PositioningPaper

The Impact of Robots
on Productivity,
Employment and Jobs

published byInternational
Federation of Robotics
Frankfurt, Germany
April 2017
updated April 2018

A positioning paper by the International Federation of Robotics

INTRODUCTION AND PURPOSE OF THIS PAPER
Rapid advances intechnology have led to asurge of public interest in
automation and robotics.
As figures from theInternational Federation of
Robotics (IFR) show, salesof robots are increasing
year-on-year, with a 16%
increase in 2016 over theprevious year. The IFR
estimates that over 3 million
industrial robots will be atwork in 2020, representing
an average annual growthrate of 14% between 2017and 2020 (International
Federation of Robotics
2017).

A Note on Definitions
There is no single agreed definition of a robot although all definitions
include an outcome of a task that is completed without humanintervention. Whilst some definitions require the task to becompleted by a physical machine that moves and responds to its
environment, other definitions use the term robot in connection withtasks completed by software, without physical embodiment.
The IFR supports the International Organization for Standardisation(ISO) definition 8373 of a robot:•

An automatically controlled, reprogrammable,
multipurpose manipulator programmable in three or moreaxes, which may be either fixed in place or mobile for use inindustrial automation applications.
o Reprogrammable: whose programmed motions or auxiliary
functions may be changed without physical alterations;o Multipurpose: capable of being adapted to a different
application with physical alterations;o Physical alterations: alteration of the mechanical structure
or control system except for changes of programming
cassettes, ROMs, etc.
o Axis: direction used to specify the robot motion in a linear or
rotary mode
• A service robot is a robot that performs useful tasks forhumans or equipment excluding industrial automation
application. Note: The classification of a robot into industrial
robot or service robot is done according to its intended
application.
• A personal service robot or a service robot for personaluse is a service robot used for a non-commercial task, usuallyby lay persons. Examples are domestic servant robot,
automated wheelchair, personal mobility assist robot, and pet
exercising robot.
• A professional service robot or a service robot for
professional use is a service robot used for a commercial task,
usually operated by a properly trained operator. Examples arecleaning robot for public places, delivery robot in offices or
hospitals, fire-fighting robot, rehabilitation robot and surgery
robot in hospitals. In this context an operator is a person
designated to start, monitor and stop the intended operation ofa robot or a robot system.
IFR members manufacture industrial robots, used in manufacturing,and service robots, used in a variety of environments bothprofessional and personal to perform a useful task.

Driving the increase inpublic interest in robotics
and automation is both afascination with the potentialof these technologies tochange our lives, and a fearof the impact of automation– including robotics – onjobs. These fears are tied
into broader geo-politicaland social shifts driven by
issues such as trade policy
and immigration that,
overall, contribute to asense of insecurity about
the employment prospectsof current and future
generations. Consequently,
many headlines focus on
the potential negative
outcomes of automation.
These risks overshadowing
the very real positive
contribution of automation
and robotics to productivity, competitiveness and job creation. In addition, it could undermine
discussion and action on the measures that should be taken to enable countries, organizations andindividuals to reap the benefits of automation.
This paper provides the IFR’s opinion on the impact of automation - specifically of robots - on
productivity, competitiveness and employment. IFR is not a policy institute. However, this report
includes the main conclusions from a variety of experts on appropriate policy responses to ensureongoing positive outcomes from automation and the ongoing development and uptake of robots,with which we concur.
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The Impact of Robots on Employment

THE IFR’S POSITION IN SUMMARY
The IFR believes that:•

Robots increase productivity and competitiveness. Used effectively, they enable
companies to become or remain competitive. This is particularly important for small-to-medium
sized (SME) businesses that are the backbone of both developed and developing country
economies. It also enables large companies to increase their competitiveness through fasterproduct development and delivery. Increased use of robots is also enabling companies in highcost countries to ‘re-shore’ or bring back to their domestic base parts of the supply chain thatthey have previously outsourced to sources of cheaper labour. Currently, the greatest threat to
employment is not automation but an inability to remain competitive.

•

Increased productivity can lead to increased demand, creating new job opportunities.
These ‘spillovers’ can be seen within an individual organization, along an industry sector’svalue chain, and in other sectors, particularly services.

•

Automation has led overall to an increase in labour demand and positive impact on
wages. Whilst middle-income / middle-skilled jobs have reduced as a proportion of overall
contribution to employment and earnings – leading to fears of increasing income inequality –the skills range within the middle-income bracket is large. Robots are driving an increase indemand for workers at the higher-skilled end of the spectrum, with a positive impact on wages.
The issue is how to enable middle-income earners in the lower-income range to upskill or
retrain.

•

Robots complement and augment labour: The future will be robots and humans
working together. Robots substitute labour activities but do not replace jobs. Less than 10% ofjobs are fully automatable. Increasingly, robots are used to complement and augment labour
activities; the net impact on jobs and the quality of work is positive. Automation provides theopportunity for humans to focus on higher-skilled, higher-quality and higher-paid tasks.

•

The IFR believes recent calls for the introduction of a robot tax are unwarranted given theproven positive impact of robotics on employment and wages. It would deter badly-needed
investment in robots, undermining the competitiveness of companies and states. Governments
may need to assess the means of generating revenues to cover social payments due to a large
number of structural factors – but there is no valid foundation for taxing a capital investmentthat improves productivity, increases competitiveness, creates more jobs than it replaces, andleads to workers moving up the skills/ income ladder.

•

Governments and companies must focus on providing the right skills to current andfuture workers to ensure a continuation of the positive impact of robots on employment,
job quality and wages. This is the argument brought by all the experts cited in this paper, withwhich the IFR concurs. Governments must invest in robotics research and development toreap the employment benefits of this rapidly growing sector. They must also provide the policy
incentives and education systems to support the acquisition of skills necessary to secure andthrive in jobs that are created or changed by the deployment of robots and automation.
Companies must engage actively in appropriate retraining programmes for employees to equipthem with appropriate skills. These goals will require intensified and coordinated public-private
sector collaboration.

