Demographics and Automation

By Daron Acemoglu, P. Restrepo. 2021

536 citations recorded by the source corpus.

We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions.

Subfields: Demography, Economics and Econometrics, Economics & Operations Research

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Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “The introduction of automation technologies, including LLMs, has previously been linked to heightened economic disparity and labor disruption, which may give rise to adverse downstream effects (Acemoglu and Restrepo, 2022a; Acemoglu, 2002; Moll et al., 2021; Klinova and Korinek, 2021; Weidinger et a”
Robots, reshoring, and the lot of low-skilled workers (2018, background): “There are no robots employed abroad because of the abundance of labor and the associated low wages such that the incentive to automate production is limited (see Abeliansky and Prettner, 2017; Acemoglu and Restrepo, 2018).”
Exposure of occupations to technologies of the fourth industrial revolution (2021, background): “Acemoglu & Restrepo (2018) show in their seminal theory that the effects of new technologies crucially depend on the type of technological progress.”
Shift-Share Designs: Theory and Inference* (2018, background): “More recently, shift-share strategies have been applied to investigate the local labor market impact of various shocks, including international trade competition (Topalova, 2007, 2010; Kovak, 2013; Autor, Dorn and Hanson, 2013; Dix-Carneiro and Kovak, 2017; Pierce and Schott, 2018), credit supply (G”
Robot Imports and Firm-Level Outcomes (2020, methodology): “Using the first-order condition for ki (5), into the first-order condition for automation (10), we can write: hκi ρiδriki = 1 δ ln ( w ri ) .”
Artificial intelligence and big data in entrepreneurship: a new era has begun (2019, background): “…ahead, but we can see plenty there that needs to be done” (Turing 1950: 460). as foundational for entrepreneurship research, such as economics (Acemoglu and Restrepo 2018; Brynjolfsson et al. 2017; Einav and Levin 2014), economic policy (Agrawal et al. 2019), innovation (Aghion et al.…”
The Rise of Robots in China (2019, background): “Several recent studies have investigated the link between robots and jobs using data aggregated at the country or industry level (for example, Graetz and Michaels 2018; Acemoglu and Restrepo 2017, 2018).”
Uneven Growth: Automation's Impact on Income and Wealth Inequality (2021, unknown): “…focus on wage inequality (Autor, Levy and Murnane, 2003; Autor, Katz and Kearney, 2006; Acemoglu and Autor, 2011; H´emous and Olsen, 2018), or study the eﬀect of automation on aggregates and wages using a representative household framework (Acemoglu and Restrepo, 2018; Caselli and Manning, 2018).”