uni-
versity see on this topic, Knapp, 1995, p. 130.
3
Besides Mingat and Eicher 1982, recent important contri- butions were made by Mortenson 1990 and Altonji 1993.
Mortenson 1990 notes, that low-income families may be more risk-averse and that the latter could explain their reluctance to
ficiencies observed on the labour markets could then be compatible
with a
rational individual
behaviour Oosterbeek Webbink, 1997.
4
Mutatis mutandis, one could advance a very similar explanation in terms of ability, another important
resource constraining the students free choice of disci- plines. Some subjects might be simply less academically
demanding, i.e. less difficult or less abstract than tech- nical ones. Students of lower ability might prefer such
orientations rather than more demanding ones because they expect higher chances of success in such subjects.
Here also this choice results from a risk-averse strategy, and an avoidance of more difficult subjects, i.e. riskier
ones for less able students. In this line of thought, stu- dents avoid more rewarding orientations because they
feel they are not sufficiently able to succeed.
The results of such kind of researches could be extremely important in terms of policy-making. If the
thesis of Mingat and Eicher 1982 is right, it would be wiser for the State to try to limit as much as possible
the weight of the social background of the students through
some “corrective”
measures as
positive discrimination. An educational policy aimed at increas-
ing equity would also be the best means to increase efficiency in terms of the skills provision to the econ-
omy. However, if the ability explanation is true, then the State could only improve upon the current state of affairs
only as much as acquired ability is responsible for it innate ability cannot be changed by assumption. The
former is indeed partly conditioned by the social and cul- tural background of the students, and an increased invest-
ment in primary and secondary education might be fruit- ful in alleviating the impact of a poorer family
background.
This paper tries to provide some pieces of answer in this debate by evaluating the impact of expected chances
of success in the students discipline choice process. Moreover, we will also try to take into account the role
of socio-economic background versus ability in the choice behaviour of the students.
2. Empirical methodology
As already mentioned, we use in this article a three- step methodology based upon Heckman 1979, Lee
use loans to finance college. Altonji 1993 explores theoreti- cally the extent to which students make sequential decisions
about whether to attend college, and once there, what field in which to major, and whether to drop out, based on uncertainties
related to labor market returns, personal tastes, and abilities.
4
Mingat and Eicher 1982 give some descriptive statistics on France confirming partly their assumptions. However, they
provide no thorough econometric analysis.
17 D. Rochat, J.-L. Demeulemeester Economics of Education Review 20 2001 15–26
1983 and Trost and Lee 1984, Lee 1983 and Trost Lee 1984. The idea is to estimate the risk
component of discipline choice process as the a priori probability of success for each student in every subject,
and to test whether this variable influences or not the choice of orientation by means of a conditional logit
methodology, while also controlling for the future expected economic benefits i.e. the return component
in the discipline choice process. By an analysis of the behaviour of various groups of students, i.e. the poorest
and the richest, we try to test for the validity of Mingat and Eicher’s main contentions, namely that the poorest
students would pay more attention to the chances of suc- cess than the richest ones. We also try to take ability into
account as a potential source of risk-averse behaviour by analysing the behaviour of both the brightest and the
“dullest” students, in order to check whether the former the latter are indeed less more responsive to their
expected chances of success when choosing a discipline.
In order to account for a potential self-selection prob- lem associated with the fact that the probability of suc-
cess can depend on the discipline chosen, we first esti- mate a multinomial logit model of orientation choice
based on socio-demographic characteristics of the stu- dents in order to compute a self-selection variable
Heckman, 1979; Lee, 1983; Trost Lee, 1984. The latter is introduced as an explanatory variable in a binary
probit model explaining observed success and failure rates. The results of the latter estimations are used to
forecast the probabilities of success of students in each of the alternative disciplines. Finally, a conditional logit
model is tested to determine to which extent these a priori probabilities of success affect the choice of orien-
tations, besides expected future economic benefits and length of studies this variable might be interpreted both
as another proxy for the risk component and as an indi- cator of the global financial cost necessary to obtain the
final degree.
