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Solutions 2020: Future of Work Policy Working Group

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On Thursday, May 9, Business Forward will welcome Al Fitzpayne from the Aspen Institute and Massachusetts State Senator Eric Lesser for a conference call on the Future of Work.

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Solutions 2020: Future of Work Policy Working Group

  1. 1. FUTURE OF WORK: AUTOMATION & A CHANGING ECONOMY ISSUE BRIEF : MAY 2019 ALASTAIR FITZPAYNE, EXECUTIVE DIRECTOR, FUTURE OF WORK INITIATIVE AT THE ASPEN INSTITUTE ERIC LESSER, MASSACHUSETTS STATE SENATOR
  2. 2. 2 EXECUTIVE SUMMARY Technology is creating and disrupting work. This has happened before. Automation has created jobs directly and indirectly over the past century, changing the composition of work. This time might be different. The automation we are experiencing now is deeper, faster, broader, and may be more disruptive than the past. Up to a third of the U.S. workforce may need to change occupations by 2030. In the past, we have made smart investments in education and a social safety net to address the changes brought on by automation. But now we are trailing other advanced economies in training our workforce and preparing for the jobs of tomorrow. We need to implement policies for shared prosperity to address the challenges and opportunities of automation: • Encourage employers to lead a human-centric approach to automation; • Enable workers to access skills training, good jobs, and new economic opportunities; • Help people and communities recover from displacements; and • Understand the impact of automation on the workforce
  3. 3. 3 U.S. WORKFORCE HAS GROWN OVER TIME EVEN WITH WAVES OF AUTOMATION 180 160 140 120 100 80 60 40 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 CIVILIAN EMPLOYMENT LEVEL, 1950-2019 EMPLOYMENT(INMILLIONS) SOURCE: ANALYSIS OF CURRENT POPULATION SURVEY DATA, U.S. BUREAU OF LABOR STATISTICS. HISTORY OF AUTOMATION – WAVES OF NEW TECHNOLOGIES (THE PERSONAL COMPUTER, INTERNET, IPHONE) HAVEN’T LED TO WIDESPREAD JOB LOSS. BUT THE COMPOSITION OF JOBS IN OUR ECONOMY HAS BEEN CHANGING
  4. 4. 4 IMPACT OF TECHNOLOGY ON ROUTINE VS. NONROUTINE JOBS EMPLOYMENT LEVELS OF ROUTINE AND NONROUTINE OCCUPATIONS, 1983-2017 NONROUTINE COGNITIVE: MANAGEMENT, PROFESSIONAL, AND RELATED OCCUPATIONS ROUTINE COGNITIVE: SALES AND OFFICE OCCUPATIONS ROUTINE MANUAL: PRODUCTION, TRANSPORTATION, AND MATERIAL MOVING OCCUPATIONS + INSTALLATION, MAINTENANCE, AND REPAIR OCCUPATIONS + CONSTRUCTION AND EXTRACTION OCCUPATIONS NONROUTINE MANUAL: SERVICE OCCUPATIONS RELATED TO ASSISTING OR CARING FOR OTHERS EMPLOYMENT(INMILLIONS) 70 60 50 40 30 20 10 0 1985 1990 1995 2000 2005 2010 2015 SOURCE: ANALYSIS OF EMPLOYMENT LEVEL DATA, U.S. BUREAU OF LABOR STATISTICS. RETRIEVED FROM FRED, FEDERAL RESERVE BANK OF ST. LOUIS.
