Investing in skills pays off : the economic and social cost of low-skilled adults in the EU.
Date: 2018-10-05
Institutions: Cedefop
Subjects: adult education, labour market, professional qualifications, skill obsolescence, social cost, unemployment, unskilled worker, updating of skills

Investing in skills pays off: 
the economic and social cost 
of low-skilled adults in the EU

This  Cedefop  study  seeks  to  provide  a  comprehensive  and 
robust  evidence  base  on  low-skilled  adults  in  the  European 
Union. It analyses trends in low skills among adults as well as 
characteristics, determinants and risks of being low-skilled. The 
study  also  provides  an  estimation  of  the  individual  and  social 
value in monetary terms of a faster increase in the general level 
of skills in Member States compared to the current trend. Fully 
appreciating  the  benefits  of  updating  the  skills  of  individuals 
through adult and lifelong learning is essential to strengthening 
the rationale for public intervention in this area and to designing 
and  implementing  effective  policies  tailored  to  this  particular 
target group.

5
5
6
0
Ε
Ν

-
T
I
-

B
C
-
1
7
-
0
0
2
-
E
N
N

-

-

d
o
i
:
1
0
.
2
8
0
1
/
2
3
2
5
0

Europe 123, 570 01 Thessaloniki (Pylea), GREECE
Postal address: PO Box 22427, 551 02 Thessaloniki, GREECE
Tel. +30 2310490111, Fax +30 2310490020
E-mail: info@cedefop.europa.eu
www.cedefop.europa.eu

Investing in skills pays off: 
the economic and social 
cost of low-skilled adults 
in the EU

 
 
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults 
in the EU 

Luxembourg: Publications Office of the European Union, 2017 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Please cite this publication as: 
Cedefop (2017). Investing in skills pays off: the economic and social cost of low-skilled 
adults in the EU. Luxembourg: Publications Office. Cedefop research paper; No 60. 
http://dx.doi.org/10.2801/23250 

A great deal of additional information on the European Union is available on the 
internet.  
It can be accessed through the Europa server (http://europa.eu). 

Luxembourg: Publications Office of the European Union, 2017 

PDF  

ISBN 978-92-896-2412-1 

doi:10.2801/23250 

TI-BC-17-002-EN-N 

EPUB  

ISBN 978-92-896-2412-1 

doi: 10.2801/086601 

TI-BC-17-002-EN-E 

© European Centre for the Development of Vocational Training (Cedefop), 2017 
All rights reserved. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The European Centre for the Development  
of Vocational Training (Cedefop) is the European Union’s  
reference centre for vocational education and training.  
We provide information on and analyses of vocational education and 
training systems, policies, research and practice. 
Cedefop was established in 1975  
by Council Regulation (EEC) No 337/75. 

Europe 123, 570 01 Thessaloniki (Pylea), GREECE 
PO Box 22427, 551 02 Thessaloniki, GREECE 
Tel. +30 2310490111, Fax +30 2310490020 
E-mail: info@cedefop.europa.eu 
www.cedefop.europa.eu 

Joachim James Calleja, Director 
Micheline Scheys, Chair of the Governing Board 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Foreword  

In  recent  years,  the  continuous  process  of  labour  market  change  has  been 
dramatically  accelerated  by  a  long-lasting  economic  crisis.  The  consequences 
still affect most European countries. 

Labour  market  change  involves  costs  for  individuals  and  the  economy  as 
whole.  The  role  of  policy  and  labour  market  institutions  is  crucial  to  avoiding 
negative consequences and ensuring that the costs and benefits of adjustments 
are, as far as possible, equally distributed across workers and firms.  

In  this  context,  the  growing  number  of  low-educated  and  low-skilled  adults 
out  of  work  in  most  European  countries  will  require  increasing  attention  in  the 
years to come.  

levels  will 

Long-term  projections  show  that,  on  average,  labour  demand  for  the  low-
skilled  is  expected  to  decrease  while  labour  demand  for  medium  and  high 
qualification 
increase.  Unemployment,  social  exclusion  and 
disengagement from the labour market by the low-skilled can permanently lower 
potential  growth  and  harm  social  cohesion.  The  (re)integration  of  low-skilled 
workers  into  labour  markets  and  their  upskilling  is  a  key  policy  challenge  for 
European economies. 

The  European  Commission  initiative  New  skills  agenda  for  Europe  and the 
recommendation Upskilling pathways: new opportunities for adults recognise the 
urgency  of  the  low-skills  issue  and  the  importance  of  ensuring  that  every 
European acquires the skills and competences to realise fully his/her talent and 
potential. 

For policy-makers to design and implement policies tailored to this particular 
group there is a need to develop a comprehensive and robust evidence base in 
relation to low-skilled adults in the EU, their volume and characteristics as well as 
their economic and social costs.  

This  Cedefop  study  seeks  to  provide  such  evidence.  Providing  a  thorough 
evidence-based  assessment  of  the  consequences  associated  with  being  low-
skilled, the benefits of updating the skills of individuals through adult and lifelong 
learning  can  be  identified  and  appreciated,  supporting  the  rationale  for  public 
intervention in this area. 

1 

 
 
 
Acknowledgements 

This  publication  was  produced  by  Cedefop,  Department  for  learning  and 
employability, under the supervision of Antonio Ranieri. Lidia Salvatore, Cedefop 
expert,  was  responsible  for  the  publication  and  research  conducted  under  the 
Economic and social cost of low-skilled project.  

This publication is based on research undertaken by a consortium led by the 
Institute for Employment Studies (IES) with Istituto per la Ricerca Sociale (IRS). 
The  publication  was  peer-reviewed  by  Pedro  Moreno  Da  Fonseca,  Cedefop 
expert. 

Cedefop  is  grateful  to  those  who  actively  participated  in  the  validation 
workshop  organised  by  Cedefop  in  October  2015  to  discuss  preliminary 
findings (1). 

The work was carried out under Cedefop’s contract  
2014-0126/AO/ECVL/ARANI-LSALVA/LowSkilled/005/14 

(1)  A detailed list of workshop participants is available on request. 

2 

 
 
 
 
                                                
Table of contents 

Foreword .............................................................................................................. 1 

Acknowledgements .............................................................................................. 2 

List of tables, figures and boxes ........................................................................... 5 

Executive summary .............................................................................................. 8 

Introduction: Low-skilled adults in the EU: why skills matter ............................... 21 

1.

  Low-skilled in the EU: conceptual and measurement challenge .................. 23 

1.1.  Conceptualisation and measurement of the low-skilled ...................... 24 
1.1.1.  Main definitions in use ............................................................. 24 
1.1.2.  Wider conceptualisation: low-skilled status as a 

multidimensional phenomenon ................................................ 25 

2.

  Understanding low skills: trends in low-skilled adults in the EU ...................... 29 

2.1.  Low-skilled adults and jobs in the Member States .............................. 29 

2.1.1.  Recent trends in educational attainment and cognitive 

skills ........................................................................................ 29 

2.1.2.  Labour market status of low-skilled adults and the 

financial crisis ......................................................................... 33 
2.1.3.  Low-skilled jobs and associated working conditions ................ 37 
2.1.4.  Low-skilled adult participation in formal and informal 

training .................................................................................... 39 

2.2. 

Institutions and policies addressing the low-skilled ............................. 42 

2.3.  Future demand and supply scenarios for low-skilled adults ................ 51 
2.3.1.  Low-skilled adults .................................................................... 52 
2.3.2.  Low-skilled adults as part of the EU-28 labour force ............... 54 

3.

  Who are the low-skilled? Characteristics, determinants and risks 

among EU adults ......................................................................................... 56 

3.1.  Characteristics of low-skilled adults: cognitive skills and other 

factors ................................................................................................ 56 

3.2.  Determinants of low skills ................................................................... 60 

3.3.  The risk of being low-skilled................................................................ 66 

4.

  The consequences of low skills ................................................................... 69 

4.1.  Benefits of higher skills for individuals ................................................ 70 
4.1.1.  Employability ........................................................................... 70 
4.1.2.  Individual returns ..................................................................... 80 
4.1.3.  Impact on individual health ...................................................... 85 
4.1.4.  Impact on individual criminal behaviour ................................... 86 

3 

 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

4.2.  Benefits of higher skills levels for society ............................................ 88 

4.3.  Benefits of higher skills levels for employers/firms .............................. 89 

4.4.  Benefits of higher skills levels for the economy................................... 91 

4.5.  Conclusions ........................................................................................ 92 

5.

  The costs of low-skilled adults ..................................................................... 94 

5.1.  Theoretical background ...................................................................... 94 

5.2.  The impact of skills at microeconomic level ........................................ 96 
5.2.1.  Individual costs of low skills for young adults .......................... 96 
5.2.2.  Empirical estimates of the impact of skills on health .............. 103 
5.2.3.  Valuation of the impact of skills on crime............................... 109 
5.2.4.  Costs of low skills to firms ..................................................... 110 
5.2.5.  The public budget costs of low skills: a satellite account ....... 113 
5.2.6.  Limitations in the valuation of microeconomic impacts .......... 118 

5.3.  Aggregate economic benefits of reducing the share of low-

skilled adults by 2025 ....................................................................... 119 
5.3.1.  Income effect from upskilling ................................................. 120 
5.3.2.  Employer benefits from upskilling .......................................... 121 
5.3.3.  Health, crime and fiscal benefits from upskilling .................... 121 
5.3.4.  Aggregated net effects .......................................................... 123 

5.4.  Skills impact at macroeconomic level ............................................... 123 
5.4.1.  Empirical estimates of the impact of skills on growth ............. 124 
5.4.2.  The long-term impact on the steady-state growth of 

GDP in 2025-50 .................................................................... 129 

Conclusions...................................................................................................... 132 

List of abbreviations ......................................................................................... 134 

References ....................................................................................................... 135 

ANNEXES ........................................................................................................ 149 

1.  Low skills definitions used in this report: by chapter................................... 150 

2.  Cluster analysis ......................................................................................... 152 

3.  Characteristics, determinants and risks of being low-skilled among 

adults in the EU ......................................................................................... 154 

4.  Effects of low skills on employability .......................................................... 158 

5.  Cost of low skills: empirical specification for the macroeconomic 

approach ................................................................................................... 161 

4 

 
 
 
Table of contents 

List of tables, figures and boxes 

Tables 

1.  Aggregated costs and benefits for individual agents: scenarios 2015-25 

(million EUR) ............................................................................................... 18 
2.  Additional categories (to that of low-educated) of low-skilled people ........... 27 
3.  Policy approaches by clusters of countries, unweighted average values 

of the indicators for each cluster, 2003-07 and 2012-13 .............................. 44 
4.  Population by qualification level, aged 25 to 64, EU-28, 2015-25 ................ 52 
5.  Change in population aged 25 to 64, EU-28, 2015-25 ................................. 53 
6.  Labour force aged 25 to 64, EU-28, 2015-25 ............................................... 54 
7.  Change in labour force aged 25 to 64, EU-28, 2015-25 ............................... 54 
8.  Proportion of the labour force aged 25 to 64 with low qualifications, EU 

Member States ............................................................................................ 55 

9.  Pooled OLS regression on literacy and numeracy scores: coefficient 

estimates 

 ............................................................................................... 57 

10.  Pooled OLS regression on literacy and numeracy scores on 

employees: selected coefficient estimates (*) .............................................. 63 
11.  Predicted probabilities of being employed ................................................... 70 
12.  Labour market occupations yearly transition matrix, longitudinal 

population 2011-12 ...................................................................................... 75 

13.  Labour market occupations transition matrix, longitudinal population 

2005-08 and 2008-11 .................................................................................. 76 
14.  Determinants of labour market transition: relative risk ratios ........................ 79 
15.  Approaches used to estimate the cost of low skills ...................................... 96 
16.  Incremental returns on skills ...................................................................... 100 
17.  Costs of low level skills to the individual .................................................... 102 
18.  Skills and health: systematic differences in the proportion of ISCED 2 

and 3 respondents (ISCED 3 – ISCED 0-2) ............................................... 105 
19.  Lifetime health costs of low skills ............................................................... 108 
20.  Compensation of employees and operating surplus in 2015 ...................... 111 
21.  GDP components: surplus/compensation ratio 2011-15 ............................ 112 
22.  Implications for tax revenue ....................................................................... 115 
23.  Differences in life course receipt of benefits between ISCED 0-2 and 3 

(based on EU-SILC 2012) ......................................................................... 116 
24.  Public spending in ISCED 3 qualifications 2012/13 ................................... 118 
25.  Aggregate cost-benefit for individual agents: upskilling (7.4%) and zero 

low-skilled scenarios (0%) (million EUR) ................................................... 123 
26.  Macroeconomic model estimates: GDP per capita growth rate .................. 129 

5 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figures 

1.  European countries by cluster ..................................................................... 12 
2.  Characteristics, determinants and risk of being low-skilled .......................... 13 
3.  Benefits of higher levels of skills .................................................................. 15 
4.  Scenarios used to derive the net benefit from upskilling .............................. 17 
5.  GDP growth under different scenarios, macroeconomic approach, 

2025-50  ...................................................................................................... 20 
Incidence of low-educated adults by gender and age in EU-28, 2015 .......... 30 

6. 
7.  Level and percentage change of adult population with low educational 

8. 

attainment level* in EU-28, 2005-15 ............................................................ 31 
Incidence of adults (25 to 64) with low proficiency scores in literacy, 
numeracy and problem solving, by country*, 2012, and educational 
attainment in 2012 ....................................................................................... 32 

9.  Gap in average proficiency scores between adults (25 to 65) with high 

education and those with low education by country*, 2012 .......................... 32 
10.  Activity rate by educational attainment levels (25 to 64), 2015 ..................... 33 
11.  Share of inactive adults wanting to work by educational attainment, 

2013  ........................................................................................................... 34 
12.  Unemployment rate by education level (25 to 64), 2015 .............................. 35 
13.  Employment rate of adults with low qualifications (25 to 64 years old), 

2007-15 versus employment rate of adults with high qualifications in 
2015  ........................................................................................................... 36 
14.  Employment rate by educational attainment and age (25 to 64), 2013 ........ 36 
15.  Share of adult workers (25 to 65) with low skills in literacy and 

numeracy by occupational category***, 2012............................................... 38 

16.  Share of low-educated adult workers by type of employment in EU-28, 

2013  ........................................................................................................... 40 

17.  Gap in participation rates of adults in lifelong learning by educational 

attainment (2011), literacy score (2012), and country .................................. 41 
18.  European Member States by cluster ............................................................ 46 
19.  Proportion of adults with low levels of education by cluster and 

percentage change by gender, 2008-13 (%) ................................................ 47 

20.  Low-skilled adults (25 to 65) among the total adult population by type 

of cognitive skill and cluster (%)................................................................... 48 

21.  Employment rates for adults with low and high levels of education and 

change in percentage points by cluster, 2008-14 ......................................... 49 

22.  Risk of poverty for adults with low and high levels of education and 

change in percentage points by cluster, 2008-14 ......................................... 50 
23.  Projections of population aged 25 to 64 by qualification level, 2015-25 ....... 52 
24.  Share of low-skilled adults aged 25 to 64 by country, 2015-25 (%) .............. 53 
25.  Characteristics of adults with low cognitive skills  ........................................ 59 
26.  Determinants of the variation in numeracy and literacy proficiency 

scores.......................................................................................................... 61 
27.  Determinants of the variation in numeracy scores by age group .................. 62 

6 

Table of contents 

28.  Relationship between the level of literacy and numeracy proficiency 

and occupation held .................................................................................... 64 
29.  Relationship between years of work experience and numeracy score ......... 65 
30.  Predicted probabilities of being low-skilled in numeracy by education 

level, migrant status of parents and highest level of education attained 
by parents ................................................................................................... 66 

31.  Predicted probabilities of being low-skilled by work experience and 

highest education attained (%) .................................................................... 67 
32.  The consequences of low skills ................................................................... 69 
33.  Predicted probabilities of being employed by education level, low skills 

in numeracy and computer use experience (35 to 44 years old) .................. 72 

34.  Labour market flows from low-skilled jobs, by gender and education, 

longitudinal population 2008-11; 2005-08 .................................................... 77 

35.  Labour market flows from unemployment for people with low 

qualifications (ISCED 0-2), longitudinal population 2005-08 and 2008-
09 (%).......................................................................................................... 78 

36.  Predicted probability of moving to a high-skilled/semi-skilled job by 

education and age, 2008-11 ........................................................................ 80 

37.  Steady-state GDP per capita growth rate in relation to proportion of 

low-skilled adults ....................................................................................... 131 

Boxes 

1.  Scenarios used to derive the net benefit from upskilling .............................. 16 
2.  Adopted OECD/PIAAC definition of low skills in literacy, numeracy and 

problem solving in technology-rich environments ........................................ 29 
3.  Cluster analysis: aims and methodological approach .................................. 43 
4.  Endogeneity and reverse causality among skills factors .............................. 58 
5.  The differing importance of educational qualification for employment 

probabilities ................................................................................................. 71 
6.  The EU-SILC data ....................................................................................... 73 
7.  Classification of occupations into job levels ................................................. 74 
8.  Specification adopted to estimate earnings differentials due to 

differences in skills ...................................................................................... 99 

7 

Executive summary  

European economies have recovered to varying degrees from the economic and 
financial  crisis  of  2008  but  the  effects  on  labour  market  dynamics  in  many  EU 
Member States have proved profound and lasting. Persistent lack of employment 
opportunities,  especially  among  the  young  and  low-skilled,  may  lead  to  serious 
long-term  consequences.  Growing  social  exclusion,  disengagement  from  the 
labour market and underuse of human resources can lead to permanently lower 
potential growth. More generally, improving and maintaining high-level skills and 
workforce  competences  is  essential  to  ensure  that  Europe  remains  competitive 
and  innovative  against  increasing  global  competition,  fast-changing  labour 
market needs and demographic challenges. 

Policy-makers have long recognised the importance of skilled human capital 
for  economic  and  social  development  and  there  is  broad  consensus  on  the 
vulnerability of the low-skilled. However, the low-skilled are now clearly at the top 
of  the  European  policy  agenda:  the  New  skills  agenda  for  Europe  includes  a 
specific  action  (Upskilling  pathways:  new  opportunities  for  adults)  aimed  at 
supporting Member States in ensuring that every European acquires a minimum 
level of skills necessary to realise talent and potential fully.  

Cedefop’s  Investing  in  skills  pays  off:  the  economic  and  social  cost  of  low-
skilled adults in the EU seeks to provide comprehensive and robust evidence on 
low-skilled adults in the EU, their volume and characteristics, and their economic 
and  social  costs.  Fully  appreciating  the  benefits  of  updating  the  skills  of 
individuals through adult and lifelong learning is essential support to the rationale 
for  public  intervention  in  this  area  and  to  designing  and  implementing  effective 
policies tailored to this particular group. 

Low skills: conceptual and measurement challenge 

Analysis  of  low-skilled  status  in  the  labour  market  to  date  has  been  primarily 
conducted  using  the  level  of  educational  attainment  of  the  population  (2). 
However,  this  definition  is  widely  recognised  as  oversimplifying  the  concept  of 

(2)  Low-skilled are typically defined as individuals whose schooling is below any level of 

secondary education corresponding to levels 0-2 of the ISCED classification. 

8 

 
 
 
                                                
Executive summary 

being low-skilled, as it does not take into account different types of skills, abilities 
and factors which can result in low-skilled status:  
(a)
long-term unemployment and/or disengagement from the labour market;
(b) skill  obsolescence  due  to  ageing,  technological  change,  changes  in

production processes and/or work organisation;

(c) gaps between individual job skills and changing skills demands of the labour

market;

(d) socioeconomic factors such as migrant background and gender.

A narrow conceptualisation of being low-skilled also fails to capture the role
of  skills  and  competences  gained  outside  formal  education  environments,  such 
as those acquired through training, informal learning and work experience.  

Low-skilled status must, therefore, be conceptualised as a multidimensional 
and  dynamic  phenomenon  which  goes  beyond  educational  attainment  and 
considers  both  its  determinants  and  effects.  It  should  also  include  a  wider 
typology  of  people  with  low  skills,  such  as  those  with  obsolete  skills  and 
mismatched workers. 

Understanding low skills and EU trends 

Educational attainment has increased substantially in recent decades, especially 
among younger generations. In 2015, low-qualified adults in the EU decreased to 
23.5% and Cedefop forecasts this will fall substantially to 14.7% by 2025. When it 
comes  to  the  labour  force,  low-qualified  adults  are  expected  to  fall  by  33% 
between  2015  and  2025,  despite  a  total  adult  labour  force  basically  stable 
over the same period. 

Despite this long-term trend, in 2015 one in four European adults aged 25 to 
64  (about  64  million  adults)  still  held  only  low  qualifications.  PIAAC  data  also 
shows  that  the  share  of  the  adult  population  with  low  cognitive  skills  in  literacy 
and  numeracy  is  18%  and  20%  respectively  (3),  with  low  achievement  in  these 
areas  more  prevalent  among  those  with  low  qualifications  than  those  with 
medium  or  high  qualifications.  However,  data  also  show  that  educational 
attainment does not fully explain adults’ skills. On average, 33% of individuals (4) 

(3)  PIAAC  covers  17  Member  States:  Belgium  (the  Flemish  Community),  Czech
Republic,  Denmark,  Germany,  Estonia,  Ireland,  Spain,  France,  Italy,  Cyprus, 
Netherlands,  Austria,  Poland,  Slovakia,  Finland,  Sweden  and  the  UK  (Cyprus  not 
available for our analysis). 
(4)  People aged 16 to 64 years old.

