Using digital traces to analyze software work: skills, careers and programming languages

[2504.03581] Using digital traces to analyze software work: skills, careers and programming languages Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > General Economics arXiv:2504.03581 (econ) [Submitted on 4 Apr 2025 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: Using digital traces to analyze software work: skills, careers and programming languages Authors: Xiangnan Feng , Johannes Wachs , Simone Daniotti , Frank Neffke View a PDF of the paper titled Using digital traces to analyze software work: skills, careers and programming languages, by Xiangnan Feng and 3 other authors View PDF HTML (experimental) Abstract: Recent waves of technological transformation are reshaping work in uncertain and hard-to-predict ways. However, jobs at the forefront of the digitizing economy offer an early glimpse of these changes and leave rich activity traces. We exploit such traces in tens of millions of Question and Answer posts on Stack Overflow for the creation of a fine-grained taxonomy of software skills to analyze human capital in the global software industry. Constructing a software skill space that maps relations among these skills reveals that real-world software jobs demand highly coherent skill sets and that programmers learn through a process of related diversification. The latter process often leads to the acquisition of lower-value skills. However, when programmers use Python they preferentially target higher-value skills, offering a potential explanation for Python's successful rise as a dominant general purpose language. Comments: 30 pages, 10 figures Subjects: General Economics (econ.GN) ; Computers and Society (cs.CY) Cite as: arXiv:2504.03581 [econ.GN] (or arXiv:2504.03581v2 [econ.GN] for this version) https://doi.org/10.48550/arXiv.2504.03581 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xiangnan Feng [ view email ] [v1] Fri, 4 Apr 2025 16:39:20 UTC (2,197 KB) [v2] Wed, 8 Apr 2026 21:04:57 UTC (4,146 KB) Full-text links: Access Paper: View a PDF of the paper titled Using digital traces to analyze software work: skills, careers and programming languages, by Xiangnan Feng and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.GN < prev | next > new | recent | 2025-04 Change to browse by: cs cs.CY econ q-fin q-fin.EC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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