Four Global Forces Breaking All The Trends - Richard Dobbs McKinsey Global Ins.
-
Upload
informa-australia -
Category
Business
-
view
847 -
download
2
Transcript of Four Global Forces Breaking All The Trends - Richard Dobbs McKinsey Global Ins.
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
Download our reports at www.mckinsey.com/mgiFollow us on @mckinsey_mgi
Like what you see?Be part of our conversation.
informa.com.au
Join our e-newsletter