Assessing Robustness to Varying Clustering Methods and Samples in Ambuehl, Bernheim, and Lusardi (2022): Replication and Sensitivity Analysis - Chi Danh Dao; Guidon Fenig; Georg Sator; Jin Young Yoon (Institute for Replication (I4R), 2024)
Ambuehl et al. (2022) explore ways to evaluate interventions designed to enhance decision-making quality when individuals misjudge the outcomes of their choices. The authors propose a novel outcome metric that can distinguish between interventions better than conventional metrics such as financial literacy and directional behavioral responses. The proposed metric, which transforms price-metric bias into interpretable welfare loss measures, can be applied to evaluate various training programs on financial products. Table 4 of the paper reports the authors' significant main point estimates at the 1% level. In this replication exercise, we first replicate the main findings of the original paper. Then, we modify the clustering method by using k-means with demographic variables as inputs, then we re-calculate standard errors with jackknife estimators. Finally, we include subjects who were excluded by the authors due to multiple switching in the multiple price lists. We find that all of these replications result in robust findings. Additionally, we successfully replicate Figure 4 from the paper. Notably, this replication demonstrates the insensitivity of the results to the choice of distance metric.
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