9 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

without  upper  secondary  education  are  low-skilled  in  literacy  and  38%  in 
numeracy.  Even  among  those  with  higher  educational  attainment,  5%  are  low-
skilled in literacy and 6% in numeracy (European Commission, 2014a). 

There  is  widespread  agreement  in  literature  on  the  positive  effect  of 
education  on  labour  market  outcomes.  People  with  at  least  upper  secondary 
education are generally more likely to participate in the labour force, less likely to 
be  unemployed,  and  more  likely  to  receive  higher  earnings  compared  to  those 
with only lower educational attainment. 

In 2015,  only  63.6%  of adults  with  low  qualifications  actively  participated  in 
the  labour  market,  compared  to  79.9%  with  a  medium  educational  level  and 
88.8% of those with tertiary education. Eurostat data suggest that low-educated 
adults are more likely to be discouraged (5) on the labour market than their more 
educated peers, resulting in disengagement and social exclusion.  

Besides  enhancing  labour  market  participation,  education  also  seems  to 
provide a shield from unemployment. Rates are substantially higher among those 
with  lower  educational  attainment  in  all  the  EU-28:  on  average,  16.3%  among 
those  holding  low  qualifications  are  unemployed  compared  to  5.2%  of  those 
holding higher tertiary qualifications.  

The  recent  economic  crisis  exacerbated  the  already  vulnerable  labour 
market  position  of  workers  with  low  qualifications.  Between  2007  and  2015  the 
employment  rate  of  adults  with  low  qualifications  decreased  in  the  EU-28  from 
57.1% to 53.2%, compared to a decline of only 1.1 percentage points for highly 
educated  individuals  (from  85.2%  to  84.1%).  While  unemployment  rates 
increased  across  all  education  levels  during  the  crisis,  the  economic  downturn 
has  negatively  affected  especially 
low  qualifications  whose 
unemployment rate increased on average from 9.2% in 2007 to 16.3% in 2015. 

those  with 

Once in employment, low-skilled adults are also more likely to be employed 
in  low-skilled  occupations.  PIAAC  data  shows  that  among  workers  employed  in 
low-skilled occupations, 35% possess low skills in literacy and 40% in numeracy. 
Low  proficiency  in  literacy  and  numeracy  is  also  high  in  semi-skilled  manual 
occupations,  particularly  in  Italy,  Spain  and  France  where  more  than  30%  of 
workers  have  low  scores  on  cognitive  skills.  Analysis  of  EU-SILC  2011  data 
suggests that adults with a low level of education are more likely to get trapped in 
low-skilled  occupations:  adults  with  low  qualifications  generally  have  a  higher 

(5)  Discouraged  workers  are  those  who  desire  to  work  but  who  are  not  in  the  labour 
force, believing that there is no work available for various reasons.OECD: Employment 
database: http://www.oecd.org/els/emp/onlineoecdemploymentdatabase.htm 
[accessed 3.3.2017]. 

10 

                                                
 
Executive summary 

probability  of  remaining  in  low-skilled  jobs  at  any  age  and  job  mobility  tends  to 
decrease with age.  

Low-skilled workers and those employed in low-skilled occupations also tend 
to experience more precarious employment than their higher skilled peers. They 
are  more  likely  to  be  self-employed  than  those  with  medium  or  high  levels  of 
education  and are  also more  likely  to be  employed  under  a temporary  contract. 
Workers  in  low-skilled  jobs  usually  experience  poorer  working  conditions  (6) 
compared to people in intermediate and highly skilled ones. They are reportedly 
less  satisfied  with  their  pay  and  career  prospects,  receive  fewer  benefits  from 
extra payments, fringe benefits and performance-related schemes, and are also 
more likely to be employed in dangerous occupations and report higher accident 
rates. 

Although  the  low-skilled  are  most  in  need  of  education,  training  and 
upskilling, empirical evidence tells us they are less likely to participate in learning 
activities.  Both  the  adult  education  survey  and  PIAAC  provide  evidence  on  the 
unequal  participation  in  learning  activities  and  reveal  strong  disparities  in  the 
participation rates of different categories of adults in lifelong learning. 

Trends in low skills among adults vary substantially across Member States. 
This is why consensus has emerged in literature on the importance of institutional 
settings  and  policies  in  explaining  differences  across  countries.  Two  sets  of 
policy  approaches  are  consistently  found  in  the  empirical  literature  aimed  at 
exploring ways to improve the labour market conditions of the low-skilled: 

(a)  remedial measures targeted at the current stock of low-skilled adults;  
(b)  preventive  measures  targeted  at  young  school  dropouts,  NEETs  and 

disadvantaged groups (Cedefop, 2016; OECD, 2014).  

A cluster analysis allowed grouping of European countries according to the 
policy  approach  addressing  low-skilled/qualified  adults.  Five  country  clusters 
were identified:  
(a)  remedial  policy  approach:  countries  with  high  labour  market  policies  (LMP) 
expenditure,  particularly  in  training,  direct  job  creation  and  income  support. 
This cluster is also characterised by high levels of product and labour market 
regulation. Work-life balance policies are also substantial and increasing;  

(6)  Eurofound: Fifth European working conditions survey – 2010. 

http://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/fifth-
european-working-conditions-survey-2010 [accessed 3.3.2017]. 

11 

                                                
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

(b) 

liberal policy approach: countries with the lowest of market regulation, and a 
high  adult  participation  in  lifelong  learning.  Despite  growth  in  recent  years, 
LMP expenditure remains low except for direct job creation; 

(c)  preventive  policy  approach:  countries  with  high  support  for  education  and 
work-life  balance,  and  expenditure  on  LMP  also  above  the  EU  average. 
Market  regulation  indicators  are  in  line  with  the  EU  average,  while  the 
degree of union coverage and density is the highest in Europe;  

(d)  regulatory  policy  approach  and  less  intensive  investment  in  education  and 
training:  countries  with  the  highest  levels  of  market  and  employment 
regulation.  Expenditure  on  LMP  and  on  education  and  training  are  lower 
than  the  EU  average  (particularly  for  training),  as  are  adult  participation  in 
lifelong learning and work-life balance policies; 

(e)  mixed  policy  approach:  countries  with  the  lowest  level  of  expenditure  on 
ALMP,  education,  formal  childcare  and  income  support.  Levels  of  market 
and  employment  regulation  are  in  line  with  the  European  average,  while 
union coverage and density are the lowest in Europe. 

The cluster analysis suggests that the preventive policy approach helps not 
only  preventing  a  high  share  of  low-skilled  adults,  but  also  supporting  labour 
market  participation  and  living  conditions  of  low-skilled  adults.  Countries  in  the 
mixed policy and regulatory policy clusters display similar negative patterns in the 
labour  market  and  living  conditions  of  the  low-skilled  adult  population,  although 
the incidence of the low-skilled population across these clusters is very different: 
in both groups skills gaps in employment rates are high, employment rates for  

Figure 1.  European countries by cluster  

12 

 
Executive summary 

low-qualified  adults  are  the  lowest,  and  low-skilled  adults  are  at  a  high  (and 
increasing) risk of poverty. Cluster analysis also suggests that high levels of LMP 
expenditure observed in the remedial policy approach cluster may counteract the 
negative  effects  of  being  low-skilled.  Countries  in  the  liberal  policy  cluster,  with 
their  high  level  of  adult  participation  in  lifelong  learning  but  lower  than  EU 
average  LMP  expenditure,  generally  display  low  rates  of  adults  with  low 
qualifications, but substantial share of adults who are low-skilled in numeracy. 

EU low-skilled adults: characteristics, determinants and 
risks 

While  future  trends  in  low  skills  suggest  that  shares  of  low-skilled  adults  will 
continue  to  decrease,  current  trends  also  indicate  how  low-skilled  people  are 
particularly  disadvantaged  and vulnerable on  the labour  market.  Effective policy 
interventions tackling low skills require a clear understanding of who are the low-
skilled and what are the risk factors of becoming low-skilled.  

Figure 2.  Characteristics, determinants and risk of being low-skilled  

Source:  Cedefop. 

Characteristics of low skills 
Findings from a pooled regression analysis of PIAAC data suggest low levels of 
cognitive skills are associated with lower levels of educational attainment, lack of 
work experience and spells of unemployment and inactivity.  

13 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Low  cognitive  skills  are  also  associated  with  disadvantaged  background, 
particularly  migrant  background,  although  the  strength  of  the  association  varies 
widely  across  and  within  countries,  between  different  age  groups.  The 
educational  level  of  parents  is  also  associated  with  cognitive  skills  proficiency 
(intergenerational persistence). 

Determinants of low skills 
Results  from  a  variance  decomposition  analysis  suggest  that  most  of  the 
observed  difference  in  cognitive  skills  is  explained  by  the  level  of  formal 
education  attained  and  frequency  of  use  of  information  processing  skills  in 
everyday life (reading, writing, and numeracy). Personal characteristics (gender, 
age,  migrant  status  and  language  spoken)  and,  especially,  family  background 
(migrant status, parental education level) are also responsible for a large portion 
of this difference. To a lesser extent, variance in cognitive skills is also explained 
by labour market attachment and having participated in training. 

Along  with  these  personal  and  human  capital  variables,  job  characteristics 
may  play  a  role  in  determining  low  cognitive  skills  among  employed  adults. 
Results from an OLS regression on employed adults confirm a strong relationship 
between level of cognitive skills and type of occupation. The positive relationship 
between  work  experience  and  the  level  of  proficiency  in  numeracy  skills  is  also 
true for  employed  adults  (7). The  effect  of  work  experience on  cognitive skills  is 
more pronounced for older workers, probably because of a self-selection effect. 

Risks of being low-skilled 
A probit regression analysis (8) on numeracy (9) skills shows results consistent with 
the analysis of the characteristics and determinants of low skills. The probability 
of being low-skilled is strongly related to level of education attained and parental 
background.  Having  a  migrant  background  seems  to  play  an  important  role  in 
determining the probability of having low proficiency in cognitive skills.  

A strong relationship also exists between work experience and the probability of 
being low-skilled in numeracy: this is higher among individuals who never worked 
and among individuals experiencing unemployment or inactivity spells.  

(7)  Similar results are found for literacy. 
(8)  Analysis  uses  average  adjusted  predictions  and  adjusted  predictions  at  representative 

values. 

(9)  This part of the analysis concentrates and presents results on numeracy skills, since 
literacy  and  numeracy  proficiency  scores  are  highly  correlated  (coefficient  is  0.86) 
and produce similar results. 

14 

                                                
Executive summary 

Consequences of being low-skilled  

The idea that education and higher skills levels are associated with a wider range of 
benefits for individuals (and their families), employers, society, and the economy 
as a whole, is largely agreed in the literature. Alongside increased employability 
and higher earnings for individuals, as well as higher productivity and economic 
growth for the economy as a whole, a more recent strand of analysis focuses on 
the  social  and  non-market  benefits  of  education  and  skills:  improved  health, 
social and civic engagement, and lower involvement in criminal activities.  

Figure 3 shows the different dimensions impacted by higher levels of skills. 
While, at individual level, education can define major labour market outcomes, it 
also contributes to improving individual satisfaction, well-being and health status. 
Higher skills are also positively related to lower involvement in criminal activities 
and may promote trust, civic engagement, active citizenship and social inclusion. 
Investment  in  human  capital  also  affects  what  could  be  called  Schumpeterian 
growth:  investment  in  education  leads  to  a  more  skilled  and  competent 
population,  which  is  able  to  generate  and  adopt  new  ideas  that  stimulate 
innovation and technological progress. 

All  these  benefits  are  interlinked  and  spill  into  all  four  dimensions.  For 
example, higher employability and higher returns also lead to higher revenues for 
governments in increased returns from taxes, as well as reduced spending on 

Figure 3.  Benefits of higher levels of skills  

Source:  Cedefop.  

15 

 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

benefits,  such  as  income  support.  Education  is  not  only  associated  with  private 
benefits, but also with large gains to economies and societies. 

Costs and benefits of low-skilled adults  

The  ultimate  aim  of  this  study  is  to  estimate  the  individual  and  social  value  in 
monetary  terms  of  a  faster  increase  in  the  level  of  skills  in  Member  States, 
compared to the current trend. It uses empirical data for all EU-28 Member States 
and –  where data are not available – builds on  findings from literature research 
on  the  impact  of  skills  on  main  socioeconomic  variables.  Applying  robust 
methodological  approaches,  estimates  are  provided  on  both  microeconomic 
approaches (costs and benefits for individual agents such as families, firms and 
the public sector), and macroeconomic approaches, considering the implications 
for the economy as a whole. Both approaches offer advantages and limitations. 

Following standard principles, microeconomic analysis assumes that wages 
correspond to marginal productivity and so include returns on education/training. 
It  implies  that  non-individual  costs  and  benefits,  such  as  externalities  and 
spillover effects, are not captured by aggregation of microeconomic outcomes of 
education/training  without  further  assumptions.  At  the  same  time,  the  implicit 
assumption  of  constant  returns  on  skills  is  unrealistic  as  it  does  not  take  into 
account deadweight losses, substitution and displacement effects.  

Box 1. 

Scenarios used to derive the net benefit from upskilling  

A baseline scenario (business as usual), which assumes that population 15 to 54 
observed in 2015 would age to the key adults cohorts in 2025 (25 to 64 year-olds) 
and would be affected by the past trend of decreasing levels of low skills, gradually 
reaching a proportion of low-skilled adults of 14.7% by 2025. This target was chosen 
in line with Cedefop’s forecast scenarios. 
An upskilling scenario, which assumes a further decreasing trend in the proportion of 
low-skilled adults to reach 7.4% by 2025 (about half of the baseline target). A further 
assumption is that the increased reduction is higher for younger people and gradually 
lower for older cohorts. This scenario is challenging as it assumes that is possible to 
double the current trend in the reduction in the proportion of low-skilled population, 
but not impossible when considering that a share of low-skilled around 10% is already 
a reality in several Member States. 
A high hypothetical zero low-skilled scenario, which assumes that, by 2025, the 
proportion of low-skilled in the adult population would completely disappear. This 
scenario is largely unrealistic because of the extensive (and difficult to sustain) 
investment in adult learning facilities and policies it would require. However, it was 
included because it represents an interesting reference point for the analysis. 

16 

 
Executive summary 

However,  macroeconomic  approaches  also  suffer  from  limitations,  initially 
because  of  the  lack  of  consideration  of  non-market  values  and  distributional 
effects which estimates based on national accounts cannot include. Further, data 
available  for  the  estimate  in  this  exercise,  which  includes  an  unprecedented 
period of economic downturn, reduces the ability of the models to evaluate fully 
the  role  of  spillovers  and  externalities  arising  from  public  investments  in  human 
capital. 

Figure 4.  Scenarios used to derive the net benefit from upskilling 

Source: Cedefop. 

Microeconomic approach: aggregated economic net benefit of reducing the 
share of low-skilled adults by 2025 
The first step in the microeconomic approach is to provide a series of estimates 
of  costs  and  benefits  of  reducing  the  share  of  low-skilled/qualified  to  the 
individual agents: 
(a) 

individuals/families  (individual  wage  return,  cost  of  upskilling,  higher 
probability to be employed, improved health, reduced crime tendency); 
(b)  employers/firms (productivity gains and higher returns on investment, saving 
of downtime due to lack of skilled staff, and saving of recruitment costs); 
(c)  public  sector/tax  revenue  (higher  activity  rate  and  lower  unemployment, 
reduction  of  unemployment  and  out-of-work  social  benefits,  reduction  of 
ALMP public expenditure, public costs of upskilling, effects on tax revenues, 
healthcare spending, legal and social assistance systems). 

17 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Aggregation  of  individual  agent  costs  and  benefits  resulting  from  upskilling 
the  low-skilled  population  of  different  ages  is  presented  in  Table  1.  The  main 
components  of  costs  and  benefits  are  considered  comparing  the  upskilling  and 
the  zero  low-skilled  scenarios  against  the  baseline,  assuming  that  the  lower 
share of low-skilled/qualified adults can be achieved within a 10-year span (2015-
25). 

Results  from  the  microeconomic  analysis  show  substantial  gross  earnings 
increases,  including  tax  revenues,  as  well  as  substantial  positive  effects  for 
individuals in terms of health and crime benefits. The estimate of the aggregated 
economic  net  benefit  of  reducing  the  size  of  the  low-skilled  adult  population 
account for a total present value over the 10  years of EUR 2 013 billion (yearly 
average  value  of  about  EUR  200  billion)  in  the  upskilling  scenario,  and  of 
EUR 3 529 billion in the zero low-skilled scenario (yearly average value of about 
EUR 350 billion). 

Table 1.  Aggregated costs and benefits for individual agents: scenarios 2015-25 

(million EUR) 

Main components 

Upskilling scenario 
(7.4%) 

Zero low-skilled 
scenario (0%) 

(+)  Aggregate net income 

(-)  Opportunity costs (foregone earnings) 

Net benefit (~GVA) 

(+)  Surplus/compensation 

Net benefit including surplus 

(-)  Net public spending 

(+)  Health and crime economic benefits 

Total net benefit (+/-) 

903 618 

287 936 

615 682 

523 330 

1 139 012 

156 267 

1 030 044 

2 012 789 

1 614 877 

453 946 

1 160 932 

986 792 

2 147 724 

345 010 

1 725 841 

3 528 554 

NB: 

All figures are expressed in net present value. Aggregate GVA is computed as the difference between 
aggregate income increase and the opportunity costs (foregone earnings). 

Aggregated gross earnings: returns of acquiring ISCED 3 as opposed to ISCED 0-2 qualifications, 
including higher earnings and employment rate (microeconomic estimates applied to evaluate gains in 
lifetime income resulting from upskilling of low-skilled adults). 

Opportunity cost of education/training investment: cumulative foregone earnings for the 
education/training spell required to acquire ISCED 3 qualification (based on empirically observed 
duration of acquiring ISCED 3 qualifications in EU-28). 

Surplus/compensation ratio: value-added created during the production process which remunerates 
capital and employers’ work more generally (based on Eurostat data on GDP income components). 

Health and crime benefits: benefits for individuals of better quality of health and crime reduction effects 
of education (estimates based on: QALY differentials between low-skilled/qualified adults and those with 
upper secondary education; evidence from literature as for crime-related benefits). 

Aggregated public spending/savings: effects on public budget (further to tax revenues due to higher 
earnings and employment) related to education/training public spending and out-of-work benefits 
savings. 

Source:  Cedefop estimations, based on EU-SILC. 

18 

 
 
 
 
 
 
Executive summary 

Macroeconomic  approach:  long-term  impact  on  the  steady-state  growth  of 
GDP in 2025-50 
To  estimate  the  possible  long-term  impact  of  reducing  the  share  of  low-skilled 
adults,  a  valuation  exercise  was  conducted  using  a  macroeconomic  approach. 
This  provides  alternative  estimates  of  the  costs  of  low  skills  to  the  European 
economy, making use of skill levels and macroeconomic output measures rather 
than aggregating individual returns. 

The impact of skills can be best estimated by quantifying the output growth 
foregone due to low skills, in models which explain GDP growth per capita based 
on factors of production and further variables relevant to macroeconomic output 
(savings,  depreciation  of  capital,  population  growth  and 
labour  market 
participation). While cross-country comparisons have the advantage of capturing 
externalities  otherwise  omitted  in  the  micro  approach,  GDP  per  capita  cannot 
represent  the  full  value  of  skills  for  society,  as  this  includes  additional  aspects 
such  as  unpriced  values,  externalities,  distributional  considerations  and  other 
important determinants of social well-being. 

the 
The  model’s  empirical  specification  combines  Eurostat  data  on 
qualifications of the population aged 25 to 65 years with further macroeconomic 
aggregates  from  the  AMECO  database  and  the  total  economy  database  of  the 
Conference Board for all 28 EU Member States. 

Results  from  the  panel  data  regression  show  that  a  10  percentage  point 
reduction  in  the  long-term  proportion  of  low-skilled  adults  (with  ISCED  0-2 
qualification)  would  increase  long-term  GDP  per  capita  growth  rate  by  0.1 
percentage  point.  We  apply  our  scenarios  (Box  1),  assumed  as  the  long-term 
proportion  of  low-skilled  adults  in  steady-state  that  is  no  longer  changing  as  of 
2025,  and  the  impact  of  the  steady  state  growth  rate  of  the  GDP  per  capita  is 
based on the model (10). 

Applying the steady-state growth rate to the 2015 GDP per capita, we obtain 
the  expected  GDP  per  capita  in  2025  in  both  upskilling  and  zero  low-skilled 
scenarios. In the upskilling scenario, after 25 years the GDP per capita would be 
EUR  52  909,  compared  to  EUR  52  007  for  the  baseline.  With  an  expected 
population  in  the  EU-28  increasing  by  about  15  million  by  2050,  the  long-term 
annual GDP for the EU-28 would be about EUR 480 billion larger in the upskilling 
scenario  than  in  the  baseline.  Following  the  same  approach,  in the  hypothetical 

(10) 

In  both  scenarios  it  is  assumed  that  the  reduction  in  the  proportion  of  low-skilled 
adults  translates  into  a  similar  increase  in  proportion  of  adults  with  ISCED  3-4  (no 
impact on ISCED 5-8). 

19 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

zero  low-skilled  scenario  the  long-term  annual  GDP  differential  would  be  about 
EUR 965 billion in 2050. 

In the 25 years taken as reference period (2025-50) the increase in annual 
GDP due to the reduction in the share of low-skilled adults set in the ‘upskilling’ 
and the zero low-skilled scenarios would be over EUR 200 billion and EUR 410 
billion respectively. 

