Robots at work : a report on automatable and non-automatable employment shares in Europe.
Date: 2018-06-11
Institutions: Directorate-General for Employment, Social Affairs and Inclusion
Subjects: EU employment policy, Europe, automation, new technology, report, robotics, robotisation, working conditions

Robots at work 

A report on automatable and non-automatable 
employment shares in Europe 

Written by Dr Grace Lordan  
London School of Economics and Political Science 
January – 2018 

Social  
Europe 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Directorate-General for Employment, Social Affairs and Inclusion 
Directorate A — Employment and Social Governance 
Unit A.4 — Thematic Analysis 

Contact: Katarina Jaksic 

E-mail:  EMPL-A4-UNIT@ec.europa.eu 

European Commission 
B-1049 Brussels 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Robots at work 

A report on automatable and non-automatable 
employment shares in Europe 

Directorate-General for Employment, Social Affairs and Inclusion 

Directorate A — Employment and Social Governance 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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More information on the European Union is available on the Internet (http://www.europa.eu). 

Luxembourg: Publications Office of the European Union, 2018 

ISBN: 978-92-79-80236-2 
doi: 10.2767/821645 

© European Union, 2018 
Reproduction is authorised provided the source is acknowledged. 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 

OVERVIEW ................................................................................................................... 6 

Headline Points ...................................................................................................... 6 

METHODS: CLASSIFICATION OF AUTOMATABLE JOBS ....................................................... 7 

Definition 1 ........................................................................................................... 7 

Definition 2 ........................................................................................................... 8 

Methods ............................................................................................................. 10 

OVERVIEW OF THE ANALYSIS ....................................................................................... 12 

 
 
 
 
 
 
OVERVIEW 

This work documents the shares of non-automatable and automatable jobs in 24 European 

countries  over  the  last  three  decades.  Knowledge  of  this  distribution  is  important  as  it 

reveals the countries, and the demographics within these countries whose employment is 

the most vulnerable to disappearing because of automation, as well countries who have 

tended  towards  substituting  labour  with  automation  at  a  faster  rate  over  the  last  two 

decades.  The same distribution also reveals the jobs that are likely to stay with us in the 

future, to the extent that they are non-automatable.  This information has an obvious place 

in any public policy debate.  

We  consider  two  definitions  of  automatable  work.  The  first  captures  jobs  that  were 

automatable  in  the  last  three  decades.  This  is  a  great  measure  of  automation 

retrospectively. The second captures the jobs that are recently automatable (so captures 

the most recent advances in technology and allows us to consider the shares of jobs that 

will be automatable over the next ten years). Our analysis gives an overview of differences 

in  the  shares  of  retrospectively  and  recently  automatable  jobs  across  the  EC  countries 

included in our analysis.  

Headline Points 

The headline points are: 

  Many  more  jobs  can  now  be  automated,  and  the  shares  of  these  recently 

automatable jobs vary greatly from country to country. The higher these shares the 

more vulnerable jobs are in a particular country to disappearing in the next decade. 

The  estimates  in  this  report  range  from  37%  of  the  total  jobs  being  recently 

automatable for Norway, to 69% for the Czech Republic (see Table 3 for estimates 

specific to each country).  

  The  analysis  further  divides  recently  automatable  shares  into  jobs  that  are  fully 

automatable and polarized automatable (see Table 1 for the categorisation of each 

individual  occupation).  The  former  describes  jobs  that  we  expect  to  be  fully 

substituted  by  technology  in  the  next  decade.  These  shares  range  from  21% 

(Ireland)  to  45%  (Italy).  That  is,  in  Ireland  1  in  5  of  the  jobs  currently  held  are 

recently fully automatable. Jobs that are recently fully automatable include vehicle 

drivers, packers and bank tellers.  

  Recently  polarized  automatable  shares  relate  to  jobs  where  technology  is  now 

available which is expected to take on some aspects of a job but not others over 

the next decade. Examples of recently polarized automatable jobs include lawyers, 

6 

 
 
sales demonstrators and library assistants. Over time, it is expected that the shares 

of  these  jobs  will  decrease  but  the  occupation  itself  will  not  disappear.  35%  of 

occupations  in  Belgium  are  estimated  to  be  recently  polarized  automatable, 

comparing  to  13%  in  Norway.  Estimates  for  the  24  countries  in  this  analysis  are 

provided in Table 3.  

  The occupations that still remain non-automatable require either deep thinking or 

high levels of interpersonal skills. We do not expect these shares to decrease in the 

next decade.  

  Our  country  level  analysis  supports  the  premise  that  the  technology  coming  on 

stream now allows for large shares of jobs with ‘low’ people to be automated.  In 

contrast, lesser shares of jobs with ‘high’ people content are potentially automatable 

with recent technological advances.  

  Our  country  level  analysis  reveals  that  recent  automation  is  not  divisive  along 

demographic  lines  (like  age  and  gender)  when  it  comes  to  substituting  for  jobs. 

Rather,  the  largest  shares  of  jobs  that  can  be  potentially  lost  belong  to  low  skill 

individuals  who  do  low  income  jobs.  So,  the  greatest  losers  in  the  advent  of 

technology are likely to be low skilled individuals who currently earn a low wage.  

  Future research needs to consider what at the country level explains the shares of 

recently  automatable,  fully  automatable  and  polarized  automatable  jobs.  This 

information is invaluable for policy makers as  they make decisions on the  extent 

and  exactly  how  they  will  intervene  in  the  diffusion  of  technology  which  can 

substitute workers.  