April 2017 (updated 2018)

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A positioning paper by the International Federation of Robotics

ROBOTS, PRODUCTIVITY, COMPETITIVENESS AND GROWTH
Robots improve productivity when they are applied to tasks that they perform more efficiently andto a higher and more consistent level of quality than humans. In a study focused specifically on
robotics for the Centre for Economic Performance at the London School of Economics, Georg
Graetz and Guy Michaels concluded that robot densification increased annual growth of GDP andlabor productivity between 1993 and 2007 by about 0.37 and 0.36 percentage points respectively
across 17 countries studied, representing 10% of total GDP growth in the countries studied over
the time period and comparing with the 0.35 percentage point estimated total contribution of steam
technology to British annual labor productivity growth between 1850 and 1910 (Graetz andMichaels 2015)1. A more recent study found that investment in robots contributed 10% of growth in
GDP per capita in OECD countries from 1993 to 2016. The same study found that a one-unit
increase in robotics density (which the study defines as the number of robots per million hours
worked) is associated with a 0.04% increase in labour productivity (Centre for Economics andBusiness Research 2017). And a study focused on Germany found that robot adoption led to a
GDP increase of 0.5% per person per robot over 10 years from 2004 to 2014 (Institute for
Employment Research, CEPR and Düsseldorf Institute for Competition Economics 2017). Looking
ahead, the McKinsey Global Institute predicts that up to half of the total productivity growth needed
to ensure a 2.8% growth in GDP over the next 50 years will be driven by automation (McKinsey
Global Institute 2017).
A report by Accenture in collaboration with Frontier Economics forecasts the potential of
automation to double Gross Value Added (GVA) across 12 developed economies by 2035, withlabour productivity improvements of up to 40% (Accenture 2016).2 The Boston Consulting Group
forecasts productivity improvements of 30% over the next 10 years, spurred particularly by theuptake of robots in SMEs as robots become more affordable, more adaptable and easier toprogram (Boston Consulting Group 2015).
Increased productivity is enabling some firms – such as Whirlpool, Caterpillar and Ford Motor
Company in the US and Adidas in Germany – to restructure their supply chains, bringing backparts of the manufacturing process to the country of origin. Citigroup and the Oxford Martin School
point to existing signs of a slowdown in goods production fragmentation and see robot density as a
key driver in this process. In a survey of 238 Citigroup clients, 70% believed that automation would
encourage companies to move their manufacturing closer to home and consolidate production (Citiand Oxford Martin School 2016). The Reshoring Initiative in the US estimates that 250,000 jobs
have been brought back to the country by reshoring and inward-bound foreign direct investment
since 2010 (Reshoring Initiative 2015). Not only does automation enable reshoring, companies that
deploy robots are less likely to relocate or offshore in the first place according to a report prepared
for the European Commission by the Fraunhofer Institute for Systems and Innovation Research(European Commission 2015). Reshoring brings advantages at the national level, with the potential
for demand spillovers into other sectors, and the accumulation of specialist manufacturing knowhow that is critical for attracting and expanding talent, and for national competitiveness.
Productivity gains due to robotics and automation are important not just at the company level but
also for both industry and national competitiveness. Both US manufacturing productivity andindustrial production have risen steadily since the financial crisis (PwC 2016) and a report by

1 Graetz and Michaels point out that the productivity increases driven by robot densification have been achievedin one quarter of the time of the GDP increase due to steam technology.
2 The Accenture forecast is based on the category of artificial intelligence, which Accenture defines as ‘multiple
technologies that can be combined in different ways to sense, comprehend and act’. Artificial intelligence, also
referred to as machine intelligence, will impact robotics by expanding the range of tasks that robots are able toperform without human intervention. Accenture argues that artificial intelligence does not only impact labour
productivity, but also makes capital more productive, for example by reducing factory downtime through moreaccurate preventive maintenance.

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Barclays estimates that an accelerated level of investment in robots would raise manufacturing
Gross Value Added in the UK by 21.0% over 10 years (Barclays 2015). And BCG estimates that
A Note on the Productivity Paradox
Global productivity has been slowing down since the turn of the century, albeit at different times andrates for mature, developing and emerging economies. Some scholars such as Robert Gordon,
have asked whether economic growth, as we have known it in the past, is now over, and havequestioned why the third industrial revolution represented by computers and the internet does notseem to be delivering the same boost to productivity as the first and second industrial revolutions
which gave us steam engines, railroads, electricity, the internal combustion engine and indoor
plumbing. (Gordon, 2012).
This is a complex topic beyond the scope of this paper, but a few points are worth note:
•

•
•

•

•

•

•

Gordon posits that one of the reasons information and communications technology (ICT)
innovation has not (yet) led to productivity increases overall is that the main focus ofdevelopment over the past decade has been on personal entertainment, which does not
drive worker productivity. This is borne out by findings that manufacturing productivity –which has been driven by innovations in automation rather than consumer technologies has grown more strongly than productivity in the services sector of the economy in mostmature economies (The Conference Board, 2015).
A number of economists believe that productivity gains are not yet showing up because
technology takes longer to be adopted at scale than commonly assumed 3.
An additional reason given for the slow adoption of automation technology is that the skills
to make it work in the specific company context are expensive and therefore the total costof capital and skilled labour is higher than using cheaper labour to complete the task (Avent2017).
Other scholars such as economist Erik Brynjolfsson point to the fact that the impact of freeservices such as search functionality and free communications services are not reflected in
GDP and therefore also not in productivity figures 4.
The OECD points to structural dimensions such as a lack of investment in ‘knowledgebased capital’ which includes: R&D, firm specific skills, organisational know-how,databases, design and various forms of intellectual property. Knowledge based capital
influences the degree to which firms realise the productivity potential of new technology
with ‘frontier firms’ showing significantly superior productivity rates to technology ‘laggards’ 5(OECD, 2015).
A report by the Oxford Martin School and Citigroup argues that firms may not yet haveadjusted structurally to reap the benefits of automation technologies. The report also cites
limitations in the current measurement of productivity, with 81% of respondents to a surveyof Citigroup clients stating that technological developments were inadequately reflected in
the productivity statistics (Citi and Oxford Martin School, 2016).
Other factors than productivity impact growth. For example, Citigroup estimates that the
impact of demographics on labour supply could slow down average growth prospects inindustrial countries by around 0.5% per annum over the next 20 years compared to the1990 – 2010 period (Citi and Oxford Martin School, 2016). McKinsey Global Instituteargues that roughly half the sources of economic growth from the past half century will
evaporate as populations age (McKinsey Global Institute, 2017). The World Economic