Formally, the first step consists in estimating the choice probability of an orientation in a set of seven
orientations, see later by means of a multinomial logit procedure. In this step, we only consider socio-demo-
graphic variables. The probability that an individual i, with the set of characteristics Y
i
chooses the orientation j is given by the following expression:
P
ij
5 exp
a
j
9y
i
O
m k
51
exp a
k
9y
i
j 51,...m
1
The aforementioned sample selection issue is treated following Lee’s 1983 work. In a polytomous choice
model, the self-selection variables l
ij
obtained for each individual after a first step of multinomial logit might be
written as follows: l
ij
5f F
− 1
P ˆ
ij
P ˆ
ij
2 where P
ˆ
ij
is the estimated probability that an individual i with the set of characteristics Y
i
chooses the orientation j
5
, f is the standard normal density function and F the
standard normal cumulative function. This new variable is introduced as an explanatory variable in the second
step which consists in estimating the probabilities of suc- cess in each of the six disciplines by means of a binary
probit model. From the estimated coefficients, we can compute the probabilities of success in each of the six
disciplines for all of the students in our sample.
These estimations of expected probabilities of success are based on actual successfailure rates for freshmen in
the first year. In other words, we estimate students prob- abilities of success based on average socio-economic and
demographic profile of successful students within each orientation. A shortcoming of such an approach is that
we cannot disentangle within the determinants of chances of success between the part due to the peculiar
socio-economic variables and the one linked with the general ability within a particular orientation. Following
Willis and Rosen 1979, students might indeed select orientations on the basis of their absolute skill advan-
tages. However, even if our procedure is indeed based upon the actual success rate, it nevertheless takes into
account both the selection bias problem i.e. the possible correlation between the choice for a specific discipline
and the chances of success in it, as well as the impact of the socio-economic characteristics of the students on
the probabilities of success. By this procedure, we obtain estimated coefficients on the various explanatory vari-
ables which are partly sample-neutral and allow us to predict probabilities of success in each orientation, even
those not actually chosen by the student.
Finally, the last step of the procedure consists in the estimation of a conditional logit model with the expected
probability of success as well as characteristics related to each major. In such a model, the probability of student
i choosing major j is obtained from the following equ- ation:
P
ij
5 exp
b9x
ij
+ d9Z
j
O
m k
51
exp b9x
ik
+ d9Z
j
j 51,...m
3 where x
ij
is the predicted probability of success in the jth orientation j
= 1,...6 for student i and Z
j
is a vector of characteristics
6
of discipline j. The impact of the vari- able x
ij
is assumed to be constant across alternatives. This coefficient
b has an expected positive sign, i.e. that an individual should choose the discipline where he has
5
Estimated by means of Eq. 1.
6
Wage at entry-level for young graduates, a measure of easi- ness of insertion on the labour market and length of study.
18 D. Rochat, J.-L. Demeulemeester Economics of Education Review 20 2001 15–26
Table 1 Definition of orientations
Orientation 1 Short cycle 2 to 3 years in Economic and Social Sciences
Orientation 2 Short cycle 2 to 3 years in Paramedical studies
Orientation 3 Short cycle 2 to 3 years in Artistic and Pedagogical studies
Orientation 4 Long cycle curricula 4 years or university degree 4 to 7 years in Natural and Medical Sciences
Orientation 5 Long cycle curricula 4 years or university degree 5 years in Engineering
Orientation 6 Long cycle curricula 4 years or university degree 5 years in Business, Economics and Social Sciences
Orientation 7 Long cycle curricula 4 years or university degree 4 to 5 years in Humanities and Psychology
the highest probability of success, given his socio-econ- omic background. This methodology has so far only been
applied by Cannings et al. 1993 to a quite close topic namely
the major
choices in
undergraduate concentrations.
3. Data and model specification
