The automation index measures the risk of an occupation for automation. It is presented on an index with a base of 100–occupations with a score above 100 have a greater-than-average risk of automation, and occupations with a score below 100 have a lower-than-average risk of automation.
This methodology starts with the underlying work on task content. We use estimated task time shares, derived from O*NET work activities, and regress them for each occupation based on Frey and Osborne’s published “computerization probabilities” (2013). This helps us identify which tasks are positively and negatively correlated with automation risk.
This classification is then linked with the task time shares to identify the share of each occupation’s time spent in high- and low-risk work, from an automation perspective.
Then we look at the place of an occupation in the broader context of labor market automation risk. Using occupation compatibility scores, we look at all similar roles (defined as having an O*NET compatibility score over 75) and find the percentage of jobs in those similar roles that are at risk of automation.
Finally, using staffing pattern data, we multiply the share of an occupation’s jobs in 3-digit NAICS industries by that industry’s share of at-risk jobs to calculate the overall industry automation risk.
We then standardize all these measures and scale the index so that 100 = the “average worker,” defined as the average index across all occupations, weighted by job numbers in 2018. The index has a standard deviation of 15. Note that the share of time spent on low-risk work is a negative contributor to an occupation’s index score (making the index score lower) while the other three measures are positive contributors (making the index score higher).
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