3 The McKinsey Global Institute finds it can take between eight and 28 years for technologies to be adopted atscale, from the point at which they become commercially available (McKinsey Global Institute 2017). Economists
Brynjolfsson and Syverson claim that it has taken 25 years for general purpose technologies such as portable
power to reach peak adoption. This is because additional investments in complementary technology, but also inprocess and organisational change, are required to implement them (Brynjolfsson, Rock, and Syverson 2017).
4 For example, Professor Brynjolfsson and his colleague Dr. JooHee Oh estimate the increase in consumer

surplus created by free internet services to be over $30 billion per year in the U.S. alone, about 0.23% of averageannual GDP during the period of 2002-2011. (Brynjolfsson and Oh n.d.)
5 The OECD report shows that the productivity of the most productive firms grew at double the speed of theaverage manufacturing firm over the same period. This gap was even more extreme in services. Private, nonfinancial service sector firms on the productivity frontier saw productivity growth of 5%, eclipsing the 0.3%
average growth rate.

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4A positioning paper by the International Federation of Robotics•

Forum also concludes that ‘by far the biggest expected drivers of employment creation are
demographic and socio-economic in nature.’ (World Economic Forum, 2016). The OECD
points out that ‘the slowdown in productivity in OECD countries predates both the crisis andthe current technological wave which has created the digitalised economy’ and notes that ‘anumber of factors may be behind the paradox such as skills mismatches, sluggish
investment, and declining business dynamism, particularly post crisis’ (OECD, 2016). See
also (Autor and Salomons 2017).
Finally, as private equity executive William Janeway points out, it may simply be too early totell, given that we are not even at the halfway point of the run of previous industrial
revolutions (Janeway, 2013).

In sum, whilst it is not clear whether productivity levels will return to previous heights, there arestrong indications that we may be still in the midst of a structural adjustment, as firms, industries
and countries implement the measures – from capital investments to training and policy instruments– needed to reap the full productivity potential of technology in general, and automation inparticular.

South Korea, which has the highest robot density6 - is projected to improve its manufacturing costcompetitiveness by 6 percentage points relative to the US by 2025, assuming all other cost factors
remain unchanged (Boston Consulting Group 2015).
Fears that increased productivity will result in job losses are not borne out in studies exploring thelink. For example, a study of more than 35 countries over 19 years found that productivity growthhas, in aggregate, been employment-augmenting rather than employment-reducing (Autor andSalomons 2017). This study finds that the key driver of employment growth or decline is notproductivity but population growth – i.e. the number of workers able to provide supply of goods andthe number of consumers providing demand for those goods. Given shrinking working age
populations in most developed economies, productivity increases will be critical to ensure enoughworkers to maintain a supply of goods and services7.
Regional and national variation
The current and future impact of automation varies between regions and between countries and acomparison is beyond the scope of this paper. However, data from IFR and other sources suggest:• China will emerge as a major robotics manufacturer and user of robots, benefiting from jobs
created by robot manufacturing and productivity gains from robot use. China has topped salesof robots to any one single market every year since 2013. In 2016 almost as many robots weresold to China than to Europe and the Americas combined (International Federation of Robotics
2017). The Chinese government has included a focus on robotics in its 10-year strategy. In
2016, China used 68 robots per 10,000 workers – over 2.5 times the number of robots per
10,000 workers in 2013.
• In 2016 China had, for the first time, more robots in operation than Japan, which now takes
second place. Driven by a rapidly aging population and low productivity rates, the Japanese
government has set its sights on a 20-fold increase in the use of robots in the nonmanufacturing sector and a three-fold growth rate of labour productivity in the service sector,
both by 2020 (Ministry of Economy, Trade and Industry, Japan, 2015).
• Some emerging and developing economies – notably Indonesia and Thailand - are installing
robots at a high rate, recognising not only productivity but also quality advantages fromautomation. (Boston Consulting Group, 2015)

6 Robot density is measured by the number of robots per 10,000 workers. Whilst South Korea occupied the top
position for robot density, the highest robot sales were to China, at 69,000 units in 2015 (more than all of Europe).
7 For example, a study commissioned by the German Ministry of Labour and Social Affairs finds that evenassuming continued immigration and a scenario of high automation, Germany’s workforce in 2040 will not beenough to sustain target economic growth (Economix 2016).

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Other studies show a link between productivity, company competitiveness and increased demand,(Graetz and Michaels 2015). If the increase in production results in wage increases or increased
employment overall, increased demand spills over into other sectors of the economy (Zierahn,
Gregory and Arntz 2016), creating a virtuous circle of increased productivity, increased demand,increased wages and spending power, leading to increased demand for other products andsectors. Economist Tyler Cowen points out that manufacturing, in particular, seems to createstrong spillover effects, both within the sector and in complementary sectors (Cowen 2016).
Automation is also changing the nature of demand, in particular by enabling increased
personalisation and so-called mass customization. For example, robots are being used in one
factory to cut out customized flip-flops based on the 3D laser scan of customers’ feet (International
Federation of Robotics 2016). This level of personalisation would not be feasible without advancesin automation technologies.

