US Cities With the Most Employees at Threat of Automation

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In the previous few years, it has turn into extra frequent to order meals from a kiosk, see machines cleansing airport flooring, and discuss to a chatbot as an alternative of a customer support agent.

The COVID-19 pandemic has accelerated the adoption of those applied sciences in addition to others, lots of which can be utilized to carry out duties that people used to do. Machines don’t name out sick or unfold illness and might exchange staff to help in social distancing.

Whereas some jobs and duties, particularly those who require creativity and interpersonal abilities, will not be conducive to automation, many others are. In accordance with knowledge from the Bureau of Labor Statistics and Oxford College, 42% of U.S. staff are at excessive danger of automation.

Decrease expert jobs, particularly those who contain repetition, usually tend to be automated. A Brookings research on automation’s influence on individuals finds that jobs in workplace administration, manufacturing, transportation, and meals preparation are probably the most susceptible to automation.

These jobs are extra conducive to automation as a result of they contain both routine, bodily labor, or data assortment and processing actions. Typically most of these jobs are lower-paying, however some jobs at low danger of automation embody low-paying private care and home service work.

Knowledge from the Bureau of Labor Statistics mixed with automation danger knowledge from a College of Oxford research exhibits a correlation between the danger of automation and annual median wages. Playing Sellers, who’ve a likelihood of automation of 96%, earn a median annual wage of lower than $24,000. On the other finish of the spectrum, Chief Executives have only a 1.5% danger of automation and earn a median annual wage of $186,000. Most occupations fall someplace between these extremes.

lower paying jobs at risk of automation

Whereas automation will occur in every single place, its impacts will probably be felt extra closely in some components of the nation than others attributable to native business make-up and employee talent set. The Brookings automation research finds that rural communities are inclined to have a a lot bigger share of duties which might be vulnerable to automation than do extra populated areas.

On the state degree, Nevada and South Dakota have the very best share of staff at excessive danger of automation—outlined right here as occupations with automation dangers of 0.7 or increased — at 48.4% and 46.9%, respectively. Nevada is one among simply two states the place casino-style playing is authorized state-wide, and playing sellers are at a really excessive danger of automation.

Nevada and South Dakota risk of job automation

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To find out the U.S. metropolitan areas with probably the most staff susceptible to automation, researchers at Commodity.com analyzed the newest knowledge from the U.S. Bureau of Labor Statistics and the College of Oxford.

Researchers ranked metros in accordance with the share of staff at excessive danger of automation, the overall variety of staff at excessive danger of automation, the share of staff at medium danger of automation, and the share of staff at low danger of automation. To enhance relevance, solely metropolitan areas with not less than 100,000 individuals had been included within the evaluation.

Listed below are the metros with probably the most staff susceptible to automation.

Small and midsize metros at risk of automation

Giant Metros With the Most Employees at Threat of Automation

Los Angeles
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15. Los Angeles-Lengthy Seaside-Anaheim, CA

  • Share of staff at excessive danger of automation: 42.6%
  • Whole staff at excessive danger of automation: 1,644,440
  • Share of staff at medium danger of automation: 19.4%
  • Share of staff at low danger of automation: 38.0%

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Miami
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14. Miami-Fort Lauderdale-West Palm Seaside, FL

  • Share of staff at excessive danger of automation: 42.7%
  • Whole staff at excessive danger of automation: 769,020
  • Share of staff at medium danger of automation: 22.9%
  • Share of staff at low danger of automation: 34.4%
Dallas
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13. Dallas-Fort Price-Arlington, TX

  • Share of staff at excessive danger of automation: 42.8%
  • Whole staff at excessive danger of automation: 1,046,720
  • Share of staff at medium danger of automation: 21.5%
  • Share of staff at low danger of automation: 35.6%
St Louis
Photograph Credit score: Sean Pavone / Shutterstock

12. St. Louis, MO-IL

  • Share of staff at excessive danger of automation: 43.1%
  • Whole staff at excessive danger of automation: 383,540
  • Share of staff at medium danger of automation: 19.4%
  • Share of staff at low danger of automation: 37.5%
Jacksonville
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11. Jacksonville, FL

  • Share of staff at excessive danger of automation: 43.2%
  • Whole staff at excessive danger of automation: 205,280
  • Share of staff at medium danger of automation: 22.3%
  • Share of staff at low danger of automation: 34.5%
Birmingham-Hoover
Photograph Credit score: Sean Pavone / Shutterstock

