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UNIVERSITE DE LAUSANNE
ECOLE DES HAUTES ETUDES COMMERCIALES
Macroeconomic Modelling
Determinants of Academic Performance of
HEC-Lausanne Graduates
Awa Sakho Urién1
June, 2003
Professor: Jean-Christian Lambelet
Assistant: Alexander Mihailov
1 I am grateful to all the former students who kindly accepted to fill in the questionnaire in which this study is based, as well as to all the people working in HEC-Lausanne who contributed to make this project possible.
2
Abstract
This paper aims to provide empirical evidence of the determinants of academic
performance for the case of HEC-Lausanne graduates. It analyses econometrically the
relationship between different variables and the average grade obtained during the
licence studies by 156 students. Our findings suggest that a large number of different
factors related with the personal and family background, with the work and study
discipline and with the type of degree interact together in order to explain the variation
of HEC students’ performance.
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1. Introduction
What are the main determinants of academic performance in the specific case of
HEC-Lausanne graduates? In fact, if we think about why some students perform better
than others many ideas come to our mind: is it because they study more? Or because
they have a higher capacity to learn? Or maybe, the personal background, way of life
and environment of the student can also favour his/her performance?
By providing empirical evidence, this study tries to identify whether there exist
specific objective factors explaining the variation in students’ achievement and also to
look at the comparative importance of these factors. The set of variables considered can
be classified in four main categories: 1)own student’s characteristics, 2)family related
characteristics, 3)characteristics related to the study and work discipline during the
university years, 4)type of degree in which the student was enrolled. Therefore, this
paper attempts to see if the variables included in each of these four groups are
significant factors underlying the students’ academic performance, and to evaluate their
relative weight.
Our empirical analysis is based on individual survey data. In fact, we have
conducted a survey among a random sample of former HEC-Lausanne students.
This survey allowed us to compile the required information about the above-mentioned
characteristics.
By giving an in-depth analysis of the determinants of graduates’ success, we
hope that the results of this study can be of interest for HEC-Lausanne in its permanent
goal of improving the education offered to the students’ community.
The paper is organised into six main parts. In the next section, we will review
the literature that has previously analysed the role of different factors in student’s
achievement. In section 3, we will provide a description of the data that we used. Then,
we will present our econometric model. After this, we will describe and analyse the
results and finally in section 6, we will offer our conclusions.
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2. Literature Review
Numerous studies have analysed the factors behind the performance of students.
Identifying the variables that influence the achievement of young individuals at school,
high school or university is of great importance for two different communities. On one
hand, it is an essential tool for the public authorities in charge of the definition of
optimal and efficient education policies. On the other hand, this kind of analysis can
help the educational institutions to improve the quality of their programmes. Also, some
authors have suggested that there is a relationship between the performance of students
during their university studies and their future earnings.
In this direction, the OECD conducted in 2000 a Programme for International
Student Assessment (PISA 2000): “Knowledge and Skills for Life”. It is an
international study (32 countries) assessing the performance of young students at age 15
in three main domains: reading literacy, mathematical literacy and scientific literacy.
PISA also collected from these students information about their background and
institutional factors. As a consequence, the study gives an extremely rich set of results
that are used by researchers as well as by policy-makers in order to better understand
what determines different levels of performance in education.
For instance, Fertig and Schmidt (2002) used the PISA 2000 study in order to
analyse econometrically the relationship between the national reading test scores and
the family, school and class characteristics of the 15-year-old students. They conclude
that students from many countries (like for example, Finland, Korea and Australia)
show a performance statistically significant better than students from the US. They also
found evidence that being a female, having both parents working, living in an intact
family and a high level of parental education are factors positively related with the
reading test scores.
Other authors have been interested in finding the variables explaining the
variation in academic performance for university students. Betts and Morell (1998) try
to identify the determinants of undergraduate success using as a measure the Grade
Point Average (GPA). Their results suggest that factors like gender, ethnicity, and
family income as well as the socio-economic environment of the school have an
important role in explaining why students obtain different GPA.
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Also, Stricker and Rock (1995) conduct a similar analysis by assessing the
impact of the examinees’ initial characteristics (gender, ethnicity, parental education,
geographic region and age), college-related characteristics and college-related
performance variables in the performance on the Graduate Record Examinations (GRE)
General Test. They found that the students' initial characteristics have a modest impact
on the GRE results and among them parental education is the most significant. On the
contrary, the college-related characteristics (major, institutional quality and research
university) seem to have a more important role in explaining the difference in GRE
scores among students.
The common point of our study and previous research is that we aim to analyse
the relationship between the academic achievement of HEC-Lausanne graduates and
their background characteristics. Besides these factors, the present study examines
whether different variables related with the study and work discipline are determinants
of the performance of HEC-Lausanne students.
3. Data
The data employed in this study are the result of a survey: we sent a multiple-
choice questionnaire to a random sample of former HEC-Lausanne students (the
questionnaire can be found at the end of the appendix). This sample included licence
students, who finalized their studies after 1994 and who are currently members of the
“Association des Gradués HEC”. The respondents are asked about different aspects of
their socio-economic and demographic characteristics such as gender, age, canton of
residence at the beginning of the university studies, mother tongue, socio-professional
status and education of the parents2, source of finance of their university studies.
Moreover, other questions ask about how often the respondent used to go to the
university library, to use the internet, or to do personal research and about the
professional experience during the university years. Finally, the participants are asked to
specify in which degree they were enrolled (management, economics, actuarial science,
computer science in management) and give their average grade obtained at the end of
their licence studies. The last question ask the respondents if they agree to give their
2 Education levels 1: compulsory school; 2: compulsory school and vocational training (e.g. apprentissage); 3: maturité, baccalauréat or equivalent diploma; 4: university graduate; 5: post graduate degree.
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authorisation in order to check with the university files the average grade obtained. The
questionnaire was sent to 589 former HEC-Lausanne graduates, among which 163
completed the questionnaire, i.e. the response rate was 27.6%. However, we were not
able to use all the 163 questionnaires as 7 of them were not correctly completed. As a
consequence we used the questionnaires of 156 former HEC-Lausanne graduates. It is
worth mentioning, that among the 156 respondents, 18 of them refused to give their
authorisation to check their average grade with the university files and 16 of them forgot
to answer this question. In these cases we used the grade provided by the respondent. As
a consequence, we were able to compare the average score from the questionnaire with
the one of the university files for 122 former students. This comparison allowed us to
see that in most of the cases the average grade provided in the questionnaire was very
close to the real one. This shows that most of the people who gave their average score of
licence studies in the questionnaire remembered it with good accuracy. Moreover, the
majority of the respondents tend to approximate their average grade by rounding it (e.g.
if they had a 7.3, they said in the questionnaire that they had a 7). Also, among the 156
questionnaires, 38 persons answered that they could not remember their grade but gave
the authorisation to check it in the university files.
In fact, it is interesting to look at the descriptive statistics of the obtained dataset
(table 1 in the appendix). Almost 30% of the students were women, most have French
as their mother tongue (80%), half of them lived with their parents during their
university studies and almost all of them financed their studies mainly or partially
thanks to their parents (94%). The largest majority of the respondents were enrolled in a
management degree (80%) and slightly more than half of them had some kind of
professional experience during the university studies. It is also striking to see that only
21% of the respondents used the university library very often and 14% of them did
frequently personal research. However, the fact that 43% of former HEC-Lausanne
students recognize to use very often internet, leads us to think that maybe internet is
used as a research tool and thus becoming a substitute for the library research.
Finally, we measure the academic performance of HEC-Lausanne graduates
with the average grade obtained during the licence studies. The average grade ranges
from 6 (parity grade) to 10. The mean average grade in our sample is 7.34 and the
median 7.7.
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Chart: Distribution of average grade
0%2%4%
6%8%
10%12%
14%16%18%
6.3
6.6
6.8 7 7.
27.
47.
67.
8 8 8.2
8.4
8.6
average grade
per
cen
tag
e o
f st
ud
ents
Source: Questionnaire, Number of observations: 156.
