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Improving the effectiveness of financial education programs. A targeting approach

Author

Listed:
  • Ginevra Buratti

    (Bank of Italy)

  • Alessio D'Ignazio

    (Bank of Italy)

Abstract

We investigate whether targeting algorithms can improve the effectiveness of financial education programs by identifying the most appropriate recipients in advance. To this end, we use micro-data from approximately 3,800 individuals who recently participated in a financial education campaign conducted in Italy. Firstly, we employ machine learning (ML) tools to devise a targeting rule that identifies the individuals who should be targeted primarily by a financial education campaign based on easily observable characteristics. Secondly, we simulate a policy scenario and show that pairing a financial education campaign with an ML-based targeting rule enhances its effectiveness. Finally, we discuss a number of conditions that must be met for ML-based targeting to be effectively implemented by policymakers.

Suggested Citation

  • Ginevra Buratti & Alessio D'Ignazio, 2023. "Improving the effectiveness of financial education programs. A targeting approach," Questioni di Economia e Finanza (Occasional Papers) 765, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_765_23
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2023-0765/QEF_765_23.pdf
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    References listed on IDEAS

    as
    1. Annamaria Lusardi & Olivia S. Mitchell, 2008. "Planning and Financial Literacy: How Do Women Fare?," American Economic Review, American Economic Association, vol. 98(2), pages 413-417, May.
    2. Aaron Chalfin & Oren Danieli & Andrew Hillis & Zubin Jelveh & Michael Luca & Jens Ludwig & Sendhil Mullainathan, 2016. "Productivity and Selection of Human Capital with Machine Learning," American Economic Review, American Economic Association, vol. 106(5), pages 124-127, May.
    3. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    4. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    5. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    financial education; machine learning; policy targeting; randomized controlled trials;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • G5 - Financial Economics - - Household Finance

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