IDEAS home Printed from https://ideas.repec.org/a/eee/injoed/v93y2022ics0738059322000815.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0738059322000815
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijedudev.2022.102631?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    10. 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.
    11. 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).
    12. Del Popolo, Fabiana & Oyarce, Ana María & Ribotta, Bruno, 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Hall,Gillette H. & Patrinos,Harry Anthony (ed.), 2012. "Indigenous Peoples, Poverty, and Development," Cambridge Books, Cambridge University Press, number 9781107020573, November.
    20. 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.
    21. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    22. Mariama Khan, 2014. "Indigenous languages and Africa's development dilemma," Development in Practice, Taylor & Francis Journals, vol. 24(5-6), pages 764-776, August.
    23. 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.
    24. 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.
    25. Delprato, Marcos & Frola, Alessia, 2022. "Zones of educational exclusion of out-of-school youth," International Journal of Educational Development, Elsevier, vol. 88(C).
    26. 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.
    27. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    2. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    3. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    5. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    7. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    8. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    10. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    12. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    13. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    14. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    15. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    16. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    17. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    19. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    20. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:injoed:v:93:y:2022:i:c:s0738059322000815. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/international-journal-of-educational-development .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.