4. Econometric Models
The main purpose of this paper is to identify what objective factors determine the
academic performance of HEC students and to evaluate their relative importance. More
precisely, we want to see whether the initial characteristics, the family related
characteristics, the work and study related variables or the type of degree are factors
explaining the variation of students’ success, and if this is the case, to evaluate their
relative weight. As a consequence our dependent variable is the average grade obtained
by the student during his university years on a scale of 6 to 10 (6 is the minimum
average grade a student can get in order to graduate). We use a Tobit model (Tobin,
1958) because the dependent variable is limited in its range: with lower limit 6 and
upper limit 10. The specification of our model is as follows:
Y =X �
Where:
� Y is our dependent variable, the average grade obtained for the licence
studies
� c is the constant
� is the vector of the coefficients of the explanatory variables
� X is the vector of the explanatory variables
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� is the error term.
From the information contained in the constructed database, we obtain our set of
explanatory variables, which can be classified in four main categories (table 2 in the
appendix gives a detailed description of all the variables):
1. Own initial and demographic characteristics: gender, mother tongue, canton
of residence at the beginning of the university studies, age.
2. Family related characteristics: parents employment situation, level of
education and socio-professional status of the parents, source of finance of
the studies, parental co-living status.
3. Study and work discipline related characteristics: type of
Maturité/Baccalauréat, use of the university library, use of internet resources,
class attendance, professional experiences during the studies (internship,
assistantship, others), personal research, membership in students’
organisation.
4. Type of degree: management, economics, actuarial sciences, and computer
sciences in management.
All the explanatory variables except age are dummy variables.
The questionnaire provides us with a very large number of independent variables.
As a consequence, we believe that the best strategy to follow is a top-down procedure.
In other words, we will start by including all the 68 explanatory variables in our
regression (model 1) and we will reduce progressively the number of independent
variables by doing a stepwise selection (models 2, 3 and 4). The stepwise selection
removes from the model the variables with a p-value larger than a specified significance
level.
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5. Results
As we mentioned in the previous part, we start by including all the 68 independent
variables in our regression. The obtained results (model 1, table 4 in appendix) look
overall satisfactory: the pseudo R2 is 52% and although some variables are far from
being significant, it seems that we have a good number (22) of independent variables
which are significant at a 10% level.
The next step is to reduce our model using a stepwise selection. We choose 20% as
the significance level for removal from the model. The resulting model (model 2, table 5
in appendix) shows also a high pseudo R2 at 44%, i.e. the model explains 44% of the
variation of the dependent variable and the rest of the variation is explained by very
specific factors. With the stepwise selection we have removed 33 variables, and
therefore we obtain a model with 35 variables. Looking at the p-values, we can observe
that almost all the explanatory are significant at a 10% level. In fact, only three of them
are not significant at a 10% level. Moreover, if we restrict ourselves to a significance
level of 5%, 10 variables have a p-value above this level. As a consequence, the results
of model 2 are quite satisfactory in terms of significance.
We have also removed these variables, which are not significant at a 10% and at a
5% level (models 3 and 4, tables 6 and 7 in the appendix) in order to see if the model is
improved. However, we notice that the pseudo R2 is in both cases lower (41% and 31%
respectively) suggesting that the removal of these variables does not provide a better
explanation of the variation of the average grade obtained by HEC-Lausanne graduates.
Thus, we will focus on model 2 and we will interpret the results looking at each
category of independent variables separately:
Own initial characteristics
First, we observe that students whose mother tongue is French perform significantly
better. Given the fact that the large majority of the licence courses is given in French
and that most of the material is in this language, it is an obvious advantage to have this
language as mother tongue. It is interesting to see, that the variables “Spanish mother
tongue” and “English mother tongue” are significant as well, but with different signs.
According to our results, the students whose mother tongue is Spanish perform better
and the students whose mother tongue is English perform worse which may be
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surprising. One possible explanation of this finding, is that Spanish is more similar to
French than English and hence, Spanish speaking students might experience less
difficulties with French. Secondly, the canton of residence at the beginning of the
university studies is not statistically significant, which implies that the students adapt
themselves easily to a new environment. Thirdly, the average grade obtained by HEC-
Lausanne students declines with the age at which the student started his/her licence
studies. In fact, students leaving a long time between the end of the high school and the
beginning of the university studies, will probably forget more of what they have learned
at school and high school. This is likely to be an disadvantage with respect to the
students who go straight to the university after obtaining their maturité/baccalauréat and
therefore who do not need to “refresh their memories”. However, contrary to previous
studies (Stricker and Rock, 1995; Betts and Morell, 1998) we have not been able to find
evidence of a relationship between academic performance and gender.
Family related characteristics
Regarding, the parents’ employment situation, we perceive that there exists a
significantly positive correspondence between the fact that the mother is retired and the
academic achievement of HEC-Lausanne students. If the mother is retired, it means that
she has previously worked and therefore this can represent an example to follow for the
students and increase their motivation at university. However, it is worth mentioning
that in our sample only 1% of the students’ mothers are retired, and therefore, the result
that we found might just be a coincidence. On the contrary, students whose father is
retired or works only part time obtain a lower average grade. In all the cases, if the
mother or the father is not alive, it tend to have a negative impact on the performance of
the student.
Among all the variables related to parental education, the only ones that are
statistically significant are the ones “father level of education 3” and “father level of
education 4”. In fact, it seems that students with a father who completed the
maturité/baccalaureat or/and graduated from a university perform significantly worse.
We were not expecting this result, as previous studies have shown that a higher level of
parental education affects positively the performance of students. A possible
explanation of this result could be that students whose father have a high education
level are less motivated because for them going to the university is a “normal” step after
their high school studies, i.e. they do not see the university studies as a new challenge.
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Therefore their level of effort will be lower than for someone who sees the fact of going
to university as an opportunity that not everyone can have. The results also suggest that
in the case of our sample, the mother’s level of education is not relevant or can not be
identified in our sample.
Although these results are somehow surprising, we want to stress the fact that our
findings may be related to the specific sample at hand.
Looking at the results, we also see that the variables “mother employee” and “father
employee” display a significantly positive coefficient estimate. More interesting is the
significant positive link between having a father who is Executive/manager or who is
self employed (artisan, retail trader, company manager) and the average grade obtained.
If the student’s father is an executive or a manager, the socio-economic status of his/her
family is likely to be high. This implies that students with high parental income perform
better. However, we can think another type of explanation: individuals who are
executives, managers or self-employed have usually a high entrepreneurial spirit, i.e.
they have initiative and a highly motivated attitude for what they do. This can exert a
positive influence on the student, if he/she sees his/her father as a model to follow.
Model 2 also shows that there is a negative link between the fact that the student
only lives with his/her father and the academic performance during the university
studies. Similarly, the PISA survey shows that 15 years old students living in single-
parent families tend to perform less well than their peers.
It is also very interesting to notice that studies financed by parents is negatively
related with the academic performance whereas the financing through a loan, a
scholarship or/and own gains of the student is positively related and significant. A
possible explanation of this result is that students who depend on a loan, a scholarship
and/or on their own gains are more aware of the economic cost, including the
opportunity cost, involved in studying for a university degree and therefore their efforts
tend to be higher than those exerted by students who depend financially on their parents.
Study and work discipline
Having a Maturité/Baccalauréat in economics or in languages3 exhibits a
significantly positive correspondence with academic success, suggesting that these
3 The variable Maturité/Baccalauréat in languages includes the variables “maturité moderne” and “maturité Latin”.
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types of diplomas prepare the students better in order to study one of the four degrees
offered at HEC-Lausanne (management, economics, actuarial science and computer
science in management).
A low course attendance has a significant and negative coefficient estimate. This
supports the idea that class attendance plays a relevant role in the learning process as it
contributes to a better understanding of the subjects treated and allows for an interaction
between the student and the teachers and also between the student and his/her peers.
With regard to the professional experiences of the student, we observe that having
some professional experience before entering the university and/or during the university
studies has a significantly positive effect on the academic success of the student.
However, if the student does both internships and has other jobs during his/her studies,
the sign of the coefficient associated to this variable is negative. The results suggest that
having some contact with the labour market during and before the university years is
positive. Nonetheless an excess of working experience during the university years can
also impact the academic performance negatively, probably because it reduces the time
dedicated to study.
Repeating a year is a highly significant predictor of a lower average grade. Also, all
the variables related with the use of the internet are negatively associated with the
academic performance.
Type of degree
Finally, the average grade obtained can also depend on the type of degree in which
the student is enrolled. Looking at the results we detect that students enrolled in
actuarial science tend to have higher average grade, whereas among management
students the academic performance is lower.
We have used a Tobit model because we have a limited dependent variable.