Figure 5.  GDP growth under different scenarios, macroeconomic approach, 2025-

50 

Source:  Cedefop estimation on data from Eurostat (labour force survey custom aggregation); annual 

macroeconomic database (AMECO); and total economy database (Conference Board). 

20 

 
 
 
Introduction  
Low-skilled adults in the EU: why skills 
matter  

Skills are a driving force of economic and social development. When citizens are 
engaged in the labour market and in society, countries prosper not only in terms 
of growth but also in terms of well-being and social cohesion.  

The  economic  and  financial  crisis  has  profoundly  affected  labour  market 
dynamics in the Member States. Soaring unemployment rates, especially among 
certain  groups  of  the  population,  (the  young  and  the  low-skilled)  may  have 
negative consequences and lead to social exclusion and disengagement. Having 
such a pool of talent out of the labour market is even more concerning, in light of 
future demographic  challenges,  increasing global  competition  and fast-changing 
labour-market needs resulting from innovation processes. 

Despite the magnitude and persistence of the low-skill phenomenon among 
adults in the EU, policy-makers have generally not paid as much attention to low-
skilled  adults  as  to  other  groups  at  risk  of  social  and  labour-market  exclusion 
(such as young people). However, in view of the future labour-market and skills 
forecasts,  low  skills  started  to  attract  growing  interest  at  policy  level.  Initiatives 
such  as  the  New  skills  agenda  for  Europe  (European  Commission,  2016a)  and 
the  Upskilling  pathways:  new  opportunities  for  adults  (Council  of  the  EU,  2016) 
have  been  developed  at  EU  level  to  support  Member  States  in  ensuring  that 
every European acquires the skills to realise fully his/her talent and potential.  

For policy-makers to design and implement policies tailored to this particular 
group there is a need to develop a comprehensive and robust evidence base on 
low-skilled adults in the EU, their volume and characteristics, as well as economic 
and  social  costs.  There  is  the  need  first  to  identify  clearly  this  target  group,  in 
terms  of  size,  characteristics,  causes  and  trends  and,  then,  measure  the 
economic and social costs and benefits both for individuals and society, in order 
to build a robust  evidence  base  on  the  phenomenon.  From this  it  is  possible  to 
identify  and  appreciate  the  benefits  of  updating  the  skills  of  individuals  through 
adult and lifelong learning, which will support the rationale for public intervention. 
The  Cedefop  study  Investing  in  skills  pays  off:  the  economic  and  social  cost  of 
low-skilled adults in the EU seeks to provide this evidence. 

To  be  able  to  design  and  implement  effective  policies  targeted  at  the  low-
skilled  requires  accurately  defining  and  measuring  this  population.  Yet,  to  date, 
the  low-skilled  phenomenon  has  been  analysed  rather  narrowly  and  primarily 
using the level of educational attainment of the population (typically with, at most, 

21 

 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

lower  secondary  education:  ISCED  0-2).  This  is  mainly  because  available  data 
are  scarce  and  mostly  confined  to  Eurostat  databases,  such  as  the  adult 
education  survey  and  the  European  Union  labour  force  survey  (EU-LFS). 
Although  these  provide  reliable,  updated  and  comparable  data  across  the  EU, 
their  scope  is  narrow  and  fails  to  capture  other  dimensions  that  are  highly 
relevant  to  defining  and  conceptualising  the  low-skilled.  As  a  result,  analysis 
based on these datasets tends to oversimplify the low-skill concept. Chapter 1 of 
this  report  aims  at  articulating  a  ‘holistic’  and  broader  definition  of  low-skilled 
status for adults, which will move beyond relying only on the level of educational 
attainment  and  embrace  the  different  dimensions  which  comprise  the  overall 
skills  and competences of  workers. This  is  expected to help with understanding 
the characteristics which define low-skilled people and investigating mechanisms 
through which low skills and competences affect the labour market outcomes of 
workers in low-skilled jobs.  

Chapter  2  provides  a  comprehensive  and  up-to-date  comparative  overview 
of  current  labour  market  conditions  and  recent  and  future  trends  in  low-skilled 
adults in the 28 Member States; it is based on a wide range of existing European 
and  international  data  sources  and  on  the  quantitative  and  qualitative  evidence 
within  existing  literature.  In  this  chapter  we  also  explore  the  impact  of  different 
institutional  settings  and  policies  in  explaining  differences  in  low-skilled  trends 
across Member States.  

Against  a  background  of  current  and  future  trends  in  low  skills,  effective 
policy  interventions  require  clear  understanding  of  who  are  the  low-skilled  and 
what  are  the  risk  factors  of  becoming  low-skilled.  Chapter  3  investigates  the 
characteristics  of  the  low-skilled  adult  population  in  the  EU,  aiming  to  identify 
what are the determinants of low-skilled status as well as the factors increasing 
the likelihood of becoming low-skilled.  

Chapter 4 provides an overview of the consequences of low skills. The idea 
that  education  and  higher  levels  of  skills  are  associated  with  a  wider  range  of 
benefits for individuals (and their families), employers, society and the economy 
as a whole, is largely shared in literature. In this chapter we explore the benefits 
associated with higher levels of skills in these four dimensions.  

Finally,  Chapter  5  provides  an  estimation  of  the  costs  of  low  skills  to 
individuals,  businesses,  the  economy  and  society  at  large,  with  a  robust 
methodological approach using empirical data for all EU-28 Member States and – 
where  data  are  not  available  –  summarising  the  findings  from  existing  literature 
on the impact of skills on a number of variables. Estimating such costs is part of 
the  required  evidence  that  will  contribute  to  raising  the  low-skill  issue  higher  on 
the  political  agenda  and  mobilising  policy-makers  and  decision-makers  at  all 
levels to take immediate action and develop effective policies. 

22 

Chapter 1.
Low-skilled in the EU: conceptual and 
measurement challenge  

To date, analysis of low-skilled status in the labour market has been narrow and 
primarily  based  on  the  educational  attainment  of  the  population.  It  has  used  a 
definition of lower secondary education – ISCED 0-2 – applied to main Eurostat 
data  sources,  such  as  the  EU-LFS,  which  are  the  only  ones  providing  reliable, 
updated and comparable data across all EU Member States. 

However,  conceptualising  low-skilled  status  based  only  on  educational 
attainments fails to capture other dimensions that are no less relevant for labour 
market  analysis  and  for  vocational  education  and  training  policy  development. 
First,  educational  attainment  does  not  take  into  account  different  types  of  skills 
and  abilities,  and  factors  that  can  result  in  low-skilled  status,  especially  among 
adults:  long-term  unemployment  and/or  disengagement  from  the  labour  market, 
skill  obsolescence  due  to  ageing,  rapid  technological  change,  product/process 
innovation,  changes  in  production  processes  and/or  work  organisation,  and 
socioeconomic  factors  such  as  migrant  background  and  gender.  Second,  this 
narrow  conceptualisation fails  to  capture  the  role  of  other  knowledge,  skills  and 
competences  gained  outside  formal  education  environments,  such  as  those 
acquired through training, informal learning and work experience.  

Also, there is little consensus among scholars on the meaning of the concept 
of skills which, in many cases, is blurred with other terms such as competences 
and  abilities.  Recent  theoretical  work  has  argued  that  skills  are  a  reflection  of 
‘value’, which may be personal, economic or social (Green, 2012).  According to 
this definition skill involves the following dimensions: 
(a)  using skill produces value for an individual or organisation, signalled through 

price of goods produced or income earned by an individual;  

(b)  skills are expandable and can be enhanced by training and development, so 
personal attributes such as physical characteristics, which are not amenable 
to change, do not meet the definition; 

(c)  skills are socially determined.  

Within this context, this report sought to articulate a broader definition of low-
skilled  status  for  adults,  moving  beyond  educational  attainment  to  embrace  the 
different dimensions which comprise the overall skills and competences of adults. 
This is expected to help with understanding the characteristics which define low-
skilled  people  and  investigating  mechanisms  through  which  low  skills  and 

23 

 
   
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

competences  affect  the  labour  market  outcomes  of  adults  and  workers  in  low-
skilled jobs.  

1.1.  Conceptualisation and measurement of the low-

skilled  

1.1.1.  Main definitions in use  
Most  literature  identifies  low-skilled  adults  as  those  with  a  low  level  of  formal 
education. This  is  typically  defined as individuals  whose  schooling  is  below  any 
level  of  secondary  education  –  ISCED  0-1  –  extended  (11)  at  most  to  lower 
secondary education (ISCED 0-2) (12).  

Alternative  definitions  use  job  characteristics  to  identify  the  low-skilled 
population,  those  working  in  elementary  occupations  (ISCO-88  major  group  9) 
(13).  However,  this  can  be  controversial  for  some  subgroups  of  the  population 
such  as  immigrant  workers  (14).  Some  studies  (Eurofound,  2008)  use  job 
characteristics to identify the low-skilled but in addition to ISCO-88 major group 9, 
elementary  occupations,  they  also  includes  major  groups  5  to  8.  Other  studies 
(Dieckhoff, 2008) consider low-skilled workers as those working only in ISCO-88 
submajor groups 81 to 93 (15).  

(11)  E.g.  Steedman  and  McIntosh  (2001)  who  present  an  analysis  of  the  international 
adult  literacy  survey  (IALS)  test  data  concluding  that  ISCED  0-2  is  a  valid  working 
definition (and measure) of the low-skilled in the Member States considered. 

(12) 

ISCED  1997  (international  standard  classification  of  education):  0  –  Pre-primary 
education;  1  –  Primary  education  or  first  stage  of  basic  education;  2  –  Lower 
secondary  education  or  second  stage  of  basic  education;  3  –  Upper  secondary 
education (3C – programme not designed to lead ISCED 5A/B); 4 – Post-secondary 
non-tertiary  education;  5  –  First  stage  of  tertiary  education;  6  –  Second  stage  of 
tertiary education 

ISCED  2011:  0  –  Early  childhood  education;  1  –  Primary  education;  2  –  Lower 
secondary  education;  3  –  Upper  secondary  education;  4  –  Post-secondary  non-
tertiary education; 5 – Short-cycle tertiary education; 6 – Bachelor or equivalent; 7 – 
Master or equivalent; 8 – Doctoral or equivalent. 

(13)  This  definition  is  used  by  manuals  and  research  practices  of  international 
organisations, such as ILO or OECD which equate ISCO-88 major group 9 with the 
low-skilled level. 

(14)  The position of immigrants in the EU labour markets raises the issue of whether low-
skilled should be defined through the skills they possess, or the jobs they  perform: 
some  lower-skilled  jobs  are  occupied  by  highly  educated  immigrants,  whose 
qualifications are not recognised in the host country. 

(15) 

ISCO-08, 2008 (international standard classification of occupations): 0 – Armed forces 
occupations;  1  –  Managers;  2  –  Professionals;  3  –  Technicians  and  associate 

24 

                                                
 
 
Chapter 1. 
Low-skilled in the EU: conceptual and measurement challenge 

Finally, in some economics literature, such as in Manning (2004), low-skilled 
people  are  also  defined  by  wage  changes  associated  with  specific  jobs 
previously performed by low-skilled workers. Some scholars refer to the changes 
associated with the ‘usual’ attribution of some routine jobs to low-skilled people. 
In  ‘a  model  of  changing  task  specialisation  in  which  ‘routine’  clerical  and 
production tasks are displaced by automation’ (Autor and Dorn, 2009, abstract), 
less-educated workers tend to lose out: technological change and/or automation 
produce displacement of routine labour input. This should, in turn, lead to shifts in 
job  specialisation  because  ‘computer  adoption  […]  implies  greater  demand  for 
computer capital’ (Autor and Dorn, 2009, p. 26). ‘Computer capital substitutes for 
workers  in  carrying  out  a  limited  and  well  needed  set  of  cognitive  and  manual 
activities,  those  that  can  be  accomplished  by  following  explicit  rules  (what  we 
term ‘routine tasks’); and that computer capital complements workers in carrying 
out  problem-solving  and  complex  communication  activities  (‘non-routine’  tasks)’ 
(Autor et al., 2003, p. 1280). 

1.1.2.  Wider  conceptualisation:  low-skilled  status  as  a  multidimensional 

phenomenon 

A broader look at the causes of becoming low-skilled leads to identifying various 
‘typologies’ of vulnerable people likely to be classified as low-skilled: people with 
obsolete  skills  (even  if  they  possess  upper  secondary  education)  and/or  people 
who  do  not  possess  enough  non-cognitive  skills.  The  term  ‘non-cognitive  skills’ 
usually refers to a set of attitudes, behaviours, and strategies that are thought to 
underpin success in school and at work, such as motivation, perseverance, and 
self-control. They are sometimes described using terms such as character skills, 
competences, personality traits, soft skills and life skills. Despite growing interest 
in this topic (Morrison et al., 2013; Nelson, 2010; Kureková et al., 2013b), while 
the  relationship  between  cognitive  skills  and  later  outcomes  in  life  has  been 
extensively  studied,  evidence  on  the  causal  relationship  between  non-cognitive 
skills  and  later  outcomes  is  not  well  established.  Dickerson  and  Green  (2004), 
Smits  and  Zwick  (2004),  and  Heckman  et  al.  (2006)  find  that  transversal 
competences  (or  non-cognitive  generic  skills),  such  as  communication  skills 
and/or language skills, attract positive wage premia and increase employability. It 

professionals;  4  –  Clerical  support  workers;  5  –  Service  and  sales  workers;  6  – 
skilled agricultural, forestry and fishery workers; 7 – Craft and related trades workers; 8 
– Plant and machine operators and assemblers; 9 – Elementary occupations. 

The  texts  we  are  quoting  refer  to  ISCO-88.  However,  in  March  2008,  the  ISCO-08 
was adopted. See ILO website for the correspondence between ISCO-88 and ISCO-
08: http://www.ilo.org/public/english/bureau/stat/isco/isco08/ [accessed 1.3.2017]. 

25 

                                                                                                                                 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

is worth highlighting that the possession of transversal non-cognitive skills (16) is 
not always linked to the formal qualification(s) acquired and/or level of education 
attained.  

In addition to the innate characteristics and qualifications held by individuals, 
skills requirements are dynamic and may evolve over time, leading to changes in 
the types of characteristics which define the low-skilled.  

Innovation,  including  increased  use  of  ICTs  and  changes  in  production 
processes  and/or  work  organisation,  requires  higher  and/or  new  skills.  Due  to 
what  is  commonly  referred  as  skill-biased  technological  change  (SBTC)  the 
average job is getting more demanding in terms of skills requirements (Kureková 
et  al.,  2013a).  Job  complexity  is  increasing  across  all  sectors  and  occupations 
and  inflation  in  relative  skills  demand,  for  instance  requiring  more  demanding 
non-routine  tasks,  even  for  low-skilled  jobs  in  some  service  sectors  (European 
Commission, 2008). 

Changes in skills demand are more likely to affect older workers negatively 
than  other  groups  (Desjardins  and Warnke,  2012).  As  the  younger  age  cohorts 
have much lower shares of low-educated people (ISCED 0-2) than older cohorts, 
the  thresholds  at  which  qualifications  are  defined  as  ‘low’  in  the  labour  market 
differ across the age cohorts. This is likely to have implications for how the same 
level of education is valued over time and between people of different ages. 

In addition to older people, workers employed in sectors experiencing rapid 
technological and organisational change and those working in low-skilled jobs are 
at  higher  risk  of  experiencing  skills  obsolescence.  Workers  experiencing 
unemployment  and  inactivity  spells  may  also  face  obsolescence  of  their  human 
capital  (Arthur  et  al.,  1998),  as  may  those  working  in  jobs  for  which  they  are 
overqualified. (Kureková et al., 2013a; De Grip et al., 2008). Over recent years, 
due to fierce competition in the labour market resulting from fewer suitable work 
opportunities,  a  growing  number  of  highly  and  medium-qualified  workers  have 
been  willing  to  accept  jobs  at  a  lower  skills  level.  Females,  young  people  and 
third  country  nationals  are  the  most  represented  among  overqualified  workers 
(Goldring and Yamina, 2013).  

Beyond  the  immediate  private  and  public  fiscal  costs,  skills  underutilisation 
can have longer-term consequences: people who do not use their skills fully are 
likely to lose them over time, which can result in adverse consequences for future 

(16) 

In  the  PIAAC  adult  survey  (programme  for  the  international  assessment  of  adult 
competencies),  non-cognitive  skills  are  explored  through  non-cognitive  modules 
referring  to  behavioural  performance  competencies,  subjective  well-being  and 
health, career interest and intentionality, and work/training history and skills transfer. 

26 

                                                
Chapter 1. 
Low-skilled in the EU: conceptual and measurement challenge 

employment and well-being, as well as lower participation in further training, with 
further consequences in terms of lower future earnings and productivity. This can 
be  particularly  detrimental  for  young  people  who  have  a  longer  working  life 
ahead.  

In recent years, the increased focus on lifelong learning has led to growing 
attention to the fact that individuals not only acquire skills over their lifetime, but 
are  also confronted  with skill  loss  and a general  decline  in the  ability  to acquire 
and  retain  new  knowledge  and  skills.  This  is  true  both  when  dealing  with  skills 
defined  as  basic  cognitive  skills  (such  as  reasoning,  episodic  memory, 
vocabulary or processing speed) and those defined as cognitive foundation skills 
such as literacy, numeracy and problem solving (Willms and Murray, 2007).  

The  conceptualisation  of 

low-skilled  status  as  a  multidimensional 
phenomenon  goes  beyond  educational  attainment  and  qualification  levels  to 
capture the different dimensions of low skills (Table 2). 

Table 2.  Additional categories (to that of low-educated) of low-skilled people 

Additional categories 
of low-skilled people 

People with obsolete 
skills  

Mismatched, 
overqualified workers  

Main characteristics  

People with higher education than ISCED 0-2 but experiencing skills 
obsolescence. 
This group may include: 
  workers who have obtained an obsolete education which no longer 
holds currency in the face of structural and labour market changes. 
This is in particular the case with older workers who have not 
refreshed their initial educational attainment with more recent training;  

  workers who have not sufficiently applied in real work settings their 
specialised skills acquired in education. This is particularly the case 
for individuals with long spells out of the labour market, including 
females experiencing career interruptions (e.g. because of child 
rearing) and those suffering from long-term sickness absence;  

  unemployed and inactive people who may be facing barriers in their 

labour market entry or re-entry, many might be unmotivated or lacking 
the interest and/or foundation skills to undertake training which would 
develop new skills required by a changed labour market and by the 
new (knowledge) economy.  

People who have worked in a sector and/or in a job that did not make 
use of their educational attainment and associated skills/qualifications.  
This group may include: 
  immigrants working in roles which do not make use of the 

qualifications gained in their country of origin (such as high-skilled 
immigrants whose qualifications are not recognised in the host 
country);  

  young people with higher level qualifications but working in entry level 
positions because of their lack of experience and difficulty in gaining 
work experience, resulting, in some countries, from the continuing 
effects of the economic crisis;  

  females, because of labour market segregation and over-

representation in precarious employment. 

Source:  Adapted from Kureková et al., 2013a.  

27 

 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Developments  in  literature  along  the  broader  classification  summarised  in 

Table 2, call for a different approach to measuring low-skilled status which: 
(a)  considers  other  dimensions  as additional to the acquired  and  certified  level 

of education (educational attainment); 

(b)  stresses  the  importance  of  demographic  factors  such  as  age,  ethnicity, 

gender and occupational status during one’s lifespan. 

(a) 

(b) 

An holistic definition of low skills may include:  
low-skilled people: with educational attainment ISCED 0-2 and ISCED 3 who 
have  experienced  skills  obsolescence  and/or  skills  mismatch  by  age,  sex 
and nationality; 
low-skilled  jobs:  people  working  in  elementary  occupations  (ISCO  major 
group 9) and in some cases also semi-skilled non-manual occupation (ISCO 
major groups 4-5) and in semi-skilled manual occupations (major groups 6-8). 

However,  limitations  in  data  availability  imply  that  a  wider  definition  of  low-
skilled  population  can  be  applied  only  to  those  dimensions  which  can  be 
measured at EU level (for all Member States). This is why, throughout the report, 
different  operational  definitions  (based  on  existing  data)  have  been  adopted 
depending on the context of use.  

28 

Chapter 2.
Understanding low skills: trends in low-skilled 
adults in the EU 

A comprehensive and up-to-date overview of recent and future trends of low-skilled 
adults in the EU is possible from a wide range of European and international data 
sources  (EU-LFS,  EU-SILC,  EWCS,  PIAAC,  Cedefop  skills  forecasts)  and  the 
quantitative and qualitative evidence from existing literature. Available data allow 
us to consider three main dimensions of low-skilled status for individuals: 
(a) 
(b) 

the level of formal education and qualifications: ISCED 0-2; 
the level of cognitive skills: people who have obtained a low score on measures 
of literacy, numeracy or problem solving in technology-rich environments as 
assessed by the OECD survey of adult skills, PIAAC (Box 2); 
the level of skill required for different occupations: low-skilled jobs are those 
in  elementary  occupations  (ISCO-08  major  group  9),  which  require  a  low-
skill level corresponding to a primary level of education. 

(c) 

Box 2. 

Adopted OECD/PIAAC definition of low skills in literacy, numeracy and 
problem solving in technology-rich environments 

The definitions of low skills in literacy, numeracy and problem solving in technology-
rich environments used in this report are based on proficiency score used by the 
OECD (2016):  
  low literacy or numeracy skills are defined as scores less than 226 points at or 

below proficiency 1;  

  low skills in problem solving are defined as scoring less than 241 points at 

proficiency level below 1. 

