SOURCE: Jutta Bolt and Jan Luiten van Zanden, The first update of the Maddison Project: Re-estimating growth before 1820, Maddison Project working paper number 4, University of Groningen,

January 2013; UN Population Division; McKinsey Global Institute analysis

Unprecedented levels of GDP growth since the 1950s

2

Contributions to global GDP growth

Compound annual growth rate, %

1500–

1600

<0.1

1000–

1500

1–1000

0.1 0.10.3

1600–

1700

1700–

1820

1870–

1900

1820–

1870

1900–

1913

1913–

1940

1940–

1950

1950–

1964

1964–

1974

1974–

1984

1984–

1994

1994–

2004

2004–

2010

0.5

0.9

1.9

2.5

1.91.7

4.74.8

3.13.0

3.83.6

Population growth

GDP per capita growth

Long-term government interest rates in select developed economies

%

SOURCE: International Monetary Fund International Financial Statistics; IHS Global Insight; Bloomberg; Organization for Economic Co-operation and Development; McKinsey Global Institute analysis

Capital has become increasingly cheaper

3

Nominal

values

Ex-post

real values

14

10

8

6

4

2

0

12

1975 1980 1985 1990 1995 2000 2005 2010 2014

4

The global corporate profit pool has risen over 30 years

22%

19.4

23.7

Total size of profit pool1

$ trillion, 2013 dollars

1 Calculated using macroeconomic data combined with financial data for 28,250 companies (16,850 publicly listed firms and 11,400 privately held firms) with more than $200 million in annual revenue.

Corporate profit pool

% of world GDP

1980 2013

SOURCE: World Bank; OECD; Bureau van Dijk; European Commission AMECO database; US Bureau of Economic Analysis; IHS; Oxford Economics; McKinsey Corporate

Performance Analysis Tool; McKinsey Global Institute analysis

24%

10.5

13.0

9.8

7.6

29%

7.6

4.4

73%

Gross pre-tax

Earnings before interest,

taxes, depreciation, and

amortization (EBITDA)

Net pre-tax

Earnings before interest

and taxes (EBIT)

Net post-tax

Net operating profit

less adjusted taxes

(NOPLAT)

Net income

5.0

17.3

2.49.5

2.07.2

1.15.6

Four disruptive forces changing the picture

Industrialization

and urbanization

in emerging

economies

Disruptive

technologies

An aging

world

Greater global

interconnections

5

0 10 20 30 40 50 60 70 80 90 100

Urban population %

Per capita GDP rises in parallel with urbanization

7SOURCE: UN population Division; The Conference Board; McKinsey Global Institute analysis

1820

United States

2013

Japan

2013

1891

Brazil

2013

1930

South Korea

2013

1950

China

2013

1920

India

2013

1950

Per capita GDP

1990 PPP $ (log scale)

30,000

10,000

3,000

1,000

300

3,000 times larger than the UK Industrial Revolution

Country

South Korea

India

China

Japan

United Kingdom

United States

Germany

Years to double per capita GDP Population at start

of growth period (million)

2000190018001700

9

10

154

28

48

22

1,023

822

53

65

33

10

12

16

8

Nearly 3 billion people will join the consuming class by 2025

SOURCE: Homi Kharas; Angus Maddison; McKinsey Global Institute Cityscope 2.0

World population, billion

1.01.3

1.6

2.5

3.7

5.3

6.8

7.9

<1%3%

7%

13%

23%

23%

36%

53%

4.4

1900

0.30.9

20252010

1.2

2.2

0 0.1

2.8 4.2

1970 1990

2.4

1.0

4.0

0.1

1820

1.5

1870

3.7

1950

1.2

Below consuming class

Consuming class

Share of population in consuming class

9

Technological breakthroughs are speeding up

First phone call

1876

First website

1991

First iPhone

2007

115 years 16 years

Hargreaves’

Jenny 1764

GM’s Unimate

1962

Google’s Schaft

2010

198 years 48 years

Computer printer

1953

3D printer

1984

Printing press

1448

505 years 31 years

SOURCE: McKinsey Global Institute analysis 11

Mobile

Internet

Advanced

robotics

3D

Printing

Changing the building

blocks of everythingIT and how we use it

Rethinking energy comes of ageMachines working for us

Twelve technologies have significant potential to disrupt

Disruptive Dozen

Mobile internet

Cloudtechnology

Internet of Things

Automation of knowledge work

Next-generation genomics

Advanced materials

Advanced robotics

Autonomous and near-autonomous

vehicles

3D printing Energy storage

Advanced oil and gas exploration and recovery

Renewable energy

SOURCE: McKinsey Global Institute analysis 12

SOURCE: UN Population Division; McKinsey Global Institute analysis

Without migration and policy changes, many countries will see their labor forces shrink dramatically

