Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement savings in Mexico

Abstract

Individuals regularly struggle to save for retirement. Using a large-scale field experiment (N = 97, 149) in Mexico, we test the effectiveness of several behavioral interventions relative to existing policy and each other geared toward improving voluntary retirement savings contributions. We find that an intervention framing savings as a way to secure one's family future significantly improves contribution rates. We leverage recursive partitioning techniques and identify that the overall positive treatment effect masks subpopulations where the treatment is even more effective and other groups where the treatment has a significant negative effect, decreasing contribution rates. Accounting for this variation is significant for theoretical and policy development as well as firm profitability. Our work also provides a methodological framework for how to better design, scale, and deploy behavioral interventions to maximize their effectiveness.