ROBOTS, AUTOMATION AND EMPLOYMENTThe big question is whether increased productivity and competitiveness result in an increase inemployment and a rise in wages.
Various scholars have painted a dark picture of what could happen if machines are able to entirely
substitute for jobs, resulting in downward pressure on the wages of low-skilled workers andincreasing returns to owners of capital (Sachs and Kotlikoff 2012), (Berg, Buffie and Zanna 2016).
But even these scholars agree that the link between automation and wage inequality – and theprobability of a downward spiral – are not a given. In their article for IMF’s Finance & Development
Journal, Andrew Berg, Edward Buffie and Louis-Felipe Zanna state, for example, that ‘technology
does not seem to be the culprit for the rise in inequality in many countries [which is] concentrated ina very small fraction of the population.’ Jeffrey Sachs and Laurence Kotlikoff point to globalisation
as another factor (Sachs and Kotlikoff 2012). In a paper for the IZA World of Labour, economist
Richard Freeman also discusses the shift of income from labour to capital, concluding that, ‘there isevidence that factors such as trade and immigration and the weakening of tradeunions…have…contributed to increased skill differentials and inequality’ (Freeman 2015).
Other scholars point to the negative impact of business model shifts – in particular thedisaggregation of the supply chain through outsourcing – on wages (Weill 2014), (Berger 2014).
David Weill, for example, argues that ‘large corporations have shed their role as direct employers of
the people responsible for their products, in favor of outsourcing work to small companies thatcompete fiercely with one another. The result has been declining wages, eroding benefits,
inadequate health and safety conditions, and ever-widening income inequality.’ Economist Harry
Holzer also sees evidence of companies being unwilling to invest substantially in training
employees, yet not able to attract graduates with the required skills (Holzer 2015).
Meanwhile, there is ample evidence that automation does not lead to job substitution, but rather toa re-allocation of both jobs and tasks in which robots complement and augment human labour by
performing routine or dangerous tasks8.. This in turn places a premium on higher-skilled labour inthe sectors in which automation has substituted for labour, but also may create new lower-skilledjobs in other sectors due to spillover effects. As economist James Bessen comments, ‘Althoughcomputer automation is not causing a net loss of jobs, it does imply a substantial displacement ofjobs from some occupations to others.’ (Bessen 2016).

8 For example, a study by the McKinsey Global Institute concludes that between 3 and 14 percent of

the global workforce will need to switch occupational categories by 2030. However, the report predicts
that, with sufficient economic growth, innovation and investment, there will be enough new job creation
to offset the impact of automation (McKinsey Global Institute 2017). A study by CISCO and Oxford
Economics puts the job displacement figure at 8.4% of the U.S. workforce by 2027 (CISCO andOxford Economics 2017).
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Various studies show a positive correlation between automation and jobs. For example a 2016
discussion paper for the Centre for European Economic Research found that, ‘Overall, labordemand increased by 11.6 million jobs due to computerization between 1999 and 2010 in the EU
27, thus suggesting that the job-creating effect of RRTC9 overcompensated the job-destructing
effect.’10 (Zierahn, Gregory and Arntz 2016). A study on the impact of technology in Asian countries
found that productivity gains through technology led to a net increase of 33 million jobs per annum
between 2005 and 2015 (Asian Development Bank 2018).
A review of the economic impact of industrial robots across 17 countries found that robots
increased wages whilst having no significant effect on total hours worked (Graetz and Michaels
2015). And although manufacturing jobs have been declining over a number of years Brookings
Institution analysts report that countries that invested more in robots lost fewer manufacturing jobs
than those that did not (Muro and Andes 2015). Indeed a study by Barclays in the UK argues thatan investment in automation of £1.24 billion over the next decade could safeguard 73,500
manufacturing jobs and create over 30,000 jobs in other sectors. (Barclays 2015). According toanalysis by PwC of data from the U.S. Bureau of Labor Statistics, the most robotics-intensive
manufacturing sectors in the US as a proportion of the total workforce - i.e., automotive, electronics
and metals - employ about 20% more mechanical and industrial engineers and nearly twice thenumber of installation maintenance and repair workers than do less robotics-intensive
manufacturing sectors and pay higher wages than other manufacturing sectors. These sectors also
tend to have a higher proportion of production-line workers - and these workers earn higher wages
than sectors that are less robotics-intensive. (PwC 2014). Consultants Deloitte argue that, ‘While
technology has potentially contributed to the loss of over 800,000 lower-skilled jobs (in the UK)
there is equally strong evidence to suggest that it has helped to create nearly 3.5 million newhigher-skilled ones in their place.’ (Deloitte LLP 2015). Countries with the highest robot density,
notably Germany and Korea, have among the lowest unemployment rates. A study focused onGermany found that robot adoption has not even caused aggregate job losses for low-skilledworkers – commonly assumed to be those at most at risk of automation. Indeed, robot adoption
increased the likelihood of workers staying with their existing employer (Institute for Employment
Research, CEPR and Düsseldorf Institute for Competition Economics 2017).
Economist David Autor sums it up with the statement that ‘Automation does indeed substitute forlabor – as it is typically intended to do. However, automation also complements labor, raises output
in ways that lead to a higher demand for labor, and interacts with adjustments in labor supply. Even
expert commentators tend to overstate the machine substitution for human labor and ignore thestrong complementarities between automation and labor that increase productivity, raise earnings
and augment demand for labor.’ (Autor 2015).

9 Routine-Reducing Technological Change
10 The study found that whilst RRTC decreased labor demand by 9.6 million jobs, this was compensated byproduct demand and spillover effects that increased labor demand by around 21 million jobs

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THE SHRINKING MIDDLE
A recent, well-documented trend
is the decline in middle-skilled,
middle-income jobs which,combined with wage stagnation,bring a fear of increased income
inequality. In a study of labour
market polarization in selectedOECD countries between 1993
and 2010 economists Maarten
Goos, Alan Manning and Anna
Salomons found decreases inhours worked by middle-skilledemployees of between 5 and 15
percentage points (Goos,
Manning and Salomons 2014).
However, the middle-skilled,
middle-income bracket covers avery wide range of jobs andassociated skills sets andalthough jobs in the bracket are
shrinking overall, there are
significant niches in which
demand is not being met. Holzer
points out, for example that‘middle-skills jobs in….healthcare, mechanical maintenanceand repair and some services is consistently growing, as are
skill needs within traditionallyunskilled jobs’ to the extent that
employers are struggling to filldemand (Holzer 2015). Thisapplies to sectors such as
manufacturing that have alreadyinvested heavily in automation.
For example, 60% of US
manufacturers surveyed by
PwC said that there is either
already a skills shortage or there
will be within the next three
years (PwC 2016).
The study by Goos, Manningand Salomons shows far higher
gains to high-skilled hours
worked than low-skilled labour.
The OECD also finds thatdeclines in the share of middleskill occupations in total
employment between 1995 and2015 have been entirely offset
by the growth in top occupations
in most industries (OECD
April 2017 (updated 2018)

