Improving the effectiveness of financial education programs. A targeting approach

Ginevra Buratti () and Alessio D'Ignazio ()
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Ginevra Buratti: Bank of Italy
Alessio D'Ignazio: Bank of Italy

No 765, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

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.

Keywords: financial education; machine learning; policy targeting; randomized controlled trials (search for similar items in EconPapers)
JEL-codes: C38 G5 I21 (search for similar items in EconPapers)
Date: 2023-04
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-edu and nep-fle
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:bdi:opques:qef_765_23

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