Technology scenario : employment implications of radical automation.
Date: 2019-04-10
Institutions: Eurofound
Subjects: automation, digitisation, hourly wage, impact of information technology, industrial manufacturing, industrial policy, job cuts, labour market, processing industry, research report, simulation, technological change

RESEARCH REPORT

Future of manufacturing
Technology scenario:  
Employment implications 
of radical automation

SERIES Manufacturing employment outlook

Future of manufacturing
Technology scenario: 
Employment implications 
of radical automation

When citing this report, please use the following wording: 
Eurofound (2019), Technology scenario: Employment implications of radical automation, Publications Office of the 
European Union, Luxembourg. 

Authors: Richard Lewney and Eva Alexandri (Cambridge Econometrics), Donald Storrie (Eurofound)

Research manager: Donald Storrie

Eurofound project: Future of Manufacturing in Europe

Acknowledgements: This overview report is the result of the analysis carried out by Cambridge Econometrics, together 
with the Warwick Labour Market Extension model and Eurofound’s European Jobs Monitor.

Luxembourg: Publications Office of the European Union

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Contents

Executive summary 

Introduction 

1  Scenario design 

Qualitative description 

Detailed description of the modelling assumptions 

How the modelling represents the impacts 

2  Results 

Global impacts 

EU impacts 

Sectoral impacts 

Occupational impacts 

3  Concluding remarks 

References 

Annex: Occupations that potentially could be automated 

1

3

5

5

6

9

11

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12

14

15

21

23

25

Executive summary

In this report, a global macrosectoral model is used 
to make a quantitative assessment of the potential 
employment impact of accelerated automation, primarily 
in light of digitisation. The model encompasses the major 
global regions but focuses mainly on the EU. It covers 
the whole economy but has particular relevance for the 
manufacturing sector. See Box 1 for a brief description of 
the applied E3ME model.

Since 2008, there have been many estimates of the 
possible employment implications of automation covering 
many countries and sectors. Typically, these examine 
the task content, which means what is actually done on 
the job, to assess susceptibility to automation. While 
often not reported as such, this will typically be well in 
excess of the likely rate of automation over time. In this 
study, the analysis extends previous studies to assess 
the impact on jobs by 2030. First, estimates are found 
from previous studies of the proportion of jobs in each 
sector and country that it would be technically feasible to 
automate. At least for manufacturing in Europe, rates are 
typically very high, approaching, and indeed sometimes 
exceeding, 50%. These are then set in relation to estimates 
of the investment required to automate these jobs and 
serve as an upper limit on the additional investment 
that is regarded plausible, as a percentage of GDP. This 
includes assumptions for the reduction in the cost of 
investment over time. The model applied also estimates 
the employment impact of changes in the supply chains 
to reflect the impact of digitisation on the material and 
transport intensity of production. These are applied to 
the model to find the economic consequences of the 
pass-through of lower unit costs, supply chain and income 
multiplier effects, distinguishing the initial assumptions 
for direct job losses due to automation from the final 
consequences for jobs once these wider economic 
transmission mechanisms are taken into account.

Because the future investment cost of automation is very 
uncertain, two cases are explored: a high-cost case, which 
implies slower uptake and hence fewer direct job losses, 
and a low-cost case in which uptake is faster and direct job 
losses are greater. Because the impact depends crucially 
on how the benefits of greater automation are distributed 
in society, a variant of the low-cost case in which workers 
enjoy a reduction in working hours, while maintaining pay, 
is also modelled. This mitigates some of the shift in the 
share of national income from wages to profits that would 
otherwise occur and that has been a driver of the increase 
in inequality experienced in many countries over the past 
three decades.

Capital equipment is substituted for labour, and the 
producers of capital equipment therefore see a substantial 
increase in demand. The additional value generated from 
the increase in labour productivity is shared among higher 
profits for firms, lower prices for consumers and higher 
wages for workers who retain their jobs. In the past, the 
large-scale introduction of labour-saving technological 
progress has displaced jobs in the short term, but in the 

longer term the income generated from lower production 
costs is spent on other goods and services – including 
products that did not previously exist – and new jobs 
are created elsewhere in the economy. In the scenarios 
modelled here, the job displacement effect still dominates 
results at the end of the forecasting period of 2030. There 
is a shift in the distribution of income from wages to profits 
and a related shift in final expenditure from consumer 
spending to investment. The negative impact on real 
household disposable income and spending of the loss 
of wage income outweighs the positive impact of lower 
consumer prices. Although not explicitly modelled, the 
shift in the income shares of labour and capital would 
increase the inequality of income distribution and depress 
consumer spending, because richer households spend a 
smaller proportion of their income. This shift is mitigated 
somewhat in the scenario that assumes that workers 
enjoy a pay-compensated reduction in working hours, 
although whether this could be achieved by regulation, as 
is assumed, is a matter of political judgement.

The scale of job loss expected in 2030, as a proportion 
of the jobs projected for 2030 in a baseline scenario with 
no acceleration in automation, is highest in the EU (10% 
in the high-cost scenario, 16% in the low-cost scenario). 
The corresponding numbers for the United States (US) are 
9% and 14% respectively. The potential job loss is high in 
China and India as the rate of automation there is curbed 
by the very large scale of investment implied.

Within the EU, manufacturing, utilities, and transport 
and communications are the sectors with the largest 
proportional job losses in all three scenarios. Employment 
in manufacturing and utilities is expected to be 20% lower 
than the baseline in the high cost/low uptake case, rising 
to 30–35% in the low cost/high uptake case. All sectors see 
a significant improvement relative to the low-cost case 
if the benefits of automation are shared among workers 
by raising the hourly wage while reducing hours worked, 
particularly distribution, retail, hotels and catering, which 
is the sector most directly affected by household spending.

In contrast to the baseline – in which there are growth 
areas among many white-collar, non-manual occupations 
in the professional and associate professional categories 
as well as among a few blue-collar, manual occupations, 
including some less skilled occupations such as cleaners 
and labourers – all three technology scenarios paint a 
much more negative picture. Significant job losses are 
still projected for clerks and many skilled manual trades, 
as they are in the baseline, but these are now sharper 
and accompanied by significant declines for many other 
skilled, semi-skilled and unskilled occupations.

We do not model occupational or geographical frictions in 
the redeployment of workers made redundant, but past 
experience of industrial restructuring has shown that these 
can be significant with severe consequences for long-term 
unemployment, withdrawal from the labour force and 
social exclusion.

1

The results therefore highlight the importance of:

  a competitive market environment to promote full 
pass-through of productivity benefits to consumers

  an innovative and globally competitive production 

capability in Europe in the supply of automation and 
digitisation equipment and software, to capture the 
jobs stimulated by investment in the new technologies

 

 

the social and political issues raised by the further 
projected shift in income from labour to capital 
and the possibility of mitigating this through 
regulation

retraining and other active labour market policies to 
mitigate the impact of restructuring on those whose 
jobs are at risk

Box 1: The E3ME model

E3ME is a global macroeconometric model designed to address major economic and economy-environment policy 
challenges. Developed over the last 20 years by Cambridge Econometrics, it is one of the most advanced models of its 
type. Its strengths are listed below.

	 It offers a high level of disaggregation, enabling detailed analysis of sectoral and country-level effects from a 

wide range of scenarios. Social impacts are important model outcomes.

	 Its econometric specification addresses concerns about conventional macroeconomic models and provides a 
strong empirical basis for analysis. It can fully assess both short- and long-term impacts and is not limited by 
many of the restrictive assumptions common to computable general equilibrium models.