  5. 5. 5 WHY MIGHT THIS TIME BE DIFFERENT? Machine learning may dramatically expand the types of tasks that can be automated. 1. DEEPER Digital advancements could lead to a higher pace of change. 2. Digital technology has applications in nearly every industry and occupation. 3. Automation’s adverse labor market impacts may be intensifying. 4. FASTER BROADER MORE DISRUPTIVE
  6. 6. 6 NUMBER OF U.S. WORKERS DISPLACED BY AUTOMATION BY 2030 (IN RAPID AUTOMATION SCENARIO) WORKERS POTENTIALLY DISPLACED BY AUTOMATION SOURCE: MCKINSEY GLOBAL INSTITUTE, DECEMBER 2017 BASED ON RECENT MCKINSEY ANALYSIS, UP TO 33% OF THE U.S. WORKFORCE MAY NEED TO CHANGE OCCUPATIONS BY 2030 19 MIL 93 MIL (56%) 54 MIL (33%) (11%) DISPLACED, NO OCCUPATIONAL CHANGE DISPLACED, NEEDS TO CHANGE OCCUPATION NO DISPLACEMENT
  7. 7. 7 POTENTIAL IMPACT BY INDUSTRY AND EDUCATION LEVEL SOURCE: WWW.BROOKINGS.EDU/RESEARCH/AUTOMATION-AND-ARTIFICIAL-INTELLIGENCE-HOW-MACHINES-AFFECT-PEOPLE-AND-PLACES/ AUTOMATIONPOTENTIALBY2030(%) A C C O M M O D ATIO N & FO O D SERV IC ES M A N U FA C TU R IN G TR A N SPO R TATIO N & W A R EH O U SIN G A G R IC U LTU R E,FO R ESTRY,FISH IN G & H U N TIN G R ETA IL TR A D E M IN IN G ,Q U A R RY IN G & O IL & G A S EX TR A C TIO N O TH ER SERV IC ES (EXC EPT PU B LIC A D M IN ISTR ATIO N ) C O N STR U C TIO N W H O LESA LE TR A D E U TILITIES FIN A N C E & IN SU R A N C E A R TS,EN TER TA IN M EN T & R EC R EATIO N A D M IN ISTR ATIO N & SU PPO R T & W A STE M A N - A G EM EN T & R EM ED IATIO N SERV IC ES R EA L ESTATE & R EN TA L & LEA SIN G G O V ER N M EN T H EA LTH C A R E & SO C IA L A SSISTA N C E IN FO R M ATIO N M A N A G EM EN T O F C O M PA N IES & EN TER PR ISES PR O FESSIO N A L,SC IEN TIFIC & TEC H N IC A L SERV IC ES ED U C ATIO N A L SERV IC ES 80 70 60 50 40 30 20 10 AVERAGE POTENTIAL 46% AUTOMATION POTENTIAL BY INDUSTRY GROUP PERCENT OF WORKERS IN HIGHLY AUTOMATABLE JOBS BY EDUCATION LEVEL 44% LESS TH A N H IG H SC H O O L H IG H SC H O O L D EG R EE O R EQ U IVA LEN T TR A D E SC H O O L C ER TIFIC ATE A SSO C IATES D EG R EE B A C H ELO R S D EG R EE G R A D U ATE D EG R EE SHAREOFJOBSWITH HIGHAUTOMATIBILITY 45 50 40 35 30 25 20 15 10 5 19% 8% 6% 1% 0% The potential impact varies by industry and education level. Industries with a higher potential for automation are those having more routine, manual work while other industries like education are seen to be less vulnerable to automation. Employees with less than a high school degree are extremely vulnerable to automation.
  8. 8. 8 INDUSTRY SPOTLIGHT: TRANSPORTATION $2.6 BILLION invested in companies developing technology to support autonomous cars 25%OF MILES DRIVEN on U.S. roads by 2030 could be by shared self-driving vehicles 305,100 taxi drivers in the U.S.– plus rideshare drivers and 750,000 workers in U.S. auto repair industry AUTONOMOUS CARS AUTONOMOUS TRUCKS 65%of U.S. manufacturers believe self-driving trucks will be mainstream within the next 10 years 30%in total transportation cost savings for manufacturers using autonomous long-haul trucking through 2040 1.3-1.7 MILLION truck drivers in the U.S. could lose their jobs
  9. 9. 9 IN THE PAST: INVESTMENTS IN EDUCATION, SAFETY NET, SOCIAL CONTRACT HIGH SCHOOL MOVEMENT GI BILL MANPOWER DEVELOPMENT TRAINING ACT WORK STUDY PROGRAM HIGHER EDUCATION ACT UNEMPLOYMENT INSURANCE SOCIAL SECURITY WORKS PROGRESS ADMINISTRATION AID TO FAMILIES WITH DEPENDENT CHILDREN MEDICARE FOOD STAMP PROGRAM EDUCATION SAFETY NET COLLECTIVE BARGAINING POWER 40-HOUR WORK WEEK OVERTIME MINIMUM WAGE LABOR MOVEMENT
  10. 10. 10 HOW THE U.S. COMPARES TO THE REST OF THE WORLD IN TRAINING ITS WORKFORCE SOURCE: MCKINSEY GLOBAL INSTITUTE ANALYSIS OF OECD DATA, DECEMBER 2017 TOTAL PUBLIC SPENDING ON WORKER TRAINING, 2015 (% OF GDP) DENMARK ITALY UNITED STATES FRANCE SPAIN JAPAN GERMANY CANADA UNITED AUSTRALIA 0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% TERTIARY HIGHER EDUCATION SPENDING AS % OF GDP, OECD COUNTRIES LU X EM B O U R G G ER M A N Y B ELG IU M A U STR IA B R A ZIIL PO LA N D TU R K EY A U STR A LIA IN D O N ESIA A R G EN TIN A LITH U A N IA N EW ZEA LA N D G R EEC E JA PA N D EN M A R K C O LO M B IA IR ELA N D SPA IN LATV IA ESTO N IA SLO V EN IA M EX IC O N ETH ER LA N D S C O STA R IC A H U N G A RY IC ELA N D SLO VA K R EPU B LIC KO R EA R U SSIA FR A N C E FIN LA N D C A N A D A ITA LY PO R TU G A L SW ED EN U N ITED K IN G D O M C ZEC H R EPU B LIC ISR A EL N O R W AY C H ILE U N ITED STATES 3.0% 2.5% 2.58% 2.0% 1.5% 1.0% .5% 0% The United States outspends OECD countries on higher education, but we are comparatively weak in training our workforce beyond college. And we are moving in the wrong direction. As the need for increased workforce training increases, a McKinsey report noted U.S. spending on workforce training programs fell as a percentage of GDP from 0.08% to 0.03% (or less than $5.5 billion) between 1993 and 2015. SOURCE: OECD DATA, “EDUCATION SPENDING.” HTTPS://DATA.OECD.ORG/EDURESOURCE/EDUCATION-SPENDING.HTM.