2.1. 

Low-skilled adults and jobs in the Member States  

2.1.1. 

Recent trends in educational attainment and cognitive skills  

Educational  attainment  has  increased  substantially  in  recent  years,  especially 
among younger generations, but in 2015 one in four European adults aged 25 to 
64  years  (equal  to  64  million  adults)  still  held  only  low  qualifications  (Eurostat, 
LFS). The incidence is higher in older cohorts, especially for females. As shown 
in Figure 6, while females are more educated than men in the younger cohorts, 
the opposite is true in the older group. Given that low-skill incidence in the older 

29 

 
   
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

cohorts  is  higher  among  females,  they  represent,  on  average,  a  slightly  higher 
share of low-educated people than men.  

Figure 6. 

Incidence of low-educated adults by gender and age in EU-28, 2015 

Source:  Eurostat, EU-LFS, [edat_lfse_03]. 

Despite  a  declining  trend  in  the  share  of  the  adult  population  with  low 
qualifications  in  all  Member  States  (except  for  Denmark,  due  to  a  break  in  the 
series), at country level the situation is diverse: the incidence is particularly dramatic 
in Mediterranean countries, while it is less than 10% in east European countries.  
Large country variations also exist in cognitive skills, both in average proficiency 
scores  and  the  incidence  of  low  proficiency  in  cognitive  skills  among  the  adult 
population (Figure 8). Italy and Spain are the weakest performers in both literacy and 
numeracy, while Finland, the Netherlands and Sweden are the top EU performers. 
On average, the share of the adult population with low cognitive skills in literacy and 
numeracy  is  18%  and  20%  respectively  (17).  When  the  incidence  of  adults  with 
low cognitive skills is plotted against the incidence of adults with low educational 
attainment, both aspects seem to follow the same trends. However, while the share 
of adults with low cognitive skills varies little across Member States, the same is 
not true for shares of adults with low qualifications. In countries such as Belgium, 
Spain,  Italy  and  the  Netherlands,  educational  attainment  rates  are  substantially 
higher  than  the  share  of  the  adult  population  with  low  cognitive  skills,  while  the 

(17)  PIAAC  covers  17  Member  States:  Belgium  (Flanders),  Czech  Republic,  Denmark, 
Germany,  Estonia, Ireland,  Spain, France, Italy, Cyprus, Netherlands, Austria,  Poland, 
Slovak  Republic,  Finland,  Sweden,  the  UK  (England  and  Northern  Ireland).  However, 
data for Cyprus were not available for our analysis. 

30 

 
 
                                                
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

opposite is observed in the east European countries. East European countries are, 
however, characterised by lower than average rates of adults with low qualifications.  

Figure 7.  Level and percentage change of adult population with low educational 

attainment level* in EU-28, 2005-15 

NB: Population in the age class 25 to 64. Low educational attainment: Pre-primary, primary and lower 

secondary education (ISCED 0-2).  

Source:  Eurostat, [edat_lfs_9903]. 

PIAAC  data  also  show  a  close  positive  relationship  between  educational 
attainment and proficiency in information-processing skills. As shown in Figure 8, 
adults with low educational attainment, on average, score lower than adults who 
have  attained  secondary  or  tertiary  education,  especially  in  problem  solving. 
However,  accounting  for  the  effects  of  other  socio-demographic  characteristics, 
such  as  age,  reduces  the  strength  of  the  relationship  between  educational 
attainment  and  proficiency  in  all  countries  (OECD,  2013a).  Differences  in  skills 
proficiency related to educational attainment vary considerably among countries. 
Possible reasons for this include differences in the quality of schooling, the nature of 
adult-learning  systems,  and  differences  in  patterns  of  participation  in  education 
(OECD, 2013a). The impact of different institutional settings and policies on the 
incidence of the low-skill population is explored in more detail in Section 2.2. 

31 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure 8. 

Incidence of adults (25 to 64) with low proficiency scores in literacy, 
numeracy and problem solving, by country*, 2012, and educational 
attainment in 2012  

*   UK (England and Northern Ireland);  **  Belgium (Flanders).  

NB:  Percentages are calculated not accounting for missing values. For Spain, France and Italy proficiency 

scores on problem solving in technology-rich environments are not available. 

 and 

 Proficiency score up to level 1 (below 226 points); 

 Proficiency score below level 1 (below 241 points). 

Source:  Cedefop calculation on PIAAC 2012, Eurostat LFS [edat_lfse_03]. 

Figure 9.  Gap in average proficiency scores between adults (25 to 65) with high 
education and those with low education by country*, 2012 

*  UK (England and Northern Ireland).  

** Belgium (Flanders). High education: tertiary education (ISCED 5-8); low education: below upper secondary 

education (ISCED 0-2). 

NB:  Percentages are calculated not accounting for missing values. For Spain, France and Italy proficiency 

scores on problem solving in technology-rich environments are not available.  

Source: Cedefop calculation on PIAAC 2012. 

32 

 
 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

2.1.2. 

Labour market status of low-skilled adults and the financial crisis 

There is widespread agreement in literature on the positive effect of education on 
labour  market  outcomes.  People  with  at  least  upper  secondary  education  are 
generally  more  likely  to  participate  in  the  labour  force,  less  likely  to  be 
unemployed  and  more  likely  to  receive  higher  earnings  compared  to those  with 
only lower educational attainment.  

Education seems to be an important driver for labour market participation. In 
2015,  only  63.6%  of  adults  with  low  qualifications  actively  participated  in  the 
labour market, compared with 79.9% for adults with a medium educational level 
and 88.8% for highly educated adults. While there are no significant differences 
among EU countries in the participation rate of highly educated adults, there is a 
higher  level  of  country  variation  for  those  with  low  qualifications  (Figure  10). 
Analysis  by  gender  reveals  that  while  activity  rates  do  not  vary  much  at  higher 
educational levels, females with low education are particularly disadvantaged: at 
EU  level  only  51.8%  of  females  with  low  education  participate  in  the  labour 
market against 75.7% of men.  

Figure 10.  Activity rate by educational attainment levels (25 to 64), 2015 

Source:  Eurostat, EU-LFS, [lfsa_argaed]. 

Low  activity  rates  among  adults  with  low  qualifications are  also of  concern; 
they  may  conceal  discouragement  and  possibly  result  in  disengagement  and 
social exclusion. Low-educated adults seem more likely to be discouraged (18) on 

(18)  Discouraged workers are persons who desire to work, but who are not in the labour 
force, believing that there is no work available for various reasons. OECD: Employment 

33 

 
 
                                                
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

the  labour  market  than  their  more  educated  peers.  As  shown  in  Figure  11,  the 
share  of  inactive  adults  wanting  to  work  is  lower  among  those  with  low 
qualifications than among the medium and highly educated in most EU  Member 
States, except for Bulgaria, Hungary and Romania.  

Figure 11.  Share of inactive adults wanting to work by educational attainment, 2013 

Source:  Cedefop analysis of Eurostat EU-LFS, microdata 2013 (latest year available for microdata). 

Besides enhancing labour market participation, education also seems to provide 
a shield from unemployment. Figure 12 shows that unemployment is substantially 
higher among those with lower educational attainment in all the EU-28.  

The economic crisis has negatively affected those with low qualifications, their 
unemployment rate increasing on average from 9.2% in 2007 to 16.3% in 2015. 
Only  Germany,  Hungary  and  Slovakia  registered  a  decline  in  unemployment 
among those with a low level of education.  

The financial crisis exacerbated the already vulnerable labour market position of 
workers  with  low  qualifications  (Kyndt  et  al.,  2013;  Kaufman,  1995;  Rocco  and 
Thijssen, 2006). Between 2007 and 2015 the employment rate of adults with low 
qualifications  decreased  in  the  EU-28  by  3.9  percentage  points  from  57.1%  to 
53.2%, compared to a decline of only 1.1 percentage points for highly educated 
individuals (from 85.2% to 84.1%); this widened the employment gap in level of 
education over the financial crisis years.  

database.   http://www.oecd.org/els/emp/onlineoecdemploymentdatabase.htm 
[accessed 3.3.2017]. 

34 

 
 
                                                                                                                                 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Figure 12.  Unemployment rate by education level (25 to 64), 2015 

Source:  Eurostat, EU-LFS [lfsa_urgaed]. 

The  EU  average  conceals  large  country  differences.  Employment  rates  for 
low-qualified  adults  have  been  decreasing  in  most  Members  States,  with  the 
sharpest  declines  of  about  10  percentage  points  in  Bulgaria,  Denmark,  Ireland, 
Greece,  Spain,  Cyprus,  Hungary  and  Portugal.  In  contrast,  employment  rates 
have increased by about five percentage points in Germany, Malta and Slovakia, 
although  the  last  still  has  the  lowest  employment  rate  for  low-qualified  adults 
(Figure 13).  

During  the  crisis  employment  rates  of  tertiary  educated  adults  also  fell 
considerably  in  some  Member  States,  including  Ireland,  Greece,  Spain  and 
Cyprus.  In  contrast  employment  rates  for  this  group  increased  by  about  three 
percentage points in Germany, Hungary, Malta and Poland.  

Analysis  by  gender  confirms  the  importance  of  education  in  influencing  the 
labour market attachment of females (Goldin and Olivetti, 2013). Highly educated 
females tend to remain in employment even after childbirth (Goldin, 2006), so the 
gap in employment rate by education is much larger among women than among 
men. The gap in employment rates between those with low qualifications and the 
highly educated is higher for females than males in all Member states except for 
Slovenia.  Female gaps by  education  range from  57.3 percentage  points (Malta) 
to  25.3  percentage  points  (Portugal);  for  men  the  differences  range  from  48.4 
percentage points (Slovakia) to 12.9 percentage points (Greece). 

The impact of education on employment outcomes is much greater for older 
adults (Figure 14), though the low-educated young are particularly disadvantaged 
compared to low-educated adults aged 30 to 49. Low participation in employment 

35 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

is a particular concern for low-educated females: only 38.4% of females with low 
qualifications  aged  25  to  29  are  employed,  compared  to  76.4%  of  females  with 
tertiary qualifications.  

Figure 13.  Employment rate of adults with low qualifications (25 to 64 years old), 

2007-15 versus employment rate of adults with high qualifications in 
2015 

Source:  Eurostat, EU-LFS, lfsa_ergaed (employment rates by sex, age and highest level of education 

attained). 

Figure 14.  Employment rate by educational attainment and age (25 to 64), 2013 

Source:  Cedefop elaboration on Eurostat EU-LFS, microdata 2013 (latest year available for microdata). 

36 

 
 
 
 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Low-skilled jobs and associated working conditions  

2.1.3. 
According  to  economic  theory,  there  is  a  bidirectional  association  between  human 
capital  and  occupation  held.  On  the  one  hand,  the  worker’s  skill  and  education 
levels  influence  their  probability  of  being  employed  in  a  high  or  low-skilled 
occupation.  On  the  other  hand,  the  job’s  characteristics,  in  particular  work 
experience and on-the-job training, may provide opportunities for maintaining and 
developing cognitive skills in the case of high-skilled occupations, or contribute to 
their obsolescence in the case of low-skilled occupations.  

Looking  at  the  distribution  of  workers  by  education  and  sector  of  employment 
(19)  it  is  not  surprising  to  observe  a  higher  share  of  workers  with  low  levels  of 
education  in  agricultural  activities  (10%  compared  to  3.3%  for  those  with 
intermediate to high levels of education) and industrial economic activities (29.4% 
compared  to  23.8%),  especially  for  men  and  the  youngest  adults.  Also  PIAAC 
data confirm that proficiency in literacy, numeracy and problem solving is strongly 
associated with the occupation held (OECD, 2013a). As shown in Figure 15, the 
incidence of low scores for both literacy and numeracy among workers employed in 
low-skilled occupations is very high in many Member States surveyed. On average, 
35% of workers in low-skilled occupations possess low skills in literacy and 40% 
in numeracy. Low proficiency in literacy and numeracy is also high in semi-skilled 
manual  occupations,  particularly  in  Spain,  France  and  Italy,  where  more  than 
30% of workers have low scores on cognitive skills. 

Low-skilled workers and workers employed in low-skilled occupations tend to 
experience more precarious employment relations and poorer working conditions 
than their higher skilled peers. As shown in Figure 16, workers with low levels of 
education are more likely to be self-employed than those with medium or high levels 
of  education.  Self-employment,  which  can  be  seen  as  a  measure  of  potential 
entrepreneurships, is not necessarily a good indicator when associated with low-
educated men and older workers. Adults with low educational attainment are also 
more  likely  to  be  employed  under  a  temporary  contract  (11.5%  versus  8.4%  of 

(19)  We  have  grouped  the  Nace  Rev.2  one-digit  sections  in  four  major  categories.  The 
breakdown by economic activity is based on the classification in NACE Rev.2 with the 
following  interpretations:  NACE  section  A,  agriculture;  sections  B  to  F,  industry 
(including construction); sections G to N, market services; and sections O to U, non-
market  services  (including  public  administration,  education,  healthcare,  arts  and 
entertainment  and  others).  For  details  see  Eurostat,  Ramon,  reference  and 
management  of  nomenclatura:  metadata:  Statistical  classification  of  economic 
activities in the European Community, Rev. 2 (2008): 
http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_
DTL&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HI
ERARCHIC&IntCurrentPage=1 [accessed 3.3.2017]. 

37 

                                                
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

those with intermediate and high levels of education). This is of particular concern for 
the younger segment of the adult population: 15.9% of those aged 25 to 44 years 
old with only low education hold a temporary contract compared to only 8.4% of 
those  aged  45  to  64  years.  Analysis  of  European  working  conditions  surveys 
(EWCS)  data  also  shows  that  workers  in  low-skilled  occupations  face  more  job 
instability  and  more  uncertainty  about  the  possibility  of  finding  another  job  if 
dismissed than workers in high-skilled occupations, and they experienced fewer 
change in wages and hours worked between 2009 and 2010.  

Figure 15.  Share of adult workers (25 to 65) with low skills in literacy and numeracy 

by occupational category***, 2012 

*   UK (England and Northern Ireland).  

**  Belgium (Flanders). 

***  Low skills in literacy: below level 2 (i.e. scores to less than 226 points). Highly skilled: managers; 

professionals, technicians and associate professionals (ISCO-08 groups 1-3); semi-skilled non-manual: 
clerical support workers; service and sales workers (ISCO-08 groups 4-5); semi-skilled manual: skilled 
agricultural, forestry and fishery workers; craft and related trades workers; plant and machine operators, 
and assemblers (ISCO-08 groups 6-8); low-skilled: elementary occupations (ISCO-08 group 9).  

Source:  Cedefop calculations on PIAAC (2012). 

38 

 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Semi-skilled  manual  occupations  concentrated  in  the  manufacturing  and 
construction sectors were the most negatively affected by the financial crisis, with 
a  higher  percentage  experiencing  wage  reduction  relative  to  hours  worked. 
Occupations  with  different  skills  intensity  illustrate  clear  differences  in  the 
composition of earnings: workers in low-skilled jobs are less likely to benefit from 
extra payments, fringe benefits and performance-related schemes, while workers 
in semi-skilled manual occupations receive several types of extra payment: overtime 
(48%); extra pay for working on Sundays (18%); extra compensation for dangerous 
working  conditions  (14%);  and  piece-rate  pay  or  productivity  payment  (19%). 
Consequently, low-skilled workers are the least satisfied with their pay and career 
prospects.  

Low-skilled adult participation in formal and informal training  
2.1.4. 
Adult  learning  is a key component of the European  lifelong learning policy. One of 
the Education and training 2020 (ET 2020) strategy’s key benchmarks is that by 
2020,  15%  of  adults  (25  to  64)  should  participate in lifelong learning  across the 
EU (20).  

However, empirical evidence on participation in lifelong learning in European 

countries shows that in 2014 only six Member States had reached the ET 2020 
target (21) and that people most in need of education, training and upskilling, are 
less likely to participate in learning activities. Older people participate less frequently 
than young people; the unemployed receive less training than the employed; and 
low-skilled individuals participate less frequently than their highly skilled counterparts 
(Roosma and Saar, 2012). Eurostat’s adult education survey provides evidence 
about unequal participation in learning activities and reveals strong disparities in 
the participation rates of different categories of adults in lifelong learning. 

According  to  socioeconomic  literature  various  factors  affect  participation  in 
education  and  training,  including  educational  attainment,  employment  status, 
occupational  category  and  age.  Education  level  is  widely  acknowledged  as  an 
important predictor for participation in learning activities (e.g. Boeren et al., 2010; 
Gvaramadze,  2010;  Fritsche,  2012;  Jones  et  al.,  2008)  with  participation  three 
times higher for adults with tertiary attainment compared to those with only lower-
secondary education.  

(20)  European  Commission:  Education  and  training:  Strategic  framework  –  Education 

and training 2020.   http://ec.europa.eu/education/policy/strategic-
framework/index_en.htm [accessed 3.3.2017]. 

(21)  The EU-LFS is the data source for the EU benchmark indicator on adult participation 

in lifelong learning. 

39 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure 16.  Share of low-educated adult workers by type of employment in EU-28, 2013  

Source:  Cedefop elaboration on Eurostat EU-LFS, microdata 2013 (latest year available for microdata).

40 

 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Figure  17  shows  the  difference  in  lifelong  learning  participation  rates  for 
highly  educated/highly  skilled  adults  and  poorly  educated/low-skilled  adults  in 
Member  States,  according  to  Eurostat’s  adult  education  survey  and  OECD 
PIAAC  data.  Adults  with  low  education  and/or  low  basic  skills  are  less  likely  to 
take part in education and training compared to those who have higher education 
and/or skill levels. For Member States for which PIAAC data are available, gaps 
in  participation  by  skill  are  similar  to  those  registered  by  education  level.  There 
are,  however,  wide  differences  across  Member  States  in  lifelong  learning 
participation  rates  between  low-skilled/educated  and  highly  skilled/educated 
adults.  

Figure 17.  Gap in participation rates of adults in lifelong learning by educational 

attainment (2011), literacy score (2012), and country 

NB:  

 Difference in percentage points between participation rates in formal or non-formal education and 
training in the past 12 months of adults (25-64) with tertiary education (ISCED 5-8) compared to those 
with lower than secondary education (ISCED 0-2,).  

 Difference in percentage points between participation rates in formal or non-formal education and 
training in the past 12 months of adults (25-65) with high proficiency scores in literacy (levels 4 and 5) 
compared to those with low proficiency scores in literacy (below level 1 and level 1). 

Source:  Cedefop analysis of Eurostat metadata, adult education survey (2011) and PIAAC (2012). 

The lower participation of low-qualified employees in training and education can 
be  explained  by  a  combination  of  worker  attitudes  toward  training  and  the  few 
training opportunities offered by employers to low-qualified employees (Kyndt et 
al.,  2013).  Although  adults  with  low  levels  of  educational  attainment  are  usually 
employed in occupations which do not require special skills to perform their jobs 
(Calero  and  Escardíbul,  2014),  lack  of  self-confidence  and  negative  attitudes  of 
low-qualified workers towards training and education might be a major barrier to 

41 

 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

participating in educational activities (e.g. Hillage et al., 2000; Illeris, 2006; cited 
in  Kyndt  et  al.,  2013).  According  to  Kyndt  et  al.  (2013)  there  is  evidence,  albeit 
not  conclusive  (22),  that  low-qualified  employees  differ  significantly  in  learning 
intentions by sex and age, with female workers having higher learning intentions 
than  male  workers  and  middle-aged  workers  (aged  36  to  45)  having  a  much 
higher intention to participate in educational activities than other age groups (23) 

2.2. 

Institutions and policies addressing the low-skilled  

Trends  in  low  skills  among  adults  vary  substantially  across  Member  States. 
Recently,  consensus  has  emerged  in  literature  on  the  importance  of  different 
institutional  settings  and  policies  in  explaining  these  national  differences  (e.g. 
Gesthuizen et al., 2011; OECD, 2014; Rovny, 2014; Oesch, 2010). 

Two sets of policy approaches consistently emerge from empirical literature 
aimed  at  exploring  ways  to  improve  the  labour  market  conditions  of  the  low-
skilled: 
(a)  remedial measures targeted at the current stock of low-skilled adults;  
(b)  preventive  measures  targeted  at  young  school  dropouts,  NEETs  and 

disadvantaged groups (Cedefop, 2016; OECD, 2014).  
Remedial  measures/policies  include  specific  training  and  lifelong  learning 
measures  to  improve  low-educated  workers’  skill  acquisition  and  maintenance. 
There  are  also  those  active  labour  market  policies  (ALMPs)  targeted  at  low-
skilled  adults,  especially  job  search  services  and  hiring  subsidies  aimed  at 
reducing  the  duration  of  unemployment  or  inactivity  spells  to  avoid  skill 
obsolescence.  

Preventive  policies  include  programmes  aimed  at  skills  upgrading,  career 
counselling,  information,  advice  and  guidance  (IAG),  sustained  investment  in 
education  and  childcare  (24)  and  specific  measures  targeted  at  potential  early 

(22)  Hazelzet  et  al.  (2009,  cited  in  Kyndt  et  al.,  2013),  using  a  different  sample  of  low-
qualified employees, did not find a significant correlation between any variables and 
learning intention in their sample of low-qualified employees. 

(23)  These results are, however, not confirmed by Hazelzet et al. (2009, cited in Kyndt et 
al., 2013). Hazelzet et al. used for their analysis a different sample of low-qualified 
employees. 

