In a study published in Journal of the Royal Society Interface in January 2021, SFI external professor Doyne Farmer, first author of Maria del Rio-Chanon, and their colleagues at Oxford University investigated the impact of automation on low-wage workers. The COVID-19 pandemic is accelerating the pace of automation and found that low-wage workers face a double difficulty of being more likely to lose their jobs due to automation and less likely to have the skills to move to newly created jobs.
This paper is based on a data-driven model created to analyze the movement of workers through an empirically derived network of professional mobility in response to automation scenarios. By identifying workers who are most at risk of long-term unemployment, the researcher model can better target worker support and retraining programs to help low-wage workers adapt to a changing economy.
The study also found that the risks of unemployment are not limited to those directly displaced by automation. Kindergarten workers, who are at low risk of automation, are likely to face a much more challenging labor market due to other displaced workers trying to enter their industry.
“Without proper action, automation could cause further deep trouble,” Farmer says. “But with the right policy framework in place, including well-targeted support for low-wage workers, it could drive a better economy for all.”
“Professional mobility and automation: a data-driven model” was published in the journal Journal of the Royal Society Interface.
Millions of Latinos are at risk of relocating automation
R. Maria del Rio-Chanona et al. Professional mobility and automation: network model based on data, The Royal Society Interface (2021). DOI: 10.1098 / rsif.2020.0898
Provided by the Santa Fe Institute
Citation: Low-wage workers at risk of automation: study (2021, February 17) retrieved February 17, 2021 from https://phys.org/news/2021-02-low-wage-workers-automation.html
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