"Even as the economic pressures that
drove millions of white working-class voters to the right are moderating, the
hostility this key segment of the electorate feels toward the Democratic Party
has deepened and is less and less amenable to change.
“You cannot really understand the
working-class rightward shift without discussing what the Democratic Party is
doing,” Daron Acemoglu, an
economist at M.I.T., wrote by email:
Many of the trends that negatively
impacted workers, especially non-college workers, including rapid automation
and trade with China, were advocated and supported by Democratic politicians.
Perhaps worse from a political point of view, when these politicians were
advocating such policies, they were also viewed as adopting a tone of
indifference to the plight of non-college workers.
Poll data suggest that Democratic
struggles with the white working class are worsening. In “Elections and Demography:
Democrats Lose Ground, Need Strong Turnout,” an Oct. 22 American Enterprise
institute report by Ruy Teixeira, Karlyn Bowman and Nate Moore write:
The gap between non-college and
college whites continues to grow. For the first time this cycle, the difference
in margin between the two has surpassed an astounding 40 points, well above the
33-point gap in 2020’s presidential contest. Republicans trail with white
college voters by 13.6 points but lead with non-college whites by more than 27
points. Democrats appear stuck in the low 30s with non-college whites — no poll
this month has them above 34 percent — so a repeat of Biden’s 37 percent mark
appears unlikely.
David Autor, an economist at
M.I.T. who has written on the role of the trade shocks that have driven white
working-class voters into the arms of the Republican Party, described his
assessment of the current mood of these voters in an email:
The class and cultural resentments
that were inflamed by the China trade shock (alongside other technological,
cultural, and political forces) are now so burned-in that I strongly suspect
that they are self-perpetuating. Like a forest fire, these resentments and
frustrations create their own wind that carries them forward. While the
economic forces that initially fanned those flames might have abated for now,
there is plenty of fuel left to consume.
“The pandemic,” Autor noted, “has
actually compressed earnings inequality sharply over the last two years. This
potentially reduces some of the political pressure accompanying the decline of
manufacturing and erosion of non-college wages.”
While this trend would seem to favor
Democrats, Autor pointed out:
Inflation has risen so fast that the
fall in inequality has not actually meant earnings growth for almost anyone;
rather, middle- and upper-income workers have seen larger falls in earnings
power than low-income workers. It’s unfortunately cold comfort to discover that
your star is rising relative to the rich because their star is falling faster
than yours.
In a 2020 study, “The Work of the Future:
Building Better Jobs in an Age of Intelligent Machines,” Autor, David Mindell,
professor of the history of engineering and manufacturing at M.I.T., and Elisabeth Reynolds, executive director of the
M.I.T. Industrial Performance Center, contend that the United States is unique
among developed countries in failing to counter the negative effects of
technological change on workers:
What sets the United States apart are U.S.-specific
institutional changes and policy choices that failed to blunt, and in some
cases magnified, the consequences of these pressures on the U.S. labor market.
The United States has allowed traditional channels of worker voice to atrophy
without fostering new institutions or buttressing existing ones. It has
permitted the federal minimum wage to recede to near-irrelevance, lowering the
floor under the labor market for low-paid workers. It has embraced a
policy-driven expansion of free trade with the developing world, Mexico and
China in particular, yet failed to direct the gains toward redressing the
employment losses and retraining needs of workers.
Acemoglu sounded a pessimistic note in his email: “Elites
are making choices that are not good news for non-college workers. In fact,
they are bad news for most workers.”
He also predicted that “robots and artificial intelligence —
and especially A.I. — will continue to automate a broad range of jobs, and
their main impact will be to destroy ‘good’ or ‘medium-quality’ jobs for
non-college workers, as well as increasingly perhaps for workers with college
degrees but without postgraduate degrees. They will tend to increase
inequality.”
Robots will continue to spread throughout U.S. industry,
Acemoglu continued, “but there are fewer and fewer non-college jobs in this
sector, so perhaps robots will not be the main issue for non-college workers.”
Instead, he argued,
Artificial intelligence and other digital technologies are
likely to have a bigger impact. This is both via automation and worker
surveillance. Digital technologies are being increasingly used to monitor
workers closely and impose worse working arrangements on them.
In a September 2022 paper, “Tasks, Automation and The
Rise In U.S. Wage Inequality,” Acemoglu and Pascual Restrepo, an economist at Boston
University, found that automation “accounts for 50 percent of the changes in
the wage structure” from 1980 and 2016, reducing “the real wage of high-school
dropout men by 8.8 percent and high-school dropout women by 2.3 percent.”
Task displacement — the replacement
of workers with machines — has wide-ranging adverse impacts, they write: “A 10
percentage point higher task displacement is associated with a 4.4 percentage
point decline in employment between 1980 and 2016, and a similar 3.5 percentage
point increase in nonparticipation (in the work force).”
