'Should We Fear the Robot Revolution? (The Correct Answer is Yes)' (IMF Working Paper 18/116) by Andrew Berg, Edward F. Buffie, and Luis-Felipe Zanna
argues
Advances in artificial intelligence and robotics may be leading to a new industrial revolution.
This paper presents a model with the minimum necessary features to analyze the implications
for inequality and output. Two assumptions are key: “robot” capital is distinct from
traditional capital in its degree of substitutability with human labor; and only capitalists and
skilled workers save. We analyze a range of variants that reflect widely different views of
how automation may transform the labor market. Our main results are surprisingly robust:
automation is good for growth and bad for equality; in the benchmark model real wages fall
in the short run and eventually rise, but “eventually” can easily take generations.
The authors comment
The factory of the future will have only two employees, a man and a dog. The
man will be there to feed the dog. The dog will be there to keep the man from
touching the equipment. (Warren Bennis, management consultant)
There is a growing perception that advances in artificial intelligence (AI) and robotics will
radically transform the workplace in upcoming decades (Brynjolfsson and McAfee, 2014;
Ford, 2015). Robots load, unload, retrieve and send out products with minimal human supervision
at Symbolic LLC’s automated distribution centers. AI programs have started working
as paralegals, accountants, and teaching assistants, and self-driving vehicles may soon eliminate
millions of jobs held by truck, bus, and taxicab drivers. Uber aims to be driverless by
2030. Robots staff more assembly lines each year, kiosks are replacing cashiers at fast-food
restaurants, and Watson recently co-authored a song. The list goes on, with each week bringing
a new or imminent application of smart machines. According to estimates by Frey and
Osborne (2017), Chui, Manyika, and Miremadi (2015), and the World Bank (2016), anticipated
advances in automation threaten 45-57% of all jobs in the United States. The White
House’s Council of Economic Advisors projects that automation will affect 83% of jobs paying
$20 an hour or less.
The sense that we are on the cusp of a robot revolution has sparked a lively debate among
economists, journalists, and technophiles about the likely impact of automation on growth
and the distribution of income. Broadly speaking, there are two camps with starkly different
views of what the future holds. Technology pessimists fear that we are headed toward an economic
dystopia of extreme inequality and class conflict: "Without ownership stakes, workers
will become serfs working on behalf of robots’ overlords [in] a new form of economic feudalism"
(Freeman, 2015). Summers (2016) shares Freeman’s vision (if not his colorful language),
predicting that the prime-age employment rate for American males will drop below 2
% by mid-century in the absence of an aggressive policy response.
Technology optimists do not deny that automation will prove disruptive in the short run. They
point out, however, that historically periods of rapid technological change have created more jobs than they have destroyed and have raised wages and per capita income in rough proportion.
The AI revolution may be different, but there are good reasons to believe that a resilient,
adaptable economy will again vanquish the specter of technological unemployment:
income growth raises the demand for labor in sectors that produce non-automatable goods
and for workers that perform manual-intensive tasks; higher productivity stimulates investment
throughout the economy in cooperating capital inputs; and while automation renders
some jobs obsolete, it complements many others, especially jobs that place a premium on creativity,
flexibility, and abstract reasoning. Criticizing technology pessimists for missing the
big picture, Autor (2014) argues that "journalists and even expert commentators tend to overstate
the extent of machine substitution for human labor and ignore the strong complementarities
between automation and labor that increase productivity, raise earnings, and augment
the demand for labor . . . Focusing only on what is lost misses a central economic mechanism
by which automation affects the demand for labor: raising the value of the tasks that workers
supply uniquely." ....
This paper analyzes the short and long-run effects of robots on output and its distribution in
a family of dynamic general equilibrium models designed to include the minimum necessary
features. The models depart from the existing literature by making two critical additions to
the standard neoclassical framework. First, they incorporate investment in both robots and traditional
capital. In standard production functions, an increase in the supply of labor stimulates
investment by raising the productivity of capital. Since the same logic applies when robots increase
the effective supply of labor services, the two types of capital should be gross complements.
The positive impact of robot labor services on traditional capital accumulation is missing,
however, in existing models. In Acemoglu and Restrepo (2016a), robots are the only type
of capital. Both types of capital are present in Sachs, Benzell, and LaGarda (2015), but not as
cooperating inputs; robots reside in a separate production function and do not therefore affect
the productivity of non-robot capital. At the core of the story here is that robot capital substitutes
for human labor as it complements traditional capital, while increases in traditional
capital spur demand for robot capital that attenuates the usual effect of capital accumulation
on labor demand.
Second, in keeping with the empirical evidence for the U.S. and other developed countries,
we allow for two types of agents in the economy: capitalists who save and invest (and in some
variants perform skilled labor), and workers who live check to check. Thus we do not assume
that everyone saves, as in the representative agent model of Acemoglu and Restrepo (2016a),
or that only young wage-earners save, as in the OG models of Sachs and co-authors. We can
thus look at inequality along two dimensions: the distribution of income between capitalists
and workers, and (in variants with skilled workers) the skilled/unskilled wage differential.
