The impact of new technologies on the labour market and the social economy.
Date: 2018-07-19
Institutions: Directorate-General for Parliamentary Research Services
Subjects: digitisation, employment policy, impact of information technology, innovation, job creation, labour market, new technology, organisation of work, professional qualifications, social economy, social inequality, technological change

The impact of 
new technologies 
on the labour 
market and the 
social economy 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The impact of new technologies  
on the labour market and the social economy 

Study 

IP/G/STOA/FWC/2013-001/LOT 8/C1 

February 2018 

Abstract 

PE 614.539 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
STOA - Science and Technology Options Assessment 

The STOA project 'The impact of new technologies on the labour market and the social economy' was 
requested by the European Parliament's Science and Technology Options Assessment (STOA) Panel. It 
was carried out by the Austrian Institute of Technology (AIT) and managed by the Scientific Foresight 
Unit  (STOA)  within  the  Directorate-General  for  Parliamentary  Research  Services  (DG  EPRS)  of  the 
European Parliament. 

AUTHOR 

Bernhard Dachs (Austrian Institute of Technology, AIT) 

STOA ADMINISTRATOR RESPONSIBLE 

Mihalis Kritikos 
Scientific Foresight Unit (STOA) 
Directorate for Impact Assessment and European Added Value 
Directorate-General for Parliamentary Research Services 
European Parliament, Rue Wiertz 60, B-1047 Brussels 
E-mail: STOA@ep.europa.eu 

LINGUISTIC VERSION 

Original: EN 

ABOUT THE PUBLISHER 

To contact STOA or to subscribe to its newsletter please write to: STOA@ep.europa.eu 
This document is available at: http://www.europarl.europa.eu/stoa/ 

Manuscript completed in February 2018 
Brussels, © European Union, 2018 

DISCLAIMER 

This document is prepared for, and addressed to, the Members and staff of the European Parliament as 
background material to assist them in their parliamentary work. The content of the document is the sole 
responsibility of its author(s) and any opinions expressed herein should not be taken to represent an 
official position of the Parliament. 

Reproduction  and  translation  for  non-commercial  purposes  are  authorised,  provided  the  source  is 
acknowledged and the European Parliament is given prior notice and sent a copy. 

PE 614.539 
ISBN 978-92-846-2586-4 
doi: 10.2861/68448  
QA-02-18-074-EN-N

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The impact of new technologies on the labour market and the social economy 

Table of contents 

Executive Summary ............................................................................................................................................ 4 

1. 

Introduction.................................................................................................................................................. 7 

2.  The labour markets in EU countries ........................................................................................................ 7 

3.  The relationship between innovation and employment ..................................................................... 9 

3.1. 

Product innovation, process innovation and productivity .......................................................... 10 

3.2. 

Productivity and demand effects ..................................................................................................... 11 

3.3.  Other effects ........................................................................................................................................ 13 

3.4.  Differences between sectors .............................................................................................................. 13 

4. 

Innovation, Skills and Unemployment................................................................................................. 15 

4.1. 

Skill-biased technological change .................................................................................................... 16 

4.2.  Routine-biased technological change .............................................................................................. 17 

4.3. 

The role of globalisation .................................................................................................................... 21 

4.4. 

The future organisation of work ...................................................................................................... 21 

5.  The impact of new technologies on employment: empirical results ............................................... 23 

5.1. 

Employment effects of information and communication technologies ...................................... 23 

5.2. 

Employment effects of other emerging technologies .................................................................... 28 

6.  Technology and inequality...................................................................................................................... 29 

6.1. 

The relationship of technology and inequality .............................................................................. 29 

6.2. 

Evidence for inequality in Europe ................................................................................................... 31 

7. 

ICT, employment and the Social Economy .......................................................................................... 38 

8.  Workshop summary ................................................................................................................................. 41 

9.  Policy options............................................................................................................................................. 43 

9.1. 

Industrial renaissance in Europe ...................................................................................................... 43 

9.2. 

Europe's industrial misery ................................................................................................................ 44 

9.3. 

Europe's growing double digital divide ......................................................................................... 45 

9.4.  Measures for policy ............................................................................................................................ 45 

10. 

References ............................................................................................................................................... 54 

11.  Annex ....................................................................................................................................................... 59 

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STOA - Science and Technology Options Assessment 

Executive Summary 

The  relationship  between  new  technologies,  employment  and  inequality  has  gained  a  lot  of 
attention in recent years. One reason for this interest  is alarming reports about possible negative 
consequences for employment from  the widespread use of  new information and communication 
technologies  (ICTs),  including  machine  learning,  digitalisation  of  production,  robotics  and 
automated vehicles. (Frey and Osborne 2013, 2017, Brynjolfsson and McAfee 2014, Ford 2015). 

Innovation and employment 

innovation,  analysing 

literature  on  employment  and 

The pessimistic view on the impacts of new technologies is the starting point for a short review of 
the 
the  successful  creation  and 
commercialisation of new products and processes. On examining the existing literature, we can be 
optimistic  about  the  future:  in  the  past,  innovations  were  mainly  labour-friendly;  the  literature 
regards innovation and technology as the main drivers of economic growth and new employment, 
at least in the long run. A number of scientific contributions show that innovation destroys, but also 
creates employment. The race between job creation through new products and job destruction from 
new process technologies has in the past been won by the job-creating effects of innovation. There 
is no guarantee for a happy end this time; however, an important lesson from the past is that we 
tend  to  under-estimate  the  job-creating  potential  of  fundamental  technological  transformations, 
because we lack sufficient knowledge and imagination about the types of jobs that will be created 
under the new technological paradigm.  

New technologies and the demand for skills 

The  literature  on  new  technologies  and  employment  also  features  some  worrying  findings,  in 
particular,  that  technological  change  is  not  skill-neutral.  New  technologies  tend  to  favour  some 
particular skills while devaluing and making others redundant, but also lowering the demand for 
them by firms that use  such  new technologies. The literature  describes this trend as  'skill-biased 
technological  change'.  Moreover,  some  authors  have  found  that  new  technologies  increasingly 
substitute  routine  tasks,  and  have  labelled  this  phenomenon  as  'routine-biased  technological 
change'. There is a high share of routine tasks not only in manual occupations, but also in white-
collar and administrative ones. 

Acemoglu and Autor (2011) show that the demand for routine jobs and tasks has fallen considerably 
in the US, regardless of whether these jobs and tasks have a cognitive or a manual character. As a 
consequence, demand for middle-skilled people has decreased, while demand for both high-skilled 
and low-skilled (paid accordingly) ones has risen. This trend has been labelled 'job polarisation'. 

The development of the European labour markets in the last 20 years has followed the path of skill-
biased technological change (see the figure below). The number of jobs and occupations that require 
only  low  skills  has  constantly  decreased.  It  is,  however,  difficult  to  say  if  this  is  due  to  the 
introduction  of new technologies or  to globalisation.  There are also some contributions that have 
identified trends of job polarisation in Europe, but the evidence for Europe is weaker than for the US. 

Innovation will create jobs in the future, but they will be in occupations other than those destroyed 
by technology, and will be characterised  in particular by a low share of routine tasks and a high 
share of tasks that require creative and social skills. This includes many health, education and social 
occupations.  

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The impact of new technologies on the labour market and the social economy 

Employment and skills levels, 1995-2015, EU-28, in 1 000 persons 

Source: EUROSTAT, Labour Force Survey. 

Technology and inequality 

The challenge of the future – besides increasing innovation to spur employment growth  – lies in 
coping  with  rising  inequality  as  a  result  of  technological  change. Skill-biased and  routine-biased 
technological  changes  are  two  mechanisms  that  may  increase  inequality,  because  they  favour 
particular groups of the workforce and reduce the employability of other groups, in particular low-
skilled workers who already bear a considerable share of the adjustment costs of innovation. The 
risk of displacement of their jobs is higher, and the number of available jobs that require only low 
qualification decreases. 

Evidence shows that Europe witnessed growing income inequality between the mid-1980s and the 
late 2000s. In the past 10 years, there has not been any overall increase in income inequality in the 
EU. However, we find evidence that inequality between people engaged in elementary occupations 
and others in high-skilled ones has increased. The wage gap between managerial and elementary 
occupations has widened, which points to the role played by skill-biased technological change with 
regard  to  inequality  in  European  economies.  Romania,  Portugal  and  Bulgaria  reveal  the  highest 
inequality between occupations, while Sweden, Denmark and Ireland have the lowest wage spread. 

The future organisation of work 

Digitalisation is expected to change not only the volume of work and the demand for different skill 
levels, but also  the  organisation of work. Individual  tasks performed by  people  will  increasingly 
become tradable over the internet. Experts believe that the share of tasks performed outside the firm 
and the share of self-employed people who work on a project-by-project basis for various clients 
will  increase  ('platform  workers').  As  a  consequence,  firms  will  gradually  shift  to  more  project-
oriented organisational structures instead of fixed hierarchies. Such a 'platform' or 'gig' economy 
may lead to more self-determination and a better work-life-balance for  employees, but may also 
result  in  more  insecurity  and  periods  of  involuntary  unemployment.  Moreover,  self-employed 
platform workers often lack legal protection and the various social benefits to which employees are 
entitled.  

5 

25%24%23%22%22%21%20%19%19%18%17%16%16%50%50%50%50%50%50%49%49%49%48%48%48%48%26%27%27%28%28%29%30%31%32%34%35%36%36%020,00040,00060,00080,000100,000120,000140,000160,000180,00020032004200520062007200820092010201120122013201420151,000 personsTertiary education (levels 5-8)Upper secondary and post-secondary non-tertiary education (levels 3 and 4)Less than primary, primary and lower secondary education (levels 0-2) 
STOA - Science and Technology Options Assessment 

Digitalisation and the social economy 

How will digitalisation affect employment in the social economy or the third sector, including social 
assistance  services,  education  and  training,  or  work  integration?  The  social  economy  provides 
employment to many people who would not find work in other areas of the economy, because they 
lack the necessary skills or have special needs. Increased pressure on the job market for elementary 
occupations will also put strain on the jobs in the social economy. However, the social economy also 
includes  a  large  number  of  non-routine  tasks  in  health,  social  work,  education  and  other  areas, 
which are difficult to substitute by ICTs. Moreover, new ICTs may also provide new tools for the 
social economy to organise help in a more efficient way. 

Policy options 

The possible  negative consequences  of digitalisation for  employment  have inspired a number  of 
suggestions for policy intervention in recent years. 

First, in a world where skill levels are closely related to inequality, it seems essential to invest in 
education;  these  investments  should  encompass  all  levels  of  the  education  system.  New  results 
point to the importance of the early years for later learning, which could be a particular focus for 
policy intervention. 

The  promotion  of  research  and  development  (R&D)  should  be  another  focus  of  public  policy. 
Potential returns from R&D are higher at the societal level than at the enterprise level, because other 
organisations  can  benefit  from  knowledge  created  as  well.  In  addition,  Europe  should  invest  in 
upgrading its internet infrastructure and promote entrepreneurship to transform new technologies 
into growth. 

New types of self-employment, such as platform work, also call for new employment regulations. 
Policy  should  review  the  status  of  these  types  of  employment  and,  if  necessary,  extend  social 
security legislation to platform work. This would also include relying on the owners of platforms 
for social security contributions. 

There may be a need for tax systems to shift from the taxation of labour to a taxation of capital and 
value  added  as  the  main  contributor  of  economic  wealth.  Machines  may  also  contribute  to  the 
financing of social security. Such a shift, however, only seems feasible if the overall tax burden does 
not increase and the tax burden on labour is substantially reduced. Some experts think that taxing 
the super-rich may be a way to decrease inequality and raise funds for the state. Piketty (2014), for 
example, proposes a progressive global tax on wealth of up to 2 %, and additional income taxes. 
However, he also admits that such a tax is impossible to implement today because it would require 
a degree of political coordination which seems very difficult to agree on.  

Historically,  productivity  increases  generated  by  new  technologies  have  led  to  reductions  of 
working  time,  allowing  to  bring  more  people  into  work  or  dampen  the  negative  effects  of 
automation  on  the  labour  market.  Such  reductions  may  also  be  appropriate  when  digitalisation 
leads to further automation.  Reductions of working time  can be easily implemented, and can be 
done in small steps. 

If the pessimistic scenario of massive job losses and a huge increase in inequality from the economic 
effects  of  digitalisation  comes  true,  this  may  lead  to  a  massive  erosion  of  the  income  basis  of 
European  countries.  In  such  a  situation,  some  authors  have  suggested  to  move  to  a  system  that 
provides  a  basic  income  for  every  citizen,  regardless  of  whether  they  are  employed  or  not.  This 
should  prevent  the  most  severe  economic  inequality  and  provide  the  means  for  participation  in 
society. However, the idea of a basic income is disputed, mainly because many people think that it 
provides  adverse  incentives  for  employment  and  entrepreneurship,  and  may  create  and  foster a 
culture of living on social benefits. 

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The impact of new technologies on the labour market and the social economy 

1.  Introduction 

This report investigates the relationship between innovation, new technologies, employment and 
inequality.  It  is  a  follow-up  of  a  study  commissioned  by  the  Science  and  Technology  Options 
Assessment (STOA) Unit of the European Parliament on the same topic (Krings and Muellner 2007). 

Since the predecessor study of 2007, the topic of this report has gained a lot of prominence. This is 
because of a number of well-received books on the topic (Brynjolfsson and McAfee 2014, Ford 2015), 
but also due to alarming reports about possible negative consequences for employment from new 
information  and  communication  technologies,  including  machine  learning,  digitalisation  in 
production,  automated  vehicles  etc.  This  is  why  the  focus  of  this  report  is  on  the  effects  of 
information and communication technologies, or digitalisation, on labour markets and inequality. 

This  report  will  review  the  literature  on  information  and  communication  technologies  and 
employment, which has evolved in the last years. Moreover, it will review the academic literature 
on  innovation  and  employment,  and  investigate  what  researchers  think  about  technology  and 
inequality.  A  special  chapter  is  devoted  to  the  social  economy  and  possible  consequences  of  the 
aforementioned technologies for it. Finally, a chapter on policy concludes the report. 

2.  The labour markets in EU countries 

The labour markets of the EU member states are in a painfully slow recovery from the recession 
following the global Financial Crisis of 2008/09. A number of recent reports analyse the status of 
EU labour markets (for example EUROFOUND 2015; European Commission 2015), so we will only 
focus on some important facts. 

Unemployment  in  the  EU  rose  from  2008  to  2012,  and  only  stopped  rising  in  2013.  In  2013, 
unemployment started to decrease in most EU member states (Figure 1 below).  

Figure 1: Changes in unemployment, 2013-2015, EU-28 and member states 

Source: EUROSTAT, Labour Force Survey 

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-4-3-2-1012Change 2013-15, percentage points 
STOA - Science and Technology Options Assessment 

We see the largest decreases in unemployment in some of the countries that were most severely hit 
by  the  crisis,  including  Spain,  Portugal,  Ireland,  and  Greece.  In  other  countries  with  high 
unemployment,  most  notably  France  and  Italy,  decreases  are  only  small  and  unemployment 
persists. 

Overall, unemployment in the  EU fell from its post-crisis peak of 10.9% in 2013 to 9.4% in 2015. 
Compared to the development of overall economic activity measured by GDP, the recovery of EU 
labour markets is remarkably modest (European Commission 2015, p. 12). Unemployment remains 
high in most EU countries.  

The  following  figure  compares  pre-crisis  (2008)  levels  of  unemployment  with  the  latest  annual 
figures. For the EU, unemployment is still 2.4 percentage points higher than in 2008. We can also 
observe considerable differences to the year 2008 in Greece, Spain, Croatia and Cyprus. There are 
only four countries with a lower unemployment rate in 2015 compared to 2008: Poland, Hungary, 
Malta and Germany.  

In absolute terms, on average 22.872 million people were unemployed in the European Union in 
2015. The largest number of unemployed people is found in Spain (five million people), France and 
Italy  (three  million  each),  and  Germany  (1.9  million).  Like  employment,  the  number  of  hours 
worked has also fallen between 2009 and 2013, and only rebounded in 2013 (European Commission 
2015, p. 16). 

Figure 2: Unemployment in the EU-28 and EU member states, 2008 and 2015 

Source: EUROSTAT, Labour Force Survey 

One aspect of the painfully slow recovery is the rise in long-term unemployment. The number of 
people  being  unemployed  for  one  year  or  more  nearly  doubled,  from  6.1  million  to  12.3  million 
between 2008 and 2013. In 2015, the number of long-term unemployed was 10.9 million people on 
average. If we relate the number of long-term unemployed to the total number of unemployed, we 
see a drastic increase, from 37% in 2008 to 49.3% in 2014. This is a fundamental change in the quality 
of unemployment. Long-term unemployment is most persistent in Greece, Slovakia, Croatia and 
Bulgaria, where more than 60% of all unemployed people were out of employment for at least a 
year. The lowest shares of long-term unemployed are found in Sweden and Finland. 

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051015202530Percent20152008 
The impact of new technologies on the labour market and the social economy 

Another worrying aspect of the recent labour market performance in the EU member countries is 
the high level of youth unemployment. Unemployment among people less than 25 increased from 
15.9% (2008) to 23.7% (2013), and dropped in the two following years to 20.2% (2015). We see the 
highest levels of youth unemployment in Greece, Spain, Croatia and Italy with more than 40%. 