However, we do not have many observations close to the limits, i.e. in our sample only
a few cases have average grade close to 6 and none of the students has an average grade
very close to 10. Under these circumstances, even though the Tobit approach is the
theoretically correct one to use, OLS yields the same results (Tables 8, 9, 10 in the
appendix). In fact the OLS estimated coefficients are exactly the same than the ones
13
obtained using the Tobit estimation. However, the standard errors are higher in the OLS
case, and therefore we can conclude that OLS is less efficient than Tobit.
After analysing these results, the next question is: among all the significant variables
which ones are the most important? In order to answer to this question, we ����������� �
coefficients4, i.e. the coefficient estimate from a regression in which the variables have
been standardized. We use this coefficient to measure the relative strength of the
explanatory variables in influencing the regressand. The results are shown in table 8.
We can observe that among all the variables the one that has the strongest influence in
the average grade obtained is having a father who is Executive/manager. Apart from this
variable, having French as one’s mother tongue, studying actuarial science and having
an internship experience during the university studies are the variables with a strongest
impact on the academic performance of HEC-Lausanne students.
6. Conclusions
The existing literature trying to identify the determinants of university students’
performance, focuses on personal background characteristics, college related
characteristics and the degree in which the student is enrolled. However, this paper also
includes variables related to the type of study and work discipline of the students.
Our empirical analysis based on an individual data level survey for the specific case
of HEC-Lausanne graduates, leads to the following conclusions. First of all, we observe
that most of our 35 explanatory variables, belong to the category of family related
characteristics. Secondly, the variable father Executive/manager or self-employed is the
one with the strongest influence on the average grade of HEC-Lausanne students.
Another interesting feature is that the self-financed studies through own gains, a loan
and/or a scholarship is positively and significantly related with the academic
achievement of the students whereas financial support by the parents exhibits a negative
effect. Finally, a Maturité/Baccalauréat in economics or in languages, class attendance,
and the acquisition of some professional experience during and/or during university
studies, have a significant and positive impact on the score of HEC students. It is worth
4 = (estimated coefficient*standard error of the explanatory variable)/ standard error of the dependent variable
14
mentioning that the size of our sample or its characteristics might explain why in some
cases we do not find the results that we expected.
It would be of great interest to replicate this analysis with a sample including not
only graduates, but also students who did not finish their licence studies.
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REFERENCES
� Betts, Julian R. and Morell, Darlene (1998) The Determinants of Undergraduate
Grade Point Average: The Relative Importance of Family Background, High
School Resources, and Peer Group Effects. The Journal of Human Resources,
34, 268-293.
� Fertig, Michael and Schmidt, Christoph M. (2002) The Role of Background
Factors for Reading Literacy: Straight National Scores in the PISA 2000 Study.
� Greene, William H. (2003) Econometric Analysis, Fifth Edition. Prentice Hall.
� Kennedy, Peter (1998) A guide to Econometrics, Fourth Edition, Blackwell.
� Organisation for Economic Co-operation and Development (OECD) (2002)
Knowledge and Skills for Life: First Results from PISA 2000. Paris.
� Stricker, Lawrence J. and Rock, Donald A. (1995) Examinee Background
Characteristics and GRE General Test Performance. Intelligence, 21, 49-6.
� Tobin, J. (1958) Estimation for Relationships for Limited Dependent Variables.
Econometrica, 26, 24-36
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APPENDIX
Table 1: Descriptive Statistics Females 28% Socio-professional status of the mother
French mother tongue 79% Agricultural worker 1%
Type of Maturité/Baccalauréat Self-employed worker 8%
Maturité/Baccalauréat in sciences 29% Executive/manager 6%
Maturité/Baccalauréat in econonomics 59% «Profession libérale» 3%
Maturité/Baccalauréat in Latin 7% Employee 34%
Maturité/Baccalauréat in modern languages 5% Worker 1%
Canton of residence before studies Retired 1%
Canton Vaud 58% Mother not employed 46%
French speaking part of Switzerland 13% Mother not alive 1%
German speaking part of Switzerland 12% Socio-professional status of the father
Ticino 4% Agricultural worker 3%
Bilingual part of Switzerland5 10% Self-employed worker 16%
Abroad 3% Executive/manager 39%
Parents employment situation «Profession libérale» 7%
Mother working full time 29% Employee 22%
Mother working part time 24% Worker 4%
Mother not employed 46% Retired 4%
Mother not alive 1% Father not employed 7%
Father working full time 88% Father not alive 2%
Father working part time 3% Work and study discipline characteristics
Father not employed 7% Used UNIL library very often 21%
Father not alive 2% Used UNIL library often 34%
Live with both parents 50% Hardly ever used UNIL library 30%
Live with the father 1% Never used UNIL library 15%
Live with the mother 6% Used the internet often 43%
Finance source of university studies Did personal research very often 14%
Parents (mainly and/or partially) 94% Did personal research often 34%
Own gains, scholarship and/or loan (mainly and/or partially)40% Hardly ever did personal research 46%
Parental education: Never did personal research 6%
Mother education level 1 19% Repeating a year 24%
Mother education level 2 44% Attendance to all the courses 51%
Mother education level 3 16% 22%
Mother education level 4 12% Attendance to less than half of the courses 11%
Mother education level 5 3% Professional Experiences
Father education level 1 9% Professional experience before the university studies 32%
Father education level 2 41% Internship during the studies 52%
Father education level 3 16% Other professional experience during the studies 54%
Father education level 4 22% Assistants 16%
Father education level 5 12% Type of degree
Management 79%
Economics 12%
Actuarial Science 3%
Computer Science in management 6%
5 Bilingual part of Switzerland includes: Fribourg, Neuchatel and Valais. Source: Questionnaire, 156 observations
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Table 2: Description of variables
Variable Description
Average grade (grade) Average grade obtained at the end of the licence studies
Gender (gender) 1 if student is female; 0 otherwise
French (fr) 1 if the student’s mother tongue is French; 0 otherwise
German (gr) 1 if the student’s mother tongue is German; 0 otherwise
Italian (it) 1 if the student’s mother tongue is Italian; 0 otherwise
