Impacts

The databases and the expected deliverables of this application will be an invaluable resource for informing government policies. Furthermore, the project will have technological, scientific, social, and economic impact. For the first time in Italy, it will be possible to produce national longitudinal maps (in terms of student mobility) from high school to job placement. A follow-up project, which will require a computer science team, could create a national data warehouse that could be made publicly available. This database could include several layers, such as time, space, student profiles, MIUR funding, and university profiles.

The project will provide several databases/datasets and papers and reports with social, economic, and scientific benefits. The main beneficiaries will be educational institutions, public institutions as well as students, households, other stakeholders, and policy makers.

The (L-ANS) database is the first longitudinal micro-level, career-focused database to provide significant statistical analysis to both educational and non-educational institutions. The (L-ANS-ALM) database will supplant currently fragmented data and build more uniform student/graduate/worker biographies, based on a longitudinal approach. In general, these two databases will provide the basis to calculate statistics/indicators/measurements for universities, for local, national, and European politicians, and for labour market stakeholders. For the first time in Italy, information on transitions from BA to MA levels will become available with a longitudinal approach. The main benefits are that universities will be able to evaluate the efficacy of the higher education system in real time, while politicians will be able to plan investments into disadvantaged areas to improve academic quality and expand work opportunities to stop intellectual migration. At the same time, the connection drawn between high school, university and the job market will provide insights into the shortfalls of the educational system; especially one in which territorial divides seemed to have increased. Most importantly, the southern part of the country could take advantage of having better information that can be used to reduce the migration of intellectual capital toward the North. Moreover, these databases could be utilized to construct predictive models of student mobility, using different middleware and software tools.

University and national policy makers will be able to utilise a new geographical and historical “visualisation” of student mobility, universities hiring policies, and differences between northern and southern territories. Moreover, universities and policy makers will be able to manage and assess the limits and the benefits of the current competitive quasi-market system. At the same time local stakeholders will be informed about educational migration flows, which in turn can support/enhance territorial excellence in order to face local weaknesses. In summary, this project will provide the necessary information for the local, regional, and national political decision makers to create policies that can reform higher education, so that institutions in charge of creating policies can be more innovative and able to improve the university system based on current information.

Expected Results, in brief

• Geographical high school mobility maps at 1st and 2nd level.
• Geographical thematic (according to the variables above mentioned) mobility maps at BA and MA level, and at placement.
• Chain migration maps; profile of the “weak” and “strong” student.
• Probability mobility profiles of graduates (and indicators) in terms of degree completion, field of study, performance etc.
• A description of push and pull factors that lie beyond mobility and a deeper understanding of individual motivations.
• Network representation of Universities in terms of attractiveness, quality of services, socio-economic indicators and mobility.
• Definition of a comprehensive notion of “centrality” for universities, moving probabilities conditioning on network centrality, identification of clusters of similar institutions.
• Identification of the driving factors of university performance and attractiveness and assessment of the relation between Higher Education reform, budgeting and student mobility.