METHODS: CLASSIFICATION OF AUTOMATABLE JOBS   

Definition 1 

To achieve our goal the report considers two definitions which capture whether or not a 

job is automatable. Both definitions are available at the three-digit occupation code level. 

The  first  definition  that  we  consider  is  owed  to  Autor  and  Dorn  (2013)  and  Autor  et  al. 

(2015) with routine task intensity in each three-digit occupation defined as:  

(1)  

In equation 1 Tk

R, Tk

M, and Tk

A are the levels of routine, manual, and abstract task inputs 

RTIk=ln(TkR)-ln(TkM)-ln(TkA) 
 
 
 
  
 
 
 
 
 
for  occupation  k  measured  at  the  3-digit  occupation  code  level.  Thus,  Equation  (1)  is 

increasing  in  the  absolute  and  relative  quantity  of  tasks  that  are  automatable  within 

occupation k. The levels of each of these variables are defined using variables from versions 

of the Dictionary of Occupation Titles, where incumbents are asked to grade the level of 

their occupation with respect to particular attributes. Routine tasks involve a predictable 

sequence  of actions which are easily codifiable. Therefore, it is already possible to have 

these tasks carried out by technology as well as humans. Conversely, manual tasks require 

actions that are not generally predictable, so substitution with technology has so far been 

limited.  Abstract  tasks  then  require  high-level  thinking  that  is  complementary  with 

technology (Autor, 2013). So, a job will have high levels of routine task intensity if it has 

high levels of routine tasks, and low levels of manual and abstract tasks. An occupation is 

then defined as automatable if it is in the top tertile of this distribution. This definition is a 

great measure for thinking about the automation of work retrospectively.   

Definition 2 

The second definition is owed to Lordan and Josten (2017). Lordan and Josten (2017) take 

the occupations classified by Autor and Dorn (2013) as given, but also reclassify additional 

occupations as automatable to take into account the seismic change on the horizon with 

respect to jobs that will be automated in the future. Therefore this definition allows us to 

provide  estimates  of the shares  of jobs within countries that  could be automated within 

the next decade. These are jobs where the technology is now becoming available to allow 

for automation, but the jobs are not currently labelled as ‘automatable’ by Autor and Dorn 

(2013).  These  jobs  are  recently  automatable.  For  example,  face-to  face-  service 

occupations are classified as non-automatable by Autor and Dorn (2013), however, already 

there is a hotel in Tokyo that is staffed by robots1 and a restaurant in Germany that relies 

on a robot chef2. We note that decision to take up this automation will differ from country 

to country depending on their wage structure, level of unionisation and local labour supply, 

among other things.  This underlines the importance of considering definition 2. That is, it 

allows policy makers to consider how they will handle the automation of employment going 

forward, and whether they will allow technology to diffuse at its own rate, or if they will 

intervene3.  

Autor  and  Dorn  (2013)  define  the  first  tertile  of  equation  1  as  automatable.  Given  that 

there  are  323  occupation  codes,  this  implies  that  there  201  codes  classified  as  non-

1 Multi-lingual robots provide the receptionists duties, a robotic arm tends to the luggage room and 
porter robots carry luggage to room. Lost keys are not an issue as rooms respond to face recognition 
software.  
2 Huis Ten Bosch: Here robots prepare food, with one robot being in charge of making pancakes, it 
mixes batter, and uses robotic arms to flip the pancakes before dressing them.   
3 For instance, a newly automatable job with large shares is vehicle driver. In many countries the 
law currently requires a person to be in a vehicle. By construction this law serves to slow down  job 
loss associated with driverless technologies.   

                                                 
automatable.  In  order  to  decide  on  whether  an  occupation  in  this  subset  is  recently 

automatable Lordan and Josten (2017) explore patent data. They argue that a significant 

amount  of  promising  research  and  development  that  is  searching  for  a  substitute  for  a 

particular job, defined as having filed a relevant patent, signals that this job is highly likely 

to be automatable in the next decade. To determine the level of patent activity they rely 

on  Google  Patents4.  For  the  remaining  3-digit  occupations  labelled  as  non-automatable 

according to Autor and Dorn (2013), Lordan and Josten (2017) search Google Patents to 

determine if a technology is actively being developed as a substitute for that occupation. 

Specifically,  they  search  for  variants  of  the  occupation  name,  plus  either  of  the  terms 

‘robot’,  ‘automation’  or  “artificial  intelligence”.  For  example,  for  registered  nurses  they 

search for ‘nurse’ and ‘robot’ or ‘automation’ or “artificial intelligence”. In this case they 

find  one  patent.  For  truck-driver  they  search  for  ‘truck’  and  ‘driverless’  or  ‘robot’  or 

‘automation’  or  “artificial intelligence.”    In  this  case  they  find  many  patents.  Finally,  for 

waiter they search for ‘waiter’ and robot’ or ‘automation’ or “artificial intelligence.”  Here 

they find five patents5.  

Based on their review Lordan and Josten classify each three-digit occupation into one of 

the  following  categories:  i)  non-automatable;  ii)  polarized  automatable;  or  iii)  fully 

automatable. The first are jobs where inter personal skills are required in order to gauge 

the specific tasks that are needed and are unlikely to be automated. Nursing is an example 

in this category. The second are jobs where there is a lot of R&D which require interpersonal 

interaction but there is a predictable sequence of events. Here, technology has had some 

success,  with  some  examples  of  promising  patents  that  are  likely  to  be  successful. 

