Bay Area Council Economic Institute Chair and McKinsey & Company Western Region Managing Partner Kausik Rajgopal's presentation for the BACEI's 10th Annual Economic Forecast

1. 1McKinsey & Company
The Future of Work
San Francisco
January 20, 2017

2. 2McKinsey & Company
Perspectives on the Future of Work
▪ Four major forces impacting the future of work
▪ Gig economy workers – answering Disraeli’s question
– Bay Area independent workforce: 30% of the working-age population
– Four segments: Free Agents, Casual Earners, Reluctants & Financially
Strapped
– Lessons learned and stakeholder considerations
▪ Automation – the fourth revolution?
– Activities vs. Jobs
– 51% of economic activity automatable with current technology
– 5-100/ 60-30

3. 3McKinsey & Company
The ‘Gig Economy’ – a Bay Area view
 Similar share of the working-age population, with comparable demographics to that of the US overall.
Independent workers make up 30% of the Bay Area working-age population, similar to the US overall (27%)
 More Digital than the US overall. Nearly twice as many independent workers in the Bay Area report using a
digital platform such as Uber or Thumbtack, 29% in the Bay Area vs. 16% in the US overall
 Work by necessity twice as often (29%) as they do in the US overall (13%), but at a similar rate as non-
digital independent workers (~30%). Several possible drivers of this phenomenon, including a different digital
independent workforce or different types of digital independent work in the Bay, and more research is needed.
 Subject to tighter financial constraints than workers in the US overall and in the Bay Area. 3x more likely to
have been recently unemployed than other workers in the Bay Area and are significantly more likely to have
dependents than other workers. Qualitative research suggests that digital independents work to back-fill faltering
traditional jobs, to support a high cost of living, or to buffer uneven income.
 Likely a leading indicator of a digital future, because the drivers of digital independent work - high
awareness and clear regulation – are portable to other cities. Awareness and clear regulation drive digital
penetration, rather than demographics (e.g., age), economic climate (e.g., household income), or infrastructure
(e.g., public transit ridership). Independent work elsewhere may become as or more digital than Bay Area today

4. 4McKinsey & CompanySOURCE: McKinsey Global Institute survey
Bay Area independent workers compared to U.S. overall
NOTE: Numbers may not sum due to rounding.
% of independent workforce in each geographic region, based on MGI survey
UNITED STATES
Primary
Income
Supplemental
income
“Free Agents”
32%
22M
46% 54%
72%28%
“Reluctants”
14%
10M
“Casual
earners”
40%
27M
“Financially
strapped”
14%
9M
68 million independent workers
By
choice
Out of
necessity
51% 49%
73%27%
“Free Agents”
35%
0.5M
“Reluctants”
15%
0.2M
“Casual
earners”
36%
0.5M
“Financially
strapped”
13%
0.2M
1.5 million independent workers
Primary
Income
Supplemental
income
BAY AREA

5. 5McKinsey & Company
Bay Area and U.S. independent workers by demographic
SOURCE: BLS; McKinsey Global Institute survey
1 Defined as the percent of the working age population who are earners; 2 Defined as ages 15 to 24; 3 Defined as ages 25 to 65; 4 Defined as ages 65+; 5 Defined as below $25,000; 6 Individuals who self-reported as having immigrated to the US 7
Earners are defined as survey respondents who reported earning income in the last year NOTE: Numbers may not sum due to rounding
53
54
13%
23%
30
27
47
49
8%
8%
39
35
79%
69%
35
38
42%
48%
47
40
58%
52%
77% 29%82% 75%71%100%
68% 27%81% 74%65%100%
100
100
Bay Area
US
Labor force participation rate1
Percent of earners in this
demographic who do independent
work7
Percent of the independent
workforce
“How large is this demographic? “How independent is this demographic?”
▪ The participation rate in independent work follows national averages across all demographics
▪ Bay Area women participate in independent work at a higher rate as compared to the US, a statistically
significant difference, but consistent with differences in overall labor force participation rates
YOUTH2 WOMEN IMMIGRANTS6SENIORS4 LOW-INCOME5OVERALL MIDDLE-AGE3 MEN
35
36
19%
11%
48
49
8%
21%
40%
55%
77%
77%
GENDERAGE OTHER
Key finding

