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Don't blame it on the machines: Robots and employment in Europe

David Klenert, Enrique Fernández-Macías, José-Ignacio Antón 24 February
2020

Opinion polls reveal that Europeans are greatly concerned about the
economic consequences of advanced technologies, but our understanding
of this relationship is still incomplete. This column assesses the
impact of one such technology – industrial robots – on employment in
Europe over the last two decades. Combining industry-level data on
employment with data on robot adoption, it finds that robot use is
linked to a small but significant increase in employment. Contrary to
some previous studies, it does not find evidence of robots reducing the
share of low-skill workers across Europe.

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[63]A

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Wolfgang Dauth, Sebastian Findeisen, Jens Südekum, Nicole Woessner

In the last couple of decades, the Digital Revolution has unleashed
rapid progress in many advanced technologies such as artificial
intelligence and robotics. Especially in the aftermath of the Great
Recession, this has sparked an intense debate on the future of work,
both in academic circles and amongst the general public. In this
debate, the image of robots replacing workers – most notably,
low-skilled workers – is a recurring theme. For instance, according to
a recent Eurobarometer survey 72% of Europeans believe that “robots and
artificial intelligence steal peoples’ jobs” (European Commission
2017).

Until recently, there were few reliable sources of data on the use of
robots that would allow for an empirical analysis of their impact on
employment. But in the last few years, the International Federation of
Robotics (a global association of robot producers) has provided data on
the deployment of one such technology – industrial robots – by country
and industry, starting in the early 1990s (IFR 2019). There have been
several papers published in recent years based on this dataset that
report significant negative employment effects of robots. For instance,
Acemoglu and Restrepo (2019) find that each robot installed in the US
replaces six workers; Chiacchio et al. (2018) find a replacement rate
of between three and four workers per robot in Europe using a similar
approach.

This column summarises the main findings of our recent study analysing
the effect of industrial robots on total and low-skill employment,
using more up-to-date data on robots and employment and relaxing some
of the assumptions of earlier studies (Klenert et al. 2020).

Industrial robots in Europe

Industrial robots can be characterised as more or less stationary
reprogrammable robotic ‘arms’ that perform manual tasks such as
handling, welding, and moulding. They are far from humanoid robots in
any sense.^1 These types of industrial robots have been around since
the 1980s, but their broad deployment took place over the last two and
a half decades (see Figure 1). The overwhelming majority of industrial
robots are used in manufacturing, with the automotive sector (NACE Rev.
2 sectors 29-30) accounting for roughly half of the operational robot
stock in Europe in 2015 (see Figure 2).

Figure 1 Robots in operation in different EU countries

Note: Countries are grouped as follows. Nordic countries and UK:
Denmark, Sweden, Finland, UK. Other EU15: Austria, Belgium, Greece,
Ireland, Netherlands, Portugal. Other EU28: Bulgaria, Croatia, Estonia,
Latvia, Lithuania, Malta, Romania, Slovenia. Visegrad: Hungary, Poland,
Czech Republic and Slovakia. Luxembourg and Cyprus are not contained in
the sample.

Figure 2 Distribution of robots by sector as a share of total robots

Since the 1970s, the share of employment in manufacturing in Europe has
been consistently decreasing relative to other sectors (see Figure 3),
while the number of industrial robots has been on the rise since the
mid-1990s (Figure 1). Whether robots have contributed to this trend
and, if so to what extent, can only be answered by a thorough
econometric analysis, taking into account structural differences
between countries and industries, as well as parallel trends regarding
the formation of ICT capital, the capital/labour ratio, and other
factors.

Figure 3 Employment shares of manufacturing in Europe

There have been several studies on this subject in recent years, which
can be classified by the type of data used. Studies based on
microeconomic data tend to find a neutral or positive correlation
between employment and robot deployment, suggesting a complementarity
between robots and jobs, even for low-skilled employment (Dauth et al.
2017, Domini et al. 2019, Jäger et al. 2016, Koch et al. 2019). By
contrast, studies relying on aggregate data – usually measured at the
sectoral and national level – tend to find a negative correlation
between robots and employment, especially for low-skilled workers
(Acemoglu and Restrepo 2019,Chiacchio et al. 2018, Graetz and Michaels
2018).

