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Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model

Author

Listed:
  • Tommaso Agasisti

    (Politecnico di Milano)

  • Francesca Ieva

    (Università degli Studi di Milano)

  • Anna Maria Paganoni

    (Politecnico di Milano)

Abstract

With the aim of assessing the extent of the differences in the context of Italian educational system, the paper applies multilevel modeling to a new administrative dataset, containing detailed information for more than 500,000 students at grade 6 in the year 2011/2012, provided by the Italian Institute for the Evaluation of Educational System. Data are grouped by classes, schools and geographical areas. Different models for each area are fitted, in order to properly address the heteroscedasticity of the phenomenon. The results show that it is possible to estimate statistically significant “school effects”, i.e., the positive/negative association of attending a specific school and the student’s test score, after a case-mix adjustment. Therefore, the paper’s most important message is that school effects are different in terms of magnitude and types in the three geographical macro areas (Northern, Central and Southern Italy) and are dependent on specific students’ and schools’ characteristics.

Suggested Citation

  • Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    • Handle: RePEc:spr:stmapp:v:26:y:2017:i:1:d:10.1007_s10260-016-0363-x
    DOI: 10.1007/s10260-016-0363-x
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    3. Chiara Masci & Francesca Ieva & Tommaso Agasisti & Anna Maria Paganoni, 2021. "Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients," Computational Statistics, Springer, vol. 36(4), pages 2337-2377, December.
    4. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    5. Silvia Bianconcini & Stefania Mignani & Jacopo Mingozzi, 2023. "Assessing maths learning gaps using Italian longitudinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 911-930, September.
    6. Tommaso Agasisti & Veronica Minaya, 2018. "Evaluating the Stability of School Performance Estimates for School Choice: Evidence for Italian Primary Schools," Working papers 67, Società Italiana di Economia Pubblica.
    7. Antonella D’Agostino & Francesco Schirripa Spagnolo & Nicola Salvati, 2022. "Studying the relationship between anxiety and school achievement: evidence from PISA data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 1-20, March.

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