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.

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
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
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
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
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
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
POTENTIAL IMPACT BY INDUSTRY AND EDUCATION LEVEL
SOURCE: WWW.BROOKINGS.EDU/RESEARCH/AUTOMATION-AND-ARTIFICIAL-INTELLIGENCE-HOW-MACHINES-AFFECT-PEOPLE-AND-PLACES/
AUTOMATIONPOTENTIALBY2030(%)
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AVERAGE
POTENTIAL
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AUTOMATION POTENTIAL BY INDUSTRY GROUP PERCENT OF WORKERS IN
HIGHLY AUTOMATABLE JOBS
BY EDUCATION LEVEL
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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
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
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
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
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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
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
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
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
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

15. INFO@BUSINESSFWD.ORG
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