Robots and employment

Despite the fact that we also rely on aggregate data, our analysis
shows that in Europe, in the period 1995 to 2015, industrial robot use
is positively associated with total employment.^2 One additional robot
per 1,000 workers (in 1995) is correlated with an increase in total
employment of 1.3 (+/−0.2)%. Furthermore, we find no evidence of a
negative relationship between robot use and low-skill employment, which
also contrasts with some previous studies. Our results are very robust
across a wide range of assumptions, estimator choices, sector
selections and time periods.

There are several factors behind this outcome. First, even if robots
are a labour-saving technology, they can induce demand effects that can
lead to employment growth (Bessen 2018). Second, investment in robots
and employment may move together, since both reflect underlying
variables such as the resilience, competitiveness, or innovative
capacity of national industries. Third, robots and automation
technologies generally do not replace entire jobs but only certain
tasks (Acemoglu and Autor 2011, Tolan et al. 2020). This may lead to a
restructuring of the task content of different jobs, enhancing labour
productivity and, potentially, employment.

With regard to earlier findings, our results are more in line with
studies based on microeconomic data (Domini et al. 2019, Jäger et al.
2016, Koch et al. 2019). By contrast, some of our results differ
significantly from studies using similar aggregate data, most
importantly Acemoglu and Restrepo (2019), Chiacchio et al. (2018) and
Graetz and Michaels (2018). In the full paper, we discuss in detail the
reasons behind these differences, mostly due to the different data
sources used (we use the EU Labour Force survey for all employment
analysis), the fact that we cover eight or more additional years and
that we relax some key assumptions of those earlier studies (for
instance, with respect to how robot density is calculated for each
sector and country).

There is significant variation between sectors both within and between
countries, which is the reason why in our analysis we account for
specific country-sector characteristics by including country-sector
fixed effects. When these structural differences are not accounted for,
we still find positive (and in most cases, significant) correlations
between total employment and robotisation, but the explanatory power is
lower. Furthermore, to account for macroeconomic trends such as
economic crises or a general downward trend in manufacturing jobs that
affect all sectors to a similar extent, we control for time fixed
effects that are not country or sector specific. If time trends are not
controlled for, these general negative employment trends can be wrongly
attributed to robotisation. In general, our results are very robust
with regard to including additional control variables (such as the ICT
capital share, capital accumulation, capital/labour share), including
non-manufacturing sectors, using different time periods, using
different calculations of the stock of robots, using different
indicators for robotization, and using different estimators.

There are some caveats to keep in mind when interpreting these results.
First, these findings refer to recent and ongoing trends, but cannot be
generalised into the future. The kinds of industrial robots analysed in
this study have been around since the 1990s and should not be confused
with more advanced technologies such as robots enhanced by artificial
intelligence, which are not yet deployed at a significant scale. Once
they are used in mainstream manufacturing, such technologies may have
more disruptive potential. Second, since the overwhelming majority of
industrial robots is used in manufacturing sectors, we cannot interpret
these results outside of the manufacturing context. That said, the use
of robots outside manufacturing is still mostly experimental or
anecdotal and has only limited economic significance at the moment (of
course, this could and possibly will change in the future). Third, the
positive correlation between robotisation and employment is significant
and robust but small compared to other variables. Fourth, we could not
find a convincing instrumental variable (IV) for robot adoption so we
cannot claim causality in the relationship between robots and
employment. However, most previous research on the impact of robots on
the labour market does not report significant differences between OLS
and IV estimates, and our main findings hold for many different OLS
specifications.

Summary and policy implications

In summary, we found no evidence that industrial robots have destroyed
jobs or reduced the employment share of low-skill workers in Europe in
recent years. In fact, robot adoption tends to be positively associated
with aggregate employment, although the relationship is small compared
to other factors affecting European employment in recent years.

This has important implications for policy. Blaming robots – or in a
broader sense, automation – for recent troubling developments in
European labour markets, such as rising wage inequality or the
polarisation of employment opportunities, may shift public attention
from other, more prominent causes such as labour market deregulation,
the weakening of collective bargaining structures or a general lack of
public spending (Mishel and Bivens 2017, Krugman 2019). The obsessive
focus on robots and automation in the recent debates on the future of
work may have also contributed to unjustified feelings of economic
anxiety and fatalism, potentially damaging the political
debate. Finally, our findings also suggest that recent policy
proposals, such as a specific tax on robots, might in fact be
ineffective for its own purposes and counterproductive in other ways,
since robot use is associated with increased productivity (Graetz and
Michaels 2018, Jungmittag and Pesole 2019).

