A Comment on "Motivated Errors" by Exley and Kessler (2024) - Tobias Auer; Maria Ulasik; Felix Holzmeister (Institute for Replication (I4R), 2024)
This report evaluates the computational reproducibility and analytical robustness of Exley and Kessler's (2024) investigation into "motivated errors," which suggests that individuals may rationalize selfish behavior by attributing their errors to confusion. Using the original data and code, we could regenerate all results reported in the manuscript and online appendices with full precision. However, our re-analysis identified significant limitations, including insufficiently annotated code, ambiguous variable naming, and the absence of essential participant-level data, which obstruct comprehensive robustness checks. These challenges underscore the importance of best practices in data and code sharing to enhance the transparency and credibility of economic research. Our reflection not only contributes to discussions on empirical rigor but also advocates for improved standards in sharing scholarly resources.
Keywords: C18; C81; C91; D91; 330; reproducibility; robustness; credibility; data/code sharing