Spanish (sp) 1 if the student’s mother tongue is Spanish; 0 otherwise
English (eng) 1 if the student’s mother tongue is English; 0 otherwise
Other language (olang) 1 if the student’s mother tongue is other than the French, German, Italian, Spanish or English; 0 otherwise
Maturité/Baccalauréat in science (matsc) 1 if the student has a Maturité/Baccalauréat in science; 0 otherwise
Maturité/Baccalauréat in economics (mateco) 1 if the student has a Maturité/Baccalauréat in economics; 0 otherwise
Maturité/Baccalauréat in Latin (matlat) 1 if the student has a Maturité/Baccalauréat in Latin; 0 otherwise
Maturité/Baccalauréat in modern languages (matmod)
1 if the student has a Maturité/Baccalauréat in modern languages; 0 otherwise
Maturité/Baccalauréat in languages (matlang) 1 if the student has a Maturité/Baccalauréat in Latin or in modern languages; 0 otherwise
Age (age) Age at which the student started her/his university studies
Canton Vaud (vd) 1 if the student at the beginning of the university studies was a resident in the Canton Vaud; 0 otherwise
French speaking Switzerland (chrom) 1 if the student at the beginning of the university studies was a resident in a French speaking canton; 0 otherwise
German speaking Switzerland (chgerm) 1 if the student at the beginning of the university studies was a resident in a German speaking canton; 0 otherwise
Ticino (ticino) 1 if the student at the beginning of the university studies was a resident in the Ticino; 0 otherwise
Bilingual Switzerland (chbil) 1 if the student at the beginning of the university studies was a resident in a bilingual canton; 0 otherwise
Abroad (abroad) 1 if the student at the beginning of the university studies was not living in Switzerland; 0 otherwise
Mother working full time (mothfull) 1 if the student’s mother was working full time at the beginning of the university studies; 0 otherwise
Mother working part time (mothpart) 1 if the student’s mother was working part time at the beginning of the university studies; 0 otherwise
Mother not alive (mothna) 1 if the student’s mother was not alive at the beginning of the university studies; 0 otherwise
Father working full time (fathfull) 1 if the student’s father was working full time at the beginning of the university studies; 0 otherwise
Father working part time (fathpart) 1 if the student’s father was working part time at the beginning of the university studies; 0 otherwise
Father not alive (fathna) 1 if the student’s father was not alive at the beginning of the university studies; 0 otherwise
Living with parents (livepar) 1 if the student was living with his/her parents during the university studies; 0 otherwise
Living with mother (livemoth) 1 if the student was only living with his/her mother during the university studies; 0 otherwise
Living with father (livefath) 1 if the student was only living with his/her father during the university studies; 0 otherwise
Studies financed by the parents (finpar) 1 if the university studies were mainly or/and partially financed by the parents; 0 otherwise
Studies financed by own gains, loan or/and scholarship (ownscholloan)
1 if the university studies were financed mainly or partially by a loan, the student’s own gains and/or by a scholarship; 0 otherwise
Mother’s education level 1 (mothedu1) 1 if the student’s mother completed the compulsory school; 0 otherwise
Mother’s education level 2 (mothedu2) 1 if the student’s mother completed the compulsory school and vocational training; 0otherwise
Mother’s education level 3 (mothedu3) 1 if the student’s mother completed the “maturité”, baccalauréat or equivalent diploma; 0 otherwise
Mother’s education level 4 (mothedu4) 1 if the student’s mother is a university graduate (licence or equivalent diploma); 0 otherwise
Mother’s education level 5 (mothedu5) 1 if the student’s mother completed a post graduate degree; 0 otherwise
Father’s education level 1 (fathedu1) 1 if the student’s father completed the compulsory school; 0 otherwise
Father’s education level 2 (fathedu2) 1 if the student’s father completed the compulsory school and vocational training; 0 otherwise
Father’s education level 3 (fathedu3) 1 if the student’s father completed the “maturité”, baccalauréat or equivalent diploma; 0 otherwise
Father’s education level 4 (fathedu4) 1 if the student’s father is a university graduate (licence or equivalent diploma); 0 otherwise
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Father’s education level 5 (fathedu5) 1 if the student’s father completed a post graduate degree; 0 otherwise
Mother agricultural worker (mothagri) 1 if the student’s mother was an agricultural worker at the beginning of the university studies; 0 otherwise
Mother self-employed (mothself) 1 if the student’s mother was self-employed at the beginning of the university studies; 0 otherwise
Mother Executive/manager (mothexec) 1 if the student’s mother was an Executive/manager at the beginning of the university studies; 0 otherwise
Mother «Profession libérale» (mothlib) 1 if the student’s mother had a «Profession libérale» at the beginning of the university studies; 0 otherwise
Mother employee (mothemp) 1 if the student’s mother was an employee at the beginning of the university studies; 0 otherwise
Mother worker (mothw) 1 if the student’s mother was a worker at the beginning of the university studies; 0 otherwise
Mother retired (mothret) 1 if the student’s mother was retired at the beginning of the university studies; 0 otherwise
Father agricultural worker (fathagri) 1 if the student’s father was an agricultural worker at the beginning of the university studies; 0 otherwise
Father self-employed (fathself) 1 if the student’s father was self-employed at the beginning of the university studies; 0 otherwise
Father Executive/manager (fathexec) 1 if the student’s father was an Executive/manager at the beginning of the university studies; 0 otherwise
Father «Profession libérale» (fathlib) 1 if the student’s father had a «Profession libérale» at the beginning of the university studies; 0 otherwise
Father employee (fathemp) 1 if the student’s father was an employee at the beginning of the university studies; 0 otherwise
Father worker (fathw) 1 if the student’s father was a worker at the beginning of the university studies; 0 otherwise
Father retired (fathret) 1 if the student’s father was retired at the beginning of the university studies; 0 otherwise
Repeating a year (repeat) 1 if the student repeated one year during his/her university studies; 0 otherwise
Used UNIL library very often (library 4) 1 if the student used UNIL library very often; 0 otherwise
Used UNIL library often (library 3) 1 if the student used UNIL library often; 0 otherwise
Hardly ever used UNIL library (library 2) 1 if the student hardly ever used UNIL library; 0 otherwise
Never used UNIL library (library1) 1 if the student never used UNIL library; 0 otherwise
Attendance to all courses (course3) 1 if the student attended all the courses; 0 otherwise
Attendance to almost all the courses (course2) 1 if the student attended almost all the courses; 0 otherwise
Attendance to less than half of the courses (course1)
