The choice to enrol in a small university: A case study of

ISSN: 2038-7296
POLIS Working Papers
[Online]
Istituto di Politiche Pubbliche e Scelte Collettive – POLIS
Institute of Public Policy and Public Choice – POLIS
POLIS Working Papers n. 205
October 2013
The choice to enrol in a small university:
A case study of Piemonte Orientale
Tiziana Caliman and Alberto Cassone
UNIVERSITA’ DEL PIEMONTE ORIENTALE “Amedeo Avogadro” ALESSANDRIA
Periodico mensile on-line "POLIS Working Papers" - Iscrizione n.591 del 12/05/2006 - Tribunale di Alessandria
The Choice to Enrol in a Small University:
A Case Study of Piemonte Orientale
Tiziana Caliman* and Alberto Cassone **
Abstract1
In the recent past, expectations concerning universities have emphasised their active role in
enhancing economic and regional development. The universities in geographical areas suffering from
structural problems are particularly required to play this role. Moreover, the correlation between the
socioeconomic status (and the education) of parents and that of their adult offspring is positive and
significant, in both the statistical and practical senses. This paper investigates the experience of a
small Italian University (Piemonte Orientale “Amedeo Avogadro”), in order to evaluate its role in
human capital accumulation, necessary to economic development. The aim of this article is to verify
whether this small university satisfies a specific demand which would never be satisfied by a larger
university. We found important role of small Universities in the human capital accumulation in the
recruitment basin, a phenomenon with medium and long term implications. The empirical results show
that the representative graduate student of Piemonte Orientale is characterized by modest parental
socioeconomic conditions and education. Its demographic recruitment basin is a specific and well
defined geographical area. These factors have a positive impact on the choice of enrolment (Piemonte
Orientale versus other Universities). The choice is modelled by a probit (logit) binary outcomes model
using the Almalaurea cross-section sample on graduates in year 2008. We also update the dataset and
re-estimate the models in order to verify the robustness of empirical results and to identify changes in
the representative student, using the Almalaurea cross-section sample on graduates for year 2010.
The crucial role of the modest socioeconomic background and the low mobility of the students are
confirmed. The 2010 analysis does not confirm a result for year 2008, i.e. that a poor performance in
secondary school increases the probability to choose Piemonte Orientale vs larger and well
1
The paper has been conceived and written together, while Tiziana Caliman is responsible for the econometric analysis.
This paper is the result among others, of a research project supported by the University of Piemonte Orientale A. Avogadro –
CESPA, including a grant sufficient for the acquisition data from AlmaLaurea. The authors express their gratitude to
prof.Andrea Cammelli (Alma Laurea Director) and particularly to dr.Angelo di Francia (Alma Laurea scientific staff) for
assistance and comments.
The authors are also indebted to colleagues for comments during the presentation at POLIS Dept. Seminar ,Spring 2011
and Winter 2013.
1
established universities: the result underlines a positive evolution of this small university recruitment
performance.
Keywords: Performance; Human Capital Accumulation; Small Universities
JEL Classification: I20, I21, I23, R00
* CerTet, Bocconi
Via Roentgen 1,20136 Milan, Italy
e-mail: [email protected]
* * Institute of Public Policy and Public Choice – POLIS, University of Piemonte Orientale
Via Cavour 84, 15121 Alessandria, Italy
e-mail: [email protected]
1.
Introduction
As in many EU member countries, and in the particular context of the Bologna Convention on
Tertiary Education, the Italian university system has experienced substantial reforms in recent years2.
The key reform aims include increasing the participation, progression and retention rates of students in
higher education. Reform has reduced, on the one hand, the length of undergraduate degree programs
to three years with the intention that students should be able to graduate at an earlier age than in the
past, in line with graduates from other European countries. On the other hand, the reform has
introduced (at least at the beginning) greater flexibility in the degree structure along with a wider
range of curricula offered to students. With more autonomy for each University, a territorial
decentralization process occurred with the proliferation of new small universities and/or of new
branches of historical universities.
More recently (2009 – 2013) a reversal of policy is taking place: many branches of the
historical universities are closing due to new requirements for university degree programs, in terms of
2
For a comprehensive English summary of the Italian university system, its evolution within the Bologna Process, and the
Almalaurea surveys main results see A. Cammelli, G. Antonelli, A. di Francia, G. Gasperoni, M. Sgarzi (2010).
2
numbers of students enrolled and stable teaching staff. Furthermore the role, and thus the existence, of
recently established small universities is put in doubt.
According to a document by CUN3, the Italian university system suffers from severe
emergencies:
(i) the percentage of GNP for higher educations is less than 1 %, compared with an OECD
average of 1,6 %. Italy ranks 32nd, out of 37 countries;
(ii) the public financing of University (FFO, Fondo di Finanziamento Ordinario) decreased
in absolute inflation adjusted terms of about 20% since 2009;
(iii) a strong reduction in teaching staff occurred through a freezing of the turnover: five
teacher out for one teacher in;
(iv), even if graduates are only about 19% of the population aged 30-34 years (the European
average is 30%), the public financing for scholarships and other services for low income students has
been heavily cut in recent years;
(v) the total annual enrolment passed from 340.000 in 2003-4 to about 280.000 in 2011-12,
mostly because of the vanishing boom of new enrolment from the stock of older people, enhanced by
the 3+2 reform at the beginning of the century;
(vi) on the supply side, the numbers of courses of studies offered is steadily decreasing after
the peak of more than 5.500 in year 2007-08. Now (2012) they are about 4.3004.
This paper analyses the experience of a small Italian university, the Piemonte Orientale5,
assumed as a case study, in order to evaluate its role in human capital accumulation, a necessary, even
though not sufficient condition, for economic development. The crucial point is: does this small
university satisfie a specific demand otherwise not satisfied by larger existing universities? Actually,
without this new University, a relevant number of potential students could not enroll, attend and
graduate without huge economic cost for their families and strong personal effort.
The important role of small universities in the human capital accumulation in the geografichal
recruitment basin, is a phenomenon with medium and long term implications: small universities not
3
Consiglio Universitario Nazionale, National University Council, the official representative institution of all professors and
researchers.
4
CUN Consiglio Universitario Nazionale, Dichiarazione del Consiglio Universitario Nazionale per l’Università e la Ricerca
Gennaio 2013 “Le emergenze del sistema”, avallabile at www.cun.it
5
The University of Piemonte Orientale ”Amedeo Avogadro”, a spin-off of the University of Torino, became an independent
University in 1998. It is based in three medium sized main county (provincial) cities (Alessandria, Novara and Vercelli), has
(2012) seven Faculties and twelve Research Departments. Enrolled students are about 10,000 and teaching and
administrative staff, are each about 350 people.
3
only could ease the congestion that plagues larger universities but also perform a key role in the
development of peripheral territorial systems both on the demand and supply side.
The evaluation of the impact of small universities becomes urgent due to the recently
instituted university assessment process based on rankings6 (productivity, research, teaching,
professors’ CVs, international relations) and efficiency course requirements. The paper also analyzes
the public policy implications of recent regulation by the so-called Gelmini Reform. The new law and
its administrative implementation reduce the opportunities for a decentralized supply of academic
programs and therefore strengthen the role of student mobility.