	 It enables integrated treatment of the world’s economies, energy systems, emissions and material demands. 

This enables it to capture two-way links and feedback among these components.

E3ME covers 59 global regions, with a detailed sectoral disaggregation in each one, and projects annually up to 2050. 
It is frequently applied at national level, in Europe and beyond, as well as for wider (European and global) policy 
analysis (Cambridge Econometrics, undated).

The baseline projection to which the projections in this report are compared incorporate the Eurostat population 
forecast available in 2017 and the short-term macroeconomic forecast produced by DG ECFIN in May 2017 (see 
Cedefop and Eurofound, 2018).

Introduction

Automation has a long historic association with human 
and economic development. Currently, most prominence 
is placed on digital information and communication-
based technologies, not least robotics and, most recently, 
artificial intelligence (Eurofound, 2018). While these 
technologies can be applied even outside manufacturing, 
it is still arguably in material production that most of the 
productivity-enhancing potential will be realised. Thus, 
the subject matter of this report is an important part of 
the Future of Manufacturing in Europe pilot project as 
proposed by the European Parliament and delegated to 
Eurofound by the European Commission (DG GROW).

Much of the discussion and policy focus in manufacturing 
in Europe has been structured around the concept of 
Industry 4.0. This term was coined in Germany to capture 
ongoing and prospective automation in manufacturing. 
Chancellor Angela Merkel defined it as ‘the comprehensive 
transformation of the whole sphere of industrial 
production through the merging of digital technology 
and the internet with conventional industry’ (Davies, 
2015, p. 10). McKinsey Global Institute defined Industry 
4.0 as ‘digitization of the manufacturing sector, with 
embedded sensors in virtually all product components 
and manufacturing equipment, ubiquitous cyber-physical 
systems, and analysis of all relevant data’ (2017).

Public debate about automation has centred on the 
possibility of massive job loss. The typical approach 
followed in the literature has been to identify the tasks 
that are most vulnerable to automation and then identify 
the occupations in which workers spend a substantial 
proportion of their time doing those kinds of tasks. Several 
studies have attempted to estimate the impacts on jobs, 
typically focusing on the current technical feasibility of 
automating particular kinds of jobs rather than the likely 
actual rate of automation over time.

McKinsey Global Institute (2017) found that, for the big EU5 
(France, Germany, Italy, Spain and the United Kingdom 
– UK), 54 million full-time equivalent jobs are associated 
with technically automatable activities and the potential 
impact due to automation is 46% of work activities. 
Building on Frey and Osborne’s original work (Frey and 
Osborne, 2013), data from the World Bank suggest that 
in the OECD, on average 57% of jobs are susceptible to 
automation (Citi, 2016). A more conservative estimate was 
published by Arntz et al (2016) which reported that 9% 
of jobs across the 21 OECD countries were automatable. 
This estimate was revised upwards by Nedelkoska and 
Quintini (2018) who found that about 14% of jobs in 32 
OECD countries were ‘highly automatable’: in the authors’ 
view these are jobs that have a probability of automation 
of over 70%. But the figure varies considerably across 
countries, from 33% in Slovakia to just 6% in Norway. 
The same study reported that a further 32% of jobs have 
a probability of automation of between 50% and 70%. In 
these jobs, a significant share of regular tasks could be 
automated, but not all, which implies a substantial change 
in the skills requirements for these jobs.

Some studies have attempted to go further and to 
gauge the likely extent of automation over a given time 
horizon. McKinsey Global Institute (2018) estimated that 
artificial intelligence will reduce the share of job profiles 
characterised by repetitive activities or that require a low 
level of digital skills in total employment to around 30% 
by 2030, and will increase the share of jobs characterised 
by non-repetitive activities and requiring high digital 
skills from roughly 40% to more than 50% over the same 
period. The World Economic Forum (undated) suggested 
that automation and technological advancements could 
lead to a net employment impact of more than 5.1 million 
jobs lost to disruptive labour market changes between 
2015 and 2020. This net figure was made up of a loss of 7.1 
million jobs, two-thirds of which are concentrated in the 
office and administrative job family, and a gain of 2 million 
jobs in several smaller job families (in 15 major developed 
and emerging economies). In Germany, the Institute for 
Employment Research (IAB, 2016) found that 1.5 million 
jobs compared to their no digitisation baseline will be 
eliminated by 2025 in a digitised world and, at the same 
time, Economy 4.0 will create 1.5 million new jobs due to 
structural changes towards the expansion of the services 
sector.

There is huge uncertainty associated with the type 
of scenario modelled in this report. The range of 
estimates of job loss that could potentially occur in the 
initial substitution of workers by machines testifies to 
uncertainty even in this first basic step. The next step 
is to explore how potentially automatable tasks pan 
out in terms of actual job loss. This is not just about 
how the technological frontier will shift over time. It is 
fundamentally about the introduction of economics 
into an analysis that, thus far, is purely technical. At 
the microeconomic level, it is hardly the case that all 
that is technologically feasible will be economically 
rational for the firm. Most obviously, the price of the 
automation technology relative to labour must be taken 
into consideration, but also the rationale of automating 
specific tasks in the context of the overall organisation of 
work. Moreover, from the macroeconomic perspective, 
the scale of investment required to replace workers with 
machines, especially to the extent predicted by some of 
the estimates presented above, may just be unrealistic in 
terms of the share of GDP of such investment. Then there 
are the effects along the supply chain from the increased 
demand for these new technologies by firms. Many other 
economic interactions also need to be accounted for. The 
most important issues are probably how the productivity 
gains affect consumer demand and how the competitive 
position of European companies evolves compared to 
companies around the world.

What these economic complexities, together with other 
social and political developments in a global economy, 
actually mean for employment in Europe is thus highly 
uncertain. Compared to the two scenarios previously 
conducted in Eurofound’s Future of Manufacturing in 
Europe project, on the employment implications of a 

3

possible global trade war (Eurofound, forthcoming) and the 
implementation of the Paris Climate Agreement (Eurofound, 
2019), this technological scenario could be viewed as highly 
speculative. However, we consider the value of this report 
to be twofold. First, it outlines many of the factors that 
should be considered when evaluating the final effects of 
a large initial displacement of employees by technological 
change and makes an attempt to both quantify and 
model them. Second, it indicates that the somewhat 

cataclysmic assumptions of potential job loss from the 
mass introduction of digital technologies are somewhat 
mitigated when a fuller economic analysis comes into play. 
However, as with past experiences of radical technological 
change, the more positive effects derived from the income 
generated from lower production costs and spending on 
other goods and services are dwarfed by the initial job 
displacement effect; they still dominate the model results at 
the end of the forecasting period of 2030.

4

Technology scenario: Employment implications of radical automation1  Scenario design

This section starts with a qualitative description that 
summarises the approach taken. It then proceeds with 
a detailed description of the modelling assumptions, 
comprising the automation rates determined on the basis 
of technological potential and automation rates taking into 
account economic feasibility. Some focus is then placed 
on the treatment of digitisation of the supply chain and 
investment changes. Finally, there is a description of how a 
scenario was developed to explore the impact of reduced 
working hours alongside automation.

Qualitative description
The purpose of this study is to synthesise the insights from 
the literature to produce quantified global scenarios and 
to compare the potential impacts on the EU with those 
of other major global blocs. The E3ME model is a global 
model that distinguishes countries (including each of the 
EU Member States) and global regions in considerable 
sectoral detail, allowing the introduction of different 
scales of impact by country and industry (Cambridge 
Econometrics, undated). We draw on the task-based 
approach outlined above and translate it into an approach 
that can be applied to jobs classified by sector.

