January 11, 2024
Morgan Frank (assistant professor, Department of Informatics and Networked Systems) examines specialized skill categories and city-specific labor markets to explain career mobility, network embeddedness, and spatial mobility in cities in his new publication, “Network Constraints on Worker Mobility,” in Nature Cities. Frank co-authored the paper with Esteban Moro (associate professor, Universidad Carlos III de Madrid), Tobin South (research assistant, Media Laboratory, Massachusetts Institute of Technology), Alex Rutherford (senior research scientist, Center for Humans and Machines, Max Planck Institute for Human Development), Alex Pentland (professor of Media Arts and Sciences, Massachusetts Institute of Technology), Bledi Taska (chief economist, Burning Glass Technologies), and Iyad Rahwan (deputy managing director, Max Planck Institute for Human Development).
Labor categories in the US are broadly divided into either high-skill or low-skill occupations. Skill specialization is when a worker excels at a narrow, specific set of skills. When network embeddedness – how connections to an organization and others in that organization affect job retention – is higher, workers have more connections within their organization, and are more likely to stay working for that organization. Frank and his co-authors hypothesize that the binary of high-skill versus low-skill occupations is too broad to explain career and spatial mobility, and specialized skill categories should be used instead.
Analyzing three resume datasets and nationally representative data of workplace skills, career mobility, and transportation patterns in cities, these researchers found that workers have higher career and spatial mobility, but lower network embeddedness, as their skill specialization increases. Frank and his co-authors state, “a measure for skill specialization based on a workers’ position in their city’s occupation network may predict future career dynamics.” The findings suggest an improved method of predicting career dynamics, as specialized skill categories offer more accurate career and spatial mobility insights than the low-skill versus high-skill labor binary.
Learn more about this research by reading the published article here.
--Alyssa Morales