1 if the student attended less than half of the courses, or even only 10% of the courses; 0 otherwise
Professional experience before the studies (prejob)
1 if the student had some kind of professional experiences before entering the university; 0 otherwise
Internship (internship) 1 if the student did an internship during the university studies; 0 otherwise
Professional experience (profexp) 1 if the student had a professional experience during the university studies; 0 otherwise
Internship and professional experience (intprofexp)
1 if the student did an internship and a professional experience during the university studies; 0 otherwise
Assistantship (assist) 1 if the student was an assistant during his/her university studies; 0 otherwise
Organisation (org) 1 if the student was a member of a students’ organisation; 0 otherwise
Used internet often (web4) 1 if the student used very often the internet; 0 otherwise
Hardly ever used the internet (web3) 1 if the student used hardly ever the internet; 0 otherwise
Never used the internet (web2) 1 if the student never used internet; 0 otherwise
No internet (web1) 1 if during the university years, the internet did not exist or was starting; 0 otherwise
Did personal research very often (research4) 1 if during the studies, the student did personal research very often; 0 otherwise
Did personal research often (research3) 1 if during the studies, the student did personal research often; 0 otherwise
Hardly ever did personal research (research2) 1 if during the studies, the student hardly ever did personal research; 0 otherwise
Never did personal research (research1) 1 if during the studies, the student did never do personal research; 0 otherwise
Management (manag) 1 if the student was enrolled in the management degree; 0 otherwise
Economics (eco) 1 if the student was enrolled in the economics degree; 0 otherwise
Actuarial science (actu) 1 if the student was enrolled in the actuarial science degree; 0 otherwise
Computer science in management (comp) 1 if the student was enrolled in the computer science management degree; 0 otherwise
19
Table 3: Determinants of Academic performance of HEC-Lausanne graduates
Explanatory variables Model 1 Model 2 Model 3 Model 4 Gender .0849
(.0876)
French .3821** (.1559)
.3985** (.0965)
.3019** (.0891)
.2452** (.0090)
German -.0609 (.1602)
Italian -.1800 (.2757)
Spanish .4363* (.2443)
.4146** (.2004)
.3411* (.1956)
.1471 (.1938)
English -.3547 (.3560)
-.5553* (.2956)
-.4749 (.3014)
Maturité/Baccalauréat in science .3525 (.4485)
Maturité/Baccalauréat in economics .4458 (.4452)
.1531** (.0777)
.1471 (.0795)
.1706 (.0823)
Maturité/Baccalauréat in languages .6783 (.4757)
.3381** (.1132)
.3444** (.1159)
.2032* (.1070)
Maturité/Baccalauréat in Latin -.1552 (.2145)
Age -.0332** (.0163)
-0.3355** (.0152)
-.0350** (.0156)
-.0401** (.0159)
Vaud -.0354 (0.1349)
French speaking Switzerland -.0782 (.1615)
German speaking Switzerland .0885 (.1582)
Ticino .2654 (.3636)
.2917 (.1959)
Abroad .0155 (.2347)
Mother working full time -.3376* (.2003)
Mother working part time -.2154* (.2051)
Mother not alive -.7850* (.4044)
-.8119** (.3938)
-.6533** (.3957)
-.5420 (.4168)
Father working full time -.1806 (.2467)
Father working part time -.6270* (.3263)
-.3524* (.2090)
-.3495* (.2030)
Father not alive -.5464** (.2652)
-.4275* (.2212)
-.4188 (.2206)
-.4781 (.2230)
Living with parents -.1351 (.0881)
-.1179 (.0748)
Living with mother -.2233 (.1734)
Living with father -1.5934* (.5423)
-1.522** (.0501)
-1.557* (.0506)
-1.198** (.5066)
Studies financed by parents (mainly and/or partially) -.1482* (.0503)
-.1016** (.0472)
-.1118** (.0476)
-.1037** (.0497)
Studies financed by own gains, schoolarship, loan (mainly and/or partially)
.1342 (.0894)
.1824** (.7935)
.2128** (.0793)
.2509** (.0794)
Mother’s education level 1 -.2925 (.2400)
Mother’s education level 2 -.2365 (.2172)
Mother’s education level 3 -.1970 (.2066)
Mother’s education level 4 -.1541 (.2155)
Father’s education level 1 .1734 (.2154)
Father’s education level 2 .01877 (.1435)
Father’s education level 3 -.1817 (.1492)
-.2359** (.0913)
-.2105** (.0927)
-.1326 (.0975)
Father’s education level 4 -.1660 (.1328)
-.1877**
(.0917)
-.1336 (.0877)
-.1106 (.0921)
Mother agricultural worker .9469** (.3602)
.6109** (.2856)
.6056** (.2887)
.6134** (.3023)
20
Mother self-employed .1877 (.2490)
Mother executive/senior management .3694 (.2582)
Mother «Profession libérale» .1477 (.3366)
Mother employee .4347** (.2130)
.1228* (.0725)
.1368* (.0733)
Retired mother 1.6555** (.4820)
1.690** (.4577)
1.381** (.4372)
1.238** (.4647)
Father agricultural worker .1076 (.3014)
Father self-employed .2482 (.2002)
.2191* (.1117)
.2548** (.1080)
.1549 (.0987)
Father executive/senior management .3912* (.2136)
.3805** (.1022)
.3721** (.0975)
.2214** (.0851)
Father «Profession libérale» .0046 (.2436)
.1907 (.1061)
Father employee .1939 (.2074)
.1938* (.1122)
.1907* (.1061)
Retired father -.4585* (.2607)
-.2787 (.1958)
Repeating a year -.2781** (.0782)
-.2827** (.0733)
-.3034** (.0744)
-.2852** (.0787)
Used UNIL library very often .0325 (0.991)
-.2939 (.1080)
Used UNIL library often .0526 (.0813)
Never used UNIL library .1996 (.1220)
.1686* (.0963)
.1474* (.0970)
Attendance to all courses .0894 (.0835)
Attendance to less than half of the courses -.1847 (.1234)
-.2811** (.1061)
-.2896** (.1086)
-.3971** (.1107)
Professional experience before university .1617** (.0794)
.1241* (.0747)
.0854 (.0753)
Internship during university .2598** (.1016)
.2870** (.0943)
.2946** (.0947)
.2505** (.0984)
Professional experience during university .2005* (.1059)
.2231** (.0902)
.2109** (.0915)
.1965** (.0960)
Internship and professional experience during university -.2484 (.1522)
-.2965** (.1335)
-.3144** (-1357)
-.3118** (.1444)
Assistanship .01085 (.1120)
Member of students’ organisation -.0847 (.0824)
Use internet often -.3820** (.1262)
-.3338** (.1082)
-.2939 (.1080)**
-.2070* (.1090)
Use hardly ever internet -.3231** (.1304)
-3254** (.1163)
-.3280** (.1182)
-.2361 (.1228)
Internet did not exist -.4595 (.1203)
-.4040** (.1168)
-.3434** (.1173)
-.1759 (.1118)
Did personal research very often .0461 (.1264)
Did personal research often .2289 (.0802)
Never did personal research -.1177 (.1769)
Management -.3028** (.1533)
-.2312** (.0901)
-.2078** (.0898)
-.1273 (.0927)
Economics -.0405 (.1865)
Actuarial Science 1.155 (.3797)
1.032** (.3277)
1.222** (.3150)
0.922** (.3008)
Pseudo R2 0.52 0.44 0.40 0.31 Notes: Each cell contains the coefficient estimate and (in parentheses) its standard error. The dependent variable is the average
grade obtained during the undergraduate studies. Significant coefficients at the 5% level are marked with two asteriks, significant
coefficients at the 10% level are marked with one asterik. The reference categories are: other languages, Maturité/Baccalauréat in
modern languages, bilingual part of Switzerland, mother’s education level 5, father’s education level 5, mother worker, father
worker, hardly ever used the library, attendance to almost all the courses, never used internet, hardly ever did personal research,
computer science in management degree.
