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Is Technological Unemployment Real? An Assessment and a Plea for Abundance Economics

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Abstract

In our increasingly automated economy, technology has replaced much of the need for non-elective human labor: in others words, we increasingly face a situation of technological unemployment. Thus automation is a double-edged sword. On the one hand, technological unemployment worsens income inequality and wealth disparity. On the other hand, there are purported gains in productivity and economic growth. I posit Abundance Economics as a new theory of economics that addresses this problematic disparity in two phases. First, in the automation economy phase, there would be an alleviation of material-goods scarcity for human survival, and second, in the actualization economy phase, there would be a focus on social goods for greater human thriving.
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Is Technological Unemployment Real? An Assessment
and a Plea for Abundance Economics
By Melanie Swan
Introduction
A persistent contemporary economic worry is technological unemployment (job loss due
to automation). In some sense, technological unemployment is a thinkability problem similar to
global warming: political incentives are packaged in shorter time frames than are appropriate for
tackling the problem. Here, I suggest a larger frame of conceptualization that sees technological
unemployment as a partial inevitability that some economies are already addressing with
comprehensive solutions. In general my view takes on the challenge, if not fully the optimism, of
President John F. Kennedy’s remark in 1962 that “if people have the talent to invent new
machines that put people out of work, then they have the talent to put those people back to work”
(Thompson 2015). Specifically, I argue that a new philosophy of economics, Abundance
Economics, is necessary for the contemporary moment, and that the most successful economies
of the future will understand economics as a way to manage the production and consumption of
social goods in addition to material goods. In Part I of this chapter, I discuss the theme of “the
future of work” and address technological unemployment, jobless growth, and income
inequality. In Part II, I describe Abundance Economics as an economic theory of the future.
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Part I: The Problem - Technological Unemployment, Income Inequality, and the
Automation Economy
The Nature of Technological Unemployment
On one hand, technological unemployment is the dream and apogee of humans’
achievement in the world. Arthur C. Clarke, in his literary depiction of the human future, has
noted that “the goal of the future is full unemployment” (Kreider 2012). Likewise, as far back as
the 1930s, the economist John Maynard Keynes envisioned a fifteen-hour work week, because
he thought that the economies of our time would outrun the need for labor faster than we could
find new uses for it, and he also predicted a society in which the accumulation of wealth would
no longer be of high social importance (Keynes, 1933). However, while technological
unemployment seems to have arrived, it does not appear to be utopian, because one of its results
is uneven economic consequences. The problem is that those who become unemployed by
technology are not being reabsorbed or planned for comprehensively in today’s society. A
broader, systems-level approach to technological unemployment, such as one that includes
efforts to train and direct individuals towards the jobs of the future and to coordinate planning
activities between business, governmental, and educational entities, would be more effective than
the haphazard approach we currently have. This could help facilitate the smooth the trajectories
of the arrival of technological unemployment, as opposed to its current arrival in haphazard
bursts with unintended consequences.
To grasp the current size, magnitude, and pace of technological unemployment, several
studies and publications provide guidance. Overall, they make the case that technological
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unemployment could have a significant near-term impact, primarily one in which the gains could
outweigh the costs, particularly if society were to influence outcomes with policy incentives and
job-retraining programs. Studies confirm that faster technological progress may increase
unemployment, at least during a transition period (Feldmann 2013, 1099). One analysis estimates
that nearly half (47%) of all U.S. employment is at risk of being automated in the next two
decades, and lists 702 jobs that could be impacted (Frey 2013, 44). By extension, this could
apply to many other countries worldwide. A report from the World Economic Forum highlights
the trend of the overall net loss of jobs: 5.1 million global jobs lost in the period 2015-2020
(WEF 2016, 1). Other examinations offer a different view, for example wondering in fact why
there are still so many jobs in a world that could be automating more quickly (Autor 2015).
The Pew Research Center presents a balanced stance, discussing both the benefits and the
detriments of technological unemployment (Smith 2014, 5). Some of the potential benefits are
that technological advances have always been a net creator of jobs, including in situations of
high-magnitude change. Even if jobs are displaced in the short-term, this job loss could be seen
in the context of longer economic time cycles that ultimately result in growth. Humans are good
at adapting to new situations, and this includes inventing new types of work to adapt to changing
economic circumstances. The central point is that technology is continuing to deliver on its
promise to free humans from drudgery, and it is up to us to organize society around this fact.
Further, technological unemployment is already starting to produce the social good of defining
“work” in a more positive and socially beneficial wayas productive human effort instead of
sustenance-labor.
