Reproducibility is defined as obtaining consistent results using the same data and code as the original study. Most of the discussion of reproducibility has centered around the many obvious benefits. Reproducible research advances knowledge for several reasons. It reduces the risk of errors. It also makes the processes that generate results more transparent. This second advantage has an important educational component, because reproducible research helps disseminate not just results but processes, thus enhancing learning for graduate students and young scholars. However, reproducibility is not without costs. Good research procedures consume resources both in terms of a researcher’s own efforts and in terms of the involvement of arms-length parties in actually reproducing the research. This second cost is not just a time cost; it is pecuniary as well.
Thus, reproducibility is a good that is costly to produce and that has many positive externalities. Researchers internalize many of the benefits of reproducibility, especially in terms of research extendability and personal reputation. However, they do not internalize any of the benefits to the research community at large. Because reproducibility is costly, it is unlikely to be produced at a socially optimal rate by any individual researchers. Thus, the questions are the extent to which reproducibility should be subsidized and who should subsidize it. Should all research be reproduced by arms-length parties, and what are the least costly policies that facilitate reproducible research? The rest of this note is organized around policies regarding actual reproduction and proprietary data.
One low-cost and easily implementable set of policies that enhances the reproducibility of research is journals’ data and code disclosure policies. In the age of inexpensive data storage and an abundance of public repositories, the costs of these policies are potentially small, and the policies should be implemented. They impose some costs on researchers in terms of organizing data and code, with varying levels of costs depending on the complexity of the project and the practices of the researcher. If research practice evolves toward a norm in which well-organized data and code are an essential part of the research process, these costs should be small.
While simple to implement, this low-cost policy is not without non-pecuniary drawbacks for journals. The code and data can be incomplete, poorly documented, or unusable. In economics, these concerns have prompted journals to start arms-length reproduction of results. The primary benefit of this policy is that authors and journals can be confident that the code submitted with an article actually works to reproduce the results.
However, the pecuniary costs of this policy can be substantial. It is expensive for journals to hire data editors and well-trained research assistants, and many academic journals run on tight budgets. It is often time-consuming for authors to comply with reproducibility requirements, although evolving research documentation standards are likely to lower these costs over time. This last issue is particularly burdensome for authors who cannot afford research assistance. As such, reproducibility requirements are likely to make it harder for underfunded researchers to compete.
While the above issues involve costs, the following issues are more fundamental. First, although some disagree, I believe that reproducibility policies have the potential to give researchers incentives to do research that is easier to reproduce, thus restraining research innovation that requires either large data or intense computing. Because this type of research is costly for journals to reproduce, authors face a long wait and a potentially time-consuming arms-length reproduction. Second, and more importantly, code that can run on data and reproduce results can still contain errors.
These arguments imply that while individual researchers are likely to underproduce reproducibility, it is not optimal for the advancement of research in economics that all research be reproduced before publication. Some papers, even those in the very best journals, rarely get read or cited, and the benefits of reproducing these papers are small.
However, ex ante, it is hard to know which papers will attract attention and which will not. One solution that lies between data and code disclosure and arms-length reproduction is verification. It is much less expensive to verify the contents of a replication package than to do an actual reproduction. Verification might consist of checking for the existence of replication instructions, an execution script, or either data or pseudo-data. Slightly more time-consuming verification might consist of checking to see whether the code actually runs. I am currently using a data editor to audit replication packages in this way. While this type of service could be provided by journals, one likely outcome of a need for more reproduction packages is the emergence of third parties that could perform these tasks for a fee. Once code and data packets have been checked, reproducibility would be left up to the academic community, with the more important pieces of research being subject to greater scrutiny.
Of course, this intermediate solution does not guarantee that the code and data submitted can reproduce the results in an article. As such, one drawback to this policy is that journal editors have to publish corrigenda or retract articles that, after publication, cannot be reproduced. A second drawback is the weakness of the replication culture in economics and parts of finance. One field with a strong replication culture is asset pricing, because it uses widely accessible sources of data.
A final issue with reproducibility is education. In economics and finance, students are not taught how to create reproducible research. An improvement that would go a long way toward improving the culture surrounding reproducibility would be to teach PhD students how to organize research projects and write code in such a way that others can reproduce results easily. This type of education would lower the costs to individual researchers of making their own research reproducible.
Another reproducibility challenge, possibly larger than verification or arms-length execution of code, is proprietary data. To be clear, not all types of data with restricted access are completely secret; that is, available only to the data provider and a researcher. For example, commercial data sets are not secret, just costly to obtain. Similarly, administrative data sets are not secret; they just require special permission. In contrast, proprietary data cannot be offered to the research community at large for the purposes of reproducing the results. So the question is whether journals should discourage the use of this type of data or require that verifiers have access to the data. Given the large number of studies using proprietary data, this issue is possibly more important than the issue of running code.
In conclusion, the reproducibility of research is essential for the advancement of science. However, it is not without costs, so blanket statements that all research should be reproducible are not feasible. Instead, feasible policies include those that lower the costs for others to reproduce research. Data and code disclosure is a low-cost policy that should be implemented widely. Verification of code and data packages is a slightly more costly option. Arms-length reproduction is a much more costly alternative. One piece missing from this discussion of costs and benefits is evidence on the magnitudes of the costs of reproducibility, both for journals and researchers. Understanding these magnitudes would be useful to editors and academic societies interested in adopting or expanding reproducibility policies. Finally, perhaps the most important issue that impedes reproducibility in finance and economics is the use of proprietary data.
This article is an expanded version of remarks given in the Conference on Reproducibility and Replicability in Economics and the Social Sciences (CRRESS) webinar series, which is funded by National Science Foundation Grant #2217493.
©2023 Toni Whited. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.