6. 6McKinsey & Company
Based on MGI survey
More frequent use of Digital Platforms
% of earners in each category
who have used digital platforms1
% of digital
independent workforce
29
100%
22
66%
78
24% 639%
78
44% 6950%
29
6%
Workers who
provide labor
Workers
who
lease assets
Workers who
sell goods
All independent
workers
16
100%
Bay Area
US
63
8 69
▪ Bay Area independent
workers use digital platforms
at a higher rate than
independent workers in the US
overall, 29% vs 16%, a
statistically significant gap
▪ Digital penetration is higher
across all categories and
statistically significant for
workers who provide labor
▪ Workers who provide labor
are a larger share of the
digital independent workforce
in the Bay, 66% vs 44%, a
statistically significant gap
SOURCE: McKinsey Global Institute survey, Brookings Institute, “Tracking the Gig Economy: New Numbers,” 2016
1 Earners are defined as survey respondents who reported earning income in the last year

7. 7McKinsey & CompanySOURCE: McKinsey Global Institute survey
NOTE: Numbers may not sum due to rounding.
% of independent workforce in each geographic region, based on MGI survey
Bay Area digital independent workers are more frequently out of
necessity relative to digital independent workers in the US, 29% vs
13%, a statistically significant finding
Often working out of necessity
28
29
31
13
72
71
69
87
Out of Necessity
Digital
By Choice
Non-digital
Digital
Non-digital
+16
▪ Digital independent workers in
the Bay Area resemble non-
digital independent workers
both in the US overall and in the
Bay Area in terms of the fraction
of workers doing so by necessity
▪ There are several possible
drivers of the fraction of Bay
Area digital independent
workers doing so by
necessity, including that
expansion of the work in the Bay
Area has made it a more
common option for workers who
do independent work out of
necessity, or that the work in the
Bay Area is less appealing than
that in other regions; more
research is needed to conclude

8. 8McKinsey & CompanySOURCE: McKinsey Global Institute survey
Tighter financial constraints
1 Defined as credit scores of 649 or lower
16
26
41
53
9
21
24
30
6
8
13
33
+10
Dependent children Dependent elderly
+18
+28
Have a low
credit score1
Unemployed in the
past 12 months
+20
Bay Area traditional workers
Bay Area non-digital independent workers
Bay Area digital independent workers
Qualitative research
suggests that digital
independents frequently
work in order to earn when
traditional jobs falter, to
provide extra income for high
cost of living, or to buffer
uneven income streams
Financial constraints of workers in the Bay Area
% of workers reporting a constraint

9. 9McKinsey & Company
Considerations for stakeholders
SOURCE: McKinsey Global Institute
POLICY MAKERS ORGANIZATIONS INNOVATORS
Address gaps in worker
protections, benefits, and
income security
▪ Use digital platforms to
distribute educational, health, or
financial bulletins (e.g., Covered
California deadlines)
▪ Tailor educational or health
offerings to digital independent
workers (e.g., community
college classes offered outside
of rush hour)
Consider how digital allows you
to utilize external talent
▪ Design human resource
information systems to interface
with independent worker
platforms
▪ Identify jobs that can be broken
into discrete tasks to apply
specialized talent available
through online platforms
Build businesses to meet the
needs of independent workers
▪ Attract workers by creating
opportunities to offer
differentiated services
▪ Retain workers by offering
sticky benefits like health care
or education
Develop differentiated skills
▪ Use digital platforms to increase
customer base and clarify value
proposition
▪ Use digital platforms to build
skills or credentials
Collect better data
▪ Set a standard for worker data
collected by digital platforms
▪ Begin tracking independent
work (e.g., advocating for
changes to Federal or State
surveys, funding a region-
specific survey)
Rethink the boundaries of your
organization
▪ Incorporate digital platforms as
part of broader transformations
of human resources or hiring
▪ Consider addressing digital
independents as a market
segment
Create new marketplaces and
tools
▪ Expand digital platforms to new
sectors (e.g., office work,
professional services)
▪ Adjust business tools to appeal
to digital independents (e.g.,
offering comprehensive tools for
sole proprietors)
Think like a business
▪ Articulate a set of priorities in
order to help define regulations
for independent work
▪ Use digital tools to manage
multiple income streams,
market services, and comply
with laws
INDEPENDENT WORKERS