Because the future investment cost of automation is 
very uncertain, we model a high-cost case, which implies 
slower uptake and hence fewer direct job losses, and 
a low-cost case in which uptake is faster and direct job 
losses are larger. Because the impacts depend greatly on 
how the benefits of greater automation are distributed 
in society, we also model a variant of the low-cost case 
in which workers enjoy a higher hourly rate of pay and a 
reduction in working hours: this mitigates some of the shift 
in the share of national income from wages to profits that 
would otherwise occur and that has been a driver of the 
increase in inequality experienced in many countries since 
the 1990s. The three scenarios modelled are:

1.  high cost, lower uptake

2. 

3. 

low cost, higher uptake

low cost with reduced working hours but no pay 
reduction

The initial potential job loss arising from 
automation
In each country we consider both the country’s sectoral 
composition (i.e. the employment shares across sectors) 
and the relative proportion of jobs at a high risk of 
automation in each of those sectors. The jobs that are 
at a high risk of automation are those in which a high 
proportion of tasks normally carried out have a routine, 
repeatable character that can readily be translated into 
rules to guide the operation of robots and software. For the 

EU Member States, we have access to the 2018 Cedefop and 
Eurofound skills forecast projections of the number of jobs 
by occupation in manufacturing sectors expected in 2030, 
and we can use the occupational classification as a proxy 
for the likely extent of routine tasks. Otherwise we draw on 
estimates made in the literature, although these estimates 
are available only for selected countries and in less sectoral 
detail.1

Macroeconomic feasibility of investment in 
potential automation
However, since the potential for automation in much of 
the literature is determined on technical grounds alone, 
it is necessary to take a view as to how much of this 
potential will be fulfilled by 2030. This depends on the 
cost of automation and the scale of investment that can 
reasonably be assumed.

With respect to the costs of automation, we make 
assumptions for the cost in 2018 of automating a job 
from within the wide range of industry estimates, with 
separate scenarios for high and low assumptions. 
The costs are assumed to fall over time as technology 
develops. Multiplying these costs by the number of jobs 
assumed to be displaced gives a required investment 
figure.

Initial analysis showed that the scale of investment 
required to achieve the full technical automation levels 
suggested for China by 2030 amounted to more than 
double the level of gross fixed capital formation for the 
country as a whole. For India, the required investment 
was even higher. Rather than adopting a case-by-case 
approach in which different adjustments were made to 
different sectors in different countries, the approach 
taken has been to scale the technical automation 
potentials across the board until the feasibility bounds 
were no longer breached in an attempt to suggest levels 
and phasing of investment between now and 2030.

Distribution and demand
The assumptions described above make clear that the 
uptake of technology to automate work envisaged in the 
scenarios involves substantial direct job losses, and the 
estimates show that this is even the case when constraining 
initial job loss in light of the macroeconomic feasibility. 
In many respects, the big macroeconomic issue is where 
the productivity gains are eventually distributed, not least 
in the context of how the distribution affects demand. In 
the model, the benefits of increased productivity accrue 
to capital, ownership that is unequally distributed. For 
most households, incomes are depressed by the loss of 
wage incomes. Distribution is skewed further by high-
skilled workers, whose labour productivity is boosted in 
the form of a moderately higher wage. This implies greater 

1 

To check that the two different methodologies are broadly compatible we have reviewed what the literature we are using for non-EU countries reports for EU 
countries and compared this with the result that we obtain by applying the occupational method. PwC (2017) reports potential job losses in manufacturing of 46% 
in the UK and 48% in Germany, which compares with the direct job losses in EU manufacturing of 40% that we assume in the low-cost scenario described below in 
this report.

5

polarisation between rich and poor in the distribution of 
income.

On the other hand, some of the cost savings could be 
distributed in the form of reduced working hours and a 
higher hourly pay rate for workers. For this reason, we 
modelled a version of the low-cost scenario in which 
labour succeeds in securing a reduction in working hours 
but not pay.

Another knock-on effect calculated in this scenario 
is the employment created in the automation and 
digitisation producers’ supply chain. The number of such 
jobs is expected to be rather limited as these sectors 
are themselves introducing productivity-enhancing 
automation.

Summary of the implemented model
The main inputs to the scenario, described in more detail 
below, are assumptions for:

 

 

 

 

the direct loss in employment attributable to 
automation

the investment required to replace workers by 
machines/software and to give additional training for 
the remaining workers

changes to each sector’s supply chain to reflect a 
different production process, notably a shift towards 
purchases of IT equipment and software and away 
from raw materials and transport

the reduction in working hours directly attributable 
to automation and, in one scenario, a compensating 
increase in average hourly wages

What effects should we expect to see in the scenario 
outcomes? Ernst et al (2018, p. 9) note the conclusions in 
the literature that automation affects jobs growth through 
three channels:

  a displacement effect, i.e. direct substitution of 

technology for workers

  a ‘skills-complementarity’ effect, meaning an 

increased demand for workers with the skills to use 
and supervise the new technology

  a productivity effect, where the cost reductions 

brought about by the new technology are passed on in 
the form of lower prices and higher incomes, which in 
turn stimulates spending in the economy

In our analysis, we recognise three important channels:

  an investment effect in which the additional demand 

for machines and software stimulates activity and jobs 
in the sectors that produce the investment goods and 
services

  a supply chain effect in which a larger share of the 

supply chain for products and services is associated 
with automation and a smaller share for the 
production and shipping of physical materials

  an income effect in which there is a loss of wage 

income and a shift from wages to profits in the 
distribution of income, the net effect of which is likely 
to be lower real consumer spending

Given the modelling approach, we expect these effects to 
appear in model outcomes as follows.

 

In the scenarios in which there is no compensating 
adjustment to the hourly wage, we expect a decrease 
in wage incomes because of the loss in employment 
(displacement effect) and hence a reduction in 
consumer expenditure because of the shift in the 
distribution of income from wages to profits.

 

In all scenarios we expect an increase in investment 
in machines and software as capital is substituted for 
labour (investment effect).

  We expect changes in the supply chain that boost 

the sectors supplying the products and services 
associated with automation/digitisation and reduce 
demand for producers and shippers of materials 
(supply chain effect).

  We expect lower unit costs for business because the 
savings in labour costs more than offset the cost of 
the new technology, feeding into lower product prices 
(productivity effect).

  There is likely to be an overall reduction in the 

demand for labour, depending on the net effects of 
these changes in the model.

The modelling we have undertaken does not represent 
explicitly the skills-complementarity effect. The demand 
for different occupations that we report below takes 
account of changes in the number of jobs in different 
sectors, reflecting the extent to which each sector has jobs 
that are more easily automated, but we do not adjust the 
shares of each occupation within any given sector because 
the present state of knowledge is inadequate to estimate 
the potential quantitative scale of such adjustments.

Detailed description of the 
modelling assumptions
This section describes how modelling assumptions were 
developed for the three scenarios.

Automation rates determined on the basis of 
technological potential
The starting point for the possible extent of automation 
is based on an assessment of the technological potential 
for different kinds of jobs to be automated and is 
implemented as follows.

