[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/123139.html
   My bibliography  Save this paper

Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout

Author

Listed:
  • Crespo, Cristian
Abstract
This paper offers a methodology to improve targeting design and assessment when two or more groups need to be considered, and trade-offs exist between using different targeting mechanisms. The paper builds from the multidimensional targeting challenge facing conditional cash transfers (CCTs). I analyze whether a common CCT targeting mechanism, namely, a proxy means test (PMT), can identify the poor and future school dropouts effectively. Despite both being key target groups for CCTs, students at risk of dropping out are rarely considered for CCT allocation or in targeting assessments. Using rich administrative data sets from Chile to simulate different targeting mechanisms, I compare the targeting effectiveness of a PMT and other mechanisms based on a predictive model of school dropout. I build this model using machine learning algorithms. Using two novel metrics, I show that combining the outputs of the predictive model with the PMT increases targeting effectiveness except when the social valuation of the poor and future school dropouts differs to a large extent. More generally, public officials who value their key target groups equally may improve policy targeting by modifying their allocation procedures.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:123139
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/123139/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Coady & Margaret Grosh & John Hoddinott, 2004. "Targeting of Transfers in Developing Countries : Review of Lessons and Experience," World Bank Publications - Books, The World Bank Group, number 14902.
    2. David Coady & Emmanuel Skoufias, 2004. "On the Targeting and Redistributive Efficiencies of Alternative Transfer Instruments," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 50(1), pages 11-27, March.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    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.
    5. Carl Lamote & Jan Van Damme & Wim Van Den Noortgate & Sara Speybroeck & Tinneke Boonen & Jerissa Bilde, 2013. "Dropout in secondary education: an application of a multilevel discrete-time hazard model accounting for school changes," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(5), pages 2425-2446, August.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    7. Marina Bassi & Matias Busso & Juan Sebastian Muñoz, 2015. "Enrollment, Graduation, and Dropout Rates in Latin America: Is the Glass Half Empty or Half Full?," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2015), pages 113-156, October.
    8. Paul Schultz, T., 2004. "School subsidies for the poor: evaluating the Mexican Progresa poverty program," Journal of Development Economics, Elsevier, vol. 74(1), pages 199-250, June.
    9. Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 351-371, December.
    10. Ariel Fiszbein & Norbert Schady & Francisco H.G. Ferreira & Margaret Grosh & Niall Keleher & Pedro Olinto & Emmanuel Skoufias, 2009. "Conditional Cash Transfers : Reducing Present and Future Poverty," World Bank Publications - Books, The World Bank Group, number 2597.
    11. Felipe Barrera-Osorio & Marianne Bertrand & Leigh L. Linden & Francisco Perez-Calle, 2011. "Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 167-195, April.
    12. 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.
    13. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    14. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2014. "Finite Population Causal Standard Errors," NBER Working Papers 20325, National Bureau of Economic Research, Inc.
    15. Glewwe, Paul, 1992. "Targeting assistance to the poor : Efficient allocation of transfers when household income is not observed," Journal of Development Economics, Elsevier, vol. 38(2), pages 297-321, April.
    16. Susan Athey & Guido Imbens, 2015. "A Measure of Robustness to Misspecification," American Economic Review, American Economic Association, vol. 105(5), pages 476-480, May.
    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. Del Rey, Elena & Estevan, Fernanda, 2013. "Conditional cash transfers and education quality in the presence of credit constraints," Economics of Education Review, Elsevier, vol. 34(C), pages 76-84.
    2. Jacobus de Hoop & Furio C. Rosati, 2014. "Cash Transfers and Child Labor," The World Bank Research Observer, World Bank, vol. 29(2), pages 202-234.
    3. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    4. M. Caridad Araujo & Mariano Bosch & Norbert Schady, 2017. "Can Cash Transfers Help Households Escape an Intergenerational Poverty Trap?," NBER Chapters, in: The Economics of Poverty Traps, pages 357-382, National Bureau of Economic Research, Inc.
    5. Karthik Muralidharan & Nishith Prakash, 2017. "Cycling to School: Increasing Secondary School Enrollment for Girls in India," American Economic Journal: Applied Economics, American Economic Association, vol. 9(3), pages 321-350, July.
    6. Jacobus de Hoop & Jed Friedman & Eeshani Kandpal & Furio C. Rosati, 2019. "Child Schooling and Child Work in the Presence of a Partial Education Subsidy," Journal of Human Resources, University of Wisconsin Press, vol. 54(2), pages 503-531.
    7. Henderson, Heath & Follett, Lendie, 2022. "Targeting social safety net programs on human capabilities," World Development, Elsevier, vol. 151(C).
    8. Kate Ambler & Diego Aycinena & Dean Yang, 2015. "Channeling Remittances to Education: A Field Experiment among Migrants from El Salvador," American Economic Journal: Applied Economics, American Economic Association, vol. 7(2), pages 207-232, April.
    9. 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.
    10. Verónica Amarante & Mery Ferrando & Andrea Vigorito, 2013. "Teenage School Attendance and Cash Transfers: An Impact Evaluation of PANES," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2013), pages 61-102, August.
    11. 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).
    12. Richard Akresh & Damien de Walque & Harounan Kazianga, 2013. "Cash Transfers and Child Schooling: Evidence from a Randomized Evaluation of the Role of Conditionality," Economics Working Paper Series 1301, Oklahoma State University, Department of Economics and Legal Studies in Business.
    13. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    14. Zurab Abramishvili & Lasha Lanchava, 2015. "Education for the Poor," CERGE-EI Working Papers wp542, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    15. Lendie Follett & Heath Henderson, 2022. "A hybrid approach to targeting social assistance," Papers 2201.01356, arXiv.org.
    16. Zhou, Lei & Jiang, Bo & Wang, Jingxi, 2020. "Do cash transfers have impacts on student Academic, cognitive, and enrollment outcomes? Evidence from rural China," Children and Youth Services Review, Elsevier, vol. 116(C).
    17. Della Guardia, Anne & Lake, Milli & Schnitzer, Pascale, 2022. "Selective inclusion in cash transfer programs: Unintended consequences for social cohesion," World Development, Elsevier, vol. 157(C).
    18. Stephen Smith, 2018. "Development Economics Meets the Challenges of Lagging U.S. Areas: Applications to Education, Health and Nutrition, Behavior, and Infrastructure," Working Papers 2018-7, The George Washington University, Institute for International Economic Policy.
    19. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    20. 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).

    More about this item

    Keywords

    multidimensional targeting; conditional cash transfers; school dropout prediction; machine learning;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • I39 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Other

    Statistics

    Access and download statistics

    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:ehl:lserod:123139. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.