Dani Rodrik, an economist at Harvard’s Kennedy
School, emailed me to say that “it is extremely unlikely that we will create an
employment miracle in manufacturing.” Even if the CHIPS and Science Act,
which President Biden signed in August, is “successful in reshoring some
manufacturing,” he argued,
I am afraid that will do very little
to create good jobs for U.S. workers without college or advanced degrees.
Semiconductors and advanced manufacturing are among the most capital- and
skill-intensive sectors in the economy and ramping up investment in them — as
worthwhile as it may be on geopolitical grounds — is one of the least effective
ways of increasing demand for labor where it is most needed.
In addition, Rodrik wrote:
Many of America’s competitors have
successfully increased the share of manufacturing in G.D.P., including Taiwan
and South Korea. But in none of these cases has the employment share of
manufacturing bounced back up. In fact, to my knowledge, there has never been a
case of sustained reversal in the downward trend of the manufacturing
employment among advanced economies.
There is, Rodrik observed,
broad and compelling evidence, from Europe as well the
United States, that globalization-fueled shocks in labor markets have played an
important role in driving up support for right-wing populist movements. This
literature shows that these economic shocks often work through culture and
identity. That is, voters who experience economic insecurity are prone to feel
greater aversion to outsider groups, deepening cultural and identity divisions
in society and enabling right-wing candidates to inflame (and appeal to)
nativist sentiment.
In an April 2021 paper, “Why Does Globalization Fuel
Populism? Economics, Culture, and the Rise of Right-Wing Populism,”
Rodrik wrote that he studied
the characteristics of “switchers”
in the 2016 presidential election — voters who switched to Trump in 2016 after
having voted for Obama in 2012. While Republican voters were in general better
off and associated themselves with higher social status, the switchers were
different: they were worried about their economic circumstances and did not
identify themselves with the upper social classes. Switchers viewed their
economic and social status very differently from, and as much more precarious
than, run-of-the-mill Republican voters for Trump.
In addition to expressing concern about economic insecurity,
switchers were also hostile to all aspects of globalization — trade,
immigration, finance.
I asked Gordon Hanson, a professor of
urban policy at Harvard’s Kennedy School, whether there was any reason for
these adverse economic trends to abate. “I see none,” he said, “at least in the
medium run.”
The Democrats, he continued, “have come to be seen as the
party of free trade, given President Clinton pushing through both NAFTA and
China’s entry to the W.T.O. and President Obama championing the Trans-Pacific
Partnership — they are seen as the engineers of manufacturing job loss.”
The strongest rightward push for the
non-college educated, Hanson wrote,
came during the period of major
manufacturing job loss of the early 2000s, which is when we document increasing
support for the right wing of the G.O.P. The absence of recovery in the 2010s
in regions hurt by this job loss means that forces luring non-college workers
back to the Democrats were weak. We’ve not seen new shocks that would push more
of the non-college educated to the G.O.P. But nor have we seen significant
recovery in manufacturing that would help them make up for lost ground.
Reshoring in the
aggregate looks to have been quite small.
In 2024, Hanson predicted, “the
G.O.P. will be in position to restate its 2016 message. And, at least in places
hurt by globalization, Democrats will not have obvious arguments to make in
their defense.”
In a July 2022 paper, “The Labor Market Impacts of
Technological Change: From Unbridled Enthusiasm to Qualified
Optimism to Vast Uncertainty,” Autor describes how artificial intelligence
radically enlarges the potential of robotics and automation to replace workers
not only performing routine tasks but more complex procedures: “What makes a
task routine is that it follows an explicit, fully specified set of rules and
procedures. Tasks fitting this description can in many cases be codified in
computer software and executed by machines.”
Conversely, Autor goes on to say, tasks that rely on “tacit
knowledge (e.g., riding a bicycle, telling a clever joke) have historically
been challenging to program because the explicit steps for accomplishing these
tasks are often not formally known.”
“Artificial intelligence,” Autor writes, “overturns the
second piece of the task framework — specifically, the stipulation that
computers can accomplish only explicitly understood (i.e., ‘routine’) tasks.
A.I. tools surmount this longstanding constraint because they can be used to
infer tacit relationships that are not fully specified by underlying software.”
Autor uses the manufacture of a
chair to explain the power of A.I.:
It is extraordinarily challenging to
explicitly define what makes a chair a chair: must it have legs, and if so, how
many; must it have a back; what range of heights is acceptable; must it be
comfortable; and what makes a chair comfortable, anyway? Writing the rules for
this problem is maddening. If written too narrowly, they will exclude stools and
rocking chairs. If written too broadly, they will include tables and
countertops.
