MARK MURO ROBERT MAXIM JACOB WHITON - With contributions from Ian Hathaway - and - Brookings Institution
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EXECUTIVE SUMMARY
and
How machines are affecting people and places
MARK MURO
ROBERT MAXIM
JACOB WHITON
With contributions from Ian Hathaway
January 2019
EXECUTIVE SUMMARY The power and prospect of automation and artificial intelligence (AI) initially alarmed technology experts for fear that machine advancements would destroy jobs. Then came a correction, with a wave of reassurances. Now, the discourse appears to be arriving at a more complicated, mixed understanding that suggests that automation will bring neither apocalypse nor utopia, but instead both benefits and stresses alike. Such is the ambiguous and sometimes-disembodied nature of the “future of work” discussion. Which is where the present analysis aims to help. Intended to clear up misconceptions on the subject of automation, the following report employs government and private data, including from the McKinsey Global Institute, to develop both backward- and forward-looking analyses of the impacts of automation over the years 1980 to 2016 and 2016 to 2030 across some 800 occupations. In doing so, the report assesses past and coming trends as they affect both people and communities and suggests a comprehensive response framework for national and state-local policymakers. 2 Metropolitan Policy Program at Brookings
In terms of current trends, the report finds that: • Approximately 25 percent of U.S.
employment (36 million jobs in 2016) will
1. Automation and AI will affect tasks in face high exposure to automation in the
virtually all occupational groups in the future coming decades (with greater than 70
but the effects will be of varied intensity—and percent of current task content at risk of
drastic for only some. The effects in this sense substitution).
will be broad but variable:
• At the same time, some 36 percent of U.S.
• Almost no occupation will be unaffected employment (52 million jobs in 2016) will
by the adoption of currently available experience medium exposure to automation
technologies. by 2030, while another 39 percent (57
million jobs) will experience low exposure.
FIGURE 5
Most jobs are not highly susceptible to automation
Shares of employment by automation potential
25%
36 million jobs
Potential for automation
(volume of tasks within the job that 39%
are susceptible to automation) 57 million jobs
High (70% of more)
Medium (30% - 70%)
Low (0% - 30%)
36%
52 million jobs
Source: Brookings analysis of BLS, Census, EMSI, and McKinsey data
Automation and Artificial Intelligence | Executive summary 3
2. The impacts of automation and AI in the educational requirements, to low-paying
coming decades will vary especially across personal care and domestic service work
occupations, places, and demographic groups. characterized by non-routine activities or the
Several patterns are discernable: need for interpersonal social and emotional
intelligence.
• “Routine,” predictable physical and
cognitive tasks will be the most vulnerable Near-future automation potential will be
to automation in the coming years. highest for roles that now pay the lowest
wages. Likewise, the average automation
Among the most vulnerable jobs are potential of occupations requiring a
those in office administration, production, bachelor’s degree runs to just 24 percent,
transportation, and food preparation. less than half the 55 percent task exposure
Such jobs are deemed “high risk,” with faced by roles requiring less than a bachelor’s
over 70 percent of their tasks potentially degree. Given this, better-educated, higher-
automatable, even though they represent paid earners for the most part will continue
only one-quarter of all jobs. The remaining, to face lower automation threats based on
more secure jobs include a broader array of current task content—though that could
occupations ranging from complex, “creative” change as AI begins to put pressure on some
professional and technical roles with high higher-wage “non-routine” jobs.
FIGURE 8
Smaller, more rural places will face heightened automation risks
County distribution by community size type, 2016
90th
75th
54% Percentile 50th
25th
52% 10th
Average automation potential
50%
48%
46%
44%
42%
40%
Nonmetro areas Metro areas Nonmetro Metro
Small Medium Large Small Medium Large
20K
FIGURE 6
The lowest wage jobs are the most exposed to automation
Automation potential. United States, 2016
80%
70%
60%
50%
40%
30%
20%
10%
0%
0 10 20 30 40 50 60 70 80 90 100
Occupational wage percentile, 2016
Note: Figures have been smoothed using a LOWESS regression
Source: Brookings analysis of BLS, Census, EMSI, and McKinsey data
• Automation risk varies across U.S. variations in task exposure to automation.
regions, states, and cities, but it will be Along these lines, the state-by-state variation
most disruptive in Heartland states. While of automation potential is relatively narrow,
automation will take place everywhere, ranging from 48.7 and 48.4 percent of the
its inroads will be felt differently across employment-weighted task load in Indiana
the country. Local risks vary with the local and Kentucky to 42.9 and 42.4 percent in
industry, task, and skill mix, which in turn Massachusetts and New York, as depicted in
determines local susceptibility to task Map 2.
automation.