21
Table 4: Model 1, Tobit estimates Tobit estimates Number of obs 156 LR chi2(68) 119.46
Prob > chi2 0.0001 Log likelihood -54.7022 Pseudo R2 0.5220 grade Coef. Std. Err. t P>|t| [95% Conf. Interval] gender 0.084947 0.087673 0.97 0.335 -0.08928 0.259178 fr 0.382169 0.155962 2.45 0.016 0.072228 0.69211 ger -0.06092 0.16022 -0.38 0.705 -0.37933 0.25748 it -0.18007 0.275713 -0.65 0.515 -0.72799 0.367854 sp 0.43631 0.244361 1.79 0.078 -0.04931 0.921926 engl -0.35473 0.356043 -1 0.322 -1.06229 0.352826 matsc 0.352507 0.448526 0.79 0.434 -0.53884 1.243858 mateco 0.445858 0.445262 1 0.319 -0.43901 1.330723 matlang 0.678325 0.475702 1.43 0.157 -0.26703 1.623683 matlat -0.15526 0.214556 -0.72 0.471 -0.58164 0.271129 age -0.03322 0.016314 -2.04 0.045 -0.06564 -0.0008 vd -0.03547 0.134953 -0.26 0.793 -0.30366 0.232725 chrom -0.07827 0.161571 -0.48 0.629 -0.39936 0.242819 chgerm 0.088599 0.158251 0.56 0.577 -0.22589 0.403088 ticino 0.265421 0.363699 0.73 0.467 -0.45735 0.988196 abroad 0.015514 0.234764 0.07 0.947 -0.45103 0.482058 mothfull -0.33766 0.200395 -1.68 0.096 -0.73591 0.060579 mothpart -0.21542 0.205137 -1.05 0.297 -0.62309 0.192248 mothna -0.78505 0.404476 -1.94 0.055 -1.58886 0.018763 fathfull -0.18068 0.246761 -0.73 0.466 -0.67107 0.309702 fathpart -0.62706 0.326393 -1.92 0.058 -1.2757 0.021574 fathna -0.54645 0.265209 -2.06 0.042 -1.07349 -0.0194 livepar -0.13514 0.088194 -1.53 0.129 -0.31041 0.040125 livemoth -0.22332 0.173451 -1.29 0.201 -0.56802 0.121377 livefath -1.59349 0.542361 -2.94 0.004 -2.67132 -0.51566 finpar -0.14825 0.050331 -2.95 0.004 -0.24827 -0.04823 ownschooll~n 0.134264 0.089452 1.5 0.137 -0.0435 0.312031 mothedu1 -0.29258 0.24008 -1.22 0.226 -0.76969 0.18453 motheedu2 -0.2366 0.217296 -1.09 0.279 -0.66843 0.195233 mothedu3 -0.19707 0.206621 -0.95 0.343 -0.60768 0.213549 mothedu4 -0.15415 0.215527 -0.72 0.476 -0.58246 0.274165 fathedu1 0.173403 0.215446 0.8 0.423 -0.25475 0.601556 fatheedu2 0.018772 0.143577 0.13 0.896 -0.26656 0.304101 fathedu3 -0.18177 0.14924 -1.22 0.226 -0.47835 0.114812 fathedu4 -0.166 0.132833 -1.25 0.215 -0.42998 0.097978 mothagri 0.946984 0.360245 2.63 0.01 0.231073 1.662895 mothself 0.187701 0.249046 0.75 0.453 -0.30722 0.682627 mothexec 0.369454 0.258284 1.43 0.156 -0.14383 0.882739 mothlib 0.147789 0.336656 0.44 0.662 -0.52124 0.816822 mothemp 0.434712 0.213062 2.04 0.044 0.011297 0.858128 mothret 1.655747 0.482026 3.43 0.001 0.697822 2.613673 fathagri 0.107635 0.301419 0.36 0.722 -0.49137 0.706641 fathself 0.248221 0.200219 1.24 0.218 -0.14967 0.646114 fathexc 0.391252 0.213618 1.83 0.07 -0.03327 0.815773 fathlib 0.004607 0.243602 0.02 0.985 -0.4795 0.488715 fathemp 0.193981 0.207463 0.94 0.352 -0.21831 0.60627 fathret -0.45852 0.260701 -1.76 0.082 -0.97661 0.05957 repeat -0.27818 0.078264 -3.55 0.001 -0.43371 -0.12264 libra4 0.032506 0.099144 0.33 0.744 -0.16452 0.229534 libra3 0.052631 0.081365 0.65 0.519 -0.10907 0.214327 libra1 0.199632 0.122033 1.64 0.105 -0.04288 0.442146 course3 0.089445 0.083558 1.07 0.287 -0.07661 0.255499 course1 -0.18479 0.123413 -1.5 0.138 -0.43005 0.060467 prejob 0.161752 0.079431 2.04 0.045 0.0039 0.319605 internship 0.259866 0.101682 2.56 0.012 0.057795 0.461937 profexp 0.200579 0.105939 1.89 0.062 -0.00995 0.411109 intprofexp -0.24843 0.15228 -1.63 0.106 -0.55105 0.054197 assist 0.108505 0.112044 0.97 0.335 -0.11416 0.331168 org -0.08472 0.082437 -1.03 0.307 -0.24854 0.079108 web4 -0.38207 0.126228 -3.03 0.003 -0.63292 -0.13122 web3 -0.32319 0.130456 -2.48 0.015 -0.58245 -0.06394 web1 -0.45952 0.120341 -3.82 0 -0.69868 -0.22037 research4 0.046179 0.126459 0.37 0.716 -0.20513 0.29749 research3 0.022896 0.080232 0.29 0.776 -0.13655 0.182341 research1 -0.11779 0.176922 -0.67 0.507 -0.46938 0.233807 manag -0.30287 0.153329 -1.98 0.051 -0.60758 0.00184 eco -0.04055 0.18652 -0.22 0.828 -0.41122 0.330121 actu 1.155189 0.379713 3.04 0.003 0.400588 1.909789 _cons 8.064654 0.642147 12.56 0 6.788523 9.340786 _se 0.343598 0.019452 (Ancill ary parameter)
22
Table 5: Model 2, Tobit estimates
Tobit estimates Number of obs 156
LR chi2(35) 101.41 Prob > chi2 0
Log likelihood -63.7297 Pseudo R2 0.4431 grade Coef. Std. Err. t P>|t| [95% Conf. Interval] intprofexp -0.29654 0.133515 -2.22 0.028 -0.56087 -0.03221 fr 0.398577 0.096534 4.13 0 0.207462 0.589691 fathemp 0.193812 0.112224 1.73 0.087 -0.02836 0.415989 prejob 0.124106 0.074756 1.66 0.099 -0.02389 0.272105 sp 0.414694 0.20049 2.07 0.041 0.017771 0.811617 engl -0.55533 0.295695 -1.88 0.063 -1.14074 0.030075 libra1 0.168605 0.096372 1.75 0.083 -0.02219 0.3594 mateco 0.153158 0.077707 1.97 0.051 -0.00068 0.306999 matlang 0.338163 0.113247 2.99 0.003 0.11396 0.562366 mothret 1.690693 0.457739 3.69 0 0.784478 2.596909 age -0.03355 0.01528 -2.2 0.03 -0.0638 -0.0033 web3 -0.32544 0.116302 -2.8 0.006 -0.55569 -0.09519 mothagri 0.610917 0.285659 2.14 0.034 0.04538 1.176454 web4 -0.33387 0.108287 -3.08 0.003 -0.54825 -0.11948 ticino 0.291794 0.195933 1.49 0.139 -0.09611 0.679694 internship 0.287091 0.094328 3.04 0.003 0.100344 0.473839 profexp 0.223102 0.090229 2.47 0.015 0.04447 0.401734 actu 1.032745 0.327751 3.15 0.002 0.383877 1.681614 mothna -0.81194 0.393834 -2.06 0.041 -1.59164 -0.03224 web1 -0.40402 0.116854 -3.46 0.001 -0.63537 -0.17268 fathpart -0.35249 0.209036 -1.69 0.094 -0.76633 0.061351 fathna -0.42754 0.221271 -1.93 0.056 -0.8656 0.010527 livepar -0.11799 0.074841 -1.58 0.118 -0.26616 0.030174 mothemp 0.122853 0.072507 1.69 0.093 -0.02069 0.2664 livefath -1.52237 0.50195 -3.03 0.003 -2.51611 -0.52863 finpar -0.10168 0.047289 -2.15 0.034 -0.1953 -0.00806 ownschooll~n 0.18244 0.079354 2.3 0.023 0.025337 0.339542 fathexec 0.380516 0.102245 3.72 0 0.178095 0.582937 repeat -0.28271 0.073334 -3.86 0 -0.4279 -0.13753 fathret -0.27877 0.195807 -1.42 0.157 -0.66643 0.108879 course1 -0.28116 0.106111 -2.65 0.009 -0.49123 -0.07108 fathself 0.219107 0.111751 1.96 0.052 -0.00213 0.440347 manag -0.2312 0.090183 -2.56 0.012 -0.40974 -0.05266 fathedu3 -0.236 0.091397 -2.58 0.011 -0.41694 -0.05505 fathedu4 -0.18772 0.091733 -2.05 0.043 -0.36933 -0.00611 _cons 7.928913 0.331561 23.91 0 7.272502 8.585325 _se 0.364068 0.020607 (Ancillary parameter))
23
Table 6: Model 3, Tobit estimates
Tobit estimates Number of obs 156
LR chi2(32) 93.66 Prob > chi2 0
