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Predicting school dropout with administrative data: new evidence from Guatemala and Honduras


  • Melissa Adelman
  • Francisco Haimovich
  • Andres Ham
  • Emmanuel Vazquez


School dropout is a growing concern across Latin America because of its negative social and economic consequences. Identifying who is likely to drop out, and therefore could be targeted for interventions, is a well-studied prediction problem in countries with strong administrative data. In this paper, we use new data in Guatemala and Honduras to estimate some of the first dropout prediction models for lower-middle income countries. These models correctly identify 80% of sixth grade students who will drop out within the next year, performing better than other commonly used targeting approaches and as well as models used in the U.S.

Suggested Citation

  • Melissa Adelman & Francisco Haimovich & Andres Ham & Emmanuel Vazquez, 2018. "Predicting school dropout with administrative data: new evidence from Guatemala and Honduras," Education Economics, Taylor & Francis Journals, vol. 26(4), pages 356-372, July.
  • Handle: RePEc:taf:edecon:v:26:y:2018:i:4:p:356-372
    DOI: 10.1080/09645292.2018.1433127

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    References listed on IDEAS

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    Cited by:

    1. Rafaella L. S. Nascimento & Roberta A. de A. Fagundes & Renata M. C. R. Souza, 2022. "Statistical Learning for Predicting School Dropout in Elementary Education: A Comparative Study," Annals of Data Science, Springer, vol. 9(4), pages 801-828, August.
    2. Francisco Haimovich & Emmanuel Vazquez & Melissa Adelman, 2021. "Scalable Early Warning Systems for School Dropout prevention: Evidence from a 4.000-School Randomized Controlled Trial," CEDLAS, Working Papers 0285, CEDLAS, Universidad Nacional de La Plata.
    3. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    4. Delprato, Marcos & Frola, Alessia, 2022. "Zones of educational exclusion of out-of-school youth," International Journal of Educational Development, Elsevier, vol. 88(C).
    5. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    6. Andres Ham & Emmanuel Vazquez & Monica Yanez-Pagans, 2023. "The Effects of Differential Exposure to COVID-19 on Educational Outcomes in Guatemala," CEDLAS, Working Papers 0313, CEDLAS, Universidad Nacional de La Plata.
    7. Wodon, Quentin, 2022. "Global report on integral human development 2022: measuring the contributions of Catholic and other faith-based organizations to education, healthcare, and social protection," MPRA Paper 114809, University Library of Munich, Germany.
    8. Malte Toetzke & Nicolas Banholzer & Stefan Feuerriegel, 2022. "Monitoring global development aid with machine learning," Nature Sustainability, Nature, vol. 5(6), pages 533-541, June.

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