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Kausik Rajgopal - The Future of Work


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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

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Kausik Rajgopal - The Future of Work

  1. 1. 1McKinsey & Company The Future of Work San Francisco January 20, 2017
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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