Log likelihood -67.6055 Pseudo R2 0.4092 grade Coef. Std. Err. t P>|t| [95% Conf. Interval] intprofexp -0.31444 0.135787 -2.32 0.022 -0.5832 -0.04568 fr 0.301988 0.089191 3.39 0.001 0.125453 0.478522 fathemp 0.190798 0.106147 1.8 0.075 -0.0193 0.400892 prejob 0.085401 0.075323 1.13 0.259 -0.06368 0.234487 sp 0.341134 0.195639 1.74 0.084 -0.04609 0.728359 engl -0.47498 0.301463 -1.58 0.118 -1.07166 0.121702 libra1 0.147484 0.097094 1.52 0.131 -0.04469 0.33966 mateco 0.147125 0.079592 1.85 0.067 -0.01041 0.30466 matlang 0.344461 0.115974 2.97 0.004 0.114916 0.574005 mothret 1.381666 0.437252 3.16 0.002 0.516223 2.24711 age -0.03507 0.015631 -2.24 0.027 -0.06601 -0.00413 web3 -0.32804 0.118217 -2.77 0.006 -0.56203 -0.09406 mothagri 0.605645 0.288779 2.1 0.038 0.034071 1.177218 web4 -0.29397 0.108022 -2.72 0.007 -0.50778 -0.08017 internship 0.294667 0.094755 3.11 0.002 0.107121 0.482213 profexp 0.210965 0.091518 2.31 0.023 0.029826 0.392104 actu 1.222702 0.315098 3.88 0 0.599035 1.846369 mothna -0.65337 0.395761 -1.65 0.101 -1.43669 0.129955 web1 -0.34343 0.11734 -2.93 0.004 -0.57568 -0.11118 fathpart -0.3496 0.203078 -1.72 0.088 -0.75155 0.052351 fathna -0.41888 0.220632 -1.9 0.06 -0.85557 0.017817 mothemp 0.13686 0.073384 1.87 0.065 -0.00839 0.282107 livefath -1.55778 0.506145 -3.08 0.003 -2.55958 -0.55598 finpar -0.11185 0.047673 -2.35 0.021 -0.20621 -0.0175 ownschooll~n 0.212835 0.079343 2.68 0.008 0.055793 0.369878 Fathexec 0.37217 0.097531 3.82 0 0.17913 0.56521 Repeat -0.30348 0.0745 -4.07 0 -0.45093 -0.15602 course1 -0.28965 0.108622 -2.67 0.009 -0.50464 -0.07466 fathself 0.254808 0.108017 2.36 0.02 0.041011 0.468604 manag -0.2079 0.089846 -2.31 0.022 -0.38573 -0.03007 fathedu3 -0.21056 0.092731 -2.27 0.025 -0.3941 -0.02702 fathedu4 -0.13365 0.087726 -1.52 0.13 -0.30728 0.039989 _cons 7.938994 0.33282 23.85 0 7.28025 8.597738 _se 0.373226 0.02113 (Ancillary parameter)
24
Table 7: Model 4, Tobit estimates
Tobit estimates Number of obs 156 LR chi2(24) 71.55 Prob > chi2 0
Log likelihood -78.6576 Pseudo R2 0.3126 grade Coef. Std. Err. t P>|t| [95% Conf. Interval] intprofexp -0.31187 0.144454 -2.16 0.033 -0.59761 -0.02612 fr 0.245229 0.090962 2.7 0.008 0.065297 0.425162 sp 0.147121 0.19839 0.74 0.46 -0.24531 0.539555 matlang 0.203286 0.107064 1.9 0.06 -0.0085 0.415069 mothret 1.238248 0.46477 2.66 0.009 0.318887 2.157608 age -0.04015 0.015959 -2.52 0.013 -0.07171 -0.00858 web3 -0.23614 0.122852 -1.92 0.057 -0.47915 0.006876 mothagri 0.613438 0.302325 2.03 0.044 0.01541 1.211467 web4 -0.19008 0.111993 -1.7 0.092 -0.41161 0.031452 internship 0.250552 0.098456 2.54 0.012 0.055796 0.445309 profexp 0.196555 0.096065 2.05 0.043 0.006529 0.386582 actu 0.922953 0.300891 3.07 0.003 0.327761 1.518146 mothna -0.54203 0.416853 -1.3 0.196 -1.36661 0.282542 web1 -0.17593 0.118142 -1.49 0.139 -0.40962 0.057767 livefath -1.19893 0.506695 -2.37 0.019 -2.20122 -0.19664 finpar -0.10374 0.049743 -2.09 0.039 -0.20214 -0.00535 ownschooll~n 0.250987 0.079468 3.16 0.002 0.093791 0.408184 fathexec 0.221404 0.085113 2.6 0.01 0.053041 0.389766 fathself 0.154975 0.098722 1.57 0.119 -0.04031 0.350256 repeat -0.28528 0.0787 -3.62 0 -0.44096 -0.1296 course1 -0.39713 0.110767 -3.59 0 -0.61623 -0.17802 manag -0.12739 0.09276 -1.37 0.172 -0.31087 0.056101 fathedu3 -0.13267 0.097581 -1.36 0.176 -0.32569 0.060358 fathedu4 -0.1106 0.092107 -1.2 0.232 -0.29279 0.071599 _cons 8.209417 0.331518 24.76 0 7.553641 8.865192 _se 0.400628 0.022681 (Ancillary parameter)
25
Table 8: Model 2, OLS estimates
Source SS df MS Number of obs 156
------------ -------------- ---------------- F( 35, 120) 3.14
Model 18.93267 35 .540933349 Prob > F 0
Residual 20.67708 120 .172308997 R-squa red 0.478
------------ -------------- ---------------- Adj R- squared 0.3257
Total 39.60975 155 .255546754 Root M SE 0.4151
grade Coef. Std. Err. t P>|t| Beta
intprofexp -0.29654 .1522422 -1.95 0.054 -0.23483
fr 0.398577 .1100743 3.62 0 0.323041
fathemp 0.193812 .1279648 1.51 0.133 0.157083
prejob 0.124106 .0852417 1.46 0.148 0.114939
sp 0.414694 .2286117 1.81 0.072 0.144956
engl -0.55533 .33717 -1.65 0.102 -0.12398
libra1 0.168605 .10989 1.53 0.128 0.118631
mateco 0.153158 .0886061 1.73 0.086 0.149849
matlang 0.338163 .1291319 2.62 0.01 0.219482
mothret 1.690693 .5219436 3.24 0.002 0.267773
age -0.03355 .0174234 -1.93 0.056 -0.15405
web3 -0.32544 .1326153 -2.45 0.016 -0.25454
mothagri 0.610917 .3257268 1.88 0.063 0.136394
web4 -0.33387 .1234755 -2.70 0.008 -0.32798
ticino 0.291794 .2234148 1.31 0.194 0.111361
internship 0.287091 .1075591 2.67 0.009 0.284662
profexp 0.223102 .1028849 2.17 0.032 0.220485
actu 1.032745 .3737222 2.76 0.007 0.323953
mothna -0.81194 .4490748 -1.81 0.073 -0.1286
web1 -0.40402 .1332439 -3.03 0.003 -0.34719
fathpart -0.35249 .2383559 -1.48 0.142 -0.11057
fathna -0.42754 .252307 -1.69 0.093 -0.14945
livepar -0.11799 .085338 -1.38 0.169 -0.11708
mothemp 0.122853 .0826772 1.49 0.14 0.116484
livefath -1.52237 .5723561 -2.66 0.009 -0.24111
finpar -0.10168 .0539217 -1.89 0.062 -0.16033
ownschooll~n 0.18244 .0904848 2.02 0.046 0.177651
fathexec 0.380516 .1165863 3.26 0.001 0.368499
repeat -0.28271 .0836201 -3.38 0.001 -0.24083
fathret -0.27877 .223272 -1.25 0.214 -0.10639
course1 -0.28116 .1209948 -2.32 0.022 -0.17387
fathself 0.219107 .1274255 1.72 0.088 0.159514
manag -0.2312 .102832 -2.25 0.026 -0.18527
fathedu3 -0.236 .1042167 -2.26 0.025 -0.16898
fathedu4 -0.18772 .1045997 -1.79 0.075 -0.15541
_cons 7.928913 .3780666 20.97 0 .
26
Table 9: Model 3, OLS estimates
Source SS df MS Number of obs 156
F( 32, 123) 3.16
Model 17.87928 32 .558727343 Prob > F 0
Residual 21.73047 123 .176670503 R-squared 0.4514
Adj R-squared 0.3087
Total 39.60975 155 .255546754 Root MSE 0.42032
grade Coef. Std. Err. t [95% Conf. Interval]
intprofexp -0.31444 0.1529207 -2.06 -0.61713 -0.01174
fr 0.301988 0.1004459 3.01 0.103161 0.500814
fathemp 0.190798 0.1195409 1.6 -0.04583 0.427422
prejob 0.085401 0.0848278 1.01 -0.08251 0.253312
sp 0.341134 0.2203259 1.55 -0.09499 0.777256
engl -0.47498 0.3395029 -1.4 -1.147 0.197048
libra1 0.147484 0.1093458 1.35 -0.06896 0.363927
mateco 0.147125 0.0896355 1.64 -0.0303 0.324553
matlang 0.344461 0.1306077 2.64 0.085931 0.602991
mothret 1.381666 0.4924262 2.81 0.406939 2.356394
age -0.03507 0.0176033 -1.99 -0.06991 -0.00022
web3 -0.32804 0.1331338 -2.46 -0.59157 -0.06451
mothagri 0.605645 0.3252181 1.86 -0.0381 1.249394
web4 -0.29397 0.1216528 -2.42 -0.53478 -0.05317
internship 0.294667 0.1067115 2.76 0.083438 0.505896
profexp 0.210965 0.1030656 2.05 0.006953 0.414977
actu 1.222702 0.3548588 3.45 0.520281 1.925123
mothna -0.65337 0.4457004 -1.47 -1.5356 0.228869
web1 -0.34343 0.1321469 -2.6 -0.605 -0.08185
fathpart -0.3496 0.2287035 -1.53 -0.8023 0.103108
fathna -0.41888 0.2484727 -1.69 -0.91071 0.07296
mothemp 0.13686 0.0826435 1.66 -0.02673 0.300448
livefath -1.55778 0.5700126 -2.73 -2.68608 -0.42947
finpar -0.11185 0.0536881 -2.08 -0.21812 -0.00558
ownschooll~n 0.212835 0.0893553 2.38 0.035962 0.389709
fathexec 0.37217 0.1098374 3.39 0.154754 0.589586
repeat -0.30348 0.0839004 -3.62 -0.46955 -0.1374
course1 -0.28965 0.1223279 -2.37 -0.53179 -0.04751
fathself 0.254808 0.1216475 2.09 0.014014 0.495601
manag -0.2079 0.1011832 -2.05 -0.40818 -0.00761
fathedu3 -0.21056 0.1044327 -2.02 -0.41727 -0.00384
fathedu4 -0.13365 0.0987953 -1.35 -0.3292 0.061914
_cons 7.938994 0.374817 21.18 7.197066 8.680921
27
Table 10: Model 4, OLS estimates
Source SS df MS Nu mber of obs 156
F( 24, 131) 3.18
Model 14.57135 24 .6 713977 Pr ob > F 0
Residual 25.03839 131 .19 1132766 R- squared 0.3679
Ad j R-squared 0.2521
Total 39.60975 155 .25 5546754 Ro ot MSE 0.43719
grade Coef. Std. Err. t P>|t| [95% Conf. Interval]
intprofexp -0.31187 0.157637 -1.98 0.05 -0.62371 -2.6E-05
fr 0.245229 0.099263 2.47 0.015 0.048863 0.441595
sp 0.147121 0.216494 0.68 0.498 -0.28116 0.575397
matlang 0.203286 0.116834 1.74 0.084 -0.02784 0.434411
mothret 1.238248 0.507183 2.44 0.016 0.234919 2.241577
age -0.04015 0.017415 -2.31 0.023 -0.0746 -0.00569
