Ravi Jagadeesan, Davide Viviano
arXiv 29 Apr 2025 · Econometrics
arXiv:2504.21156 · PDF · DOI · OpenAlex · Extracted main text
The publication process both determines which research receives the most attention, and influences the supply of research through its impact on researchers' private incentives. We introduce a framework to study optimal publication decisions when researchers can choose (i) whether or how to conduct a study and (ii) whether or how to manipulate the research findings (e.g., via selective reporting or data manipulation). When manipulation is not possible, but research entails substantial private costs for the researchers, it may be optimal to incentivize cheaper research designs even if they are less accurate. When manipulation is possible, it is optimal to publish some manipulated results, as well as results that would have not received attention in the absence of manipulability. Even if it is possible to deter manipulation, such as by requiring pre-registered experiments instead of (potentially manipulable) observational studies, it is suboptimal to do so when experiments entail high research costs. We illustrate the implications of our model in an application to medical studies.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Frankel, A. and M. Kasy (2022) Which findings should be published? | 1.000 | 8 | 4 | 100% |
| 2 | Head, M. L., L. Holman, R. Lanfear, A. T. Kahn, and M. D. Jennions (2015) The extent and consequences of p-hacking in science | 0.971 | 12 | 3 | 92% |
| 3 | Elliott, G., N. Kudrin, and K. Wüthrich (2022) Detecting $p$-hacking | 0.737 | 3 | 2 | 100% |
| 4 | Kasy, M. and J. Spiess (2023) Optimal pre-analysis plans: Statistical decisions subject to implementability | 0.737 | 3 | 2 | 100% |
| 5 | Bernard, D. R., G. Bryan, S. Chabé-Ferret, J. De Quidt, J. Fliegner,… (2024) How much should we trust observational estimates? accumulating evidence using randomized controlled trials with imperfect compli… | 0.737 | 3 | 2 | 100% |
| 6 | Spiess, J. (Forthcoming (2025) Optimal estimation when researcher and social preferences are misaligned | 0.737 | 3 | 2 | 100% |
| 7 | Bartos, F., W. M. Otte, Q. F. Gronau, B. Timmers, A. Ly, and E.-J. W… (2023) Empirical prior distributions for Bayesian meta-analyses of binary and time to event outcomes | 0.644 | 2 | 2 | 100% |
| 8 | Mirrlees, J. A (1971) An exploration in the theory of optimum income taxation | 0.644 | 2 | 2 | 100% |
| 9 | Myerson, R. B (1981) Optimal auction design | 0.644 | 2 | 2 | 100% |
| 10 | Niederle, M (2025) Experiments: Why, how, and a users guide for producers as well as consumers | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2510.21178 | 0.511 | 2 | 1 |
| 2 | Testing the Fairness-Accuracy Improvability of Algorithms | 0.405 | 1 | 1 |