Robustness Reproduction with Machine Learning
Year: 2026
Type: other
Abstract: We conduct a robustness reproduction of Ammann et al. (2023), published in Food Quality and Preference, who examine how consumers evaluate the importance of animal welfare as an agricultural policy goal relative to potentially competing objectives. Using the publicly available data, we first provide a reproduction of the original results and then perform a robustness reproduction that relaxes the linearity assumption through a Machine Learning (ML) workflow. The original association patterns are reproducible, while the ML extension uncovers modest nonlinearities with limited gains in explanatory power. Overall, this study illustrates a simple and transparent workflow that complements standard OLS-based replication with data-driven specifications, thereby supporting transparency and reproducibility in applied agricultural economics.
Source: ZBW Journal Data Archive
Authors: Bittmann, Thomas, Maruejols, Lucie
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7163195052
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