However,  Lordan  and  Josten  (2017)  suggest  that  developments  will  progress  to  a 

polarization,  with  human  employees  being  kept  in  establishments  where  a  personal 

interaction still holds value and robots being utilized where it does not, at least for the next 

couple of decades.  An example here is wait staff where robots may have had some success 

substituting  humans  in  low  cost  or  quirky  establishments  where  the  level  of  service 

required is minimal, but are unlikely to displace waiters offering a fine dining experience 

where the interactions expected with customers are less predictable. Another is lawyers, 

where machines can substitute for a previous need to synthesis large volumes of text from 

books  of  law  but  cannot  substitute  for  the  abstract  thinking  evidenced  in  top  barristers 

during serious trials. Last are the jobs where there is recently a lot of successful R&D and 

people may care less about whether the job is done by a robot or a human. So, the added 

value to the customer of having a human carry out these roles is zero. Here, there have 

4 Specifically, Google patents covers over 87 million patent publications from the following patent 
offices: United States, Europe, Japan, China, South Korea, WIPO, Russia, Germany, United Kingdom, 
Canada, France, Spain, Belgium, Germany, Finland, Luxembourg and the Netherlands. Patents that 
are non-English have been translated by Google patents and indexed. 
5 A documentation of this review can be found in the appendix of Josten and Lordan (2017). 

 
 
 
                                                 
been significant developments to the extent where Lordan and Josten (2017) predict a cull 

of jobs in the next decade. An example here is truck drivers6.   

Table 1 documents each three-digit occupation and their associated classification according 

to Autor and Dorn (2013) and Lordan and Josten (2017). A reader can think of the Lordan 

and  Josten  (2017)  classification  as  ‘recently  automatable’  –  that  is,  recently  technology 

has  become  available  which  allows  for  the  automation  of  this  occupation,  with  an 

expectation that this automation over the next decade will erode the occupation’s shares 

in the absence of policy intervention.   

Occupations classified as automatable under the two definitions include accountants, air 

traffic controllers, typists, and bank tellers. Those classified as non-automatable under the 

two  definitions  include:  aerospace  engineers,  mathematicians,  nurses,  teachers, 

economists, psychologists and hairdressers. These are the jobs that will not be affected by 

technology  in  the  coming  decades.  Notably  these  occupations  centre  around  two  main 

characteristics  deep  thinking  and/or  interactions  with  people.  Jobs  that  are  recently 

polarized  automatable  according  to  Lordan  and  Josten  (2017)  are  speech  therapists, 

librarians, teacher’s aides, wait staff and masons. In general, these are jobs where there 

is heterogeneity in the tasks complexity with requirements at the low end of complexity 

being  automatable,  and  those  at  the  high  end  being  non-automatable.  Jobs  that  are 

recently fully automatable according to Lordan and Josten (2017) are vehicle drivers of all 

kinds, packers, power plant operators and mail carriers. Notably, the Lordan and Josten 

(2017) classification suggests that automation will hollow out occupations that are mid and 

low skill to a far greater extent than those at the top of the skill distribution in the next 

decade.   

Methods 

Our  overall aim is to provide information on which European countries have the highest 

shares of occupations classified as automatable by Autor and Dorn (2013), and classified 

as  recently  fully  automatable  and  recently  polarized  automatable  by  Lordan  and  Josten 

(2017). We calculate these shares for three decades, the 90’s, 00’s and 10’s for Autor and 

Dorn (2013) to get an idea of whether some countries have adopted automation at a faster 

rate than others retrospectively. For Lordan and Josten (2017) we focus on the 10’s period 

only when calculating recently automatable shares.   

To calculate the shares, we calculate the total number of jobs that are automatable in each 

country for a specific time period (the 90’s, 00’s or 10’s ), the denominator is then equal 

6  We  note  that  Lordan  and  Josten  (2017)  also  consider  another  stricter  definition  of  automatable 
employment  which  looks  to  see  if  the  jobs  classified  as  automatable  under  definition  2  have  a 
successful pilot or trial already in industry. This definition has a correlation of 0.88 with definition 2 
so we are not worried about significant finding differences across these classifications.  

                                                 
to the total number of jobs in the same time period. We draw on the EU LFS, and in general 

rely on data from 1993-1996, 2003-2006 and 2013 – 2016. We choose these time periods 

to avoid having periods of recession in our calculations7. We include 24 countries in our 

analysis. Unfortunately, three-digit occupation codes are not available in the 1980s so we 

may  not  see  trends  in  diffusion  of  information  technology  over  this  period8,  however 

countries that have had a period of diffusion in the 80’s will have relatively low and flat 

shares of Autor and Dorn (2013) automatable employment in the 90’s, which we will see 

in the analysis.  

We next extend our country level analysis, to provide for  each  country a more nuanced 

description of their shares of automatable employment. Specifically, for each country we 

present figures that document the shares of automatable jobs across countries according 

to both definitions. We also do these calculations by specific strata.  The numerator is then 

the  share  of  automatable  employment  in  a  country,  strata  and  time  period,  and  the 

denominator is total employment in a country, strata and time period.  

We focus on the following strata:  

1)  high skill, mid skill and low skill jobs 

2)  high compensation and low compensation jobs 

3) 

4) 

 high in ‘people’ and low in ‘people’ jobs 

 older, middle aged and young workers  

5)  male and female workers   

So,  for  example  when  we  calculate  the  high  skill  shares,  the  numerator  is  the  share  of 

automatable employment in a country that relates to high skill workers and a specific time 

period.  