On the other hand, some of the potential costs of technological unemployment result from
its disproportionate impact on society. One example of this is the bifurcation of the labor force:
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highly-skilled workers in certain industries are better poised to succeed, while others are being
displaced into lower-paying service industry jobs or into a state of permanent unemployment.
Also, blue-collar employment is being impacted more than white-collar employment, and
women more than men (Brinded 2016). Pew Research further notes that the educational system
is inadequate for future work preparation, a topic addressed in more detail by David Gunkel in
another chapter of this book.
Jobless Growth
Technological unemployment cannot be evaluated as a standalone phenomenon since
productivity, jobs, and economic growth are highly interrelated. The main economic question is
whether technological unemployment is a “new” situation or not. Are there structural changes to
the economy, or is today’s technological unemployment part of persistent long-term trend, albeit
one that we have not recognized? While it is unclear if the current moment of technological
unemployment is a symptom of a structural change or instead merely the continuation of a long-
term trend, the situation of jobless growth at present does seem clear. In the wake of the 2008
financial crisis, gains have been seen in most measures of economic health, particularly
productivity, however there has not been a corresponding growth in jobs. One study points to
evidence of jobless growth by indicating that unemployment increased by more than 5.7 %
between May, 2007, and October, 2009, simultaneous with increases in automation
(Brynjolfsson 2012). Another study finds that 44% of companies that have cut employees since
2008 did so by replacing their functions with automation (McKinsey 2011). These examples
suggest that technological unemployment could be one explanation for the recent jobless growth.
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This could persist because capital, in the form of technology, is being effectively substituted for
labor (The Economist 2014).
The fact that the nature of technological unemployment is changing could also influence
the velocity and reach of the substitution of technology for labor. Automation is no longer being
confined to routine tasks, since machine learning algorithms, cloud-based big data, and
predictive analytics are quickly enabling new kinds of technology applications. Self-driving
vehicles are one example of how technology is assuming more complicated tasks. Commercial
driving is anticipated to be one of the next sectors of labor to be automated (Nuwer 2015). By
one estimate, long-distance truck driving in the U.S. could be fully automated by 2025 (Collins
2015). The complexities of commercial driving require a second order of innovation in the form
of vehicle-to-vehicle communication networks to coordinate autonomous vehicles. In addition to
driving, other sectors to see greater degrees of automation and technological unemployment in
the immediate future could include manufacturing, distribution and logistics, administrative
functions, and financial and legal services (WEF 2016, 3; Croft 2016).
The current situation of technological unemployment can be better understood by
considering some analogous economic examples. One such example is outsourcing, where over
the last several decades a significant number of jobs have shifted to countries with more efficient
cost profiles. There were fears of job loss, but the worldwide economy eventually adjusted to the
situation. Indeed, in one sense, technological unemployment can be seen as a continuation of
outsourcing in the sense that it arises from online outsourcing and technological outsourcing. The
same diversity of arguments as to whether outsourcing’s overall impact has been favorable or
detrimental would apply to technological outsourcing. One lesson could be that adjustment takes
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time on the order of years or decades but eventually occurs, and that it is a combination of
structural change and the continuation of long-term trends.
An illustration of this is the industrial revolution. Similar to the current case of
automation, there were diverse approaches to the industrial revolution. Some countries quickly
embraced the new technologies (UK, Belgium), while others (France) had a more measured
implementation. In some sense, both the industrial revolution and outsourcing are examples of
the more general case of adopting any new technology. The best program could be one of smart
adoption, as opposed to forced adoption or fearful non-adoption. Smart adoption in the case of
technological unemployment suggests a long-term multi-sector economic planning effort. A
change on the order of the industrial revolution took 50-100 years to fully propagate through
worldwide nation-state economies. Therefore, it is difficult to make statements regarding
technological unemployment because it is a recent situation that has arisen most clearly since
2008. If technological unemployment is a significant macro-level structural change to the
economy, longer time frames will be needed to fully assess its impact. Further, any complex
economic situation is difficult to gauge while in progress. The example above regarding the
industrial revolution also underlines that while dramatic economic changes eventually have a
universal impact, the benefits accrue unevenly.
Overall, technological unemployment and jobless growth could be long-term trends that
precipitate structural economic change. Irrespective of measurability challenges, they should be
addressed, particularly through macroeconomic policy. A related issue highlighted by
technological unemployment, for which there might be better, targeted interventions, is income
inequality.
