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Socioeconomic inequality and regional disparities in educational achievement: The role of relative poverty

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  • Daniele, Vittorio

Abstract

The relationship between PISA 2012 maths test scores and relative poverty was tested in a sample of 35 Italian and Spanish regions, together with a larger sample that included Australian, Belgian, and Canadian regions. The correlation between mean scores in mathematics, adjusted for students' socioeconomic and cultural backgrounds, and poverty rates is ‐−0.84 for the Italian and Spanish sample, and −0.68 for the complete sample. In the regressions, the effect of relative poverty on mean scores in mathematics is highly significant (p < 0.01), robust to different specifications, and independent from students' backgrounds and regional development levels. It is proposed that disparities in average scores in mathematics across regions depend on the shares of low-performing students which, in turn, depend on the degree of relative poverty within regions. The implications for the thesis according to which, in Italy and Spain, regional disparities in educational achievements reflect genetic differences in the IQ of populations are discussed.

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  • Daniele, Vittorio, 2021. "Socioeconomic inequality and regional disparities in educational achievement: The role of relative poverty," Intelligence, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:intell:v:84:y:2021:i:c:s0160289620300933
    DOI: 10.1016/j.intell.2020.101515
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    3. Tang, Kai & Li, Zhenshan & He, Chun, 2023. "Spatial distribution pattern and influencing factors of relative poverty in rural China," Innovation and Green Development, Elsevier, vol. 2(1).
    4. Pal, Anita & J.V., Madhusudan & Kumari, Dolly & Kamal, Vineet Kumar, 2024. "Socioeconomic disparities in school life expectancy among India and its major states of India in the last decade: Insights from large scale household surveys data," Children and Youth Services Review, Elsevier, vol. 156(C).
    5. Giorgia Zotti, 2024. "Regional Differences in Italian School Efficiency: A Conditional DEA Approach," SERIES 05-2024, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Nov 2024.
    6. Daniel Doz & Mara Cotič & Darjo Felda, 2023. "Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades," Mathematics, MDPI, vol. 11(19), pages 1-19, September.

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