IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v160y2022i1d10.1007_s11205-021-02801-9.html
   My bibliography  Save this article

Social Integration of Second Generation Students in the Italian School System

Author

Listed:
  • Francesco Giovinazzi

    (University of Bologna)

  • Daniela Cocchi

    (University of Bologna)

Abstract

Cultural divides and prejudice complicate the processes of integration and acculturation of migrant families living in a foreign country. Evaluating the impact of such phenomenon can be crucial for social stability and policy making. In this context, the education system has a leading role in fostering and attaining social integration, in particular when it comes to younger sections of the migrant population. In this work, we propose a method for the construction of a quantitative indicator capturing social integration of second generation students in the Italian school system according to areas defined by nationality of the students and administrative region in which they attend school. The indicator, based on survey data, is estimated by means of a 2-step methodology. In the first step, we choose an individual qualitative variable capturing social integration at the unit level, and we compute a first direct estimate of the indicator as the proportion of highly integrated students in each area. Such variable is isolated following alternatively a proxy variable approach or a latent variable model approach. In the second step, we make use of two alternative small area models to improve the estimates, dealing with missing values, low sample size and high variability in smaller domains. At the end, the 2-step methodology results in 4 alternative versions of a synthetic indicator of social integration, that can be used to rank nationalities and administrative regions.

Suggested Citation

  • Francesco Giovinazzi & Daniela Cocchi, 2022. "Social Integration of Second Generation Students in the Italian School System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 287-307, February.
  • Handle: RePEc:spr:soinre:v:160:y:2022:i:1:d:10.1007_s11205-021-02801-9
    DOI: 10.1007/s11205-021-02801-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-021-02801-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11205-021-02801-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    2. Figueroa-Zúñiga, Jorge I. & Arellano-Valle, Reinaldo B. & Ferrari, Silvia L.P., 2013. "Mixed beta regression: A Bayesian perspective," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 137-147.
    3. Angelo Moretti & Natalie Shlomo & Joseph W. Sakshaug, 2020. "Multivariate Small Area Estimation of Multidimensional Latent Economic Well‐being Indicators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 1-28, April.
    4. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    5. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    6. Enrico Fabrizi & Giorgio E. Montanari & M. Giovanna Ranalli, 2016. "A hierarchical latent class model for predicting disability small area counts from survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 103-131, January.
    7. Ryan Janicki, 2020. "Properties of the beta regression model for small area estimation of proportions and application to estimation of poverty rates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(9), pages 2264-2284, May.
    8. White, Arthur & Murphy, Thomas Brendan, 2014. "BayesLCA: An R Package for Bayesian Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i13).
    9. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cepeda-Cuervo Edilberto & Garrido Liliana, 2015. "Bayesian beta regression models with joint mean and dispersion modeling," Monte Carlo Methods and Applications, De Gruyter, vol. 21(1), pages 49-58, March.
    2. Zhou, Haiming & Huang, Xianzheng, 2022. "Bayesian beta regression for bounded responses with unknown supports," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    3. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    4. Paulus, Anne & Hagemann, Nina & Baaken, Marieke C. & Roilo, Stephanie & Alarcón-Segura, Viviana & Cord, Anna F. & Beckmann, Michael, 2022. "Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes," Land Use Policy, Elsevier, vol. 121(C).
    5. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
    6. Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.
    7. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    8. Jillian M Rung & Leonard H Epstein, 2020. "Translating episodic future thinking manipulations for clinical use: Development of a clinical control," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    9. Zhang, Dengjun & Xie, Yifan, 2022. "Customer environmental concerns and profit margin: Evidence from manufacturing firms," Journal of Economics and Business, Elsevier, vol. 120(C).
    10. Buntaine, Mark T., 2011. "Does the Asian Development Bank Respond to Past Environmental Performance when Allocating Environmentally Risky Financing?," World Development, Elsevier, vol. 39(3), pages 336-350, March.
    11. Yukako Sado-Inamura & Kensuke Fukushi, 2018. "Considering Water Quality of Urban Rivers from the Perspectives of Unpleasant Odor," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    12. Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.
    13. Dengjun Zhang, 2022. "Capacity utilization under credit constraints: A firm‐level study of Latin American manufacturing," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1367-1386, January.
    14. Jodrá, P. & Jiménez-Gamero, M.D., 2016. "A note on the Log-Lindley distribution," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 189-194.
    15. Abbasiharofteh, Milad & Kogler, Dieter F. & Lengyel, Balázs, 2023. "Atypical combinations of technologies in regional co-inventor networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 52(10), pages 1-1.
    16. Francisco Cribari-Neto & Sadraque E.F. Lucena, 2015. "Nonnested hypothesis testing in the class of varying dispersion beta regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 967-985, May.
    17. Frank A. La Sorte & Alison Johnston & Toby R. Ault, 2021. "Global trends in the frequency and duration of temperature extremes," Climatic Change, Springer, vol. 166(1), pages 1-14, May.
    18. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    19. Barbiero, Tommaso & Grillenzoni, Carlo, 2019. "A statistical analysis of the energy effectiveness of building refurbishment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    20. Tariq Maqsood & Mark Edwards & Ioanna Ioannou & Ioannis Kosmidis & Tiziana Rossetto & Neil Corby, 2016. "Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1625-1650, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:160:y:2022:i:1:d:10.1007_s11205-021-02801-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.