What
the future of work will mean for jobs, skills, and wages
In an era marked by rapid
advances in automation and artificial intelligence, new research assesses the
jobs lost and jobs gained under different scenarios through 2030.
The technology-driven world in which we live is a world
filled with promise but also challenges. Cars that drive themselves, machines
that read X-rays, and algorithms that respond to customer-service inquiries are
all manifestations of powerful new forms of automation. Yet even as these
technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform—a development that has
sparked much public concern.
Building on our January 2017 report
on automation, McKinsey Global
Institute’s latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation , assesses the number and
types of jobs that might be created under different scenarios through 2030 and
compares that to the jobs that could be lost to automation.
The results reveal a rich mosaic of
potential shifts in occupations in the years ahead, with important implications
for workforce skills and wages. Our key finding is that while there may be
enough work to maintain full employment to 2030 under most scenarios, the
transitions will be very challenging—matching or even exceeding the scale of
shifts out of agriculture and manufacturing we have seen in the past.
1. What impact will automation have on work?
2. What are possible scenarios for employment growth?
3. Will there be enough work in the future?
4. What will automation mean for skills and wages?
5.
How do we manage the upcoming workforce transitions?
1. What impact will automation have on work?
We previously found that about half
the activities people are paid to do globally could theoretically be automated using currently demonstrated technologies. Very
few occupations—less than 5 percent—consist of activities that can be fully
automated.
However, in about 60 percent of
occupations, at least one-third of the constituent activities could be
automated, implying substantial workplace transformations and changes for all
workers.
While technical feasibility of
automation is important, it is not the only factor that will influence the pace
and extent of automation adoption. Other factors include the cost of developing
and deploying automation solutions for specific uses in the workplace, the
labor-market dynamics (including quality and quantity of labor and associated
wages), the benefits of automation beyond labor substitution, and regulatory
and social acceptance.
Interactive
Taking these factors into account,
our new research estimates that between almost zero and 30 percent of the hours
worked globally could be automated by 2030, depending on the speed of adoption.
We mainly use the midpoint of our scenario range, which is automation of 15
percent of current activities. Results differ significantly by country, reflecting the mix of activities currently performed
by workers and prevailing wage rates.
The potential impact of automation on employment varies by occupation and sector (see interactive
above). Activities most susceptible to automation include physical ones in
predictable environments, such as operating machinery and preparing fast food.
Collecting and processing data are two other categories of activities that
increasingly can be done better and faster with machines. This could displace
large amounts of labor—for instance, in mortgage origination, paralegal work,
accounting, and back-office transaction processing.
It is important to note, however,
that even when some tasks are automated, employment in those occupations may
not decline but rather workers may perform new tasks.
Automation will have a lesser
effect on jobs that involve managing people, applying expertise, and social interactions,
where machines are unable to match human performance for now.
Jobs in unpredictable
environments—occupations such as gardeners, plumbers, or providers of child-
and eldercare—will also generally see less automation by 2030, because they are
technically difficult to automate and often command relatively lower wages,
which makes automation a less attractive business proposition.
2. What are possible scenarios for
employment growth?
Workers displaced by automation are
easily identified, while new jobs that are created indirectly from technology
are less visible and spread across different sectors and geographies. We model
some potential sources of new labor demand that may spur job creation to 2030,
even net of automation.
For the first three trends, we
model only a trendline scenario based on current spending and investment trends
observed across countries.
Rising incomes and consumption, especially in emerging
economies
We have previously estimated
that global consumption could grow by $23 trillion between 2015 and 2030, and most of this will come from
the consuming classes in emerging economies. The effects of these new consumers
will be felt not just in the countries where the income is generated but also
in economies that export to these countries. Globally, we estimate that 250
million to 280 million new jobs could be created from the impact of rising
incomes on consumer goods alone, with up to an additional 50 million to 85
million jobs generated from higher health and education spending.
Aging populations
By 2030, there will be at
least 300 million more people aged 65 years and older than there were in 2014. As
people age, their spending patterns shift, with a pronounced increase in
spending on healthcare and other personal services. This will create
significant new demand for a range of occupations, including doctors, nurses,
and health technicians but also home-health aides, personal-care aides, and
nursing assistants in many countries. Globally, we estimate that healthcare and
related jobs from aging could grow by 50 million to 85 million by 2030.
Development and deployment of technology
Jobs related to developing and
deploying new technologies may also grow. Overall spending on technology could
increase by more than 50 percent between 2015 and 2030. About half would be on
information-technology services. The number of people employed in these
occupations is small compared to those in healthcare or construction, but they
are high-wage occupations. By 2030, we estimate that this trend could create 20
million to 50 million jobs globally.
For the next three trends, we model
both a trendline scenario and a step-up scenario that assumes additional
investments in some areas, based on explicit choices by governments, business
leaders, and individuals to create additional jobs.