Working Together:
Examples of Human Robot Collaboration
Industrial robots have until recently been separated from humans– often by physical cages. Due to recent advances in technology,
a newer trend, which is also spilling out of the factory into nonmanufacturing sectors and into the home, is for collaborative
robots that respond to and work alongside humans safely.
In the factory, improvements in mobility and flexibility – such as;
improved gripping techniques and the ability to handle a diverse
range of shapes and materials; integrated vision guidance andenhanced sensors that enable robots to sense and respond totheir environment; and the ability to respond to both voice andgestured commands – are bringing robots out of their cages andon to the factory floor in close proximity to their human coworkers, performing tasks such as packing finished items intoboxes and removing defective items from production lines. These
collaborative robots are not replacing human work, but are
increasing the productivity of human workers, whilst
simultaneously reducing the risk of workplace injury – for example
due to repetitive heavy lifting.
Collaborative robots are particularly positive for SMEs, as theserobots can be easily set up by workers rather than specialist
systems integrators and also adapted quickly to new processes
and production run requirements. Humans are still needed tocarry out tasks such as refinishing, but the robot assistant that
fetches and carries parts will significantly increase workers’
productivity. A frequently-quoted example is BMW’s American
factory in Spartanburg, where collaborative robots help to fit doors
with sound and moisture insulation, a task that used to cause
wrist-strain for workers. Canadian electronics manufacturer
Paradigm Electronics uses robots to carry out delicate polishingand buffing tasks on loudspeakers, working with employees who
handle the final finish and quality check. These robots have led toa 50% productivity increase, but with no job losses as employees
who previously carried out these tasks have been promoted frommachine operators to robot programmers (Collaborative Robots).
The category of service robots is predicted to grow rapidly in bothprofessional and domestic usage. The IFR forecasts that over
300,000 professional service robots will be sold to bothmanufacturing and non-manufacturing sectors between 2016 and2019. These include Automated Guided Vehicles (AGVs) that canmove around factories, warehouses, hospitals and other publicareas to move products through production processes, fetch
goods and parts, load pallets and check status levels of machines
and stocks, functioning either independently or as humanassistants.
The IFR projects an increase of 42 million service robots for
personal and domestic use between 2016 and 2019 in categories
such as floor cleaning, lawn-mowing, entertainment and elderly
assistance. The pace of development, and social acceptance, ofdomestic robots is being partly driven by very rapid advances invoice recognition and natural language programming.
Healthcare is a particularly promising sector for service robots,with applications ranging from exoskeletons that enable workers
to deal ergonomically with heavy loads as well as recover frominjury or substitute for limbs that are no longer mobile - to robotassisted surgery.

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2017)11. A number of studies show that automation is a key driver of the shift to the high-skilled
category but find no or minimal correlation between technological change and job polarisation
towards low-skilled occupations12. Other studies find that technology adoption accelerates demandfor high-skilled workers. For example, Bessen finds that that jobs have grown faster in occupations
that use computers (Bessen 2016) and economists Guy Michaels, Ashwini Natraj and John Van
Reenen find a positive correlation between growth in ICT use and demand for highly educatedworkers (Michaels, Natraj and Van Reenen 2014).
Many of these higher-skilled jobs command a wage premium. For example, Deloitte estimates thatin the UK, the higher-skilled jobs that have replaced lower-skilled ones pay on average £10,000
more per annum, adding £140 billion to the UK’s economy. (Deloitte LLP 2015).
Jeffrey Lin finds significant (30%) positive differences between the wages of those in ‘new work’(new additions to the US Census Bureau categorisations, a high proportion of which aretechnology-related) than workers not employed in new occupations (Lin 2009).
Other research points to various factors unrelated to automation that have caused wage
stagnation, which is more severe in lower- than higher-income jobs. Aside from the obvious impactof the recent recession, experts point to the impact of new working arrangements such as zerohours contracts13, the ‘gig economy’14, and lower union participation on wages15 (Pennycook,
Cory and Alakeson 2013), (Katz and Krueger 2016), (Rosenfeld, Denise and Laird 2016).
A picture emerges of a rapid restructuring of jobs traditionally – though not exclusively –concentrated in the middle-skill bracket, with positive opportunities for those with the right skills, yeta shortfall in employees with these skills, and a downward pressure on wages in the lower-skilled
and lower end of the middle-skilled brackets due primarily to structural issues.
Concerns about the impact of automation on jobs – and as a result on social security funds – have
led recently to calls from a number of quarters for the introduction of a robot tax. This argument
ignores the complex and systemic nature of employment and wage development described above.
A robot tax would not address factors such as trade, globalisation of supply chains, demographics
and the changing nature of employment contracts which play a significant role in job and wage
development.
The IFR believes a robot tax is unwarranted given the proven positive impact of robotics on
employment and wages. It would deter badly-needed investment in robots, undermining thecompetitiveness of companies and states. Profits, not the means of making them, should be taxed.
The European Parliament recently recognised this by rejecting a motion to request the European
Commission to develop legislation for a robot tax.
Governments may need to assess the means of generating revenues to cover social payments
due to a large number of structural factors – but there is no valid foundation for taxing a capital
investment that improves productivity, creates more jobs than it replaces, and leads to workers
moving up the skills/ income ladder.

11 The exceptions are hotels and restaurants; wholesale and retail trade and; repairs.
12 See for example (OECD 2017) and (Michaels, Natraj and Van Reenen 2014)
13 A report by the Resolution Foundation found that the gross weekly pay of workers on zero-hours contracts in

the UK was under half of that of employed workers and that more zero-hours contract workers (37%) were in the
16-24 age-bracket than older workers. (Pennycook, Cory and Alakeson 2013)14 Research for the National Bureau for Economic Research shows that all of the 9.1 million jobs added in the US
between 2005 and 2015 are based on ‘alternative working arrangements’ (Katz and Krueger 2016).
15 A study by the Economic Policy Institute argues that the decrease in union membership has depressed wages

for non-union members. Union membership in the US has declined from 1 in 3 in the 1950s to about 1 in 20
today. The authors calculate that for non-union private-sector men, weekly wages would be an estimated 5
percent ($52) higher in 2013 if private-sector union density (the share of workers in similar industries and regions
who are union members) remained at its 1979 level (Rosenfeld, Denise and Laird 2016).