Authors’ note: The views expressed in this column are those of the
authors and cannot be taken as representing the official position of
the European Commission.

References

Acemoglu, D and P Restrepo (2019), “[68]Robots and jobs: Evidence from
US labor markets”, Journal of Political Economy (advance online
publication).

Acemoglu, D and D Autor (2011), “Skills, tasks and technologies:
Implications for employment and earnings”, in Handbook of Labor
Economics, Vol. 4, Elsevier, pp. 1043-1171.

Bessen, J (2018), “[69]AI and Jobs: The Role of Demand”, NBER Working
Paper No. 24235.

Chiacchio, F, G Petropoulos and D Pichler (2018), “[70]The impact of
industrial robots on EU employment and wages: a local labour market
approach”, Bruegel Working Paper Issue 2, April.

Dauth, W, S Findeisen, J Südekum and N Wößner (2017), [71]“German
robots: the impact of industrial robots on workers”, IAB Discussion
Paper No. 30/2017.

Domini, G, M Grazzi, D Moschella and T Treibich (2019), “[72]Threats
and opportunities in the digital era: automation spikes and employment
dynamics”, LEM Working Paper Series 2019/22, Sant'Anna School of
Advanced Studies.

European Commission (2017), “Attitudes towards the impact of
digitisation and automation on daily life”, Eurobarometer Special
Report 460, European Commission.
[73]https://ec.europa.eu/jrc/communities/sites/jrccties/files/ebs_460_e
n.pdf

Graetz, G and G Michaels (2018) “Robots at work”, Review of Economics
and Statistics 100(5): p.753-768.

IFR (2019), World Robotics 2019 edition, dataset, International
Federation of Robotics.

Jäger, A, C Moll and C Lerch (2016), “[74]Analysis of the impact of
robotic systems on employment in the European Union—2012 data update”,
Publications Office of the European Union.

Jungmittag, A and A Pesole (2019), “The impact of robots on labour
productivity: A panel data approach covering nine industries and 12
countries”, JRC Working Papers on Labour, Education and Technology,
JRC118044, European Commission.

Klenert, D, E Fernández-Macías and J I Antón-Pérez (2020), “[75]Do
robots really destroy jobs? Evidence from Europe”, JRC Working Papers
on Labour, Education and Technology, 2020/01.

Koch, M, I Manuylov and M Smolka (2019), “[76]Robots and firms”, CESifo
Working Paper No. 7608.

Krugman, P (2019), “[77]Democrats, Avoid the Robot Rabbit Hole. The
automation obsession is an escapist fantasy”, The New York Times, 17
October.

Mishel, L and J Bivens (2017), “[78]The Zombie Robot Argument Lurches
on”, Economic Policy Institute Report.

Tolan, S, A Pesole, F Martínez-Plumed, E Fernández-Macías, J
Hernández-Orallo and E Gómez (2020), “Measuring the occupational impact
of AI beyond automation: tasks, cognitive abilities and AI benchmarks”,
JRC Working Papers on Labour, Education and Technology, forthcoming.

Endnotes

1 An industrial robot is defined by the International Organisation for
Standardisation as "an automatically controlled, reprogrammable,
multipurpose manipulator programmable in three or more axes, which can
be either fixed in place or mobile for use in industrial automation
applications" (ISO 8373:2012).

2 Our analysis combines annual data on robot installations in Europe
from the International Federation of Robotics (IFR) with data on
aggregate employment and employment by skill level from the EU Labour
Force Survey (EU LFS). Additional control variables are taken from EU
KLEMS growth and productivity accounts. The resulting sample covers the
years 1995 to 2015, 14 different sector aggregates and 14 EU countries.

[79]a

[80]A

Topics: [81]Labour markets [82]Productivity and Innovation

Tags: [83]robots, [84]robotisation, [85]automation, [86]Job
destruction

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* [91]Printer-friendly version

[92]David Klenert

Economist, European Commission
[93]Enrique Fernández-Macías

Researcher, Joint Research Centre of the European Commission
[94]José-Ignacio Antón

Associate Professor, University of Salamanca

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