Yet, the map of state automation exposure
Large regions and whole states—which is distinctive. Overall, the 19 states that
differ less from one another in their overall the Walton Family Foundation labels as
industrial compositions than do smaller the American Heartland have an average
locales like metropolitan areas or cities—will employment-weighted automation potential
see noticeable but not, in most cases, radical of 47 percent of current tasks, compared with
45 percent in the rest of the country. Much
Automation and Artificial Intelligence | Executive summary 5
MAP 2
Average automation potential by state Routine-intensive occupations
Employment share, 1980
2016
42.4 -- 20%
18.1% 44%
44%
20% - 45%
- 25%
45% - 46%
25% - 30%
46%
30% - 47%
- 35%
35% - 38.9%
47% - 48%
Source: Brookings analysis of BLS, potential
Automation Census, EMSI, Moody’s, and McKinsey data
ial 2016
42.4% - 44% 44% - 45% 45% - 46% 46% - 47% 47% - 48.7%
of this exposure reflects Heartland states’ average. By contrast, small university towns
44% - 45% longstanding
45% - 46% and continued 46% - specialization
47% in like Charlottesville, Va. and Ithaca, N.Y., or
47% - 48.7%
Automation potential
2016 manufacturing and agricultural industries. state capitals like Bismarck, N.D. and Santa
42.4% - 44% 44% - 45% 45% - 46% 46% - 47% 47% - 48.7%
Fe, N.M., appear relatively well-insulated.
• At the community level, the data reveal
sharper variation, with smaller, more rural As to the 100 largest metropolitan areas, it
communities significantly more exposed is also clear that while the risk of current-
to automation-driven task replacement— task automation will be widely distributed, it
and smaller metros more vulnerable than won’t be evenly spread. Among this subset
larger ones. The average worker in a small of key metro areas, educational attainment
metro area with a population of less than will prove decisive in shaping how local
250,000, for example, works in a job where labor markets may be affected by AI-age
48 percent of current tasks are potentially technological developments.
automatable. But that can rise or decline. In
small, industrial metros like Kokomo, Ind. Among the large metro areas, employment-
and Hickory, N.C. the automatable share weighted task risk in 2030 ranges from 50
of work reaches as high as 55 percent on percent and 49 percent in less well-educated
6 Metropolitan Policy Program at Brookings
MAP 4
Routine-intensive occupations
Average automation potential by metropolitan area Employment share, 1980
2016 39.1%- 20%
18.1% - 44%
44% - 46%
20% - 25%
46%
25% - 48%
- 30%
48% - 50%
30% - 35%
35%
50% - 38.9%
- 56.0%
Source: Brookings analysis of BLS, potential
Automation Census, EMSI, Moody’s, and McKinsey data
ial 2016
39.1% - 44% 44% - 46% 46% - 48% 48% - 50% 50% - 56.0%
locations like Toledo, Ohio and Greensboro- • Men, young workers, and underrepresented
44% - 46% 46% - 48% 48% - 50% 50% - 56.0%
High Point, N.C., to just 40 percent and 39
Automation potential
2016
communities work in more automatable
percent
39.1% - 44% in44%
high
- 46% education
46% - 48% attainment
48% - 50% 50%metros
- 56.0% occupations. In this respect, the sharp
like San Jose, Calif. and Washington, D.C. segmentation of the labor market by gender,
age, and racial-ethnic identity ensures
Following Washington, D.C. and San Jose that AI-era automation is going to affect
among the larger metros with the lowest demographic groups unevenly.
current-task automation risk comes a “who’s
who” of well-educated and technology- Male workers appear noticeably
oriented centers including New York; more vulnerable to potential future
Durham-Chapel Hill, N.C.; and Boston— automation than women do, given
all with average current-task risks below their overrepresentation in production,
43 percent. These metro areas relatively transportation, and construction-installation
protected by their specializations in durable occupations—job areas that have above-
professional, business, and financial services average projected automation exposure.
occupations, combined with relatively large By contrast, women comprise upward of 70
education and health enterprises. percent of the labor force in relatively safe
Automation and Artificial Intelligence | Executive summary 7
occupations, such as health care, personal automation potentials of 47 percent, 45
services, and education occupations. percent, and 44 percent for their jobs,
respectively, figures well above those likely
Automation exposure will vary even more for their white (40 percent) and Asian (39
sharply across age groups, meanwhile, with percent) counterparts.
the young facing the most disruption. Young
workers between the ages of 16 and 24 face Underlying these differences is the stark
a high average automation exposure of over- and underrepresentation of racial and
49 percent, which reflects their dramatic ethnic groups in high-exposure occupations
overrepresentation in automatable jobs like construction and agriculture (Hispanic
associated with food preparation and serving. workers) and transportation (black workers).