(24)  The European Commission (2013a) launched the social investment package (SIP), 
aiming  to  redirect  Member  states’  policies  toward  social  investments  (through 
country-specific recommendations). With the support of European Social Funds, the 
SIP  provides  guidance  to  improve  skills  formation,  development  and  use,  with 
particular attention to children and young people. (See also European Commission, 
2013b). 

42 

                                                
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

school  leavers  (e.g.  Cedefop,  2016).  Measures  supporting  work-life  balance, 
such  as  the  provision  of  affordable  care  services,  flexible  working  time 
arrangements  and  parental  leave,  as  well  as  tax  regimes  (25)  which  do  not 
discourage labour market participation among second earners in households are 
also  important  in  supporting  labour  market  participation  and  continuity  of 
employment  among 
favouring  skill  acquisition  and  preservation 
(Jaumotte, 2003).To shed light on the relationships between institutional settings 
and the incidence of adults with low levels of education and skills, we performed 
a cluster analysis (Table 3) to: 
(a) 

identify similar groups of Member States according to the policy mix adopted 
in  areas  we  expect  to  affect  the  volume  and  trends  in  volumes  of  low-
qualified adults; 

females, 

(b)  analyse  how  each  cluster  performs  in  terms  of  differences  in  employment 
rates and risk of poverty by skill level comparing the low and high-skilled. 

Box 3. 

Cluster analysis: aims and methodological approach 

The analysis is based on an original dataset of policy indicators for 27 Member States 
over the 2003-13 period (*). 
To cluster EU Member States we considered the 2003-07 pre-crisis period average 
for the following policy indicators: 
  labour market policies (LMP) expenditure, distinguishing between active and 

passive measures as a % of GDP; 

  education system in terms of (i) public expenditure in education as a % of GDP 

and (ii) adult lifelong learning measured as the participation rate of 25 to 64 year-
old people in formal and non-formal education and training in the last four weeks; 

  market regulation of both product and labour markets, including through union 
activity – as representation may be higher for low-skilled adults - and minimum 
wages; 

  taxation on second earners in households, since empirical literature has shown 
that this is a key factor in deciding whether to participate in the labour market;  

  work-life balance policies in terms of (i) public expenditure in maternity and 

paternity leave as a % of GDP, and (ii) formal childcare availability rate for children 
aged up to two years.  

We used a hierarchical cluster technique, with the method of complete linkage, also 
known as ‘farthest neighbour clustering’, which tends to find compact clusters of 
approximately equal diameters. 

(*)  The main sources of data are Eurostat and OECD. See Table A3 in Annex 2 for further details. As data 
for Croatia are not available for most of the policy indicators considered, Croatia was not included in this 
analysis. 

(25)  Especially fiscal policies for secondary earners (European Commission, 2015).  

43 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Table 3.  Policy approaches by clusters of countries, unweighted average values of the indicators for each cluster, 2003-07 and 

2012-13 

Remedial  
policy approach 

BE DE FR  
LU NL AT PT  

Liberal  
policy  
approach 

Preventive  
policy  
approach 

Regulatory policy 
approach and less 
intensive investment in 
education and training  

IE SI UK 

DK FI SE 

EL ES CY IT MT PL 

Mixed  
policy  
approach  

Unweighted 27 
Member States 
average 

BG CZ EE LV  
LT HU RO SK 

2003-07  2012-13  2003-07  2012-13  2003-07  2012-13 

2003-07 

2012-13 

2003-07  2012-13  2003-07  2012-13 

LMP Expenditure (% GDP) 

Training 
Employment incentives 
Supported employment/rehabilitation 
Direct job creation 
Start-up incentives 
Income maintenance support 

Education system 

Expenditure on education  
Lifelong learning 

Markets regulation 

Product market regulation 
Employment protection legislation 
(regular) 
 Employment protection legislation 
(temporary) 
Unions density 
Unions coverage 
Minimum wage 

Taxation on second earner 

Work life balance policies 

Parental leave (% GDP) 
Formal childcare (0-2 years) 

0.23 
0.13 
0.12 
0.15 
0.02 
1.54 

0.23 
0.12 
0.11 
0.08 
0.02 
1.43 

0.10 
0.03 
0.01 
0.09 
0.00 
0.47 

0.15 
0.04 
0.01 
0.11 
0.02 
1.26 

0.35 
0.29 
0.28 
0.04 
0.02 
1.73 

0.35 
0.40 
0.33 
0.04 
0.02 
1.27 

5.13 
8.75 

5.43 
11.63 

5.19 
15.96 

5.94 
12.08 

7.11 
22.68 

7.44 
27.87 

1.72 

1.35 

1.39 

1.45 

1.49 

1.39 

2.69 

2.55 

1.50 

1.63 

2.30 

2.32 

2.13 

2.13 

0.70 

0.89 

1.46 

1.25 

28.28 
83.56 
0.50 

25.68 
82.90 
0.52 

33.03 
58.17 
0.49 

29.19 
55.13 
0.52 

72.54 
88.66 
0.47 

68.83 
88.50 
0.49 

0.09 
0.11 
0.04 
0.02 
0.03 
0.70 

5.10 
5.63 

1.89 

2.63 

2.68 

34.91 
61.89 
0.45 

0.08 
0.15 
0.05 
0.02 
0.04 
1.09 

5.66 
6.54 

1.39 

2.24 

0.04 
0.04 
0.02 
0.06 
0.01 
0.26 

4.56 
4.45 

1.62 

2.24 

0.04 
0.05 
0.04 
0.09 
0.01 
0.33 

4.43 
5.53 

0.14 
0.11 
0.08 
0.08 
0.02 
0.88 

0.14 
0.13 
0.09 
0.07 
0.02 
0.99 

5.18 
9.13 

5.47 
10.55 

1.11 

1.67 

1.30 

2.16 

2.37 

2.24 

2.19 

1.05 

1.38 

1.70 

1.69 

31.31 
59.84 
0.47 

19.81 
34.46 
0.43 

17.04 
25.55 
0.46 

32.69 
61.94 
0.46 

29.56 
58.32 
0.49 

0.36 

0.36 

0.26 

0.25 

0.34 

0.30 

0.21 

0.20 

0.25 

0.28 

0.28 

0.28 

0.26 
27.43 

0.26 
36.43 

0.26 
27.00 

0.43 
32.17 

0.62 
48.56 

0.67 
49.17 

0.17 
17.67 

0.20 
20.42 

0.59 
9.50 

0.80 
10.88 

0.38 
22.25 

0.47 
26.24 

Source:  Cedefop analysis on Eurostat, OECD and ICTWSS data. Full details on methodological approach and data used can be found in Annex 2 Table A3. 

44 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

As shown in Figure 18, five country clusters were identified (26) by policy mix, 

consistent with those of the traditional welfare system classification: 
(a)  countries  characterised  by  a  remedial  policy  approach  (Belgium,  Germany, 
France,  Luxembourg,  the  Netherlands,  Austria  and  Portugal),  with  high 
levels  of  LMP  expenditure,  particularly  in  training,  direct  job  creation  and 
income maintenance support relative to the EU average. This cluster is also 
characterised by high levels of product and labour market regulation. Work-
life balance policies are also substantial and increasing;  

(b)  countries  characterised  by  a  liberal  policy  approach  (Ireland,  Slovenia  and 
the UK) with the lowest level for all market regulation indicators, and a high 
level  of  adult  participation  in  lifelong  learning  (16%  compared  to  9.1%  of 
European  average),  while  expenditure  on  education  is  in  line  with  the  EU 
average. LMP expenditure is lower than the EU average except for direct job 
creation  despite  growth  in  the  period  from  2012-13,  especially  for  training, 
direct job creation and income maintenance;  

(c)  countries  characterised by  a  preventive  policy  approach  (Denmark,  Finland 
and  Sweden),  with  high  support  for  education  and  work-life  balance:  all 
these  indicators  show  the  highest  values  relative  to  other  clusters. 
Expenditure on LMP, both active and passive, is also above the EU average. 
Market  regulation  indicators  are  consistent  with  the  EU  average,  while  the 
degree of union coverage and density is the highest in Europe;  

(d)  countries  characterised  by  a  regulatory  policy  approach  and  less  intensive 
investment  in  education  and  training  (Greece,  Spain,  Italy,  Cyprus,  Malta 
and  Poland),  with  the  highest  levels  of  product  market  and  employment 
regulation.  Expenditure  on  labour  market  policies,  both  active  and  passive, 
and on education and training are lower than the EU average (particularly for 
training),  as  are  use  of  work-life  balance  policies  and  adult  participation  in 
lifelong 
in  unemployment  have  pushed  up 
increases 
expenditure in income support during the recent financial crisis; 

learning.  High 

(e)  countries  characterised  by  a  mixed  policy  approach  (Bulgaria,  Czech 
Republic,  Estonia,  Hungary,  Lithuania,  Latvia,  Romania and Slovakia), with 
the  lowest  level  of  expenditure  on  ALMP,  education,  formal  childcare  and 
income support, even though this increased in the 2012-13 period due to the 
high rise in unemployment. Levels of market and employment regulation are 
consistent  with  the  EU  average,  while  union  coverage  and  density  are  the 
lowest in the EU.  

(26)  A range of solutions were processed and analysed (from 3 to 8 clusters).  

45 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure 18.  European Member States by cluster  

Available  data  do  not  show  significant  changes  in  the  clusters’  policy  mix 
between the pre-financial crisis period of 2003-07 and post-financial crisis period 
of  2012-13;  the  exception  is  the  increase  in  passive  labour  market  measures, 
especially  in  the  liberal  policy  approach  cluster,  due  to  the  sharp  increase  in 
unemployment. The policy indicators also register a significant increase in formal 
childcare expenditure in all the clusters identified. 

Results from the cluster analysis  (Figure 19) show that in 2013 the highest 
incidence of low-educated adults (35%) was found in the cluster characterised by 
a  regulatory  policy  approach  and  lower  levels  of  investment  in  education  and 
training.  Conversely,  the  lowest  rates  are  reported  in  the  preventive  policy 
approach cluster (17.2%)  and in  the  mixed  policy  approach  cluster  (12.7%) (27). 
The  volume  of  people  with  low  levels  of  education  has  been  decreasing  since 
2008  in  all  clusters  for  both  males  and  females;  however,  there  is  a  higher 
reduction  in  the  incidence  of  low-education  in  the  total  adult  female  population, 
especially  in  the  regulatory  (-7.1  percentage  points)  and  the  remedial  (-
6.8 percentage points) policy approach clusters. These clusters are, nonetheless, 
characterised by a particularly high proportion of low-educated females. 

(27)  This  is  probably  due  to  the  strong  investment  in  education  before  the  transition  to 
market  economy.  Until  1970,  expenditures  on  education  in  transition  economies 
were  much  larger  than  in  OECD  countries  and  the  rest  of  the  world;  real  public 
education  expenditure  for  students  in  secondary  school  was  increasing  until  1980 
when it started to fall and did not recover. In transition economies the rates of grade 
repetition  and  school  dropout  both  in  primary  and  secondary  education  are  small 
(Beirne and Campos, 2006).  

46 

  
 
                                                
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Figure 19.  Proportion of adults with low levels of education by cluster and 

percentage change by gender, 2008-13 (%) 

NB:  Remedial policy approach: BE, DE, FR, LU, NL, AT and PT; liberal policy approach: IE, SI and the UK ; 
preventive policy approach: DK, SE, FI; regulatory policy approach and less intensive investment in 
education and training: EL, ES, IT, CY, MT, PL; mixed policy approach: BG, CZ, EE, LV, LT, HU, RO, SK. 

Source:  Cedefop analysis of LFS data. 

Similar  trends  result  from  analysis  of  cognitive  skill  data  (Figure  20). 
Preventive  and  mixed  policy  approach  clusters  report  the  lowest  incidence  of 
adults  with  low  numeracy  and  literacy  skills.  Conversely  the  highest  share  of 
adults  with low  cognitive skills  is  reported  in the regulatory  policy  approach  and 
less  intensive  investment  in  education  and  training  cluster.  In  all  five  clusters 
there  are  more  low-skilled  in  numeracy  than  in  literacy.  This  gap  is  particularly 
high in the liberal policy approach cluster (7.7 percentage points). 

47 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure 20.  Low-skilled adults (25 to 65) among the total adult population by type of 

cognitive skill and cluster (%) 

NB:  Remedial policy approach: BE, DE, FR, LU, NL, AT and PT; liberal policy approach: IE, SI and the UK  
Preventive policy approach: DK, SE, FI; regulatory policy approach and less intensive investment in 
education and training: EL, ES, IT, CY, MT, PL; mixed policy approach: BG, CZ, EE, LV, LT, HU, RO, SK. 

Source:  Cedefop analysis of PIAAC data. 

Figure 21 presents the employment indicator associated with each cluster. 
Adults with low levels of education have lower employment rates in all clusters 
compared to those who are highly educated but there are differences across 
clusters.  

Countries adopting a preventive policy approach generally perform better than 
the  others:  employment  rates  are  high  across  all  qualifications.  In  contrast,  the 
lowest employment rate for adults with low levels of education are reported in the 
mixed policy approach cluster (the gap with tertiary educated adults stands at over 
38 percentage points) and in countries adopting a regulatory policy approach and 
less  intensive  investment  in  education  and  training  (employment  gap  between 
adults with low levels of education and those who are highly educated of around 
30 percentage points).  

Education affects employment rates more strongly for females than males in all 
clusters. The employment gap between males with high and low levels of education 
is under 20 percentage points in all clusters except for countries in transition; for 
females, employment gaps between those with low and high levels of education are 
over 30 percentage points in all clusters, except in the preventive policy approach 
cluster. Skill gaps are particularly marked in the  regulatory and the mixed policy 
approach cluster. 

During the recession, employment for people with low qualifications dropped 
in all but the mixed policy approach cluster. However, in the regulatory approach 

48 

 
 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

and  less  investment  in  education  and  training  cluster  the  highly  educated  have 
also  been  considerably  affected.  Further,  during  the  crisis  no  significant  gender 
differences  in  employment  rates  were  observed;  one  exception  was  the 
regulatory  and  mixed  policy  approach  clusters,  where  the  employment  rate  of 
highly educated females declined more than that of the low-educated ones.  

Figure 21.  Employment rates for adults with low and high levels of education and 

change in percentage points by cluster, 2008-14 

NB:  Remedial policy approach: BE, DE, FR, LU, NL, AT and PT; liberal policy approach: IE, SI and the UK  
Preventive policy approach: DK, SE, FI; regulatory policy approach and less intensive investment in 
education and training: EL, ES, IT, CY, MT, PL; mixed policy approach: BG, CZ, EE, LV, LT, HU, RO, SK. 

Source:  Cedefop analysis of LFS data. 

The adverse labour market conditions have also affected the risk of poverty, 
with  a  growing  share  of  adults  at  risk  of  poverty  compared  to  levels  before  the 
financial crisis, across all educational attainment levels (Figure 22). While policy 
mix does not affect the risk of poverty for the highly educated (around 10% in all 

49 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

clusters), the poverty risk for adults with low levels of education is particularly high in 
countries  with  a  liberal  or  mixed  policy  approach,  and  comparatively  low  in  the 
preventive policy approach cluster. In all clusters the risk of poverty increased more 
for people with low levels of education than the highly educated, with a worsening 
of the gap by education level more evident in the preventive, and regulatory, and 
mixed  policy  approach  clusters.  Also,  in  the  liberal  policy  approach  cluster  the 
change  in  the  risk  of  poverty  increased  considerably  for  both  the  low-  and  the 
highly educated.  

Figure 22.  Risk of poverty for adults with low and high levels of education and 

change in percentage points by cluster, 2008-14 

NB:  Remedial policy approach: BE, DE, FR, LU, NL, AT and PT; liberal policy approach: IE, SI and the UK; 
preventive policy approach: DK, SE, FI; regulatory policy approach and less intensive investment in 
education and training: EL, ES, IT, CY, MT, PL; mixed policy approach: BG, CZ, EE, LV, LT, HU, RO, SK. 

Source:  Cedefop analysis of EUSILC data. 

50 

 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

Cluster  analysis  may  suggest  that  the  preventive  policy  mix  helps  not  only 
preventing  a  high  share  of  low-skilled  adults,  but  also  supporting  labour  market 
participation and living conditions of low-skilled adults. However, while this cluster 
reports  the  lowest  share  of  those  low-educated  at  risk  of  poverty  (30%),  the 
dramatic increase observed during the crisis (+9%), is of particular concern. 

Countries  in  the  regulatory  and  mixed  policy  approach  clusters  display 
similar  negative  patterns  in  the  labour  market  and  living  conditions  of  the  low-
skilled  adult  population,  although  the  incidence  of  the  low-skilled  population 
across these clusters is very different. In both clusters, which display low levels of 
expenditure in ALMP, the low-skilled population represent a vulnerable segment 
of the adult population: on the one hand, skills gaps in employment rates are high 
and  employment  rates  for  adults  with  low  qualifications  are  the  lowest;  on  the 
other  hand,  low-skilled  adults  are  also  at  a  high  risk  of  poverty  with  increasing 
rates since the onset of the crisis.  

Analysis also suggests that high levels of LMP expenditure observed in the 
remedial  policy  approach  cluster  may  counteract  the  negative  effects  of  being 
low-skilled.  Despite a higher  than EU  average  incidence  of  low-educated  adults 
(26.5%),  this  cluster  displays  the  second  highest  employment  rate  for  low- 
educated adults and the lowest poverty rate (33.8%). These rates have increased 
relatively  little  with  the  crisis  (+3%).  Countries  in  the  liberal  policy  approach 
cluster,  with  their  high  level  of  adult  participation  in  lifelong  learning  but  lower 
than EU average LMP expenditure, generally display low rates of adults with low 
qualifications,  but  a  substantial  share of  adults  who  are  low-skilled  in  numeracy 
(25.1%). Since the crisis  started, the share of those at risk of poverty increased 
sharply  not  only  among  the  low-educated  but  also  among  those  with  higher 
qualifications. 

2.3.  Future demand and supply scenarios for low-

skilled adults  

This  section  uses  data  provided  by  Cedefop’s  labour  force  and  skills  forecast 
2015 (28).  Baseline  projections  are  provided  using  current  economic  and 
in  macroeconomic 
demographic 
circumstances. The  underlying  assumption (29)  in the  baseline forecast model  is 

trends  along  with  projected  changes 

(28)  Year 2015 values represent forecasts (i.e. not historical values) for consistency and 

comparability reasons with the 2020 and 2025 values. 

(29)  Model  assumptions  are  based  on  DG  ECFIN’s  GDP  growth  projections  from 

November 2014. 

51 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

for the EU-28 to achieve, on average, a modest economic recovery following the 
recession of recent years; average GDP growth will be about 2% a year between 
2015  and  2025,  although  with  significant  variation  between  individual  countries 
(Cedefop, 2015).  

Table 4.  Population by qualification level, aged 25 to 64, EU-28, 2015-25  

Level of 
qualifications  

High 
Medium 
Low 

EU-28 total 

2015 

Population 
(million) 

% of the  
EU-28 

Population 
(million) 

84.9 
129.8 
61.7 

276.4 

30.7 
47.0 
22.3 

100.0 

100.1 
128.6 
39.5 

268.2 

2025 

(million) 

100.1 
128.6 
39.5 

268.2 

% of the 
EU-28 

37.3 
48.0 
14.7 

100.0 

Source: Cedefop, skills forecasts, 2015 database and own elaborations. 

Low-skilled adults  

2.3.1. 
The skills profile of the EU-28 population aged 25 to 64 is anticipated to upgrade 
by 2025 (Table 4). The Cedefop forecast shows that during this period there will 
be a significant reduction in the share of the EU-28 total population aged 25 to 64 
holding  low  qualifications  to  14.7%,  while  the  share  of  adults  with  high-level 
qualifications  will  further  increase  to  reach  37.3%  of  the  total,  and  adults  with 
medium-level qualifications will broadly remain stable (Figure 23). 

Figure 23.  Projections of population aged 25 to 64 by qualification level, 2015-25 

Source: Cedefop, skills forecasts, 2015 database and own elaborations. 

52 

 
 
 
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

The  number  of  adults  aged  25  to  64  with  low  qualifications,  is  projected  to 
fall  by  about  22  million  (-36%)  between  2015  and  2025,  while  the  total  adult 
population is projected to decrease only slightly (-3%) (Table 5). 

Table 5.  Change in population aged 25 to 64, EU-28, 2015-25 

Level of 
qualifications 

High 

Medium 

Low 

EU-28 total 

2015 

2025 

Change  

Population (million) 

84.9 

129.8 

61.7 

276.4 

100.1 

128.6 

39.5 

268.2 

15.2 

-1.2 

-22.2 

-8.3 

% 

17.9 

-0.9 

-36.0 

-3.0 

Source: Cedefop, Skills forecasts, 2015 database and own elaborations. 

At country level, while the shares of low-skilled adults are forecast to reduce 
in  all  Member  States,  changes  in  the  expected  proportion  of  low-skilled  adults 
tend to confirm current disparities (Figure 24). 

Figure 24.  Share of low-skilled adults aged 25 to 64 by country, 2015-25 (%)  

Source: Cedefop, skills forecasts, 2015 database and own elaborations. 

When  considering  the  adult  population,  the  older  adult  age group  45  to  64 
accounts for the greatest  proportion of low-qualified. Looking ahead, the current 
age profile of the low-skilled is expected to change only slightly over the next 10 
years.  

53 

 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Low-skilled adults as part of the EU-28 labour force  

2.3.2. 
In  line  with  the  trends  projected  for  the  total  adult  population,  the  number  of 
adults  with  low  qualifications  in  the  labour  force  (30)  will  decrease  from  39.1 
million in 2015 (18.1% of the total) to 26.2 million in 2025 (12.2%). The share of 
adults  with  medium-level  qualifications  in  the  labour  force  is  forecast  to  remain 
broadly stable, while highly qualified will reach 40.5% of the total (Table 6).  