  For EU manufacturing, any jobs from the 2018 Cedefop 
and Eurofound skills forecast that are not managerial, 
professional or technical are considered at risk of 
automation, because these occupations are likely to 
involve a large proportion of the kinds of tasks that are 
most readily automated.2

2  Managerial, professional and technical occupations are defined as ISCO major groups 1–3.

6

Technology scenario: Employment implications of radical automation  Outside of EU manufacturing, potential automation 

rates are drawn from the literature cited above where 
this provides quantified estimates, such as PwC (2017) 
and McKinsey Global Institute (2017).

  For non-EU regions, the potential automation rates 

are based on the estimates reported in PwC (2017) and 
McKinsey Global Institute (2017).

The estimates from the literature are summarised in the 
Annex. These studies have mainly taken the standard 
approach, namely to determine the tasks most susceptible 
to automation and then assess how important these 
tasks are for jobs in different sectors. McKinsey Global 
Institute (2017) also seeks to take into account some other 
factors, such as technical feasibility, technology costs 
and potential competition with labour. The potential 
automation rates can be quite high and vary considerably 
among sectors and regions.

Investment in automation
There is considerable uncertainty about the economic 
feasibility of potential automation indicated in the 
previous section and industry estimates of the costs 
of automation vary widely (Engineering 360, undated; 
Robotworx, undated); we have selected rates that lie 
within that wide range. In the high-cost scenario, the 
cost of automating a single job in 2018 is assumed to be 
€103,060 (in the 2005 price base in which E3ME operates 
as of February 2019), and in the low-cost scenario we 
reduce that value by 40%. The economic feasibility of 
the rate of automation between now and 2030 clearly 
depends on the cost of automation. While there is now 
some literature providing estimates to support our 
assumptions for direct job losses, there is little in the 
way of quantified estimates for other elements of the 
automation narrative. This includes the rate at which 
the cost of automation equipment will fall over time as 
production expands and further technological advances 
are made. Also, one would require some estimate of 
the expected lifetime of the new capital equipment and 
hence the period over which its cost could be recovered 
in the prices that firms charge.

Rather than ignore these effects and hence understate 
the economic impacts of automation, we have made 
assumptions on the basis of our own judgement and 
experience in modelling structural change using an input–
output model. With respect to the application of robot 
technology, in all three scenarios we assume:

  a 5% per annum decrease in costs due to 

technological progress

  a 5% depreciation rate – effectively that a new robot 

has a lifetime of 20 years

 

the cost of investment is recovered over the lifetime of 
the robot

Scenario design

Using these cost assumptions, the implied investment 
required for high rates of potential automation is 
implausibly large in relation to the size of GDP. We have 
therefore applied lower automation rates than the 
potential rates using our judgement as to the viability 
of the scale of investment between now and 2030. The 
choice of the upper bound for what cumulative investment 
might be feasible, expressed as a percentage of GDP, is 
necessarily arbitrary and a rather ad hoc approach has 
been taken here, allowing lower levels for the developed 
countries and higher ones for China and India. This is 
partly reflected in recent actual levels where, in the period 
2005–2017, the ratio of investment to GDP in the US and 
the EU was about 20%, in India it was over 30% and in 
China over 40%.3

The rates of direct job reduction finally adopted in the 
scenarios, when aggregated across the sectors in the E3ME 
model,4 are presented in Table 1.

Table 1: Direct job reduction rates assumed in the 
scenarios

% of baseline 
jobs in 2030

High cost, 
lower uptake

Low cost, higher uptake/
low cost, adjusted 
working hours

EU28

US

Japan

China

India

South Korea

Rest of the world

12.6

11.8

5.9

6.4

2.6

6.5

3.5

17.2

16.6

8.2

8.9

3.6

9.1

4.7

Source: Cambridge Econometrics analysis based on PwC (2017) and 
McKinsey Global Institute (2017)

Table 1 shows that in the high-cost variant it is assumed 
that, by 2030, 12.6% of the baseline jobs in the EU28 will 
be replaced by robots or another form of automation, 
and that this number rises to 17.2% in the two low-cost 
variants. This range is broadly consistent with the figure 
of 14% in the results reported by Nedelkoska and Quintini 
(2018) and the range 14–18% in the results from Suta et al 
(2018), cited earlier. They are, however, considerably lower 
than the range of automation rates suggested in many 
other studies, for example by McKinsey Global Institute 
(2017) for Germany (27–47%).

The implication of applying the automation rates (Table 2) 
is that there would be 30–42 million fewer people in 
employment, depending on the scenario, than in the 
baseline by 2030 before wider effects such as the stimulus 
to jobs in equipment-supplying sectors are considered.

3 
4 

Source: Statistics sourced from Eurostat (EU), OECD (US) and World Bank (India and China).
For EU Member States, E3ME distinguishes over 60 sectors, defined approximately at the two-digit NACE level. The list of sectors can be found in Appendix B of the 
E3ME manual (Cambridge Econometrics, undated).

7

Table 2: Direct employment losses in 2030 as a result 
of automation (millions)

High cost, lower 
uptake

Low cost, higher 
uptake/low cost, 
adjusted working 
hours

EU28

US

Japan

China

India

South Korea

Rest of the world

30.8

21.3

4.8

49.0

20.2

2.4

67.4

Source: Cambridge Econometrics analysis

42.0

29.8

6.7

68.6

28.2

3.3

89.2

From all the countries modelled, Japan and Korea will be 
the least affected in terms of number of people, reflecting 
the automation rates reported in the literature.5

The level of investment required in 2030 to achieve the 
level of automation is summarised in Table 3.

The highest levels of required cumulative investment in 
the period 2018–2030 as a percentage of GDP are in India 
and China. The rate for the EU28 is higher than that in the 
US, reflecting the higher number of jobs assumed to be 
automated in the EU28, while the lowest investment levels 
are in Korea and Japan.

Digitisation of the supply chain and investment 
changes
The literature offers little in the way of quantified 
estimates for the impact of digitisation on the supply 
chain of purchases of inputs and on the investment 

in equipment and software required, but it seems 
unreasonable to ignore these impacts. The following 
assumptions are made in all three scenarios:

  manufacturing purchases of information and 
communications technology (ICT) services are 
expected to double by 2030 compared to 2016 levels 
(IAB, 2016)

 

 

services sector purchases of ICT are expected to 
increase by 80% by 2030 compared to 2016 levels (IAB, 
2016)

logistics/transport purchases by industry decrease 
by 1% by 2030 from current values, and logistics/
transport purchases by services decrease by 0.6% by 
2030 from current values

 

raw material and other purchases by industry and 
service drop by 1% by 2030 compared to current levels

  purchases of education are 2% higher by 2030 

compared to 2016 levels to reflect additional training

  an additional €102.8 billion at February 2019 prices 

spread over 12 years will be spent on updating existing 
equipment, data storage systems, etc. in the EU28 as a 
whole

  new investment in systems and equipment amounting 
to an extra 0.5% annual investment by agriculture and 
manufacturing (cf. IAB, 2016) and 0.2% by services

Sharing the benefits by reducing working 
hours
An additional scenario was developed to represent a 
future in which automation proceeds rapidly, at the same 
rate and with the same level of investment as in the low-
cost scenario, but in which the impact on wage incomes 
is mitigated by a reduction in average hours worked. At 