1,000

849

988

China

Japan

81 69 55

4755 40

Russia

Germany

24

20302010 2050

1927

Working-age population forecast, with current migration rates (15–64

years)

Million

Poland

88 75103

Total decline (2010–2050)

-151

-28

-26

-15

-8

Million %

-15

-27

-32

-27

-29

14

SOURCE: The Conference Board Total Economy Database; UN Population Division; McKinsey Global Institute analysis

At past rates of productivity growth, GDP growth would slow by about 40 percent

NOTE: Numbers may not sum due to rounding.

15

GDP of G19 and Nigeria

Compound annual growth rate, %

1.7

0.3

1.8

1.8Employment

growth

Productivity

growth

Next 50 years at

historical

productivity

growth

2.1

-40%

Past 50 years

3.6

Correlation between oil prices and commodities

Correlation with oil prices

1980–1999 2009–2014

17

1 Updated till 2013Q1

SOURCE: World Bank; International Monetary Fund Organisation for Economic Co-operation and Development;

Food and Agriculture Organization of the United Nations; UN Comtrade; McKinsey Global Institute analysis

Sugar

Steel

0.18

-0.26

Timber

0.36

-0.60

Beef

Wheat 0.33

Corn 0.36 0.85

0.97

0.82

0.761

0.31

0.471

The world’s first truly global recession in a linked world

Real GDP growth, PPP adjusted, %

20102000908070601950

-2

-1

0

1

2

3

4

5

6

7

18SOURCE: World Bank; McKinsey Global Institute analysis

19

20

By 2025, the China region alone will be home to almost one-quarter of Fortune Global 500 companies

SOURCE: Fortune Global 500; MGI CompanyScope; McKinsey Global Institute analysis 21

Eastern Europe

& Central Asia

Southeast Asia

Developed

regions

China region

Africa &

Middle East

South Asia

Latin America

477

2013

500

370

130

2000

500

476

24

1990

500

477

23

1980

500

23

34

26

271

2025

500

1226

11

120

Number of Fortune Global 500 companies

22

23

24

25

26

Compound annual

growth rate (%)

We forecast the global corporate profit pool will continue to grow but slower than GDP

Total size of

profit pool

$ trillion, 2013

dollars

Corporate profit

pool

% of world GDP

SOURCE: World Bank; OECD; Bureau van Dijk; European Commission AMECO database; US Bureau of Economic Analysis; IHS; Oxford Economics; McKinsey Corporate

Performance Analysis Tool; McKinsey Global Institute analysis

23.7

19.619.4

-17%

10.710.513.0

-18%

7.99.8

7.6

-19%

6.07.6

4.4

-21%

5.0

17.3 21.4

2.49.5 11.7

2.07.2 8.6

1.15.6 6.5

1980

2013

2025

1980–2013

3.8

2013–25

1.8

1980–2013

4.3

2013–25

1.8

1980–2013

4.0

2013–25

1.5

1980–2013

5.1

2013–25

1.2

Gross pre-taxEarnings before interest,

taxes, depreciation, and

amortization (EBITDA)

Net pre-taxEarnings before interest

and taxes (EBIT)

Net post-taxNet operating profit less

adjusted taxes (NOPLAT) Net income

27

Technological advancement has placed pressure on traditionally middle-class transaction jobs

SOURCE: US Bureau of Labor Statistics 1972–2010; McKinsey Global Institute analysis 28

Decline in transaction jobs between 1970 and 2010

% workforce share decline for select highly automatable jobs

-86

-80

-59

-43

-37

Typists

General clerks

Bookkeeping jobs

Secretaries

Telephone

operators

29

While few jobs are 100% automatable, 60% of all jobs have at least 30% technically automatable activities

SOURCE: BLS 2014; O*Net; Global Automation Impact Model; McKinsey analysis

Automation potential based on demonstrated technology of job titles in the US (cumulative)

