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Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach

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  • Delprato, Marcos
  • Frola, Alessia
  • Antequera, Germán

Abstract

Despite the renewed emphasis in equity for SDG4, Indigenous learning gaps persist. Indigenous barriers for learning are intersectional -a combination of multi-layered and heterogeneous causes. In this paper, we use data from PISA for Development to estimate the Indigenous learning gap in Guatemala, Paraguay and Senegal for out and in school samples. We employ machine learning which allows to employ numerous controls and their interactions, accounting for intersectionality. We find that negative learning gaps remain for both samples (with some differences by level by of performance) even after controlling for around 66–217 covariates, showing the extent of Indigenous-driven inequality and discrimination.

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  • Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:injoed:v:93:y:2022:i:c:s0738059322000815
    DOI: 10.1016/j.ijedudev.2022.102631
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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Naila Kabeer & Ricardo Santos, 2017. "Intersecting inequalities and the Sustainable Development Goals: Insights from Brazil," WIDER Working Paper Series wp-2017-167, World Institute for Development Economic Research (UNU-WIDER).
    3. Delprato, Marcos, 2019. "Parental education expectations and achievement for Indigenous students in Latin America: Evidence from TERCE learning survey," International Journal of Educational Development, Elsevier, vol. 65(C), pages 10-25.
    4. Hugo Storm & Kathy Baylis & Thomas Heckelei, 2020. "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 849-892.
    5. Del Popolo, Fabiana & Ribotta, Bruno & Oyarce, Ana María, 2009. "Indígenas urbanos en América Latina: algunos resultados censales y su relación con los objetivos de desarrollo del milenio," Notas de Población, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    6. Mandy Li-Ming Yap & Krushil Watene, 2019. "The Sustainable Development Goals (SDGs) and Indigenous Peoples: Another Missed Opportunity?," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 20(4), pages 451-467, October.
    7. Christelle Dumas, 2012. "Does Work Impede Child Learning? The Case of Senegal," Economic Development and Cultural Change, University of Chicago Press, vol. 60(4), pages 773-793.
    8. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    10. Hall,Gillette H. & Patrinos,Harry Anthony (ed.), 2012. "Indigenous Peoples, Poverty, and Development," Cambridge Books, Cambridge University Press, number 9781107020573.
    11. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    12. Lentz, E.C. & Michelson, H. & Baylis, K. & Zhou, Y., 2019. "A data-driven approach improves food insecurity crisis prediction," World Development, Elsevier, vol. 122(C), pages 399-409.
    13. Delprato, Marcos & Frola, Alessia, 2022. "Zones of educational exclusion of out-of-school youth," International Journal of Educational Development, Elsevier, vol. 88(C).
    14. Delprato, Marcos & Akyeampong, Kwame & Dunne, Máiréad, 2017. "Intergenerational Education Effects of Early Marriage in Sub-Saharan Africa," World Development, Elsevier, vol. 91(C), pages 173-192.
    15. Aidan Mulkeen & Dandan Chen, 2008. "Teachers for Rural Schools : Experiences in Lesotho, Malawi, Mozambique, Tanzania, and Uganda," World Bank Publications - Books, The World Bank Group, number 6423, December.
    16. Lyliana E. Gayoso de Ervin, 2021. "Can Compulsory Schooling Reduce Language-Based Educational Gaps? Evidence from a Policy Change in Paraguay," Economic Development and Cultural Change, University of Chicago Press, vol. 69(2), pages 569-589.
    17. Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020. "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers 27879, National Bureau of Economic Research, Inc.
    18. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    19. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    20. Cristian Garcia Palomer & Ricardo Paredes, 2010. "Reducing the Educational Gap: Good Results in Vulnerable Groups," Journal of Development Studies, Taylor & Francis Journals, vol. 46(3), pages 535-555.
    21. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    22. Naila Kabeer & Ricardo Santos, 2017. "Intersecting inequalities and the Sustainable Development Goals: Insights from Brazil," WIDER Working Paper Series 167, World Institute for Development Economic Research (UNU-WIDER).
    23. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    24. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    25. Hernandez-Zavala, Martha & Patrinos, Harry Anthony & Sakellariou, Chris & Shapiro, Joseph, 2006. "Quality of schooling and quality of schools for indigenous students in Guatemala, Mexico, and Peru," Policy Research Working Paper Series 3982, The World Bank.
    26. Mariama Khan, 2014. "Indigenous languages and Africa's development dilemma," Development in Practice, Taylor & Francis Journals, vol. 24(5-6), pages 764-776, August.
    27. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    28. Alberto Chong & Hugo Ñopo, 2008. "The Mystery of Discrimination in Latin America," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 79-115, January.
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