10. 10McKinsey & Company
Likely a leading indicator for other regions Potential driver
Disproven driver
1 On-demand services are a sub-set of digital independent work that includes grocery delivery, ridesharing, and errands
SOURCE: BLS, US Census Bureau, Moody’s Analytics, McKinsey Global Institute survey, team analysis
Hypothesis Bay Area status
▪ First market for nine of the nine digital work platforms reviewed, including
Uber, UberX, Lyft, Lyft Line, Taskrabbit, Postmates, and Instacart
▪ Home to 64% of the 39 digital work platforms identified during the study
▪ Early entrance of platforms gives companies time to
grow and advertise their services
▪ High visibility of platforms increases likelihood that a
consumer will use a digital platform
▪ Clear regulations on ride-sharing, including being part of the first state to
formally regulate it and being one of the first cities to require registrations
▪ Clear regulations for room-sharing
▪ Clear regulations encourage participation in markets
▪ Clear regulations support companies building products
and platforms for independent workers
▪ The Bay Area has the highest per capita GDP of the surveyed cities, but
there does not appear to be a link between per capita GDP and digital
penetration
▪ There does not appear to be a relationship between the surveyed
likelihood to pay for independent work services and digital penetration
across surveyed cities
▪ Neither unemployment nor labor force participation are significantly
correlated with digital penetration
▪ High household incomes or GDP growth increases
demand for services provided by independent workers
▪ A high likelihood to pay for independent services
encourages consumption of services provided by
digital independent workers
▪ A high unemployment rate combined with a high labor
force participation rate increases the labor pool for
digital independent work
▪ Inadequate public transit drives demand for ride-
sharing services
▪ Lower transit ridership should therefore correlate with
higher digital penetration
▪ Except in New York, transit ridership is a not a major driver of independent
work, possibly because no other system provides a strong enough
alternative to displace ridesharing services
▪ The Bay Area is highly educated, but there does not appear to be a
relationship between digital penetration and age or education levels
▪ A younger and more educated population drives usage
of adoption of on-demand1 services and increases the
fraction of digital independents
High awareness
of
digital platforms
Clear regulations
Economic
climate
Infrastructure
Demographics
The Bay Area may be a leading indicator for other cities since the drivers of high digital penetration
are not rooted in inherent facts of the Bay Area such as demographics, economic climate, or infrastructure

11. 11McKinsey & Company
There are more platforms for digital independent work
in the Bay Area than in the rest of the US combined…
Number of headquarters, 2016
…and Bay Area consumers use online platforms to pay for on-demand
services more than other cities
Power of high awareness: Bay Area vs. other Regions
SOURCE: McKinsey Global Institute survey, Press search, Google Maps
Uber, Thumbtack, Airbnb, Getaround, Stride Health, Upwork, Lyft,
and Craigslist are all on the same 3.5 mile walk
0
0
0
1
25
Chicago
14
New York
4
Los Angeles
Other
Detroit
4
5
Bay Area
Houston
Atlanta
64% of US
headquarters
Total 25
3
4
2
3
3
4
5
Bay Area
Other
22
21
18
18
17
16
29
Chicago
NYC
Atlanta
Detroit
LA
Houston
Bay Area
Digital penetration
% of total independent workers
Usage of online platforms
% of working age population used
online platforms for services

12. 12McKinsey & Company
Power of high awareness: UberX case study
SOURCE: McKinsey Global Institute, McKinsey team analysis, Jonathan Hall and Alan Krueger, “An Analysis of the Labor Market for Uber’s Driver-Partners in the United States,” 2015. Detroit was not included in the
study. Figures are approximate.
22
21
18
18
17
16
29
NYC
Chicago
Detroit
LA
Atlanta
Bay Area
Houston15,000
0
22,500
7,500
05 25 20302000 10 15 20
New York
AtlantaSan Francisco
Los Angeles Houston
Chicago
Percentage of digital workers
% of total independent workers
Time to 5,000
UberX drivers
Months
17
NA
27
NA
22
17 25
24
NA
NA
NA
27
19 25
Time to 10,000
UberX drivers
Months
Growth of the UberX platform
Number of active Uber driver-partners by city
Months since uberX launched
▪ The digital work platform UberX grew about as fast in Los Angeles, New York, Chicago, and Houston, all cities with a
lower penetration of digital independent work, suggesting that demand for digital independent work is not limited to the Bay Area
▪ The platform grew slower in Atlanta, which had high penetration of digital work in the MGI survey, suggesting that
ridesharing is only one part of a larger story of digital independent work

13. 13McKinsey & CompanySOURCE: Press search, R Street
Supportive Moderate Restrictive
Power of clear regulations
EXHIBIT 13
1 R Street – ride and room sharing industry lobbying group. Roomscore is 2016, RideScore is 2015 2 Illegal unless the owner is staying there.
▪ Regulations are relatively
clear, if somewhat
restrictive, in the
Bay Area
▪ The presence of stable
regulations, even if they
are not permissive, may
encourage ridesharing
and room renting as
supplemental income,
especially for those with
existing jobs
Ridesharing regulations in cities around the US
San Jose
Houston
Legal?
Yes
Yes
Insurance required?
Yes
Yes
Ridesharing co.
require permit?
Yes
Yes
No
Yes - $200
City
San Francisco
San Jose
Houston
Renting a unit for
<30 day legal?
Yes
Yes
Not regulated
Insurance required?
Yes - $500,000
No
Not regulated
Tax? What fraction
of listing price?
14%
10%
7%
Limit to days per
year?
90
180
Not regulated
Los Angeles Illegal No 12% - hotels 180 - proposed
New York City Illegal2 - 5.9% -
Chicago Yes Yes, $1,000,000 8.5% 90
Detroit Not regulated Not regulated Not regulated Not regulated
Atlanta Illegal - - -
Roomsharing regulations in cities around the US
Los Angeles Yes Yes Yes No
New York City Yes Yes Yes Yes - $84
Chicago Yes Yes Yes Yes - TBD
Detroit Yes Yes No No
Atlanta Yes Yes Yes No
Permit required?
Cost per year?
Regulation score
by RoomScore1
D+
B
F
D
D-
C-
A-
F
Regulation score
by RideScore1
A
A
D+
C-
B
B+
B
City
Digital penetration (% of
independent workers)
San Francisco Yes Yes Yes Yes - $91 A 29%
29%
18%
21%
16%
17%
18%
22%