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Income Inequality
Income inequality refers to the uneven distribution of income within a society, and a case
can be made that it is a worsening global problem that has both economic and social
consequences. An Organization for Economic Cooperation and Development report finds that
“in OECD countries, the richest 10% of the population earn 9.6 times the income of the poorest
10%” (DeSilver 2015). Another study claims that the world’s wealthiest 0.1% of individuals
control a concentrated portion of income, the size of which has not been seen since before World
War I (Piketty 2014). In countries such as the U.S. and the U.K., corporate top-to-bottom pay
ratios are routinely 300:1 for the CEO as compared with the lowest-paid worker (Anderson 2015;
Wilkinson 2014). In the U.S., the Census Bureau reports that “the top 5% of households received
21.8% of income in 2014, while the bottom 60% received 27.1%” (DeSilver 2015). Further, the
American middle class has been shrinking. In 2015, after more than four decades of being the
nation’s economic majority, the middle class was overtaken in number by those in other
economic tiers (120.8 million adults in middle-income households as compared with 121.3
million in lower and upper-income households combined) (Fry 2015).
A related phemomenon is that income inequality is not an isolated problem but has
widespread negative effects on the whole of society. One study finds that all social problems are
more common in less equal societies. These include violence, mental illness, drug addiction,
obesity, imprisonment, and poorer social conditions for children. Health and social problems
were found to be two to ten times more prevalent in societies with greater income inequality
(Pickett 2011). In the case of mental illness, income rank was seen as a better predictor of
developing an illness than absolute income. Other studies found effects on stress, cognitive
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performance, and emotional well-being: for example, links between income inequality and child
maltreatment and bullying (Eckenrode 2014; Due 2009). Other examinations documented the
literal “pollution effect” of income inequality on health outcomes (Subramanian 2004). Further,
the social costs of income inequality were found to be endemic, persisting across all countries,
states, and provinces, for example that the more equal provinces of China tend to fare better than
the less equal ones (Pickett 2011). This evidence supports the case that income inequality exists,
is worsening, and has significant social effects beyond the economic domain.
The important question, then, is how we can resolve this problem and its attendant social
consequences. In terms of policy, how do we balance the promotion of income inequality with
the social costs of doing so. Even if some countries wanted to make improvements to income
inequality, the degree to which it might actually be possible could be problematic, given country
ideology, size, and diversity. For example, such policies might be more readily deployable in
smaller countries with greater homogeneity in values, and thus the cohesion and trust necessary
for implementation. In other cases, the sheer size and diversity of a country could be a challenge.
The U.S. is many times the size of some countries with greater income equality, for example,
such as Denmark, and does not have as homogeneous a population as that country does. Also, in
some sense income inequality is an example of a “first-world” problem, in that only wealthy
societies are equipped to identify and address it. Moreover, cultural attitudes may stand in the
way of resolving income inequality: given the value systems of certain countries such the U.S.,
where capitalism is the norm, income inequality seems more likely to persist there than in other
countries such as Scandinavia, where socialist economics is more accepted and so where income
inequality reduction has already been a long-term policy objective.
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Part II: The Solution - A New Philosophy of Economic Theory
To address the long-term structural effects of automation as outlined in Part I, one
foundational resource that might be helpful is a new overall philosophy of economic theory, and
as such I propose Abundance Economics. The challenges of automation arise from outdated and
monolithic economic principles. Increasingly, traditional economic notions of material scarcity
are no longer valid in today’s digital economies. Traditional premises of economic theory will
prove even less tenable as the automation economy progresses.
The cornerstone of most economic theory has been the idea of scarcity. Traditionally
conceived, economic systems are those engaged in the production and distribution of scarce
material goods. However, there are existing and emerging situations in the world where scarcity
is not a parameter, or in any case not the governing parameter. For example, with electronic
goods such as software and digital images, there is essentially no cost to marginal production: the
production and distribution of an additional unit is simply done by copying and sending the
goods electronically (Rifkin 2015). There is no additional cost to one person or one million
people listening to a song. Additionally, a broad share of the goods valorized in the
contemporary economy are intangible. These include non-monetary currencies such as
reputation, intention, attention, access, influence, choice, autonomy, recognition, and creativity.
Intangible goods have properties that are different from material goods; they are often
complementary and non-rival, and they can make more of themselves when consumed (In
economic terms, they can agglomerate). Thus a new philosophy of economic theory is needed to
make sense of digital economics.
One first step in articulating a new philosophy of economic theory that more
appropriately corresponds to the automation economy is setting forth some mind-set shifts: from
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labor to fulfillment,scarcity to abundance, and hierarchy to decentralization. The first principle,
transitioning from labor to fulfillment means reorienting our thinking from a labor-based
economy to a fulfillment-based economy. The second principle, shifting from scarcity to
abundance means seeing the world’s resources in a paradigm of availability as opposed to
paucity. The third principle, moving from notions of hierarchy to decentralization, means
apprehending that modes of organization may be centralized or decentralized (or both), where
decentralization may be better in certain cases, particularly for very large-scale endeavors. The
first two relate most to the situation of automation and technological unemployment.