The paper is organized as follows. Section 2 breathly describes tertiary education in Italy,
Section 3 analyzes the general context of student territorial and social mobility, Section 4 surveys the
earlier empirical evidence literature, Section 5 provides information about the dataset employed in the
empirical analysis, with summary statistics, Section 6 analyzes the selected models, describes the
estimation procedures and comments on the empirical findings. Finally, Section 7 summarizes the
discussion and suggest some policy implications.
2.
Tertiary Education in Italy
In this version (Almalaurea May 2013), we skip this paragraph which is intended to summarize the
long season of reform and counter reform of higher education in Italy from 1995 to 2013.
This process, long overdue, started with the Italian budget law for 1995, which granted each
University greater autonomy on expenditures decisions. The full reform (law 509/90) introduced,
according to the Bologna Process, the 3 + 2 system (bachelor + master degrees system). As soon as
the universities started to fully exploit their higher financial and organizational autonomy, the supply
of new courses of study boomed. In order to control this outcome, a policy reversal occurred in 2005
with the introduction of “Minimun Requirements” for teaching staff and other characteristics. A long
series of budget cuts were introduced in the following years, both because of macroeconomic
constraints for the public expenditure and in order to control the behaviour of single universities.
This progressive counter reform has been consistently arranged in a sistematic way in the so called
6
Censis, for example, provides Italian universities rankings. Recently the MIUR and the National Evaluation Committee
(now ANVUR) started gathering data on research and teaching in order to evaluate the university system.
4
Riforma Gelmini which, under the cover of efficiency, rationalization and better governance and
organization, actually produced an increasing centralization and rigidity of the system.
3. Territorial and Social Mobility in Italy: an Overview.
An interesting picture of the Italian University System (as it appears from the set of Academic
Institutions belonging to the AlmaLaurea Consortium) can be obtained simply analyzing two
indicators: (i), the percentage of local graduates (living in the same county (provincia) where the
university is based, and (ii), the percentage of graduates with at least one parent holding a university
degree. Plotting the normalized values of both indicators we get a cloud (Graph1), with a triangular
shape.
We associate each university with a different label: Urban Niche Universities7, (specializing
most often in peculiar curricula, such as gastronomique science in Bra), Historical Universities8
mainly based in regional main city and the New Small Universities9, the picture shows a surprising
significance. We can identify three well defined horizontal belts from the top educated families to the
bottom. The top belt includes all Urban Niche Universities, the middle belt includes all Historical
Universities with some outliers. The bottom belt includes all the New Small Universities. In each belt
we find institutions with a higher or lower value of the local graduates indicator, which is consistent
with the perceived quality of the university (the lower, the better).
Table 1 Social and Territorial Mobility Indicators for Graduates in Almalaurea Universities (average 2003-2011)
7
The thirteen Universities considered are: LIUC Castellanza, LUM Casamassima, Milano IULM, Milano San Raffaele,
Perugia Stranieri, Roma Campus Bio Medico, Roma Foro Italico, Roma LUMSA, Roma LUSPIO, Roma TRE, Scienze
Gastronomiche Bra, Siena Stranieri, Venezia IUAV
8
The twenty-six Universities considered are: Bari, Bari Politecnico, Bologna, Cagliari, Camerino, Catania, Ferrara, Firenze,
Genova, Marche Politecnica, Messina, Modena e Reggio Emilia, Napoli Federico II, Napoli L'Orientale, Napoli Parthenope,
Padova, Parma, Perugia, Roma La Sapienza, Sassari, Siena, Torino, Torino Politecnico, Trieste, Urbino, Venezia Ca' Foscari.
9
The twenty-two Universities considered are: Basilicata, Bolzano, Calabria, Cassino e Lazio Meridionale, Catanzaro Magna
Grecia, Chieti e Pescara, Foggia, Insubria, L'Aquila, Molise, Napoli Seconda Università, Piemonte Orientale, Reggio
Calabria Mediterranea, Salento (Lecce), Salerno, Sannio, Teramo, Trento, Tuscia, Udine, Valle D'Aosta, Verona.
5
Absolute Value
University
Local
Graduates
(%)
Scienze Gastronomiche Bra
Normalized Value
Parent(s) Holding
University Degree (%)
Local
Graduates
Parent(s) holding
University Degree
4,70
40,70
0,00
0,70
Venezia IUAV
22,83
27,63
0,23
0,41
Chieti Pescara
27,10
16,53
0,28
0,16
Siena Stranieri
28,90
29,10
0,30
0,44
Padova
31,03
20,38
0,33
0,25
Bologna
31,13
30,35
0,33
0,47
Camerino
33,80
21,08
0,36
0,26
Perugia Stranieri
34,05
25,15
0,37
0,35
Trieste
34,28
26,38
0,37
0,38
Parma
34,70
25,50
0,37
0,36
Tuscia
35,13
14,33
0,38
0,11
Urbino
35,30
21,10
0,38
0,26
Venezia Ca' Foscari
35,50
21,55
0,38
0,27
L'Aquila
36,85
19,85
0,40
0,23
IULM Milano
37,65
30,80
0,41
0,48
Siena
37,85
23,33
0,41
0,31
LIUC Castellanza
38,07
35,53
0,42
0,59
Ferrara
42,33
22,03
0,47
0,28
Firenze
44,33
25,50
0,49
0,36
Roma LUSPIO
44,50
36,70
0,50
0,61
San Raffaele Milano
45,30
43,93
0,51
0,78
Teramo
45,55
18,45
0,51
0,20
Marche Politecnica
48,90
21,50
0,55
0,27
Roma Campus Bio-Medico
49,30
53,83
0,56
1,00
Molise
49,58
13,95
0,56
0,10
Piemonte Orientale
49,68
13,45
0,56
0,09
Udine
49,85
17,23
0,56
0,18
All Almalaurea Universities
53,47
23,40
0,61
0,31
Modena e Reggio Emilia
54,53
22,23
0,62
0,29
Cagliari
55,03
17,07
0,63
0,17
6
Absolute Value
University
Local
Graduates
(%)
Normalized Value
Parent(s) Holding
University Degree (%)
Local
Graduates
Parent(s) holding
University Degree
Perugia
55,18
24,08
0,63
0,33
Cassino e Lazio Meridionale
56,65
13,88
0,65
0,10
Torino Politecnico
57,68
30,50
0,66
0,47
Calabria
57,93
18,00
0,66
0,19
Sannio
59,10
22,75
0,68
0,30
Roma La Sapienza
59,43
28,78
0,68
0,44
Catania
59,65
23,78
0,69
0,32
Napoli L'Orientale
60,30
26,30
0,69
0,38
Roma LUMSA
60,60
38,80
0,70
0,66
Trento
60,93
20,85
0,70
0,26
Verona
61,75
15,95
0,71
0,15
Salerno
61,80
22,85
0,71
0,30
LUM Casamassima
61,90
41,30
0,71
0,72
Genova
65,20
28,28
0,75
0,42
Lecce Salento
65,85
14,35
0,76
0,11
Sassari
66,73
12,23
0,77
0,06
Napoli Seconda Università
67,23
19,93
0,78
0,24
Catanzaro Magna Grecia
68,30
9,43
0,79
0,00
Roma Foro Italico
68,40
23,53
0,79
0,32
Messina
68,68
21,33
0,80
0,27
Insubria
71,10
13,20
0,83
0,08
Bari Politecnico
71,60
26,20
0,83
0,38
Bolzano
72,33
20,90
0,84
0,26
Torino
72,55
21,78
0,85
0,28
Bari
75,60
16,18
0,88
0,15
Napoli Federico II
75,70
27,10
0,89
0,40
Reggio Calabria Mediterranea
77,23
27,38
0,90
0,40
Basilicata
77,75
13,90
0,91
0,10
Roma TRE
78,68
34,95
0,92
0,57
Valle D'Aosta
80,83
12,33
0,95
0,07
Napoli Parthenope
81,10
20,80
0,95
0,26
Foggia
84,90
14,80
1,00
0,12
7
Graph 1 Dispersion Diagram by Territorial and Social Mobility: the Position of the Urban Niche Universities
8
Graph 2 Dispersion Diagram by Territorial and Social Mobility: the Position of the Historical Universities
9
Graph 3 Dispersion Diagram by Territorial and Social Mobility: the Position of the New Small Universities
10
4. The Empirical Evidence Literature
The empirical literature about territorial mobility of university students or graduates is very
limited.
