Slates Star Codex:
Technological unemployment: much more than you wanted to know
19 February 2018
This is potentially consistent with a story where the jobs that have been easiest to automate are middle-class-ish. Some jobs require extremely basic human talents that machines can’t yet match – like a delivery person’s ability to climb stairs. Others require extremely arcane human talents likewise beyond machine abilities – like a scientist discovering new theories of physics. The stuff in between – proofreading, translating, records-keeping, metalworking, truck driving, welding – is more in danger. As these get automated away, workers – in accord with the theory – migrate to the unautomatable jobs. Since they might not have the skills or training to do the unautomatable upper class jobs, they end up in the unautomatable lower-class ones. There’s nothing in economic orthodoxy that says this can’t happen. David Autor and his giant block of citations agree: Because jobs that are intensive in either abstract or manual tasks are generally found at opposite ends of the occupational skill spectrum—in professional, managerial, and technical occupations on the one hand, and in service and laborer occupations on the other—this reasoning implies that computerization of “routine” job tasks may lead to the simultaneous growth of high-education, high-wage jobs at one end and low-education, low-wage jobs at the other end, both at the expense of middle-wage, middle education jobs—a phenomenon that Goos and Manning (2003) called “job polarization.” A large body of US and international evidence confirms the presence of employment polarization at the level of industries, localities, and national labor markets (Autor, Katz, and Kearney 2006, 2008; Goos and Manning 2007; Autor and Dorn 2013; Michaels, Natraj, and Van Reenen 2014; Goos, Manning, and Salomons 2014; Graetz and Michaels 2015; Autor, Dorn, and
Hanson 2015)
Related publications
Goos, M., A. Manning, and A. Salomons, 2014, Explaining Job Polarization: Routine-Biased Technological Change and Offshoring, American Economic Review, 104(8), 2509-2526. https://www.aeaweb.org/articles?id=10.1257/aer.104.8.2509
Goos, M., A. Manning, and A. Salomons, 2009, Job Polarization in Europe, American Economic Review: Papers & Proceedings, 99(2), 58-63. https://www.aeaweb.org/articles?id=10.1257/aer.99.2.58
Michaels, G., A. Natraj, and J. Van Reenen, 2014, Has ICT Polarized Skill Demand? Evidence from Eleven Countries Over Twenty-Five Years, The Review of Economics and Statistics, 96(1), 60-77. https://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00366