Jobs lost, jobs gained

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Jobs lost, jobs gained: Workforce transitions in a time of automation SUSAN LUND, MCKINSEY GLOBAL INSTITUTE CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of McKinsey & Company is strictly prohibited September 21, 2017

Transcript of Jobs lost, jobs gained

Page 1: 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

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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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