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The software complexity of nations

Sándor Juhász et al.

Research Policy2026https://doi.org/10.1016/j.respol.2026.105422preprint
FT50AJG 4*ABDC A*
Weight
0.44

Abstract

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records—e.g. data on exports, patents, and employment—that have blind spots when it comes to the digital economy. In this paper we use data on the geography of programming languages used in open-source software to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity index (ECI software ) and show that it complements the ability of measures of complexity based on trade, patents, and research to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's entries and exits in programming languages are partly explained by its current pattern of specialization. Together, these findings help extend economic complexity ideas and their policy implications to the digital economy. • We measure countries' software economic complexity using GitHub data • Software economic complexity predicts GDP, income inequality and emissions • We show that countries diversify into related programming languages over time • We extend economic complexity methods and their policy implications to the digital sector

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https://doi.org/https://doi.org/10.1016/j.respol.2026.105422

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@article{sándor2026,
  title        = {{The software complexity of nations}},
  author       = {Sándor Juhász et al.},
  journal      = {Research Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.respol.2026.105422},
}

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The software complexity of nations

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.