Example

occupations

Sewing machine

operators

Logging equipment

operators

Stock clerks

Travel agents

Dental lab technicians

Bus drivers

Nursing assistants

Web developers

Fashion designers

Chief executives

Statisticians

INTERIM FINDINGS

1 We define automation potential according to the work activities that can be automated by adapting currently demonstrated technology

87

71

60

50

4134

2619

10

1

>90 >80 >70100 >10

% of roles

(100% =

775 roles)

>20>30>50>60 >40

Percent of automation potential

30

40

56

46

50

39

40

34

49

35

55

48

43

64

43

52

36

35

60

24

35

Educational services

Mining

Agriculture, forestry, fishing, and hunting

Arts/entertainment/recreation

Real estate/rental and leasing

Other services

Utilities

Information

Management of companies/enterprises

Transportation/warehousing

Administrative/support/waste management

Finance/insurance

Construction

Retail trade

Manufacturing

Accommodation/food services

Federal, state, and local government

Wholesale trade

Professional, scientific, and technical services

Healthcare/social assistance

Variations exist across the economic potential of automation by industry

SOURCE: BLS 2014; O*Net; Global Automation Impact Model; McKinsey analysis

FTE weighted percent of technically

automatable1 activities by industry

Percent

All industries are

likely to have

significant share of

automatable

activities

However the share

varies across

industries – 35% in

healthcare vs. 56%

in agriculture

15%

10%

9%

9%

8%

8%

5%

5%

5%

5%

5%

4%

3%

3%

2%

1%

1%

1%

1%

0%

INTERIM FINDINGS

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated technology

Impact of automation by industry

in the USTotal economic wages

by industry

100%= $5.8 Trillion

31

Cumulative annual wages by technical automatability1 for the US

Technically automatable activities represent ~$2.2Trillion of addressable wages in the US, ~39% of the total labor market

SOURCE: BLS 2014; O*Net; Global Automation Impact Model; McKinsey Analysis

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated technology

2 Based on median wages and FTE (130 Million) from BLS; 2,080 hrs/year; excludes occupations without DWAs ($0.1T in wages, 2 Million FTEs)

3 Ratios are not equal due to FTE weighting

$ Trillion

5.8

3.6

(62%)

Total wages2 Automatable wages Non-automatable wages

2.2

(38%)

FTEs3

Percent (100%= 130M) 44 56

INTERIM FINDINGS

32

Technical automation potential of work activities1 by job zone in the USPercent of time

Occupations requiring higher levels of education and experience have a lower proportion of activities that can be automated

SOURCE: BLS 2014; O*Net; Global Automation Impact Model; McKinsey analysis

4956

78

5144

22 22

78

36

64

High school or

some experience

Less than

high school

Automatable

Non-automatable

Some post-

secondary

education

Graduate degreeBachelor’s degree

No. of FTEs

persons, Total =

130 Million

18 48 33 23 7

Example

occupationsTaxi drivers,

animal

caretakers

Sheet metal

workers, fire

fighters

Electricians,

legal

secretaries

Accountants,

teachers

Lawyers, doctors

▪ As occupations

require more

education or

experience, technical

potential for

automation

decreases

▪ The biggest change

in technical

automatabliity occurs

between jobs that

require a Bachelor’s

degree and those

that do not

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated technology

2 Job zone is based primarily on education required, adjusted for experience required

INTERIM FINDINGS

Job zone2

Graduation rates in science and engineering vary widely

College graduates with Science, Technology, Engineering, or Mathematics (STEM) degree

42

35

35

28

27

26

24

23

22

22

21

21

19

15

15

11Brazil

United States

Russia

Australia

Canada

Japan

United Kindom

Italy

World

Spain

France

Mexico

Germany

South Korea

Taiwan

China

33SOURCE: National Science Foundation, Science and Engineering indicators 2012, First University Degrees by selected region and country/economy: 2008 or most recent; McKinsey Global

Institute analysis

1 Only includes countries with more than 100,000 college graduates in 2008 or most recent year. STEM fields are defined as physical and biological sciences, mathematics, computer sciences,

architecture, and engineering

% of 2008 graduating class1

Growing wage inequality in the advanced nations

34

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0.2

0.5

0.9

College

graduate

Some

college

Graduate

school

High school

graduate

High school

drop-out

0603200097949188858279767370671963 2008

Growth in real, composition adjusted weekly wages for full-time male workers (1963 = 1)

1963-2008

35

Heading

Externally

focused to

reset

intuition

Agile and

low cost

Optimist

36

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