Data 

Our main data source is the EU-LFS data. These data are matched to the Autor and Dorn 

(2013) 3-digit occupation definition of automatable jobs by cross walks provided by Lordan 

and Pischke (2017)9. Age and gender strata are determined from within the EU –LFS data. 

7 For a small number of countries we need to use different years in calculating the 90’s shares. This 
is noted in the country level description of the results. A few countries had to be excluded from the 
analysis owing to a lack of data (we require at least two-time points and three-digit occupation codes. 
These are Bulgaria, Switzerland, Malta, Poland and Romania.  
8  1980’s  was  notable  period  of  diffusion  for  information  technology.   Nordhaus  (2007)  show  that 
overall after a slow-down in the progress of computing adoption in the 1960s and 1970 (owing to a 
slow  in  annual  price  declines),  an  acceleration  followed  in  the  1980’s  onwards   when larger price 
declines ensued.   

9 In the EU LFS we first cross walk ISCO 2010 to ISCO 88. Next we crosswalk ISCO 88 to the three-
digit occupation codes. Details of occupations that correspond to these codes can be found in Table 
1.  

 
 
 
                                                 
 
Classifications  of  three-digit  occupation  codes  into  high  and  low  people  content  are 

consistent with definitions provided by Lordan and Pischke (2016)10.  Considering whether 

there is a bifurcation by people content adds value as it is informative of the value of inter 

personal (no cognitive) skills in the years going forward which are currently not taught in 

an official capacity on education curriculum. High and low-income jobs are determined by 

classifications from the ONET database11, given that the income data in the EU LFS is not 

available.  Finally, high, mid and low skilled jobs are classified based on information on the 

required  education,  experience  and  training  (both  on  and  off  site)  for  each  three-digit 

occupation code12.   

OVERVIEW OF THE ANALYSIS  

Table 2 documents the Autor and Dorn (2013) (AD) shares of automatable employment 

over  three  decades  (90’s,  00’s  and  10’s).  For  Germany,  Italy,  Czech  Republic,  France, 

Sweden,  Austria,  UK,  Spain,  Estonia,  Lithuania,  Greece  and  Croatia  we  do  not  see  any 

significant  evidence  of  continued  automation  over  the  three  decades.  However,  for 

Hungary,  Netherlands,  Slovakia,  Portugal,  Denmark,  Finland,  Cyprus,  Norway,  Iceland, 

Luxemburg,  Ireland  and  Belgium  we  do  see  evidence  of  shares  of  AD  automatable 

employment decreasing over the three decades. Notably, even in the 10’s there is a large 

variation in the AD shares of automatable employment across countries. This suggests that 

some  countries  with  higher  AD  automatable  shares  (like  Germany,  Italy  and  the 

Netherlands who have shares of almost 40%) have for one reason or another not adopted 

technology or re organised work to the extent of other countries in the EC (like Ireland and 

Belgium who have AD shares of less than 20%). 

Table 3 calculates the shares of recently automatable employment using the definition put 

forward by Lordan and Josten (2017) (LJ). This captures the jobs that are now automatable 

given the technologies recently available. There is great variation across the 24 countries, 

with Ireland and Norway having the lowest shares of LJ recently automatable jobs. Still, 

even in these countries the estimates suggest that 38 out of 100 jobs currently available 

are automatable. For the UK this number is 43 out of 100. The estimates suggest that the 

Czech Republic and Germany have the highest shares of LJ recently automatable jobs. For 

10 Lordan and Pischke (2016) create three latent factors based on the 79 context and activities ONET 
variables. They follow the psychometric literature and use exploratory factor analysis to reduce the 
dimensionality  first  (Gorsuch,  1983;  Thompson,  2004).  They  follow  Heckman  et  al.  (2012)  and 
extract three latent variables that they   label ‘people,’ ‘brains,’ and  ‘brawn’. The ‘people’ variable 
relates to jobs that have a significant amount of engagement with third parties in some way or other.  
11 Specifically, ONET has a variable in its values database which is on a scale of 1 – 7 that denotes 
how  high  compensation  is  for  each  three-digit  occupation  code.  We  classify  occupations  as  high 
compensation if they are in the top tertile of this variable. Conversely occupations are denoted as 
low compensation if they are in the bottom tertile.  
12 We draw on information from the ONET database which defines the levels required for education, 
experience,  on  site  and  off-site  training.  We  combine  these  variables  using  an  exploratory  factor 
analysis. The tertiles of the final variable then define our low, mid and low skill classifications.  

                                                 
Germany the estimates suggest that 66 out of 100 jobs are currently automatable. For the 

Czech Republic this number is 69 out of 100. 

We further divide the LJ shares into jobs that are recently fully automatable and recently 

polarized automatable (see Table 1 for the categorisation of each individual occupation). 

By  construction  the  sum  of  recently  fully  automatable  and  recently  polarized  optimal  is 

equal to recently automatable jobs as defined by LJ. Recently fully automatable are jobs 

that can now – or soon - be fully substituted by technology. From Table 3, these shares 

range from 21% (Ireland) to 45% (Italy). That is, in Ireland 1 in 5 of the jobs currently 

held are recently fully automatable. For Italy this number is close to 1 in 2.  

