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Universities’ responses to crises: the influence of competition and reputation on tuition fees

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Abstract

Modern societies regularly face crises that have major disruptive effects. Learning from past crises can inform better choices and policies when facing a new one. Following the 2008 global financial crisis, higher education scholars explored its effects on students’ tuition fees through cuts in public funding. This article instead investigates how universities’ decisions on tuition fees have been affected by other factors, beyond the decrease in public funds. As such, it explores the role of competition and reputation in affecting universities’ decisions on tuition fees when facing a crisis. Using data from 59 public Italian universities in the period between 2003 and 2014, we found that universities increased tuition fee by an average of 27% per student in response to the crisis. At the same time, high competition mitigated the increase of tuition fees, except for the case of highly reputed universities, which charged even higher tuition. These findings highlight the importance of monitoring fees in times of crises, as well as the complex role of competition and reputation in containing or inflating university tuition fees.

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Notes

  1. Twenty-seven Italian private universities, six public doctoral universities, as well as three universities for foreigners were excluded as their model of price setting is not comparable due to their different business models.

  2. https://www.istruzione.it/

  3. The share of core-funding allocated according to the institutional performance increased from 7 to 20% between 2008 and 2015 (Donina et al., 2017).

  4. To generate the dependent variable, we used the number of total students enrolled, rather than the number of paying enrolled students. This is due to the restraint of the percentage of Italians that are exempt (an average of 1% over the period). The University of L’Aquila, where the whole student body is exempt from paying tuition, represents the only notable exception. This is a consequence of the earthquake that hit the city of L’Aquila in 2009.

  5. Using the department as the level of analysis simplifies the investigation and objective because of the presence of a large variety of degree programmes in Italy, each of which is characterized by its own peculiarities. After the implementation of the Law 240/2010, the department became the internal organizational structure of Italian universities, which unifies both teaching and research activities (Donina et al., 2015). Adopting the department as the level of analysis allows us to take into account the disciplinary heterogeneity of the educational offerings in the Italian context (see for example Cattaneo, Malighetti, et al., 2017a, b). An analysis at the micro-level would instead require the degree programmes as its level of analysis.

  6. https://www.shanghairanking.com/

  7. Natural logs of all variables, except dummies, are taken to interpret the results in terms of elasticity.

  8. We have tested the validity of the results by adopting a panel data model with university fixed effects. Results generally stayed stable.

  9. All regressions include standard errors clustered at the regional level, with year fixed effects and with a standardized coefficient for competition. Results also stayed stable by adopting a panel data model with a university fixed effect.

  10. During the period of investigation, some universities were ranked and some were taken off the rankings, such that they belonged to two different categories in different years.

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Correspondence to Alice Civera.

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Appendices

Appendix

Definition of the competitors’ centrality index

The definition of the competitors’ centrality index dates back to the work of Hotelling (1929) and has been referenced in several other studies since. This index accounts for distance of a university from its competitors in both physical (km) and operative terms:

$${F}_{i,j,t}= {O}_{({\mathrm{Prov}}_{i,t})}{D}_{{\mathrm{Univ}}_{j,t};{\mathrm{ComPI}}_{j,t}}{f}_{({d}_{i,j})}$$

where:

$${\mathrm{ComPI}}_{j,t}= \sum_{\begin{array}{c}m=1\\ m\ne j\end{array}}^{N}{(\mathrm{Univ}}_{m,t}) f{(d}_{j,m})$$

ComPIj,t is the competitors’ proximity index for university j in year t, N is the total number of other m institutions different from university j within the Italian HE context, Univm,t represents the extent to which students are attracted to university m and f (dj,m) is a function of the Euclidean distance between university j and university m.

The intensity of the competition influences the distribution and flow of students; moreover, if the coefficient of the index is negative, competition is the dominant force at work, while if the coefficient is positive, the agglomeration effect prevails.

Definition of the competitors’ centrality index with discipline overlapping

Since two or more universities can be geographically close but too different to compete, ComPIj,t is corrected in terms of departments for the set of choices students face when the universities’ educational offers are considered. In the Italian context, the department has been adopted as the level of analysis (see for example Cattaneo, Malighetti, et al., 2017a, b), as it is a good proxy for the variety of the university’s educational offering. According to the Law 240/2010, the department has become the internal organizational structure of Italian universities, unifying both teaching and research activities (Donina et al., 2015).

For each pair of universities, the departments in common are first identified:

$${\mathrm{DepOV}}_{i-j}={\mathrm{Dep}}_{i}\cap {\mathrm{Dep}}_{j}$$

where DepOVij is the department overlap (weighted by the number of students enrolled in a given department) between university i and university j, where DEPi stands for the departments of university i and DEPj those at university j. Second, the overlapping index between universities i and j is computed by multiplying the relative shares of students enrolled in the departments of university i also existing at university j

$${\mathrm{IDepOV}}_{i-j}= \frac{\sum (\mathrm{Stud} {\mathrm{Univ}}_{i})\in {\mathrm{DepOV}}_{i-j}}{\sum (\mathrm{Stud} {\mathrm{Univ}}_{i})} \times \frac{\sum (\mathrm{Stud} {\mathrm{Univ}}_{j})\in {\mathrm{DepOV}}_{i-j}}{\sum (\mathrm{Stud} {\mathrm{Univ}}_{j})}$$

As an example, the University of Bergamo and Milan Polytechnic are geographically close and compete for engineering students. Yet, only Bergamo provides courses in law, foreign languages, literature and communication studies, human and social sciences and literature and philosophy, whereas only Milan Polytechnic has an architecture and design department. The first ratio is 0.14, calculated as the number of students enrolled in engineering at Bergamo (which is also offered by Milan) (344), relative to the total number of students (2449). Similarly, the second ratio (0.67) is measured excluding those students belonging to the departments of architecture and design (4415) in Milan. The product of these two ratios is used to weigh the competitors’ proximity index.

The product of these two ratios \({\mathrm{IDepOV}}_{m-j,t}\) is used to weigh the competitors’ proximity index as follows:

$${\mathrm{ComPI}}_{j,t}= \sum_{\begin{array}{c}m=1\\ m\ne j\end{array}}^{N}{(\mathrm{Univ}}_{m,t}{\mathrm{IDepOV}}_{m-j,t}) f{(d}_{j,m})$$

Additional robustness check

See Table 8.

Table 8 Alternative measure of competition. This table reports the results of dynamic panel regressions on the level of tuition fees per student charged by 59 public Italian universities in the period between 2003 and 2014. Competition is measured by two alternative measures based on the number of publications (model 1) and on the relative national recognition (model 2), respectively. Competitors’ number of publications is provided by Scopus database, and the number of articles in national, regional and local newspapers is provided by the Factiva news media database

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Civera, A., Cattaneo, M., Meoli, M. et al. Universities’ responses to crises: the influence of competition and reputation on tuition fees. High Educ 82, 61–84 (2021). https://doi.org/10.1007/s10734-020-00622-2

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