10. Birmingham-Hoover, AL

  • Share of staff at excessive danger of automation: 43.4%
  • Whole staff at excessive danger of automation: 155,150
  • Share of staff at medium danger of automation: 20.8%
  • Share of staff at low danger of automation: 35.9%
Nashville
Photograph Credit score: jdross75 / Shutterstock

9. Nashville-Davidson–Murfreesboro–Franklin, TN

  • Share of staff at excessive danger of automation: 43.4%
  • Whole staff at excessive danger of automation: 289,600
  • Share of staff at medium danger of automation: 19.6%
  • Share of staff at low danger of automation: 37.0%
Orlando
Photograph Credit score: Sean Pavone / Shutterstock

8. Orlando-Kissimmee-Sanford, FL

  • Share of staff at excessive danger of automation: 44.0%
  • Whole staff at excessive danger of automation: 361,400
  • Share of staff at medium danger of automation: 23.3%
  • Share of staff at low danger of automation: 32.6%
New Orleans
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7. New Orleans-Metairie, LA

  • Share of staff at excessive danger of automation: 44.3%
  • Whole staff at excessive danger of automation: 158,550
  • Share of staff at medium danger of automation: 19.5%
  • Share of staff at low danger of automation: 36.2%
Indianapolis
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6. Indianapolis-Carmel-Anderson, IN

  • Share of staff at excessive danger of automation: 44.6%
  • Whole staff at excessive danger of automation: 309,530
  • Share of staff at medium danger of automation: 20.4%
  • Share of staff at low danger of automation: 35.0%
Wyoming
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5. Grand Rapids-Wyoming, MI

  • Share of staff at excessive danger of automation: 44.9%
  • Whole staff at excessive danger of automation: 158,220
  • Share of staff at medium danger of automation: 21.6%
  • Share of staff at low danger of automation: 33.5%
Louiseville
Photograph Credit score: Sean Pavone / Shutterstock

4. Louisville/Jefferson County, KY-IN

  • Share of staff at excessive danger of automation: 45.1%
  • Whole staff at excessive danger of automation: 185,580
  • Share of staff at medium danger of automation: 21.6%
  • Share of staff at low danger of automation: 33.3%
Memphis
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3. Memphis, TN-MS-AR

  • Share of staff at excessive danger of automation: 47.4%
  • Whole staff at excessive danger of automation: 202,640
  • Share of staff at medium danger of automation: 20.4%
  • Share of staff at low danger of automation: 32.2%

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Riverside-San Bernardino-Ontario
Photograph Credit score: Jon Bilous / Shutterstock

2. Riverside-San Bernardino-Ontario, CA

  • Share of staff at excessive danger of automation: 48.8%
  • Whole staff at excessive danger of automation: 476,660
  • Share of staff at medium danger of automation: 20.1%
  • Share of staff at low danger of automation: 31.1%
Las Vegas
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1. Las Vegas-Henderson-Paradise, NV

  • Share of staff at excessive danger of automation: 49.3%
  • Whole staff at excessive danger of automation: 307,650
  • Share of staff at medium danger of automation: 22.7%
  • Share of staff at low danger of automation: 28.0%

Detailed Findings & Methodology

To find out the U.S. metropolitan areas with probably the most staff susceptible to automation, researchers at Commodity.com analyzed the newest knowledge from the U.S. Bureau of Labor Statistics’ Occupational Employment Survey and a College of Oxford research The Way forward for Employment: How Prone Are Jobs to Computerization?

Researchers ranked metros in accordance with the share of staff at excessive danger of automation. Within the occasion of a tie, the metro with the upper proportion of staff at excessive danger of automation was ranked increased. Researchers additionally calculated the shares of staff at medium danger and low danger of automation.

Occupations at a excessive danger of automation are outlined as these jobs with dangers of automation of 0.7 and better. Occupations at medium danger of automation are outlined as jobs with automation dangers between 0.3 and 0.7, whereas occupations at low danger of automation are outlined as jobs with automation dangers lower than 0.3.

To enhance relevance, solely metropolitan areas with not less than 100,000 individuals had been included within the evaluation. Moreover, metro areas had been grouped into the next cohorts primarily based on inhabitants dimension: 

  • Small metros: 100,000-350,000
  • Midsize metros: 350,000-1,000,000
  • Giant metros: greater than 1,000,000