Major replication efforts reveal widespread issues with replicating published findings.
A large-scale project re-analyzing 110 articles from top economics and political science journals found that while 84% of claims could be computationally reproduced, the robustness of these findings is a separate, and more concerning, matter. This effort, involving six independent research teams examining 12 prespecified hypotheses, suggests that experience in replication can paradoxically lead to finding lower levels of robustness, with no clear link between robustness and author characteristics or data availability.
Further contributing to the discourse, a broader study across social sciences, including economics and political science, indicates that findings from roughly half of all published papers are not independently replicable. This project, known as Systematizing Confidence in Open Research and Evidence (SCORE), investigated over 100 papers across numerous leading journals. The availability of data and computer code emerged as a crucial factor, with papers making this information readily accessible showing significantly higher reproduction rates. Conversely, only one-third of papers in the SCORE sample had their data and code publicly available.
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The replication initiatives encountered significant hurdles. The lack of raw data severely restricted the scope of re-analyses across various categories. While some results were reproducible, the study highlighted a high prevalence of coding errors. These re-analyses, which involved altering weighting schemes, control variables, estimation methods, or employing new data, often led to a decrease in the statistical significance of originally published test statistics. The robustness checks, categorized into eight groups, underscored the fragility of many published conclusions.
These findings emerge from a confluence of recent scholarly outputs, including a report in Nature, discussions on blogs such as Nevin Manimala's and Ali Niazi's, and a feature in AoI (Articles of Interest) on the replication network. The research highlights a systemic challenge within these fields, where the initial publication of results, even with mandatory data and code sharing policies, does not guarantee their long-term reliability or independent verification. The implication is that a significant portion of the scholarly record in economics and political science may not withstand rigorous scrutiny, presenting a sobering reflection on the foundations of knowledge in these disciplines.