Pankaj C Patel1, Srikant Devaraj2, Michael J Hicks3, Emily J Wornell4. 1. Villanova School of Business, Villanova University, 800 E. Lancaster Ave, Villanova, PA 19085, USA. Electronic address: pankaj.patel@villanova.edu. 2. Center for Business and Economic Research, Miller College of Business, Ball State University, 2000 W. University Ave., Muncie, IN 47306, USA. Electronic address: sdevaraj@bsu.edu. 3. Center for Business and Economic Research, Miller College of Business, Ball State University, 2000 W. University Ave., Muncie, IN 47306, USA. Electronic address: mhicks@bsu.edu. 4. Indiana Communities Institute, Center for Business and Economic Research, Rural Policy Research Institute, Ball State University, 2000 W. University Ave., Muncie, IN 47306, USA. Electronic address: ejwornell@bsu.edu.
Abstract
RATIONALE: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
RATIONALE: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.