 New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level dataFrank Fossen and Alina Sorgner Technological Forecasting and Social Change, 2022, vol. 175, issue C Abstract: We analyze heterogeneous effects of new digital technologies on individual-level wage and employment dynamics in the United States from 2011-2018. To this end, we employ four digital technology measures from recent literature: computerization probabilities of occupations, occupational impacts of artificial intelligence, and the suitability of tasks for machine learning and their within-occupation variance. Based on CPS and ASEC panel data, the results indicate that labor-displacing digital technologies are associated with slower wage growth and higher probabilities of switching one's occupation and becoming non-employed. In contrast, labor-reinstating digital technologies improve individual labor market outcomes. Workers with high levels of formal education are most affected by the new generation of digital technologies. Keywords: Digitalization; Artificial intelligence; Machine learning; Wage dynamics; Employment stability; Unemployment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:175:y:2022:i:c:s004016252100812x DOI: 10.1016/j.techfore.2021.121381 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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