Investments in infrastructure and buildings
Infrastructure and buildings are
two areas of historic underspending that may create significant additional
labor demand if action is taken to bridge infrastructure gaps and overcome housing shortages. New demand could be created for up to 80 million jobs
in the trendline scenario and, in the event of accelerated investment, up to
200 million more in the step-up scenario. These jobs include architects, engineers,
electricians, carpenters, and other skilled tradespeople, as well as
construction workers.
Investments in renewable energy, energy efficiency, and
climate adaptation
Investments in renewable energy, such as wind and solar; energy-efficiency
technologies; and adaptation and mitigation of climate change may create new
demand for workers in a range of occupations, including manufacturing,
construction, and installation. These investments could create up to ten
million new jobs in the trendline scenario and up to ten million additional
jobs globally in the step-up scenario.
‘Marketization’ of previously unpaid domestic work
The last trend we consider is the
potential to pay for services that substitute for currently unpaid and
primarily domestic work. This so-called marketization of previously unpaid work
is already prevalent in advanced economies, and rising female workforce
participation worldwide could accelerate the trend. We estimate that this could
create 50 million to 90 million jobs globally, mainly in occupations such as
childcare, early-childhood education, cleaning, cooking, and gardening.
When we look at the net changes in
job growth across all countries, the categories with the highest percentage job
growth net of automation include the following:
·
healthcare providers
·
professionals such as
engineers, scientists, accountants, and analysts
·
IT professionals and
other technology specialists
·
managers and
executives, whose work cannot easily be replaced by machines
·
educators, especially
in emerging economies with young populations
·
“creatives,” a small
but growing category of artists, performers, and entertainers who will be in
demand as rising incomes create more demand for leisure and recreation
·
builders and related
professions, particularly in the scenario that involves higher investments in
infrastructure and buildings
·
manual and service jobs
in unpredictable environments, such as home-health aides and gardeners
Upcoming workforce transitions could be very large
The changes in net occupational
growth or decline imply that a very large number of people may need to shift occupational
categories and learn new skills in the years ahead. The shift could be on a
scale not seen since the transition of the labor force out of agriculture in
the early 1900s in the United States and Europe, and more recently in in China.
Seventy-five million to 375 million may need to switch
occupational categories and learn new skills.
We estimate that between 400
million and 800 million individuals could be displaced by automation and need
to find new jobs by 2030 around the world, based on our midpoint and earliest
(that is, the most rapid) automation adoption scenarios. New jobs will be
available, based on our scenarios of future labor demand and the net impact of
automation, as described in the next section.
However, people will need to find their
way into these jobs. Of the total displaced, 75 million to 375 million may need
to switch occupational categories and learn new skills, under our midpoint and
earliest automation adoption scenarios; under our trendline adoption scenario,
however, this number would be very small—less than 10 million .
In absolute terms, China faces the
largest number of workers needing to switch occupations—up to 100 million if
automation is adopted rapidly, or 12 percent of the 2030 workforce. While that
may seem like a large number, it is relatively small compared with the tens of
millions of Chinese who have moved out of agriculture in the past 25 years.
For advanced economies, the share
of the workforce that may need to learn new skills and find work in new
occupations is much higher: up to one-third of the 2030 workforce in the United
States and Germany, and nearly half in Japan.
3. Will there be enough work in the future?
Today there is a growing concern
about whether there will be enough jobs for workers, given potential
automation. History would suggest that such fears may be unfounded: over time,
labor markets adjust to changes in demand for workers from technological
disruptions, although at times with depressed real wages.
We address this question about the
future of work through two different sets of analyses: one based on modeling of
a limited number of catalysts of new labor demand and automation described
earlier, and one using a macroeconomic model of the economy that incorporates
the dynamic interactions among variables.
If history is any guide, we could
also expect that 8 to 9 percent of 2030 labor demand will be in new types of
occupations that have not existed before.
Both analyses lead us to conclude
that, with sufficient economic growth, innovation, and investment, there can be
enough new job creation to offset the impact of automation, although in some
advanced economies additional investments will be needed as per our step-up
scenario to reduce the risk of job shortages.
A larger challenge will be ensuring
that workers have the skills and support needed to transition to new jobs. Countries that fail to
manage this transition could see rising unemployment and depressed wages.
The magnitude of future job
creation from the trends described previously and the impact of automation on
the workforce vary significantly by country, depending on four factors.
Wage level
Higher wages make the business case
for automation adoption stronger. However, low-wage countries may be affected
as well, if companies adopt automation to boost quality, achieve tighter
production control, move production closer to end consumers in high-wage
countries, or other benefits beyond reducing labor costs.
Demand growth
Economic growth is essential for
job creation; economies that are stagnant or growing slowly create few if any
net new jobs. Countries with stronger economic and productivity growth and
innovation will therefore be expected to experience more new labor demand.