Table 3: Investment required in the automation scenarios

High cost, lower uptake

Low cost, higher uptake/ 
Low cost, adjusted working hours

Cumulative, 2018–2030

€ trillion, 2005 
prices

% of cumulative 
GDP

2030

% of GDP

Cumulative, 2018–2030

€ trillion, 2005 
prices

% of cumulative 
GDP

2030

% of GDP

EU28

US

Japan

China

India

South Korea

Rest of the world

3.8

2.6

0.6

6.2

2.4

0.3

8.3

Source: Cambridge Econometrics analysis

2.1

1.3

1.0

5.7

6.6

1.2

3.8

7.3

4.3

3.6

15.5

18.3

3.7

12.2

3.1

2.2

0.5

5.3

2.0

0.2

6.6

1.7

1.1

0.8

4.9

5.5

1.0

3.0

6.0

3.6

3.0

13.1

15.4

3.1

9.7

PwC (2017, p. 42) reports the ‘lower average automatability of most individual sectors in Japan’, even in wholesaling and retailing which are relatively labour 
intensive in Japan. ‘Retail sales workers [spend] a lower proportion of time conducting manual tasks compared with management tasks, such as planning or 
organising.’ The report notes that this could change in the future if retailing in Japan moves to a more self-service model, ‘reducing the need for skilled sales staff 
and increasing the need and scope for automation’. The report also notes that South Korea faced a similarly low risk of automation as Japan (PwC, 2017, p. 33).

5 

8

Technology scenario: Employment implications of radical automationScenario design

How the modelling represents the 
impacts
Figure 1 illustrates the model inputs and how these link to 
other model variables. It shows the economic logic of how 
the changes in policy are expected to affect the economy. 
The modelling inputs are shown in the blue panel. The 
grey panel shows the initial impacts on the economy and 
in which model variables this will be felt. The white panel 
summarises the main model links and interactions, that is, 
the knock-on effects to the wider economy.

In the top right-hand part of Figure 1, the direct 
employment losses affect employment in each sector and 
the total employment in a country, which feeds through 
to disposable incomes in the bottom right-hand part of 
the figure. The loss in incomes leads to lower consumer 
expenditure and lower demand for products (bottom 
left-hand part of the figure). On the other hand, lower 
employment results in a lower wage bill for companies, 
allowing them to realise a reduction in unit costs. The 
investment in automation in the top left-hand part of the 
figure leads to an increase in demand (bottom left-hand 
part of the figure) for the products of the sectors that 
supply the equipment for the additional investment (such 
as electronics, electrical and mechanical engineering), 
leading to additional output and employment in these 
sectors and additional imports (‘trade’ in the figure). But 
the investment must be paid for and so this is reflected 
in an increase in unit costs. The net effect of higher 

the same time, we assume no reduction in the average 
wage per job, implying an increase in the average hourly 
wage. The employment losses in Table 1 and Table 2 are 
the same for both low-cost scenarios (the right-hand 
column), but they are now interpreted as the reduction in 
labour input (measured, say, in millions of hours worked) 
equivalent to the loss of jobs shown in the table if average 
working hours remained unchanged. The impact of the 
reduction in labour input seen in both low-cost scenarios 
on the final number of jobs lost is mitigated when average 
working hours are reduced.

The question then arises as to how this reduction in 
working hours might come about. In the scenario we have 
assumed that it is achieved by regulation, introduced in 
all countries and affecting all sectors, rather than targeted 
on sectors according to the extent of the reduction in their 
labour input achieved through automation. With regard 
to scale, we have assumed a 5% reduction applied across 
the board. Hence, workers working a 40-hour week would 
now work a 38-hour week but for the same pay. Part-time 
workers currently working, say, a 20-hour week would 
now work a 19-hour week. The effect is both to mitigate 
somewhat the impact of the reduction in labour input on 
jobs and, because the direct cost is borne by employers, 
to increase the wage bill and household employment 
incomes compared with the low-cost scenario. We make 
no further assumption about possible consequences, for 
example an increase in productivity of workers because 
their working hours are shorter.

Figure 1: Technology scenario inputs and model links

Investment in 
robots

Shift in production 
inputs towards IT / 
software

Reduction in 
employment

Average 
working 
hours

Average 
hourly 
wage

Model inputs

Investment in a 
sector

Sector costs

Demand

Unit costs

Trade

Industry prices

Source: Cambridge Econometrics analysis

Intermediate 
demand for each 
sector’s products 
(+ and -)

Sector output

Directly feed
into

Employment in a 
sector

Model
interactions
and outputs

Wage bill Total employment

Disposable income

Consumer 
prices

Consumer 
spending

9

capital costs and lower wage costs is a net reduction in 
unit costs which is passed on, at least in part, to prices. 
This, in turn, has two main impacts. First, on a domestic 
level, the lower price levels boost the purchasing power 
of consumers, mitigating in part the loss in disposable 
incomes linked to the loss of wage income. Second, lower 
prices improve firms’ competitiveness in export markets 
(‘trade’ in the figure). Changes to the supply chain shown 

in the middle of the upper panel shift the structure of 
demand towards production inputs associated with 
new technologies. In the scenario in which it is assumed 
that the labour input is reduced through lower average 
working hours but that wage incomes are maintained 
through a higher hourly wage, the impact on consumer 
expenditure is mitigated, but so is the reduction in costs 
for businesses.

10

Technology scenario: Employment implications of radical automation2  Results

The results are presented relative to a baseline projection 
in which there is no acceleration in automation and 
digitisation. The baseline incorporates the Eurostat 
population forecast available in 2017 and the short-term 
macroeconomic forecast produced by DG ECFIN in May 
2017 (see Cedefop and Eurofound, 2018).

Global impacts
Figures 2 and 3 show the impacts on GDP and employment 
in 2030 for the three scenarios compared to the baseline.

The differences in impacts on GDP among the countries 
shown in Figure 2 reflect the scale of the assumptions for 
the additional investment for automation shown in Figure 
3. Hence, the assumption of a higher rate of automation 
investment in the EU than in the US and Japan is reflected 
in greater impacts on GDP. The greatest impacts on GDP 
by 2030 are in India and China, but Figure 3 shows that the 
scale of investment required is by then reaching shares of 
GDP in the order of 15–20%, which may be larger than is 
feasible. In most countries the GDP impact is dominated 
by the investment impact, which is why GDP effects are 
generally larger in the high-cost case – in which the scale of 
investment is assumed to be higher – than in the low-cost 
case. But when the two low-cost cases are compared, the 
case in which the benefits of automation are assumed to 

Figure 2: Impact on GDP in 2030, by scenario

Rest of the world

be shared among workers through a higher hourly wage 
yields a higher GDP impact because households spend a 
higher proportion of wage income than capital income.

The differences in percentage employment impacts among 
the countries shown in Figure 3 reflect the combined 
effect of the scale of investment and the scale of baseline 
employment in 2030 in each country. In India, China 
and the rest of the world, the scale of investment is 
substantial, as shown in Table 3 (p. 8), but baseline output 
per job in 2030 is lower in these countries and so baseline 
employment is large. Consequently, their direct job losses 
are lower as a percentage of baseline employment in 2030 
than in the EU, Japan, Korea and the US, even though the 
GDP impact, driven by the scale of investment spending, 
is higher.

The employment losses are largest in the low-cost 
scenario with no working hours adjustment: in this case 
the stimulus to spending coming from investment is less 
(resulting in less of a stimulus to GDP) while the scale of 
automation is greater. In all countries there is a marked 
improvement in the employment outcome in the scenario 
in which working hours are adjusted, compared with the 
low-cost case. In India and the rest of the world, this is 
sufficient to change the outcome for employment from a 
small net loss in the low-cost case to a small net gain in the 
case with adjusted working hours.