Cappellari and Lucifora (2008) estimate the impact of the italian reform on the decision to go
to college: they find that individuals leaving secondary school, after the reform have a probability of
going to college that is 10 % higher, compared to individuals making the choice under the old system.
This increase is concentrated among individuals with good high-school performance and low parental
educational background. This result may be interpreted as an indication of the existence of constraints
(pre-reform) – for good students from less affluent households – on the optimal schooling decision.
Rotaris, Danielis and Rosato (2012) study the choice between commuting and staying in the
university town by university students in the Trieste area. Using a theoretical model derived from
urban and regional science, they find that the crucial indifference distance between living in the
university or home town is about 65-75km. This result may be assumed to imply that the household
economic constraints prevent students to enroll in the university if there is none within the above
range.
According to Ciriaci and Nuzzi (2012, p. 376) the marginal effect of the research quality on
the probability to migrate for the enrolment in the University is equal to 0,69 (an increase of 1% of the
avarege value of the University rating implied an increase of the probability of migrating of 0,69%).
The higher the education level of the parents, the higher the probability of migrating.
Hertz et al. (2001) focus on the intergenerational persistence of educational inequality. The
paper analyzes 42 countries according to educational persistence across generations. Empirical
evidence shows significant differences among countries, in particular between Latin America and
European Nordic countries. The first group is characterized by a higher intergenerational persistence
of educational differences, the second exhibits a low serial correlation. In Italy the significant
correlation is further evidence to support the claim that “weak” students (on account of their social
economic and cultural background) find it more difficult to graduate. For this reason, it is important to
analyze the demand satisfied by small universities to check if they play a specific educational role.
According to Dotti, Fratesi, Lenzi, Percoco (2010) and Ciriaci (2005), Coniglio and E. Prota
(2008), Fratesi e Percoco (2010), migrant graduates from southern Italy who obtained their degree in
northern Italy show a low probability of returning home. A gravitational model is used to analyse the
11
students mobility flows, with the result that a good University and a dynamic labour market are the
main drivers. In particular long distance mobility depends on labour market conditions, while short
distance mobility appears linked to the University reputation.
Aina C. and Baici E. (2012) analyse the student choice between to stay or to move. In
particular, the paper analyses the effects of the number of university degree courses available in the
province of residence on the probability of studying in that province rather than moving to a different
one. The supply of degree courses outside the province of residence is weighted by a spatial matrix
where the distance between the province of residence and any other province is imputed in minutes.
The results confirm that the probability of enrolling in a universities located in the same province of
residence is positively correlated to the number of courses available. In particular, the probability of
enrolling at university in the province of residence is higher for the departments where the attendance
is compulsory (i.e. Engineering). The enrolment behaviour of freshmen differs according to gender,
with females more sensitive to the supply of local degree courses.
5.
The data
The data on individual socio-economic conditions of the Piedmont graduates are drawn from
the Almalaurea survey, for 2008 and 2010.
Almalaurea (www.almalaurea.it) is a consortium of Italian universities with the support of the
Ministry for Education, University and Research. Almalaurea covers 77% of Italian graduates and, for
the year 2010, the total number of personal CV for graduates in 56 Italian universities was about 1,47
million. The Almalaurea database includes many quantitative and qualitative social and economic
variables on Italian graduates, based on answers to a questionnaire completed by all graduates from
AlmaLaurea universities.
The survey includes information on Italian graduates which are gathered to the eve of the
degree. The survey covers the 95% of the graduates. The sample data used in this work are two cross
section. The cross section related to 2008 contains 949010 individual observations: students awarded a
10
We dropped the observations for which the students have no choice (unique curricula or faculties) because the curricula
chosen is not offered by the Piemonte Orientale (for example veterinary or engineering), thus our sample contains 8703
individual observations.
12
bachelor’s degree11 in 2008 (regardless of the year of first enrolment) living in Piedmont (i.e. those
who, answering the questionnaire, give as current residence a town in Piedmont). The cross section
for 2010 contains 8737 individual observations. Even though the samples (2008 and 2010) do not
include data on Pavia and Milan Universities (also potential and actual choices according to MIUR
data), the samples are representative respectively of 87.4% and of 88.7% of Piedmont graduates.
In Table 2 we compare the samples (2008 and 2010) with MIUR data on Piedmont graduates
in order to consider the fraction of Piedmont students who graduated in non Almalaurea universities
(the flag Affiliated to Almalaurea consortium takes the values “1” for the universities recently
associated to Almalaurea, “2” for Almalaurea universities at the time of the survey, in 2008 and 2010
respectively, and “ blank” for those currently (2012) not associated to Almalaurea).
11
Among graduates there are three types of students due to Italian university reform (law n. 509/1999): those who enrolled
before 2001 in an old system degree program and who graduated within the same program, those enrolled before 2001 in an
old system degree program who decided to switch to a “new” degree program and who therefore graduated in a 3 years
program, and those who enrolled after 2001 in a 3 years program.