Overall, the labour markets of the member states of the European Union show a divergent picture; 
on the one hand, some countries have held unemployment constant or reduced it considerably in 
the last years; examples include the UK and Germany. On the other hand, there are countries with 
persistently high unemployment: France, Italy, Greece, Portugal or Spain. The challenge, thus, is 
not only to reduce unemployment, but also to deal with a larger degree of disparity in the economic 
conditions throughout the  European Union, a can be seen in the  figure below. In the  mid-1990s, 
differences in unemployment rates between the largest EU countries were much smaller than they 
are today. 

Figure 3: Unemployment rates in Italy, France, the UK, Germany and the EU-28, 1993-2015 

Source: EUROSTAT, Labour Force Survey 

3.  The relationship between innovation and employment 

What can be done to bring down unemployment? A look in the literature reveals that innovation is 
seen by many authors as the driving force behind employment growth, in particular in the long run 
(Freeman et al. 1982; Van Reenen 1997; Edquist et al. 2001; Pianta 2005; Vivarelli 2014).  

A first look at the relationship between innovation and employment is given in the following figure. 
It has been taken from an EU-funded study on the impact of innovation on employment growth at 
firm level in the EU over the period 1998-2010 (Peters et al. 2014). The figure reveals that in terms 
of  employment  growth,  innovating  firms  perform  better  than  non-innovators  regardless  of  the 
phase  of  the  business  cycle;  they  create  more  in  upswings  and  boom  periods,  and  loose  less 
employment in recessions1.  

1 Readers should notice that employment growth as illustrated in this figure is the average growth at firm level, not at the level of the whole economy, 
which is lower The main difference between the two values is the fact that firms which exited the market are not included in the firm level data. 

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0246810121419931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015Unemployment rate -annual average,  %  EUGermanyFranceUnited KingdomItaly 
                                                           
STOA - Science and Technology Options Assessment 

Figure  4:  Employment  growth  of  innovators  and  non-innovators  in  different  phases  of  the 
business cycle, manufacturing, 1998-2010 

Source: Peters et al. (2014) 

3.1.  Product innovation, process innovation and productivity 

Before  we  investigate  the  relationship  between  innovation  on  employment  in  more  detail,  it  is 
useful  to  make  a  few  distinctions:  first,  between  product  innovation,  the  introduction  of  new 
products on the market, and process innovation, which is the implementation of new processes for 
the  production  of  products  (OECD  2005).  The  OECD  considers  both,  product  and  process 
innovation, as technological innovations. 

In  addition,  firms  may  also  invest  in  non-technological  innovation  such  as  organisational 
innovation  or  marketing  innovation.  Whereas  process  innovations  refer  to  changes  in  material 
goods which are improved through technical change, organisational innovations are new ways to 
organise work (OECD 2005), including the introduction of new business processes and new ways 
of workplace organisation. Business model innovation also falls in this category (Massa and Tucci 
2014). 

Finally, marketing innovations involve significant changes in product design or packaging, product 
placement,  product  promotion  or  pricing  (OECD  2005).  We  can  distinguish  between  marketing 
innovations  and product innovations by looking at product characteristics; if  the  function or  the 
way people use the product has changed, then it is product innovation.  

The  distinction  between  technological  and  non-technological  innovation  says  nothing  about  the 
economic value of the innovation for the firm and its potential for jobs creation. Organisational and 
marketing innovations often complement technological innovations and  help  to unfold their full 
potential (Brynjolfsson and Hitt 2000).  

Another important term related to the topic of this report is  productivity (Bartelsman and Doms 
2000; OECD 2015). Increases in productivity are improvements of the ratio between the factors of 
production employed and the output of the firm – if a firm becomes more productive, it can produce 

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-8-6-4-2024681012UpswingBoomDownswingRecessionEmployment growth over two year-periodInnovatorno Innovator 
The impact of new technologies on the labour market and the social economy 

more with the  same inputs. Productivity is central to all discussions on employment and output 
growth.  Empirical  evidence  suggests  that  increases  in  productivity  account  for  a  considerable 
proportion of the employment growth in many sectors in the European Union, in particular in high-
technology sectors (Peneder 2009, p. 14). 

Unfortunately,  productivity  growth  is  slowing  down  in  the  long  term  in  Europe  and  North 
America, as can be seen in the following figure. It was highest in the post-war period 1950-1972. 
After the recession of 1972/73, most countries were not able to return to the productivity growth 
rates of the 1950s and 1960. 

Figure 5: Labour productivity growth in the long run, 1950-2013 

Source: OECD (2015), p. 16 

Productivity growth is the link between innovation and employment growth; innovation may lead 
to changes in productivity, which in turn may lead to employment changes (Hall 2011; Mohnen and 
Hall  2013).  The  linkage  between  innovation,  productivity  and  employment,  however,  is  not 
straightforward,  and  different  forms  of  innovation  may  therefore  have  different  effects  on 
employment growth (Edquist et al. 2001; Chennells and Van Reenen 2002; Pianta 2005; Hall et al. 
2008; Harrison et al. 2014; Vivarelli 2014). 

3.2.  Productivity and demand effects 

The following Figure 6 illustrates the relationships between process and product innovation on the 
one hand and employment changes on the other hand. 

We start with the effect of process innovations on employment. Process innovation  – for example 
the installation of new production equipment – usually allows the firm to produce the same amount 
of output with less capital and/or labour. As a consequence, process innovation most often leads to 
an  increase  in  productivity  and  a  negative  effect  on  employment  (Productivity  effect  of  process 
innovation, in  the  lower  right side of the  figure). The size  of this  negative effect depends on the 
current production technology and, thus, the rate of substitution between input factors as well as 
on  the  direction  of  the  technological  change.  This  labour-saving  effect  also  varies  significantly 
between sectors (Edquist et al. 2001). 

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01234567AustriaCanadaFranceGermanyItalyNetherlandsSwedenUKUnited StatesAverage annual growth1950-19721972-19951995-20042004-2013 
STOA - Science and Technology Options Assessment 

Figure 6: Effects of product and process innovation on employment 

 Source: Peters et al. (2014), own illustration. 

In  recent  years,  a  growing  literature  discussed  the  labour-saving  effects  of  digitalisation.  Some 
authors  (for  example  Frey  and  Osborne  2013,  2017;  Brynjolfsson  and  McAfee  2014)  predict  vast 
potentials for productivity increases and corresponding employment losses due to the application 
of information technologies on a wide  range  of routine  activities in  manufacturing and services. 
These predictions will be summarised and discussed in chapter five of this report. 

Despite the predictions of vast future employment losses due to process innovation, the empirical 
evidence on  past employment effects of process innovations is  ambiguous. In the  studies  of van 
Reenen (1997) and Entorf and Pohlmeier (1990), the impact of process innovations turns out to be 
small and not significant at all. Greenan and Guellec (2000) or Lachenmaier and Rottmann (2011), 
in contrast, report a significant positive effect of process innovations on employment growth. The 
latter two studies even establish that process innovation creates more new employment at the firm 
level than product innovation. On contrary, Blechinger and Pfeiffer (1999) find evidence of labour 
displacement by process innovation, the effect being more pronounced in larger firms. 

The  second  important  effect  of  innovation  on  employment  is  the  demand  effect  depicted  in  the 
upper  left sector of  Figure  6. If a firm introduces a new product to the  market,  this product will 
create  new  demand  for  the  firm  if  consumers  and  user  firms  find  it  useful.  The  degree  product 
innovation can create new employment depends on the price elasticity of demand and the existence 
of  substitutes  or  complementary  products  into  our  considerations.  Altogether,  the  empirical 
literature  suggests  that  the  demand  effect  is  certainly  the  most  important  positive  outcome  for 
employment from innovation. 

Despite  the  large number  of studies that report employment gains from product innovation, the 
demand effect is rarely mentioned in current discussions on digitalisation. This may be due to the 
fact that our ability to imagine the future is limited, which also includes limitations to imagine major 
future product innovations and their impact on jobs creation. Employment losses due to process 
innovation, in contrast, seem to be much more obvious from today's perspective because they apply 
to changes in products and occupations we all know very well. 

This lack of clarity is also reflected by current discussions on future prospects for productivity and 
innovation; the OECD (2015, p. 28) nicely sums up the arguments of techno-pessimists and techno-
optimists;  the  former  argue  that  the  current  slowdown  in  growth  is  a  permanent  phenomenon 

12 

  Employment - -  Process innovation + + Demand effect Productivity effect Price effect Productivity effect  Product innovation  
The impact of new technologies on the labour market and the social economy 

caused  by  the  exhaustion  of  opportunities  for  significant  useful  innovation.  The  latter  group,  in 
contrast, sees no evidence for diminishing returns to innovation. 

3.3.  Other effects 

The  demand  and  the  productivity  effect  are  two  major  channels  how  innovation  affects 
employment;  they  are,  however,  not  the  only  ones.  Process  innovation  often  allows  the  firm  to 
manufacture a new product at substantially lower costs than the old one; if the firm passes on this 
decrease in production costs to their consumers by reducing the price of the product, this step may 
also lead to overall market expansion and employment growth.  

Empirical evidence for this price effect is provided by (König et al. 1995), (Greenan and Guellec 2000), 
or (Lachenmaier and Rottmann 2011). The magnitude of the price effect is determined by the size 
of  the  price  reduction,  the  reaction  of  demand  on  price  changes  (elasticity  of  demand)  and  the 
reactions of competitors to the price reduction. Thus, the price effect also shows the importance of 
competition policy for employment, since we can assume that firms in a competitive market will set 
prices closer to the new, lower production costs than in a market with only a few competitors. 

In  addition,  productivity  effects  from  product  innovation  may  also  enter  into  the  equation  (see 
Figure  6).  New  products  sometimes  utilize  new  inputs,  which  allow  to  produce  them  with  less 
inputs  and  a  higher  productivity  than  the  old  product  (see  Harrison  et  al.  2014).  Thus,  product 
innovation  can  lead  to  productivity  changes,  even  if  product  innovation  is  not  associated  with 
simultaneous  process  innovation.  On  the  other  hand,  new  products  may  help  firms  to  increase 
productivity  by  moving  resources  from  the  production  of  old  products  to  new  products  with  a 
higher value so that productivity measured in monetary terms increases. 

Employment gains from product innovation are a major driver of employment growth; however, 
they come at a price. New products with a higher utility and/or a lower price will displace existing 
products offered by the firm or by its competitors. Thus, the demand for a new product may come 
at the expense of existing 'old' products. Product innovation may incur two types of these negative 
effects:  first,  a  negative  effect  to  innovator  itself  (known  as  the  'cannibalisation'  effect);  second,  a 
negative  effect  to  competitors  (the  'business  stealing'  effect).  Both  effects  may  reduce  the  initial 
employment-creating effect of product innovation.  

We all know these effects from mobile phones, cars or other consumer products; demand for older 
mobile phones by the same firm or competitors will decrease once a new model has been introduced 
to  the  market.  Moreover,  a  new  mobile  phone  with  increased  functionality  may  also  negatively 
affect sales of related product categories, for example tablets, digital cameras, digital music players, 
electronic route guiding systems, etc. 

The literature shows that cannibalisation and business stealing effects are frequent: in fact, some 
cannibalisation  is  almost  inevitable  for  firms  that  launch  new  products,  in  particular  for  multi-
product firms (Lomax et al. 1997). The degree of these negative effects depends on the existence of 
substitutes  and  the  reactions  of  competitors.  Exceptions  may  exist  when  a  new  product 
complements an existing one, or when a new product extends the product range of a firm into new 
fields, but benefits from the established brand (Lomax et al. 1997).  

3.4.  Differences between sectors 

The linkage between innovation and employment creation is also influenced by the sector of the 
firm.  Industries  differ  in  a  number  of  technology-related  factors  including  the  richness  of 
technological opportunities, the cumulativeness of the knowledge base, or the means to protect and 
appropriate  the  economic  benefits  from  innovation  (Marsili  2001;  Cohen  2010;  Dosi  and  Nelson 
2010).  We  can  observe  the  results  of  these  factors  in  persistent  differences  in  the  technological 

13 

STOA - Science and Technology Options Assessment 

trajectories  between  sectors  and  distinct  technological  regimes  that  shape  the  directions  of 
technological search in industries (Breschi et al. 2000; Marsili 2001; Castellacci 2007).  

These  technological  differences  also  transfer  into  different  employment  performances  between 
sectors, as can be seen in the following figure which illustrates employment growth in the EU by 
technology intensity of the sector2. 

Figure 7: Employment by technology intensity of the sector, EU, 2008 and 2013 

Source: (European Commission 2016), p. 301 

The fastest growing sector, market knowledge-intensive services, is a diverse sector including water 
and air transport, but, more important, legal and accounting activities; activities of head offices and 
management consultancy activities; architectural and engineering activities, technical testing and 
analysis; advertising and market research; and other professional, scientific and technical activities. 
Moreover,  it  also  includes  employment  activities,  security  and  investigation  activities.  We  can 
therefore imagine very diverse drivers that have fuelled growth of this sector. High-tech knowledge 
intensive  services,  the  second  fastest  growing  sector,  includes  the  television,  music  and  film 
industry,  telecommunications,  commercial  R&D  services  and  information  and  communication 
technology (ICT) services. 

The  figure  also  reveals  a  considerable  gap  between  manufacturing  and  service  industries  in 
employment  growth.  All  manufacturing  sectors  perform  below  average,  which  high-tech 
manufacturing  performing  best.  This  gap  between  services  and  manufacturing  is  confirmed  by 
macroeconomic data which shows a higher average employment growth in services compared in 
manufacturing across European countries (Veugelers 2013). The gap has been explained by a higher 
income elasticity of service products compared to manufacturing products and a higher degree of 
tradability of manufacturing products that allows a stronger shift of manufacturing towards lower-
cost countries and brings a more openness in manufacturing compared to services (Veugelers 2013).  

2 Readers should note that Figure 1 relates to the firm level, while Figure 7 relates to the sectoral level. Employment growth cannot be the same for the 
two figures, since firm-level data do not take into account the effects of firm entries and firm closings. 

14 

-15-10-50510Low-tech manufacturingMedium-low-tech manufacturingMedium-high-tech manufacturingHigh-tech manufacturingTotalLow knowledge-intensive servicesHigh-tech manufacturing and servicesOther knowledge-intensive servicesKnowledge-intensive servicesHigh-tech knowledge-intensive servicesMarket knowledge-intensive servicesEmployment growth 2008-13, (%) 
                                                           
The impact of new technologies on the labour market and the social economy 

A  second  explanation  is  productivity  growth,  which  is  usually  considerably  higher  in 
manufacturing than in services due to larger opportunities for process innovation (Baumol 2012). 
There  is  a  long  discussion  on  how  innovation  in  service  firms  differs  from  innovation  in 
manufacturing,  and  if  service  and  manufacturing  innovation  can  be  measured  with  the  same 
metrics (Tether 2005; Gallouj and Savona 2009). From this literature we know that innovation in 
services is more often non-technical, is based to a lesser degree on the application of scientific and 
formal  knowledge,  includes  more  interaction  with  clients,  requires  more  of  the  necessary  social 
skills for interaction, and can be protected less efficiently with formal intellectual property rights 
than  innovation  in  manufacturing.  Moreover,  service  products  are  far  less  standardised  than 
manufacturing products, because services are often the result of user-producer interaction (Tether 
et al. 2001; Miles 2005). All these factors speak for a lower degree – or at least for different innovation 
trajectories - in services, and for fewer opportunities for process innovation to raise productivity 
compared to manufacturing. 

If  we  move  from  the  firm  level  to  the  macroeconomic  level,  another  implication  of  employment 
change driven by innovation is the  reallocation of employment and capital between sectors. The 
fact  that  not  all  firms  are  successful  with  their  innovation  projects  implies  that  some  firms  and 
sectors grow fast, while others stagnate or even decrease. Thus, innovation is a major determinant 
of structural change and aggregate productivity growth, together with firm entries and exits (Caves 
1998; Bartelsman and Doms 2000; Krüger 2008; Dosi and Nelson 2010). This reallocation is driven 
by innovation and the use of new technologies. A famous illustration of long-term structural change 
as a result of major product, process and organisational innovations is Joseph Schumpeter's (1942) 
notion of 'creative destruction'. 

4.  Innovation, Skills and Unemployment 

So  far,  this  survey  has  assumed  that  no  differences  exist  between  people  in  terms  of  skills, 
experience,  creativity,  etc.,  so  that  labour  is  homogenous.  This  is,  of  course  a  very  simplistic 
assumption. 

People  have  different  levels  of  training  and  different  skills  which  in  turn  provide  different 
contributions  to  innovation.  Recent  empirical  research  has  shown  that  a  range  of  different  skills 
(engineering,  design,  multimedia,  graphical  arts,  software  development,  marketing  research) 
available in a firm has a positive impact on its innovation activities: the more skills are employed in 
the  innovation  process,  the  more  likely  is  it  that  this  firm  brings  up  new  products,  and  market 
novelties (Peters 2016). 

However, innovation and new products and processes also change the types of skills firms demand. 
Firms  which  are  based  on  ICT  demand  different  skills  sets  than  firms  based  on  mechanical 
technologies. We will discuss this in chapter 4.1 and 4.2. New technologies may also change the way 
people work and the future organisation of work, as we will see in chapter 4.4. 

This linkage between innovation and skills is the background of all estimations of the future impact 
of digitalisation  on employment  which  will be presented in chapter  5  – what  new products  will 
firms offer in the future, and what occupations and skills will the need for this? Can technology take 
over  tasks  and  occupations  where  specific  skills  are  needed?  What  if  the  development  of  skills 
cannot keep pace with technological change? We can be quite sure that innovation will also create 
jobs in the future, but they will be in other occupations as the jobs destroyed by technology. 