Insofar as in reality the number of capitalists is relatively small and that their capital income
is generally much greater than wage income, the we interpret the capital share as an index
of income inequality. When we allow for skilled as well as unskilled workers in the models,
skilled workers are also the capitalists, so that both an increase in the wage premium for
skilled workers and the capital labor ratio increase inequality.
While we are confident that the right model features complementary capital inputs and households
too poor to save, a number of potentially important design decisions are less clearcut.
The basic problem is that nobody knows what the world will look like in say 2035. There
is considerable disagreement among and between economists and technology experts about
whether automation will (i) destroy jobs for just low-skill labor or labor at all skill levels; (ii)
penetrate most or only a small subset of sectors; (iii) reduce the demand for labor in all tasks
or decrease it in some and increase it in others. The lack of consensus coupled with the uncertainty
inherent in predicting a "future that ain’t what it used to be" (Yogi Berra) makes it hard
to justify choosing one set of answers over any other.
Our solution to this problem is to start with a benchmark model and then examine the implications
of plausible variations that reflect widely different views about how automation may
transform the labor market. In Model 1, robots compete against all labor in all tasks. Subsequent
extensions of the model assume that robots (i) compete only for some tasks (Model 2);
substitute only for unskilled labor while complementing skilled labor (Model 3); and contribute
to production only in one sector — elsewhere, production requires only labor and traditional
capital (Model 4). We examine the implications of an increase in the level of robot productivity
on the level of output and its distribution, both in the long run and during the transition.
Our main results, previewed below, are surprisingly robust. Automation is very good
for growth and very bad for equality in all variants, including those reputed to be conducive to
technological optimism:
• Real per capita income increases 30 - 240% in the long run. The large positive impact
on growth does not require dramatic advances in robot technology. Small improvements
suffice when robots and human labor are very close substitutes. In runs for this scenario, the direct gains from more investment in more productive robots account for
only 6 - 16% of the increase in GDP. The remaining 84 - 94% reflects the strong positive
feedback effects between robot and non-robot capital accumulation.
• In the benchmark model, the real wage decreases in the short run under weak conditions.
In the long run, however, growth in the non-robot capital stock raises the demand
for labor and the real wage. Both the long-run increase in the real wage and the depth
and duration of the low-wage phase are greater the higher the elasticity of substitution
between robots and labor. The intertemporal trade-off for labor is thus sharply defined:
more short-run pain for a larger long-run gain.
• Following up on the hint in the last statement, the transition path is difficult for labor.
When multiple parameters happen to fall within specific narrow ranges, it takes as little
as twelve years for positive real wage growth to materialize. In other scenarios, the low wage
phase lasts 20 - 50+ years. (The "short run" can consume an entire working life.)
• Although the real wage increases in the long run, labor’s share in income decreases
most when real output increases most. The bigger the increase in the GDP pie, the less
equitable the distribution of the pie.
• In the limiting case of perfect substitutability between robots and labor, the long run
never comes. There is a dramatic "singularity": the increase in the level of robot productivity
sends the economy on a trajectory that converges to endogenous growth, in
which the accumulation of robot and traditional capital continues forever, wages fall
and stay below initial levels forever, and the labor share of income converges to zero.
• The distributional outcome is much worse when robots substitute only for low-skill
labor (Model 3). While skilled labor enjoys continuous large gains, the wage for lowskill
labor decreases in the short/medium run under conditions much weaker than in
the benchmark model. Nor is there any assurance that growth eventually raises the lowskill
wage. Quite the contrary: there is a strong presumption the real wage decreases
more in the long run than in the short run. And the magnitude of the worsening in inequality
is horrific. In our base case calibration, the skilled wage increases 56 - 157%
in the long run while the wage paid to low-skill labor drops 26 - 56% and the group’s
share in national income decreases from 31% to 8 - 18%.
• The most common arguments for technology optimism do not stand up to scrutiny. Neither
the assumption that robots complement labor in some production tasks (Model 2) or that a non-automatable sector co-exists alongside the automation-vulnerable sector
(Model 4) delivers optimistic results. Rather, they tend to underscore an underlying
trade-off: variations in which inequality worsens by less also tend to deliver less output
growth and lower wage growth.
The rest of the paper is organized into five sections. Section II lays out the benchmark model
in which robots compete with homogeneous human labor in a single production task. Following
this, Sections III – V develop models with low- and high-skill labor, automatable and
non-automatable sectors, and human-specific production tasks. Section VI revisits the debate
between optimists and pessimists, concluding with an appeal for research aimed at finding
policy measures that promote a more equitable distribution of the gains from automation-led
growth.
