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Inequality of Opportunity in Education in Spanish Regions: A Machine Learning Approach

Author

Listed:
  • PABLO BENCOMO-MESA

    (Universidad de La Laguna)

  • GUSTAVO A. MARRERO

    (Universidad de La Laguna and EQUALITAS)

  • GABRIELA SICILIA

    (Universidad Autónoma de Barcelona)

Abstract

Inequality of Opportunity in Achievement (IOpE) measures the importance of factors beyond the student’s control in explaining differences in academic performance. Using PISA 2018, we estimate the IOpE for Spanish regions using conditional inference tree (CIT) and forest (CIF). Using CIFs, IOpE is twice as high as those obtained using traditional approaches (on average, 43% compared to 20%). Murcia and Extremadura are among those with the highest IOpE, while Castilla-La Mancha and the Pais Vasco have the lowest IOpE. The circumstances that contribute most to IOpE are the cultural environment at home (number of books) and parental occupation.

Suggested Citation

  • Pablo Bencomo-Mesa & Gustavo A. Marrero & Gabriela Sicilia, 2025. "Inequality of Opportunity in Education in Spanish Regions: A Machine Learning Approach," Hacienda Pública Española / Review of Public Economics, IEF, vol. 252(1), pages 113-162, March.
    • Handle: RePEc:hpe:journl:y:2025:v:252:i:1:p:113-162
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Inequality of opportunity; Academic achievement; Machine learning; PISA; Spanish regions;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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