13
14
Table 2: Graduates by Degree in all Universities and Almalaurea Universities (2010 and 2008)
2010
Affiliated to
Almalaurea
Consortium
(at 2010)
2
2008
CDL
L
LS/LM
LSCU
/LMCU
Aosta
1
10
2
0
13
Affiliated to
Almalaurea
Consortium
(at 2008)
2
Bari
2
2
1
0
5
2
Bergamo
0
2
0
0
2
Bologna
5
45
30
3
83
2
2
Bolzano
0
1
0
0
1
2
1
Bra - Scienze
Gastronomiche
Brescia
0
7
6
0
13
0
1
0
2
3
2
Cagliari
0
1
1
0
2
2
Calabria
1
1
0
0
2
Camerino
0
1
0
2
Casamassima J.Monnet
Cassino
0
1
0
0
2
2
2
TYPOLOGY OF DEGREE
Universities
Total
TYPOLOGY OF DEGREE
Universities
Total
CDL
L
LS
LSCU
/LMCU
Aosta
1
6
3
0
10
Bari
3
1
5
0
9
Bergamo
1
6
0
0
7
Bologna
8
64
29
3
104
Bolzano
0
0
1
0
1
Bra - Scienze
Gastronomiche
0
17
0
0
17
2
Cagliari
0
1
2
1
4
2
2
Calabria
3
2
0
0
5
0
1
2
Camerino
2
4
0
0
6
0
0
1
0
3
0
0
3
1
0
1
2
Casamassima J.Monnet
Cassino
0
1
0
0
1
Castellanza
LIUC
Catania
1
13
13
5
32
1
2
2
1
6
Castellanza
LIUC
Catania
0
20
16
4
40
2
1
1
0
0
2
2
2
Catanzaro
0
2
1
0
3
2
Catanzaro
1
1
0
1
3
2
Chieti e Pescara
0
30
30
0
60
2
Chieti e Pescara
0
121
15
0
136
1
Enna - KORE
0
0
0
3
3
Enna - KORE
0
2
0
0
2
2
Ferrara
0
4
2
1
7
2
Ferrara
1
4
4
1
10
2
Firenze
1
53
13
0
67
2
Firenze
0
24
14
0
38
2
Foggia
0
2
0
0
2
2
2
Genova
27
318
150
52
547
2
Genova
59
270
99
38
467
2
Insubria
0
17
9
3
29
Insubria
0
19
5
4
28
2
L'Aquila
0
4
0
0
4
L'Aquila
0
26
3
0
29
2
2
2
15
Affiliated to
Almalaurea
Consortium
(at 2010)
1
2
2
2
2
2
1
2
2
1
2
2
TYPOLOGY
Universities
Macerata
Total
CDL
L
LS/LM
LSCU/LMCU
0
5
1
0
Affiliated to
Almalaurea
Consortium
(at 2008)
6
2
TYPOLOGY OF DEGREE
Universities
Total
CDL
L
LS/LM
LSCU/LMCU
Macerata
2
7
0
0
9
Marche
0
2
1
0
3
Messina
36
30
15
4
85
Messina
1
0
0
0
1
Milano
23
218
134
54
429
Milano
40
225
113
21
399
Milano Bicocca
12
81
42
5
140
Milano Bicocca
17
96
37
0
150
Milano Bocconi
2
95
64
0
161
Milano Bocconi
6
108
102
0
216
Milano
Cattolica
Milano IULM
17
156
110
21
304
30
162
102
7
301
1
46
13
0
60
Milano
Cattolica
Milano IULM
3
58
20
0
81
Milano
Politecnico
Milano San
Raffaele
Modena e
Reggio Emilia
14
180
107
0
301
0
20
9
7
36
0
5
2
0
7
Napoli Federico
II
Napoli II
1
1
3
0
5
6
7
3
1
17
Napoli
L'Orientale
0
2
0
0
2
Padova
4
42
15
0
61
Palermo
1
2
0
0
3
15
8
51
2
Milano
Politecnico
Milano San
Raffaele
Modena e
Reggio Emilia
Molise
4
182
105
3
294
0
18
8
2
28
2
1
10
3
2
16
2
0
0
0
1
1
Napoli Federico
II
Napoli II
2
4
3
1
10
1
13
6
2
22
Napoli
L'Orientale
Napoli
Parthenope
Napoli Pegaso
0
0
1
0
1
0
1
0
0
1
0
4
0
4
8
Novedrate - eCampus
Padova
0
13
0
0
13
2
27
11
0
40
Palermo
0
2
0
0
2
Parma
1
13
7
4
25
2
2
2
Parma
11
17
16
Affiliated to
Almalaurea
Consortium
(at 2010)
TYPOLOGY
Universities
Total
CDL
L
LS/LM
LSCU/LMCU
Pavia
19
254
169
85
527
2
Perugia
1
3
3
1
8
2
Perugia
Stranieri
Piemonte
Orientale
Pisa
2
2
2
2
2
1
2
2
Affiliated to
Almalaurea
Consortium
(at 2008)
TYPOLOGY OF DEGREE
Universities
Total
CDL
L
LS/LM
LSCU/LMCU
Pavia
41
277
181
67
566
2
Perugia
1
7
1
1
10
Perugia
Stranieri
Piemonte
Orientale
Pisa
0
4
1
0
5
95
1027
219
107
1449
2
9
32
0
43
2
Reggio
Calabria
1
0
0
0
1
2
Roma La
Sapienza
Roma LUISS
4
12
7
0
23
1
1
1
0
3
Roma LUMSA
0
4
1
0
5
0
7
1
0
8
2
31
983
210
157
1.381
2
0
17
20
4
41
Reggio
Calabria
Roma
Mercatorum
Roma La
Sapienza
Roma LUISS
1
1
0
0
2
0
4
0
0
4
3
13
8
1
25
0
3
0
1
4
Roma LUMSA
0
2
1
0
3
Roma Marconi
0
37
6
8
51
Roma Marconi
0
31
1
0
32
Roma San Pio
V
Roma
TEL.M.A.
Roma Tor
Vergata
Roma Tre
0
2
1
0
3
0
6
1
0
7
1
7
10
0
18
1
1
3
1
6
Roma
UNINETTUNO
Roma UNISU
0
1
0
0
1
0
1
0
1
2
2
Salento
0
1
1
0
2
2
Salerno
1
1
0
0
2
2
Sannio
0
1
0
0
1
Roma San Pio
V
Roma
TEL.M.A.