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THE IMPACT OF AUTOMATION ON INDUSTRY SECTORS AND JOBS
In aggregate, automation, and more specifically robotics, has a positive impact on employment.
However, the picture varies across different sectors, job types and skills level. Of particular concern
in public debate has been the fear that certain jobs will be wiped out entirely as a result of
automation (Frey and Osborne 2013).
Which tasks can and can’t be automated?
According to most experts, the types of tasks that can most easily be automated are those thathave a high degree of repetition in either physical tasks or data processing. Advances in artificial
intelligence make it hard to define the borders or development trajectory of automation tasks,
though currently, a level of predictability in either physical or processing tasks is a fundamental
component of the ability to automate. What is clearer are the kinds of tasks robots or otherautomation tools will not be able to perform any time soon, even assuming the successful adoptionof advancing developments in artificial intelligence that are focused on autonomous learning. Tasks
requiring high levels of creativity, empathy, persuasion, an understanding of which knowledge toapply in which situation to reach a productive decision 16 and a high level of sensorimotor skills 17 areconsidered unlikely to be automatable in the near future 18.

More recent analysis takes a more nuanced view of tasks rather than jobs being automated (Arntz,
Gregory and Zierahn 2016), (McKinsey Global Institute 2017). This analysis suggests that lessthan 10% of jobs can be automated entirely and that the level of potential automation of tasks
within a job varies greatly according to job and industry. As McKinsey puts it, ‘More occupations willchange than will be automated away’ (McKinsey Global Institute 2017). This is an importantdifferentiation as it paints a picture, supported by the IFR’s experience, of a future in which robots
and humans will work together, each doing what each does best, with positive effects not only forthe competitiveness of the firm, with attendant repercussions for employees, but also on the qualityof work carried out by people19.
Economist David Autor argues that ‘When automation or computerization makes some steps in awork process more reliable, cheaper or faster, this increases the value of the remaining humanlinks in the production chain’ (Autor 2015). Economist James Bessen points out that the
introduction of ATMs in the 1990s did not lead to a reduction in the number of bank tellers thatthese machines theoretically replaced. Although individual bank branches did reduce the numberof tellers per branch, they also opened more branches to remain competitive, and tellers’ jobs
focused on more high-value tasks in customer interaction. (Bessen, Toil and Technology 2015).
Other research finds that ‘although automation tends to reduce employment and the share of laborin national income, the creation of more complex tasks has the opposite effect, and both types ofinnovations contribute to economic growth…. Under reasonable conditions, there exists a stable
16 In his 1966 book, ‘The Tacit Dimension’ scholar Michael Polanyi argued that ‘We know more that we can tell’ –in other words, we don’t necessarily know why we carry out a particular task or reach a decision in the way we do.
Applying this theory to a discussion of which tasks can be automated, David Autor argues that the hardest tasks
to automate are those that require, ‘flexibility, judgment, and common sense—skills that we understand only
tacitly’. (Autor, Why Are There Still So Many Jobs? The History and Future of Workplace Automation 2015)17 Moravec’s paradox, named after Hans Moravic, a computer expert who, with other scholars, discovered thathigh-level reasoning requires very little computation, but low-level sensorimotor skills require enormous
computational resources.
18 Artificial intelligence has been able to produce artworks and music that its makers claim are indistinguishable

from human creations, but it is unclear whether if produced at scale, these kind of ‘creations’ would be seen byaudiences as being genuinely creative, or simply derivatives of original works, or whether it will matter if they are.
19 An OECD working paper goes further, arguing that even taking the same industry and the same task, acomparison between OECD countries reveals differences in automatibility, which the OECD ascribes to culturaldifferences in management style. In OECD countries such as the UK and US where levels of communication
between workers are high, automatibility is lower than in Germany and Italy, where communication is lower.
(Arntz, Gregory and Zierahn 2016).

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balanced growth path in which the two types of innovations go hand-in-hand’ (Acemoglu andRestrepo 2015).
It is also important to note that the fact that the labour force in one sector is shrinking does not
automatically equate to aggregate job losses – the issue is whether the losses in one sector or job
type are balanced by gains in another. For example, Bessen argues that so far, new jobs in othersectors such as manufacturing and services have replaced jobs lost in sectors such as agriculture,in which employment has fallen from around 40% of the US labour force in 1900 to less than 2%
today (Bessen 2016). Similarly, a survey by the World Economic Forum of member companies
predicted robotics will be a strong driver of employment growth in the survey’s Architecture andEngineering job family (World Economic Forum 2016). In some cases, job gains will be in new
industry sectors. For example, the relatively new ecommerce sector added 355,000 jobs in the
U.S. from 2007 to 2016 - seven times more than were lost in the general retail sector during that
period (Mandel 2017). In the UK, since 2010, jobs in warehousing have increased by 115,000,
sixteen times more than were lost in retail (RSA 2017).

SHAPING POSITIVE OUTCOMES
Labour market dynamics is a highly complex field with so many factors influencing outcomes thatmapping causality and forecasting trends is impossible to do with a high degree of accuracy.
But for all the differences of opinion among experts on which tasks, jobs and industries will be mostaffected by automation, there is a common consensus that the imperative must be to equip thecurrent and future workforce with the skills to profit from the higher-skilled – and higher paying opportunities that are being driven by automation including robotics. At the same time, a clearer
picture of low-skilled job opportunities that will be created indirectly by technology-driven innovation
should be used to enable low-skilled workers who are, for whatever reason, unable to upskill tomove into other sectors 20.
The task of equipping current and future generations of workers to benefit from the opportunities
offered by automation falls to both government and the private sector. Government must provide
policies, incentives and programmes to up- and re-skill its workforce. Meanwhile, the private sector
must invest more in skills training. As Manpower Inc.’s Senior Vice President of Innovation &Workforce Solutions, Tammy Johns put it at an Aspen Institute Roundtable on Information
Technology, increasingly, ‘career management has been outsourced to its owners’ (Bollier 2010).
The main conclusions from cited experts, with which IFR members concur, are listed below21:•

Businesses should review their organisation’s activities to assess where potential value
from automation is highest and create a strategic plan that includes both capital
investment 22 and reskilling workers (McKinsey Global Institute 2017) (Boston Consulting
Group 2015).

•

Companies must invest more in training to increase their stock of intangible capital which is
key to a sustainable productivity growth path (The Conference Board 2015).