Recently  polarized  automatable  shares  relate  to  jobs  where  technology  is  now  available 

which will be able to take on some aspects of a job but not others. Examples of recently 

polarized  automatable  jobs  include  lawyers,  sales  demonstrators  and  library  assistants. 

Over  time,  it is  expected  that  the  shares  of  these  jobs  will  decrease  but  the  occupation 

itself  will  not  disappear.    35%  of  occupations  in  Belgium  are  estimated  to  be  polarized 

automatable, comparing to 13% in Norway.  

Overall Table 3 suggests that all countries in the EC hold enough recently automatable jobs 

that we can be certain that technology can shape the nature and number of jobs available 

to a significant extent. The extent of this shaping will depend on local labour markets and 

their  laws,  as  well  as  the  countries  own  characteristics  and  political  landscape.    Future 

research  should  consider  exactly  which  factors  help  explain  the  shares  of  automatable 

employment  in  Table  2  and  3.    It  is  crucial  that  these  linkages  are  understood  so  as  to 

inform policy makers on their potential policy. 

Table 1: Occupations Classified by Autor and Dorn (2013) and Lordan and Josten 
(2017) 

Lordan 
Josten  

and 

labour 

relations 

resources  and 

Autor and Dorn 

Non-Automatable  Non- Automatable 
Automatable  

Occupation 
Chief  executives,  public  administrators,  and 
legislators 
Financial managers 
Human 
managers 
Managers and specialists in marketing, advert., 
Non-Automatable 
PR 
Managers in education and related fields 
Non-Automatable  Non- Automatable 
Managers of medicine and health occupations  Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Managers of properties and real estate 
Automatable 
Funeral directors 
Fully Automatable 
Non-Automatable  Non- Automatable 
Managers and administrators, n.e.c. 
Fully Automatable 
Automatable 
Accountants and auditors 
Fully Automatable 
Automatable 
Insurance underwriters 
Automatable 
Other financial specialists 
Fully Automatable 
Non-Automatable  Non- Automatable 
Management analysts 

Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Non-Automatable 

 
 
 
labour  rel. 

training,  and 

Occupation 
Personnel,  HR, 
specialists 
Purchasing agents and buyers of farm products  Automatable 
Buyers, wholesale and retail trade 
Purchasing  managers,  agents,  and  buyers, 
n.e.c. 
Business and promotion agents 
Construction inspectors 
Inspectors and compliance officers, outside 

and 

Autor and Dorn 

Lordan 
Josten  
Polarized 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable 

Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Management support occupations 
Architects 
Aerospace engineers 
Metallurgical and materials engineers 
Petroleum, mining, and geological engineers 
Chemical engineers 
Civil engineers 
Electrical engineers 
Industrial engineers 
Mechanical engineers 
Engineers and other professionals, n.e.c. 
Computer  systems  analysts  and  computer 
scientists 
Operations  and  systems  researchers  and 
analysts 
Actuaries 
Mathematicians and statisticians 
Physicists and astronomists 
Chemists 
Atmospheric and space scientists 
Geologists 
Physical scientists, n.e.c. 
Agricultural and food scientists 
Biological scientists 
Foresters and conservation scientists 
Medical scientists 
Physicians 
Dentists 
Veterinarians 

Optometrists 
Podiatrists 
Other health and therapy occupations 
Registered nurses 

Pharmacists 
Dieticians and nutritionists 
Respiratory therapists 
Occupational therapists 
Physical therapists 

Speech therapists 
Therapists, n.e.c. 

Physicians' assistants 
Subject instructors, college 

Polarized 
Non-Automatable 
Automatable 
Non- Automatable 
Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable  Non- Automatable 

Non-Automatable  Non- Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Automatable 
Non- Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non- Automatable 
Automatable 
Polarized 
Automatable 

Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 
Automatable 
Fully Automatable 
Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 

Occupation 
Kindergarten and earlier school teachers 
Primary school teachers 
Secondary school teachers 
Special education teachers 
Teachers, n.e.c. 
Vocational and educational counselors 

Librarians 
Archivists and curators 
Economists, market and survey researchers 
Psychologists 
Social scientists and sociologists, n.e.c. 
Urban and regional planners 
Social workers 
Clergy and religious workers 
Welfare service workers 

Lordan 
Josten  

and 

Autor and Dorn 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Automatable 
Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 
Automatable 
Automatable 
Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Lawyers and judges 
Writers and authors 
Technical writers 
Designers 
Musicians and composers 
Actors, directors, and producers 
Painters,  sculptors,  craft-artists,  and  print-
Fully Automatable 
Automatable 
makers 
Automatable 
Fully Automatable 
Photographers 
Dancers 
Non-Automatable  Non- Automatable 
Art/entertainment performers and related occs  Non-Automatable  Non- Automatable 
Non- Automatable 
Editors and reporters 
Non- Automatable 
Announcers 
Polarized 
Automatable 

Athletes, sports instructors, and officials 
Clinical laboratory technologies and technicians  Non-Automatable  Fully Automatable 
Fully Automatable 
Dental hygienists 
Automatable 
Automatable 
Health record technologists and technicians 
Fully Automatable 
Non-Automatable  Fully Automatable 
Radiologic technologists and technicians 
Non-Automatable  Non- Automatable 
Licensed practical nurses 