Table 6.  Labour force aged 25 to 64, EU-28, 2015-25 

Level of 
qualifications 

2015 

Labour force 
(million) 

High 

Medium 

Low 

All levels/EU-28 

74.0 

102.4 

39.1 

21.4 

2020 

Labour force 
(million) 

81.08 

102.87 

32.73 

% 

34.3 

47.5 

18.1 

% 

37.4 

47.5 

15.1 

100.0 

216.67 

100.0 

2025 

Labour force 
(million) 

86.8 

101.5 

26.2 

214.5 

% 

40.5 

47.3 

12.2 

100.0 

Source: Cedefop, skills forecasts, 2015 database and own elaborations. 

Low-qualified adults in the labour force are projected to fall by 33% between 
2015 and 2025, despite a total adult labour force basically stable over the same 
period (Table 7). Since the share of adults in the labour force with medium-level 
qualifications  will  decrease  only  marginally,  a  substantial  shift  of  the  active 
population towards high qualifications (+17.4) is expected (Table 7).  

Table 7.  Change in labour force aged 25 to 64, EU-28, 2015-25 

Labour force (million) 

Level of qualifications 

High 

Medium 

Low 

All levels/EU-28 

2015 

74.0  

102.4  

39.1  

215.4  

2025 

86.8 

101.5 

26.2 

214.5 

Source: Cedefop, Skills forecasts, 2015 database and own elaborations. 

% 

Change 

17.4 

-0.8 

-33.0 

-0.4 

(30)  The labour force represents people in the population 25 to 64 who are economically 
active,  i.e.  employed  and  actively  seeking  jobs.  People  who  are  not  considered  as 
labour force are those voluntary unemployed (not seeking a job and, even if offered, 
likely  to  refuse  it),  disabled,  retired  or  on  parental  leave  etc.  The  calculations  are 
based  on  the  demographic  forecasts  and  assumptions  on  the  future  development 
participation (activity) rates by different age groups, genders and countries. 

54 

 
 
 
                                                
Chapter 2. 
Understanding low skills: trends in low-skilled adults in the EU 

In  line  with  the  predicted  decline  in  low-skilled  adults  as  a  share  of  the 
population, the share of the labour force aged 25 to 64 with low qualifications is 
also  projected  to  reduce  considerably  by  2025  across  the  EU  Member 
States(Table 8).  

Table 8.  Proportion of the labour force aged 25 to 64 with low qualifications, EU 

Member States  

Member States 

2015 (%) 

2025 (%) 

Belgium 

Bulgaria 

Czech Republic 

Denmark 

Germany 

Estonia 

Ireland 

Greece 

Spain 

France 

Croatia 

Italy 

Cyprus 

Latvia 

Lithuania 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Romania 

Slovenia 

Slovakia 

Finland 

Sweden 

UK 

EU-28 

17.9 

12.1 

4.0 

14.7 

9.7 

5.9 

15.1 

26.5 

35.6 

17.0 

10.8 

32.2 

16.8 

6.5 

n/a 

14.0 

11.1 

45.2 

17.7 

11.2 

5.6 

50.9 

15.4 

9.2 

4.8 

8.3 

12.1 

16.9 

18.1 

11.5 

7.4 

2.1 

14.7 

7.6 

4.4 

8.6 

17.8 

21.6 

11.2 

6.2 

22.6 

9.5 

4.6 

n/a 

5.8 

7.2 

29.3 

12.7 

7.0 

3.2 

41.7 

13.3 

5.3 

2.7 

3.7 

8.4 

9.8 

12.2 

Source:  Cedefop, skills forecasts, 2015 database and own elaborations. 

55 

Chapter 3.
Who are the low-skilled? Characteristics, 
determinants and risks among EU adults  

While  future  trends  in  low  skills  suggest  that  shares  of  low-skilled  adults  will 
continue  to  decrease,  current  trends  also  indicate  how  low-skilled  people  are 
particularly  disadvantaged  and  vulnerable  on  the  labour  market.  Against  this 
scenario,  effective  policy  interventions  tackling  low  skills,  require  a  clear 
understanding  of  who  are  the  low-skilled  and  what  are  the  risk  factors  of 
becoming low-skilled.  

3.1.  Characteristics of low-skilled adults: cognitive 

skills and other factors 

The purpose of this section is to investigate, using PIAAC data, the relationship (31) 
of cognitive skills and factors (both skills-related factors, such as formal education 
and training, and personal characteristics, such as family background and use of 
skills) which may influence the development of cognitive skills.  

Table  9  presents the results  of  a  pooled regression  analysis  estimating the 
relationship  between  numeracy  and  literacy  proficiency  scores  among  adults 
aged 25 to 65 in the 16 EU Member States surveyed by the PIAAC (32) and a set 
of  variables  identified  by  previous  studies  as  affecting  skill  acquisition:  personal 
characteristics,  parental  background,  educational  and  training  attainment,  work 
experience and spells of unemployment and inactivity (see Annex 3 for details). 

Although  from  this  analysis  it  is  not  possible  to  infer  causation  precisely 
(Box 4),  we  provide  evidence  on  the  existence  of  statistically  significant 
correlations which highlight the existence of association or relationship between 
cognitive skills and the other variables analysed. 

(31)  The  main  purpose  of  this  analysis  is  to  investigate  correlations  between  cognitive 
skills and formal education or training: analysis of causality is beyond the scope of 
the present study.  

(32)  PIAAC data for Cyprus are not included in this analysis.  

56 

 
   
 
 
 
                                                
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

Table 9.  Pooled OLS regression on literacy and numeracy scores: coefficient 

estimates 

Demographic background 

Age 35-44 
Age 45-54 
Age 55-65 
Female  
Foreign-born and foreign-language 

Parental background 

Both parents foreign-born 
One parent foreign-born 
Neither parent has attained upper secondary education  
At least one parent has attained secondary and post-
secondary, non-tertiary education 

Education and training  

Literacy 

Numeracy 

-1.34*** 
-6.31*** 
-11.42*** 
-2.08*** 
-24.31*** 

-13.26*** 
-2.60*** 
-10.39*** 

0.54 
-1.91*** 
-6.14*** 
-11.19*** 
-24.80*** 

-12.83*** 
-2.96*** 
-10.27*** 

-6.40*** 

-6.44*** 

Low education 
Medium education 
No formal or non-formal education and training in the previous 
12 months 

-33.28*** 
-17.79*** 

-36.73*** 
-19.26*** 

-4.35*** 

-3.46*** 

Computer skills 

No computer experience 

Work experience and unemployment 

0.18 

-8.77*** 

Never had paid work including self-employment in past 
Unemployed or inactive since maximum 12 months 
Unemployed or inactive since at least 12 months but less than 
five years  
Unemployed or inactive since at least five years 

-14.21*** 
-5.00*** 

-20.28*** 
-7.07*** 

-6.46*** 

-10.29*** 

-6.37*** 

-11.62*** 

* p<0.1 **p<0.05 ***p<0.01 

NB: Least squares regressions analysis controlled also for frequency of use of information processing skills at 
home (reading, writing and numeracy); number of books at home; perceived general health; country 
fixed effects. Dependent variables: literacy score and numeracy score.  

Sample: Population aged 25-65, excluding retired people, students, permanently disabled people and 

individuals in compulsory military or community service. The (omitted) reference categories are: aged 25-
34; male, good health; male; native born and native language; neither parents are foreign born; both 
parents having attained at least secondary education; have more than 25 books at home; high level of 
education attained; having participated into formal or non-formal education or training in the previous 
12 months; having computer experience; using frequently writing, reading, and numeracy skills at home 
(i.e. belonging to the highest quintile of the index of frequency of use of information processing skills at 
home); in employment; living in Austria.  

Source:  Our calculation on OECD survey of adult skills (PIAAC) 2012. 

As  summarised  in  Figure  25,  analysis  shows  a  strong  positive  relationship 
between the level of formal education and the level of proficiency in literacy and 
numeracy:  on  average,  the  proficiency  score  gap  between  individuals  with  high 
education and those with low education is 37 points in numeracy and 33 points in 
literacy. This is consistent with results found in literature (e.g. Green and Riddell, 
2015; Leuven et al., 2010; Banks and Mazzonna, 2012; Carlsson et al., 2012).  

57 

  
  
  
  
  
  
  
  
  
  
  
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Box 4. 

Endogeneity and reverse causality among skills factors 

The cross-sectional nature of the PIAAC survey makes it difficult to infer causality due 
to the fact that both schooling and training (as well as computer experience and use of 
skills in everyday life) are related to cognitive skills and so are potentially endogenous 
variables.  Unobservable ability may  also  affect  educational  and training choices as  well 
as cognitive skills levels. The omitted factors are likely to end up in the error term of the 
ordinary least squares (OLS) regression and likely to be correlated with the variables of 
interest (such as schooling, training, use of skills at home). As a result, an endogeneity 
problem is likely to bias the estimated effects of education and training on the level of 
cognitive skills.  
To  address  such  concerns  would  require  a  longitudinal  dataset  or  a  dataset  where 
ability  is  known  and  predetermined  with  respect  to  schooling  and  other  skill  factors. 
Another  way  to  handle  endogeneity  is  to  use  an  IV  estimator  (rather  than  OLS),  as 
some authors have done when estimating returns on education and skills. For example, 
Brunello et al. (2009); Hanusek et al. (2013); Cappellari et al. (2015); and Green and 
Riddell (2015) use compulsory school reforms or time and space variation in compulsory 
schooling laws to instrument years of schooling. Cappellari et al. (2015, abstract) find 
that ‘most of the endogeneity of skills appears to reflect the endogeneity in education, 
suggesting  that  it  is  the  same  set  of  unobservables  that  favours  human  capital 
accumulation in both dimensions’.  

Cognitive skills are also associated with work experience and labour market 
status. People who have never worked show lower levels of cognitive skills: other 
things being equal, their numeracy score is, on average, 20 points lower than their 
employed counterparts; data also show that longer unemployment spells are 
associated with lower levels of cognitive skills. Even controlling for age and level of 
formal education, people unemployed or inactive for at least five years have 
average numeracy scores 11.6 points lower than those in employment. The 
numeracy score of those unemployed or inactive for less than 12 months is, on 
average, only seven points lower the employed. This analysis shows that 
unemployment and career interruptions may cause technical skills obsolescence 
and confirms the results of a meta-analysis from psychological literature on skill 
decay and retention (Arthur et al., 1998 cited in OECD, 2011).  

Analysis  also  shows  a  strong  relationship  between  low  cognitive  skills  and 
disadvantaged  background.  There  is  a  particularly  strong  relationship  between 
proficiency in information processing skills and parental background, especially in 
migrant  background,  although  the  strength  of  the  association  varies  widely 
across and within countries between different age groups (33). For foreign-born  

(33)  For  example,  OECD  (2013a)  shows  that,  even  when  educational  attainment  and 
socioeconomic and immigrant background are accounted for, age continues to have 
a  strong  relationship  to  proficiency.  There  are,  however,  some  country  differences, 

58 

 
                                                
 
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

Figure 25.  Characteristics of adults with low cognitive skills 

Lower educational 
attainment 

Disadvantaged 
background 

Source:  Cedefop. 

Poor labour 
market status 

people  and  non-native  language  speakers  the  24  point  reduction  in  average 
proficiency  score  in  numeracy  and  literacy  is  higher  than  that  between  highly 
educated  and medium-educated  of  18  and  19  points for  literacy  and numeracy, 
respectively (34). These results are consistent with findings of a study based on the 
IAL  survey  (35)  which  found  that,  together  with  age  and  occupation,  speaking  a 
first  language  other  than  the  one  used  for  testing  is  a  major  determinant  of 
performance in literacy (OECD and Statistics Canada, 2000). The education level of 
parents is also associated with cognitive skills proficiency: individuals whose parents 
have not obtained an upper secondary education degree score 10 fewer points, on 
average, in numeracy than those with at least one parent with upper secondary 
education. Parental background plays an important role in producing both cognitive 
and non-cognitive skills (Heckman et al., 2006). Several studies have shown that 
even before pupils start school, there is a large gap in cognitive ability between 
children from high and low socioeconomic backgrounds. One British study found that 

even  though  on  average  across  countries,  the  association  between  the  parents’ 
educational  attainment  and  cognitive  skills  proficiency  is  stronger  for  the  adult 
population  as  a  whole  (16  to  65)  than  for  young  people  (15  to  24);  in  the  Czech 
Republic,  Denmark,  Estonia,  Slovakia  and  the  UK  (England/Northern  Ireland),  the 
relationship  is  stronger  among  young  people  than  among  the  overall  adult 
population (OECD, 2013a). 

(34)  The  OECD  2013a  report  shows  that  the  negative  relationship  between  skills  and 
foreign-language  background  is  stronger  than  that  between  skills  and  foreign-born 
background;  second-generation  immigrants  or  persons  belonging  to  a  language 
minority  score  higher  than  foreign-language  immigrants,  and  closer  to  the  average 
score of native‑born adults. 

(35) 

International adult literacy survey, implemented over the period 1994-98. 

59 

 
 
                                                                                                                                 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

nearly  a  fifth  of  the  gap  in  test  scores  between  the  richest  and  poorest  children  is 
explained  by  an  apparent  ‘direct’  link  between  the  childhood  cognitive  ability  of 
parents  and  that  of  their  children  (Gregg  and  Goodman,  2010  cited  in  Blanden 
and McNally, 2014). 

The relationship between cognitive skills and other skills factor variables, such 
as  education  and training,  use  of  skills  at  home,  is  mostly  bidirectional  (reverse 
causality)  and  mutually  reinforcing.  For  example, 
the  relationship  between 
proficiency in information-processing skills and participation  in  initial and continuing 
education and training, as well as engagement in activities such as reading and 
writing, use of numeracy and the use of ICTs is a two-way relationship. 

3.2.  Determinants of low skills  

To  identify  what  determines  low-skilled  status  among  adults,  we  performed  a 
variance decomposition analysis (36) which allows us to assess to what extent the 
variables considered are able to explain the differences (37) in the cognitive skills 
scores observed among the individuals in the sample.  

Results,  summarised  in  Figure  26,  show  how  the  level  of  formal  education 
attained  and  frequency  of  use  of  information  processing  skills  in  everyday  life 
(reading, writing, numeracy) explain most of the observed difference in cognitive 
skills.  Both  factors  present  a  strict  relationship  with  cognitive  skills  which  is 
bidirectional and mutually reinforcing (Box 4).  

Personal characteristics (gender, age, migrant status and language spoken) 
and, especially, family background (migrant status, parental education level) are 
also  responsible  for  a  large  portion  of  the  difference  in  cognitive  skills  scores. 
These  factors  show  a  larger  contribution  in  explaining  the  variance  of  literacy 
than numeracy.  

Lack  of  work  experience or  long  periods  of  unemployment  or  inactivity  and 
having participated in training also contribute towards explaining the variance of 
cognitive skill levels. As accumulation of human capital does not end with school, 
training is a way to adapt and enhance existing skills. This is especially relevant 
for older workers, whose skills accumulated at school are likely to be substantially 
depreciated,  and  for  the  less-educated,  who  run  the  risk  of  social  (and  labour 
market)  exclusion  (Bassanini  et  al.,  2005).  The  low  contribution  of  training 

(36)  Similar to OECD (2014), we used the fields regression-based decomposition technique, 
which  performs  an  exact  decomposition  of  the  outcome  variable  variance  into  the 
variance attributable to each explanatory variable and the residual. 

(37)  Observed variance, R2 which reflects how much of the variance (i.e. the difference) 

observed in the cognitive skill scores can be explained by the variables of the model. 

60 

                                                
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

observed in the analysis can be partly explained by the fact that the variable used 
for measuring training experience refers only to the 12 months before the survey 
and  does  not  measure  the  total  amount  of  training  accumulated  during  the  life 
course. Also, measuring the cognitive skills effects on participation (as opposed 
to  non-participation)  in  training  could  underestimate  the  real  effect  of  training, 
since  it  does  not  consider  duration,  content  and  other  qualitative  aspects  of 
training.  At  the  same  time,  the  endogeneity  problem  discussed  above  (Box  4) 
may bias estimates of the effects of training on cognitive skills. According to our 
results,  institutional  factors  (country  fixed  effects)  explain  a  small  part  of  the 
observed variance, with no major difference across age groups (Figure 27). 

Figure 26.  Determinants of the variation in numeracy and literacy proficiency 

scores  

NB:  Total variance explained in parenthesis. 

Proportion of the explained variance (R2) in literacy and numeracy explained by each factor (rescaled to 
100). Results obtained using Fields (2004) regression-based decomposition technique of Equation 1 
estimates (Annex 3).  

  Where: Education: highest level of formal education attained; Training: having undergone formal or non-
formal education or training during the 12 months preceding the survey; Use of skills at home: frequency 
of use of numeracy, reading and writing skills in every-day life; Work-experience: not having had work 
experience or experiencing short or long periods of no employment. Personal characteristics: gender, age, 
perceived health, immigrant and language status. Parent and family background: level of educational 
attainment of parents, immigrant background, number of books at home; Country: fixed effects. 

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

The results by age group show that the contribution of parental background and 
of personal characteristics to the observed difference in cognitive skills tends to 
decrease with age, while the contribution of the set of variables representing the 
frequency of use of skills at home increases with age. These results are consistent 

61 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

with  other  findings  from  empirical  literature  analysing  survey  results  on  literacy 
proficiency  scores (38).  For  example,  Desjardins  (2003),  in  analysing  the 
determinants  of  literacy  using  IALS  data,  finds  that  education  remains the  most 
important  predictor  of  literacy  proficiency  after  accounting  for  all  other  factors, 
and that home background measured by parents’ education level is also a strong 
predictor of literacy proficiency. 

Figure 27.  Determinants of the variation in numeracy scores by age group 

NB:  Proportion of the explained variance (R2) in literacy and numeracy explained by each factor (rescaled to 
100). Results obtained using Fields (2004) regression-based decomposition technique of Equation 1 
estimates (Annex 3). 

  Where: Education: highest level of formal education attained; Training: having undergone formal or non-
formal education or training during the 12 months preceding the survey; Use of skills at home: frequency 
of use of numeracy, reading and writing skills in every-day life; Work-experience: not having had work 
experience or experiencing short or long periods of no employment. Personal characteristics: gender, age, 
perceived health, immigrant and language status. Parent and family background: level of educational 
attainment of parents, immigrant background, number of books at home; Country: fixed effects. 

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

Alongside  these  personal  and  human  capital  variables,  job  characteristics 

may play a role in determining low cognitive skills among (employed) adults.  

Results from an OLS regression on employed adults, presented in Table 10 
(39) confirm a strong relationship between the level of cognitive skills and type of 

(38)  OECD  and  Statistics  Canada,  2000;  Statistics  Canada,  2005;  OECD,  2013;  2014; 

Desjardins, 2003.  

(39)  Restricting the sample to employed workers could result in sample selection bias as 
low-skilled  workers  are  more  likely  to  be  unemployed.  To  address  the  potential 
selection  into  employment,  we  modelled  jointly  both  the  selection  into  employment 
and the cognitive equation using a Heckman two-stage regression analysis. Results 
showed  no  statistically  significant  effects  of  selection  into  employment  so  the 
analysis reports the simple OLS estimates. 

62 

 
 
                                                
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

occupation. As shown in Figure 28, workers employed in elementary occupations 
score  20  fewer  points  on  average  in  cognitive  skills  than  managers.  There  is  a 
significant  gap  in  cognitive  skill  proficiency  between  workers  in  high-skilled 
occupations (ISCO 1-3) or clerical support workers (ISCO 4) and those in semi-
skilled (ISCO 5-8) and unskilled occupations (ISCO 9). 

Table 10.  Pooled OLS regression on literacy and numeracy scores on employees: 

selected coefficient estimates (*) 

Total work experience (years) 

Experience 

Job-related characteristics 
Firm 

  public sector 
  micro (1-10 employees) 
  small (11-50) 
  medium (51-250) 

Contract type 

  fixed term job 
  part time job 

Occupations (ISCO 08) 

  professionals 
  technicians and associate professionals 
  clerical support workers 
  service and sales workers  
  craft and related trades workers 
  plant and machine operators and assemblers 
  elementary occupations 

Education and training  

Low education 
Intermediate education 
Non-job-related formal or non-formal education and training in 
the previous 12 months 
Job-related formal or non-formal education and training in the 
previous 12 months 

* p<0.1 **p<0.05 ***p<0.01 

Literacy 

Numeracy 

0.13 

0.31*** 

-3.00*** 
-5.17*** 
-4.55*** 
-1.97*** 

-1.87*** 
-1.72*** 

0.76 
-4.20*** 
-3.93*** 
-12.70*** 
-15.96*** 
-16.84*** 
-19.25*** 

-24.49*** 
-11.68*** 

-5.71*** 
-5.76*** 
-4.92*** 
-3.03*** 

-2.95*** 
-3.10*** 

0.35 
-5.11*** 
-5.82*** 
-16.48*** 
-16.51*** 
-19.54*** 
-22.47*** 

-27.69*** 
-13.35*** 

5.01*** 

4.19*** 

2.11*** 

2.00*** 

NB:  Least squares regressions controlling also for frequency of use of reading, writing and numeracy skills at 
home, a squared term for work experience; number of books at home; perceived general health; country 
fixed effects. Dependent variables: literacy score and numeracy score.  

