The Complexity of the Future of Work
Rapidly advancing cognitive technologies, such as artificial intelligence (AI), have the potential to drastically impact modern society and to shape the future of work. Accordingly, policy makers and researchers seek forecasts into technological change and labor trends, including growing job polarization and income inequality as well as decreasing career mobility and spatial mobility for workers.
Although a given technology impacts demand for only a narrow set of workplace skills, modern empirical work relies on coarse labor distinctions between cognitive and physical or routine and non-routine work to explain employment trends. In this dissertation, I explore the complex ways that skills and employment undergird aggregate labor dynamics in the US.
As a motivating example, I demonstrate how simple measures for skills within a labor market contribute to the differential impact of automation across US cities of different sizes. I build on this motivation to address methodological barriers through a refined model of workplace skills and their inter-dependencies, thus connecting microscopic workplace connections to macroscopic labor trends. I perform an unsupervised analysis of specific workplace skills as a skills network whose aggregate and refined topology grant new insights into job polarization and workers' career mobility.
Since these inter-skill connections predict career mobility, I construct a map of US occupations that captures worker transition rates between employment opportunities and, in combination with urban employment data, predicts workers' spatial mobility. These refined models that connect workplace skills to both inter-city and intra-city dynamics enable new insights and new input data sources for real-time labor trends at the level of specific technologies and specific workplace skills. I conclude by exploring one novel and potentially useful source of input information: the evolution of scientific AI research.
The analyses in this dissertation provide new tools to policy makers designing viable worker retraining programs, offer new insights to individual workers navigating their careers, and present new measures for economic resilience in the face of changing technology.
Iyad Rahwan (thesis advisor, Associate Professor, Media Arts and Sciences, MIT)
Erik Brynjolfsson (Schussel Family Professor of Management Science, Sloan School of Management, MIT)
Alex Pentland (Toshiba Professor, Media Arts and Sciences, MIT)