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



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
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
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
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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.
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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
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.
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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... Along with other factors such as globalization, the so-called 'Fourth Industrial Revolution,' based on AI and robotics, has fomented research into the extent to which there has been a change in the composition of jobs that are available, the skills those jobs require, and the wages they pay (Morikawa, 2017). The main social concern is the possibility that computerization and automation are a source of job loss, a phenomenon known in the economic literature as technological unemployment (i.e., Frey & Osborne, 2017;Swan, 2017). The term refers to the possibility that machines will take over current jobs and create unemployment (Frey & Osborne, 2017). ...
... The impact of technological change as a whole, and computerization in particular, on labor market outcomes has become an important area of research (Frey & Osborne, 2017). For example, a number of studies have presented evidence that in some jobs and industries there has been a decline in the total number of largely routine jobs and a reduction in the weekly hours of work in these jobs (i.e., Arntz et al., 2016;Frey & Osborne, 2013Swan, 2017). ...
... As Swan (2017) argued, our findings also suggest that issues regarding the impact of new technologies on society will depend on the rate at which those who are displaced by them can be retrained. Similarly, we agree with Fort, Pierce, and Schott (2018) that we need to reject simple hypotheses regarding unemployment. ...
In recent years, there has been increasing social concern about the impact of automation on labor market outcomes, a phenomenon known as technological unemployment. Specific concerns revolve around the decline in wages and the increase in unemployment in occupations that are predominately routine. While there is insufficient evidence for massive unemployment scenarios, these concerns are critical due to their social and political implications. This study seeks to identify the correlates of perceptions of the effect of new technologies on job prospects, and on job loss and wage loss due to computerization. We conducted a secondary data analysis of the 2017 Pew Research Center American Trends Panel surveying the American population. Results indicate that individuals employed in jobs involving manual or physical tasks had more negative perceptions regarding the impact of technology on their careers, whereas those involved in managerial and data analysis tasks reported more positive views. Young employees with higher incomes and more education, who use the Internet almost constantly, expressed more positive views of technology’s impact on their jobs. Nonetheless, technological unemployment and under-employment were associated with age, income, race and negative perceptions regarding the intrusion of new digital technologies on the workplace. Findings provide evidence for the self-interest hypothesis concerning the effect of technology on low-income groups. Implications of the findings are discussed.
... In order to automate processes-which is the foundation of the digital transformationmore and more entities have implemented IT solutions, the key elements of which are Sustainability 2022, 14, 1333 2 of 29 software robots. Swan even points to the emergence of an automation economy, which focuses its considerations about the functioning of the economy in such conditions where robotic technology complements or replaces most of the demand for human labor [12]. ...
... According to Schallmo and Williams, digitization means fundamental changes in the way business operations and the business models of enterprises are implemented and introduced thanks to the use of digital technologies and data that are both digitized and natively digital [33] (pp. [11][12]. For the purpose of this article, the author has adopted as binding one of the most frequently quoted definitions of the business model, which is proposed by Osterwalder, Pigneur, and Tucci, according to which a business model is a conceptual tool that contains a set of elements and relations that enables the business logic of a given company to be expressed. ...
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The current digital transformation (additionally accelerated by the COVID-19 pandemic) is causing profound changes across a number of industries. Part of this revolution is the spread of Robotic Process Automation (RPA), which enables the automation of business processes by replacing human work with advanced software robots. One of the goals of the conducted research was to develop a classification of approaches to RPA positioning in enterprises. The author also identified differences in RPA positioning between individual industries. Based on conducted literature research, the author has proposed a proprietary classification for approaches to RPA positioning: conservative, efficiency improving, and strategic. This was subject to verification based on the results of empirical research using multidimensional correspondence analysis. The survey was conducted by the author in 2020 using the CAWI method: Credible (reliable) results were obtained from 238 Polish enterprises. The multidimensional correspondence analysis, conducted on the basis of the results of the empirical research confirmed that the approaches to RPA positioning in enterprises proposed by the author did occur in business practice. The outcome of the RPA classification became the basis for qualitative research (in the form of semi-structured interviews with expert practitioners) aimed at answering the question as to whether enterprises that strategically position RPA and treat it as a tool for digital transformation increase their organizational resilience. Up until now, however, no study has been found that focuses on how RPA increases organizational resilience or what its consequences are both at the research and application levels. This article fills the research gap in this area.
... Training chat bots to be empathetic to customers is the kind of complex, creative activity that will characterize more roles in the future. According to McKinsey Global Institute; Manyika et al. (2017), robots could take over jobs of 800 million people by 2030, however, the World Economic Forum (WEF, 2018) asserted that skill revolution could open ways for new opportunities, also more employees need skilling and re-skilling in order to fit in the 4thIR (Baldwin, 2019;Brynjolfsson and McAfee, 2014;Helbing, 2015;Mason, 2015;Rifkin, 2014;Schwab, 2018;Swan, 2017;Tsekeris, 2019). With the above discussion as a backdrop, it becomes a fact that organizations need to invest in humans. ...