Sample: population aged 25-65, excluding retired people, students, permanently disabled people and 
individuals in compulsory military or community service. The (omitted) reference categories are: those 
aged 25 to 34; male, good health; male; native born and native language; neither parents are foreign 
born; both parents having attained at least secondary education; having more than 25 books at home; 
high level of education attained; having participated into formal or non-formal education or training in the 
previous 12 months; having computer experience; being in highest quintile of the index of frequency of 
use of writing, reading, and numeracy skills at home; in employment; living in Austria.  

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

63 

 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

These  results  are  consistent  with  literature  which  shows  a  link  between 
occupations  requiring  the  performance  of  complex  tasks  and  levels  of  cognitive 
skills, even after controlling for education (Desjardins and Warnke, 2012). There 
are  also  indications  that  job-complexity  has  an  effect  on  skills  growth  rate. 
Analysis of production functions for adults’ verbal and non-verbal cognitive skills 
on  a  longitudinal  dataset  found  that  post-school  tenure  in  skilled  jobs  has 
significant positive effects on both types of cognitive skill (Behrman et al., 2014).  

Figure 28.  Relationship between the level of literacy and numeracy proficiency and 

occupation held 

NB:  OLS regression of numeracy proficiency score on employees aged 25 to 65 with one job only; excludes 

workers employed in non-profit organisations, in the armed forces and skilled agricultural, forestry and 
fishery workers.  

Control variables: job-related characteristics (firm size, sector – public, private –, occupation at 1-digit of 
ISCO-08, contract type, a dummy for full-time work, experience and its square), demographic 
characteristics (gender, age, perceived health, migrant status), parental background (parents’ highest 
level of education attained and migrant background), use of skills at home, country fixed effects.  

ISCO 1: Managers (omitted category); ISCO 2: Professionals, ISCO 3: Technicians and associate 
professionals; ISCO 4: Clerical support workers; ISCO 5: Service and sales workers; ISCO 7: Craft and 
related trades workers; ISCO 8: Plant and machine operators, and assemblers; ISCO 9: Elementary 
occupations.  

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

The  positive relationship between  work  experience  and proficiency  in numeracy 
skills also emerges for employed adults (40). Figure 29 shows the increase in the 
level of numeracy skills for different age groups, controlling for personal and job-
related characteristics. The effect of work experience on cognitive skills is more 

(40)  Similar results are found for literacy. 

64 

 
 
 
 
                                                
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

pronounced for older workers, probably because of a self-selection effect in that 
individuals  near  retirement  age  who  continue  working  are  predominantly  those 
working in high-skilled occupations and with higher cognitive skills than average 
workers of the same age group.  

Figure 29.  Relationship between years of work experience and numeracy score 

(*) Years of paid work during lifetime. Calculation based on coefficient estimate of a OLS regression of 
numeracy proficiency score on a sample of employees aged 25-65, with controls for: job-related 
characteristics (firm size, sector – public, private –, occupation at 1-digit of ISCO-08, contract type, a 
dummy for full-time work, experience and its square), demographic characteristics (gender, perceived 
health, migrant status), parental background (parents’ highest level of education attained and migrant 
background), use of skills at home, country fixed effects.  

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

Another  result  is  related  to  the  effect  of  training:  having  attended  non-job-
related  training  has  a  higher  positive  impact  on  cognitive  skills  than  having 
attended job-related training. This result could be explained by the fact that, other 
things  equal,  workers  who  freely  decide  to  engage  in  training  have  higher 
cognitive  skills  on  average.  Heckman  (1999,  cited  in  de  Grip  and  Zwick,  2004) 
found  that  ability  fosters  further  learning.  Analysis  of  data  from  a  Norwegian 
survey on adults in formal continuing education found that even when controlling 
for factors  that may  cause  differences in motivation,  low-skilled  adults  attending 
primary or lower secondary programmes were more likely to state that they were 
required 
to  adults  attending  education 
programmes of higher levels (Daehlen and Ure, 2009). 

training,  compared 

to  undertake 

65 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

3.3.  The risk of being low-skilled  

In  line  with  the  analysis  of  the  characteristics  and  determinants  of  low  skills,  a 
probit regression analysis (41) on numeracy skills (42) shows that the probability of 
being low-skilled is strongly related to the level of education attained (Figure 30): 
on  average,  low-educated  adults  are  three  times  more  likely  to  be  low-skilled 
(27%) than those who are highly educated (9%). 

Figure 30.  Predicted probabilities of being low-skilled in numeracy by education 

level, migrant status of parents and highest level of education attained 
by parents 

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

(41)  We  used  average  adjusted  predictions  and  adjusted  predictions  at  representative 
values. For more details see Annex 3. In the sample analysed, the incidence of low-
skilled people in numeracy is 17%. 

(42)  This part of the analysis concentrates and presents results only on numeracy skills, 
since  literacy  and  numeracy  proficiency  scores  are  highly  correlated  (coefficient  is 
0.86) and produce similar results. 

66 

 
 
                                                
Chapter 3. 
Who are the low-skilled? Characteristics, determinants and risks among EU adults 

The probability of having low levels of cognitive skills is also closely related 
to parental background: having a migrant background seems to play an important 
role in determining the probability of having low proficiency in cognitive skills. An 
average adult with low educational attainment and foreign-born parents with low 
qualifications  has  a  40%  probability  of  being  low-skilled  in  numeracy;  this 
probability declines to 22% for an average adult with low educational attainment 
and  two  native  parents,  if  at  least  one  completed  tertiary  education.  Similar 
differentials,  but  to  a  lesser  extent,  are  evident  among  adults  having  attained 
upper  secondary  education  (-14  percentage  points)  and  those  having  attained 
tertiary education (-9 percentage points).  

Figure 31.  Predicted probabilities of being low-skilled by work experience and 

highest education attained (%) 

NB:  Adjusted predictions at representative values for estimates of a probit regression where the dependent 

variable is the probability of being low-skilled scoring at or below proficiency level 1 (scores up to 226 
points).  

Sample: Population aged 25-65, excluding retired people, students, permanently disabled people and 
individuals in compulsory military or community service. The omitted categories are: aged 25-34; male, 
good health; male; native born and native language; neither parents are foreign born; both parents 
having attained at least secondary education; having more than 25 books at home; high level of 
education attained; having participated into formal or non-formal education or training in the previous 
12 months; having computer experience; using frequently writing, reading, and numeracy skills at home 
(i.e. belonging to the highest quintile of the index of frequency of use of information processing skills at 
home); in employment; living in Austria. 

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

Results  also  show  a  strong  relationship  between  work  experience  and  the 
probability  of  being  low-skilled  in  numeracy,  which  is  higher  among  individuals 
who  never  worked  and  those  experiencing  unemployment  or  inactivity  spells. 

67 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure  31  shows  that for  each  level  of  educational  attainment,  the  probability  of 
being  low-skilled  decreases  with  the  intensity  of  work  experience.  Adults  with 
tertiary education and no work experience also have a higher probability of being 
low-skilled than a worker with upper secondary education and work experience. 

68 

Chapter 4.
The consequences of low skills  

The  idea  that  education  and  higher  levels  of  skills  are  associated  with  a  wider 
range  of  benefits  for  individuals  (and  their  families),  employers,  society  and  the 
economy as a whole is largely shared in literature.  

In  addition  to  increased  employability  and  higher  earnings  for  individuals, 
and higher productivity and economic growth for the economy as a whole, a more 
recent  strand  of  analysis  focuses  on  the  social  and  non-market  benefits  of 
education and skills, such as improved health, social and civic engagement, and 
lower involvement in criminal activities.  

The  analysis  presented  in  this  section,  based  on  PIAAC  and  EU-SILC 
(Box 5)  data,  will  lay  the  foundations  for  a  costing  framework  presented  in  the 
next chapter. 

Figure 32.  Benefits of higher levels of skills  

Source:  Cedefop.  

69 

 
   
 
 
 
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

4.1.  Benefits of higher skills for individuals  

Employability 

4.1.1. 
Several studies in literature explore the positive impact of education on individual 
employability  (e.g.  Cedefop,  2013;  Dorsett  et  al.,  2010;  Dickson  and  Harmon, 
2011; Dickson and Smith, 2011; Heinrich and Hildebrand, 2005). In line with this 
evidence,  descriptive  statistics  presented  in  Chapter  2  suggest  that  low-skilled 
adults  are  more  likely  to  experience  spells  of  inactivity  and  unemployment  and 
that, once employed, they tend to be employed in low-skilled occupations.  

Table 11.  Predicted probabilities of being employed 

Education 

Low education 

Medium education 

Cognitive skills 

Low-skilled in numeracy 

Computer skills 

No computer experience 

Personal and family characteristics 

Age 35-44 

Age 45-54 

Age 55-65 

Foreign-born and foreign-language as first language 

Perceived general health: poor/fair 

Female  

Have children 

Female* having children 

Constant 
Pseudo R2 

Observations 

* p<0.1 **p<0.05 ***p<0.01 

Coefficients 
(1) 

Margins 
(2) 

-1.23*** 

-0.65*** 

-0.14*** 

-0.07*** 

-0.47*** 

-0.05*** 

-0.63*** 

-0.07*** 

0.04*** 

0.08*** 

0.04*** 

-0.07*** 

-0.09*** 

-0.09*** 

-0.03*** 

0.39*** 

0.66*** 

0.38*** 

-0.58*** 

-0.77*** 

-0.12*** 

0.33*** 

-0.72*** 

3.09*** 

0.1526 

70 186 

70 186 

NB:  Logistic regression. Dependent variable: being employed = 1, 0 otherwise (including both unemployed 

and inactive people). Low-skilled in numeracy = scoring less than 226 points (on a scale of 0-500 points).  

Sample: population aged 25-65, excluding retired people, students, permanently disabled people and 
individuals in compulsory military or community service. The omitted reference categories are: high level 
of education attained; not being low-skilled in numeracy; having computer experience; aged 25-34; good 
health; native born and native language; male, not having children; being male without children; living in 
Austria. 

Source:  Cedefop calculation on OECD survey of adult skills (PIAAC) 2012. 

70 

  
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 4. 
The consequences of low skills 

Box 5. 

The differing importance of educational qualification for employment 
probabilities 

Brozovicova  et  al.  (2012)  analysed  the  odds  of  being  employed  in  five  EU  Member 
States (Bulgaria, Spain, Hungary, Romania and Slovakia) and found a high degree of 
heterogeneity in the effects of low levels of education (ISCED 0-2) on labour market 
outcomes. Estimating a probabilistic binary choice model for each country using 2010 
EU-LFS data showed that having low qualifications reduced individuals’ odds of being 
employed  by  about  10%  in  Bulgaria,  14%  in  Spain  and  Hungary,  17%  in  Romania, 
and  of  23%  in  Slovakia.  The  role  of  low  educational  attainment  also  varied  within 
countries, being more negative in sparsely populated areas than others. 
Other  empirical  research  focusing  on  the  demand  for  labour  found  that  having 
educational  qualifications  is  less  important  for  low-skilled  occupations.  For  example, 
results  emerging  from  a  qualitative  analysis  of  36  case  studies  and  employer 
interviews  in  five  UK  industries  employing  a  large  share  of  low-skilled/low-paid 
workers  (call  centres,  hotels,  food  processing,  retailing  and  hospitals),  showed  that 
qualifications play a marginal role in recruitment, retention and progression (Lloyd and 
Mayhew, 2010).  
Similar results are found by Kureková et al. (2012) and literature they review.  
For  example,  Jackson  (2001)  in  analysing  322  job  adverts  chosen  from  national, 
regional  and  local  British  newspapers,  found  that  only  40%  included  a  qualification 
requirement  of  any  kind.  Educational  qualifications  were  very 
for 
managerial  and  professional  positions,  and  vocational  qualifications  were  important 
for  the  remaining  positions.  Similarly,  in  a  more  recent  study  Jackson  et  al.  (2005), 
performing a content analysis of around 5 000 jobs adverts, found that qualifications 
appeared as a requirement in only 26% of all advertised jobs, but were required only 
in around 10% of advertisements for the technical and operative, sales and personal 
service occupational categories. 

important 

Results from a logistic regression (43) on PIAAC (44)(45) data (Table 11) confirm 
that the level of education enhances the likelihood of being employed (46): compared 
to adults with tertiary education, the chance of being employed decreases by 7% 
for those with an upper secondary degree, and by 14% for people with less than 
upper secondary education. The negative effect of low education on employment 
probabilities is different across and within countries, depending on the characteristics 
of labour demand and the countries’ institutional settings (Box 5).  

(43)  Details  on  the  methodological  approach  adopted  and  the  selected  sample  are 

provided in Annex 4.  

(44)  PIAAC data for Cyprus are not included in this analysis.  
(45)  As  for  Section  3.3,  this  part  of  the  analysis  concentrates  and  presents  results  only 
on  numeracy  skills,  since  literacy  and  numeracy  proficiency  scores  are  highly 
correlated (coefficient is 0.86) and produce similar results. 

(46)  The  analysis  used  average  adjusted  predictions  and  adjusted  predictions  at 

representative values. 

71 

 
                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

The level of cognitive skills plays a significant role in increasing the odds of 
being  employed:  for  people  low-skilled  in  numeracy  the  chances  of  being 
employed are reduced by 5% (47). Computer experience is another important skill 
factor increasing the likelihood of being employed (+7% for adult population) (48). 

Figure 33.  Predicted probabilities of being employed by education level, low skills 
in numeracy and computer use experience (35 to 44 years old) 

NB: Low-skilled in numeracy = scoring less than 226 points on a scale of 0-500 points. 

Adjusted predictions at representative values on estimates of a logistic regression, where the dependent 
variable is the probability of being employed = 1, 0 otherwise including both unemployed and inactive 
people. Sample: population aged 25-65, excluding retired people, students, permanently disabled people 
and individuals in compulsory military or community service. The omitted reference categories are: high 
level of education attained; not being low-skilled in numeracy; having computer experience; aged 25-34; 
good health; native born and native language; male, not having children; being male without children; 
living in Austria.  

Source:  Cedefop on OECD survey of adult skills (PIAAC) 2012. 

(47)  This is consistent with other studies, like those reported in Dench et al. (2006) reporting 
the results of several UK studies showing that, although different employment impacts 
emerge  depending  on  the  data  used  and  analytical  approach  adopted,  all  find  that 
higher  levels  of  cognitive  skills  are  associated  with  greater  probabilities  of  being 
employed.  

(48)  Similar findings emerge from Kureková et al. (2012). 

72 

 
 
                                                
Chapter 4. 
The consequences of low skills 

Having  both  low  skills  in  numeracy  and  no  computer  experience  has  a 
higher impact on reducing employment probability than level of formal education 
(Figure 33). For example an average adult aged 35 to 44 not having attained an 
upper secondary degree but possessing computer and numeracy skills has more 
chances of being employed (78%) than an upper secondary school graduate with 
low skills in numeracy and no computer experience (69%).  

4.1.1.1.  Labour market transitions among low-skilled adults: a low-skills trap? 

Having  established  that  educational  level  and  cognitive  skills  play  a  role  in 
determining  the  probability  of  being  employed,  analysing  the  labour  market 
transitions  of 
the 
determinants of transitions across labour market statuses.  

low-skilled  workers  offers 

the  opportunity 

to  explore 

Box 6. 

The EU-SILC data 

The analysis of the labour market transitions is based on the European survey on 
income and living conditions (EU-SILC). The EU-SILC is a rotating panel survey, 
where individuals are interviewed for a maximum of four years, and the sample is 
refreshed regularly with new members; over two years there is a 75% overlap in the 
longitudinal sample, and over four years there is a 25% overlap. The longitudinal EU-
SILC microdata cover 27 Member States (excluding Germany, for which data are not 
released for research) and other non-Member States like Norway and Iceland.  
The 2012 dataset is the most recent available, though its use for our analysis is 
constrained by microdata being unavailable as yet for Ireland, Croatia, Romania, 
Slovakia and Sweden, reducing the aggregate sample, and the change in the ISCO 
adopted since 2011, as it is not possible to convert the two classifications without 
some loss or distortion of information. 
The analysis considers the 2008-11 and 2005-08 periods using the ISCO-88 for 
transitions over a three-year period to analyse the changes over time in transition 
patterns, and, separately, the new ISCO-08 for the yearly transitions between 2011 
and 2012. 
The dataset contains yearly individual labour market information. The descriptive 
analysis, with the exception of the econometric estimation, is performed with the 
appropriate longitudinal sample weight supplied by Eurostat.  
Since the EU-SILC survey gives no information on individual cognitive skills, we use 
the ISCED level of education attained as a proxy for skills (low education: ISCED 0-2, 
intermediate education: ISCED: 3-4, high education: ISCED 5-8). The EU-SILC 
survey is also lacking information on in-job training.  

Using EU-SILC data for individuals aged 25 to 65 (Box 6), allows analysing 
the  transitions  of  employed  workers  into  employment,  unemployment  and 

73 

 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

inactivity.  To  classify  ‘activity  status’  (49)  and  ‘occupation’  (50)  four  groups  are 
considered  (51):  high-skilled  jobs  (HSJ),  semi-skilled  jobs  non-manual  (SSJ), 
semi-skilled manual jobs (SSMJ) and low-skilled jobs (LSJ).  

Box 7. 

Classification of occupations into job levels 

The ISCO changed in 2012 and the two classifications are not directly comparable at 
the 2-digit level. We used 2-digit level ISCO-88 up to 2011 and ISCO-08 from 2012 
onwards.  
Using the ISCO-88, we have grouped occupations as follows: 
  the major groups between 1 and 3 are classified as high-skilled jobs (HSJ);  
  the major groups 4 and 5, excluding the minor group 51, are classified as semi-

skilled non-manual jobs (SSJ); 

  the major groups 6, 7 and 8, excluding the minor group 61, are classified as semi-

skilled manual job (SSMJ); 

  the 9 major groups and the minor groups 51 and 61 are classified as low-skilled 

jobs (LSJ). 

Using the ISCO-08, we have grouped occupations as follows:  
  HSJ: the major groups between 1 and 3;  
  SSJ: the major groups 4 and 5, excluding the minor group 51 and 53;  
  SSMJ: the major groups 6, 7 and 8, excluding the minor groups 61 and 62;  
  LSJ: the 9 major groups and the minor groups 51, 53, 61 and 62.  

Transitions have been calculated using transition matrices at the aggregate 
EU level (52). These matrices report the unconditional transition probability of an 

(49)  The  reference  variable  is  ‘self-defined  current  economic  status  (PL030)’,  that 
captures  the  respondent’s  perception  of  their  main  activity  status  for  the  current 
period and it may differ from the strict ILO definition. 

(50)  The reference variable is the ‘occupation (PL050 for ISCO-88 and PL051 for ISCO-
08)’, and it refers to the main job (the current job for employed people and the last 
main  job  for  people  who  do  not  work). The  ISCO-88  (2  digits)  is  used.  From  2012 
onwards ISCO-08 is used. If multiple jobs are held or were held, the main job is the 
one  with  the  greatest  number  of  hours  usually  worked. When  identifying  the  ISCO 
code,  interviewers  should  have  referred,  if  applicable,  to  the  code  given  the 
preceding year(s), to avoid unjustified changes in the variable.  

(51)  We  have  considered  the  groups  used  in  the  descriptive  analysis  in  Chapter  2,  but 

also the ‘distribution’ of PIAAC numeracy scores between occupations. 

(52)  The sample for 2012-11 covered Member States but excluding DE, IE, HR, RO, SK 
and  SE,  plus  two  EFTA  countries  (NO  and  IS).  The  sample  for  2011-08  covered 
Member  States  except  for  DE  and  IE,  plus  two  EFTA  countries  (NO  and  IS).  The 
sample for the 2008-05 period covered Member States except for BG, DE, HR and 
RO, plus two EFTA countries (NO and IS). Note: DE  is not in the sample because 

74 

 
                                                
 
Chapter 4. 
The consequences of low skills 

individual to move to state 𝑗 in the current period, given that s/he was in the state 
𝑖 in  the  previous  one  (53).  The  estimated  transition  probabilities  are  likely  to 
depend on individual characteristics and on job characteristics.  

Over a one-year period (2011-12) stability rates are quite high for all types of 
occupation,  except  for  the  unemployed.  However,  low-skilled  workers  have  a 
higher  probability  of  losing  their  job  than  others.  Specifically,  6.3%  move  to 
unemployment  and  3.3%  move  to  inactivity.  Conversely,  high-skilled  workers 
enjoy  higher  employment  stability,  with  a  90.7%  probability  of  remaining  in  the 
same occupation category (Table 12). 

Table 12.  Labour market occupations yearly transition matrix, longitudinal 

population 2011-12 

Origin 2011 

HSJ 

SSJ  

SSMJ  

LSJ  

Unemployed 

Inactive 

Total 

HSJ 

SSJ 

SSMJ 

LSJ 

Unemployed 

Inactive 

Total 

Destination 2012 

90.7 

5.9 

2.9 

3.5 

5.6 

2.7 

2.9 

84.4 

1.2 

1.7 

5.0 

2.3 

1.3 

1.2 

86.3 

2.3 

6.3 

1.4 

1.2 

1.8 

2.3 

82.9 

8.1 

3.7 

31.5 

13.5 

14.4 

14.8 

2.4 

4.1 

5.7 

6.3 

63.8 

7.7 

10.7 

1.5 

2.5 

1.7 

3.3 

11.1 

82.3 

15.1 

100 

100 

100 

100 

100 

100 

100 

NB:  HSJ: high-skilled job; SSJ: semi-skilled non-manual job; SSMJ: semi-skilled manual job; LSJ: low-skilled 

job. Sample: population aged 25-65, excluding students and retired persons. 

