IMPACTS FROM AUTOMATION DIFFUSE LOCALLY – A NOVEL APPROACH TO ESTIMATE JOBS RISK IN US CITIES

Teresa Farinha

No 2029, Papers in Evolutionary Economic Geography (PEEG) from Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography

Abstract: Workers that become automated may transfer productivity gains to their co-workers or make it easier to automate their jobs too. In this paper, I empirically investigate how automatable jobs have diffused impacts to neighbouring jobs in North American cities between 2007 and 2016. Results indicate that jobs that share similarities with neighbouring high-risk jobs grew less, even when controlling for their own technical risk of automation. Conversely, jobs that share complementarities with neighbouring high-risk jobs grew faster, possibly indicating productivity gains from working with recently automated jobs. In addition to the analysis in this paper, I provide an adjusted index of job automation risk that accounts for local diffusion of impacts (negative and positive) in US cities.

Keywords: automation; Cities; complementarity; diffusion; jobs; similarity (search for similar items in EconPapers)
JEL-codes: J21 O20 R10 (search for similar items in EconPapers)
Date: 2020-07, Revised 2020-07
New Economics Papers: this item is included in nep-geo, nep-lma, nep-tid and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:egu:wpaper:2029

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