The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market

By David H. Autor, David Dorn. 2013

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GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, neutral): “Several studies have shown that automation technologies have resulted in wage inequality in the US, driven by relative wage declines for workers specializing in routine tasks (Autor et al., 2006; Van Reenen, 2011; Acemoglu and Restrepo, 2022b).”
The future of employment: How susceptible are jobs to computerisation? (2017, background): “Historically, computer isation has largely been confined to manual and cognitive routine tasks involvin g explicit rulebased activities (Autor and Dorn, 2013; Goos, et al., 2009).”
Earnings Dynamics, Changing Job Skills, and STEM Careers* (2020, methodology): “11 We follow the major classiﬁcation scheme for the NLSY used by Altonji, Kahn, and Speer (2016), and we generate consistent occupation codes across NLSY waves using the census occupation crosswalks developed by Autor and Dorn (2013).”
The comparative advantage of cities (2020, unknown): “(31)Autor and Dorn (2013) rank occupations by their skill level according to their mean log wage.”
Where Have the Middle-Wage Workers Gone? A Study of Polarization Using Panel Data (2016, unknown): “φj(z) is continuous and increasing in z so that a (17)See Autor et al. (2003) and Acemoglu and Autor (2011) for a discussion of why computers may be thought of as substitutes for routine workers and complements for non-routine workers.”
The Geography of Remote Work (2020, unknown): “We use the crosswalks provided by Autor and Dorn (2013) to map PUMA identifiers in the ACS data to commuting zones.”
Do low-skilled workers gain from high-tech employment growth? High-technology multipliers, employment and wages in Britain (2019, background): “Similarly, the literature on skills-biased technological change has highlighted the growth in these low wage service jobs to fulfil functions outsourced by the affluent, but time poor, workers whose incomes have been boosted by new technology (Autor and Dorn, 2013). Empirical studies of human capita”
Artificial Intelligence in Service (2018, background): “Traditionally, it has been considered that service jobs, even low-skilled ones, are more difficult to automate due to their relying more heavily on contextual understanding and spontaneous interactive communication than manufacturing jobs (Autor and Dorn 2013).”
New Frontiers: The Origins and Content of New Work, 1940–2018 (2022, methodology): “Following Autor and Dorn (2013), “routine” task content averages “set limits, tolerances, and standards” and “finger dexterity”; and “abstract” task content averages “direction, control and planning” and “GED math”.”
Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share (2018, unknown): “, Elsby, Hobijn and Sahin, 2013; Karabarbounis and Neiman, 2013; Piketty 2014; Barkai, 2017; Autor et al. 2017b; Dao et al. 2017; Gutiérrez and Philippon, 2017). Reviewing an array of within- and cross-country evidence, Karabarbounis and Neiman (2014) argue that labor’s falling share of value-added ”
Industrial Robots, Workers' Safety, and Health (2022, background): “One may be worried that our measure of robot exposure may be correlated with other economic shocks, particularly to the exposure to import competition from China (Autor and Dorn, 2013).”
Is Automation Labor Share-Displacing? Productivity Growth, Employment, and the Labor Share (2018, background): “, Elsby, Hobijn and Sahin, 2013; Karabarbounis and Neiman, 2013; Piketty 2014; Barkai, 2017; Autor et al. 2017b; Dao et al. 2017; Gutiérrez and Philippon, 2017). Reviewing an array of within- and cross-country evidence, Karabarbounis and Neiman (2014) argue that labor’s falling share of value-added ”
People versus machines: The impact of minimum wages on automatable jobs (2018, methodology): “For example, less-educated, male, and young workers have been the most susceptible to reductions in employment that is intensive in routine tasks (Autor and Dorn, 2013; Autor and Dorn, 2009).”
Heterogeneous Labor Market Impacts of the COVID-19 Pandemic (2020, unknown): “(2020), who use data from the Nielsen Homescan panel; Bartik et al. (2020), who use data from Homebase; and Chetty et al. (2020), who build a database that tracks economic activity at a granular level in real time using anonymized data from various private companies.”
Environmental Policy and Innovation: A Decade of Research (2019, background): “Scholars are now giving increased attention to the effects of automation and globalization on different sets of workers (Autor and Dorn 2013, Autor et al. 2013).”
Shift-Share Instruments and the Impact of Immigration (2018, unknown): “The definition of commuting zones is based on Tolbert and Sizer (1996), and applied to Censuses using codes provided by Autor and Dorn (2013).”