Automatable 
Automatable 

Non-Automatable 

Health technologists and technicians, n.e.c. 
Engineering technicians 
Drafters 
Surveryors, 
scientists/techs 

cartographers, 

mapping 

Biological technicians 

Chemical technicians 

Other science technicians 

Airplane pilots and navigators 
Air traffic controllers 

Broadcast equipment operators 
Computer software developers 

Polarized 
Automatable 

Non-Automatable 

Non-Automatable 

Non-Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Fully Automatable 
Polarized 
Automatable 

Non-Automatable 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable 

 
 
 
Occupation 
Programmers 
machine tools 
Legal assistants and paralegals 

of 

numerically 

controlled 

Technicians, n.e.c. 
Sales supervisors and proprietors 
Insurance sales occupations 
Real estate sales occupations 
Financial service sales occupations 
Advertising and related sales jobs 
Sales engineers 
Salespersons, n.e.c. 
Retail salespersons and sales clerks 
Cashiers 
Door-to-door  sales,  street  sales,  and  news 
vendors 

Autor and Dorn 

Lordan 
Josten  

and 

Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 

Non-Automatable  Non- Automatable 

Non-Automatable 
Automatable 

Polarized 
Automatable 
Sales demonstrators, promoters, and models 
Office supervisors 
Fully Automatable 
Computer and peripheral equipment operators  Non-Automatable  Non- Automatable 
Fully Automatable 
Secretaries and stenographers 
Fully Automatable 
Typists 
Non- Automatable 
Interviewers, enumerators, and surveyors 
Fully Automatable 
Hotel clerks 
Fully Automatable 
Transportation ticket and reservation agents 
Fully Automatable 
Receptionists and other information clerks 
Correspondence and order clerks 
Fully Automatable 
Human  resources  clerks,  excl  payroll  and 
timekeeping 

Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 

Automatable 

Library assistants 
File clerks 
Records clerks 
Bookkeepers  and  accounting  and  auditing 
clerks 
Payroll and timekeeping clerks 
Billing  clerks  and  related  financial  records 
processing 
Mail and paper handlers 
Office machine operators, n.e.c. 
Telephone operators 
Other telecom operators 
Postal clerks, exluding mail carriers 
Mail carriers for postal service 
Mail clerks, outside of post office 
Messengers 
Dispatchers 
Shipping and receiving clerks 
Stock and inventory clerks 
Meter readers 
Weighers, measurers, and checkers 
Material 
expediting cl. 
Insurance 
investigators 
Customer service reps, invest., adjusters, excl. 
insur. 

recording,  sched.,  prod.,  plan., 

examiners, 

adjusters, 

and 

Non-Automatable 
Automatable 
Automatable 

Fully Automatable 
Polarized 
Automatable 
Fully Automatable 
Fully Automatable 

Automatable 
Automatable 

Fully Automatable 
Fully Automatable 

Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Fully Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 

Automatable 

Fully Automatable 

Automatable 

Fully Automatable 

Automatable 

Fully Automatable 

Occupation 
Eligibility  clerks  for  government  prog.,  social 
welfare 
Bill and account collectors 
General office clerks 
Bank tellers 
Proofreaders 
Data entry keyers 
Statistical clerks 

Teacher's aides 
Administrative support jobs, n.e.c. 

Autor and Dorn 

Lordan 
Josten  

and 

Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 

Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Polarized 
Automatable 
Fully Automatable 
Polarized 
Automatable 
Non-Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable 
Automatable 

Housekeepers, maids, butlers, and cleaners 
Laundry and dry cleaning workers 
Supervisors of guards 
Fire fighting, fire prevention, and fire inspection 
Non-Automatable 
occs 
Police and detectives, public service 
Non-Automatable  Non- Automatable 
Sheriffs, bailiffs, correctional institution officers  Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Crossing guards 
Fully Automatable 
Automatable 
Guards and police, except public service 
Polarized 
Automatable 

Polarized 
Automatable 

Protective service, n.e.c. 
Supervisors of food preparation and service 

Bartenders 

Waiters and waitresses 
Cooks 

Food preparation workers 
Miscellanious  food  preparation  and  service 
workers 
Dental Assistants 
Health and nursing aides 
Supervisors of cleaning and building service 
Superv. 
landscaping, 
groundskeeping 

service, 

lawn 

of 

Janitors 
Pest control occupations 
Barbers 
Hairdressers and cosmetologists 

Recreation facility attendants 

Guides 

Ushers 

Baggage porters, bellhops and concierges 

Recreation and fitness workers 

Non-Automatable 
Non-Automatable  Non- Automatable 

Automatable 

Non-Automatable 
Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Fully Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Non-Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable 

Non-Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Non-Automatable 

Non-Automatable 
Non-Automatable  Fully Automatable 
Non- Automatable 
Automatable 
Non- Automatable 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Non-Automatable 

Non-Automatable 

Non-Automatable 

Non-Automatable 

Gardeners and groundskeepers 

Non-Automatable 

 
 