Demographics
Countries with a rapidly growing
workforce, such as India, may enjoy a “demographic dividend” that boosts GDP
growth—if young people are employed. Countries with a shrinking workforce, such
as Japan, can expect lower future GDP growth, derived only from productivity
growth.
Mix of economic sectors and occupations
The automation potential for
countries reflects the mix of economic sectors and the mix of jobs within each
sector. Japan, for example, has a higher automation potential than the United
States because the weight of sectors that are highly automatable, such as
manufacturing, is higher.
Automation will affect countries in different ways
The four factors just described
combine to create different outlooks for the future of work in each country
(see interactive heat map). Japan is rich, but its economy is projected to grow
slowly to 2030. It faces the combination of slower job creation coming from
economic expansion and a large share of work that can be automated as a result
of high wages and the structure of its economy.
Interactive
However, Japan will also see its
workforce shrink by 2030 by four million people. In the step-up scenario, and
considering the jobs in new occupations we cannot envision today, Japan’s net
change in jobs could be roughly in balance.
The United States and Germany could
also face significant workforce displacement from automation by 2030, but their
projected future growth—and hence new job creation—is higher. The United States
has a growing workforce, and in the step-up scenario, with innovations leading
to new types of occupations and work, it is roughly in balance. Germany’s
workforce will decline by three million people by 2030, and it will have more
than enough labor demand to employ all its workers, even in the trendline
scenario.
At the other extreme is India: a
fast-growing developing country with relatively modest potential for automation
over the next 15 years, reflecting low wage rates. Our analysis finds that most
occupational categories are projected to grow in India, reflecting its
potential for strong economic expansion.
However, India’s labor force is
expected to grow by 138 million people by 2030, or about 30 percent. India
could create enough new jobs to offset automation and employ these new entrants
by undertaking the investments in our step-up scenario.
China and Mexico have higher wages
than India and so are likely to see more automation. China is still projected
to have robust economic growth and will have a shrinking workforce; like Germany,
China’s problem could be a shortage of workers.
Mexico’s projected rate of future
economic expansion is more modest, and it could benefit from the job creation
in the step-up scenario plus innovation in new occupations and activities to
make full use of its workforce.
Displaced workers will need to be reemployed quickly to
avoid rising unemployment
To model the impact of automation
on overall employment and wages, we use a general equilibrium model that takes
into account the economic impacts of automation and dynamic interactions.
Automation has at least three distinct economic impacts. Most attention has
been devoted to the potential displacement of labor. But automation also may
raise labor productivity: firms adopt automation only when doing so enables
them to produce more or higher-quality output with the same or fewer inputs
(including material, energy, and labor inputs). The third impact is that
automation adoption raises investment in the economy, lifting short-term GDP
growth. We model all three effects. We also create different scenarios for how
quickly displaced workers find new employment, based on historical data.
The results reveal that, in nearly
all scenarios, the six countries that are the focus of our report (China,
Germany, India, Japan, Mexico, and the United States) could expect to be at or
very near full employment by 2030. However, the model also illustrates the
importance of reemploying displaced workers quickly.
If displaced workers are able to be
reemployed within one year, our model shows automation lifting the overall
economy: full employment is maintained in both the short and long term, wages
grow faster than in the baseline model, and productivity is higher.
However, in scenarios in which some
displaced workers take years to find new work, unemployment rises in the short
to medium term. The labor market adjusts over time and unemployment falls—but
with slower average wage growth. In these scenarios, average wages end up lower
in 2030 than in the baseline model, which could dampen aggregate demand and
long-term growth.
4. What will automation mean for
skills and wages?
In general, the current educational
requirements of the occupations that may grow are higher than those for the
jobs displaced by automation. In advanced economies, occupations that currently
require only a secondary education or less see a net decline from automation,
while those occupations requiring college degrees and higher grow.
In India and other emerging
economies, we find higher labor demand for all education levels, with the
largest number of new jobs in occupations requiring a secondary education, but
the fastest rate of job growth will be for occupations currently requiring a
college or advanced degree.
Workers of the future will spend
more time on activities that machines are less capable of, such as managing
people, applying expertise, and communicating with others. They will spend less
time on predictable physical activities and on collecting and processing data,
where machines already exceed human performance. The skills and capabilities
required will also shift, requiring more social and emotional skills and more
advanced cognitive capabilities, such as logical reasoning and creativity.
Wages may stagnate or fall in
declining occupations. Although we do not model shifts in relative wages across
occupations, the basic economics of labor supply and demand suggests that this
should be the case for occupations in which labor demand declines.
Our analysis shows that most job
growth in the United States and other advanced economies will be in occupations
currently at the high end of the wage distribution. Some occupations that are
currently low wage, such as nursing assistants and teaching assistants, will
also increase, while a wide range of middle-income occupations will have the
largest employment declines.
