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 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