The most immediate concept to revamp is scarcity, specifically the presumption of
scarcity as the core precept of most economic systems. Even scarcity’s opposite, abundance, is
an impoverished formulation as currently conceived. This is because abundance is primarily
understood quantitatively to be the zero-sum alleviation of scarcity, which it is, but it is also
more (see Figure 2.1). In the first sense, abundance is the eradication of scarcity in terms of
having material needs met, recouping a quantitative baseline for survival. In the second sense,
abundance is also an important upside formulation concerning the quality of life. Abundance
means a qualitative sense of open-ended possibility, boundless improvement trajectories up from
the baseline metric into new territory. Abundance starts to attend to the social goods that humans
need to thrive, those goods that pertain to their quality of life, not merely the material goods they
need to survive.
Figure 2.1. Abundance Economics.
[INSERT FIGURE 2.1 HERE]
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Social goods traditionally mean goods or services that benefit all persons in a society, for
example clean air, clean water, electricity, literacy, and Wi-Fi. Here I extend the term to mean
quality-of-life social goods such as autonomy, recognition, and trust. Other important social
goods include agency, mutuality, respect, acknowledgement, contribution, collaboration,
creativity, participation, and belonging. For example, societies with a higher level of trust (a
direct result of better income equality) have been able to modernize more quickly and remain
more globally competitive than others (for example in the digitization of health, finance,
banking, and payment systems).
While material goods enable survival, social goods enable thriving. Abundance
economics is concerned with both. Scarcity creates negative social goods, or social pathologies,
such as income inequality. Addressing potential technological unemployment from a policy
perspective can help to reduce negative social goods, including “technological anxiety” (Mokyr
2015), and uncertainty about the effects of automation. Whereas the Scarcity Economy is a fixed-
pie, zero-sum game and focuses directly or indirectly on creating social pathologies, the
Abundance Economy is an expanding-pie model with open-ended possibility.
There are two phases for achieving abundance economics. The first step is an eradication
of material-goods scarcity by way of the automation economy, recouping a baseline ideal. The
second step is the creation of social goods through the actualization economy. The automation
economy, if well-executed, can help in the first phase to meet the survival needs of all people.
However, to truly extend human quality of life beyond sustenance, the open-ended formulation
of abundance as the production and consumption of social goods is needed. The bigger issue is
attending to quality of life, not merely the impact of automation.
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Abundance Economics Phase I:
Automation Economy Alleviates Material-Goods Scarcity
The Automation Economy comprises the phase of Abundance Economics that alleviates
scarcity and reaches a baseline of material-goods sustenance. It is one in which technology has
supplemented or replaced non-elective human labor. Presumably, labor-based “work” would not
fully disappear, but could be executed out of choice as opposed to necessity. In the labor-to-
fulfillment mind-set shift, work becomes a concept of optional productive engagement for the
purpose of personal fulfillment, not a sustenance requirement. Decoupling labor-based work
from sustenance-remuneration is an idea different countries are exploring. One proposal is to
institute programs such as guaranteed basic income (GBI) initiatives, paying individuals a
monthly basic income to cover survival needs, a concept discussed in a number of other chapters
in the present collection of essays. Some universal or guaranteed basic income pilot programs are
being tested in Europe and in Canada. The test-cases are both a forward-looking experiment for
bringing about a smooth transition to the automation economy, and a practical response to the
inefficiencies of welfare systems. The electorate has not so much resisted the essential concept of
GBI programs as much as the possibility it might increase immigrationwhich only serves to
confirm their perceived value (Foulkes 2016).
A new form of jobs, jobs of the future,could be necessary to produce and maintain the
future economy. There might be many fulfilling and remunerative employment categories of the
future. Some possible examples we could imagine based on current developments are: neuro-
implant technician, urban farmer, virtual reality experience designer, 3D printing specialist,
smart-home handyperson, remote health care specialist, and freelance professor (Grothaus 2015).
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Other jobs of the future could include blockchain smart-contract writers, audio interface
designers, and social robotics interaction specialists (Swan 2015). While the need for labor-work
requiring human expertise and ingenuity might not go away, it could be reshaped to offer a wider
range of participation and compensation choices to individuals. The economy is already
configuring demand for some of these job categories of the future. Entrepreneurs could target the
productive fulfillment market directly, by designing jobs of the future that offer intrinsic
meaning and fulfillment.
