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2015 | OriginalPaper | Chapter

12. The Effect of University Costs and Institutional Incentives on Enrolments: Empirical Evidence for Italian Regions

Authors : Claudia Pigini, Stefano Staffolani

Published in: Geographical Labor Market Imbalances

Publisher: Springer Berlin Heidelberg

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Abstract

We study the relationship between the enrollment decisions of Italian secondary school graduates and the cost of participating in higher education. In particular, we look into the role of incentives, such as scholarship grants, and of the supply of under-priced accommodation which are policy tools in the hands of regional institutes (Enti Regionali per il diritto allo Studio Universitario, ERSU). We provide empirical evidence by estimating a conditional logit model using the survey of 2004 secondary school graduates issued by the Italian Institute of Statistics (ISTAT). We find that enrollment costs are determinant in students university choices: on average, the elasticity of the probability of enrollment to tuition fees is − 0. 062, the one to expected grants is 0. 028, and the one to expected rent is − 0. 022. Differences between regions are considerable: southern regions show lower elasticities, while small central and northern regions exhibit the largest ones.

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Appendix
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Footnotes
1
A series of conferences in Paris (1998), Bologna (1999), Prague (2001), Berlin (2003), and Bergen (2005) whose goal was to achieve a higher degree of comparability between European HE systems.
 
2
Regional agency for the right to education (Enti Regionali per il diritto allo Studio Universitario).
 
3
The application of this estimation strategy to model HE choices was first proposed by Manski and Wise (1983) and followed in recent analyses by Long (2004) and Gibbons and Vignoles (2012). Drewes and Michael (2006) and Verboven and Kelchtermans (2010) use some variations of the conditional logit model: the rank-ordered conditional logit and the nested logit model, respectively.
 
4
Staffolani and Pigini (2012) propose a theoretical model that describes the enrollment and university choices of secondary school graduates and an empirical analysis aimed to test its prediction. The work focuses on a general framework for students’ choices that is based on HE costs as well as university quality, while it takes no account of the role of regional incentives. The reader will, however, be referred to Staffolani and Pigini (2012) for a more extensive description of the data.
 
5
Earlier results can be found in Jackson and Weathersby (1975), Leslie and Brinkman (1987), Kane (1995), and Kane (1995). A compact review of these references can be found in http://​www.​hanoverresearch.​com/​2012/​06/​tuition-elasticity-student-responsiveness-to-tuition-increases/​.
 
6
More flexible tools that accommodate random utility models, such as multinomial probit or mixed logit models, are, in principle, the best choice in these cases. However, given the high number of student–university combinations in our dataset, the adoption of such models is computationally unfeasible.
 
7
The faculties of Medicine and Architecture pose as an exception. However, applicants who cannot access these faculties have no obstacles in enrolling into other faculties without being selected.
 
8
Such assignment will be explained in detail in Sect. 12.4.
 
9
In the appendix, Fig. 12.3, based on UNESCU data, shows the number of foreign students enrolled in Italy and the number of Italian students enrolled abroad for the period 1999–2010.
 
10
Detailed percentages are displayed in Table 12.5 in the appendix, where the first and third columns are plotted in the left and right panels of Fig. 12.1, respectively.
 
11
Table 12.6 in the appendix, based on MIUR data, shows the evolution over time for the period 2001–2007 of the “attractiveness” of regional universities, computed by the ratio between the share of students enrolled in regional universities coming from outside the region on students enrolled in the region and the share of students enrolled in universities outside the region on enrolled secondary school leavers living in the region.
 
12
The methodology note that describes the computation of the university ranking can be found in http://​www.​repubblica.​it/​speciale/​2002/​censis/​indicatori.​html.
 
13
Their relevance is discussed in Agasisti and Dal Bianco (2007).
 
14
We want to control for dimension as the return to skill may be higher in big cities. See Addario and Patacchini (2007).
 
15
More detailed descriptive statistics on all the variables included in the conditional logit specification, disaggregated by universities and Italian provinces, are available in Staffolani and Pigini (2012).
 
16
Average tuition fees are 720 euros in public universities and 2,480 euros in private ones.
 
17
The coefficient presented in Table 12.3 are strongly influenced by the familiar background of students. For instance, by selecting the sample of students coming from the richest families (the ones where the highest job position is chief executive officers, executive or self-employed), we obtain the following coefficients: tuition fees − 0. 007, rent − 0. 089, grants are not statistically significant. By selecting students coming from poorer families (the one where the breadwinner is executive white collar, blue collar, or unemployed), the three coefficients are strongly higher (in absolute value): − 0. 07 for fees, 0. 007 for grants, and − 0. 114 for rent. Therefore, regional policies aimed to provide incentives in terms of cost reduction have a strong redistributing effect of enrollment opportunities and university choices for different subgroups of the population (see Staffolani and Pigini 2012).
 
18
Quartic specifications in distance and university ranking have also been tested. Results, however, are not remarkably different.
 
19
We also computed the average elasticity between universities, obtaining the result of − 0. 3, that is the same presented in Staffolani and Pigini (2012). It is higher, in absolute value, than the average elasticity computed between regions. These results are nevertheless coherent: in fact, considering regions, we do not take into account the substitution between enrolling in universities located in the same region.
 
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Metadata
Title
The Effect of University Costs and Institutional Incentives on Enrolments: Empirical Evidence for Italian Regions
Authors
Claudia Pigini
Stefano Staffolani
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-55203-8_12