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

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  • Adelman,Melissa Ann
  • Haimovich,Francisco
  • Ham,Andres
  • Vazquez,Emmanuel Jose

Abstract

Across Latin America, school dropout is a growing concern, because of its negative social and economic consequences. Although a wide range of interventions hold potential to reduce dropout rates, policy makers in many countries must first address the basic question of how to target limited resources effectively for such interventions. Identifying who is most likely to drop out and, therefore, who should be prioritized for targeting, is a prediction problem that has been addressed in a rich set of research in countries with strong education system data. This paper makes use of newly established administrative data systems in Guatemala and Honduras, to estimate some of the first dropout prediction models for lower-middle-income countries. These models can correctly identify 80 percent of sixth grade students who will drop out in the transition to lower secondary school, performing as well as models used in the United States and providing more accurate results than other commonly used targeting approaches.

Suggested Citation

  • Adelman,Melissa Ann & Haimovich,Francisco & Ham,Andres & Vazquez,Emmanuel Jose, 2017. "Predicting school dropout with administrative data: new evidence from Guatemala and Honduras," Policy Research Working Paper Series 8142, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8142
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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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