Abundance Economics Phase II:
Actualization Economy Creates Social Goods for Human Thriving
Whereas jobs of the future (elective work, possibly with augmented incentives) are
needed to achieve a new form of economy based on self-fulfillment, lives of the futureare
needed to achieve the second phase of abundance economics, the actualization economy. The
Actualization Economy more fully incorporates the mind-set shift from labor to fulfillment,
wherein humans are thriving not merely surviving. Articulating lives of the future exposes our
impoverished concept of work, and our division of life into work and leisure. Beyond the work-
leisure binary of the labor economy, there could be many different categories of life activities
such as life-long learning, unpaid vocations (teaching, mentoring, coaching, leading,
facilitating), health and sports (movement, exercise, team and league participations), creative
expression (art, music, singing), community participations (civic, political), collaboration
(engaging with others on projects or goals), interaction (friends, family, acquaintances,
associates), spiritual and mindfulness activities, and entertainment (relaxation, play, fun,
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discovery). Beyond work for pay, these opportunities for meaningful engagement could create as
much work as needed, and produce many valuable social goods.
In the contemporary labor economy, what seems to account for the “good life” is the idea
of some sort of work-life balance, but in abundance economics the definition is much broader.
The good life expands to a fuller multi-category experience of life in which self-directed agents
produce and consume social goods, and in which labor-work no longer centrally defines human
existence. Thus, with an orientation to both social and material goods production, abundance
economics is a model for generating an improved quality of life that goes beyond sustenance
needs.
Discussion and Limitations
There are many potential limitations to the Abundance Economics proposed here.
Abundance Economics might be overly optimistic and unrealistic to achieve. It would be nice to
foster the growth of social goods, but precisely how to accomplish this in practice is not clear.
One problem is that qualitative measurement metrics are not yet fully established, despite some
promising emerging methods such as cliodynamics(Turchin 2005).
Measurement is difficult, but a more intractable challenge is social incentives. It may be
that political hierarchies will have little reason to adopt policies supporting social goods
production if there is a risk of eroding their power base. Social goods can be generated by other
means such as crowdsourcing, but this has proved difficult so far (Murray 2015). Hierarchical
social organization presents further challenges because the current structure of the ownership of
the means of production is likely to persist. At present, the funders of new technology still
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become the owners of new technology, and accrue wealth and influence from this; and that, in
turn, contributes to income inequality. The present power structure is likely to continue unless
alternative models of the ownership of the means of production are implemented. While there
might be less of a requirement for physical plant means-of-production in the digital era, we could
nevertheless expect that new forms of influence and control that favor existing hierarchies would
be similarly instantiated in the automation and actualization economy.
Further, perhaps one of the most intransigent limitations to future change is complacency.
Depending upon the level of remuneration built into potential guaranteed basic income
initiatives, there might be little incentive for anyone to be interested in the production of any
goods, whether social or material. In fact, arguably, complacency is already a social good (or
social pathology) produced by many economies, even if mostly as an unintended consequence.
However, the hopeful view is that the human drive to apply energy productively and enjoyably
towards challenge and meaning will persist. As discussed, the entrepreneurial call to action is
precisely to design the experiences of the future that cater to meaningful engagement of
productive energy and improved quality of life.
Conclusion
In this chapter, I proposed a new philosophy of economic theory, Abundance Economics,
to address the contemporary moment of technological automation and technological
unemployment. Automation and its effects are likely to persist as crucial economic drivers.
Abundance economics appropriates automation by rethinking the traditional economic principles
of scarce resource distribution in two phases. First, there is an alleviation of quantitative
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material-goods scarcity in the Automation Economy to support human survival needs. Second,
there is the creation of qualitative social goods in the Actualization Economy to enable human
thriving. I suggest that the most successful future economies will be those that enact economics
as systems for the production and consumption of social goods in addition to material goods.
Such an emphasis on social goods that improve human quality of life could be crucial in helping
to transition to a potential situation of rapid automation across multiple sectors of the economy.
Overall, automation and technological unemployment should be a substantial long-term
positive gain for the worldwide economy. The key challenge is to implement these structural
changes in ways that benefit all persons. There is no economic law that producing a good or
service must require human labor (Huff 2015), and we should not limit our imagination to
projects achievable only by human labor. Instead, we can be thinking about much larger,
Kardashev-level (i.e., planetary) projects that might be possible through automation, such as
large-scale environmental cleanup, agricultural monitoring, and space settlement. These are the
abundant futures towards which Clarke and Keynes both gestured.
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