14. 14McKinsey & Company
Emerging Regional archetypes
High or clear Low or difficult
EXHIBIT 14
AttributesOpportunities
Example cities
Policymakers
Independent
workers
Innovators
Organizations
Clear
regulations
High
awareness
Early adopter Early majority
Late majority –
awareness
Bay Area
Atlanta, Houston, Los
Angeles Chicago, Detroit
Leverage digital penetration
to deliver services
Collaborate with other early
cities to set regulations
Consider partnering with
innovators for awareness
Use digital tools to think
like a business and
differentiate
Articulate a set of priorities
to help define regulations
for independent work
Learn from early adopters
to advocate for regulations
Attract and retain workers to
platforms with sticky benefits
Identify remaining barriers
to full adoption
Consider advertising
offerings in the market
Invest in integration software
with digital platforms
Work with innovators to
identify
Identify opportunities to begin
integrating digital workers
New York
Consider regulatory
changes to support adoption
Partner with platforms to
define regulations
Advocate for regulatory
changes
Identify regulatory changes
that would anticipate a shift
Late majority -
regulation

15. 15McKinsey & Company
Automation happens first with specific activities, not entire jobs
SOURCE: Expert interviews; McKinsey analysis
NOTE: Analysis based on currently available of demonstrated technology capabilities as of 2016.
Occupations
Retail salespeople Social1
Linguistic2
Cognitive3
Sensory perception4
Physical5▪ ...
▪ …
▪ …
~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
Based on currently demonstrated
technology capabilities as of 2016

16. 16McKinsey & Company
Most susceptible activities
▪ 51% of US economy
▪ $2.7 trillion in wages
Spectrum of automation potential
7 14 16 12 17 16 18
Process
data
Predictable
physical
Collect
data
Manage Expertise Interface Unpredictable
physical
Time spent on activities that can be automated by adapting currently demonstrated technology
%
9
18 20
26
64
69
81
Time spent
in all US
occupations
%
Total wages
in US, 2014
$ billion
596 1,190 896 504 1030 931 766
BASED ON DEMONSTRATED TECHNOLOGY

17. 17McKinsey & Company
Automation potential by Sector
Time spent in US occupations, %
50 25 10 5 1
Ability to automate, %
0 50 100
Sectors by activity type
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
Accommodation and food services
Educational services
Manage InterfaceExpertise
Unpredictable
physical Collect data Process data
Predictable
physical
BASED ON DEMONSTRATED TECHNOLOGY
Automation
potential
44%
43%
49%
36%
41%
51%
53%
47%
39%
44%
40%
36%
35%
27%
35%
58%
73%
60%
57%
Most
automatable
Least
automatable
Inthemiddle

18. 18McKinsey & Company
Automation potential by wage band
120100
60
4020
40
600
20
100
80
80
0
Hourly wage
$ per hour
Ability to technically automate
Percentage of time on activities that can be automated
by adapting currently demonstrated technology
BASED ON DEMONSTRATED TECHNOLOGY
File
clerks
Chief
executives
Landscaping and
grounds-keeping workers

19. 19McKinsey & Company
The main near-term act: automation of activities within jobs
Example
occupations
100
91
73
62
51
42
34
26
18
81
>20>30>70 >50>80>90 >60100 >40
Percent of automation potential
% of roles
(100% =
820 roles)
>0%>10
SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis
 Sewing machine
operators
 Assembly line workers
 Stock clerks
 Travel agents
 Dental lab technicians
 Bus drivers
 Nursing assistants
 Web developers
 Fashion designers
 Chief executives
 Statisticians
 Psychiatrists
 Legislators
While about
5%
of occupations could have
100%
of tasks automated,
More will have portions of their tasks automated e.g.
60%
of occupations could have
30%
of tasks automated