15 

STOA - Science and Technology Options Assessment 

4.1.  Skill-biased technological change 

Technology is not skill-neutral, but tends to favour some particular skills, while devaluates other 
skills and makes them redundant. This has been labelled 'skill-biased technological change' in the 
literature (Card and DiNardo 2002; Haskel and Slaughter 2002; Acemoglu and Autor 2011).  

The  skills  bias  of  technology  has  been  discussed  thoroughly  in  recent  years  for  information  and 
communication  technologies  (ICTs).  Applications  of  ICTs,  such  as  payroll  processing  software, 
barcode  scanners,  automated  inventory  software,  or  word  processing,  substituted  many  routine 
office tasks in the  last 30 years and have made them redundant. However, some tasks have also 
benefited  from  ICTs  because  ICTs  increased  their  productivity.  Examples  include  the  work  of 
designers, engineers, or  managers; ICTs have complemented and augmented skills necessary for 
these occupations, and increased demand for them. 

The following graph gives a first impression of the relationship between skills and unemployment. 
It shows the  share of unemployed  with different educational levels  as a percentage of the  active 
population in the EU-28 aged 25 to 64. 

We see that unemployment is particularly high among people with primary and lower secondary 
education or less. This group has the highest unemployment rate of all three groups throughout the 
whole  period.  Moreover,  people  with  the  lowest  educational  attainment  have  also  suffered 
disproportionally from the crisis, as can be seen in the Figure. Unemployment in this group nearly 
doubled during the crisis, from below 10% to 18% in 2013. 

Figure 8: Unemployment and skills levels, 1995-2015, EU-28 

Source: EUROSTAT, Labour Force Survey. 

However, employment opportunities for people with primary and lower secondary education or 
less have decreased long before the crisis 2008/09, as can be seen in the following figure. The share 
of this group on total employment in the  EU  decreased from 25% in 2003 (which equals around 
40 million people) to 16% (or 26 million people) in 2015. People with tertiary education, in contrast, 
increased  their  share  on  total  EU  employment  by  10  percentage  points  during  this  period,  from 
around 40 million people in 2003 to 59 million people in 2015. 

16 

02468101214161820199519961997199819992000200120022003200420052006200720082009201020112012201320142015PercentagePrimary and lower secondary education or lessUpper secondary and post-secondary educationTertiary education 
The impact of new technologies on the labour market and the social economy 

The size of the largest share, people with upper secondary and post-secondary education, remained 
nearly unchanged between 2003 and 2015, both in absolute and relative terms. This group holds a 
share of about 50% on total EU employment, which equals about 77 million people in 2015. 

Figure 9: Employment and skills levels, 1995-2015, EU-28, 1,000 persons 

Source: EUROSTAT, Labour Force Survey. 

4.2.  Routine-biased technological change 

Skill levels, however, may still be a too broad category to capture current developments in US and 
EU labour markets. David Autor (2013) and others suggest to look at individual tasks rather than 
skills. A task is a unit of work activity that produces output (Autor 2013). Tasks can be performed 
by labour (people) or by capital (machines, or, in other words, automation). People apply their skills 
to deliver various tasks (Autor 2013). 

The division of tasks between people and machines is fluid: (Autor 2013, p. 186) finds that “novel 
tasks  –  those  demanded  by  new  products,  techniques,  or  services  –  are  often  assigned  first  to 
workers because workers are flexible and adaptive. As these tasks are routinized and codified, they 
become fallow for automation …” A shift of tasks between workers and machines can, of course, 
also be caused by increases in the capabilities of machines due to technological change. 

Chapter 3 discussed that process innovation can destroy, but can also create employment. The tasks 
approach adds some new facets to this relationship. New process technologies can make some tasks 
provided by workers redundant, while increase the value and utility of other tasks by increasing 
their productivity. These tasks include everything related to problem-solving capabilities, intuition, 
creativity,  persuasion,  situational  adaptability,  visual  and  language  recognition  and  person-to-
person interactions (Autor et al. 2003). So, new technologies can either substitute or complement 
tasks performed by workers (Brynjolfsson and McAfee 2014; Autor 2015). 

17 

25%24%23%22%22%21%20%19%19%18%17%16%16%50%50%50%50%50%50%49%49%49%48%48%48%48%26%27%27%28%28%29%30%31%32%34%35%36%36%020,00040,00060,00080,000100,000120,000140,000160,000180,00020032004200520062007200820092010201120122013201420151,000 personsTertiary education (levels 5-8)Upper secondary and post-secondary non-tertiary education (levels 3 and 4)Less than primary, primary and lower secondary education (levels 0-2) 
 
 
STOA - Science and Technology Options Assessment 

Acemoglu and Autor (2011) suggest that a distinction of manual vs. cognitive and routine vs. non-
routine jobs may be better suited than skilled vs. unskilled. They distinguish between four types of 
tasks: 

  Analytical and interactive non-routine tasks 
  Analytical and interactive routine tasks 
  Manual routine tasks 
  Manual non-routine tasks 

They show that demand for routine jobs and tasks has fallen considerably, no matter if these jobs 
or tasks had a cognitive or manual character. As a consequence, demand for  people with middle 
skill levels has decreased, while the demand for both high-skilled and low-skilled (and accordingly 
paid) occupations has risen. This has been labelled 'routine-biased technological change' and job 
polarisation (Autor et al. 2006, 2008; Goos et al. 2014). ICTs increasingly substitute routine, middle-
skilled jobs, but are complementary to less routine high-skilled cognitive and low-skilled manual 
jobs. This may mean that firms hire more university graduates working in design, development or 
management, but they may also need cleaning staff, salespersons, etc. An example for such a non-
routine task is everything that requires face-to-face communication. An alternative explanation for 
job polarisation is globalisation, because routine jobs are likely to be more open to offshoring than 
non-routine jobs. 

The  figures  above  do  not  give  evidence  for  job  polarisation.  However,  if  we  go  at  the  level  of 
occupations, divergent trends between various groups become apparent. The graph below is taken 
from  the  report  of  Peters  (2016).  She  depicts  the  changes  in  shares  of  eight  different  occupation 
groups  on  total  employment  for  the  period  1995-2001,  2001-2008,  and  2008-2015.  The  eight 
occupation  groups  are  classified  into  high-paid,  medium-paid  and  low-paid  population  classes, 
according to the average wage at European level in each of the groups. Goos et al. (2014, p. 2513) 
suggest  that  the  routine  intensity  is  highest  among  the  middle-paid  occupations.  Due  to  data 
limitations, the figure only includes data for the EU-15. Developments in the EU-28, however, are 
similar. 

The results (see Figure 10 below) clearly indicate job polarisation; high-paid professionals, but also 
low-paid  service  and  sales  workers  could  raise  their  share  on  overall  employment  considerably. 
Medium-paid  occupations,  such  as  clerical  support  workers  or  craft  and  related  trades  workers, 
suffered  the  largest  losses  in  terms  of  employment  share.  It  seems  that  job  polarisation  even 
accelerated since the crisis of 2008/09. Overall, medium-paid occupations lost 4.6 percentage points 
on overall employment in the period 2008-2012 and nine percentage points in the period 1995-2015. 
High-paid and low-paid gained 1.3 and 3.1 percentage points in the period 2008-2012 and 9.5 and 
5.8 percentage points in the period 1995-2015, respectively. However, there are also exceptions, such 
as managers, which also lost shares. Evidence presented by Peters (2016, p. 310) based on CEDEFOP 
employment forecasts suggests that this trend will likely continue in the future. 

The European Jobs Monitor (EUROFOUND 2015) comes to a similar conclusion for the three year 
period between the second quarter 2011 and the second quarter of 2014. During this period, EURO-
FOUND  finds  “the  largest  employment  growth  in  well-paid  jobs,  some  modest  growth  in  the 
lowest-paid jobs and declining employment in middle of the wage distribution.” (EUROFOUND 
2015, p. 1). More recently, employment growth has changed, with more new employment in low-
paid  and  mid-paid  segments.  This  is  very  recent  development,  because  in  its  2014  report, 
EUROFOUND (2014) writes that “contrary to previous research in this area, a clear association was 
not found between the routine content of jobs and the polarisation of job structures. While it is true 
that  routine  content  was  negatively  associated  with  employment  growth,  this  effect  tended  to 
contribute to upgrading rather than polarisation. “ 

18 

The impact of new technologies on the labour market and the social economy 

Figure  10:  Changes  in  employment  shares  of  different  occupation  groups,  1995-2015,  EU-15, 
percentage points. 

Source: EUROSTAT, Labour Force Survey, own calculations. 

One  should  note,  however,  that  this  analysis  of  job  polarisation  and  other  labour  market  effects 
crucially depends on the proper identification of routine and non-routine tasks. Goos et al. (2014, 
p. 2513)  suggest  that  the  routine  intensity  is  higher  in  the  middle-paid  occupations.  The  OECD 
(Marcolin et al. 2016) brought forward a new classification of routine content based on individual 
responses to a survey on adult competencies (PIAAC). These results indicate that routine intensity 
increases with skill levels – the jobs with the lowest skills requirements are also the most routine-
intensive ones.  

This would – at least partly – bring down the argument of Goos et al. (2014) about routine-biased 
technological change in Europe. However, Marcolin et al. (2016) also find that medium and high 
routine-intensive  occupations  are  mostly  found  in  medium  skilled  employment,  simply  because 
there  are a lot of these medium-skill  jobs around (see  Figure  11 below). 73% of all high routine-
intensive  and  68%  of  all  medium  routine-intensive  employment  can  be  categorised  as  medium-
skilled. So, the medium-skilled group is the one with the highest affectedness to job polarisation. 
On  average,  46%  of  the  employed  work  in  non-routine  occupations  or  in  low  routine-intensive 
occupations. 

Marcolin et al. (2016) also provide data on the share of different routine intensity at country level 
that gives an interesting perspective at divergences in the European Union (see Figure 12 below). 
Similar  statistics  are  the  basis  for  estimations  on  the  employment  effects  of  information  and 
communication technologies we will discuss in the next section. 

The  figure  indicates  a  considerable  divide  within  Europe  in  terms  of  routine  intensity.  Southern 
European countries such as Italy and Spain reveal the largest share of employees with a high routine 
intensity, while Austria, Germany and Denmark have the lowest shares in this category. If we sum 
up the employment shares of medium- and high-routine  intensity, the UK and Ireland have the 

19 

-4%-3%-2%-1%0%1%2%3%4%5%6%ManagersProfessionalsTechnicians and associate professionalsClerical support workersCraft and related trades workersPlant and machine operators and assemblersSkilled agr., forestry and fishery workersService and sales workersElementary occupationsHighMediumLowin percentage pointsChange 1995-2001Change 2001-2008Change 2008-2015 
STOA - Science and Technology Options Assessment 

highest degree  of  routine intensity  in  the  EU, followed by Slovakia and Poland. However, these 
countries have also the highest shares of non-routine jobs. 

Figure 11: Employment by skill and routine intensity, 2011-2012. 

Source: Marcolin et al. (2016), p. 23. 

Figure 12: Employment by routine intensity in various OECD countries, 2011-2012. 

Source: Marcolin et al. (2016), p. 21. 

20 

0%10%20%30%40%50%60%70%80%90%100%non-routinelow routinemedium routinehigh routineskills lowskills mediumskills high0%10%20%30%40%50%60%70%80%90%100%HR: High routine intensiveMR: Medium routine-intensiveLR: Low routine intensiveNR: Non-routine intensive 
 
 
 
The impact of new technologies on the labour market and the social economy 

4.3.  The role of globalisation 

Besides technological change, there  are also other economic  forces that may reduce employment 
opportunities for low-skilled people. An obvious candidate is globalisation and the emergence of 
global  value  chains.  Globalisation  can  lead  to  a  substitution  of  domestic  low-skilled  labour  by 
foreign  low-skilled  labour,  for  example  when  firms  offshore  production  and  substitute  goods 
produced in Europe with imports (Grossman and Rossi-Hansberg 2008).  

Timmer et al. (2011) show that there is indeed a strong shift towards imported inputs in most global 
value chains. The output of countries and sectors increasingly depends on imported intermediary 
goods.  However,  this  does  not  necessarily  mean  that  domestic  low-skilled  labour  is  replaced  by 
foreign low-skilled labour. The authors show for the German automotive industry that the content 
of  domestic  low-skilled  labour  in  value  added  decreased  from  7%  to  4%  of  final  output  value 
between  1995  and  2008.  While  the  total  share  of  foreign  inputs  on  final  output  of  the  German 
automotive  industry  increased  from  21%  to  34%  in  this  period,  the  share  of  foreign  low-skilled 
labour on final output remained stable at 4%: Value added is increasingly created by  capital and 
high-skilled labour, and the share of domestic and imported low-skilled labour on output and value 
added  decreases.  Thus,  the  share  of  low-skilled  labour  embodied  in  imported  goods  cannot  be 
blamed for the decrease of domestic low-skilled labour inputs. The authors report that low-skilled 
labour inputs decreased in 91% of all combinations of countries and global value chains. So, it is 
rather  technological  change  than  globalisation  which  is  to  blame  for  the  decline  of  low-skilled 
labour.  

However,  there  are  also  results  that  point  in  a  different  direction.  Becker  et  al.  (2013)  show  that 
offshoring  is  associated  with  a  statistically  significant  shift  towards  more  non-routine  and  more 
interactive tasks, and with a shift towards highly educated workers. Crino (2009) provides more 
evidence in this direction, so it seems that both, technology and globalisation have its share on the 
skills-bias we observe in employment statistics. In addition, we have to distinguish between short-
term and long-term effects: once firms start to offshore low-skilled jobs abroad, offshoring will raise 
foreign wages and may change the direction of technological change. Acemoglu et al. (2015) predict 
that  further  offshoring  may  soon  induce  innovation  in  less  skill-intensive  sectors,  leading  to  a 
shrinking of the inequality gap. 

4.4.  The future organisation of work 

Finally,  if  information  and  communication  technologies  are  going  to  substitute  individual  tasks, 
how will the organisation of work look like in the future? A recent foresight study by the German 
Bertelsmann-Stiftung (Landmann and Heumann 2016) gives some indications for this question. Its 
main assumption – and also of similar studies – is that individual tasks performed by people will 
become  more tradeable over the Internet. Various experts labelled this a  'platform', 'freelance' or 
'gig' economy (Friedman 2014, Kenney and Zysman 2016).  

The 'gig' economy has two main consequences:  

First,  the  share  of  self-employed  people  who  offer  these  tasks  over  the  Internet  will  increase,  as 
marketplaces for these services ('platforms') become more widespread. This includes highly skilled 
and paid experts, but also low-skilled service personnel. As a positive consequence, platform work 
brings more self-determination for the people offering these tasks, and a better work-life-balance. 
The  downside,  however,  may  also  be  more  insecurity  for  the  platform  workers  and  periods  of 
involuntary unemployment.  

Second, as firms gradually rely more on self-employed contractors and independent experts instead 
of fixed staff, they can increase efficiency and be able to offer their products and services at lower 
prices because they avoid running idle with their fixed stuff. This means a shift to a more project-

21 

STOA - Science and Technology Options Assessment 

oriented  organisational  structure  instead  of  fixed  hierarchies.  The  boundaries  of  the  firm  will 
increasingly become blurred, and the share of tasks performed outside the firm will increase. Work 
related to a product or project will more rarely be carried out at the same place and time, but will 
spread geographically and in time. The office as the place to do work in a pre-defined time of the 
day  will  be  less  important  than  it  is  today,  and  homework  will  again  gain  prominence.  Crowd 
working, where many people will take over small pieces of a task, will become more widespread. 

The future organisation of work was also one of the main topics of the discussion at the workshop 
the contractor of this project organized at the European Parliament. Statements by the speakers and 
from the audience indicated concerns about the gig economy and the 'atomisation' of work relations 
by platforms. Workshop participants argued that this may have advantages for  a few  'superstar' 
experts,  but  will  bring  disadvantages  for  the  large  number  of  people  who  have  few  alternatives 
other  than  to  take  up  this  kind  of  work.  Platform  work  is  already  frequent  in  some  sectors  like 
transport  and  delivery  services;  in  these  industries,  firms  don't  have  employees,  they  have 
associates.  

It will be a challenge for  social policy to enlarge social security legislation to platform work and 
other new forms of work that have emerged or will emerge as a consequence of new information 
and communication technologies. One solution could be a contribution of the platform owners to 
social security. Moreover, trade unions, as well as the social security system, which both evolved 
based on the model of wage earners in long-term working relations, will have to cope with these 
trends.  Much  will  also  depend  if  and  how  governments  regulate  crowd  working  and  similar 
developments. 

Another topic related to the future organisation of work is work time reduction. New technologies 
and the accompanying increases in productivity allowed large reductions in the working time in 
the past. During the 20th century, work hours shortened by almost half in the United States and 
Europe. This allowed to keep unemployment low and supported the integration of women in the 
labour market after the Second World War. 

The  current  technological  changes  have  not  created  large  reductions  in  working  time  so  far. 
According to the EUROSTAT Labour Force Survey, full-time employees in the EU-28 worked on 
average 41.1 hours a week in 2015, only slightly less than 10 years before (41.9 hours). The same is 
true for part-time employees (20.2 vs. 19.8 hours per week). 