A.I. cuts through the problem of computerizing the
manufacture of a chair, according to Autor, by learning
the solution inductively by training on examples. Given a
suitable database of tagged images and sufficient processing power, A.I. can
infer what image attributes are statistically associated with the label “chair”
and can then use that information to classify untagged images of chairs with a
high degree of accuracy What rules does A.I. use for this classification? In
general, we do not know because the rules remain tacit. Nowhere in the learning
process does A.I. formally codify or reveal the underlying features (i.e.,
rules) that constitute “chair-ness.” Rather, the classification decision
emerges from layers of learned statistical associations with no human
interpretable window into that decision-making process.
In comparison with the non-college
workers hurt by earlier levels of automation, the impact of artificial intelligence
will be on better-educated, more upscale employees, in Autor’s view:
A.I. will likely eat into a lot of
management and decision-making jobs that formerly required college-educated
workers or even workers with graduate credentials, such as lawyers. Hence, A.I.
is not “more of the same.” While the last four decades of computerization have
been very good for professional, managerial workers, and not at all good for
blue-collar production and white-collar office/clerical/admin workers, the A.I.
era may erode the college premium that has been either high or rising since
1980.
In addition, Autor writes,
A.I. will reduce the number of person-to-person jobs in
sales, food service, general customer service and tech support. The jobs that
are least likely to be adversely affected at present are the lowest-wage jobs
in personal services (cleaning, home health aides, groundskeeping). These jobs
are still cheap to accomplish with humans and still hard and expensive to
accomplish with machines. On the positive side, A.I. will surely complement the
most skilled and creative people in the labor market. The question is how
narrow or broad that set will be. I’m worried that it may be narrow.
Autor joined Acemoglu in arguing
that policymakers can influence the direction that artificial intelligence
takes:
A.I. is a general-purpose technology
and could be put to many invaluable purposes: improving the quality and
accessibility of health care while reducing its cost; making education more
accessible, engaging, and affordable; providing real-time guidance to workers
who are engaged in construction, maintenance, repair, etc.; advancing medical
innovation to eradicate the worldwide disease load; improving agriculture;
finding efficiencies to reduce CO₂
emissions.
There is, however, another side to the potential of A.I.,
Autor wrote:
It could also be used for counterproductive purposes, for
example, building history’s greatest surveillance states — whether that
surveillance is done by the government (e.g., China) or by the private sector
(e.g., the U.S.).
None of these capabilities is intrinsic to A.I. But we will
develop those A.I. capabilities if that’s where we put our money. At present,
U.S. investments in A.I. seem heavily directed at (1) selling advertising; and
(2) replacing workers. If that’s where we put our money, I’m confident we’ll
achieve those ends. That’s worse than a missed opportunity.
In his May 2022 essay “The Turing Trap:
The Promise & Peril of Human-Like Artificial Intelligence,” Erik Brynjolfsson, a professor at Stanford’s
Institute for Human-Centered Artificial Intelligence, warns that “an excessive
focus on developing and deploying Human-Like Artificial Intelligence can lead
us into a trap. As machines become better substitutes for human labor, workers
lose economic and political bargaining power and become increasingly dependent
on those who control the technology.”
There is, Brynjolfsson argues, an
alternative: “When A.I. is focused on augmenting humans rather than mimicking
them, humans retain the power to insist on a share of the value created. What
is more, augmentation creates new capabilities and new products and services,
ultimately generating far more value than merely humanlike A.I.”
But, he adds, “While both types of A.I. can be enormously
beneficial, there are currently excess incentives for automation rather than
augmentation among technologists, business executives, and policymakers.”
The appeal to the technological
elite “of a greater concentration of technological and economic power to beget
a greater concentration of political power risks trapping a powerless majority
into an unhappy equilibrium” and threatens a repeat of “the backlash against
free trade” that blossomed with the election of Donald Trump.
“As the economic winners gained
power,” Brynjolfsson writes, they left “many workers worse off than before,”
fueling
a populist backlash that led to
import tariffs and other barriers to free trade. Some of the same dynamics are
already underway with A.I. More and more Americans, and indeed workers around
the world, believe that while the technology may be creating a new billionaire
class, it is not working for them. The more technology is used to replace
rather than augment labor, the worse the disparity may become, and the greater
the resentments that feed destructive political instincts and actions.
Brynjolfsson is not alone in the economic community
— in fact, he has widespread support
— for his argument that a “moral imperative of treating people as ends, and not
merely as means, calls for everyone to share in the gains of automation.
The solution is not to slow down technology, but rather to
eliminate or reverse the excess incentives for automation over augmentation.”
At the moment, calls for policies to
institute a moral imperative like this are limited to the universe of
artificial intelligence and automation technologies, with little or no momentum
in the political community. Worse yet, the bitter divisions throughout our
political system suggest that the development of this momentum will be a long
time coming.”
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