Jobs lost, jobs gained
Transcript of Jobs lost, jobs gained
Jobs lost, jobs gained: Workforce transitions in a time of automation SUSAN LUND, MCKINSEY GLOBAL INSTITUTE
CONFIDENTIAL AND PROPRIETARYAny use of this material without specific permission of McKinsey & Company is strictly prohibited
September 21, 2017
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Lessons from history
Automation impact
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Text
Throughout history, large scale sector employment declines have been countered by growth of new sectors that have absorbed workers1
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2,904
12,176
524151
Total net US jobs created and occupations with most jobs created and destroyed by PCsThousands of jobs
Utilizers–Computer utilizing industries1980 - 2015
3205Customer service reps
Computer scientists (not in computer industry)
Stock and inventory clerks
Bookkeepers and auditing clerks
Secretaries
Typists
1517
1873
-881
-823
-562
Assorted managers and administrators
Computer software developers (in-industryequipment)
Computer scientists
Office machine manufacturers (typewriters)
Direct–Computer equipment manufacturing1970 - 2015
31
18
27
-61
Indirect–Computer suppliers1970 - 2015
42Managers
Semiconductor manufacturing occupations
Printed circuit assembly occupations
Typewriter indirect occupations
26
31
-79
Enabled–Computer software and services industries1970 - 2015
686
768Software developers (SW and apps)
Computer scientists
Managers
Typewriter repair
416
-32
EXAMPLES SHOWNNOT EXHAUSTIVE
Jobs created: 19.3MJobs destroyed: 3.5M
Net jobs: 15.8M (~10% of labor force)
SOURCE: IPUMS; Moody’s; IMPLAN; US Bureau of Labor Statistics; FRED; McKinsey Global Institute analysis
New technologies create many more jobs than they destroy over time, mainly outside the industry itself: Example of PCs2
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Ford Model T – Assembly line improved productivity and number of employees as a result of higher sales at lower prices
189,088 230,78882,38853,488
550600690
780950
200
1,000400,000
400
800600
0
200,000
100,000
300,000
011
13,840
1413
394,788
10
20,727
191512
440
1909
490
6,87612,880
18,89214,366
3,9762,773
18131213
785,000
05
15
2520
10
0
20,000
15,000
10,000
10 121909
21
13111,655
191514
SOURCE: BLS, FDIC, SNL Hounshell (1984: 224) for the first two columns and Beaudreau (2008: 71), McKinsey Global Institute Analysis
Automation can stimulate employment by lowering the price of a good and unleashing latent demand: Example of Ford Model T3
# of employeesProductivity, # Model T units produced per employee per year
Model T units shipped Price, $
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The personal computer and Internet might have reduced employment for information analysts, but instead it has doubled
Total
1,500
2,000
0
1,000
500
Statisticians
Managementanalyst
Budget Analysts
Economists, market Researchers andsurvey researchers
2015
Operations researchanalyst
1990
2000
Credit Analysts
Financial Analysts
1.9M
985
364
59
55255
31
Employment in analyst occupations, US Total employment, thousands, 1990-2015
Total employmentThousands, 2015
159
4
SOURCE: IPUMS, McKinsey Global Institute Analysis
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Lessons from history
Automation impact
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To assess the impact of automation on employment, we focus on the activities within occupations and the capabilities of currently demonstrated technologies
Occupations
Retail salespeople
Social 1
Linguistic2
Cognitive3
Sensory perception4
Physical 5▪ ...▪ …▪ …
~800 occupations
Teachers
Health practitioners
Food and beverage service workers
Activities
Greet customers
▪ ...▪ …▪ …
Process sales and transactions
~2,000 activities assessed across all occupations
Clean and maintain work areas
Demonstrate product features
Answer questions about products and services
?
Capabilities required
18 capabilities utilized within each activity
SOURCE: McKinsey Global Institute analysis
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Most susceptible activities ▪ 51% of US economy ▪ $2.7 trillion in wages
Three categories of activities have higher technical automation potential
SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis
7 14 16 12 17 16 18
Processdata
Predictablephysical
Collectdata
Manage Expertise Interface Unpredictablephysical
Time spent on activities that can be automated by adapting currently demonstrated technology -- US%
918 20
26
6469
81
Time spent in all occu-pations %
Total wages in US, 2014$ billion
596 1,190 896 504 1030
931 766
1 Managing and developing people.2 Applying expertise to decision making, planning, and creative tasks.3 Interfacing with stakeholders.4 Performing physical activities and operating machinery in unpredictable environments.5 Performing physical activities and operating machinery in predictable environments.
BASED ON DEMONSTRATED TECHNOLOGY
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10091
7362
5142
3426
188
1
>90 >80 >50>60>70100 >40 >10 >0 >20>30
While few occupations are fully automatable, over 60 percent of all occupations have at least 30 percent technically automatable activities
Less than 5% of occupations consist of
activities that are 100% automatable
Example occupations Sewing machine
operators Graders and sorters
of agricultural products
Stock clerks Travel agents Watch repairers
Chemical technicians, Nursing assistants Web developers
Fashion designers
Chief executives Statisticians
Psychiatrist Legislators
Technical automation potential%
Share of roles100% = 820 roles
Over 60% of occupations have at least
30% of their activities that are automatable
Automation potential based on demonstrated technology of occupation titles in the US (cumulative)
SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis
11McKinsey & CompanySOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis
Technical potential for automation across sectors varies depending on mix of activity types
Automation potential%Sectors by activity type
Accommodation and food services
Manufacturing
Agriculture
Transportation and warehousing
Retail trade
Mining
Other services
Construction
Utilities
Wholesale trade
Finance and insurance
Arts, entertainment, and recreation
Real estate
Administrative
Health care and social assistances
Information
Professionals
Management
Educational services
0 50 100
Ability to automate (%)Size of bubble indicates % of time spent in US occupations
Collectdata
Predict-able
physicalProcess
dataManageInter-face
Unpredict-able
physicalExpertise
39
44
47
49
57
27
35
35
36
36
40
41
43
44
51
53
60
60
73
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Occupations requiring higher levels of education and experience have lower automation potential
Example occupations
Logging equipment operators Taxi drivers
Stock clerks Travel agents Dental lab
technicians Fire fighter
Nursing assistants Web developers Electricians Legal secretaries
Lawyer Doctors Teacher Statisticians Chief executives
3649
56
78
6451
44
22
High school orsome experience
Automatable
Some post-secondary education
Bachelor and graduate degree
Non-automatable
Less thanhigh school
Technical automation potential of work activities by job zone in the US %
SOURCE: BLS 2014; O*Net; Global Automation Impact Model; McKinsey analysis
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Projected impact to total employment in earliest automation scenario Share of 2014 FTE hours with potential to be automated, 2016-2030
10 10 9
78
11 125
6 5
7
2 3 000
36
55
4
0
6
64
11
4
54
0
4
2 56
5
72
33
3
47
32
1
2
US
22
2
2
1
0
China
1
Japan
4854
1
1
2
30
2
Mexico Germany
2
2 1
1
2
India
192
2
1
0
3
27
Government and Administration
Other Services
Transportation and Warehousing
Manufacturing
Retail Trade
Construction Other minimal impact sectors1
Healthcare
Wholesale TradeAgriculture
Accommodation and Food Services
By 2030, automation has the potential to replace up to 47 percent of work hours in the US in the earliest adoption scenario
Midpoint automation scenario, country aggregate
9% 13% 16% 25% 25% 28%
SOURCE: McKinsey Global Institute analysis
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