The Software Complexity of Nations

TL;DR

This paper extends economic complexity analysis to the digital economy by using open-source software data to estimate a country's software economic complexity index, revealing its relation to economic indicators and specialization patterns.

Contribution

It introduces a novel measure of digital economic complexity based on open-source software, complementing traditional trade and patent-based measures.

Findings

Software complexity correlates with GDP per capita, inequality, and emissions.

Open-source software follows relatedness principles similar to traditional economic sectors.

The new index enhances understanding of digital economy development.

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 (ECIsoftware) 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.