web3 -0.23614 0.134063 -1.76 0.081 -0.50135 0.029071
mothagri 0.613438 0.329914 1.86 0.065 -0.03921 1.266087
web4 -0.19008 0.122213 -1.56 0.122 -0.43185 0.051686
internship 0.250552 0.107441 2.33 0.021 0.038008 0.463096
profexp 0.196555 0.104832 1.87 0.063 -0.01083 0.403937
account 0.922953 0.328349 2.81 0.006 0.2734 1.572507
mothna -0.54203 0.454893 -1.19 0.236 -1.44192 0.357853
web1 -0.17593 0.128923 -1.36 0.175 -0.43097 0.079112
livefath -1.19893 0.552934 -2.17 0.032 -2.29277 -0.1051
finpar -0.10374 0.054282 -1.91 0.058 -0.21113 0.003639
ownschooll~n 0.250987 0.08672 2.89 0.004 0.079434 0.422541
fathint 0.221404 0.09288 2.38 0.019 0.037664 0.405143
repeat -0.28528 0.085882 -3.32 0.001 -0.45517 -0.11538
course1 -0.39713 0.120875 -3.29 0.001 -0.63625 -0.15801
fathind 0.154975 0.107731 1.44 0.153 -0.05814 0.368092
manag -0.12739 0.101224 -1.26 0.21 -0.32763 0.072859
fathedu3 -0.13267 0.106486 -1.25 0.215 -0.34332 0.077988
fathedu4 -0.1106 0.100512 -1.1 0.273 -0.30943 0.08824
_cons 8.209417 0.361771 22.69 0 7.493747 8.925087
28
Questionnaire
Pour la plupart des questions suivantes, il suffit de souligner ou de mettre en italiques les réponses proposées. Une fois le questionnaire rempli, je vous remercie de me l’envoyer, soit par courrier électronique à l’adresse [email protected], soit par courrier ordinaire adressé à A. Sakho, p.a. Mme M. Gillot, Association des gradués, Ecole des HEC, Université de Lausanne, CH-1012 Lausanne-Dorigny. Si vous avez des questions, vous pouvez bien sûr me joindre par courrier électronique.
Sauf indication contraire, prière de ne donner qu’une seule réponse. Les questions ne suivent aucun ordre particulier.
1. Je suis de sexe : féminin / masculin 2. Je suis né-e en (année) : 3. Ma langue maternelle est : 4. Mon diplôme de fin d’études secondaires est (par exemple, maturité
scientifique) : 5. J’ai commencé mes études universitaires à l’âge de : (ans) 6. Au début de mes études universitaires, j’étais domicilié-e : - dans le canton de : - à l’étranger 7. Au début de mes études universitaires
a) Ma mère travaillait : à temps plein / à temps partiel b) N’était pas « économiquement active » c) N’était plus en vie
8. Au début de mes études universitaires a) Mon père travaillait : à temps plein / à temps partiel b) N’était pas « économiquement actif » c) N’était plus en vie
9. Pendant mes études universitaires ou la plus grande partie de mes études a) Je vivais avec mes deux parents b) Je vivais avec ma mère c) Je vivais avec mon père d) Je ne vivais avec aucun de mes deux parents
29
10. Mes études universitaires ont été financées (réponses multiples possibles)
a) Principalement par mes parents ou ma famille b) Partiellement par mes parents ou ma famille c) Principalement par une bourse d) Partiellement par une bourse e) Principalement par mes propres gains f) Partiellement par mes propres gains g) Autre (précisez, s.v.p.) :
11. Formation de ma mère a) Scolarité obligatoire b) Scolarité obligatoire + formation professionnelle (p.ex. apprentissage) c) Maturité, baccalauréat ou titre équivalent d) Graduée universitaire (licence ou titre équivalent) e) Formation post-grade
12. Formation de mon père a) Scolarité obligatoire b) Scolarité obligatoire + formation professionnelle (p.ex. apprentissage) c) Maturité, baccalauréat ou titre équivalent d) Gradué universitaire (licence ou titre équivalent) e) Formation post-grade
13. Statut socioprofessionnel de ma mère au début de mes études universitaires
a) agricultrice b) indépendante (artisane, commerçante ou cheffe d’entreprise) c) cadre ou profession intellectuelle supérieure d) profession libérale (médecin, avocate, etc.) e) employée f) ouvrière g) retraitée h) économiquement non active i) n’était plus en vie j) autre (préciser, s.v.p.) :
14. Statut socioprofessionnel de mon père au début de mes études universitaires a) agriculteur b) indépendant (artisan, commerçant, chef d’entreprise, etc.) c) cadre ou profession intellectuelle supérieure d) profession libérale (médecin, avocate, etc.) e) employé f) ouvrier g) retraité h) économiquement non actif i) n’était plus en vie j) autre (préciser, s.v.p.) :
30
15. Pendant mes études universitaires (réponses multiples possibles) a) J’ai travaillé (prise de notes de cours, exercices, préparation des examens, etc.) le plus souvent par moi-même b) J’ai souvent travaillé en équipe, avec des camarades c) J’ai bénéficié d’aides extérieures à l’Université (leçons privées, aide des parents ou d’autres personnes, etc.)
16. Pendant mes études de licence
a) La durée minimale pour l’obtention d’une licence était de 3 / 4 ans b) Durée effective de mes études de licence : semestres. c) Si mes études de licence ont pris plus que le temps que la durée minimale, c’était pour cause de (réponses multiples possibles)
- Maladie - Redoublement(s) - Séjour à l’étranger - Service militaire - Stage ou autre expérience pratique - Occupation professionnelle pour financer mes études - Grossesse - Autre (précisez, s.v.p.) :
17. Pendant mes études et en dehors des heures de cours, j’ai travaillé principalement
a) A l’Université (bibliothèque, p.ex.) b) Chez moi c) Autre
18. Pendant mes études, j’ai utilisé les bibliothèques de l’UNIL
a) Très souvent b) Assez souvent c) Rarement d) Pour ainsi dire jamais
19. Pendant mes études, j’ai suivi
a) Tous les cours ou presque b) La plupart des cours c) Moins de la moitié des cours
20. Expérience(s) pratique(s) (réponses multiples possibles)
a) Après la fin de mes études secondaires, je suis entré tout de suite à l’Université (pas de stage ou autre expérience pratique préalable) b) Avant d’entrer à l’Université, j’ai fait un stage ou ai eu d’autres expériences pratiques préalables c) Pendant mes études, j’ai effectué un ou plusieurs stages d) Pendant mes études, j’ai eu d’autres expériences ou occupations professionnelles
31
21. Pendant mes études de licence (réponses multiples possibles) a) J’ai été assistant-étudiant b) J’ai eu d’autres fonctions de ce type (p.ex. assistant de recherche FNRS) c) J’ai été actif dans des organisations estudiantines (p.ex. Comité des étudiants, délégué des étudiants au Conseil HEC, etc.) d) Rien de ce qui précède
22. Pendant mes études de licence, j’ai utilisé les ressources du web a) Souvent b) Rarement c) Pour ainsi dire jamais d) Le web n’existait pas encore ou en était à ses tout débuts
23. Pendant mes études, j’ai effectué des recherches personnelles a) Souvent b) Assez souvent c) Rarement d) Pour ainsi dire jamais
24. J’ai obtenu une licence HEC en : (réponses multiples possibles)
a) Management-gestion b) Economie politique c) Sciences actuarielles d) Informatique de gestion
25. La moyenne que j’ai obtenue pour ma licence était de : a) /10 b) /6 c) Je ne me souviens plus bien
26. Concernant la question précédente, la mémoire peut jouer des tours. Ou je ne me souviens plus bien de ma moyenne à la licence. Par conséquent, je vous autorise à vérifier ma moyenne dans les archives de l’Ecole : a) Oui
b) Non (Du côté de HEC, l’accord du Doyen a déjà été obtenu.)
Le cas échéant, autres information pertinentes et commentaires :
Un grand merci !