'A Note on Automation, Stagnation, and the Implications of a
Robot Tax' (Diskussionsbeiträge des Fachbereichs Wirtschaftswissenschaft der Freien Universität Berlin,2017) by
Emanuel Gasteiger and Klaus Prettner comments
We analyze the long-run growth effects of automation in the canonical overlapping
generations framework. While automation implies constant returns to capital within
this model class (even in the absence of technological progress), we show that it does
not have the potential to lead to positive long-growth. The reason is that automation
suppresses wages, which are the only source of investment because of the demographic
structure of the overlapping generations model. This result stands in sharp contrast to
the effects of automation in the representative agent setting, where positive long-run
growth is feasible because agents can invest out of their wage income and out of their
asset income. We also analyze the effects of a robot tax that has featured prominently
in the policy debate on automation and show that it could raise the capital stock and
per capita output at the steady state. However, the robot tax cannot induce a takeoff
toward positive long-run growth.
The authors state
Automation and its potential economic consequences have caught the attention of
economists, policymakers, and the general public over the last few years (see, for example,
The Economist, 2014; Brynjolfsson and McAfee, 2016). While the number of industrial
robots that replace workers on assembly lines already took off in the 1990s (IFR, 2015)
and 3D printing technologies are already used to produce highly customized products
like hearing aids and prostheses (Abeliansky et al., 2015), driverless cars and lorries that
could revolutionize the transport business are currently being developed and tested. Furtheromore, automation is not confined to routine tasks that have long been considered as susceptible to replacement by machines: devices based on machine learning are starting
to beat doctors in the accuracy of diagnosing diseases, reporters in the speed of writing
newsflashes, and even authors in writing books – at least for given parameters of content
and style (see Barrie, 2014).
On the one hand, there is widespread agreement that automation has a huge potential to raise economic well-being (Steigum, 2011; Acemoglu and Restrepo, 2015; Graetz and Michaels, 2015; H´emous and Olsen, 2016; Abeliansky and Prettner, 2017; Prettner,
2017). On the other hand, there are also concerns that automation could (at least partly)
be responsible for stagnating wages of low-skilled workers, a phenomenon that we have
observed in the United States over the past few decades (Frey and Osborne, 2013; Mishel
et al., 2015; Arntz et al., 2016; Murray, 2016; Acemoglu and Restrepo, 2017; Prettner
and Strulik, 2017). As a consequence, automation might be a major driver of the rise
in inequality that has been observed in many countries (Piketty and Saez, 2003; Piketty,
2014). On top of that, by relying on a numerical analysis, it has even been argued that
automation could lead to economic stagnation in the long run (Sachs and Kotlikoff, 2012;
Benzell et al., 2015; Sachs et al., 2015).
We aim to contribute to this debate along two lines. First, we show analytically that
the long-run economic growth effects of automation crucially depend on the underlying
framework that is used to describe the process of saving and investment. While the
standard neoclassical growth models of Solow (1956), Cass (1965), Koopmans (1965), and
Diamond (1965) lead to remarkably similar predictions with regards to the growth effects of
household’s savings behavior and investment decisions, they lead to diametrically opposed
predictions with regards to the growth effects of automation. Models of automation based
on Solow (1956), Cass (1965), and Koopmans (1965), in which households save a part of
their wage income and a part of their asset income, imply that automation could lead to
perpetual long-run growth even without (exogenous or endogenous) technological progress.
However, models of automation based on the canonical overlapping generations (OLG)
framework of Diamond (1965), in which households save exclusively out of wage income,
imply economic stagnation in the face of automation. The reason for the differential
effects of automation between the two types of underlying growth models is rooted in the
demographic structure and the implied timing of events in the OLG model. The generation that builds up its assets for retirement can save only out of wage income. The resulting
assets are in turn used to invest in standard physical capital and in automation. Since
automation is, by its very definition, a substitute for labor, its accumulation suppresses
wages and therefore diminishes the only source of investment. As a result, automation is
– in a sense – digging its own grave and preventing the takeoff to long-run growth in the
OLG economy.
Our second contribution is that we analyze the effects of a robot tax coupled with a
redistribution of the proceeds of the tax from robot income to labor income in the OLG
setting. We trace the effects of such a tax-transfer scheme on the steady-state capital stock
and therefore on steady-state per capita output. While we show that such a tax-transfer
scheme cannot overcome the stagnation steady state, it has a positive effect on the per
capita capital stock and on per capita output. In the potential implementation of such a
scheme, however, we argue that it is important to coordinate with other countries. The
reason is that moving capital to jurisdictions without robot taxes is easily done in a world
of open economies.
The paper is structured as follows. In Section 2, we sketch out the basic formulation of
the OLG model with automation, in Section 3 we analyze the equilibrium dynamics and
show that such a model necessarily leads to long-run stagnation. In Section 4 we analyze
the effects of a robot tax on the dynamics of the model and on the steady-state capital
stock. In Section 5 we summarize and draw conclusions for policy makers.