Black workers have a slightly lower average
Equally sharp variation can be forecasted automation potential based on their
in the automation inroads that various overrepresentation in health care support
racial and ethnic groups will face. Hispanic, and protective and personal care services,
American Indian, and black workers, jobs which on average have lower automation
for example, face average current-task susceptibility.
FIGURE 10
Automation exposure breaks sharply along demographic lines
Average automation potential by gender and race, 2016
47%
45% 44%
43%
40% 40% 39%
Men Women Hispanic American Black White Asian and
Indian Pacific Islander
Source: Brookings analysis of 2016 American Community Survey 1-Year microdata
8 Metropolitan Policy Program at Brookings
FIGURE 11
Black and Hispanic workers are concentrated in more automatable occupations
Shares of occupation group, 2016
Other Asian Black Hispanic White
Food Preparation and Serving Related
Production
Office and Administrative Support
Farming, Fishing, and Forestry
Transportation and Material Moving
Construction and Extraction
Installation, Maintenance, and Repair
Automation potential
Sales and Related
Healthcare Support
Legal
Computer and Mathematical
Protective Service
Personal Care and Service
Healthcare Practitioners and Technical
Life, Physical, and Social Science
Management
Community and Social Services
Building and Grounds Cleaning and Maintenance
Arts, Design, Entertainment, Sports, and Media
Architecture and Engineering
Education, Training, and Library
Business and Financial Operations
0% 20% 40% 60% 80% 100%
Source: Brookings analysis of American Community Survey 1-year microdata
3. To manage and make the best of these • Promote a constant learning mindset
changes five major agendas require attention - Invest in reskilling incumbent workers
on the part of federal, state, local, business, - Expand accelerated learning and
and civic leaders. certifications
- Make skill development more financially
To start with, government must work with accessible
the private sector to embrace growth and - Align and expand traditional education
technology to keep productivity and living - Foster uniquely human qualities
standards high and maintain or increase hiring.
• Facilitate smoother adjustment
Beyond that, all parties must invest more - Create a Universal Adjustment Benefit to
thought and effort into ensuring that the labor support all displaced workers
market works better for people. To that end, the - Maximize hiring through a subsidized
appropriate actors need to: employment program
Automation and Artificial Intelligence | Executive summary 9• Reduce hardships for workers who are If the nation can commit to its people in these
struggling ways, an uncertain future full of machines will
- Reform and expand income supports for seem much more tolerable.
workers in low-paying jobs
- Reduce financial volatility for workers in
low-wage jobs
• Mitigate harsh local impacts
- Future-proof vulnerable regional
economies
- Expand support for community
adjustment
FI V E POLI CY ST RAT EG IES
FO R ADJ UST ING TO
AUTOMATI ON
Embrace growth and technology
Run a full-employment economy, both nationally and regionally
Embrace transformative technology to power growth
Promote a constant learning mindset
Invest in reskilling incumbent workers
Expand accelerated learning and certifications
Make skill development more financially accessible
Align and expand traditional education
Foster uniquely human qualities
Facilitate smoother adjustment
Create a Universal Adjustment Benefit to support all displaced workers
Maximize hiring through a subsidized employment program
Reduce hardships for workers who are struggling
Reform and expand income supports for workers in low-paying jobs
Reduce financial volatility for workers in low-wage jobs
Mitigate harsh local impacts
Future-proof vulnerable regional economies
Expand support for community adjustment
Source: Metropolitan Policy Program at Brookings
10 Metropolitan Policy Program at BrookingsAutomation and Artificial Intelligence | Executive summary 11
1775 Massachusetts Avenue, NW Washington, D.C. 20036-2188 telephone 202.797.6139 fax 202.797.2965 brookings.edu/metro 12 Metropolitan Policy Program at Brookings
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