 
Occupation 
Motion picture projectionists 
Child care workers 

Lordan 
Josten  
Autor and Dorn 
Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 

and 

Personal service occupations, n.e.c 

Non-Automatable 

Supervisors of personal service jobs, n.e.c 

Non-Automatable 

Public transportation attendants and inspectors  Non-Automatable 
Animal caretakers, except farm 
Farmers (owners and tenants) 
Farm managers 
Farm workers, incl. nursery farming 
Graders and sorters of agricultural products 
Inspectors of agricultural products 
Timber, logging, and forestry workers 
Fishers,  marine  life  cultivators,  hunters,  and 
kindred 
Supervisors of mechanics and repairers 
Automobile mechanics and repairers 
Bus, truck, and stationary engine mechanics 
Aircraft mechanics 
Small engine repairers 
Auto body repairers 
Heavy  equipement  and 
mechanics 
Industrial machinery repairers 
Machinery maintenance occupations 
Repairers of industrial electrical equipment 
Repairers of data processing equipment 
Repairers  of  household  appliances  and  power 
tools 
Telecom and line installers and repairers 
Repairers of electrical equipment, n.e.c. 
Heating,  air  conditioning,  and  refrigeration 
mechanics 
Precision makers, repairers, and smiths 

farm  equipment 

Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 

Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 

Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 

Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Locksmiths and safe repairers 
Repairers of mechanical controls and valves 
Elevator installers and repairers 
Millwrights 
Mechanics and repairers, n.e.c. 
Supervisors of construction work 

Masons, tilers, and carpet installers 
Carpenters 
Drywall installers 
Electricians 
Electric power installers and repairers 

Painters, construction and maintenance 

Non-Automatable 

Paperhangers 

Non-Automatable 

Plasterers 
Plumbers, pipe fitters, and steamfitters 

Non-Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Occupation 

Autor and Dorn 

Concrete and cement workers 
Glaziers 
Insulation workers 
Paving,  surfacing,  and  tamping  equipment 
operators 
Roofers and slaters 
Structural metal workers 
Drillers of earth 

Misc. construction and related occupations 
Drillers of oil wells 

and 

Lordan 
Josten  
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 

Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Fully Automatable 

Non-Automatable 
Non-Automatable  Fully Automatable 

Polarized 
Automatable 

Explosives workers 

Miners 

Non-Automatable 

Non-Automatable 

Other mining occupations 

Non-Automatable 

Production supervisors or foremen 
Tool and die makers and die setters 
Machinists 
Boilermakers 
Precision grinders and fitters 
Patternmakers and model makers 
Engravers 

Non-Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 
Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Polarized 
Automatable 

Other metal and plastic workers 
Cabinetmakers and bench carpeters 
Furniture/wood  finishers,  other  prec.  wood 
workers 

Dressmakers, seamstresses, and tailors 
Upholsterers 
Shoemakers,  other  prec.  apparel  and  fabric 
workers 
Hand molders and shapers, except jewelers 
Optical goods workers 
Dental 
technicians 
Bookbinders 
Other precision and craft workers 
Butchers and meat cutters 
Bakers 
Batch food makers 

laboratory  and  medical  applicance 

Water and sewage treatment plant operators 
Power plant operators 
Plant  and 
engineers 

system  operators, 

stationary 

Non-Automatable 
Non-Automatable  Non- Automatable 

Automatable 

Non-Automatable 
Automatable 

Fully Automatable 
Polarized 
Automatable 
Fully Automatable 

Non-Automatable  Fully Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Fully Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Fully Automatable 

Non-Automatable 

Non-Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Fully Automatable 
Non-Automatable  Fully Automatable 
Non-Automatable  Non- Automatable 

Other plant and system operators 
Lathe, milling, and turning machine operatives  Automatable 
Punching and stamping press operatives 
Rollers, roll hands, and finishers of metal 

 
 
 
Occupation 
Drilling and boring machine operators 
Grinding,  abrading,  buffing,  and  polishing 
workers 

Forge and hammer operators 
Molders and casting machine operators 
Metal platers 

Autor and Dorn 
Automatable 

Lordan 
Josten  
Fully Automatable 

and 

Automatable 

Non-Automatable 
Automatable 
Automatable 

Fully Automatable 
Polarized 
Automatable 
Fully Automatable 
Fully Automatable 
Polarized 
Automatable 

Non-Automatable 
Non-Automatable  Fully Automatable 

Heat treating equipment operators 
Sawing machine operators and sawyers 
Nail,  tacking,  shaping  and  joining  mach  ops 
(wood) 
Other woodworking machine operators 
Printing machine operators, n.e.c. 
Typesetters and compositors 
Winding  and  twisting  textile  and  apparel 
Fully Automatable 
operatives 
Automatable 
Fully Automatable 
Knitters, loopers, and toppers textile operatives  Automatable 
Automatable 
Textile cutting and dyeing machine operators 
Fully Automatable 
Non-Automatable  Fully Automatable 
Textile sewing machine operators 
Non-Automatable  Fully Automatable 
Shoemaking machine operators 

Fully Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 

Automatable 
Automatable 
Automatable 
Automatable 

Polarized 
Automatable 
Non-Automatable 
Fully Automatable 
Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Fully Automatable 
Fully Automatable 
Automatable 
Fully Automatable 
Automatable 

Non-Automatable  Non- Automatable 
Fully Automatable 
Automatable 

Clothing pressing machine operators 
Miscellanious textile machine operators 
Cementing and gluing machine operators 
Packers, fillers, and wrappers 
Extruding and forming machine operators 
Mixing and blending machine operators 
Separating,  filtering,  and  clarifying  machine 
operators 
Food roasting and baking machine operators 
Washing,  cleaning,  and  pickling  machine 
operators 
Paper folding machine operators 
Furnance, kiln, and oven operators, apart from 
food 
Slicing, cutting, crushing and grinding machine  Automatable 
Automatable 
Photographic process workers 

Automatable 
Automatable 

Non-Automatable 

Machine operators, n.e.c. 