Roma Tor
Vergata
Roma Tre
0
1
5
0
6
0
3
1
0
4
0
5
8
0
13
0
4
3
0
7
Roma
UNINETTUNO
Roma UNISU
0
2
0
0
2
0
3
1
5
9
Salento
1
2
0
0
3
2
2
17
Affiliated to
Almalaurea
Consortium
(at 2010)
2
CDL
L
LS/LM
LSCU/LMCU
Sassari
0
0
0
1
1
Affiliated to
Almalaurea
Consortium
(at 2008)
2
2
Siena
0
24
5
0
29
2
2
Siena Stranieri
0
0
1
0
1
2
Teramo
0
1
6
0
7
2
Torino
476
5.875
2.685
752
9.788
2
83
1.759
1.279
0
3.121
0
4
0
0
4
0
1
4
0
5
2
2
Torino
Politecnico
Torrevecchia
Teatina Leonardo
Trento da
Vinci
Trieste
2
12
8
1
23
2
Tuscia
1
12
0
0
2
Udine
0
0
2
0
1
3
24
8
4
39
1
22
12
0
2
Urbino Carlo
Bo
Venezia Ca'
Foscari
Venezia Iuav
0
2
9
2
Verona
0
2
727
10.493
2
2
TYPOLOGY
Universities
TOTAL
Total
TYPOLOGY OF DEGREE
Universities
Total
CDL
L
LS/LM
LSCU/LMCU
Sassari
0
1
0
1
2
Siena
1
101
6
1
109
Teramo
0
1
4
0
5
2
Torino
820
5769
2423
551
9564
2
188
1776
1184
0
3154
0
2
0
0
2
2
3
0
0
5
2
Torino
Politecnico
Torrevecchia
Teatina Leonardo
Trento da
Vinci
Trieste
3
11
5
1
20
13
2
Tuscia
1
36
2
0
39
2
2
Udine
0
1
2
0
3
8
25
3
1
37
35
2
1
12
10
0
23
0
11
2
Urbino Carlo
Bo
Venezia Cà
Foscari
Venezia Iuav
1
0
7
0
8
6
0
8
2
Verona
0
3
3
0
6
5.231
1.187
17.638
1.425
10.685
4.848
834
17.801
TOTAL
2010
Piedmont
Graduates
Piedmont
in
AlmaLaurea
percent
2008:
Piedmont
absolute
absent
from
value
AlmaLaurea
2010
2008
TYPOLOGY OF DEGREE
CDL
L
LS/LM
LSCU/LMCU
Total
645
9.366
4.553
987
15.551
88,72%
89,26%
87,04%
83,15%
88,17%
82
1127
678
200
2087
Piedmont
Graduates
Piedmont in
AlmaLaurea
2008:
percent
absolute value
Piedmont
absent from
AlmaLaurea
2010
TYPOLOGY OF DEGREE
CDL
L
LS/LM
LSCU/LMCU
Total
1.260
9.490
4.144
733
15.627
88,42%
88,82%
85,48%
87,89%
87,79%
165
1195
704
101
2174
18
Source Almalaurea and Miur
LEGENDA
L = Laurea (First Cycle Degree/Bachelor - 180 ECTS);
LS/LM = Laurea Specialistica/Magistrale (Second Cycle Degree/Two years Master - 120 ECTS);
LSCU/LMCU = Laurea Specialistica/Magistrale a ciclo unico (Single Cycle Degree/Combined Bachelor and Master - 300/360
ECTS).
19
The dataset includes variables which characterize socio-economic background (proxied by
parents’ education, parents’ professional status); education (secondary school typology; secondary
school graduation marks); gender; student performance and student mobility (measured by the
distance between Faculty location and student home address in both kilometers and minutes); working
student status. Table 3 shows summary statistics of the main variables12.
Table 3: Summary Statistics of the Sample Variables
2008
Variable
Description of variable
Mean
Marks average
mean of the exam marks
26,19
Std.
2010
Std.
Min
Max
2,20
19,64
30,00
26,18
2,14
3,61
2,66
-
13,00
3,58
2,60
82,32
12,63
60,00
100,00
82,46
12,57
60,00
100,00
4,34
2,85
1,31
38,95
4,44
2,79
1,58
37,10
0,05
0,22
-
1,00
0,05
0,22
26,59
5,86
20,68
71,79
26,30
5,57
Dev.
Mean
Dev.
Min
Max
19,53
30,00
a categorical variable which
takes values of 0 for
university-oriented
Secondary
School leaving
certificate
secondary schools
(specializing in classical
-
13,00
studies, science, art, modern
languages) and
progressively higher values
for technical or vocational
schools.
Secondary
School leaving
Secondary School leaving
certificate marks
certificate marks
/100
Duration
duration of university
enrolment (years)
Dummy variable :
Erasmus
1 = the student took part in
-
1,00
Erasmus program
Age average
average age at graduation
20,19
71,71
(years)
12
Most Almalaurea categorical variables are redefined by grouping together the “similar” categories in order to focus on the
effects of very different socio-economic background (low, medium and high) and the main typologies of secondary school
(university-oriented schools vs. technical or vocational schools) on university choice.
20
2008
Variable
Description of variable
Mean
2010
Std.
Dev.
Std.
Min
Max
2,00
50,00
1,74
4,86
Mean
Min
Dev.
Max
difference between actual
Enrolment
age at enrolment and
2,13
5,40
-
3,54
1,04
-
5,00
0,73
3,58
1,09
-
5,00
102,00
8,61
74,00
0,12
0,32
0,15
0,18
-
3,00
47,00
0,71
-
2,00
0,75
0,74
-
2,00
113,00
101,69
8,93
73,00
113,00
-
1,00
2,12
0,57
1,00
4,00
0,36
-
1,00
0,15
0,36
-
1,00
0,39
-
1,00
0,18
0,39
-
1,00
standard enrolment age (19
years)
a categorical regressor:
1 = no certified school
attendance;
2 = primary school
Mother
certificate;
education
3 = lower secondary school
certificate;
4 = upper secondary school
certificate;
5 = university degree.
Father education
same
Degree final
degree mark (the laude is
mark /110
accounted as 3 points)
Working Student
Dummy variable:
status
1 = studing worker
Dummy Mother
Dummy variable:
holding a Un.
1 = holding a university
degree
degree
Dummy Father
holding a Un.
same
degree
21
2008
Variable
Description of variable
Mean
Std.
Dev.
2010
Min
Max
Std.
Mean
Dev.
Min
Max
a categorical variable:
1 = self-employed, family
co-worker, partner of
worker cooperative);
Mother
professional
status
2 = entrepreneur;
3 = professional;
5,92
2,33
1,00
9,00
5,89
2,28
1,00
9,00
4,58
2,12
1,00
9,00
4,57
2,13
1,00
9,00
0,38
0,49
-
1,00
0,38
0,49
-
1,00
45,78
98,66
-
37,70
53,78
-
987,00
0,24
0,43
-
1,00
33,13
88,97
-
1.585,00
4 = senior executive,
manager;
5 = cadre;
6 = white collar worker;
7 = blue collar worker;
9 = nonworking.
Father
professional
same
status
Gender
Dummy variable:
1 = male
distance between student
Distance minutes
address and university
1467
location in minutes
Dummy variable:
Dummy of
1 = the province of the
overlapping
university is the same of the
residence
distance between student
Distance km
address and university
38,88
61,15
-
983,00
location in kilometers
22
6. The Model and Empirical Results
The probability for a Piedmont student to choose and graduate in Piemonte Orientale instead
of other universities (which offer same or similar programs) is modeled by a probit/logit:
(1)
(2)
(3)
where the observed values of yi,j are outcomes for individual ‘i’ enrolled in faculty ‘j’
generated by the regressors. X is a vector of exogenous variables representing: individual’s personal
characteristics (such as gender and age), pre-university qualifications (such as mark at Secondary
school leaving certificate and type of school attended13), indicators of family background (for example
income proxied by parents’ employment typologies), possible peer group effects, and distances to
university measured by three variables (for 2008 estimates the inverse of distance, the time necessary
to reach the Faculty for 2010 estimates a dummy variable which takes value of one if student address
and faculty location belong to the same province and the time necessary to reach the Faculty
analogously to 200814 ). β is a set of parameters to be estimated and ε is the usual white noise error
term. The model is a binomial probit/logit for the individual's probability to choose Piemonte
Orientale, where yi,j = 1 if the individual chooses it and yi,j = 0 otherwise. The Piemonte Orientale
locations are Alessandria, Novara, Vercelli, Acqui Terme, Alba-Bra, Asti, Biella and Casale
Monferrato for 2008 while in 2010 Acqui Terme and Biella are no longer active.