Supporters of a reduction argued at the workshop that such a measure may help to dampen the 
negative  effects  of  new  ICTs  on  jobs  predicted  by  some  studies.  Moreover,  it  may  also  help  to 
increase the quality of life for employees. However, a reduction of worktime while maintaining the 
same levels of income will inevitably weaken the competitive position of European export-oriented 
firms, and may even increase labour shortages in areas where firms already find it hard to hire new 
employees, in particular experts related to ICTs. So, the effects of worktime reduction are not clear, 
in particular in the long run. 

To sum up, there is a tendency that technological change favours high-skilled, non-routine or low-
routine  occupations,  while  it  reduces  employment  opportunities  in  low-skilled  and  routine 
occupations.  Many  of  these  tasks  may  be  replaced  by  technologies,  in  particular  ICTs.  The 
development of the European labour markets confirms this skill-biased or routine-biased character 
of technology. In terms of workplace organisation and organisation of work, experts expect more 
self-employment, project structures and an increase in the share of tasks contracted and performed 
over platforms outside the firm. 

22 

 
 
The impact of new technologies on the labour market and the social economy 

5.  The impact of new technologies on employment: empirical 

results 

This chapter  will give an  overview on the  current discussions  of the  employment effects of new 
technologies.  This  discussion  currently  focuses  on  the  employment  impact  of  information  and 
communication  technologies.  An  example  is  the  book  by  Brynjolfsson  and  McAfee  (2014)  which 
paints  a  rather  optimistic  picture  of  the  opportunities  from  the  computerisation  of  physical  and 
intellectual routine tasks. Ford  (2015) is more sceptical and points to the  need to adjust political, 
economic and social structures to accompany and smoothen possible negative implications of new 
ICTs. 

5.1.  Employment effects of information and communication 

technologies 

Before we begin to discuss possible implications of ICTs on employment it is helpful to consider 
what particular technologies these authors have in mind when they talk about digitalisation and 
the  social  and  economic  implications  of  new  ICTs.  A  preliminary,  certainly  non-exhaustive  list 
includes the following technologies, which are also discussed in a recent STOA study (Van Woensel 
et al. 2015): 

  Autonomous vehicles: support and/or substitution of human drivers in private cars as well 
as  in  passenger  and  freight  traffic.  Autonomous  driving  could  substitute  a  considerable 
number of professional drivers. 

  Additive  manufacturing  (3D-Printing):  a  technology  that  allows  production  of  three-
dimensional artefacts by adding successive layers of material. A blueprint at a computer 
determines the final shape of the object. Additive manufacturing is already used to produce 
a variety of manufacture. 

  Algorithmic decision-making (Big Data): substitution and/or support of human decision-
making by algorithms based on large data sets and probabilities. Examples include credit 
ratings,  planning  processes,  summarising  of  documents  etc.  Experts  regard  algorithmic 
decision making as one potential tool to substitute a number of routine office occupations. 

  Industrial  and  service  robots:  machines  that  perform  physical  tasks  without  the  need  for 
human intervention may take over a number of manual, routine tasks in manufacturing but 
also in services and substitute workers in these activities. 

  Bitcoin and block chain technologies: electronic money created by the private sector on the 
basis of a database that records transactions and ensures ownership titles. This technology 
could be the basis for a number of new entrants that challenge traditional banks. 

  Digital factory ('Industrie 4.0'): network connectivity  embedded in production equipment 
allows to exchange production data, a better control of the manufacturing process, a higher 
degree of customisation of the products and more integration with suppliers and customers 
along the value chain. 

  Smart home and Active Assisted Living (AAL): ICTs provide assistance and/or supervision 
for  people  with  disabilities.  AAL  is  related  to  home  automation,  but  also  various 
supervision technologies and also includes rudimentary decision-making, for example in 
the case of an emergency. 

23 

 
 
STOA - Science and Technology Options Assessment 

All these technologies are expected to have profound effects on future labour demand because they 
may substitute routine activities by applications of ICTs. The work of accountants, for example, may 
be at danger of being substituted because technologies today – and even more in the near future – 
will be able to correctly recognise invoices and record them according to accounting standards. This 
may put millions of accountants in Europe and North America out of work. 

In  recent  years,  a  number  of  authors  have  tried  to  estimate  the  effects  of  digitalisation  on 
employment. A tentative list of these studies can be found in the Table below; there are certainly 
some more on the way. 

Table 1: Economy-wide studies on the impact of new ICTs on employment 

Source 

Result 

Frey and Osborne (2013) 

-47% 

Peri
od 

Remarks 

-  20 

10 
years 

USA, all sectors 

Bowles (2014) 

-47 to -60% 

-  20 

10 
years 

All  EU  member  states;  follows  the 
approach of Frey/Osborne 2013 

Bonin et al. (2015) 

-12% 

DE, all sectors 

Boston Consulting Group (2015) 

+6% 

10 years 

DE, manufacturing 

Wolter et al. (2015) 

less than 1% 

25 years 

Arntz et al. (2016) 

-12% to -6% 

DE,  manufacturing,  considers  also 
economy-wide compensation effects 

OECD countries, follows the approach 
of Bonin et al. 2015 

Source: own research. 

  With around 800 citations in Google Scholar and another 280,000 search results on Google3, 
the work of Frey and Osborne (2013, 2017) is certainly the most discussed of these studies. 
Frey  and  Osborne  identify  occupations  with  the  highest  content  of  routine  tasks  and 
estimate how many jobs could be replaced by digitalisation. They find that about 47 percent 
of total US employment is at risk of being substituted by ICTs within the next 20 years. The 
remaining  jobs  are  characterised  by  one  of  these  three  features:  perception  and 
manipulation (for example finger dexterity); creative intelligence (for example the ability to 
come  up  with  unusual  or  clever  ideas  as  about  a  given  topic  or  situation);  and  social 
intelligence (social perceptiveness, negotiation skills, persuasion). 

  Bowles  (2014)  repeated  the  study  by  Frey  and  Osborne  for  the  European  Union  and 
concludes  that  between  47%  (for  Sweden  -  similar  to  the  US)  and  up  to  well  over  60% 
(Romania) of the EU work force will lose their job due to ICTs in the coming decades. 

  Bonin et al. (2015), from the German Centre for European Economic Research (ZEW), expect 
much lower jobs losses due to ICTs and technological change than the two aforementioned 
studies. He expects a reduction of 12% for Germany. Here, a main difference to Frey and 
Osborne  (2013,  2017) and  Bowles  (2015)  is  that  Bonin  et  al.  (2015) focus  on  tasks  within 
occupations, rather than on occupations itself. Occupations include various tasks, routine 
and  non-routine,  and  require  a  set  of  different  skills.  People  apply  their  skills  to  deliver 
various tasks (Autor 2013). It is therefore unlikely that ICTs makes complete occupations 

3 Retrieved May 20, 2016 

24 

 
 
                                                           
The impact of new technologies on the labour market and the social economy 

obsolete - which the central assumption of Frey, Osborne and Bowles who estimate how 
'computerisable' different jobs are. 

  The Boston Consulting Group (BGC 2015) studies the impact of Industrie 4.0 in the German 
manufacturing sector on the basis of expert interviews and statistical data. They find that 
Industrie  4.0  will  have  positive  impacts  on  productivity,  revenue,  employment  and 
investment. For employment, they see a six percent increase or 390,000 additional jobs until 
2025. However, they also expect transformations within the workforce with displacement 
among workers who perform simple, repetitive tasks. 

  Wolter et al.  (2015), from the  German  Institute for Employment  Research (IAB), employ a 
multi-sector  input-output  model  for  the  German  economy  and  estimate  the  economic 
effects of five aspects of the advance of Industry 4.0, including higher investments in ICTs 
infrastructure and equipment, changing intermediate demand and changing demand for 
various occupations. This approach also considers indirect economic effects via demand for 
investment and intermediate goods.  

They find only a very small reduction in the overall number of employees (-60,000) due to 
Industry 4.0  until 2030. However, the  model also predicts huge structural change within 
manufacturing  and  between  manufacturing  and  services:  the  small  total  effect  of  minus 
60,000 results from a loss of 420,000 jobs, but also gains of 360,000 jobs in manufacturing. 

  Finally, Melanie Arntz et al. (2016) follow the approach of Bonin et al. (2015) and consider 
routine tasks rather than routine occupations. They find that the share of workers who have 
the highest probability of being replaced by automation is largest in Germany and Austria 
(12% of the workforce), and lowest in Estonia, Finland and Belgium (6-7%). 

Which tasks are favoured or threatened by technology? A closer look at Frey/Osborne 

The study of Frey and Osborne ranks 700 occupations according to their potential for automation. 
This  ranking  is  based  on  experts'  opinions  and  the  description  of  job  profiles.  These  are  the 
occupations with the highest (99% probability) chances of being replaced by technology: 

Data  Entry  Keyers,  Library  Technicians,  New  Accounts  Clerks,  Photographic  Process  Workers  and 
Processing  Machine  Operators,  Tax  Preparers,  Cargo  and  Freight  Agents,  Watch  Repairers,  Insurance 
Underwriters, Mathematical Technicians, Hand Sewers, Title Examiners, Abstractors, and Searchers, and 
Telemarketers. 

The opposite side of the spectrum consists of occupations with a very low (less than 4% probability 
of being replaced by technology: 

Recreational  Therapists,  First-Line  Supervisors  of  Mechanics,  Installers,  and  Repairers,  Emergency 
Management  Directors,  Mental  Health  and  Substance  Abuse  Social  Workers,  Audiologists,  Occupational 
Therapists, Orthoptists and Prosthetists, Healthcare Social Workers, Oral and Maxillofacial Surgeons, First-
Line  Supervisors  of  Fire  Fighting  and  Prevention  Workers,  Dieticians  and  Nutritionists,  and  Lodging 
Managers. 

The  difference  between  the  two  groups  is  obvious:  the  latter  occupations  include  intensive 
interaction with persons (mostly patients), and require creative and social intelligence, while the 
first group does not.  

25 

 
 
 
STOA - Science and Technology Options Assessment 

In addition, three other studies with a narrower focus deserve to be mentioned here: 

  First, the analysis by Pantea et al. (2014) uses firm-level instead of country-wide data like the 
studies  mentioned  above.  Firm-level  data  has  the  advantage  to  give  an  average  effect 
instead of the aggregate effect, which may be distorted by very large or very small actors. 
The  authors  come  to  the  conclusion  that  no  negative  relationship  between  employment 
growth and the intensity of ICT use by firms exists. 

  Second,  Michaels  and  Graetz  (2015)  investigate  the  impact  of  robots  on  employment  at 
industry  level  for  the  period  1993  to 2007  for  17  countries.  They  find  a  positive  effect  of 
increased  use  of  robots  in  industry  on  productivity,  wages  and  subsequently  on  GDP 
growth. They do not find evidence of a negative effect of robots on aggregate employment, 
only  a  negative  effect  on  low-skilled  workers  and  a  weaker  effect  for  middle-skilled 
employment. 

  Third, Fraunhofer ISI (Jäger et al. 2015) analyses the impact of robots on employment for the 
period 2007-2009, but in contrast to Michaels and Graetz (2015) with firm-level data. They 
confirm  the  aforementioned  result  and  find  neither  a  positive  nor  a  negative  direct 
employment  effect.  However,  they  show  that  firms  using  robots  reveal  a  significantly 
higher productivity. An interesting side result is that firms which use robots have a much 
smaller probability to offshore production to locations abroad. 

Obviously, there is no common opinion in the literature on the future impacts of information and 
communication  technologies  on  employment.  Estimates  vary  between  employment  gains  and 
employment losses of nearly half of the workforce, depending on the method and the assumptions 
used. 

Differences in the method employed are most obvious between Frey and Osborne (2013, 2017) and 
Bowles (2014) on the one hand and Bonin et al. (2015) and Arntz et al. (2016) on the other hand. The 
latter studies try to break down occupations into various tasks and analyse substitution at the task 
level.  This  approach  assumes  that  occupations  consist  of  various  routine  and  non-routine  tasks; 
examples for  non-routine  tasks are inter-personal group work or  anything including face-to-face 
communication,  all  things  technology  is  not  very  good  at.  So,  even  if  a  robot  would  take  over 
checkout at a supermarket,  if  may not be the  same  if a robot or  a person wishes you a fine  day. 
Bonin et al. (2015) and Arntz et al. (2016) consider that jobs such as supermarket checkout include 
routine and non-routine tasks. 

It is interesting to have a look at what job profiles are regarded under threat of substitution by the 
various studies. The occupations with the highest risk of being replaced in Fry and Osborne (2013, 
2017)  are  insurance  underwriters,  mathematical  technicians,  hand  sewers,  title  examiners, 
abstractors,  and  searchers  and  telemarketers.  Other  occupations  with  a  high  probability  to 
disappear  are  tax  preparers,  cargo  and  freight  agents,  data  entry  keyers,  library  technicians, 
insurance appraisers for auto damage, order and brokerage clerks, and tellers. At the other side of 
the  spectrum  are  recreational  therapists,  emergency  management  directors,  mental  health  and 
substance  abuse  social  workers,  audiologists,  occupational  therapists,  healthcare  social  workers, 
dentists and oral surgeons, or dieticians and nutritionists. 

Such lists are more difficult to identify in the study of Bonin et al. (2015) since they focus on tasks 
and not occupations, but Arntz et al. (2016) provide examples: people working in the occupation 
'Bookkeeping,  Accounting,  and  Auditing  Clerks'  face  an  automation  potential  of  98%.  However, 
only 24% of all employees in this occupation can perform their job without group work or face-to-
face  interactions;  both  factors  may  hamper  substitution.  Moreover,  people  working  in  the 
occupation 'Retail Salesperson' face an automation potential of 92%. Despite this, only 4% of retail 
salespersons  perform  their  jobs  with  neither  both  group  work  nor  face-to-face  interactions.  This 
demonstrates the difference in the two approaches.  

26 

The impact of new technologies on the labour market and the social economy 

Another insight from the comparison of these studies is the fact that new technologies may destroy 
the demand for certain tasks, but rarely extinguishes whole occupations. This is the main difference 
between the results of Frey and Osborne (2013) and Bowles (2014) on the one hand and Bonin et al. 
(2015) and Arntz et al. (2016) on the other hand. ICTs may change the routine content of occupations, 
but this does not necessarily mean that the whole occupation becomes redundant. 

A  common  weakness  of  all  these  studies  is  that  they  restrict  themselves  to  the  estimation  of  the 
losses from new technologies based on current employment and occupation structures. There is no 
information  in  these  studies  on  future  gains  from  new  technologies.  As  discussed  in  Chapter  3, 
technology displaces employment, but also creates new employment via demand for new products, 
and price reductions for existing products which may levy demand for these products and foster 
employment.  This  creation  of  employment  via  new  products  is  difficult  to  assess,  because  it  is 
basically about products that  do not exist so far. This is also a challenge for  studies  that employ 
macroeconomic models like the one by Wolter et al. (2015).  

We  can  only  rely  on  the  historical  evidence  from  past  major  innovations,  which  show  that 
employment losses are more than compensated by employment gains from new technologies. Three 
effects are responsible for this compensation:  

  A minor effect comes from additional investments in infrastructure and new machinery for 
digitalisation,  which  creates  employment  in  the  sectors  that  produce  these  investment 
goods; 

  A second effect comes from decreasing costs due to ICTs that are passed on by the enterprises 
to  consumers  in  competitive  markets.  Lower  prices,  in  turn,  generate  new  demand,  and 
new jobs. 

  Third, we may expect a considerable number of new products from ICT-based innovations, 
which will create new demand and also new jobs. However, they may also displace existing 
products via the business stealing effect. 

The result by Wolter et al. (2015) of huge structural changes in the manufacturing workforce brings 
us back to the tasks approach. Tasks that are not substituted by new technologies benefit from them 
because  these  technologies  can  augment  the  productivity  of  non-routine  tasks  and  therefore 
complement them. This is the reason why Wolter et al. (2015) also predict considerable gains in non-
routine  tasks  which  we  find  in  technical  and  scientific  occupations,  but  also  in  teaching  while 
machine  operators  will  see  large  losses.  These  complementarities  between  specific  tasks  part  of 
different occupations and  new technologies are a second source  of employment growth, besides 
now products. They may also the reason why the three firm-level studies find no negative effects of 
ICTs or robots in particular on employment.  

There  is,  however,  a  big  caveat  to  these  considerations.  Martin  Ford  (2015)  speaks  for  many 
observers when he asks: is this time different? Ford and other authors point out that there are some 
factors  present  in  the  current  development  of  ICTs  that  have  not  been  in  place  in  previous 
technological  revolutions.  A  first  deviation  is  the  speed  of  improvement  in  microprocessor 
technology, which has been described by Moore's Law. According to Moore's Law, the number of 
integrated  circuits  on  a  chip  doubles  every  24  months,  which  also  indicates  that  the  processing 
power grows exponentially.  

Exponential  growth  of  power,  however,  is  a  feature  that  was  not  present  in  steam  power  or 
electrification. As a consequence, costs of processors, sensors, cameras etc. have fallen much faster 
than did  the  costs  of electrical power  or  steam power.  Nordhaus (2007) estimates that computer 
performance has improved by a factor of between 1.7 trillion and 76 trillion since manual computing 
of the Mid-19th century. This could mean that we still underestimate the range of future applications 
for ICTs, which may raise exponentially just as computing power (Ford 2015). 

