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

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Kausik Rajgopal - The Future of Work

  • 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