We estimate four main models: two different models, where parent occupation and parent
education are alternatively entered as additional variables into the model (due to huge correlation),
and two distributions in order to model the probability function (logit versus probit). Parent education
is proxied by either categorical variables (Mother’s education and Father’s education) or dummy
variables (Dummy Mother holding a Un. degree and Dummy Father holding a Un. degree). This
generates two further sub-models, called “Model A bis” for logit or probit distribution respectively
(Probit model A_bis and Logit model A_bis).
13
Further development of this work should consider also the endogeneity problem which could arise if secondary school
leaving certificate is chosen to credibly signal some information relevant for the labor market. Thus we are gathering
information about neighborhood secondary school leaving certificate for each province in order to control for it.
14
The change in one variable representing the distance permit to overtake the 2008 estimates inconvenient, that is the
marginal effect greater than one.
23
In all models, the main explanatory variable estimates have all turned out to be significant
and their signs are as expected and stable.
Table 4 shows empirical results for 2008 and Table 6 for 2010. Table 5 (2008) and Table 7
(2009) contain the marginal effects of each independent variable, that is the derivative of the
prediction function, which, by default, is the probability of success following probit/logit, that is the
choice of Piemonte Orientale. By default, margins evaluate this derivative for each observation and
report the average of the marginal effects.
In particular, the educational and socio-economic background crucially affects university
choice. Piemonte Orientale is chosen with higher probability by technical or vocational secondary
school students. This evidence is confirmed by 2010 estimates. Further the Secondary School leaving
certificate mark is a significant explanatory variable, but its rule is ambiguous: for 2008 estimates, the
higher the mark, the lower is the probability of Piemonte Orientale choice; for 2010 estimates, the
effects became positive. This finding can be explained by the dropout of the students of the old Italian
University system (the so called “vecchio ordinamento”) or by an improvement in the relative
positioning of the Piemonte Orientale or by both causes. An experience of study abroad (Erasmus)
negatively affects the probability of Piemonte Orientale choice, but this results is not confirmed by the
estimates on 2010. Parents education, whether proxied by graduate dummies or categorical variables,
negatively affects the small university choice. In particular, if his/her parents are graduates then the
student will more likely enrol and graduate in the historical universities which his/her parents had
chosen. Predictably, parents’ occupations affect university choice through the student’s economic
opportunities.
The small university is chosen with higher probability by “weaker” students: these would be
female, living in peripheral municipalities suffering from structural problems, with lower average
marks (which is a signal of either lower abilities or stronger selection).
The Working Student status plays an unexpected negative role, significant in 2008’
estimates, that is, if the student works then he/she has a smaller probability to choose Piemonte
Orientale. This finding can be explained as the net effect of opposite forces: if the studying workers
usually do not attend classes their choice is independent of distance so they choose the university
where they work (very likely the bigger city) and not the one where they live; studying worker choose
a local university only because it allows them to effectively keep their job and attend classes. Another
explanation focuses on the higher concentration of good jobs in bigger cities where the historical
universities are located: this is where, according to the gravitational model of regional economics,
24
government, banking, insurance and financial services, research activities and company headquarters
also tend to be located. The empirical results for 2010 does not confirm this evidence: the Working
Student status is always negative in all the model estimates but is not significant, at usual confidence
levels.
Duration is not significant in all the models. This evidence supports the idea that the
educational supply of small universities is comparable to that offered by the bigger universities.
The marginal effects for these explanatory variables are significant, sign coherent, stable and
modest in all models, except the distance factor.
Actually, distance, and therefore student mobility, does matter. The role of student mobility
in the Italian university system is crucial: the mobility of student is strictly constrained by the
accessibility of the supply point. The very low mobility of university students in Italy is mainly due to
poor and unequal availability of low cost student accommodation, expensive and inefficient
commuting opportunities, and finally to social, economic and cultural constraints.
The marginal effect of distance is always significant and very large. In 2010 estimates the
marginal effect is 13% and in 2008 estimates is greater than one15.
Our empirical results show that Piemonte Orientale, a small university satisfies a particular
tertiary education demand. Also its demographic recruitment basin is characterised by a specific
geographical area.
15
This seems like a strange result considering that the values of the probability function are bound between 0 and 1.
Nevertheless this can be explained by the computation technique of marginal effects. The marginal effect is the derivative,
that is the approximate change in the dependent variable y for a one-unit change in a regressor x. Because y is between 0 and
1, the change in y obviously cannot be greater than 1, but the marginal effect computes the approximate change. The
derivative at a point is the slope of the tangent line to the curve at that point. Thus the slope of the tangent line, at the point
Inverse_distance_km is greater than 1, therefore distance does matter.
25
26
Table 4 Model Estimations 2008
Variable
Probit model A
Probit model A_bis
Probit model B
Logit model A
Logit model A_bis
Logit model B
Secondary School leaving
0.1930736
***
0.1877444
***
0.1986058 ***
0.3514739 ***
0.3400375
***
0.3631498 ***
certificate marks /100
-0.0063332
***
-0.0060648
***
-0.0063148 ***
-0.0119645 ***
-0.01145
***
-0.0119261 ***
Erasmus
-0.2898485
***
-0.3043228
***
-0.3497474 ***
-0.5763653 ***
-0.6017015
***
-0.6990103 ***
certificate
Secondary School leaving
Parents education
Mother education
-0.0204652
Father education
-0.0993238
-0.0212551
-0.1851035 ***
***
Dummy Mother holding a
Un. degree
-0.1481552
***
-0.2780425
**
-0.1853156
**
-0.3376159
***
-0.1538005
***
-0.2806492
***
Dummy Father holding a
Un. degree
Working Student status
-0.1659365
***
-0.1527958 **
-0.3066475 ***
-0.2804307 **
Mother professional status
-0.0320895 **
-0.0553512 **
Father professional status
-0.0178927
-0.0278286
.
Gender (1=male)
-0.1531113
***
-0.1594376
***
-0.1512158 ***
-0.271704 ***
-0.2813243
***
-0.2666229 ***
Marks average
-0.0401973
***
-0.0399961
***
-0.0385565 ***
-0.0730546 ***
-0.0719707
***
-0.0695823 ***
Duration
-0.0022652
-0.0000456
-0.0077752
-0.0066242
Inverse_ Distance _km
Distance _minutes
-0.001577
-0.0040002
66.11107
***
66.3036
***
66.08145 ***
119.0929 ***
119.3543
***
119.0725 ***
-0.0121669
***
-0.0121238
***
-0.0118654 ***
-0.0230637 ***
-0.0229418
***
-0.0225088 ***
Constant
0.3126587
0.2543957
0.2638964
0.7351999
0.6203103
0.6173739
Log likelihood
-2833.603
-2912.779
-2770.9677
-2819.1214
-2898.3688
-2756.6194
1112.640
1146.010
1073.73
1141.61
1174.83
1102.42
LR χ
2
27
Prob > χ2
0.000
0.000
0.000
0.000
0.000
0.000
0.164
0.1644
0.1623
0.1684
0.1685
0.167
0.197
0.1960
0.1950
0.201
0.2
0.199
0.231
0.2310
0.2280
0.237
0.237
0.234
0.364
0.3760
0.3570
0.372
0.385
0.365
0.161
0.1610
0.1590
0.165
0.165
0.163
86.880%
86.930%
87.150%
86.88%
86.96%
87.13%
Goodness of Fit
Pseudo R
2
Efron's R2
Cragg & Uhler's R
2
McKelvey&Zavoina's R
McFadden's Adj R
Correctly classified
2
2
The similar categories of the categorical variables have been grouped in order to reduce the categories.
