Reproduce to validate: A comprehensive study on the reproducibility of economics research
Bibliographic record
Abstract
Abstract Journals have pushed for transparency of research through data availability policies. Such data policies improve availability of data and code, but what is the impact on reproducibility? We present results from a large reproduction exercise for articles published in the American Economic Journal: Applied Economics, which has had a data availability policy since its inception in 2009. Out of 363 published articles, we assessed 274 articles. All articles provided some materials. We excluded 122 articles that required confidential or proprietary data or that required the replicator to otherwise obtain the data (44.5% of assessed articles). We attempted to reproduce 152 articles and were able to fully reproduce the results of 68 (44.7% of attempted reproductions). A further 66 (43.4% of attempted reproductions) were partially reproduced. Many articles required complex code changes even when at least partially reproduced. We collect bibliometric characteristics of authors, but find no evidence for author characteristics as determinants of reproducibility. There does not appear to be a citation bonus for reproducibility. The data availability policy of this journal was effective to ensure availability of materials, but is insufficient to ensure reproduction without additional work by replicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.437 | 0.833 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".