Data and Methodology

In our analysis, the rankings of automation risk describe the technical feasibility that an occupation can be computerized or automated with start-of-the-art technology available today. This data comes from Burning Glass Technologies, and is derived largely from a well known 2013 study from two researchers at Oxford, Carl Benedikt Frey and Michael A. Osborn. To calculate the automation risk, the Oxford researchers evaluated the ability of computers to perform the underlying tasks associated with the given occupation.

1. “High risk” occupations are the top quartile of risk, with at least 85 percent risk of automation for a given occupation.
2. “Medium risk” occupations are in the second quartile of risk, between 50 percent and 85 percent risk of automation for a given occupation.
3. “Low risk” occupations are in the bottom two quartiles, with less than 50 percent risk.

A few key caveats are important to consider when interpreting the data.

First, the rankings are not a probability that a given job will actually be automated. Because a job or task can technically be done by a computer does not mean that it will. A range of legal, logistical, business, financial, political, and social factors could lower the real rate at which businesses and employers adopt technology and automate functions. Moreover, predictions about technology have a relatively high degree of uncertainty.

Second, jobs that have some tasks that can technically be automated will not necessarily be displaced. Instead, the nature of many jobs will change—in some cases, dramatically—but will not be eliminated. (McKinsey estimates that just 5 percent of jobs will be outright eliminated, but that half of job tasks could be automated.) The implication of this change is the need for workers in at risk occupations to continuously upskill to keep pace with the changing requirements of their occupation.

Finally, while technology and automation will displace some jobs and change others, new jobs will also be created and other jobs will expand. Our analysis does not capture the impact of projected job creation.

• The data on automation potential comes from Burning Glass Technologies, which is derived largely from a well known 2013 study from two researchers at Oxford, Carl Benedikt Frey and Michael A. Osborn, titled “The Future of Employment: How Susceptible are Jobs to Computerisation?”
• Occupational and wage data for the Phoenix metropolitan area is from the Bureau of Labor Statistics and covers the period from January 1, 2017 to December 31, 2017. The geographic area spans Phoenix, Mesa, and Scottsdale, including Pinal and Maricopa counties.
• Data on national averages of women in occupations comes from the Bureau of Labor Statistics.
• Data on education levels of employed individuals comes from the American Community Survey (ACS) five-year estimates (2011 – 2015).