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With the development of the 4th Industrial Revolution (4th IR), its emerging technologies and skills; there is a mismatch between 4th IR, and the skills needed by information professionals to survive. This paper bridges the gap based on the skills needed to survive and provide possible solutions to challenges faced by information professionals, which will in turn help to reduce the number of unemployed, semi-employed, non-employed, and provide economic empowerment among information professionals in this new revolution. Information professionals should adopt the missing middle model/techniques in organization which asserts that robots, by and large, will not be taking our jobs; instead, human Machine collaboration will reconfigure some of our work, making and make human skills more unique and important than ever.
... However, not even governments can absolutely control their own data: "data volume grows faster than processing power, implying that a growing share of data will never be processed" (Helbing, 2017, p. 319; see also Tsekeris, 2016). 7 Despite the coming situation of technological unemployment (Brynjolfsson & McAfee, 2014;Helbing, 2015;Swan, 2017), the rapid proliferation of a "collaborative commons" and the so-called "non-market" and "nonlinear" economic activities is making it possible for a more cooperative and just society to emerge (Mason, 2015, pp. 141-145; see also Rifkin, 2014). ...
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The central aim of this article is to sketch and outline a brief and critical presentation , overview and assessment of the (radically ambivalent) dynamics of the large family of technological developments pertaining to the Fourth Industrial Revolution (Industry 4.0), as well as of the so-called digitalisation of society. This assessment attempts to comprehensively overcome relevant analytical dualisms and the one-sided "either-or" logic, in favor of a synthetic, open and creative "both-and" framework of interdisciplinary thought.
... For example, few high tech organisations have explored the notion that the personal data and many interactions contributed by individuals to AI systems form a sort of community investment in these technologies that deserves either direct financial return or at least some significant stake in decision making concerning the technologies (Cheney-Lippold 2017). As society moves toward public policies that incorporate 'abundance economics' notions, ways of conscientiously allocating the gains from technologies across societal levels are being deliberated (Swan 2017). The notion of a 'robot tax', proposed by Mady Delvaux-Stehres and endorsed by Microsoft founder Bill Gates, has recently catalysed interest in the prospects for redistribution of some AI-related profits into education and social welfare (Rimmer 2017). ...
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The potential societal impacts of automation using intelligent control and communications technologies have emerged as topics in recent writings and public policy initiatives. Constructed entities labelled as ‘thinking machines’ (such as IBM’s Watson as well as intelligent chatbot and robotic systems) have also played significant roles in this discourse. This paper provides an historical sequencing then analyses a selection of writings produced since the 1940s concerning economic and social issues involving artificial intelligence (AI) research and applications. The paper explores how overstatements and hyperbolic themes and concepts, often stemming from AI’s early periods (including from Herbert Simon), are being employed in characterisations of current AI approaches in apparently opportunistic attempts to provide rhetorical support for various large-scale business and societal initiatives. It also addresses the relative neglect of consideration of many of AI’s sociotechnical failures and discontinued approaches in recent examinations of automation and social welfare issues. The paper discusses the moral logic of AI researchers and developers providing reasonable and measured narratives in public discourse rather than hyperbole, efforts that can empower decision makers to make sounder judgments concerning the technology’s current and future applications as well as allocate rewards of the technology more equitably.
... Another pressing challenge with which blockchain technology might be helpful is addressing the slate of issues related to the automation economy and the future of work. Many sectors of the economy are in the process of automating, with technological unemployment as the result [33]. Technological unemployment is the outsourcing of jobs to technology. ...
Conference Paper
This paper articulates the notion of the cryptocitizen who thinks freely of the traditional dictates of authority. The analysis looks beyond the immediate economic benefits and risks of blockchain technology to consider the broader implications for the individual and society. The possibility of creating and participating in different and multiple self-determined political and economic systems could mobilize how we instantiate ourselves as individuals and societies. Blockchain technology invites the possibility of making a social world with greater prominence of the values we apparently care about: freedom, trust, and dignity. There is a need for social structures that ensure and promote coexistence among diverse individuals and groups, particularly looking ahead to the possibilities of machine intelligence and space settlement. The hidden novelty and benefit of blockchain technology is serving as a tool for the design of smart city cryptopolises and cryptocitizen social structures. The bigger stakes of blockchain technology are the possibility of having new modes of social organization.