27 

STOA - Science and Technology Options Assessment 

Another  implication  from  this  explosive  growth  in  performance  is  that  ICTs  are  maybe  more 
widespread today than steam power or electricity ever was. This would also imply that ICTs are a 
part of much more workplaces, firms, and households than steam power or electricity ever were, 
and that changes related to ICTs are much deeper than with previous technological changes. 

Moreover,  digital  goods  such  as  movies,  pictures,  online  services  and  any  other  digitalised 
information  can  be  reproduced  at  very  little  cost,  even  at  zero  marginal  cost  (Brynjolfsson  and 
McAfee 2014). This is a cost advantage very difficult to beat by human labour, even it if became 
much more productive with ICTs. 

5.2.  Employment effects of other emerging technologies 

Outside information and communication technologies the employment effects of new technologies 
are hardly an issue for academic research or consulting. The World Economic Forum  (WEF 2016) 
has conducted a survey on drivers of future change in the economy (see Figure 13 below); all but 
two  of  these  technology  drivers  are  related  to  ICTs.  Respondents  see  an  economic  significance 
beyond ICTs only in advanced materials and health applications of biotechnology. 

Figure 13: Technological drivers of change, 2016 

Source: World Economic Forum (WEF, 2016), p. 7. 

It  is  therefore  not  surprising  that  only  very  few  studies  tackle  the  employment  effects  of  new 
technologies other than ICTs. Wydra (2011) investigates the employment effects of biotechnology 
in Germany and finds huge differences depending on the sector where biotechnology is applied. 
His results also indicate that indirect effects from higher intermediate demand are more important 
than  direct  effects.  In  contrast  to  ICTs,  biotechnology  leads  only  to  little  substitution  of  human 
labour by technology. 

Another relevant area is the literature on 'Green Jobs' and the employment effects of environmental 
technologies. A study by the OECD (2012) summarised the previous literature and provides model 
estimates for the employment effects of change towards a low-carbon economy. The transition to 
green  growth  would  first  have  very  diverse  employment  effects  across  sectors  with  huge 
employment  gains  for  the  manufacturers  of  solar  and  wind  electricity  equipment,  but  also 

28 

0%5%10%15%20%25%30%35%40%Advanced materials, biotechnology and genomicsAdvanced manufacturing and 3D printingArtificial intelligence and machine learningAdvanced robotics and autonomous transportCrowdsourcing, the sharing economy and peer-to-peer platformsThe Internet of ThingsNew energy supplies and technologiesAdvances in computing power and Big DataMobile internet and cloud technologyShare of respondents rating driver as top trend  
The impact of new technologies on the labour market and the social economy 

considerable employment losses related to oil and coal mining sectors. Energy generation itself is 
not labour-intensive and plays only a minor role in these calculations. Since the heavily impacted 
industries represent only a small share of total employment, these changes do not translate into a 
large overall reallocation of jobs and total employment would not be affected by mitigation policy 
(OECD  2012,  p.  27).  In  another  survey,  Meyer  and  Sommer  (2014)  include  23  studies  on 
environmental innovations and come to the conclusion that the majority of them show positive net 
employment effects for environmental innovations. However, they also point to large differences in 
the underlying technologies and the competitive position of the firms or sectors.  

A particular interesting field for employment effects is resource efficiency, because a reduction of 
material  inputs  may  lead  to  less  economic  activity  and  less  employment.  Cooper  et  al.  (2016) 
investigate this issue for materials efficiency in two case studies on steel consumption in the UK. 
Surprisingly,  they  find  that  more  efficiency  does  not  mean  less  employment.  A  similar  result  is 
found by  Sartorius  (2015) who investigates the  effects of 16 efficiency-increasing innovations for 
Germany.  The  International  Labour  Union  points  to  the  opportunities  for  creating  employment 
from sustainable refurbishment of buildings (Keivani et al. 2010). 

To sum up, a considerable literature has evolved on the employment impacts of information and 
communication technologies. The main message from some of these studies is that we should expect 
considerable reductions in employment; however, other studies predict much lower employment 
losses. Since these studies only focus on losses and hardly consider the gains from ICTs, they may 
overestimate  the  negative  effects  of  these  technologies  and  underestimate  possible  positive 
compensation effects. A second relevant field are studies that examine the employment effects of 
environmental  technologies.  A  common  finding  from  these  studies  is  that  environmental 
technologies will have only little impact on employment. 

6.  Technology and inequality 

Skill-biased and routine-biased technological change which we discussed in the previous chapter are 
two mechanisms that can increase inequality, because they favour particular groups of the workforce 
and reduce the employability of other groups, in particular low skilled workers. Erik Brynjolfsson 
of the MIT sums up many contributions to the recent debate when he states: 'My reading of the data 
is  that  technology  is  the  main  driver  of  the  recent  increases  in  inequality.  It's  the  biggest  factor'. 
(Technology Review 2014). 

Thus, technology is highly relevant in recent discussions on inequality. Many people see inequality 
as a big concern in society, and are worried about rising levels of inequality in the US and Europe. 
These worries are also confirmed by a number of recent major publications (Piketty 2014; Atkinson 
2015; Stiglitz 2015). 

6.1.  The relationship of technology and inequality 

Before we have a look at the data we have to clarify some  terms regarding inequality. There are 
many areas where one may find inequality, for example social inequality, inequality before the law, 
health and mortality inequality, etc. The current discussion mainly deals with monetary inequality.  

There are two principal ways to look at monetary inequality: first, in terms of income inequality, 
second  in  terms  of  wealth  inequality.  The  measurement  of  income  inequality  is  more  or  less 
straightforward, by looking at the income of individuals or households before (market income) as 
well as after public transfers and taxes. Further adjustments to the data can be made to account for 
part-time or full-time employment, various skills levels, sectors etc.  

Wealth  inequality  is  more  difficult  to  measure,  despite  some  progress  in  recent  years.  Key 
challenges in the collection of data on wealth inequality include the assessment of real estate, the 

29 

STOA - Science and Technology Options Assessment 

treatment of debts, non-response in the wealthiest percentiles of the population, or the assessment 
of social security claims. New empirical evidence for Europe came from the Eurosystem Household 
Finance and Consumption Survey (ECB 2013), a project co-ordinated by the European Central Bank. 
This is why there exist only very few data – except the ECB study – which allow a cross-country 
comparison of wealth and wealth distribution. We will therefore stick to income data, which also 
allows to link measures of inequality to occupations and sectors. 

Despite current discussions which regard technology as a driver of inequality, there are numerous 
examples  from  the  past  where  new  technologies  reduced  inequality.  A  common  pattern  in  the 
history  of  technology  is  that  technological  change  made  products  and  services  -  which  were 
previously reserved only for a small group of people - available for a large part of the population. 
This is basically the  story  of the  mass production of textiles, pottery and other  household goods 
during the First industrial revolution, mass transport and electricity during the Second industrial 
revolution, or advances in medicine in the 20th century (Freeman and Soete 1997).  

A  modern  example  how  new  technologies  create  equality  are  mobile  phones  in  developing 
countries. Aker and Mbiti (2010) report that the introduction of mobile phone services has reduced 
communication  costs  and  increased  access  to  vital  information  such  as  prices  for  agricultural 
products. Thus, mobile phones help to overcome the information asymmetries between farmers and 
traders  and  produced  tangible  economic  benefits,  improving  agricultural  and  labour  market 
efficiency and producer and consumer welfare (Aker and Mbiti 2010). 

Technology also contributes to equality by freeing up time and money that can be saved for other 
activities. The washing machine is perhaps the best example for the fact that time-saving contributes 
to equality because it liberated women from one of the most time-consuming household activities 
(Cardia 2008). 

However,  the  literature  has  also  identified  various  ways  technology  and  innovation  increases 
inequality. These effects are at the centre of today's discussions of technology and equality. A first 
channel  is  differences  in  skills  or  tasks.  New  technologies  require  various  sets  of  skills  and/or 
replace various types of tasks, which are unevenly distributed in the workforce; we have discussed 
skill-biased and routine-biased technological change in Chapter 4. New information technologies, 
for  example,  complement  skilled  labour  and  shape  demand  for  unskilled  and  skilled  labour  in 
favour  of the  latter;  they substitute routine  tasks, and make them increasingly replaceable. Skill-
biased and routine-biased technological change may contribute to a higher inequality, which may 
multiply when negative scenarios of digitalisation and technological unemployment become reality. 

Another way how innovation may increase inequality is the degree innovators are able to reap the 
economic benefits from their innovations (also called appropriability). Today, some economists find 
a tendency towards winner-take-all markets, a market where a small number of people or firms 
gain  most  of  the  income  or  sales  available  in  this  market  (Rosen  1981;  Frank  and  Cook  1995). 
Winner-take-all markets contribute to more income and wealth inequality because they allow that 
the profits of one sector are mostly or even entirely appropriated by one or a small number of firms. 
This is a sharp contrast to  markets where one firm holds only a small or medium fraction of the 
global  market.  Winner-take-all  markets  can  be  seen  as  a  type  of  natural  monopoly  where  new 
technologies create cost structures that lead to one firm taking the whole market.  

Krueger  (2005) explains this development  which  is fuelled by new technologies  by citing Alfred 
Marshall's explanation why a gifted opera singer in the 19th century was paid less than a superstar 
salary:  “But  so  long  as  the  number  of  persons  who  can  be  reached  by  a  human  voice  is  strictly 
limited, it is not very likely that any singer will make an advance on the £10,000 said to have been 
earned in a season by Mrs. Billings at the beginning of the last century ...” 

Technological change, from the gramophone to online streaming services has removed such barriers 
and multiplied the number of people the opera singer can reach at a time. The same happened for 

30 

The impact of new technologies on the labour market and the social economy 

actors, writers and directors with the advance of cinema, television, DVDs and streaming, and also 
in other sectors (examples are software, social networks, internet services etc.). The market share of 
the second largest search engine on the internet is only a tiny fraction of Google's market share. 

Brynjolfsson  and  McAfee  (2014)  identify  three  developments  that  make  winner-take-all  markets 
more common today: 

  The digitalisation of information, goods, and services makes the reproduction of them very 

cheap; 

  Improvements  in  telecommunications  and  transport  and  the  global  economic  integration 

increase the number of consumers a firm can potentially reach; 

  Standards, networks and network effects become increasingly important. 

A surprising fact about winner-take-all markets is that the factors that promote their emergence can 
also  lead  to  their  decline.  Digitalisation  of  information,  goods  and  services  not  only  reduces  the 
marginal  cost  of  their  reproduction  to  almost  zero,  but  also  considerably  lowers  fixed  cost  and 
therefore  entry  barriers  for  new  entrepreneurs  (Brynjolfsson  und  McAfee  2014).  New  market 
entrants may therefore have a better chance to overcome monopolies in these markets than in other 
parts of the economy. 

A  third  way  how  innovation  creates  inequality  is  the  business-stealing  effect  described  in  a 
previous  chapter.  New  innovations  compete  with  old  products  and  make  them  obsolete.  The 
business-stealing  effect  leads  to  structural  change  in  the  economy,  and  creates  growing  and 
declining sectors with increasing and shrinking employment. The automobile made horse-powered 
transportation  redundant  in  the  early  20th  century,  and  we  see  similar  examples  in  other  fields 
today. Unemployment in these declining sectors may be a major source of inequality in the society, 
if  employment  is  not  re-allocated  from  declining  to  increasing  sectors  –  which  is  often  not  easy 
because of different skills requirements between industries. 

Some economists argue that inequality from innovation is the price we have to pay for technological 
progress. This has already been pointed out by Joseph Schumpeter (1911): The main incentive for 
entrepreneurs to  introduce new  products is the  reward of gaining market power  and monopoly 
rents.  A  new  formulation  of  this  thought  is  presented  by  Mankiw  (2013).  He  confirms  that  the 
incomes of individuals at the top, especially in the top one percent, have grown much faster than 
average since the 1970s in the US. Mankiw defends the top one percent and points out that these 
high  earners  have  also  made  significant  economic  contributions  to  growth  and  income  of  many 
other people. He asks if society would be really better off without their achievements, and admits 
that many of these questions are rather in the sphere of political philosophy than economics. 

Public  debates  on  employment  often  distinguish  between  'good',  well-paid  and  secure  jobs  and 
'lousy'  jobs with  low pay and insecure  employment conditions. Will technological change create 
good or lousy jobs in the future? From the perspective of skill-biased technological change is seems 
quite  clear  that  technology  will  create  more  good  jobs  in  the  future,  simply  because  they  are 
complementary  to  the  productivity-enhancing  characteristics  of  new  technologies.  From  the 
perspective of routine-biased technological change the  picture is  more differentiated. Lousy  jobs 
can also thrive in times of technological change if they have a low routine content. This may be the 
case  for  platform  workers,  self-employed  in  transport  and  delivery  services,  cleaning,  health 
services etc. So, technological change does not inevitably push back lousy job. There is, however, a 
clear tendency that they become less frequent. 

6.2.  Evidence for inequality in Europe 

What is the evidence for a growing degree of income inequality in Europe? For the period between 
the mid-1980s and the late 2000s, the evidence clearly points to rising inequality (OECD 2011). In 
this report, we focus on the last 10 years.  

31 

STOA - Science and Technology Options Assessment 

A first, well-known measure of inequality is the share the top one, five, or 10% of the population 
have  on  income.  We  find  the  largest  degree  of  income  inequality  for  this  indicator  in  Cyprus, 
Bulgaria and Lithuania, and the lowest degree of inequality in Sweden, Slovenia and Belgium. In 
2006, Portugal revealed the highest degree of inequality, while in 2010 it was Lithuania. However, 
there is a certain degree of consistency between  the Top one percent and Top 10% rankings, and 
also a large overlap with other rankings. Rankings based on the Gini coefficient measure the overall 
concentration  of  income  across  the  income  distribution.  The  countries  with  the  smallest  Gini 
coefficient  and  the  most  equal  income  distribution  in  the  EU  are  Slovenia,  the  Czech  Republic, 
Sweden and Finland, while the most unequal countries are Latvia, Lithuania, Bulgaria and Cyprus. 
This is also the picture revealed by the Figure below. 

Figure 14: Income share of the Top one percent and top 10% of the population, EU-28, 2014 

Source: EUROSTAT, Community Statistics on Income and Living Conditions (SILC). 

The income shares of the Top one percent and Top 10% of the population were quite stable in the 
period 2006-2014. At the EU-28 level, there is no change in the income share for the Top one percent, 
and  only  a  small  gain  of  0.2  percentage  points  for  the  Top  10%.  However,  we  find  much  more 
movement at the member states level (Figure 16). The largest increases in inequality can be found 
in Cyprus, Bulgaria and Denmark, while inequality decreased in Hungary, Romania and Portugal. 
Portugal was the country with the highest degree of inequality in 2006, while Denmark was one of 
the most equal countries in those years in the EU.  

The  development  in  large  member  states  which  considerably  influence  overall  EU  development 
was unequal; a rise in the income shares of the Top one and 10 percent in Germany and France, a 
decrease in Poland and the UK. Spain saw a rise in the income share of the Top 10%, but a decrease 
in the income share of the Top one percent. There is also very little movement in the median values 
for the Top 10% and Top one percent across all EU member states. Altogether, there is no evidence 
that income inequality has changed in one or the other direction in the EU during the last nine years. 
There is some evidence of rising inequality in the period before, but we are mainly concerned with 
the  most  recent  period  since  this  is  the  time  where  effects  from  technological  change  may  have 
materialised. 

32 

05101520253035Share on total incomeTop 1%Top 10% 
The impact of new technologies on the labour market and the social economy 

Figure 15: Changes in the income share of the Top one percent and top 10% of the population, 
EU-28, 2006-2014 

Note: Data for 2006-2009 without Croatia 

Source: EUROSTAT, Community Statistics on Income and Living Conditions (SILC). 

The argument above about inequality and automation was that automation replaces workers by 
machines, which in turn leads to unemployment and to a shift of economic benefits from workers 
to the  owners of capital. So, we have to look at the  distribution of income between workers and 
capital owners. A first measure for this distribution is the share of the compensation of employees 
(wages,  social  security  contributions  by  employers)  on  gross  domestic  product  (GDP).  A  falling 
share would indicate that capital owners appropriate a higher share on GDP, which may be due to 
a higher automation and a substitution of labour by machines. 

This was, however, not the case, as the figure below indicates. The indicator remains quite flat at a 
level of 47-48% throughout the period. At country level we see the largest decreases in the United 
Kingdom  (-2.9%),  Portugal  (-3.6%),  Ireland  (-5.1%),  and  Romania  (-5.8%).  The  reasons  for  this 
decrease may be related with the crisis and high unemployment. The US also saw a declining share 
of employees' compensation on GDP and other measures (Lawrence 2015). 

However, there  are also countries in the  EU where  the  labour  share has increased, most notably 
Bulgaria, Estonia, Sweden and Austria. Germany, as the largest economy in the EU, also witnessed 
an increase in the labour share, as France and Italy. So it would be wrong to say that employees lost 
shares on GDP during the last nine years. 

33 

-5-4-3-2-10123456EU-28CyprusBulgariaDenmarkFranceAustriaGermanySpainMaltaEstoniaSwedenSloveniaLithuaniaCzech RepublicItalyNetherlandsGreeceLuxembourgUnited KingdomFinlandIrelandPolandBelgiumLatviaSlovakiaPortugalRomaniaHungaryChange in percentage pointsTop 10%Top 1% 
 
 
STOA - Science and Technology Options Assessment 

Figure 16: Compensation of employees as a share of gross domestic product, 2006-2015 

Source: Eurostat, national accounts 

We now look at the level of sectors to find more evidence for the development of the wage share 
over time. The following table shows the share of personnel costs on value added at factor costs for 
different sectors and their development over time. The value added of all sectors adds up to gross 
domestic product, however, we do not provide data for value added from governmental activities 
here. 

