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How researchers are tapping GitHub Innovation Graph data to reveal the “digital complexity” of nations
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One of their goals for the GitHub Innovation Graph was to facilitate research on the economic impact of open source software and developer collaboration.
Key facts
- As mentioned, the core data comes from the GitHub Innovation Graph, which gives them quarterly counts of developers pushing code by economy and programming language for 163 economies and 150 languages
- Four years of data (2020–2023) is enough for cross-sectional analysis but too short to credibly test long-run growth predictions, which is what economic complexity measures are designed
- A web app might combine HTML, CSS, and JavaScript; a data science project uses Python and Jupyter Notebook; systems programming pairs C with Assembly
- The reporter is happy to share an interview with these researchers, along with their Q4 2025 data release
Summary
The Research Policy paper examines whether the geography of open-source software production on GitHub can reveal the “digital complexity” of nations, and whether that complexity predicts GDP, inequality, and emissions in ways that traditional economic data misses. Sándor Juhász is a research fellow at the Corvinus University of Budapest. Johannes Wachs is an Associate Professor at Corvinus University of Budapest, Director of the Center for Collective Learning at the Corvinus Institute of Advanced Study, and a researcher at the Complexity Science Hub in Vienna. Jermain Kaminski is an Assistant Professor at the School of Business and Economics at Maastricht University. His research specializes in entrepreneurship, strategy, and causal machine learning, with a focus on how data-driven methods can improve decision-making and innovation.