Digital Engagement, Income Disparities, and Job Seeking in the United States since 2010

[2511.05294] Digital Engagement, Income Disparities, and Job Seeking in the United States since 2010 Skip to main content Search Submit Donate Log in Computer Science > Computers and Society arXiv:2511.05294 (cs) [Submitted on 7 Nov 2025 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: Digital Engagement, Income Disparities, and Job Seeking in the United States since 2010 Authors: Shaolong Wu , Yijiang River Dong , Siming He View PDF HTML (experimental) Abstract: Surveys often record how frequently people use the internet without measuring the infrastructures, skills, and support systems that make digital participation possible. Using the U.S. National Longitudinal Survey of Youth 1997 cohort, we study how internet-use frequency relates to labor income, employment attachment, and job seeking after 2010. The main digital-engagement analysis uses the comparable 2011, 2013, and 2015 waves, with 2017 retained as later labor-market context. Across repeated cross sections, daily internet use consistently marks higher income and stronger employment attachment. Relative to daily use, less-than-daily use is associated with roughly 11 to 20 percent lower income, while nonuse is associated with about 18 to 21 percent lower income in 2011 and 2013. Respondents reporting no internet use are also 13 to 23 percentage points less likely to report full-year work. Job-search estimates reveal a distinct mechanism: active search is governed by employment status, search intensity, and application support, so a frequency item sorts respondents more sharply on durable labor-market attachment than on short-window search. Education accounts for a substantial share of the raw digital gradient, and pooled lagged-outcome and doubly robust transition estimates separate durable stratification from positive adoption margins. The results establish internet-use frequency as an informative behavioral marker of digitally mediated labor-market stratification and clarify why routine use should not be treated as a simple measure of digital access. Comments: 9 pages, 10 figures, 2 tables Subjects: Computers and Society (cs.CY) ; General Economics (econ.GN) Cite as: arXiv:2511.05294 [cs.CY] (or arXiv:2511.05294v3 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2511.05294 Focus to learn more arXiv-issued DOI via DataCite Journal reference: In Proceedings of the 2026 International Conference on Information Technology for Social Good (GoodIT '26), September 2-4, 2026, Pisa, Italy. ACM, New York, NY, USA, 9 pages Related DOI : https://doi.org/10.1145/3794786.3830729 Focus to learn more DOI(s) linking to related resources Submission history From: Shaolong Wu [ view email ] [v1] Fri, 7 Nov 2025 14:55:45 UTC (1,662 KB) [v2] Mon, 13 Jul 2026 17:36:22 UTC (712 KB) [v3] Thu, 27 Aug 2026 20:31:02 UTC (785 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CY < prev | next > new | recent | 2025-11 Change to browse by: cs econ econ.GN q-fin q-fin.EC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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 (opens in new tab) (opens in new tab) (opens in new tab)

Related documents

• Digital Engagement, Income Disparities, and Job Seeking in the United States since 2010#682521
• Labour market demand for ICT specialists in online job advertisements, by NUTS 2 region - experimental statistics#818083
• Tales from the Hiring Line: Effects of the Internet and Technology on HR Processes#1071629
• Conference calls and information spillover: the role of analyst participation#195014
• The Evolution of U.S. Retail Concentration#146968