Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/303193 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
I4R Discussion Paper Series No. 161
Verlag: 
Institute for Replication (I4R), s.l.
Zusammenfassung: 
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.
Schlagwörter: 
reproducibility
robustness
credibility
data/code sharing
JEL: 
C18
C81
C91
D91
Dokumentart: 
Working Paper

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