The Impact of Robots on
Productivity, Employment
and Jobs
A positioning paper by the International
Federation of Robotics
April 2017

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 15%
increase in 2015 over theprevious year. The IFR
estimates that over 2.5
million industrial robots will
be at work in 2019,representing an averageannual growth rate of 12%
between 2016 and 2019
(International Federation of
Robotics 2016).

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 human intervention. Whilst somedefinitions require the task to be completed by a physical machine that moves andresponds 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 more axes, which may be either fixed in place or mobile
for use in industrial automation applications.
Reprogrammable: whose programmed motions or auxiliary functions may bechanged without physical alterations;
Multipurpose: capable of being adapted to a different application with physicalalterations;Physical alterations: alteration of the mechanical structure or control system
except for changes of programming cassettes, ROMs, etc.
Axis: direction used to specify the robot motion in a linear or rotary mode

Driving the increase in public- A service robot is a robot that performs useful tasks for humans or equipment
interest in robotics andexcluding industrial automation application. Note: The classification of a robot into
automation is both aindustrial robot or service robot is done according to its intended application.
fascination with the potential- A personal service robot or a service robot for personal use is a service robotof these technologies toused for a non-commercial task, usually by lay persons. Examples are domesticchange our lives, and a fear
servant robot, automated wheelchair, personal mobility assist robot, and petof the impact of automation –exercising robot.
including robotics – on jobs.
- A professional service robot or a service robot for professional use is a service
These fears are tied into
robot used for a commercial task, usually operated by a properly trained operator.
broader geo-political andExamples are cleaning robot for public places, delivery robot in offices or hospitals,social shifts driven by issues
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 of asuch as trade policy androbot or a robot system.
immigration that, overall,
contribute to a sense of
IFR members manufacture industrial robots, used in manufacturing, and service
insecurity about the
robots, used in a variety of environments both professional and personal to perform a
useful task.
employment prospects ofcurrent and future
generations. Consequently,
many headlines focus on the potential negative outcomes of automation. This risks overshadowing
the very real positive contribution of automation and robotics to productivity, competiveness and job
creation. In addition, it could undermine discussion and action on the measures that should betaken to enable countries, organizations and individuals 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:



2



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



Increased productivity can lead to increased demand, creating new job
opportunities. These ‘spillovers’ can be seen within an individual organization, along an
industry sector’s value 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 increasein demand 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% of jobs are fully automatable. Increasingly, robots are used to complement andaugment labour activities; the net impact on jobs and the quality of work is positive.
Automation provides the opportunity 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
the proven positive impact of robotics on employment and wages. It would deter badlyneeded 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 investment that improves productivity, increases competitiveness, creates
more jobs than it replaces, and leads 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.

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). Looking ahead, the McKinsey Global Institute predicts that up to half ofthe 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 by1
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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The Impact of Robots on Employment

A Note on the Productivity Paradox
Global productivity has been slowing down since the turn of the century, albeit at different times and rates for mature,developing and emerging economies. Some scholars such as Robert Gordon, have asked whether economic growth, as wehave known it in the past, is now over, and have questioned why the third industrial revolution represented by computers andthe internet does not seem to be delivering the same boost to productivity as the first and second industrial revolutions whichgave 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 of development 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 most mature economies (The Conference Board, 2015).
 Other scholars such as economist Erik Brynjolfsson point to the fact that the impact of free services such as searchfunctionality and free communications services are not reflected in GDP and therefore also not in productivity figures.
 The OECD points to structural dimensions such as a lack of investment in ‘knowledge-based 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’3 (OECD, 2015).
 A report by the Oxford Martin School and Citigroup argues that firms may not yet have adjusted 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 survey of 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 in industrial countries by around 0.5% per annum over the next 20 years
compared to the 1990 – 2010 period (Citi and Oxford Martin School, 2016). McKinsey Global Institute argues 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 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 and the current technological wave which has created the digitalised
economy’ and notes that ‘a number of factors may be behind the paradox such as skills mismatches, sluggish investment,
and declining business dynamism, particularly post crisis’. (OECD, 2016)
 Finally, as private equity executive William Janeway points out, it may simply be too early to tell, given that we are not evenat 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 are strong 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, andautomation in particular.

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
South Korea, which has the highest robot density4 - 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).
Finally, there is 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,

3

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.
4
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).