Korea

India

China

Japan

US

EU28

0

2

4

6

8

10

12

14

16

% diﬀerence from baseline, 2030

Source: Cambridge Econometrics analysis

Low cost, adjusted working hours

Low cost

High cost

11

Figure 3: Impact on employment in 2030, by scenario

Rest of the world

Korea

India

China

Japan

US

EU28

-20

-15

-10

-5

0

5

% diﬀerence from baseline, 2030

Low cost, adjusted working hours

Low cost

High cost

Source: Cambridge Econometrics analysis

Figure 4 compares the assumptions for direct job losses 
with the final outcome when the second-round effects 
of supply chain purchases and income multipliers are 
taken into account. The two low-cost scenarios in the 
lower part of the figure have the same direct reduction in 
labour requirement in each country and sector (expressed 
in the figure as ‘job losses’), but differ according to how 
the reduction in labour input is shared among workers 
throughout each country. As expected, the final job 
losses are smaller than the direct job losses, but only 
substantially smaller in the case where working hours are 
adjusted, demonstrating the importance for household 
spending and for jobs of how the benefits of higher 
productivity are shared between workers and employers: 
when working hours are reduced but the hourly wage is 
increased in compensation, the distribution of income 
between profits and wages is shifted in favour of wages, 
boosting household spending.

EU impacts
Table 4 shows the impact on macroeconomic indicators 
in the three scenarios for the EU in 2030 compared with 
a baseline in which there are no effects from accelerated 
automation and digitisation.

GDP impacts are larger in the high-cost than the low-
cost case because investment is higher and because the 
lower rate of automation means that job losses (and 
hence consumer spending impacts) are smaller. Higher 
investment costs in the high-cost variant are passed on to 

12

consumers and so consumer prices fall by less than in the 
low-cost scenario.

There is a substantial shift from consumer spending to 
investment because the net loss in employment incomes 
outweighs the benefits to consumers of lower costs and 
prices. Underlying this, there is a shift from wages to gross 
profits in the share of incomes from production, and a 
larger part of gross profits is devoted to financing the higher 
investment costs. Figure 5 shows the reduction in the share 
of wages in the sum of wages and profits in the baseline 
in 2030 and in the three scenarios. The greater the scale 
of automation, moving from the high-cost to the low-cost 
scenario, the smaller the share of wages. However, the 
reduction in share is mitigated when the low-cost case is 
adjusted to incorporate lower working hours and a higher 
wage per hour (the low-cost adjusted working hours 
scenario). Figure 4 shows that this is because job losses 
are reduced (from 16.0% to 10.9%, comparing the second 
and third columns of the table) with the result that the GDP 
impact is higher. This comes about because the reduction 
in employment is less while the average hourly wage is 
increased, and so the reduction in consumer spending is less.

In all cases, net domestic product impacts are much 
smaller than GDP impacts. Net domestic product 
excludes the replacement expenditure that firms have 
to make simply to maintain their existing capital. By 
2030 the capital stock is much larger than in the baseline 
and a substantial part of investment is going towards 
replacement of automation technology that has reached 
the end of its useful life.

Technology scenario: Employment implications of radical automationFigure 4: Direct and total job losses in 2030, by scenario

High-cost scenario

-15

-10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Low-cost scenario

-20

-15

-10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Low-cost, adjusted working hours scenario

-20

-15

-10

-5

0

5

% diﬀerence from baseline, 2030

Source: Cambridge Econometrics analysis

Direct losses

All losses

Rest of the world

South Korea

India

China

Japan

US

EU28

Rest of the world

South Korea

India

China

Japan

US

EU28

Rest of the world

South Korea

India

China

Japan

US

EU28

Results

13

Table 4: EU28 macroeconomic effects in 2030, % difference from baseline

% difference from baseline

GDP 

Net domestic product

Consumer spending

Investment

External exports

External imports

Employment

(of which direct*)

Consumer prices

High cost

Low cost

Low cost, adjusted working hours

6.6

2.9

-5.3

39.9

5.9

5.0

-10.0

-12.6

-1.9

4.4

1.4

-6.7

34.7

5.6

4.5

-16.0

-17.2

-4.8

5.9

3.7

-4.6

33.5

6.3

4.6

-10.9

-17.2

-2.1

Source: Cambridge Econometrics analysis
Note: *‘Direct’ job losses are those introduced by assumption.

Figure 5: Share of the wage bill in wages and profits in 2030, EU28

Baseline

High cost

Low cost

Low cost, adjusted working hours

0

10

20

%

30

40

Source: Cambridge Econometrics analysis

In all cases the fall in consumer spending is less than the 
fall in employment, partly because of lower consumer 
prices but also because household incomes are drawn 
from several sources, not just wage income. But there are 
clear distributional implications (a shift from wage earners 
to the owners of capital, and income from capital is very 
unequally distributed among households) in that we 
have not modelled the effect of different propensities to 
consume for households at different income levels, and so 
the reduction in consumer spending could be larger than 
shown here: richer households spend less of any given 
boost to income than poorer households.

The results illustrate the importance of:

  a competitive market environment to promote full 
pass-through of productivity benefits to consumers

  an innovative and globally competitive production 

capability in the supply of automation and digitisation 

14

equipment and software, to capture a high proportion 
of value added in the supply chain that would 
otherwise be lost to Europe

Sectoral impacts
Figure 6 shows that, in all three scenarios, the sectors 
experiencing the largest proportional impacts 
are manufacturing, utilities and transport and 
communications.

Employment in manufacturing and utilities is expected 
to be 20% lower than the baseline in the high cost/low 
uptake case, rising to 30–35% in the low cost/high uptake 
case. All sectors see an improvement relative to the low-
cost case if the benefits of automation are shared among 
workers by raising the hourly wage while reducing hours 
worked. This comes about because the difference between 
the two low-cost scenarios involves a redistribution of 

Technology scenario: Employment implications of radical automation 
Figure 6: Impacts on EU sectoral employment, 2030

Results

All sectors

Non-business services

Business services

Transport and communications

Distribution, retail, hotels and catering

Construction

Utilities

Manufacturing

Mining

Agriculture

-35

-30

-25

-20

-15

-10

-5

0

% diﬀerence from baseline, 2030

Low cost, adjusted working hours

Low cost

High cost

Source: Cambridge Econometrics analysis

income from capital to labour and so household spending 
is higher. The effects are felt across the whole economy, 
but most directly in distribution, retail, hotels and catering, 
which is the sector most directly affected by household 
spending.

Figure 7 shows that EU productivity, measured as value 
added per job, is some 20–25% higher as a result of the 
additional investment in automation. The impact on 
productivity is largest in the sectors in which automation 
is most pervasive: manufacturing, utilities and transport 
and communications. As expected, productivity growth 
is stronger when lower automation costs support faster 
take-up. The impact of automation on value added per job 
is smaller in the case where the reduction in working hours 
means that there are more jobs, but the impact on value 
added per hour worked would be similar in the two low-cost 
scenarios. However, since we assume that the hourly wage is 
increased in the reduced working hours, the unit cost faced 
by employers will be higher than in the low-cost scenario.