20 Indeed, technologies such as virtual reality and Web-connected glasses may enable lower-skilled workers to perform higherskilled tasks by providing them with information in-situ.
21 There are also implications for city planners, though these go beyond the scope of this positioning paper.
However it is worth noting Lin’s finding (Lin 2009) that ‘workers are more likely to be observed in a newoccupation when they live in cities that were initially dense in both college graduates and industry variety.’
22 Given the combination of the rapid fall in robotics hard and software – projected by BCG to fall a further 20%

over the next decade – and rapid advancements in collaborative robots that are suitable and affordable for SMEs,
companies that cannot invest now should map a break-even point for adoption (Boston Consulting Group 2015).

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The Impact of Robots on Employment•

Governments should consider and implement policies that support early adoption of
automation and investment in both automation technologies and the digital infrastructureneeded to support automation (McKinsey Global Institute 2017).

•

Governments should work closer with the private sector to identify jobs being either createdor re-shaped by automation for which supply is not matching demand and to establish
education and training possibility for those jobs (McKinsey Global Institute 2017). The
German apprenticeship model should be considered in countries such as the US withlooser links between education and the private sector (PwC 2016). Governments should
also consider incentivizing lifelong learning through funding for skills training.23•

Education curricula must focus on improving basic STEM (science, technology,
engineering and mathematics) skills but also promote the human skills that robots will not
replace (creativity, empathy, systems thinking etc.) (Deloitte LLP 2015), (McKinsey Global
Institute 2017).

•

As demonstrated in this paper, robots and automation are creating more jobs than they
destroy and contribute to a shift in demand for higher-skilled, higher-income earners.
Automation makes an easy scapegoat for fears about incomes and employment that havefar broader and deep-seated causes. 24 Governments must not only address these causes,
but also ensure that debate and policy-making on automation recognises the labourenhancing features of automation technologies.

•

Some experts argue for incentives for firms to (re)-establish a higher vertical range ofmanufacturing through the implementation of robots, driven by findings that this has apositive impact on productivity (European Commission 2015).

CONCLUSION
Concern about the future of employment and jobs is causing widespread debate and political shifts.
Attention has turned to the role of automation, with automation – and robots – more often than notpresented as ‘job killers’. But this is not borne out by the facts. Research indicates that robots
complement and augment, rather than substitute for, labour and in doing so, raise the quality ofwork and the wages of those fulfilling new tasks.
Demographics, structural changes, a focus on technology innovation in areas that do not drive
productivity – such as entertainment – and the fact that we are only part-way through thisinnovation cycle are all contributing factors to low productivity growth. Studies focused specificallyon robots show a contribution to productivity growth equal to that seen in previous industrial
revolutions, with robot-driven productivity accounting for 10% of total GDP growth over 14 years
and forecasts of continued automation-driven productivity growth of up to 1.4% annually over the
next 50 years.
A concern about the decline of middle-skilled, middle-income jobs and increasing wage inequality
is warranted but cannot be attributed solely to automation. Whilst automation appears to be
increasing the demand for high-skilled, high-income employees, its impact on low-skilled, lowincome employment is less clear. Wage stagnation appears to be far more attributable to structuralissues such as employment conditions that force down wages and dampen investment by
employers in skills training. A reduction in robot usage would neither help low-skilled workers, nor
resolve these structural issues.

23 According to a report by the World Economic Forum, workers in Denmark, which allocated funding for two

weeks’ certified skills training per year for adults, are over twice as likely to consider mid-career transitions
positively (World Economic Forum 2016).
24 See McKinsey ‘Poorer Than Their Parents’ for a more thorough discussion of these causes (McKinsey Global
Institute 2016).

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Robots and automation will increasingly shape the way we work in the future, with enormous
potential for improvements in productivity, increased national competitiveness and the improved
quality, and remuneration, of work25. Governments and firms must work to create an environmentthat will enable workers, companies and nations to reap the rewards of these improvements. Thismeans supporting investments in research and development in robotics and, most importantly,
providing education and skills re-training for existing and future workers.

25 Rapid advances in artificial intelligence will vastly expand the range of capabilities of robots, opening up newopportunities in sectors such as healthcare, which need productivity improvements to cope with ageing
populations. Fears about robots becoming smarter than, and out of the control of their makers are provoking
considerable debate. AI experts such as Stuart Russell, believe it is possible to design AI systems whose goals
do not conflict with human values and instead support productive outcomes (Russell June 2016).

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The Impact of Robots on Employment

REFERENCES
Accenture. 2016. Why Artificial Intelligence Is the Future of Growth. Accenture.
Acemoglu, David, and P Restrepo. 2015. The Race Between Man and Machine: Implications ofTechnology for Growth, Factor Shares and Employment.
Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. 2016. The Risk of Automation for Jobs in OECDCountries: A Comparative Analysis. OECD Social, Employment and Migration Working Paper No. 189,
Paris: OECD Publishing.
Asian Development Bank. 2018. Asian Development Outlook 2018.
Autor, David. 2015. Why Are There Still So Many Jobs? The History and Future of Workplace
Automation. Journal of Economic Perspectives Volumn 29, Number 3.
Autor, David, and Anna Salomons. 2017. Does Productivity Growth Threaten Employment?
Barclays. 2015. Future-proofing UK manufacturing. Barclays.
Berg, Andrew, Edward Buffie, and Luis-Felipe Zanna. 2016. Robots, Growth and Inequality. Finance &Development Vol. 53, No.3.
Berger, Susanne. 2014. How Finance Gutted Manufacturing. Boston Review. 1 April. Accessed
January 29, 2017. http://bostonreview.net/forum/suzanne-berger-how-finance-gutted-manufacturing.
Bessen, James. 2016. Computers Don’t Kill Jobs but Do Increase Inequality. Harvard Business
Review.
Bessen, James. 2016. How Computer Automation Affects Occupations: Technology, jobs, and skills.
Law & Economics Working Paper No. 15-49, Boston University School of Law.
Bessen, James. 2015. Toil and Technology. Article, Finance and Development, March 2015.
Bollier, David. 2010. The Future of Work: What it Means for Individuals, Businesses, Markets andGovernments. The Aspen Institute.
Boston Consulting Group. 2015. The Robotics Revolution.
Brynjolfsson, E, D Rock,, and C Syverson . 2017. Artificial Intelligence and The Modern Productivity
Paradox: A Clash of Expectations And Statistics. NBER Working Paper 24001.
Brynjolfsson, Erik, and JooHee Oh. n.d. MIT Initiative on the Digital Economy. Accessed April 04,
2017. http://ide.mit.edu/research-projects/attention-economy-measuring-value-free-goods-internet.
Centre for Economics and Business Research. 2017. The impact of automation. CEBR.
CISCO and Oxford Economics. 2017. The A.I. Paradox: How Robots Will Make Work More Human.
Citi and Oxford Martin School. 2016. Technology at Work V2.0.
n.d. Collaborative Robots. robotics.org. Accessed February 2, 2017.
http://www.robotics.org/userAssets/riaUploads/file/13CollaborativeRobotTechnologyandCustomerApplicationsUniversal-ScottMabie.pdf.
Cowen, Tyler. 2016. Economic Development in an 'Average is Over' World. George Martin University.
Deloitte LLP. 2015. From Brawn to Brains: The Impact of Technology on Jobs in the UK.

