  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 fail to capture
the nuances of the digital economy. In this paper we use data on the geography
of programming languages used in open-source software projects to extend
economic complexity ideas to the digital economy. We estimate a country's
software economic complexity and show that it complements the ability of
measures of complexity based on trade, patents, and research papers 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 software entries and exits are explained
by specialization in related programming languages. We conclude by exploring
the diversification and development of countries in open-source software in the
context of large language models. Together, these findings help extend economic
complexity methods and their policy considerations to the digital sector.