Non-Automatable 

Welders, solderers, and metal cutters 
Assemblers of electrical equipment 

Non-Automatable 
Automatable 

Painting and decoration occupations 
Production  checkers,  graders,  and  sorters  in 
manufacturing 
Supervisors of motor vehicle transportation 
Truck, delivery, and tractor drivers 
Bus drivers 
Taxi cab drivers and chauffeurs 

Parking lot attendants 
Railroad conductors and yardmasters 

Non-Automatable 

Automatable 
Fully Automatable 
Non-Automatable  Non- Automatable 
Non-Automatable  Fully Automatable 
Non-Automatable  Fully Automatable 
Non-Automatable  Fully Automatable 

Non-Automatable 
Non-Automatable  Non- Automatable 

Polarized 
Automatable 

Locomotive operators: engineers and firemen  Non-Automatable 

Polarized 
Automatable 

Fully Automatable 
Fully Automatable 
Polarized 
Automatable 
Fully Automatable 
Automatable  
Polarized 
Automatable 
Polarized 
Automatable 
Fully Automatable 
Polarized 
Automatable 

 
Occupation 

Autor and Dorn 

Railroad brake, coupler, and switch operators 
Ship crews and marine engineers 

Non-Automatable 
Non-Automatable  Non- Automatable 

Miscellanious transportation occupations 
Operating engineers of construction equipment  Non-Automatable  Non- Automatable 
Crane,  derrick,  winch,  hoist, 
operators 

Non-Automatable  Non- Automatable 

Non-Automatable 

longshore 

Polarized 
Automatable 

and 

Lordan 
Josten  
Polarized 
Automatable 

Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 
Polarized 
Automatable 

Excavating and loading machine operators 
Stevedores  and  misc.  material  moving 
occupations 

Non-Automatable 

Non-Automatable 

Helpers, constructions 

Non-Automatable 

Helpers, surveyors 
Construction laborers 
Production helpers 
Garbage and recyclable material collectors 

Non-Automatable 
Non-Automatable  Fully Automatable 
Automatable 
Fully Automatable 
Non-Automatable  Fully Automatable 

Machine feeders and offbearers 
Garage and service station related occupations  Automatable 
Automatable 
Vehicle washers and equipment cleaners 
Automatable 
Packers and packagers by hand 
Laborers, freight, stock, and material handlers, 
n.e.c. 

Non-Automatable 

Non-Automatable 

Polarized 
Automatable 
Fully Automatable 
Fully Automatable 
Fully Automatable 
Polarized 
Automatable 

 
 
 
 
 
 
 
 
Table 2: Autor and Dorn (2013) Shares  of Automatable Employment over three 
decades 

Germany  
Hungary  
Netherlands   
Slovakia  
Italy  
Czech Republic  
Portugal  
France  
Sweden  
Denmark  
Austria 
UK  
Spain  
Finland  
Cyprus   
Norway  
Estonia  
Iceland  
Luxemburg    
Lithuania  
Ireland  
Belgium  
Greece  
Croatia  

10’s 
0.397  
0.371 
0.383  
0.373  
0.371  
0.327  
0.322 
0.290   
0.283  
0.280  
0.284  
0.243   
0.227  
0.225  
0.220  
0.220  
0.216  
0.215  
0.213  
0.207  
0.190  
0.184   
0.184  
0.166  

00’s 
0.377  
0.338  
0.311 
0.337  
0.394  
0.290  
0.315 
0.214  
0.278  
0.285  
0.262 
0.253  
0.235  
0.234  
0.349  
0.276  
0.215 
0.234 
0.261 
0.185  
0.302  
0.205  
0.189  
0.173  

90’s  
0.382  
0.327  
0.301  
0.277  
0.363  
0.305  
0.370  
0.263  
0.289  
0.310  
0.234  
0.267  
0.220  
0.262  

0.261  
0.219  
0.252  
0.246  
0.200  
0.321 
0.215  
0.179  
0.161  

Note: Table is ordered by Autor and Dorn (2013) share value in the 10’s.  

 
 
 
 
 
 
 
  
Table 3: Lordan and Josten (2017) shares of automatable employment in the 10’s 
Notes 

Czech Republic  
Germany  
Cyprus  
Portugal  
Spain  
Hungary  
Italy   
Lithuania 
Slovakia  
Austria  
Estonia  
France  
Belgium  
Luxembourg  
Netherlands  
Finland  
Iceland  
Sweden  
Greece  
Croatia  
Denmark  
UK  
Ireland  
Norway  

Recently 
Automatable  
0.692  
0.663  
0.645  
0.644  
0.613  
0.606  
0.584  
0.583  
0.578  
0.576  
0.575  
0.575  
0.573  
0.568  
0.567  
0.546  
0.539  
0.513  
0.494  
0.480  
0.476  
0.433  
0.379  
0.374  

Fully Automatable   Polarized 

0.406  
0.446  
0.258  
0.419  
0.316  
0.439  
0.449 
0.307  
0.425  
0.335 
0.293  
0.328  
0.223  
0.269  
0.376  
0.288  
0.287  
0.289  
0.349  
0.247  
0.327  
0.286  
0.209  
0.248  

Automatable  
0.286 
0.217  
0.387  
0.225  
0.296  
0.167  
0.135  
0.276  
0.153  
0.241 
0.282  
0.247  
0.349  
0.299  
0.191  
0.258  
0.253  
0.224  
0.145  
0.233  
0.149  
0.147  
0.170  
0.126  

Note: Table is ordered from highest to lowest according to recently automatable shares of 
employment values.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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8
0
5
-
E
N
-
N

doi:10.2767/821645