In business studies and economics research, programme code is usually published as “supplementary material” as part of the publication of a paper, next to the data used (where legally possible), to show how the postulated research results were achieved. Some researchers also place code on their personal websites. This makes it available, but not necessarily Open Source – and therefore not always reusable for third parties. For reproducibility reasons, journals are also increasingly demanding code accessibility or publication. Example: There is a “Data and Code Policy” of the American Economic Association (AEA), according to which papers are only published if the data and code used are clearly documented and readers are granted access to them. Authors of papers with empirical research, simulations or experimental work must provide sufficient information about the data, programmes, and other details of the calculations as well as access to them so that they can be reproduced. 

Another use case is when business studies and economics researchers publish code themselves, which can range in scope up to complete Open Source software. They can publish it on a platform such as GitHub under an Open Source licence. Another option is to publish code on Zenodo (GitHub already has a Zenodo integration) so that it receives a DOI and thus becomes citable. This practice is not yet widespread in economics. The publication of code in Jupyter Notebooks is also a use case in economics and business studies.

In economics and business studies, for example, R, Python and Julia play a major role in Open Code.

Examples of Open Code in economics and business studies include:

• QuantEcon, founded by Thomas J. Sargent, is a non-profit organisation that offers, among other things, Open Source libraries in Python and Julia in the field of quantitative economics, which can be used to simulate and analyse economic models (associated GitHub repository). It also includes a collection of lectures, which are based on Open Source languages and open computing environments as well as on code notebooks such as Jupyter Notebooks.
• Nobel Prize winner Paul Romer considers transparent research to be more powerful and therefore relies on Python instead of proprietary software such as Stata and Mathematica and documents his code in Jupyter Notebooks. He makes it accessible via GitHub to document his research transparently.
• Vincent Arel-Bundock from the University of Montréal, who researches, among other things, international tax policy and foreign direct investment, provides R packages that have been reused by many others on GitHub.
• Macroeconomist Gregor Böhl offers access to his code on his website or on GitHub.
• Open Source Economics is a platform for reproducible research that offers tools, economic models, and applications.