The picture at sectoral level is similar to the aggregate picture, as we do not have an indication for 
a decrease in the share of employees, measured by personnel cost, on value added. There are some 
sectors where we indeed find a decrease (construction and transport), but the overall share is rather 
increasing than decreasing. Trade has rising personnel costs and a stagnant value added, resulting 
in a rising labour share.  

Table 2: Share of personnel costs on value added in various sectors, 2013 and change between 
2008 and 2013, EU-28 

Sector 

Tourism 
Manufacturing 
Construction 
Trade 
Transport 
Professional, scientific and technical activities 
Information and communication 

Share 
2013 
68.9% 
65.9% 
65.8% 
64.8% 
63.5% 
61.7% 
54.7% 

Change 
2008-13 

2.5% 
1.6% 
-0.5% 
5.5% 
-1.0% 
2.5% 
2.6% 

Total business sector 

60.3% 

2.4% 

Source: Eurostat, Structural Business Statistics, own calculations 

34 

40414243444546474849502006200720082009201020112012201320142015as a percentage of GDPEU-28EU-15 
 
 
 
 
 
 
 
 
 
 
 
The impact of new technologies on the labour market and the social economy 

An increase in the personnel costs on value added can also be seen in most countries, with only 
some exceptions. The overall development of the EU is, of course, driven by large countries, which 
all – with the exception of the UK - show rising or at least stagnant personnel costs as a share of 
value added. So, there is no indication for more inequality between workers and capital owners in 
the EU.  

Figure 17: Personnel costs as a share of value added at country level, 2008, 2010 and 2013, EU-28 

Source: Eurostat, Structural Business Statistics, own calculations 

The literature reviewed above points to differences between various occupations and skills levels 
in their income over time, so we will, in a next step, look for rising inequality within the labour force 
between different skills levels. 

The following Figure indicates that inequality between different occupational classes - which also 
resemble different skills levels - has indeed increased. It shows the annual average change in hourly 
wages  for  three  broad  occupational  groups  in  the  business  sector  between  2002/06,  between 
2006/10 and for the full period 2002 to 2010. 

There  is  clearly  wage  dispersion  with  average  losses  for  elementary  occupations  of  around 
0.5 percent a year and wage increases for skilled manual and non-manual workers. A decrease of 
0.5 percent annually adds  up to  minus 1.6 percent between 2006 and 2010, or  minus 3.6 percent 
between 2002 and 2010. Total hourly wages increased on average between 1.6 and 1.8 percent per 
year, so skilled manual workers (e.g. plant and machine operators, craft workers) had a favourable 
wage development compared to the average worker. This can be taken as an indication for skilled-
biased technological change in European labour markets. 

35 

0%10%20%30%40%50%60%70%80%90%200820102013 
 
 
STOA - Science and Technology Options Assessment 

Figure 18: Average annual change in hourly wages for three different occupational groups, 2002 
– 2010, EU-28 

EUROSTAT, Structure of earnings survey, own calculations 

A more detailed picture of the development gives the following Figure 19. We see the decrease in wages 
for elementary occupations we already know from the previous Figure. In addition, the data also show 
a decrease in the wages for clerical support workers for the period 2006-2010, which was not the case in 
2002-2006. This may be a first indication for the jobs-destroying effects of digitalisation in white-collar 
professions. Managers have by far the highest average hourly wages - around double the wage of the 
average  worker  -  but  also  saw  below-average  wage  increases  during  this  period.  Craft  workers  and 
technicians had the best wage performance. 

Finally,  we  can  also  measure  inequality  due  to  skills  differences  by  calculating  the  wage  of  the 
occupation with lowest hourly wage as a percentage of the occupation with the highest hourly wage, 
this is, managers. Inequality has  also  increased  in this indicator  between 2002 and 2010: in 2002, the 
average hourly wage of elementary occupations was 35.1% of the average hourly wage of managers; in 
2010, this value decreased to 31.9%. Inequality was even higher if we look at annual wages instead of 
hourly  wages.  Here,  full-time  employed  people  in  elementary  occupations  only  gained  31.8%  of 
managers in 2006 and 29.8% in 2010. Unfortunately, data on annual wages are not available for 2002. 

36 

-1.0%-0.5%0.0%0.5%1.0%1.5%2.0%2.5%3.0%Skilled non-manual workersSkilled manual workersElementary occupationsannual change in %2002/102006/102002/06 
 
  
 
 
The impact of new technologies on the labour market and the social economy 

Figure 19: Average annual change in hourly wages for different occupations, 2002–2006 and 2006-
10 EU-28 

EUROSTAT, Structure of earnings survey, own calculations 

Differences between the wage of the occupation with lowest and the highest hourly wage can also 
be used to compare countries (Figure 20). It turns out that the countries with the lowest degree of 
inequality between occupations are Sweden, Denmark and Ireland, while Romania, Portugal and 
Bulgaria reveal the highest degrees of inequality. This confirms the result of the comparison of the 
Top one percent and Top 10% income earners (Figure 14). 

It should be noted, however, that the average hourly pay of a manager in Romania (7.14 EUR) is 
only  a  third  of  the  average  hourly  wage  of  a  worker  in  an  elementary  occupation  in  Denmark 
(21.46 EUR). Moreover, the very high average wage for elementary occupations in Denmark raises 
some doubts about the comparability of the data. The corresponding value for Sweden is only half 
(13.43 EUR), which is nevertheless still two times the wage for managers in Romania. 

To sum up, there is evidence for a growing degree of income inequality in Europe between the mid-
1980s and the late 2000s (OECD 2011), but not for the last 10 years in the EU, although there are 
some countries where income is more unequal distributed today than it was 10 years ago. There is 
no sign that capital owners would acquire a larger share on economic welfare, either. However, we 
find evidence that inequality between workers in elementary occupations and in skilled occupations 
has increased. The wage gap between managers and elementary occupations has widened which 
points to skill-biased technological change in European economies. 

37 

-0.5%0.0%0.5%1.0%1.5%2.0%2.5%TotalTechnicians and associate professionalsCraft and related trades workersSkilled agricultural, forestry and fishery workersService and sales workersPlant and machine operators and assemblersProfessionalsManagersElementary occupationsClerical support workersAnnualized change in %2006/102002/06 
 
 
STOA - Science and Technology Options Assessment 

Figure 20: Wage of the occupation with the lowest hourly wage as a percentage of the wage of 
the occupation with the highest hourly wage, EU-28, 2010 

EUROSTAT, Structure of earnings survey, own calculations 

The previous chapter came to the conclusion that digitalisation is unlikely to destroy a large number 
of jobs. However, based on the results of this chapter, there seems to be an uneven distribution of 
the costs of digitalisation: ow skilled workers are likely to bear the brunt the adjustment costs of 
innovation, because the risk of automation and displacement of their jobs is higher. Another group 
with  a  potentially  high  burden  is  medium-skilled  white  collar  occupations  with  a  high  share  of 
routine tasks that can be found in particular in service industries. Therefore the likely challenge of 
the future lies in coping with rising inequality from technological change. 

7.  ICT, employment and the Social Economy 

We now move our focus to a specific sector of the European economy, the Social Economy or the 
Third Sector (Monzón Campos and Chaves Ávila 2012; OECD 2013). The Social Economy consists 
of  firms  and  organisations  organised  as  co-operatives,  associations,  mutuals  and  foundations. 
Hence,  experts  also  see  the  Social  Economy  defined  by  certain  organizing  principles  rather  than 
economic activities (OECD 2013, p. 16):  

  Placing services to its members or to the community ahead of profit 
  Autonomous management 
  A democratic decision-making process 
  The primacy of people and work over capital in the distribution of revenues 

A  related  concept  to  the  Social  Economy  is  also  social  innovation  (Howaldt  et  al.  2015).  Some 
authors see the Social Economy as the main field for social innovation (Moulaert and Nussbaumer 
2005),  and  the  aforementioned  principles  also  as  relevant  for  social  innovation.  The  meaning  of 
social innovation, however, is ambiguous (Reinstaller 2013), with changing emphasis on the social 
aspects of technological change, on social entrepreneurship, innovation for the poor in developing 
economies, etc. There seems to be a consensus that social innovation should lead to positive social 

38 

0%100%200%300%400%500%600% 
The impact of new technologies on the labour market and the social economy 

change. Caulier-Grice et al. (2012) define social innovation in the following way: 'Social innovations 
are new solutions (products, services, models, markets, processes etc.) that simultaneously meet a 
social need (more effectively than existing solutions) and lead to new or improved capabilities and 
relationships  and  better  use  of  assets  and  resources.  In  other  words,  social  innovations  are  both 
good for society and enhance society's capacity to act.' 

Another common goal of both social innovation and the Social Economy is employment and the 
improvement  of  employment  for  vulnerable  groups.  This  is  why  both  concepts  have  received 
increasing attention from policy in recent years.  

The following Figure 21 gives an indication of the share of the Social Economy compared to other 
sectors  in  the  EU-27  (excluding  Croatia).  With  a  total  number  of  more  than  14  million  people 
employed, the Social Economy is indeed a relevant actor, even at the European stage. The sector has 
about the same size as the construction industry.  

Figure 21: Employment in various sectors of the economy and in the Social Economy, EU-27, 2009-
2010 

Note:  Employment  figures  for  all  sectors  except  Social  Economy  are  persons  employed  for  2010.  Social 
Economy is 2009-10 

EUROSTAT, Structural business statistics, Monzón Campos and Chaves Ávila (2012), own calculations 

Monzón Campos and Chaves Ávila (2012) estimate that the Social Economy equals 6.53% of total 
paid employment in the EU, with the highest shares in Sweden, Belgium and the Netherlands, and 
the lowest shares in some Southern and Eastern European member states. 

We  also  get  a  good  impression  about  the  Social  Economy  if  we  look  at  the  fields  where  the 
organisations of the Social Economy are active in. From a large-scale case study project on the Social 
Economy,  the  OECD  (2013,  p.  27)  reports  that  these  organisations  are  mainly  active  in  social 
assistance  services  (26%),  education  and  training  (21.1%),  work  integration  (19.6%),  culture  and 
recreation (16.2%), and health and medical services (7.8%). Just 4.4% of the cases they analysed are 
active in manufacturing, another three percent in construction. Sources of revenue are the sale of 
goods and services and subsidies to a similar degree (around 30% each). 

39 

05,00010,00015,00020,00025,00030,00035,000Employment (1.000 persons employed) 
STOA - Science and Technology Options Assessment 

Figure 22: Share of the Social Economy on total paid employment in EU member states, 2009-
2010 

EUROSTAT, Monzón Campos and Chaves Ávila (2012), own calculations 

What are the relations between the Social Economy and innovation, ICTs and employment? They 
are not obvious at first sight. 

A first relation between these topics is given by the criteria for routine tasks provided above, which 
are, in the opinion of various experts, in danger of getting displaced by machines. From the list of 
sectors where Social Economy organisations are active in, it seems that most of them are in sectors 
that  are  highly  non-routine.  Social  assistance  services,  education,  work  integration,  culture,  or 
health services are associated with non-routine activities (Autor 2015), and require a great deal of 
human interaction and social perceptiveness. Thus, we may assume that the Social Economy will 
be one of the sectors least affected by the jobs-destroying effects of digitalisation. Technology may 
have a supporting, not a displacing role in these sectors in the future. Like many service industries, 
they require a direct involvement of employees in the production of their output. It seems unlikely 
that involvement and human interaction in the Social Economy can be replaced by ICT-enabled 
services. 

Second, an important part of the employment-creating activities of Social Economy organisations is 
also  related  to  vulnerable  groups.  The  OECD  study  finds  that  over  three-quarters  of  the 
organisations surveyed worked with vulnerable individuals. A lack of employment opportunities 
in the wider labour market, but also a lack of skills are main obstacles for more employment in this 
group. Main employment sources for vulnerable people are routine occupations, so there may be 
some  danger that  digitalisation will make this particular  part  of the activities of Social Economy 
organisations more difficult in the future. 

However,  this  does  not  mean  that  ICTs  have  no  use  for  the  Social  Economy.  Social  internet 
technologies such as social networks or newsfeeds have been adopted at very high speed in recent 
years, and organisations in the Social Economy are beginning to make use of them (Chui et al. 2012). 

40 

 - 2 4 6 8 10 12Share on total paid employment 
The impact of new technologies on the labour market and the social economy 

The  main  opportunities  of  these  technologies  for  the  Social  Economy  lie  in  improving 
communication and co-operation within groups and between people.  

An example is Austria, when around 90,000 refugees arrived in the country until the end of the year 
2015. The involvement of volunteers outside professional social organisations was mainly organised 
by using social networks like Facebook and Twitter. Registered volunteers were called in on a day-
to-day basis, depending on actual  demand for support. This system has been institutionalised as 
'Freiwillige Soforthilfe'4. People who register themselves are contacted by SMS or email when their 
skills are needed.  

But the opportunities of social networks for the Social Economy go beyond one-way communication 
and mobilisation. Since social networks allow two-way communication, new forms of organisations 
in the Social Economy may emerge which go beyond the centralised approach of many established 
organisations. Moreover, social networks may also facilitate  the  claim for  a democratic decision-
making  process  in  Social  Economy  organisations.  Social  networks  may  therefore  open  Social 
Economy organisations to many of the new forms of innovation described by (Leitner et al. 2012) 
such as Innovation Communities, Crowd sourcing, User Innovation or Open Innovation. 

8.  Workshop summary 

A panel of experts from academia, industry and the trade unions took up the issues discussed in 
this report at the STOA workshop 'How is digital technology changing the labour market?'. The aim 
of  the  workshop  was  to  discuss  further  the  topics  and  findings  of  the  report  together  with  the 
audience. The workshop confirmed the relevance of the topics of the report. 

The workshop took place on October  11th 2016 at the  European Parliament in  Brussels and  was 
chaired by Georgi Pirinski, MEP and STOA Panel Member. Susanne Giesecke from the AIT Austrian 
Institute Technology was the moderator of the workshop. 

Bernhard Dachs (AIT Austrian Institute of Technology) summarised the results of the study. He 
pointed out that innovation was in the past the answer to unemployment, at least in the long run. 
However,  today  some  observers  believe  that  the  labour-friendly  character  of  innovation  has 
changed. Moreover, he pointed out that we cannot be sure that new technologies still contribute to 
a reduction of inequality, like they did it in the past when they made consumer goods, transport or 
electrical energy accessible to large parts of the population. 

Jari  Kuusisto  (University  of  Vaasa,  Finland)  observed  a  deep  transformation  at  all  levels  in 
European  manufacturing  firms.  This  transformation  is  driven  by  digitalisation,  with  the  aim  to 
increase  production  efficiency  and  competitiveness,  and  reduce  costs.  In  his  presentation,  he 
regarded  a  skilled  work  force,  a  good  education  system,  and  a  capable  infrastructure  as  the 
strengths of Finland and other European countries in this transformation.  He illustrated his point 
with the example of the company ABB in Vaasa, Finland; ABB hired several engineers at PhD level 
and a number of engineers at Master and Bachelor level, but only one welder in 2015; this shows 
the importance of skills for future manufacturing. However, disruptive innovation often happens 
outside Europe, and platforms which may be decisive in the future evolve outside Europe. 

Zelko  Pazin  (European  Engineering  Industries  Association,  Belgium)  agreed  that  European 
manufacturing  is  currently  in  a  deep  transformation.  However,  from  his  perspective,  European 
manufacturing still looks healthy and is one of the most important economic sectors, accounting for 
20% of employment, 67% of exports, and 65% of R&D expenditure in the business sector. One of the 
strengths of European  manufacturing is its diversity; the  competencies of the  EU manufacturing 
industries are distributed over a broad range of subsectors, which is not the case for their US and 

4 www.fluechtlinge.wien 

41 

                                                           
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Japanese competitors. He saw more R&D, investment in skills and the establishment of favourable 
framework conditions as necessary to cope with future challenges. Europe needs to stay on the top 
of  technological  development  and  quality  of  the  industrial  workforce  in  order  to  master  the 
transformation. 

Marco  Vivarelli  (Catholic  University  of  Milano,  Italy)  explained  that  the  academic  debate  on 
technological  unemployment  is  very  old,  but  inconsistent:  the  promise  of  economics  is  that  job 
losses  are  compensated  by  new  employment  from  higher  overall  production  due  to  new 
technologies. However, in the real world, non-competitive markets, price rigidities, and pessimistic 
expectations may severely hinder and delay the compensation of the initial job losses. Today not 
only agricultural and manufacturing employment appears at risk, but also employees in services - 
including occupations that mainly rely on  cognitive skills - are no longer safe. Vivarelli saw that 
R&D leads to job creation, but this effect is often limited to high-tech sectors, while low-tech sectors 
struggle. As a consequence, he stressed the need for social safety nets, and to train the unskilled. 

Christine  Jakob  (European  Public  Service  Union,  Belgium)  pointed  to  the  need  for  an  inclusive 
transition  towards  good  and  fair  digital  work.  She  also  saw  a  need  for  upskilling,  and  more 
assessment  of the social impacts  of ICTs.  Current public discussions are scattered, and there  are 
many different approaches at sectoral and member state level; what we need is a comprehensive 
approach and a discussion. 