April 2017 (updated 2018)

Positioning Paper

14

A positioning paper by the International Federation of Robotics

Economix. 2016. Arbeitsmarkt 2030: Wirtschaft und Arbeitsmarkt im digitalen Zeitlater, Prognose
2016.
European Commission. 2015. Analysis of the impact of robotic systems on employment in theEuropean Union. Luxembourg: Publications Office of the European Union.
Freeman, Richard. 2015. Who Owns the Robots Rules the World. IZA World of Labor.
Frey, Carl Benedikt, and Michael Osborne. 2013. The Future of Employment: How Susceptible are
Jobs to Computerisation. Working Paper, Oxford Martin School.
Goos, Maarten, Alan Manning, and Anna Salomons. 2014. Explaining Job Polarization: RoutineBiased Technological Change and Offshoring. American Economic Review, Vol, 104, issue 8 pages
2509-26.
Gordon, Robert J. 2012. Is US economic growth over? Faltering innovation confronts the six
headwinds. Policy Insight No. 63, Center for Economic Policy Research.
Graetz, Georg, and Guy Michaels. 2015. Robots at Work. Centre for Economic Performance.
Holzer, Harry. 2015. Job Market Polarization and U.S. Worker Skills: A Tale of Two Middles.
Brookings Institution.
Institute for Employment Research, CEPR and Düsseldorf Institute for Competition Economics. 2017.
German Robots – The Impact of Industrial Robots on Workers.
International Federation of Robotics. 2016. IFR Press Release World Robotics Report. ifr.org. 29
September. Accessed February 01, 2017. http://www.ifr.org/news/ifr-press-release/world-roboticsreport-2016-832/.
International Federation of Robotics. 2016. World Robotics Industrial Robots 2016.
IMF. 2004. How Will Demographic Change Affect the Global Economy.
Janeway, William. 2013. Does Economic Growth Have a Future in the United States? Project
Syndicate. 17 January. Accessed February 01, 2017. https://www.projectsyndicate.org/commentary/why-economic-growth-is-not-over-by-william-janeway.
Katz, Laurence, and Alan Krueger. 2016. The Rise and Nature of Alternative Work Arrangements in
the United States, 1995-2015. NBER Working Paper No. 22667, National Bureau for Economic
Research.
Lin, Jeffrey. 2009. Technological Adaptation, Cities, and New Work. Working Paper 09-17, Federal
Reserve Bank of Philadelphia.
Mandel, Michael. 2017. The Creation of a New Middle Class?: A Historical and Analytic Perspective
on Job and Wage Growth in the Digital Sector, Part I. Public Policy Institute.
McKinsey Global Institute. 2017. A Future That Works: Automation, Employment and Productivity.
McKinsey Global Institute. 2016. Poorer Than Their Parents: Flat or Falling Incomes in Advanced
Economies. McKinsey Global Institute.
Michaels, Guy, Ashwini Natraj, and John Van Reenen. 2014. Has ICT polarized skill demand?
Evidence from eleven countries over 25 years. Review of Economics and Statistics, No. 96, pp. 60-77.
Ministry of Economy, Trade and Industry, Japan. 2015. Japan's Robot Strategy. Ministry of Economy,
Trade and Industry.

15

Positioning Paper

April 2017 (updated 2018)

The Impact of Robots on Employment

Muro, Mark, and Scott Andes. 2015. Robots Seem to Be Improving Productivity, Not Costing Jobs.
Harvard Business Review.
OECD. 2016. OECD Compendium of Productivity Indicators 2016. OECD Publishing.
OECD 2015. The Future of Productivity. OECD Publishing.
OECD. 2017. OECD Employment Outlook 2017.
Pennycook, Matthew, Giselle Cory, and Vidhya Alakeson. 2013. A Matter of Time: The rise of zerohours contracts. Resolution Foundation.
PwC. 2014. The New Hire: How a New Generation of Robots is Transforming Manufacturing.
PwC. 2016. Upskilling manufacturing: How technology is disrupting America's industrial labor force.
Price Waterhouse Coopers in conjunction with the Manufacturing Institute.
Reshoring Initiative. 2015. Reshoring Initiative Data Report 2015. reshorenow.org. Accessed February
02, 2017. http://reshorenow.org/content/pdf/2015_Data_Summary.pdf.
Rosenfeld, Jake, Patrick Denise, and Jennifer Laird. 2016. Union decline lowers wages of nonunion
workers. Economic Policy Institute.
RSA. 2017. The Age of Automation: Artificial Intelligence, robotics and the future of low-skilled work.
Russell, Stuart. June 2016. Should We Fear Supersmart Robots? Scientific American, pp. 58-59.
Sachs, Jeffrey, and Laurence Kotlikoff. 2012. Smart Machines and Long-Term Misery. Working Paper
18629, National Bureau of Economic Research.
The Conference Board. 2015. Productivity Brief 2015.
Weill, David. 2014. The Fissured Workplace: Why Work Became So Bad for So Many and What Can
Be Done to Improve It. Harvard University Press.
World Economic Forum. 2016. The Future of Jobs: Employment, Skill and Workforce Strategy for the
Fourth Industrial Revolution. Global Challenge Report, World Economic Forum.
Zierahn, Ulrich, Terry Gregory, and Melanie Arntz. 2016. Racing With or Against the Machine?
Evidence from Europe. Discussion Paper No. 16-053, ZEW Centre for European Economic Research.

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