Signif. codes Pr(>|t|): 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 .. if Pr(>|t|) around 0.1
28
Table 5 Marginal Effects 2008
Marginal Effects
Variable
Probit model A
Probit model A_bis Probit model B
Logit model A
Logit model A_bis Logit model B
0.04063 ***
0.039155 ***
0.041592 ***
0.037705 ***
0.036078 ***
0.038726 ***
Secondary School leaving certificate marks /100
-0.00133 ***
-0.00126 ***
-0.00132 ***
-0.00128 ***
-0.00121 ***
-0.00127 ***
Erasmus
-0.05231 ***
-0.05394 ***
-0.06073 ***
-0.05083 ***
-0.052 ***
-0.05878 ***
Secondary School leaving certificate
Parents education
Mother education
Father education
-0.00431
-0.00228
-0.0209 ***
-0.01986 ***
Dummy Mother holding a Un. degree
-0.02912
Dummy Father holding a Un. degree
-0.03615 ***
Working Student status
-0.03244
**
-0.02744
**
-0.02996 ***
**
-0.0331 ***
-0.02989 ***
Mother professional status
-0.00672 ***
Father professional status
-0.00375
-0.0301 ***
-0.02745
**
-0.02756
**
-0.0059
**
-0.00297
Gender (1=male)
-0.03156 ***
-0.03255 ***
-0.03104 ***
-0.02847 ***
-0.02914 ***
-0.02779 ***
Marks average
-0.00846 ***
-0.00834 ***
-0.00807 ***
-0.00784 ***
-0.00764 ***
-0.00742 ***
Duration
-0.00048
-0.00033
-9.55E-06
13.9124 ***
13.82805 ***
13.83878 ***
12.776 ***
12.66343 ***
12.6979 ***
-0.00256 ***
-0.00253 ***
-0.00248 ***
-0.00247 ***
-0.00243 ***
-0.0024 ***
Inverse_ Distance _km
Distance _minutes
-0.00083
-0.0007
-0.00043
29
Table 6 Model Estimations 2010
Variable
Probit model A
Probit model A_bis
Probit model B
Logit model A
Logit model A_bis
Logit model B
Secondary School leaving
certificate
0,1300678
***
0,1056132
***
0,0894115
**
0,2345867
***
0,19893
**
0,033846
***
0,0341418
***
0,0337214 ***
0,0679716
***
0,068234
***
0,1805844
**
Secondary School leaving
certificate marks /100
Erasmus
0,0686103 ***
Dropped for
Dropped for
Dropped for
Dropped for
Dropped for
Dropped for
collinearity
collinearity
collinearity
collinearity
collinearity
collinearity
Parents education
Mother education
-0,0845634
Father education
0,0930405
**
-0,088066
**
0,1456772
Dummy Mother holding a
-0,0983893
Un. degree
**
-0,2755668
**
Dummy Father holding a
-0,0191003
Un. degree
Working Student status
-0,0767364
-0,0278949
-0,0347461
-0,1041622
Duration
-0,1517538
-0,338123 ***
0,0200519
Father professional status
Marks average
-0,1780359
-0,1940208 ***
Mother professional status
Gender (1=male)
-0,2180589
0,1003734
-0,477122
***
-0,440261
***
-0,4589813 ***
-0,9737691
***
-0,9194377
***
-0,7736109 ***
-0,2110604
***
-0,2068851
***
-0,2034554 ***
-0,4224529
***
-0,4191691
***
-0.3497079 ***
0,00415
-0,0001892
-0,011633
0,003168
-0,0035472
-0,0809503
Dummy of overlapping (same
province for the university and
for the residence)
Distance _minutes
Constant
1,050483
***
1,026934
***
1,072493 ***
2,369154
***
2,380334
***
2,5063 ***
-0,0104464
***
-0,0111745
***
-0,011768 ***
-0,0259758
***
-0,0278186
***
-0,0300284 ***
1,005449
1,10339
1,663208
2,657045
2,938163
4,3781
30
Log likelihood
LR χ
2
Prob > χ2
-91,051427
-91,498954
-88,86218
-89,385605
-89,486758
-86,79113
47,97
47,07
52,34
51,3
51,09
56,49
0
0
0
0
0
0
0,2085
0,2046
0,2275
0,223
0,2221
0,2455
0,205
0,211
0,225
0,237
0,25
0,268
0,257
0,252
0,279
0,273
0,272
0,299
0,461
0,469
0,499
0,568
0,585
0,619
0,208
0,205
0,228
0,223
0,222
0,246
92,49%
92,49%
93,46%
93,46%
93,70%
94,19%
Goodness of Fit
Pseudo R
2
Efron's R2
Cragg & Uhler's R
2
McKelvey&Zavoina's R
McFadden's Adj R
Correctly classified
2
2
0
The similar categories of the categorical variables have been grouped in order to reduce the categories.
Signif. codes Pr(>|t|): 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 .. if Pr(>|t|) around 0.1
31
Table 7 Marginal Effects 2010
Marginal Effects
Variable
Probit model A
Probit model A_bis
Probit model B
Logit model A
Logit model A_bis
Logit model B
Secondary School leaving certificate
0,0103732 ***
0,0084459 ***
0,0065337 ***
0,0071012 ***
0,0058522 ***
0,0041623 ***
Secondary School leaving certificate marks /100
0,0026993 ***
0,0027303 ***
0,0024642 ***
0,0020576 ***
0,0020073 ***
0,0018125 ***
Erasmus
Dropped for
Dropped for
Dropped for
Dropped for
Dropped for
Dropped for
collinearity
collinearity
collinearity
Collinearity
collinearity
collinearity
Parents education
Mother education
-0,0067441
Father education
0,0074202
*
-0,0026659 *
0,0044098
Dummy Mother holding a Un. degree
-0,0076046
*
-0,0077226
*
Dummy Father holding a Un. degree
-0,0015171
**
-0,0008164
**
Working Student status
-0,0061199
-0,0027786
-0,0076116
-0,0066009
-0,0052375
-0,0083183
Mother professional status
-0,014178 ***
-0,0096646 ***
Father professional status
0,0014653
-0,0012519
Gender (1=male)
-0,0354383 ***
-0,0329195 ***
-0,0312698 ***
-0,0275918 ***
-0,0253686 ***
-0,0221489 ***
Marks average
-0,0168325 ***
-0,0165445 ***
-0,0148674 ***
-0,0127881 ***
-0,0123312 ***
-0,0110833 ***
-0,0000151
-0,0008501
-0,0001044
-0,0005724
Duration
0,000331
0,0000959
Dummy of overlapping (same
province for the university
and for the residence)
Distance _minutes
0,1373634 ***
0,1331495 ***
0,1323324 ***
0,1427796 ***
0,1403953 ***
0,1392002 ***
-0,0008331 ***
-0,0008936 ***
-0,0008599 ***
-0,0007863 ***
-0,0008184 ***
-0,0007876 ***
32
33
7. Concluding Remarks and Implication for Public Policy
Over the last twenty years, the Italian university system underwent an important reform
process which was launched in the 1990s and is still going on with the implementation of a new
Reform Law (Act 240/2010). This latter is actually a Counter reform, a typical Italian specialty,
which is going to reverse the trend experienced in the previous 15 years.
