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Scalable Early Warning Systems for School Dropout prevention: Evidence from a 4.000-School Randomized Controlled Trial

Author

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  • Emmanuel Jose Vazquez
  • Francisco Haimovich
  • Melissa Adelman

Abstract

Across many low- and middle-income countries, a sizable share of young people drop out of school before completing a full course of basic education. Early warning systems that accurately identify students at risk of dropout and support them with targeted interventions have shown results and are in widespread use in high-income contexts. This paper presents impact evaluation results from an early warning system pilot program in Guatemala, a middle-income country where nearly 40 percent of sixth graders drop out before completing ninth grade. The pilot program, which was implemented in 17 percent of Guatemala’s primary schools and largely leveraging existing government resources, reduced the dropout rate in the transition from primary to lower secondary school by 4 percent (1.3 percentage points) among schools assigned to the program, and by 9 percent (3 percentage points) among program compliers. Although the effect size is relatively modest, the low cost of the program (estimated at less than US$3 per student) and successful implementation at scale make this a promising and cost-effective approach for reducing dropout in resource-constrained contexts like Guatemala. AEA RCT ID: AEARCTR-0004091
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Suggested Citation

  • Emmanuel Jose Vazquez & Francisco Haimovich & Melissa Adelman, 2021. "Scalable Early Warning Systems for School Dropout prevention: Evidence from a 4.000-School Randomized Controlled Trial," Asociación Argentina de Economía Política: Working Papers 4529, Asociación Argentina de Economía Política.
  • Handle: RePEc:aep:anales:4529
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    File URL: https://aaep.org.ar/works/works2021b/4529.pdf
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    References listed on IDEAS

    as
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    4. 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.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Dropout prevention; Early Warning; Impact Evaluation; School Management; Guatemala;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

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    This paper has been announced in the following NEP Reports:

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