Figure 8 compares the assumptions for direct job losses 
entered into the modelling and the model outcome for all 
job losses in the three scenarios. Again, the two low-cost 
scenarios in the lower part of the figure have the same direct 
reduction in labour requirement in each country and sector 
(expressed in the figure as ‘job losses’), but differ according 
to how the reduction in labour input is shared among 
workers throughout each country. In most sectors the final 
job losses are a little less than those directly imposed, as 

the higher investment creates demand for the output of the 
producers of automation equipment and software and their 
supply chain. The exception is distribution, retail, hotels and 
catering. This sector does not benefit significantly from the 
additional investment spending, and suffers the additional 
impact of a reduction in consumer spending in the scenario 
as income is shifted from wage earners to the owners of 
capital. However, when working hours are reduced but the 
hourly wage is increased in compensation, in the low-cost 
adjusted working hours scenario, this redistribution of 
income is mitigated and so there are fewer final job losses in 
distribution, retail, hotels and catering than direct losses.

Occupational impacts
Occupational employment patterns within industries 
are assumed to be unchanged between scenarios. This 
is a simplifying assumption in the absence of any robust 
information about how these patterns might be affected. This 
is especially significant in these technology scenarios where 
(in principle) one might expect the patterns to be affected by 
automation. Table 5 shows the projected employment levels 
and changes by two-digit occupational category between the 
baseline and all of the technology scenarios.

In contrast to the baseline – in which there are growth 
areas among many white-collar, non-manual occupations 
in the professional and associate professional categories 
as well as among a few blue-collar, manual occupations, 

15

including some less skilled occupations such as cleaners 
and labourers – all three technology scenarios paint a 
much more negative picture. Significant job losses are still 
projected for clerks and many skilled manual trades, but 
these are now sharper and accompanied by significant 
declines for many other skilled, semi-skilled and unskilled 
occupations. These are especially pronounced in the 

Figure 7: Impacts on EU sectoral productivity, 2030

low-cost technology scenario. Any significant growth in 
employment is projected in only a few occupations such as 
ICT professionals, legal, social and cultural professionals, 
science and engineering associate professionals, legal, 
social, cultural and related associate professionals and 
customer services clerks.

All sectors

Non-business services

Business services

Transport and communications

Distribution, retail, hotels and catering

Construction

Utilities

Manufacturing

Mining

Agriculture

0

10

20

30

40

50

60

70

80

90

100

% diﬀerences from baseline

Source: Cambridge Econometrics analysis

Low cost, adjusted working hours

Low cost

High cost

Figure 8: Direct and all job losses in EU sectors in 2030, by scenario

High-cost scenario

All sectors
Non-business services
Business services
Transport and communications
Distribution, retail, hotels and catering
Construction
Utilities
Manufacturing
Mining
Agriculture

-50

-40

-30

-20

-10

0

% diﬀerence from baseline, 2030

16

Direct losses

All losses

Low-cost scenario

All sectors

Non-business services

Business services

Transport and communications

Distribution, retail, hotels and catering

Construction

Utilities

Manufacturing

Mining

Agriculture

All sectors

Non-business services

Business services

Transport and communications

Distribution, retail, hotels and catering

Construction

Utilities

Manufacturing

Mining

Agriculture

-45 -40 -35 -30 -25 -20 -15 -10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Low-cost adjusted working hours scenario

-45 -40 -35 -30 -25 -20 -15 -10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Technology scenario: Employment implications of radical automationHigh-cost scenario

All sectors

Non-business services

Business services

Transport and communications

Distribution, retail, hotels and catering

Construction

Utilities

Manufacturing

Mining

Agriculture

-50

-40

-30

-20

-10

0

% diﬀerence from baseline, 2030

Results

Figure 8: (continued)

Direct losses

All losses

Low-cost scenario

All sectors
Non-business services
Business services
Transport and communications
Distribution, retail, hotels and catering
Construction
Utilities
Manufacturing
Mining
Agriculture

-45 -40 -35 -30 -25 -20 -15 -10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Low-cost adjusted working hours scenario

All sectors
Non-business services
Business services
Transport and communications
Distribution, retail, hotels and catering
Construction
Utilities
Manufacturing
Mining
Agriculture

-45 -40 -35 -30 -25 -20 -15 -10

-5

0

% diﬀerence from baseline, 2030

Direct losses

All losses

Source: Cambridge Econometrics analysis

17

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20

Technology scenario: Employment implications of radical automation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3  Concluding remarks

The first point to emphasise is the very high uncertainty 
of these projections. The point of departure is a large-
scale automation driven by digitisation. There are by 
now many empirical estimates that suggest such a 
radical loss of jobs due to the substitution of labour 
by these technologies. These initial job losses are of 
a scale that transforms economies, societies even, 
and obviously it is hardly possible to predict the 
economic, social and political repercussions of such a 
transformation. A notable uncertainty in this potential 
technologically driven transformation is that, unlike 
previous ones, it will occur simultaneously in most of the 
world and not be led, as before, by Europe and the US. 
The contribution of this report is to extend the analysis 
beyond just the technologically feasible substitution of 
workers by machines by incorporating some economics 
into the analysis. This includes the macroeconomic 

feasibility of the investment cost of automation and the 
multiplier effects of loss of demand – not only because 
of initial job loss but also as a result of the shift away 
from other labour incomes – and job creation in the 
supply chain emanating from the increased demand for 
ICT equipment. There is one important empirical result 
that should be deemed reasonably credible, namely that 
while the indirect employment effects are positive, they 
only marginally compensate the job losses as were the 
initial assumptions fed into the model; in the high-cost 
alternative they reduce the initial job loss of 13% to 
a net employment decline of 10% and in the low cost 
alternative the reduction is from the initial 17% to a net 
employment decline of 16%. However, in this low cost 
alternative if one allows for a compensated working 
time reduction, this leads to the much smaller a net 
employment decline of 11%.

21

References

All Eurofound references are available at www.eurofound.europa.eu

Ambrosetti, (2017), Technology and labour: Steering the 
change, web page, accessed 28 February 2019.

Arntz, M., Gregory, T. and Zierahn, U. (2016), The risk of 
automation for jobs in OECD countries: A comparative 
analysis, Working Paper No. 189, OECD Publishing, Paris.

Cambridge Econometrics (undated), E3ME: Technical 
manual, version 6.0, available at https://www.camecon.
com/how/e3me-model/, accessed 7 February 2019.

Cedefop and Eurofound (2018), Skills forecast: Trends and 
challenges to 2030, Publications Office of the European 
Union, Luxembourg.

Citi (2016), Technology at work v2.0: The future is not what it 
used to be, New York.

Davies, R. (2015), Industry 4.0: Digitalisation for productivity 
and growth, European Parliamentary Research Service 
Briefing, European Parliament, Brussels.

Engineering 360 (undated), What is the real cost of an 
industrial robot arm?, web page, accessed 19 February 2019.

Ernst, E., Rossana, M. and Samaan, D. (2018), The 
economics of artificial intelligence: Implications for the 
future of work, International Labour Office, Geneva.

Eurofound (2018), Game changing technologies: Exploring 
the impact on production process and work, Publications 
Office of the European Union, Luxembourg.

Eurofound (2019), Energy scenario: Employment 
implications of the Paris Climate Agreement, Publications 
Office of the European Union, Luxembourg.

Eurofound (forthcoming), Trade scenario: Employment 
implications in Europe of a large increase in global tariffs, 
Publications Office of the European Union, Luxembourg.

Frey, C. B. and Osborne, M. A., (2013), The future of 
employment: How susceptible are jobs to computerisation?, 
Oxford Martin School, Oxford, UK. 

IAB (Institute for Employment Research) (2016), Economy 
4.0 and its labour market and economic impacts: Scenario 
calculations in line with the BIBB-IAB qualification and 
occupational field projections’, IAB-Forschungsbericht, 
Nuremberg.

