From Project Syndicate:
Laura Tyson, a former chair of the US President's Council of Economic
Advisers, is a professor at the Haas School of Business at the
University of California, Berkeley, a senior adviser at the Rock Creek
Group, and a member of the World Economic Forum Global Agenda Council on
Gender Parity.
BERKELEY – Advances
in artificial intelligence and robotics are powering a new wave of
automation, with machines matching or outperforming humans in a
fast-growing range of tasks, including some that require complex
cognitive capabilities and advanced degrees. This process has outpaced
the expectations of experts; not surprisingly, its possible adverse
effects on both the quantity and quality of employment have raised
serious concerns.
To listen to
President Donald Trump’s administration, one might think that trade
remains the primary reason for the loss of manufacturing jobs in the
United States. Trump’s treasury secretary, Steven Mnuchin, has
declared that the possible technological displacement of workers is “not even on [the administration’s] radar screen.”
Among economists,
however, the consensus is that about 80% of the loss in US manufacturing
jobs over the last three decades was a result of labor-saving and
productivity-enhancing technological change, with trade coming a distant
second. The question, then, is whether we are headed toward a jobless
future, in which technology leaves many unemployed, or a “good-jobless
future,” in which a growing number of workers can no longer earn a
middle-class income, regardless of their education and skills.
The answer may be some of both. The most recent major study
on the topic found that, from 1990 to 2007, the penetration of
industrial robots – defined as autonomous, automatically controlled,
reprogrammable, and multipurpose machines – undermined both employment
and wages.
Based on the study’s
simulations, robots probably cost about 400,000 US jobs each year, many
of them middle-income manufacturing jobs, especially in industries like
automobiles, plastics, and pharmaceuticals. Of course, as a recent
Economic Policy Institute report
points out, these are not large numbers, relative to the overall size
of the US labor market. But local job losses have had an impact: many of
the most affected communities were in the Midwestern and southern
states that voted for Trump, largely because of his protectionist,
anti-trade promises.
As automation
substitutes for labor in a growing number of occupations, the impact on
the quantity and quality of jobs will intensify. And, as a recent
McKinsey Global Institute study
shows, there is plenty more room for such substitution. The study,
which encompassed 46 countries and 80% of the global labor force, found
that relatively few occupations – less than 5% – could be fully
automated. But some 60% of all occupations could have at least 30% of
their constitutive tasks or activities automated, based on current
demonstrated technologies.
The activities most
susceptible to automation in the near term are routine cognitive tasks
like data collection and data processing, as well as routine manual and
physical activities in structured, predictable environments. Such
activities now account for 51% of US wages, and are most prevalent in
sectors that employ large numbers of workers, including hotel and food
services, manufacturing, and retail trade.
The McKinsey report
also found a negative correlation between tasks’ wages and required
skill levels on the one hand, and the potential for their automation on
the other. On balance, automation reduces demand for low- and
middle-skill labor in lower-paying routine tasks, while increasing
demand for high-skill, high-earning labor performing abstract tasks that
require technical and problem-solving skills. Simply put, technological
change is skill-biased.
Over the last 30 years or so, skill-biased technological change has fueled the
polarization
of both employment and wages, with median workers facing real wage
stagnation and non-college-educated workers suffering a significant
decline in their real earnings. Such polarization fuels rising
inequality in the distribution of labor income, which in turn drives
growth in overall income inequality – a dynamic that many economists,
from
David Autor to
Thomas Piketty, have emphasized.
As
Michael Spence and I argue in a
recent paper,
skill-biased and labor-displacing intelligent machines and automation
drive income inequality in several other ways, including winner-take-all
effects that bring massive benefits to superstars and the luckiest few,
as well as rents from imperfect competition and first-mover advantages
in networked systems. Returns to digital capital tend to exceed the
returns to physical capital and reflect power-law distributions, with an
outsize share of returns again accruing to relatively few actors....
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