The Italian Ministry of Education, University, and Research (MIUR) reported that, based on the 2017-2018 academic year data, there are almost 660,520 students with non-Italian citizenship across the elementary, middle, and high school levels. That is 9.4% of the total school population (MUIR, 2020). The presence of these pupils, 63% of whom were born in Italy to non-Italian parents, requires new teaching, organizational methods, and policy in the Italian education system with an eye towards critical inclusion. 

Nevertheless, despite a universal school system intended to bridge students with non-Italian citizenship, their families, and their communities (Colombo, Santagati, 2014), schools manage to perpetuate the same exclusionary systems intended to be prevented by these laws. Whether through geographic location, educational attainment, or school infrastructures, segregation leading to intercultural inequalities manifests. 

Until recently, exact data on the specific student demographics, student outcomes, school choice, and school resources broken down within geographies were unavailable for public review. Thus, the connections between policy and practice have been speculative, qualitative, and anecdotal. For example, previous studies of the educational experiences of students with non-Italian citizenship have explored questions of the system's capacity to engage with changing demographics, investigating the wide gap between Italian and non-Italian students educational outcomes (Istat, 2016; Ravecca, 2009), professional achievements (Giancola, Salmieri, 2018), or by examining social connections and feelings of belonging of students with a migratory background (Galantino, 2019). 

This study seeks to review and further investigate schools' demographic makeup in Italy, with a specific deep dive into schools in the city of Padova, and using the cases of Bologna and Torino for comparison. This study compares publicly available information from several online databases on the MIUR website, focusing on elementary, middle, and high school. By combining the results of previous qualitative research involving ethnographic participant observation with quantitative data analysis, we seek to understand the quality of educational services provided to students with non-Italian citizenship, the current state of students' trajectories, and the educational outcomes of students with non-Italian citizenship in the Italian school system.

We will compare information from several publicly available databases on the MIUR website, focusing on elementary, middle, and high schools. The databases can be grouped into four categories: Schools’ general information, including region, province, and address that we will use to map geolocalization and to create groups by grade or specialization Data on the number of students with or without Italian citizenship status for each school Data on schools based on the “Sistema Nazionale di Valutazione,” composed of self-assessments in ten categories related to the quality of services offered by each school Data on school infrastructures, including quality of the contextual environment, accessibility measures, availability of transportation options, etc Since the “Circolare Ministeriale n. 2, 8 Gennaio 2010”, the distribution of non-Italian citizenship students should be limited to a maximum of 30% of the student population for each school complex. Based on these values, we split the schools into five groups. Given n as the % of students of non-Italian citizenship: Group 1: n<2% Group 2: 2%30% This grouping will enable us to compare the performance and quality of schools that have a student population of non-Italian citizenship close to the national average (group 3), beyond the limit posed by law (group 5), minimal or null (group 1), or between these extremes (group 2 and 4). In particular, we will compare the data from the schools’ self-assessments and infrastructures status, to evaluate any correlation. This comparison will be made both at the state and local levels, focusing on the province of Padova as an example, to underline general trends and local specificities. We will also perform a geolocalization of all school buildings, enabling us to visualize schools’ distribution within each group at the state and local levels. We will focus on the cities of Padova, Bologna, and Torino as examples of how schools in distinct groups are distributed in local urban and suburban environments. Lastly, we will look at data on students’ choice of high school specialization, comparing the choices of students with and without Italian citizenship, and grouping the specializations by industry.

A preliminary overview of the data shows three key results. First, regarding the general state of the distribution of non-Italian citizenship students in schools, there are too many schools in groups 1 and 5. We discovered that non-Italian citizenship students prefer certain high school specializations (mostly technical and professional development), that their drop-out rate is higher, and that they are less likely to attend private schools. Results of schools’ self-assessment evaluations seem like reliable markers of distinguishing the groups. Notably, schools in group 5, whose population of non-Italian citizenship students exceeded the limit posed by law, tended to score lower in all fields except for the quality of their DE&I-related practices. The data available on the schools’ infrastructures, albeit promising in theory, turned out to not be statistically meaningful due to significant absences of reports from many schools, leaving the datasets too incomplete to perform an analysis. Secondly, regarding the schools’ geolocalization, observations at the State level tend to replicate (with additional, valuable details) the results published by the MIUR in their 2017-2018 report. However, zooming locally reveals complex relations between the schools’ location and the urban and suburban environments. These results demonstrate how social factors, urban planning, and transportation contribute to students’ de-facto segregation based on citizenship. Lastly, the analysis conducted raises additional questions and concerns left unanswerable due to faulty and incomplete data. In particular, a better level of granularity could be achieved if we had additional information on the citizenship status of the parents of all students, their household income, and years of residence in Italy. The data collection methodology also raises questions about which other indicators could have been collected alongside the citizenship status (like ethnicity, gender beyond binary terms, disability).

Borromini, C., De Sanctis, G., Notiziario Stranieri, Gli alunni con cittadinanza non italiana, anno scolastico 2017/2018, Gestione Patrimonio Informativo e Statistica, MIUR, published July 2019 Colombo, M., Santagati, M. (2014), Nelle scuole plurali. Misure di integrazione degli alunni stranieri, Milan: Franco Angeli. Elia, J. (2014), «Le seconde generazioni di immigrati a scuola. Integrazione sìo integrazione no?», Education et sociétés plurilingues , 36, pp. 77-91. Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. Chicago: University of Chicago Press. Galantino, M. G. (2019). ‘I’m Italian, and I’m other.’ Citizenship in the Making among Second Generation High School Students in Rome. Proceedings of the 1st International Conference of the Journal Scuola Democratica EDUCATION AND POST-DEMOCRACY VOLUME I Politics, Citizenship, Diversity, and Inclusion. ISTAT (2016), L’integrazione scolastica e sociale delle seconde generazioni– Anno 2015 , Rome: ISTAT Lalla, M. (2010). School insertion of foreign students of first and second generation in Italy. Department of Economics. Morales, A. and Hanson, W. E. (2005). Language brokering: an integrative review of the literature. Hispanic Journal of Behavioral Sciences, 27, (4), 471 - 503. Ravecca, A. (2009), Studiare nonostante. Capitale sociale e successo scolastico degli studenti immigrati nella scuola superiore , Milan: Franco Angeli. The datasets used for this project can be accessed on the MIUR website (last visited: 01/29/2021) https://dati.istruzione.it/opendata/opendata/catalogo/#Scuola.