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A positioning paper by the International Federation of Robotics
Regional and national variation
The current and future impact of automation varies between regions and between countries and a comparison is beyond thescope of this paper. However, data from IFR and other sources suggests:
 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 sales of robots to any one single market every year since 2013. The
Chinese government has included a focus on robotics in its 10 year strategy. In order to achieve its target of a robot densityof 150 units per 10,000 workers by 20205, Chinese companies will have to install around 650,000 new industrial robots
between 2016 and 2020 – 2.5 times more than were installed globally in 2015 (International Federation of Robotics, 2016)
 Japan currently has the largest stock of industrial robots in operation, primarily in the automotive industry. 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 ofrobots in the non-manufacturing 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 from automation. (Boston Consulting Group, 2015)

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).

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Robotics Development Plan (2016-2020),China

5The Impact of Robots on Employment
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 tasks. This in turn places a premium on higher-skilled labour in the
sectors in which automation has substituted for labour, but also may create new lower-skilled jobs
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).
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 RRTC6 overcompensated the job-destructing
effect.’7 (Zierahn, Gregory and Arntz 2016).
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). And countries with the highest robot
density, notably Germany and Korea, have among the lowest unemployment rates.
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).

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Routine-Reducing Technological Change
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
7

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A positioning paper by the International Federation of Robotics

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 - isconsistently growing, as are skill
needs within traditionally unskilledjobs’ to the extent that employers
are struggling to fill demand(Holzer 2015). This applies tosectors such as manufacturingthat have already invested 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, Manning andSalomons shows far higher gains
to high-skilled hours worked thanlow-skilled labour. Automation is a
key driver of the shift to the highskilled category. For example,
Bessen finds that that jobs havegrown faster in occupations thatuse computers (Bessen 2016)
and economists Guy Michaels,
Ashwini Natraj and John Van

Working Together: Examples of Human Robot
Collaboration
Industrial robots have until recently been separated from humans – oftenby physical cages. Due to recent advances in technology, a newer trend,
which is also spilling out of the factory into non-manufacturing 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 rangeof shapes and materials; integrated vision guidance and enhanced
sensors that enable robots to sense and respond to their environment;
and the ability to respond to both voice and gestured commands – arebringing robots out of their cages and on to the factory floor in close
proximity to their human co-workers, performing tasks such as packing
finished items into boxes and removing defective items from productionlines. 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 these robots
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 to carry 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 tocause wrist-strain for workers. Canadian electronics manufacturer
Paradigm Electronics uses robots to carry out delicate polishing andbuffing tasks on loudspeakers, working with employees who handle thefinal finish and quality check. These robots have led to a 50%
productivity increase, but with no job losses as employees who
previously carried out these tasks have been promoted from machine
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 both manufacturing and nonmanufacturing sectors between 2016 and 2019. These include
Automated Guided Vehicles (AGVs) that can move around factories,
warehouses, hospitals and other public areas to move products throughproduction processes, fetch goods and parts, load pallets and checkstatus levels of machines and stocks, functioning either independently or
as human assistants.
The IFR projects an increase of 42 million service robots for personaland domestic use between 2016 and 2019 in categories such as floor
cleaning, lawn-mowing, entertainment and elderly assistance. The paceof development, and social acceptance, of domestic robots is being
partly driven by very rapid advances in voice recognition and natural
language programming.
Healthcare is a particularly promising sector for service robots, withapplications ranging from exoskeletons that enable workers to deal
ergonomically with heavy loads as well as recover from injury or
substitute for limbs that are no longer mobile - to robot-assisted surgery.

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The Impact of Robots on Employment
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 contracts8, the ‘gig economy’9, and lower union participation on wages10 (Pennycook, Coryand 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.
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).

8 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 1624 age-bracket than older workers. (Pennycook, Cory and Alakeson 2013)9
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).
10 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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A positioning paper by the International Federation of Robotics

Which tasks can and can’t be automated?
According to most experts, the types of tasks that can most easily be automated are those that have a high degree of repetition ineither 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 other automation tools will not be able toperform any time soon, even assuming the successful adoption of advancing developments in artificial intelligence that arefocused on autonomous learning. Tasks requiring high levels of creativity, empathy, persuasion, an understanding of whichknowledge to apply in which situation to reach a productive decision11 and a high level of sensorimotor skills12 are considered
unlikely to be automatable in the near future.13

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 people14.
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 stablebalanced 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%

11 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 2015).
12 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.
13 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.
14 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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The Impact of Robots on Employment
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).
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. 15
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 below16:
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
investment17 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).

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.18

15

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.

16 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.’
17 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).
18 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).

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A positioning paper by the International Federation of Robotics


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.19 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.
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 work20. 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.

19 See McKinsey ‘Poorer Than Their Parents’ for a more thorough discussion of these causes (McKinsey Global

Institute 2016).
20
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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