  11. 11. 11 21ST CENTURY POLICIES FOR SHARED PROSPERITY ENCOURAGE EMPLOYERS TO LEAD A HUMAN-CENTRIC APPROACH TO AUTOMATION. - Create Worker Training Tax Credit - Expand apprenticeship programs - Promote worker voice 1. ENABLE WORKERS TO ACCESS SKILLS TRAINING, GOOD JOBS, AND NEW ECONOMIC OPPORTUNITIES. 2. HELP PEOPLE AND COMMUNITIES RECOVER FROM DISPLACEMENTS. 3. UNDERSTAND THE IMPACT OF AUTOMATION ON THE WORKFORCE. 4. - Establish lifelong learning & training accounts - Improve data on training outcomes - Promote job quality - Develop place-based policies - Provide wage insurance to older workers - Modernize unemployment insurance - Create new metrics for tracking technological progress and automation - Improve occupational projections - Develop better data on local and regional labor markets
  12. 12. 12 BOOST THE INCENTIVE FOR EMPLOYERS TO INVEST IN WORKERS BACKGROUND - Employers are uniquely positioned to play an important role in preparing the workforce for lifelong learning - Unfortunately, available data suggest that employer investment in worker training is declining PROPOSAL: CREATE BUSINESS TAX CREDIT TO OFFSET TRAINING COSTS - Tax credit to offset portion of cost of new training activities for non-highly compensated workers - Currently, there is a 20 percent R&D tax credit but no similar credit for corporate investment in training EXAMPLES - CT, GA, KY, MS, RI, and VA provide businesses with tax credits for training investments that range from 5 percent to 50 percent of training expenses. Versions of this proposal have been introduced in NJ and VA - Federal legislation to create a Worker Training Tax Credit also introduced last Congress in the U.S. Senate and House
  13. 13. 13 EMPOWER WORKERS TO INVEST IN THEIR OWN TRAINING BACKGROUND - To succeed in a rapidly changing economy, workers will need to update skills over the course of their careers, both to adapt to the evolving skills that will be needed in their jobs and/or to help transition to new jobs if their industry or occupation faces disruption PROPOSAL: CREATE LIFELONG LEARNING & TRAINING ACCOUNTS - Lifelong Learning and Training Accounts would be funded by workers, employers, and government, and could be used by workers to pay for education and training opportunities over the course of their career EXAMPLES - Demonstration programs have been implemented in ME, WA, Chicago, and New York City - In MA, legislation has been proposed to establish a Lifelong Learning and Training Account program - Federal legislation was also proposed in the U.S. Senate and House last session
  14. 14. 14 IMPROVE STATE LABOR MARKET DATA BACKGROUND - Detailed data on local and regional economies is often nonexistent or inaccessible - Better data would improve understanding of how economic forces like automation are affecting local and regional economies, to best target policymaking, service offerings, and delivery PROPOSAL: IMPROVE DATA COLLECTION AND USAGE - Add new data elements in state UI wage records - Create training program effectiveness data by matching with education program data through state longitudinal data systems - Increase funding for state labor market information systems - Develop a more effective and transparent skills-based labor market EXAMPLES - LA, OR, WA, and AK currently collect additional data elements, including occupational title - CO and IN have worked with the Markle Foundation’s Skillful Initiative to develop a more effective and transparent skills-based labor market
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