Plamen Gochev Dimitrov (Соnfederation of the Independent Trade Unions in Bulgaria) identified 
in  his  statement  a  trend  in  the  current  industrial  transformation  of  transferring  the  risk  from 
employers  to  workers.  He  saw  several  new  digital  divides,  including  one  between  Eastern  and 
Western  European  countries  and  within  the  group  of  EU  member  states  in  Middle  and  Eastern 
Europe. For example, the Digital Economy and Society Index by DG Connect reports a considerable 
gap between the Baltic States and the Czech Republic on the one hand and Bulgaria and Romania 
on the other hand. 

Dimitrov pointed out that the new member states are more at risk of job losses due to digitalisation 
than old  member  states.  Moreover, digitalisation may widen the  gap between  various groups of 
employees, including, in his own words, 'superstar workers' and 'digital galley slaves'. He agreed 
the need for new regulation to counter the effects of atomistic labour markets. 

Discussion 

The relevance of the  topic  could be seen from  the  number  of statements and questions from the 
audience  of  the  workshop.  Several  people  from  the  audience  picked  up  the  topic  of  the 
transformation of working conditions. A main concern was the worsening of working conditions 
for  'gig'  workers  -  people  offering  their  services  via  platforms  on  the  Internet.  Platform  work  is 
indeed frequent in some sectors; in these sectors, firms do not have employees, they have associates. 
The  speakers  and  the  audience  agreed  that  social  security  legislation  is  not  prepared  for  it.  The 
speakers stressed the need to give platform workers some of rights of employees. Maybe it is time 
to think about corporate digital responsibility, as one participant suggested? 

More generally, the discussion raised the  question  what exactly employment means today. This 
question  has  very  much  to  do  with  how  work  is  performed  today,  and  how  working  rights  are 
evolving.  Moreover,  it  was  also  questioned  if  trade  unions  are  up  to  the  task  of  reacting  to  the 
challenges. There is not unified view on issue related to digitalisation, platform work, etc. in trade 
unions across the EU. 

There was a consensus in the discussion that we cannot stop new technologies but must adapt our 
frameworks to it; at the moment, there is a regulatory mess. There is also a lack of data on  new 
forms of employment related to ICT, and we need these data to allow a better informed policy. 

42 

The impact of new technologies on the labour market and the social economy 

The discussion also raised the issue of a new industrial policy, which was seen as a necessity by 
the  audience, but,  of course, without the  old mistakes.  Compared with the  old industrial policy, 
much more focus should be laid on research, development and innovation, and on the development 
of skills. 

Finally, another topic of the discussion was the  reduction of working time. Statements from the 
audience pointed out that productivity increases in the past were accompanied  by a reduction of 
work  time,  which  is  not  the  case  today.  In  this  perspective,  the  current  industrial  revolution  is 
different from previous revolutions, because they have all produced something better. So far, it is 
difficult to see what the digital revolution turns for the better. Maybe the productivity growth from 
new technologies will allow a basic income in 10, 15 years.  

To sum up, there was a consensus among in the audience that there is a need for change. This need 
coming from the technology side, is based on the changes evidenced in the business sector and in 
labour relations brought forward by new forms of employment. Education and  skills are key for 
managing this change. The challenge today is to look for different jobs for the people – good, decent 
jobs. There was also a consensus that we need also some additional mechanisms to compensate the 
losers of digitalisation. 

9.  Policy options 

The final chapter will present options for policy. The future is uncertain, so we will depict  three 
small  scenarios  –  positive,  negative,  and  regional  divergence  -  how  digitalisation  and  new 
information  and  communication  technologies  may  shape  employment  dynamics  and  income 
equality in the  next 20 years. The scenarios are based on the  literature reviewed in the  previous 
chapters.  

These scenarios do not intend to present a complete set of drivers, critical factors and impacts of 
future developments; they rather serve as the framework of how policy can react if things evolve in 
a particular direction. These reactions in the form of concrete measures are discussed in the final 
section of this chapter. 

9.1.  Industrial renaissance in Europe 

Looking back from 2036, it turns out that digitalisation complements Europe's strengths to a large 
degree. In particular, digitalisation was a good match to the competences of European firms in many 
manufacturing industries. Manufacturing firms took up new production technologies fast because 
they  complemented  many  of  their  traditional  strengths.  European  firms  learned  very  quickly  to 
combine the skills of their workforce with the technological opportunities of digitalisation.  

This  has  led,  for  the  first  time  in  decades,  to  a  considerable  boost  in  productivity  and  new  jobs. 
Productivity  gains  lead  to  a  re-shoring  of  production  activities  back  to  Europe,  because 
productivity-adjusted  wage  advantages  of  Asian  locations  decreased,  and  production  in  Europe 
close to many key clients is much more capable to take up the advantages  of flexible production 
from digitalisation. 

There  was also  jobs destruction from digitalisation, in particular  in service  firms of the  banking, 
insurance,  and  professional services sector. A number  of middle-skills jobs  in these firms and in 
administrative were replaced by machines. However, many of these losses were compensated by 
new  jobs  where  non-routine  skills  are  augmented  by  digitalisation.  This  was  also  due  to  large 
investments in data infrastructure and in education in EU member states beginning in the second 
half of 2010s, which started to pay off in 2020-25. Another source of growth was Europe's traditional 
strength in culture, which became the basis of many new services. New information technologies 

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lowered fixed cost and entry barriers in many fields of services and offered new opportunities for a 
number of entrepreneurs. 

Economic growth and prosperity made it easier for European policy to dampen tendencies towards 
economic inequality. Education and a 'no child left behind' policy are seen as the main instruments 
to fight economic inequality in the long term. In social policy, one guiding principle is still flexicurity 
and enhancements of this concept, which help employees and employers to cope with the raising 
need for flexibility due to digitalisation. 

Economic  growth  also  helped  to  balance  public  budgets,  ease  the  burden  of  public  debts  and 
continue a policy of social equilibrium in Europe. Taxes on income and indirect consumption taxes 
are still important in 2036, but are gradually complemented and substituted by environmental taxes 
and taxes on value added, as contribution of capital to the creation of economic wealth increases, 
and the labour share decreases. 

9.2.  Europe's industrial misery 

Looking  back  from  2036,  economic  nightmares  come  true:  digitalisation  wiped  out  considerable 
parts  of  employment  in  Europe,  as  forecasted  by  some  employment  studies  from  the  2010s. 
Economic growth has virtually stopped, and we can witness the economic decline of regions and 
even whole countries. In retrospect, it seems that many people had grave difficulties to compete 
with new technologies based on their acquired skills, which seemed outdated when compared to 
the productivity of machines. 

European  firms  also  suffered  from  fierce  competition  by  Asian  firms  and  the  resurgence  of  the 
United States in manufacturing and services. US banks, insurance companies and software firms 
are the technology leaders in Big Data applications. In retrospect, it seemed that the US economy 
was  able  to  benefit  much  more  from  digitalisation  than  Europe,  which  some  blame  to  a  lack  of 
flexibility and entrepreneurship in Europe. 

Jobs destruction appears in all sectors of the economy, but is most pronounced in administrative 
and office occupations. The argument that job losses are compensated by new jobs turned out to be 
wrong;  new  information  technologies  created  much  less  new  jobs  than  destroyed  existing 
employment because of two reasons; first, technological progress in ICTs went much faster than 
many expected, leading to a much larger number of tasks which could be replaced by machines and 
software; second, absolute cost advantages of services provided by ICTs turned out to be so huge 
that  firms  preferred  machines  over  people  even  if  they  have  to  forego  some  non-routine  tasks 
provided  by  employees.  This  resulted  in  a  large  number  of  highly  automated  services  in  the 
economy of the year 2036. These automated services may lack a 'human touch', but can nevertheless 
be  produced  at  virtually  no  marginal  cost.  There  were  also  attempts  to  re-bundle  tasks  within 
occupations by concentrating non-routine operations in a small number of all-around occupations 
which are still performed by people. Employment and economic activity declined. 

One  consequence of these developments is that markets with winner-takes-all characteristics are 
much  more  common  in  2036  than  20  years  ago.  This  has  led  to  a  considerable  number  of  firm 
closures and market exits in many manufacturing and service sectors, and to a higher concentration 
of  value  added  and  wealth  in  a  few  firms  and  individuals  in  the  economy.  Unemployment  has 
increased  to  levels  not  known  since  the  1930s.  These  two  developments  also  raised  economic 
inequality to levels not known for a long time in Europe. 

The developments described above had also serious consequences for public households in Europe: 
on the one hand, tax revenues came under pressure as a consequence of shrinking employment and 
the economic downturn. A stronger progression in income taxes with a higher tax burden for the 
superstars of the winner-takes-all economy provided only limited relieve for tax revenues. On the 
other hand, raising inequality and high unemployment forced governments to more social policy 

44 

The impact of new technologies on the labour market and the social economy 

interventions.  Countries  escaped  this  difficult  situation  by  reducing  social  policy  to  measures  to 
prevent  absolute  poverty,  rather  than  providing  a  differentiated  system  of  social  security.  Some 
countries experiment with the introduction of an unconditional basic income which replaces all or 
most other social monetary transfers. Nevertheless, social and political stability is fragile, and the 
opposition against 'everything digital' has been growing. 

9.3.  Europe's growing double digital divide 

Digitalisation  turned  out  to  have  very  different  effects  on  the  Member  States  and  regions  of  the 
European  Union.  In  the  economic  centres  and  the  metropolitan  areas,  digitalisation  has  led  to 
renewed  growth,  a  vibrant  entrepreneurial  culture  and  growing  incomes.  In  rural  areas, 
digitalisation  had  hardly  a  positive  economic  impact,  and  economic  divergences  between 
metropolitan and rural areas have deepened.  

This development is, at first, due to the fact that digitalisation has strongly augmented the clustering 
of creative people in cities, which has already been described by Richard Florida in the 2000s. It may 
be  true  that  digital  workers  can  do  their  projects  from  everywhere;  however,  they  also  want  to 
socialise, exchange ideas and enjoy an appealing cultural surrounding. These preferences created 
entrepreneurial  ecosystems  with  a  variety  of  specialised  services  in  many  metropolitan  areas  of 
Europe  which  were  very  competitive  internationally  and  well-connected  to  similar  city  regions 
outside Europe, but with very little relationships to their surrounding rural regions which stayed 
behind.  

Regional disparities were first welcomed and seen as an indication that metropolitan areas develop 
into 'beacons' of digitalisation, entrepreneurship and economic dynamism. However, there was no 
trickle-down of the economic benefits of this growth to rural areas, and the development was partly 
self-enforcing,  by  substantial  movements  of  educated  people  from  the  rural  to  the  metropolitan 
regions who found much better employment opportunities and income there. 

As a result, the divide between metropolitan and rural areas widened. A growing divide can also 
be  observed  at  EU  member  states  level,  between  the  Nordic  and  some  Western  and  Eastern 
European  countries  on  the  one  hand  which  could  benefit  much  more  from  the  opportunities  of 
digitalisation, and Southern and Eastern European countries on the other hand which adapted only 
slowly to digitalisation. The divide between Northern and Southern EU members started to widen 
after  the  economic  crisis  of  2008/2009  and  the  sovereign  debt  crisis  of  2010.  Both  developments 
deprived  Southern  member  states  of  the  means  for  investments  in  education,  infrastructure  and 
R&D to prepare for digitalisation. There are some isles of digitalisation in Southern Europe as well, 
but they are much smaller and fewer compared to Northern Europe. 

Public  households  got  into  trouble  from  this  development,  at  least  in  federal  states.  A  growing 
divide between metropolitan and rural areas lead to higher transfers from the wealthy to the poor 
regions.  A  growing  divide  also  poses  a  dilemma  for  policy:  should  policy  invest  money  in 
stagnating  regions  to  increase  overall  coherence  of  living  standards  and  infrastructure,  or  put 
money  into  the  metropolitan  regions  to  ensure  the  future  competitiveness  of  these  regions?  A 
growing disparity between poor and rich regions also shifts the economic powers, and finally also 
the political powers in favour of metropolitan regions. 

9.4.  Measures for policy 

What are adequate policy responses to digitalisation and the developments described above? In the 
following,  we  will  present  some  conclusions  for  policy  related  to  the  three  scenarios  described 
above. As a general remark, it does not seem adequate – and also impossible– to slow down or even 
hamper  the  diffusion  of  the  technologies  described  in  this  report  to  avoid  possible  negative 
consequences. This would basically mean to cut off Europe from technological change happening 

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in other parts of the world. To our knowledge, there is no case in history where such an experiment 
did succeed. Isolating Europe from the rest of the world in terms of technological change would 
also  imply  to  take  away  the  means  to  stay  competitive  on  foreign  markets  from  European 
companies. So, technological isolation of Europe would also lead to and require economic isolation 
and de-globalisation. Countries which followed such a policy in the past, such as China and Japan 
in the 18th and 19th century, paid a very high price  in the form of economic decline and political 
dependence, before they decided to end isolation and start catching-up policies (Ferguson 2011). So, 
Europe cannot escape the consequences of new technologies, be they good or bad. 

  Education 

In a world where skills levels are closely related to inequality, because skilled-biased technological 
change  continuously  increases  the  demand  for  skilled  labour,  it  seems  essential  to  invest  in 
education, regardless what future will became reality; this is why education should also be the top 
priority for policy. A more flexible education policy will make it possible both for younger and older 
people to adapt to the new working conditions. Depending on the difference in skills between jobs 
destroyed and created, the time and cost of individual adaptation will differ. Historical experience 
has shown that by increasing the  supply of skilled labour, policy  can compensate  the  unwanted 
effects of skilled-biased technological change on inequality (Goldin and Katz 2008). 

Investments in education should encompass all levels of the education system.  More specifically, 
recent research shows that early childhood is an important period for shaping cognitive and non-
cognitive  skills  and  for  forming  the  basis  for  learning  and  the  acquisition  of  skills  in  later  years 
(Heckman  2008,  Heckman  et  al  2013).  A  critical  time  is  from  birth  to  age  five,  when  the  brain 
develops rapidly to build the foundation of cognitive and character skills necessary for later success 
in school, health, career and life.  

Early childhood education fosters cognitive skills along with attentiveness, motivation, self-control 
and sociability (Heckman et al 2013). Moreover, it is also a form of social policy, because children 
from a disadvantaged background are less likely to get such support, and are in danger of staying 
behind  in  early  years,  if  they  come  from  families  that  lack  the  social  and  economic  resources  to 
provide  the  early  developmental  stimulation.  So,  efforts  should  focus  on  the  first  years  for  the 
greatest efficiency and effectiveness.  This is  why early support of children in Kindergarten, pre-
school and elementary school is essential, and policy intervention should in particular focus on this 
age with higher investments in education.  

A second question related to education is which skills should be taught. This is even more important 
when we move away from early childhood and the basic skills mentioned above. From the literature 
on skills, ICTs and employment, it seems most relevant to foster non-routine skills. These skills are, 
on the one hand, related to human interaction and social perceptiveness, and, on the other hand, 
complement ICT applications, so that technology can augment human abilities. The problem with 
this approach, however, is that digitalisation is moving fast, and there are some abilities of machines 
that were far out of range in the opinion of most experts only a few years ago. In a new book, McAfee 
and Brynjolfsson (2017) tell the story of how a computer programme prepared by the deep learning 
unit  of  Google  managed  to  beat  a  human  master  player  in  the  game  of  Go,  a  game  which  was 
thought to be too complex to be mastered by computers. The point McAfee and Brynjolfsson (2017) 
make  is  that  it  is  difficult  to  identify  skills  that  are  'safe'  of  automation,  because  the  underlying 
technologies are progressing even faster than experts assume. Computers today challenge human 
superiority in so many fields, including pattern recognition, language, intuition, judgement,  and 
prediction.  

McAfee  and  Brynjolfsson  (2017,  see  also  Brynjolfsson  and  McAfee  2014)  give  an  answer  by 
suggesting to focus on everything related to the ability to come up with creative and unusual ideas 
on a given topic or situation – even if computers today can do more and more things that meet most 

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The impact of new technologies on the labour market and the social economy 

definitions  of  creativity,  including  composing  music,  painting,  designing  objects,  or  developing 
scientific hypotheses. However, computers are still not good in experiencing the world like humans 
do,  and  in  understanding  and  predicting  human  behaviour.  This  is  why  work  that  requires 
empathy, teamwork, coaching or other types of human interactions will continue to be done mostly 
by  humans.  It  consists  of  a  second  part  of  skills,  besides  creativity,  that  cannot  be  replaced  by 
machines, at least in the short and medium term. 

If we believe the literature, a focus on memorising facts should not be the first priority of future 
curricula. This may be a big change for some education systems. Brynjolfsson and McAfee (2014) 
stress the fact that education systems of many Western countries still include many features of 19th 
century education systems with their focus on three basic skills: good handwriting, good reading, 
and multiplication, division, addition and subtraction in the head. Clerks were the computers of the 
Victorian times, and these three skills were essential for their well-functioning. Therefore, not only 
a change of the contents, but also reform of the educational system as such should be on the agenda. 

  Research and development 

Research and development is a second key area of policy with respect to digitalisation. It  closely 
related  to  education,  in  particular  at  tertiary  level.  Investments  in  R&D  help  firms  to  build  up 
competences and are therefore an important driver of competitiveness and employment growth, at 
least in the long run (Peneder 2009, Peters et al. 2014). 

