Improving the effectiveness of financial education programs. A targeting approach

Abstract 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.