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

Universidad de La Laguna et al.

Hacienda Pública Española / Review of Public Economics2025https://doi.org/10.7866/hpe-rpe.25.1.4article
AJG 1
Weight
0.41

What the paper says

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.

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https://doi.org/https://doi.org/10.7866/hpe-rpe.25.1.4

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@article{universidad2025,
  title        = {{Inequality of Opportunity in Education in Spanish Regions: A Machine Learning Approach}},
  author       = {Universidad de La Laguna et al.},
  journal      = {Hacienda Pública Española / Review of Public Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.7866/hpe-rpe.25.1.4},
}

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

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.