Another  common  feature  of  education  and  R&D  is  that  the  potential  returns  to  investments  in 
education and R&D at the societal level are higher than at the level of individuals or firms. This is 
because also other firms and individuals are benefitting from more knowledge of one individual or 
firm,  for  example  by  knowledge  spillovers  which  is  the  voluntary  or  involuntary  transfer  of 
knowledge between firms. Knowledge spillovers exist because knowledge can often be given away 
without  any  loss  of  knowledge  to  the  sender  (Foray  2004).  As  a  consequence  of  the  difference 
between the private and the social returns of R&D, firms often invest too little in R&D, because they 
cannot fully reap the benefits of their R&D investments. This is one important reason for policy to 
support R&D of private firms (Steinmueller 2010). 

Economists estimate that the social rate of return from research and development - this is the yield 
from R&D in the form of economic growth and a higher GDP - is between four and eight percent 
(Ugur et al. 2014). Hence, the social return from R&D today is considerably higher than the current 
interest rates for public debt, a good argument for public investment in R&D.  

Despite  these favourable  characteristics of research and development, the  European Union lacks 
behind the US in terms of R&D investments (OECD 2016a). According to the OECD, China, South 
Korea and the US have expanded R&D expenditure faster than the European Union in recent years, 
so the gap between the EU and these countries has widened, not closed. China has surpassed the 
European Union in gross R&D expenditure as a percentage of GDP. The figure below shows this 
development and China's catching up since 2000. 

47 

 
 
STOA - Science and Technology Options Assessment 

Figure  23:  R&D  expenditure  as  a  percentage  of  Gross  Domestic  Product  (GDP),  EU-28,  USA, 
Japan, China, Korea, 2000-2015 

Source: OECD Main Science, Technology Indicators 01/2017 

There are also some qualitative deficits of European R&D that should worry policy: publications of 
EU researchers are cited less frequently than publications by US researchers (Dosi 2006, Sachwald 
2015). In Sachwald's interpretation, this is an indication for less relevance and quality of European 
research. Similar findings are also presented by the European Commission (2016, S. 61).  

Sachwald (2015) also points to some deficits in the sectoral composition of the EU economy which 
relate to R&D: in addition to a higher R&D intensity, the US also has a much stronger basis in some 
scientific  fields  with  a  high  relevance  for  innovation,  in  particular  ICT  and  health.  This  makes 
Europe's deficits even worse if we consider the innovation perspective. The structural deficits of 
Europe's science system are also a main concern for Moncada-Paternò-Castello who is affiliated to 
the  European  Commission's  JRC  (Moncada-Paternò-Castello  et  al.,  2010;  Moncada-Paternò-
Castello,  2016a).  By  structural  deficits,  Moncada-Paternò-Castello  means  a  lower  share  of  some 
R&D  intensive  sectors,  in  particular  the  ICT  sector.  For  him,  these  differences  explain  a  lot  of 
Europe's slow growth in high-technology sectors and the lower overall R&D intensity of Europe.  

There are some signs that Europe's comparably low level of R&D is related to funding, not to a lack 
of capabilities. For example, the European Commission (2017) reports in its mid-term evaluation of 
Horizon 2020, the main instrument of the European Commission to fund R&D in Europe, that the 
available  funding  in  Horizon  2020  is  too  small  to  provide  support  for  all  high-quality  proposals 
received. In other words, Europe is wasting good ideas. 

These are all good arguments to increase R&D expenditure – regardless what scenario may become 
reality. The arguments even gain weight when we consider that interest rates for public debts are 
currently  at  very  low  levels,  considerably  below  the  social  rates  of  return  for  R&D  which  are 
between four and eight percent. Europe should invest in R&D at all levels, from basic research to 
applied  development.  Basic  research  has  a  considerable  training  effect  for  scientific  and 
technological staff. This would include higher budgets for universities, and more public funding 
for R&D in firms.  

48 

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The impact of new technologies on the labour market and the social economy 

In recent years, indirect public funding of R&D via tax credits has gained importance in the policy 
mix of many countries (Appelt et al. 2016). Tax credits also faced some criticism, because they may 
be  more  advantageous  for  large  firms  or  do  not  target  specific  technologies.  Nevertheless, 
policymakers  should  consider  to  balance  tax  incentives  for  business  R&D  with  the  use  of  direct 
support measures to foster R&D and innovation in areas such as ICT or health where Europe has 
deficits compared to the US.  

Another important question with regard to the design of R&D policies is the degree recipients of 
public funding are free in the topics they want to research. In recent years, a number of governments 
earmarked funds for R&D in topics related to global or societal challenges. A recent report by the 
OECD (2016a) lists a number of these challenges or megatrends related to demography, resources 
and  energy,  climate  change  and  the  environment,  health,  and  the  economy.  In  topics  related  to 
global societal challenges, direct instruments may be more appropriate than tax incentives because 
direct instruments allow the government to provide directions for R&D. However, one may argue 
that government not necessarily have better knowledge  about future technological requirements 
than firms do who are under pressure from markets to bring up new products that find acceptance 
from consumers because they satisfy demands. 

  Entrepreneurship 

R&D  needs  entrepreneurs  who  transform  new  scientific  findings  into  growth  and  new 
employment. A recent report by the OECD (2016b) shows that a considerable amount of new jobs 
is created by small, newly-found enterprises. The same report demonstrates that Europe is lacking 
behind in the growth of young enterprises. Moreover, Cincera and Veugelers (2013) demonstrate 
that the lower R&D intensity of the EU compared to the USA we discussed above is, to the largest 
part,  due  to  a  lower  R&D  intensity  of  EU  young  innovating  firms  compared  to  their  US 
counterparts. The emergence of information and communication technologies in particular has been 
very much shaped by start-ups in the past, and some of these start-ups reached a considerable size 
in very short periods of time. So far, European entrepreneurial ecosystems have not generated an 
equivalent to Amazon, Facebook or Google.  Technological change proceeds fast in ICTs, and the 
knowledge bases of these technologies provide only low entry barriers for new firms. Thus, ICTs 
provide considerable opportunities for new ideas and new firms. 

In another study, Moncada-Paternò-Castello (2016b) demonstrates that the gap between the EU and 
the US in R&D is not because there are fewer young firms in the ICT sectors in the EU, but because 
the younger, R&D-intensive ICT and biotechnology sectors are smaller in the EU, being dominated 
by  the  early  entrant  and  subsequently  growing  US  companies.  This  is  why  Europe  needs  more 
support for new firms in the field of ICTs and biotechnology. Besides soft measures such as training 
and consulting for people who want to start a business, this may also include measures to increase 
the available amount of venture capital. European countries still have a considerably lower supply 
of VC as a percentage of GDP compared to the US, but also compared to Israel or Canada (OECD 
2016b).  On  average,  Nordic  countries  in  the  EU  are  better  equipped  with  venture  capital  than 
Southern  member  states.  However,  it  cannot  be  the  task  of  the  government  to  provide  venture 
capital. States should rather generate incentives for individuals to invest more in start-ups and small 
firms and strengthen capital markets in Europe. 

 

Infrastructure 

Investment in infrastructure is crucial if Europe wants to keep pace  in the ICT revolution. Policy 
can use digitalisation for modernising the welfare state since it provides a digital infrastructure that 
could be used by businesses as well as the  whole of society, for education, health and wellbeing 
services.  

In particular, Europe should invest in upgrading its Internet network infrastructure. An active role 
for policy is needed for maintaining a balance between city regions – which are likely to get a lot of 

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private investment – and rural areas, which may lack behind in terms of infrastructure. Moreover, 
there  is  also  a  danger  that  imbalances  in  internet  infrastructure  between  Eastern  and  Western 
European  member  states,  as  well  as  within  the  group  of  Middle  and  Eastern  European  member 
states may deepen. According to the Digital Economy and Society Index 2017 of DG Connect (see 
Figure  below), Bulgaria and Romania are  currently  lagging behind all other EU countries,  while 
Estonia, Lithuania Slovenia and the Czech Republic established themselves at the EU average or 
even above average. 

Figure 24: The Digital Economy and Society Index 2017 

DG Connect 

Looking at digital infrastructure, it is clearly a task for the European level to ensure equal access to 
connectivity. It seems evident that a further deepening of this digital divide in Europe  has to be 
avoided. The European Commission has already taken steps to upgrade infrastructure in Europe 
with the European Fund for Strategic Investments (EFSI), an instrument to relaunch investment and 
restore EU competitiveness. On focal area of the EFSI, besides energy and research, development 
and  innovation,  are  digital  technologies.  A  European  programme  for  broadband  on  a  European 
scale could be a first step in that direction. 

  Employment regulations 

Social security systems will face some challenges from the developments described in this report. 
In all scenarios, Europe will be well served to stick to the concept of flexicurity described in detail 
in a previous study for the European Parliament (Krings and Muellner 2007). This concept, on the 
one hand, meets the strong demand to make labour markets, employment and work organisation 
more flexible. On the other hand, it also meets the equally strong demand for providing security to 
employees  –  especially  vulnerable  groups  –  and  for  preserving  social  cohesion  in  European 
societies. 

The flexibility aspect of the flexicurity approach matches with predictions in the positive scenario 
that self-employment and more flexible working arrangements will increase in the future due to 

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The impact of new technologies on the labour market and the social economy 

ICTs, while the security aspect is needed to enable secure transitions of people from job to job and 
cope with structural change in the future. This is why flexicurity is an important element for future 
labour market policies to cope with technological change as it is already an important piece of the 
European Employment Strategy. 

More specifically, new types of self-employment such as platform or gig workers also call for new 
employment regulations. These new forms of employment offer some opportunities for more self-
determined and more flexible work; however, opportunities for more self-determination should not 
come at the price of more uncertainty, or undermine goals of promoting full employment, and rising 
and stable incomes. Platform employment can also create a new 'precariat' of isolated individuals 
living  from  job  to  job,  without  lasting  financial  or  social  connections  to  workplaces  or  to  other 
workers.  Policy  should  review  the  status  of  these  types  of  employees,  and,  if  necessary,  enlarge 
social security legislation, but also health and safety regulations, to platform work. This would also 
mean that owners of platforms should contribute to social security. However, policy should avoid 
to move from current non-regulation to over-regulation. 

Historically, productivity increases generated by the new technologies have led to  reductions of 
working  time,  and  allowed  to  bring  more  people  into  work  or  dampen  the  negative  effects  of 
automation  on  the  labour  market.  Such  reductions  may  also  be  appropriate  when  ICTs  lead  to 
further automation. The reduction of working time is a very flexible instrument which can be easily 
implemented, and reductions can also be done in small steps. Thus, a shortening of work time may 
be a more moderate form of re-distribution than the introduction of a basic income with may have 
some unintended side effects described below.  

Work time reduction, however, also means an increase in labour  costs, which may have negative 
consequences for the competitiveness of export-oriented firms if productivity does not increase in 
the same way. However, just like the universal basic income discussed below, work time reduction 
may be the right measure if the goal is to reduce inequality caused by a new unemployment. 

  Tax and social security policies 

In the positive scenario, economic activity will remain vibrant and growth will even speed up. Tax 
revenues will remain stable or even grow, which allows governments to maintain the welfare state 
and invest in education, R&D and infrastructure. However, there may be a need for  a shift in the 
tax systems of European countries from labour as the main source of revenue to a taxation of capital 
and  value added, if  these components become the  main contributors to economic wealth due  to 
digitalisation. Machines may also contribute to the financing of social security.  A shift in taxation 
away from labour towards value added and capital may seem even more important in the negative 
scenario where economic activity is decreasing due to digitalisation. 

Such a shift seems only feasible if overall tax burden does not increase, and only if the tax burden 
on  labour  is  substantially  reduced.  Otherwise,  this  move  would  mean  an  increase  in  overall  tax 
burden. Moreover, it has to be kept in mind that a higher tax on the means of production basically 
means  taxing  one  of  the  potential  future  sources  of  comparative  advantage  of  European  firms. 
Higher taxes on value added or on investment goods may reduce the willingness of European firms 
to utilize digital technologies and may be an obstacle for future competitiveness. 

The designers of tax policies should also bear in mind that capital is more mobile than labour. The 
shift  away  from  labour  towards  capital  and  value  added  may  lead  to  distortions  and  even  to 
movements of firms out of countries that lean too much on taxation of these factors. Therefore, the 
shift from labour taxation to capital taxation has to be co-ordinated between countries, at least at 
the level of EU member states. 

Empirical evidence presented by Thomas Piketty (2014) and others suggests that the concentration 
of wealth in the hands of a few very rich individuals has dramatically increased in recent decades. 

51 

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Globalisation and new technologies have disproportionately benefited the rich by rewarding capital 
more handsomely than labour. There are experts who believe that digitalisation will further increase 
this concentration because of the winner-takes-all principle and the stronger role of capital in value 
added creation compared to labour.  

Thus, taxing the super-rich – by higher income  taxes, a stronger progression in income  taxes  or 
wealth taxes - is seen by many as a way to decrease inequality and raise financial means for the 
state. Moreover, it may even have a greater psychological impact than a financial one, because it 
may reduce a feeling of injustice and discrimination among ordinary tax payers who frequently are 
confronted with reports of tax avoidance.  

There have been many propositions in recent years to tax the super-rich. Thomas Piketty (2014), for 
example,  proposes a progressive global  tax on  wealth of up to 2%, and additional income  taxes. 
However, he also admits that such a tax is impossible to implement today because it would require 
a  degree  of  political  co-ordination  with  seems  very  difficult  to  agree  on  today.  This  is  a  main 
problem of taxing the super-rich (but also of taxing capital): wealth is very mobile, and may find a 
'safe harbour' where  it is out of reach for  the taxation of their home countries. A solution to this 
dilemma can only lie in international cooperation.  

  The idea of an unconditional basic income 

If the pessimistic scenario of massive job losses and a huge increase in inequality from the economic 
effects of new ICTs comes true, some authors (for example Ford 2015) have suggested to introduce 
an  unconditional  basic  income  for  every  citizen.  Unconditional  basic  income  means  that  all 
residents or citizens of a country receive a certain sum of money from the government regardless 
of they are employed or not. This should prevent the most severe economic inequality and provide 
the means for participation in society. 

The idea of a basic income  has generated wide  interest in a number  of countries, and is actively 
discussed  by  several  political  parties.  In  a  recent  briefing  for  the  European  Parliament,  Karakas 
(2016) discusses the main arguments in favour and against a basic income: proponents of a basis 
income  point  to  the  fact  that  it  provides  people  with  more  independence  in  their  education  and 
professional choices, contributes to poverty reduction, may be a step towards more employment 
and  may  help  to  deal  with  an  economic  crisis,  because  it  stabilizes  demand.  With  respect  to 
digitalisation,  an  unconditional  basic  income  appears  for  many  to  be  a  sound  approach  for 
redistributing  the  gains  from  automation.  Advocates  of  a  basis  income  hope  that  it  will  be  an 
effective  measure  to  avoid  that  many  people  fall  into  poverty.  Moreover  it  should  avoid  a 
concentration  of  incomes  in  the  hands  of  large  enterprises  and  their  owners  which  benefit 
disproportionally  from  digitalisation  and  winner-takes-all  effects.  Thus,  basic  income  has  to  be 
accompanied by a new tax policy that focuses on capital gains and the benefits of automation and 
digitalisation instead of labour. 

However,  the  idea  of  an  unconditional  basic  income  is  also  disputed.  First,  there  are  moral 
arguments against a basic income. It may foster a culture of living on social benefits, and people 
should be paid for working (and not for not working). The political process may also be influenced 
by steady demands to increase the basic income if the majority of the voting population receives it. 
Second,  critics  think  that  an  unconditional  basic  income  provides  adverse  incentives  for 
employment  and  entrepreneurship.  This  may  lead  to  lower  employment,  less  economic  activity, 
and may even drive the economy into a recession rather than stabilize growth. Moreover, they argue 
that a basic income for every citizen is way too expensive to implement as it will require massive 
tax increases to finance it. Moreover, there are also practical problems with the implementation, for 
example how to calculate the level of basic income in a fair way. 

It is difficult to assess the relevance of the arguments of the advocates and critics of a universal basic 
income, because there is only limited empirical evidence that would back up the points they make 

52 

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in  favour  or  against  it.  Karakas  (2016)  reports  some  experiments  with  the  basic  income  and 
concludes  that  these  experiments  reveal  positive  effects  on  reducing  poverty  and  inequality. 
However, the economy-wide implementation of a basic income would be at a completely different 
level than isolated experiments: it seems a big social experiment with uncertain outcome. Moreover, 
once the unconditional income has been implemented in a country, it seems impossible to reverse 
this decision. Therefore, we argue that less revolutionary measures such as changes in taxation or a 
reduction of work time may be more appropriate than a basic income. 

To sum up, it does not seem feasible to stop digitalisation, but policy has many options to influence 
and  steer  the  process,  by  investing  in  education,  infrastructure,  and  R&D,  and  adapting  labour 
legislation and tax and social security policies to digitalisation. Perhaps most important is to think 
of digitalisation not as a threat, but as a chance to increase welfare, opportunities and social cohesion 
for all European citizens.  

53 

 
 
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11.  Annex 

Calculation of Value added at factor costs: 

 

turnover (+); 

increases (+) or decreases (-) of stocks; 

 
capitalized production (+); 
  other operating income (+); 
 
  purchases of goods and services (-); 
  other taxes on products which are linked to turnover but not deductible (-); 
  duties and taxes linked to production (-). 

 = Value added at factor costs 

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