Due to the need for greater institutional autonomy and self-regulation, the first set of reforms
affected the management model of the higher education system which moved away from external,
highly centralized control to more decentralized and internal control.
More recently a policy reversal has occurred, with a view to limit the excessive proliferation
of branches (and/or new universities) and curricula, and therefore to control the financial viability of
the system.
Within this framework, many controversial issues emerge. These include university autonomy,
efficiency and role of the recent small universities, affordability of higher education and
intergenerational mobility versus student mobility.
In this work we have provided a first analysis of the role that small universities play in human
capital accumulation, focusing on Piemonte Orientale as a case study. The model set up considers the
probability of choosing to enrol in Piemonte Orientale vs. other universities, given as explanatory
variable a set of social, economic and cultural variables. We have estimated four main binary outcome
models (plus two sub models for the parents’ education variables): two models where parent
occupation and parent education are alternatively entered as variables, and two distributions in order to
model the probability function (logit versus probit).
The econometric exercise carried out in this paper for 2008 and 2010 highlights the important
role played by small universities in both reducing the congestion in mega universities (and therefore
improving the efficiency of the educational process), and promoting the development of human
capital, and through this, contributing to economic growth and social mobility.
The most important findings of this paper can be summarized as follow:
(i) small universities satisfy a specific demand which cannot be satisfied by larger
universities, because of distance, socio-economic family background, educational background, gender,
34
parents education. This crucial role of the modest socioeconomic background and the low mobility of
the students are confirmed into 2010 estimates;
(ii) student mobility is strictly constrained by the accessibility of the supply point: the very
low mobility of university students in Italy is mainly due to poor and unequal availability of low cost
student accommodation, too expensive and inefficient commuting opportunities, and to social,
economic and cultural constraints;
(iii) family background, as measured by parental education, crucially affects the university
choice of the children, at least in the sense that a low indicator of parents education is the dominant
factor of the demand for higher education in small universities, which is mostly generated and not
“attracted”;
(iv) educational background is a choice driver, i.e. the small university is chosen with a higher
probability by vocational school students (not by university-oriented secondary school students). This
result is confirmed in 2010, while the poor performance in secondary school leaving certificate in
2010 does not increase the probability to choose Piemonte Orientale vs. mega or historical
universities, suggesting a evolution in recruitment;
(v) the small university is chosen with higher probability by “weaker” students (female, living
in peripheral small town).
Further research imply addressing the endogeneity problem which could arise if the secondary
school choice as a signal for future enrolment in university.
35
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Recent working papers
The complete list of working papers is can be found at
http://polis.unipmn.it/index.php?cosa=ricerca,polis
*Economics Series
Q
**Political Theory and Law
ε
Al.Ex Series
Quaderni CIVIS
2013 n.205*
Tiziana Caliman and Alberto Cassone: The choice to enrol in a small university:
A case study of Piemonte Orientale
2013 n.204*
Magnus Carlsson, Luca Fumarco and Dan-Olof Rooth: Artifactual evidence of
discrimination in correspondence studies? A replication of the Neumark method
2013 n.203** Daniel Bosioc et. al. (DRASD): OPAL – Osservatorio per le autonomie locali
N.2/2013
2013 n.202* Davide Ticchi, Thierry Verdier and Andrea Vindigni: Democracy, Dictatorship
and the Cultural Transmission of Political Values
2013 n.201** Giovanni Boggero et. al. (DRASD): OPAL – Osservatorio per le autonomie
locali N.1/2013
2013 n.200* Giovanna Garrone and Guido Ortona: The determinants of perceived overall
security
2012 n.199*
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entrenchment
2012 n.198*
Ugo Panizza and Andrea F. Presbitero: Public debt and economic growth: Is
there a causal effect?
2012 n.197ε
Matteo Migheli, Guido Ortona and Ferruccio Ponzano: Competition among
parties and power: An empirical analysis
2012 n.196*
Roberto Bombana and Carla Marchese: Designing Fees for Music Copyright
Holders in Radio Services
2012 n.195*
Roberto Ippoliti and Greta Falavigna: Pharmaceutical clinical research and
regulation: an impact evaluation of public policy
2011 n.194*
Elisa Rebessi: Diffusione dei luoghi di culto islamici e gestione delle
conflittualità. La moschea di via Urbino a Torino come studio di caso
2011 n.193*
Laura Priore: Il consumo di carne halal nei paesi europei: caratteristiche e
trasformazioni in atto
2011 n.192** Maurilio Guasco: L'emergere di una coscienza civile e sociale negli anni
dell'Unita' d'Italia
2011 n.191*
Melania Verde and Magalì Fia: Le risorse finanziarie e cognitive del sistema
universitario italiano. Uno sguardo d'insieme
2011 n.190ε
Gianna Lotito, Matteo Migheli and Guido Ortona: Is cooperation instinctive?
Evidence from the response times in a Public Goods Game
2011 n.189** Joerg Luther: Fundamental rights in Italy: Revised contributions 2009 for
“Fundamental rights in Europe and Northern America” (DFG-Research A.
Weber, Univers. Osnabrueck)
2011 n.188ε
Gianna Lotito, Matteo Migheli and Guido Ortona: An experimental inquiry into
the nature of relational goods
2011 n.187*
Greta Falavigna and Roberto Ippoliti: Data Envelopment Analysis e sistemi
sanitari regionali italiani
2011 n.186*
Angela Fraschini: Saracco e i problemi finanziari del Regno d'Italia
2011 n.185*
Davide La Torre, Simone Marsiglio, Fabio Privileggi: Fractals and
self-similarity in economics: the case of a stochastic two-sector growth model
2011 n.184*
Kristine Forslund, Lycia Lima and Ugo Panizza: The determinants of the
composition of public debt in developing and emerging market countries
2011 n.183*
Franco Amisano, Alberto Cassone and Carla Marchese: Trasporto pubblico
locale e aree a domanda di mobilità debole in Provincia di Alessandria
2011 n.182*
Piergiuseppe Fortunato and Ugo Panizza: Democracy, education and the quality
of government
2011 n.181*
Franco Amisano and Alberto Cassone: Economic sustainability of an alternative
form of incentives to pharmaceutical innovation. The proposal of Thomas W.
Pogge
2011 n.180*
Cristina Elisa Orso: Microcredit and poverty. An overview of the principal
statistical methods used to measure the program net impacts