The Effect of AI Across the US and How to Adjust

The Effect of AI Across the US and How to Adjust

New Brookings study says the potential effects will “vary significantly across occupations, regions, and demographic groups,” so policymakers have a lot to do to adapt to the new reality.

Whatever you look artificial intelligence is present. In order to get a handle on how and where this technology, and automation in general, will impact U.S. workers, the Brookings Institute analyzed data in a variety of industries, geographies, and demographic groups across the country.

“The next phase of automation, increasingly involving AI, seems like it should be manageable in the aggregate labor market, though there are many sources of uncertainty,” said Mark Muro, senior fellow and lead author of the report. “With that said, the potential effects will vary significantly across occupations, regions, and demographic groups, which means that policymakers, industry, and society as a whole needs to focus much more than they are on ensuring the coming transitions will work for all of those affected.”

The report, titled Automation and Artificial Intelligence: How machines are affecting people and places,  concluded that by 2030, some 25% of U.S. employment will have experienced high exposure to automation. Another 36% will experience medium exposure, and another 39% will experience low exposure.

Those with greater than 90% automation potential over the next two to three decades represented only 4% of U.S. employment in 2016.

Job tasks projected to be 100% automatable represent only half of one percent of the workforce (740,000 jobs).

In addition to those conclusions, Brookings discovered some overall trends:

The impacts of automation and AI in the coming decades will vary especially across occupations, places, and demographic groups.  

Automation risk varies across U.S. regions, states, and cities, but it will be most disruptive in Heartland states. While automation will take place everywhere, its inroads will be felt differently across the country. Local risks vary with the local industry, task, and skill mix, which in turn determines local susceptibility to task automation.

Large regions and whole states—which differ less from one another in their overall industrial compositions than do smaller locales like metropolitan areas or cities—will see noticeable but not, in most cases, radical variations in task exposure to automation. Along these lines, the state-by-state variation of automation potential is relatively narrow, ranging from 48.7% and 48.4% of the employment-weighted task load in Indiana and Kentucky to 42.9% and 42.4% in Massachusetts and New York.

The 19 states that the Walton Family Foundation labels as the American Heartland have an average employment-weighted automation potential of 47% of current tasks, compared with 45% in the rest of the country. Much of this exposure reflects Heartland states’ longstanding and continued specialization in manufacturing and agricultural industries.

Men, young workers, and underrepresented communities work in more automatable occupations.

The sharp segmentation of the labor market by gender, age, and racial-ethnic identity ensures that AI-era automation is going to affect demographic groups unevenly. Male workers appear noticeably more vulnerable to potential future automation than women do, given their overrepresentation in production, transportation, and construction-installation occupations—job areas that have above average projected automation exposure. By contrast, women comprise upward of 70% of the labor force in relatively safe occupations, such as health care, personal services, and education occupations.

Automation exposure will vary even more sharply across age groups, meanwhile, with the young facing the most disruption. Young workers between the ages of 16 and 24 face a high average automation exposure of 49%, which reflects their dramatic overrepresentation in automatable jobs associated with food preparation and serving.

Equally, sharp variation can be forecasted in the automation inroads that various racial and ethnic groups will face. Hispanic, American Indian, and black workers, for example, face average current-task automation potentials of 47%, 45%, and 44% for their jobs, respectively, figures well above those likely for their white (40%) and Asian (39%) counterparts.

Underlying these differences is the stark over- and underrepresentation of racial and ethnic groups in high-exposure occupations like construction and agriculture (Hispanic workers) and transportation (black workers). Black workers have a slightly lower average automation potential based on their overrepresentation in health care support and protective and personal care services, jobs which on average have lower automation susceptibility.

Action is Needed 

The study also suggests steps that federal, state, local, business, and civic leaders can do to work with the private sector  in an effort “to embrace growth and technology to keep productivity and living standards high and maintain or increase hiring.”

  • Promote a constant learning mindset by investing in reskilling incumbent workers and expanding accelerated learning and certifications. This includes making skill development more financially accessible as well as aligning and expanding traditional education.

 • Facilitate smoother adjustment by creating a universal adjustment benefit to support all displaced workers as well as maximizing hiring through a subsidized employment program.

 • Reduce hardships for workers who are struggling. This includes reforming and expanding income supports for workers in low-paying jobs.

• Mitigate harsh local impacts  by future-proofing vulnerable regional economies and expanding support for community adjustment.

  

Hide comments

Comments

  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Publish