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Data

17-set-2019

The data regard three entities or, in a broader sense, statistical units: the student, the high school, and the university.

  1. The Student
    The MIUR has been collecting administrative data – called Anagrafe Nazionale Studenti (ANS) – at student-level from all higher education institutions for more than a decade. These do not allow for deep-understanding of student flows, because they have all the limitations of aggregate data. However, our research group has access to the national student level micro-data archives, from 2008 to the latest available cohorts, thanks to a special agreement between the MIUR and all the universities of our group. This agreement was first signed in 2016 (by the Universities of Palermo, Cagliari, Siena, and Turin) and amended in 2017 (to include the Universities of Florence, Naples Federico II, and Sassari). The databases provide a great opportunity in terms of the advancement of knowledge and it is the basis for the creation of this research group. The database also includes the student's high school identification code as well as several information on his/her high school career. The original ANS archives have already been cleaned up by our research group and longitudinal individual datasets for each cohort are already available (L-ANS). These datasets allow, for the first time, to learn about the transition from BA graduation (1st level) to MA (2nd level) enrolment: this transition is very interesting because it encapsulates the second student migration flows.
    These datasets will be linked to data surveys on the university graduates profile (ALM1) and labour market outcomes (ALM2) collected by the AlmaLaurea Consortium (ALM) at graduation and after 1, 3, and 5 years from degree attainment. These data allow us to identify post-graduate movers and stayers within a coarse geographical partition. A periodic retrospective survey will be used to produce a longitudinal database, which will allow us to relate the labour market outcomes and residential choices 1, 3 and 5 years after the end of studies to university careers, on a fine-grained typology of fields of study and degree-types, creating a unified (L-ANS-ALM) data archive. Because of the current restrictions in data release, we will later explore how to extend the linkage to all institutions, while at first the linkage will be limited to universities covered in the current project.

  2. The High School
    The high school’s identification code reported in the (L-ANS) will be linked to data collected by the National Institute for the Evaluation of the School System (INVALSI). This dataset will allow linking the high school characteristics (region, province, type of high school, gender percentage, foreign students percentage, student attrition, teaching staff, student achievement in reading and mathematics, etc.) to the university outcomes (e.g. student attrition, mobility rates, number of credits accumulated, type of university courses, etc.). Compositional variables will be computed at the school level using data from three databases: the INVALSI student questionnaire (INVALSI-S), the INVALSI tests on the two skill areas (INVALSI-T) and the INVALSI school form (INVALSI-SCH). We will link each school in a new database (INVALSI-S-T-SC) to define Italian high school characteristics in terms of the socioeconomic and immigrant status of their students, their level of competencies and the school organisational practices. The link of (INVALSI-S-T-SC) with the ANS micro-data will allow us to investigate students’ university choices and performance in light of their previous educational experiences.

  3. The University
    The (L-ANS) datasets can provide detailed information on student indicators related to the FFO. Moreover, we will need other time series 2008-2018 regarding: the reduction of the allocation funding due to student reduction (in the South), additional funding due to new faculty positions, the geography of total university faculty, and university faculty mobility in Italy. This information will be used to create an Italian university map with several layers: total budget and its items, faculty members, and students (movers/stayers).