Overstatements and hyperbolic themes and concepts, often stemming from artificial intelligence (AI)’s early periods, are being employed in characterizations of current AI approaches in apparently opportunistic attempts to provide rhetorical support for various large-scale and consumer initiatives. The chapter addresses the relative neglect of consideration of many of AI’s sociotechnical failures and discontinued approaches. Unfortunate patterns from decades past portend comparable kinds of issues today as robots and other AI-enhanced entities enter everyday life, with many marketers and developers engaging in overstatement and “robo-hype.” The chapter discusses the moral logic of AI researchers and developers providing reasonable and measured narratives in public discourse rather than hyperbole, efforts that can empower decision makers to make sounder judgments concerning AI’s applications as well as allocate the rewards of the technology more equitably.KeywordsArtificial intelligenceRoboticsSecurityCybersecurityTechnological failuresRobo-hypeHyperboleAutomationJob lossPublic discourseEthicsScience fictionTechnological debt
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This research examines the practice of cryonics and provides empirical evidence for an improved understanding of the motivations and attitudes of participants. Cryonics is the freezing of a person who has died of a disease in hopes of restoring life at some future time when a cure may be available. So far, about 300 people have been cryopreserved, and an additional 1200 have enrolled in such programs. The current work has three vectors. First, the results of a worldwide cryonics survey (n = 316) carried out as part of this research are discussed. Second, a theoretical model is developed from the survey results to propose a Theory of Cryonic Life Extension which explains an individual’s decision to select cryopreservation. Third, the most distinctive survey result, a conceptualization of personal identity malleability, is extended with a philosophical formulation. Personal identity is found to be emergent, not fundamental, and thus may continue to evolve in concept and application, particularly in the longer time frames implicated by cryonics. The potential consequences of this work are that the conceptual norms materializing in the cryonics community could be forerunners of wider societal trends of how humans understand themselves as subjects in an era increasingly configured by science and technology.
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This concise article maintains that, in times of structural and persistent crisis, Europe needs to effectively tackle the multiple challenges and existential fears by cultivating a strong and dynamical digital skills ecosystem, based on collective values and the fundamental liberal principles of co-creation, co-evolution, and collective intelligence, over against the obsolete principles of optimisation and top-down administration and control. This will arguably result in upgrading humanism (humanism 2.0) and democracy (democracy 2.0), and in boosting responsible innovation and, therefore, adaptiveness, as well as in translating technological progress into inclusive and sustainable economic growth, and risks into creative opportunities for all citizens.
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Most people in the future will not need to work, at least in the ways in which we continue to think about work/human labour. In this chapter, we discuss the role of humans in the future economy. We begin with a discussion of the evolution of the integration of robots into the economy. Then, we turn out attention to the economics of robotics and AI, showing how these technological changes alter the economy and how markets and political responses may unfold. Then we discuss how humans can remain competitive in the new economy, developing skills that are needed and how educational institutions will have to change to address the new economic reality. Finally, we conclude, showing that humans will have to see their relationship to the job market differently and there will have to be an appropriate political response to the new economic landscape with changes in taxation and new ways of ensuring economic and political stability.
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Technology is widely considered the main source of economic progress, but it has also generated cultural anxiety throughout history. The developed world is now suffering from another bout of such angst. Anxieties over technology can take on several forms, and we focus on three of the most prominent concerns. First, there is the concern that technological progress will cause widespread substitution of machines for labor, which in turn could lead to technological unemployment and a further increase in inequality in the short run, even if the long-run effects are beneficial. Second, there has been anxiety over the moral implications of technological process for human welfare, broadly defined. While, during the Industrial Revolution, the worry was about the dehumanizing effects of work, in modern times, perhaps the greater fear is a world where the elimination of work itself is the source of dehumanization. A third concern cuts in the opposite direction, suggesting that the epoch of major technological progress is behind us. Understanding the history of technological anxiety provides perspective on whether this time is truly different. We consider the role of these three anxieties among economists, primarily focusing on the historical period from the late 18th to the early 20th century, and then compare the historical and current manifestations of these three concerns.
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In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. 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 demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.
We are suffering just now from a bad attack of economic pessimism. It is common to hear people say that the epoch of enormous economic progress which characterised the nineteenth century is over; that the rapid improvement in the standard of life is now going to slow down—at any rate in Great Britain; that a decline in prosperity is more likely than an improvement in the decade which lies ahead of us.
The Luddites of today don't necessarily oppose technology, but they do worry that labor-saving software and automation will harm lower-income workers. Although skill-biased technological change has contributed to a rise in income inequality since the early 1980s, innovations will deliver net benefits to the less affluent so long as the state's capacity for mischief is constrained. When subjected to economic logic, the Luddite fallacies are no more compelling today than they were at any time in the past, whether driven by direct animosity toward labor saving machinery or instead linked to distaste for technology based income inequality. It may be that the past politics of wealth and income redistribution have so distorted the incentives in labor supply decisions that the supply of skilled labor complementary with new technology lags the growth in demand, resulting in less productivity and output.
In this article, I present three key facts about income and wealth inequality in the long run emerging from my book Capital in the Twenty-First Century and seek to sharpen and refocus the discussion about those trends. In particular, I clarify the role played by r > g in my analysis of wealth inequality. I also discuss some of the implications for optimal taxation, and the relation between capital-income ratios and capital shares.
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.