McKinsey Digital (2015), Industry 4.0: How to navigate 
digitization of the manufacturing sector, McKinsey and 
Company, New York.

McKinsey Global Institute (2017), Jobs lost, jobs gained: 
Workforce transitions in a time of automation, McKinsey & 
Company, New York.

McKinsey Global Institute (2018), Notes from the frontier: 
Modeling the impact of AI on the world economy, McKinsey 
& Company, New York.

Nedelkoska, L. and Quintini, G. (2018), Automation, skills 
use and training, OECD Social, Employment and Migration 
Working Papers No. 202, OECD Publishing, Paris. 

PwC (PricewaterhouseCoopers) (2017), ‘Will robots steal 
our jobs? The potential impact of automation on the UK 
and other major economies’, UK Economic Outlook, March, 
pp. 30–47.

Robotworx (undated), Frequently asked questions: How 
much do industrial robots cost?, web page, accessed 
19 February 2019.

Suta, C., Barbieri, L. and May-Gillings, M. (2018), ‘Future 
employment and automation’, in Hogarth, T., Economy, 
employment and skills: European, regional and global 
perspectives in an age of uncertainty, Vol. 61, Fondazione 
Giacomo Brodolini, Rome, pp. 17–43.

World Economic Forum (undated), The future is automated, 
but what does that really mean for jobs?, web page, 
accessed 28 February 2019.

23

Annex: Occupations that could 
potentially be automated

This Annex reports on estimates, mainly drawn from the 
literature. Note that they are not the estimates plugged 
into the model assumptions as they may be capped due 
to the macroeconomic unfeasibility of the scale of the 
investment cost associated with high automation rates.

For the EU Member States, the numbers expected to be 
working in each occupation and sector in 2030 are drawn 
from the detailed baseline results taken from the latest 
Cedefop projections (Cedefop and Eurofound, 2018). We 
then calculate, for each sector in manufacturing, the share 
of manufacturing workers not employed in managerial, 
professional or technical occupations in 2030 as a proxy 

for jobs most vulnerable to automation. These rates are 
shown in Table A1 for the largest EU countries.

For non-manufacturing sectors in the EU and non-EU 
regions the assessment is drawn from the literature. The 
main sources used are:

  PwC (2017) (results summarised in Table A2)

  Ambrosetti (2017) with information about automation 

rates for Italy (results summarised in Table A3)

  McKinsey Global Institute (2017) (results summarised 

in Table A4)

Table A1: Share of manufacturing workers not in managerial, professional or technical occupations expected in 
2030

%

France

Germany

Italy

Poland

Spain

Food, drink and tobacco

Textiles and leather

Wood and wood products

Paper and paper products

Printing and reproduction

Coke and refined petroleum

Other chemicals

Pharmaceuticals

Rubber and plastic products

Non-metallic mineral products

Basic metals

Fabricated metal products

Computer, optical and electronic 
equipment

Electrical equipment

Other machinery and equipment

Motor vehicles

Other transport equipment

Furniture; other manufacturing

58.6

49.7

55.3

55.3

55.3

16.8

26.0

20.1

52.8

52.8

49.4

49.4

19.2

37.4

45.8

30.1

25.2

56.9

84.1

66.8

74.3

74.3

74.3

64.5

48.9

40.8

74.0

74.0

78.9

78.9

45.4

52.5

59.5

50.7

59.0

60.8

72.3

75.3

72.6

72.6

72.6

48.4

38.2

36.1

71.1

71.1

74.6

74.6

37.2

48.5

52.4

57.3

55.5

57.2

67.7

77.7

67.5

67.5

67.5

51.6

51.8

43.8

75.0

75.0

71.9

71.9

70.0

53.6

64.6

84.2

53.5

68.7

73.5

60.1

65.0

65.0

65.0

23.1

44.8

30.9

67.3

67.3

74.3

74.3

38.2

56.7

57.4

70.9

36.3

67.5

Source: Cambridge Econometrics analysis based on Cedefop and Eurofound (2018)

UK

69.8

65.0

67.3

67.3

67.3

49.5

36.3

28.7

62.3

62.3

52.3

52.3

25.8

47.6

59.4

59.2

38.0

54.2

25

Table A2: Summary of automation rates reported in PwC (2017)

%

Germany

Japan

Wholesale and retail trade

Administrative and support services

Transportation and storage

Professional, scientific and technical

Human health and social work

Accommodation and food services

Construction

Public administration and defence

Information and communication

Financial and insurance

Education

Arts and entertainment

Other services

Real estate

Water, sewage and waste management

Agriculture, forestry and fishing

Electricity and gas supply

Mining and quarrying

Domestic personnel and self-subsistence

Source: PwC (2017)

42

30.4

64.4

21.6

24

30.5

40.7

29.1

29.3

40.2

8.5

15.3

18.6

28.2

62.6

18.7

31.8

23.1

8.1

25

20.4

33.4

24.6

11

18.5

25.7

16.1

19.3

13.2

3.5

21.3

18.6

28.2

62.6

18.7

31.8

23.1

8.1

UK

44

37.4

56.4

25.6

17

25.5

23.7

32.1

27.3

32.2

8.5

22.3

18.6

28.2

62.6

18.7

31.8

23.1

8.1

US

47

35.4

76.4

32.6

25

44.5

34.7

33.1

45.3

61.2

11.5

17.3

18.6

28.2

62.6

18.7

31.8

23.1

8.1

Table A3: Summary of automation rates for Italy

Table A4: Summary of automation rates (2017)

%

Italy

%

China

India

Agriculture

Manufacturing

Retail trade

Construction

49

64

54

41

49

67

56

42

Source: McKinsey Global Institute (2017)

Education and health services

Information and communication services

Other collective and personal services

Real estate and business services

Hotels and restaurants

Construction

Public administration and defence

Finance and insurance

Transport and warehousing

Manufacturing

Trade

Fisheries

Source: Ambrosetti (2017)

6

9

10

12

15

15

16

17

17

19

20

25

26

Technology scenario: Employment implications of radical automationFOMEEF/18/009

T
J
-
0
2
-
1
9
-
1
2
5
-
E
N
N

-

This report looks into the impact of the 
accelerated application of automation and 
digitisation technologies on the wage and tasks 
structure of employment in Europe.

Despite the high level of uncertainty of 
these projections, the contribution of this 
report is to extend the analysis beyond just 
the technologically feasible substitution of 
workers by machines by incorporating some 
economics to the analysis. This includes the 
macroeconomic feasibility of the investment 
cost of automation, the multiplier effects of 
loss of demand – not only because of initial job 
loss, but also as a result of the shift away from 
other labour incomes – and job creation in the 
supply chain emanating from the increased 
demand for information and communications 
technology (ICT) equipment.

The analysis is carried out using the E3ME 
macroeconometric model, which provides 
information on sectoral impacts, together with 
the Warwick Labour Market Extension model 
for occupational analysis. Further analysis of 
the employment developments in Europe is 
undertaken using Eurofound’s European Jobs 
Monitor.

The European Foundation for the Improvement 
of Living and Working Conditions (Eurofound) 
is a tripartite European Union Agency whose 
role is to provide knowledge in the area of 
social, employment and work-related policies. 
Eurofound was established in 1975 by Council 
Regulation (EEC) No. 1365/75 to contribute to 
the planning and design of better living and 
working  conditions in Europe.