Income polarization could continue.
Policy choices such as increasing investments in infrastructure, buildings, and
energy transitions could help create additional demand for middle-wage jobs such
as construction workers in advanced economies.
The wage-trend picture is quite
different in emerging economies such as China and India, where our scenarios
show that middle-wage jobs such as retail salespeople and teachers will grow
the most as these economies develop. This implies that their consuming class
will continue to grow in the decades ahead.
5. How do we manage the upcoming
workforce transitions?
The benefits of artificial intelligence and automation to users and businesses, and
the economic growth that could come via their productivity contributions, are
compelling. They will not only contribute to dynamic economies that create jobs
but also help create the economic surpluses that will enable societies to
address the workforce transitions that will likely happen regardless.
Faced with the scale of worker
transitions we have described, one reaction could be to try to slow the pace
and scope of adoption in an attempt to preserve the status quo. But this would
be a mistake. Although slower adoption might limit the scale of workforce
transitions, it would curtail the contributions that these technologies make to
business dynamism and economic growth. We should embrace these technologies but
also address the workforce transitions and challenges they bring. In many
countries, this may require an initiative on the scale of the Marshall Plan,
involving sustained investment, new training models, programs to ease worker
transitions, income support, and collaboration between the public and private
sectors.
All societies will need to address
four key areas.
Maintaining robust economic growth to support job creation
Sustaining robust aggregate demand
growth is critical to support new job creation, as is support for new business
formation and innovation. Fiscal and monetary policies that ensure sufficient
aggregate demand, as well as support for business investment and innovation,
will be essential. Targeted initiatives in certain sectors could also help,
including, for example, increasing investments in infrastructure and energy
transitions.
Scaling and reimagining job retraining and workforce
skills development
Providing job retraining and
enabling individuals to learn marketable new skills throughout their lifetime
will be a critical challenge—and for some countries, the central challenge.
Midcareer retraining will become ever more important as the skill mix needed
for a successful career changes. Business can take a lead in some areas,
including with on-the-job training and providing opportunities to workers to
upgrade their skills.
Improving business and labor-market dynamism, including
mobility
Greater fluidity will be needed in
the labor market to manage the difficult transitions we anticipate. This
includes restoring now-waning labor mobility in advanced economies. Digital
talent platforms can foster fluidity, by matching workers and companies seeking
their skills and by providing a plethora of new work opportunities for those
open to taking them. Policy makers in countries with inflexible labor markets
can learn from others that have deregulated, such as Germany, which transformed
its federal unemployment agency into a powerful job-matching entity.
Providing income and transition support to workers
Income support and other forms of
transition assistance to help displaced workers find gainful employment will be
essential. Beyond retraining, a range of policies can help, including
unemployment insurance, public assistance in finding work, and portable
benefits that follow workers between jobs.
We know from history that wages for
many occupations can be depressed for some time during workforce transitions.
More permanent policies to supplement work incomes might be needed to support
aggregate demand and ensure societal fairness. More comprehensive minimum-wage
policies, universal basic income, or wage gains tied to productivity growth are
all possible solutions being explored.
Policy makers, business leaders,
and individual workers all have constructive and important roles to play in
smoothing workforce transitions ahead. History shows us that societies across
the globe, when faced with monumental challenges, often rise to the occasion
for the well-being of their citizens.
Yet over the past few decades,
investments and policies to support the workforce have eroded. Public spending
on labor-force training and support has fallen in most member countries of the
Organisation for Economic Co-operation and Development (OECD). Educational
models have not fundamentally changed in 100 years. It is now critical to
reverse these trends, with governments making workforce transitions and job
creation a more urgent priority.
We will all need creative visions for how our lives are
organized and valued in the future, in a world where the role and meaning of
work start to shift.
Businesses will be on the front
lines of the workplace as it changes. This will require them to both retool
their business processes and reevaluate their talent strategies and workforce
needs, carefully considering which individuals are needed, which can be
redeployed to other jobs, and where new talent may be required. Many companies
are finding it is in their self-interest—as well as part of their societal
responsibility—to train and prepare workers for a new world of work.
Individuals, too, will need to be
prepared for a rapidly evolving future of work. Acquiring new skills that are
in demand and resetting intuition about the world of work will be critical for
their own well-being. There will be demand for human labor, but workers
everywhere will need to rethink traditional notions of where they work, how
they work, and what talents and capabilities they bring to that work.
By James Manyika, Susan Lund, Michael Chui,
Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi
ReportMcKinsey
Global Institute November 2017
https://www.mckinsey.com/global-themes/future-of-organizations-and-work/what-the-future-of-work-will-mean-for-jobs-skills-and-wages?cid=other-eml-alt-mgi-mgi-oth-1711
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