提高金融教育计划的有效性。有针对性的方法

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-04-10 DOI:10.1111/joca.12577
Ginevra Buratti, Alessio D'Ignazio
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引用次数: 0

摘要

我们研究了定向算法能否通过事先识别最合适的受众来提高金融教育项目的有效性。为此,我们使用了约 3800 人的微观数据,这些人参加了 2021 年底在意大利开展的金融教育活动。首先,我们利用机器学习(ML)工具设计了一个目标定位规则,根据易于观察到的特征确定金融教育活动的主要目标人群。其次,我们模拟了一种政策情景,使用参加活动但未被雇用的个人随机样本来设计定位规则。我们发现,将金融教育活动与基于 ML 的目标选择规则相结合会产生更大的效果。最后,我们讨论了我们的研究结果对政策的影响,以及有效实施基于 ML 的目标选择必须满足的条件。
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Improving the effectiveness of financial education programs. A targeting approach

We investigate whether targeting algorithms can improve the effectiveness of financial education programs by identifying ex-ante the most appropriate recipients. To this end, we use micro-data from around 3800 individuals who participated in a financial education campaign conducted in Italy in late 2021. First, we employ machine learning (ML) tools to devise a targeting rule that identifies individuals who should be primarily targeted by a financial education campaign based on easily observable characteristics. Second, we simulate a policy scenario, using a random sample of individuals who took part in the campaign but were not employed to devise the targeting rule. We find that pairing a financial education campaign with an ML-based targeting rule leads to greater effectiveness. Finally, we discuss the policy implications of our findings, and the conditions that must be met for ML-based targeting to be effectively implemented.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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