Details

Autor(en) / Beteiligte

• Patel, Pankaj C.
• Devaraj, Srikant
• Hicks, Michael J.
• Wornell, Emily J.

Titel

County-level job automation risk and health: Evidence from the United States

Ist Teil von

• Social science & medicine (1982), 2018-04, Vol.202, p.54-60

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2018

Quelle

ScienceDirect Pay Per View(PPV) Titles

Beschreibungen/Notizen

• Previous studies have observed a positive association between automation risk and employment loss. Based on the job insecurity-health risk hypothesis, greater exposure to automation risk could also be negatively associated with health outcomes. The main objective of this paper is to investigate the county-level association between prevalence of workers in jobs exposed to automation risk and general, physical, and mental health outcomes. As a preliminary assessment of the job insecurity-health risk hypothesis (automation risk → job insecurity → poorer health), a structural equation model was used based on individual-level data in the two cross-sectional waves (2012 and 2014) of General Social Survey (GSS). Next, using county-level data from County Health Rankings 2017, American Community Survey (ACS) 2015, and Statistics of US Businesses 2014, Two Stage Least Squares (2SLS) regression models were fitted to predict county-level health outcomes. Using the 2012 and 2014 waves of the GSS, employees in occupational classes at higher risk of automation reported more job insecurity, that, in turn, was associated with poorer health. The 2SLS estimates show that a 10% increase in automation risk at county-level is associated with 2.38, 0.8, and 0.6 percentage point lower general, physical, and mental health, respectively. Evidence suggests that exposure to automation risk may be negatively associated with health outcomes, plausibly through perceptions of poorer job security. More research is needed on interventions aimed at mitigating negative influence of automation risk on health. •Exposure to automation risk could exacerbate health outcomes.•Using General Social Survey data, automation risk.→job insecurity→poorer health is supported•Using county-level data, automation risk lowers general, physical, and mental health.