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

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

Abstract

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.

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  • 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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    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. World Bank, 2023. "Dominican Republic Poverty Assessment 2023," World Bank Publications - Reports 40565, The World Bank Group.
    4. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    5. 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.
    6. Delprato, Marcos & Frola, Alessia, 2022. "Zones of educational exclusion of out-of-school youth," International Journal of Educational Development, Elsevier, vol. 88(C).
    7. Jorge Andres Zambrano Riveros & Guillermo Beylis & William Maloney & Guillermo Vuletin, "undated". "Latin America and the Caribbean Economic Review, October 2023 - Wired [Informe Económico América Latina y el Caribe, Octubre 2023 - Conectados]," World Bank Publications - Reports 40386, The World Bank Group.
    8. Raghul Gandhi Venkatesan & Dhivya Karmegam & Bagavandas Mappillairaju, 2024. "Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 171-196, April.
    9. 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.
    10. Elena Arias Ortiz & Maria Soledad Bos & Juliana Chen Peraza & Cecilia Giambruno & Victoria Levin & Victoria Oubiña & Jasmine Anne Pineda & Pablo Zoido, 2024. "Learning Can’t Wait [El aprendizaje no puede esperar]," World Bank Publications - Reports 41144, The World Bank Group.
    11. World Bank, 2023. "Mauritius Public Expenditure Review - From Resilience to Performance," World Bank Publications - Reports 40788, The World Bank Group.
    12. 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.
    13. 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.
    14. 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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