Source:  Cedefop analysis of Eurostat microdata, EU-SILC.  

However,  it  is  well  known  that  during  upward  business  cycles  there  are 
higher  flows  between  occupations,  higher  outflows  from  unemployment,  and 
lower long-term unemployment rates. An employed adult has a higher probability 
of entering a better job in terms of occupational level (54). During periods of crisis, 
there is a higher risk of long-term unemployment for the unemployed, as well as 
fewer  chances  for  the  employed  to  upgrade  their  occupation.  These  risks  are 
higher for low-skilled individuals who face reduced employment opportunities and 

the  dissemination of the  longitudinal microdata  is not allowed.  IE is not  included in 
the 2011 sample because the data are not clean. 

(53)  𝑃𝑖𝑗 = 𝑑𝑖𝑗/𝑁𝑖  where  𝑑𝑖𝑗  stands  for  the  number  of  individuals  in  state  𝑖 in  the  initial 
period  who  move  to  state  𝑗  in  the  following  period,  and  𝑁𝑖  is  the  number  of 
individuals in state 𝑖 in the initial period. 

(54)  Theoretical literature that focuses on labour market matching models with job-to-job 
transitions predicts that booms are times which allow employed workers to upgrade 
into  better  jobs,  while  opening  jobs  for  unemployed  workers,  albeit  of  lower  quality 
(Krause and Lubik, 2007). 

75 

 
                                                                                                                                 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

a job quality penalty if they succeeded in finding a job (OECD, 2014). To illustrate 
the  impact  of  the  economic  crisis  on  labour  market  dynamics,  Table  13  shows 
transitions over a three-year period: 2005-08 and 2008-11.  

Before the crisis, stability rates for employed people of all skills levels were 
higher,  as  were  exit  rates  from  unemployment  and  transitions  from  low-skilled 
jobs to high-/semi-skilled jobs. However, during the financial crisis stability rates 
decreased  by  2.8  percentage  points  for  semi-skilled  manual  jobs  and  by  2 
percentage points for low-skilled jobs, while for high-skilled jobs the stability rate 
decreased  by  only  1  percentage  point.  During  the  crisis  more  adults  working  in 
low-skilled jobs (from 3.8% to 9.2%) and semi-skilled manual jobs (from 4.6% to 
10.1%) lost their jobs as transitions to unemployment increased (55).  

Table 13.  Labour market occupations transition matrix, longitudinal population 

2005-08 and 2008-11 

Origin 

2005-08 

2008-11 

HSJ  SSJ  SSMJ  LSJ 

Unemp-
loyed 

Inactive  HSJ  SSJ  SSMJ  LSJ 

Unemp-
loyed 

Inactive 

Destination 

HSJ  

SSJ  

87.5 

3.7 

15.0  71.1 

2.1 

2.1 

SSMJ  

5.4 

1.7 

80.2 

2.1 

3.8 

5.1 

5.4 

3.3 

6.4  74.9 

1.9 

3.8 

4.6 

3.8 

2.7  86.5 

3.9 

4.1  13.3  70.5 

2.0 

1.8 

3.0 

4.0 

1.7 

77.4 

1.9 

3.6 

3.9 

6.2 

4.3 

3.1 

4.4  72.9 

3.4 

6.5 

10.1 

9.2 

2.5 

4.2 

2.9 

6.1 

10.5 

7.8 

13.7  15.2 

33.3 

19.4 

8.8 

6.0 

6.8  13.4 

45.3 

19.7 

LSJ  

Unemp-
loyed 

Inactive 

5.8 

4.8 

3.2 

7.5 

Total 

32.0  12.2 

18.1  16.2 

4.9 

5.9 

74.0 

5.3 

3.3 

1.8 

6.8 

15.6  32.0  11.8 

15.5  16.0 

8.2 

9.4 

74.7 

15.4 

NB:  HSJ: high-skilled job; SSJ: semi-skilled job; SSMJ: semi-skilled manual job; LSJ: low-skilled job. 

Sample: population aged 25-65, excluding students and retired persons. 

Source:  Cedefop elaboration on Eurostat microdata, EU-SILC.  

Labour  market  flows  from  low-skilled  jobs  by  education  level  presented  in 
Figure  34  show  that  workers  employed  in  low-skilled  jobs  and  with  low 
qualifications  tend  to  have  higher  exit  rates  to  unemployment  and  inactivity 
(21%),  and  lower  exit  rates  to  higher  skilled  jobs  (8%).  During  the  crisis,  the 
situation  worsened  across  all  educational  levels,  but  workers  with  lower 
qualifications experienced the worst transitions in the labour market. 

(55)  The  results  are  consistent  with  an  empirical  study  (Bachmann  et  al.,  2014)  that 
analysed  the  change  in  labour  market  transition  before  and  after  the  crisis,  and 
found  similar  results:  an  increase  in  the  transition  rate  from  employment  to 
unemployment,  higher  permanence  rates  in  unemployment,  and  lower  transition 
rates from unemployment. 

76 

 
                                                
Chapter 4. 
The consequences of low skills 

Figure 34.  Labour market flows from low-skilled jobs, by gender and education, 

longitudinal population 2008-11; 2005-08 

NB:  Permanence rate: case of ‘no-movement’ in LSJ. Exit rate to other jobs: movements from LSJ to SSMJ, 

SSJ, HSJ. Exit rate to unemployed/inactive: movements from LSJ to unemployed/inactive.  
Sample: population aged 25-65, excluding students and retired persons. 

Source:  Cedefop analysis of Eurostat microdata, EU-SILC. 

Before  the  financial  crisis  the  long-term  unemployment  rate for  people  with 
low qualifications was 41.7%. With the crisis it increased to 53.8% and exit rates 
from  unemployment  to  jobs  other  than  low-skilled  ones  shrank  drastically; 
transitions from unemployment to semi-skilled manual jobs declined from 12.8% 
to 6% (Figure 35). 

77 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Figure 35.  Labour market flows from unemployment for people with low qualifications 
(ISCED 0-2), longitudinal population 2005-08 and 2008-09 (%) 

NB:  HSJ: high-skilled job; SSJ: semi-skilled job; SSMJ: semi-skilled manual job; LSJ: low-skilled job.  

Sample: unemployed aged 25-65, with low education (ISCED 0-2). 

Source:  Cedefop elaboration on Eurostat microdata, EU-SILC. 

4.1.1.2.  Determinants of transitions 

Educational  attainment  has  an  impact  on  the  risk  of  remaining  in  a  low-skilled 
occupation.  Results from  a multinomial  logit  regression  (56)  Table 14  show  that, 
all  other  variables  constant,  adults  with  low  levels  of  education  are  three  times 
more at risk (57) of ‘remaining in a low-skilled job’ compared to ‘moving to higher 
level occupations’ than their more educated peers. The relative risk of falling into 
unemployment  or  inactivity  for  adults  with  low  levels  of  education  compared  to 
moving to higher level occupations is 4.2 times more likely for unemployment and 
4.4 times more likely for inactivity compared to highly educated adults. 

An  additional  year  of  work  experience  is  expected  to  reduce  the  risk  of 
‘remaining in a low-skilled job’ relative to ‘moving to higher level occupations’ by 
a factor of 0.97.  

(56)  The  multinomial  logit  specification  has  also  been  tested  for  the  independence  of 
irrelevant alternatives (IIA) hypothesis using Small and Hsiao’s test, which indicated 
that IIA was not violated. Methodological approach can be found in Annex 4. 

(57)  A risk ratio greater than 1 indicates an increase in the risk of the outcome of interest 
relative to the reference category, with all other variables in the model held constant. 
Further methodological details can be found in Annex 4.  

78 

 
 
                                                
Chapter 4. 
The consequences of low skills 

Table 14.  Determinants of labour market transition: relative risk ratios 

Variables 

Intermediately qualified 

Low-qualified 

Female 

Number of children (<=4 yrs old) 

Fem*N. of children(<=4 yrs old) 

Married 

Age 

Work experience 

Household size (person) 

Bad health 

Unemployment spell 

Fixed-term contract 

Self-employment 

Constant 

Observations 

(1) 
LSJ to HSJ/ 
SSJ/SSMJ 

Reference 
outcome 

(2) 
LSJ to LSJ 

(3) 
LSJ to 
unemployed 

(4) 
LSJ to  
inactive 

2.099*** 
(0.274) 
2.977*** 
(0.426) 
1.791*** 
(0.162) 
0.941 
(0.0695) 
1.158 
(0.136) 
0.912 
(0.0888) 
1.060*** 
(0.00949) 
0.972*** 
(0.00805) 
1.093** 
(0.0380) 
1.454* 
(0.328) 
0.318*** 
(0.0470) 
0.855 
(0.101) 
1.821*** 
(0.220) 
0.283*** 
(0.0835) 

3.177*** 
(0.826) 
4.210*** 
(1.132) 
1.158 
(0.160) 
0.953 
(0.111) 
1.114 
(0.203) 
0.825 
(0.118) 
1.099*** 
(0.0128) 
0.919*** 
(0.00981) 
1.017 
(0.0517) 
3.295*** 
(0.873) 
5.824*** 
(0.934) 
1.151 
(0.184) 
0.923 
(0.192) 
0.0111*** 
(0.00510) 

2.795*** 
(0.877) 
4.405*** 
(1.410) 
4.103*** 
(0.764) 
0.923 
(0.218) 
2.723*** 
(0.696) 
0.918 
(0.152) 
1.147*** 
(0.0143) 
0.917*** 
(0.00997) 
0.972 
(0.0615) 
9.348*** 
(2.422) 
0.994 
(0.221) 
1.693*** 
(0.315) 
2.156*** 
(0.443) 
0.000455*** 
(0.000268) 

6 578 

6 578 

6 578 

6 578 

* p<0.1 **p<0.05 ***p<0.01. Standard errors in parentheses. 

NB:  Multinomial logit model. Dependent variable: labour market transition from LSJ (1 for LSJ to 

HSJ/SSJ/SSMJ, 2 for LSJ to LSJ, 3 for LSJ to unemployed, 4 for LSJ to inactive). The reference 
categories are: high education, male, non-married, without health problems, not having been 
unemployed at least one time between the initial period and the reference period, employee with a 
permanent contract.  

Sample: population aged 25-65, excluding retired population, student and inactive people. 

Source:  Cedefop calculation using EU-SILC survey 2011. 

Predicted  probabilities  of  moving  from  a  low-skilled  job  to  a  high-
skilled/semi-skilled job, by level of education and age (58) (Figure 36) tells us that 
job mobility tends to decrease with age, and adults with low qualifications have a 

(58)  Predicted  probabilities  at  different  ages  were  computed  while  holding  all  the  other 

variables constant at their mean. 

79 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

higher  probability  of remaining  in low-skilled  jobs  at  any  age. While for  younger 
workers educational attainment matters much (59), the gap between workers with 
low  and  high  qualifications  drops  as  age  increases.  Younger  highly  educated 
people may enter the labour market with a low-skilled job, but are more likely to 
move  to  a  better  job  in  the  future  (even  if  also  to  become  unemployed).  In 
contrast,  older  highly  educated  people  employed  in  low-skilled  jobs  are  more 
likely  to  remain  trapped  in  low-skilled  jobs  (low-skills  trap)  probably  due  to  the 
effects of skill obsolescence. 

Figure 36.  Predicted probability of moving to a high-skilled/semi-skilled job by 

education and age, 2008-11 

NB:  Predicted probabilities obtained from multinomial logit.  

Source:  Cedefop calculation on EU-SILC survey 2011.  

Individual returns  

4.1.2. 
Having established that low-skilled adults are more likely than their more skilled 
peers  to  experience  lower  employability  and  poorer  transitions  in  the  labour 
market,  it  may  be  reasonable  to  assume  that  they  are  also  more  likely  to  earn 
less and to accumulate less work experience.  

(59)  The  probability  of  moving  to  a  higher  level  occupation  is  significantly  higher  for 
highly  educated  young  workers  (45%  for  an  individual  aged  25)  than  for  young 
workers with low qualifications (22% for an individual aged 25). Similar results have 
been found by empirical literature (Bernardi and Garrido, 2008) focused on mobility 
among unskilled people in Spain.  

80 

 
 
                                                
Chapter 4. 
The consequences of low skills 

In the traditional human capital theory (Becker, 1964; Becker and Chiswick, 
1966;  Mincer,  1958;  1974),  it  is  assumed  that  individuals  rationally  choose  to 
engage in education and give up earnings today in return for higher earnings in 
the future. The level of investment in education depends on how different people 
feel  about  giving  up  some  of  today’s  consumption  in  return  for  future  rewards 
(Borjas,  2010)  and  on  expectations  about  the  lifetime  earnings  premium 
associated with employment at higher qualification levels.  

Countless  studies  evaluate  private  returns  on  education. While  results  vary 
according to the country/ies of observation and datasets used, how education is 
captured 
the  different  assumptions,  model  specifications  and 
methodologies  (61),  consensus  emerged  on  the  positive  impact  on  earnings  of 
participation in education.  

(60)  and 

A  meta-analysis  on  the  results  of  several  studies  on  returns  on  education 
report an average varying between 6.5% and 14% (Harmon et al., 2003). Similar 
conclusions  are  reported  in  Heinrich  and  Hildebrand  (2005)  who  estimated 
returns  varying  between  about  4%  to  14%,  while  Montenegro  and  Patrinos 
(2014), building on their previous work (e.g. Psacharopoulos and Patrinos, 2004) 
report  an  average  return  on  schooling  of  about  10%.  Results  are  higher  for 
females than for males and are the highest for tertiary education. Badescu et al. 
(2011)  estimating  the  wage  differentials  caused  by  differences  in  educational 
attainment  across  24  Member  States  using  EU-SILC  2005  data  for  full-time 

(60)  Education  has  generally  been  captured  either  using  years  of  schooling  (e.g. 
Montenegro and Patrinos, 2014; Mendolicchio and Rhein, 2011; Card and Lemieux, 
2001; Flores et al., 2013) or qualifications level as a proxy for educational attainment 
to capture the non-linearity of education returns (e.g. Davia et al., 2009; Badescu et 
al., 2011; Quintini, 2014). 

(61)  Several  methodological  issues  arise  from  estimating  returns  on  education  in  the 
Mincerian  tradition.  Two  prominent  issues  are  the  endogeneity  of  the  educational 
variable  (the  possible  existence  of  heterogeneity  due  to  unobserved  ability,  which 
assumes that more able individuals invest more in education and would earn more 
at  any  level  of  education  as  they  are  able  to  convert  schooling  into  human  capital 
more  efficiently  than  the  less  able.  For  an  extensive  discussion  on  the  issue  see 
Card,  1999)  and  the  selectivity  bias  of  participation  in  employment.  Most  studies 
address  the  heterogeneity  in  skills  investment  and  returns  either  including  some 
measures of innate ability or family background information (e.g. Flores et al. (2013) 
use the Hausman and Taylor (1981) estimator, while Badescu et al. (2011) include 
variables  on  parental  education  and  a  variable  measuring  the  financial  situation  of 
the family), or using sources of exogenous variations such as reforms in compulsory 
schooling  laws  (e.g.  discussion  in  Heinrich  and  Hildebrand,  2005;  Woessman, 
2014). Bias due to selectivity in employment has been generally addressed using a 
Heckman selection model  (e.g. Hanusheck and  Schwerdt,  2013;  Mendolicchio and 
Rhein, 2011).  

81 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

employees  aged  25  to  65,  report  an  average  43%  return  associated  with 
university  degrees.  However,  the  authors  observe  great  variability  across 
countries. For those who did not attain secondary education there is, on average, 
a wage penalty of 17%.  

Education alone  does not  capture  all  the  dimensions  of  low  skills. With the 
emergence  of  international  comparative tests  of  skills  in representative  samples 
of  the  adult  population,  several  scholars  integrated  the  study  on  the  returns  on 
education using also cognitive and basic skills as measured by PIAAC (Chapter 
2),  the  international  adult  literacy  survey  (IALS,  the  predecessor  of  PIAAC)  or 
other  test  scores  coming  from  national  surveys.  These  studies  using  skills’  test 
scores  also  demonstrate  the  positive  impact  of  skills  on  earnings.  However,  as 
with  education,  results  vary  considerably  depending  on  datasets,  countries, 
assumptions  and  model  specifications.  For  example,  Hanushek  and  Schwerdt 
(2013)  estimate  earnings  returns  on  cognitive  skills  across  22  OECD  countries 
using  PIAAC  and  focus  on  full-time  employees  aged  35  to  54  (62).  Results  (63) 
show  great  variation  across  countries  but,  on  average,  one  standard  deviation 
increase in numeracy skills translates into an increase in hourly wages of 17.8%.  
Carbonaro  (2006)  studies  the  effect  of  different  institutional  mechanisms 
related  to  the  wage  setting  process  on  the  relationship  between  cognitive  skills 
and  earnings  across  different  OECD  countries  using  IALS  (64)  and  focusing  on 
full-time workers aged 25 to 65. Results (65) reveal significant and positive returns 
on literacy skills in both liberal and social market economies, although higher in 
the liberal market economies than in the social ones.  

A  number  of  studies  focus  on  the  returns  on  skills  in  individual  countries. 
However,  while  several  exist  for  the  US  (e.g.  Tyler,  2004;  De  Anda  and 
Hernandez,  2007)  fewer  look  at  EU  Member  States  and  most  of  these  are 
focused  on  the  UK.  For  example,  Vignoles  et  al.  (2011)  evaluate  the  impact  of 

(62)  According  to  the  authors,  their  decision  to  focus  on  prime-age  workers  is  due  to 
imperfect  job  matches  and  the  difficulty  of  observing  skills  in  early  career  stages 
which may understate the returns on skills for young people. 

(63)  The  authors  estimate  a  Mincerian  type  equation  with  numeracy  skills  instead  of 
years  of  schooling.  To  control  for  unobserved  heterogeneity  they  include  a  set  of 
observable characteristics such as parental education and occupation category. 

(64) 

IALS identifies three types of skill: prose literacy, document literacy, and quantitative 
literacy. In the model the scores of the three types are averaged and used as a skill 
indicator. 

(65)  Results  are  estimated  by  a  linear  regression  using  multi-level  methods  (individuals 
nested in countries) to capture the effect of national level variables on individual skill 
characteristics and to control for unobserved heterogeneity. In the model the scores 
of the three types of skills are averaged and are used as a skill indicator. 

82 

                                                
Chapter 4. 
The consequences of low skills 

basic skills on hourly wages in the UK using data from the British cohort survey 
(BCS)  and  the  national  child  development  study  (66).  Findings  from  the  BCS 
reveal  that  an  increase  in  one  standard  deviation  in  literacy  is  associated  with 
14%  higher  earnings,  while  an  additional  standard  deviation  in  numeracy  skills 
translates  into  11%  higher  hourly  wages.  Results  from  the  national  child 
development  survey  show  slightly  larger  impacts:  an  increase  of  one  standard 
deviation in literacy and numeracy skills results in 15% higher wages.  

With  the  growing  importance  of  ICT  skills  and  digital  competences  and  the 
evidence  that  they  effectively  contribute  to  increase  the  likelihood  of  being 
employed, some literature focuses specifically on returns on ICT skills.  

Sakellariou (2009) analyses the returns on computer and language skills for 
young  graduates in Vietnam  using the  higher  education  graduate tracer survey. 
Results  show  a  high  computer  wage  premium  among  tertiary  education 
graduates  at  around  80%.  Dolton  and  Pelkonen  (2004)  estimate  the  returns  on 
computer use, using the workplace employment relations survey for the UK. The 
authors  use  broader  concepts  of  ICT  skills  (office  IT  function,  manual  function, 
and creative function) and find a 3% wage premium for using a computer and a 
2.4% return for the office IT function. Borghans and ter Weel (2006) differentiate 
between  the  importance  of  IT  in  day-to-day  work,  the  level  of  specification  and 
the effectiveness of computer use to calculate the returns on ICT skills in the UK. 
Results  show  that  while  the  ability  to  write  documents  and  to  carry  out 
mathematical analyses yields significant labour-market returns, the ability to use 
a  computer  effectively  has  no  substantial  impact  on  wages.  These  estimates 
suggest  that  writing  and  maths  can  be  regarded  as  basic  skills,  but  that  the 
higher wages of computer users are unrelated to computer skills. 

As training effectively contributes to increasing and updating one’s skills and 
competences, some studies focus on the impact of training on earnings. Vignoles 
et  al.  (2004)  found  that  in  the  UK  male  workers  who  undertook  work  related 
training  between  33  and  42  years  old  experienced  a  4-5%  higher  wage  growth 
than  workers  who  did  not.  In  Italy,  Brunello  et  al.  (2012)  found  that  that  one 
additional  week  of 
increases  monthly  net  earnings  by  1.36%, 
substantially  less  than  the  3%  or  more  often  found  in  the  rest  of  literature  for 
Europe.  

training 

(66)  A  Mincer  equation  is  estimated,  substituting  years  of  schooling  by  age  34  literacy 
and  numeracy  test  scores.  To  control  for  unobserved  heterogeneity  they  include  a 
wide  set  of  personal  characteristics  as  well  as  the  lags  of  previous  skill  levels  and 
wages.  Heckman  selection  model  is  used  to  correct  the  bias  due  to  selection  into 
employment. 

83 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

In  addition  to  concepts  of  skills  supply  using  educational  attainment  and 
skills  acquisition,  skills  demand  concepts  have  been  used  as  an  alternative 
classification to understand the economic impact of skills on individuals.  

Ingram  and  Neuman  (2006)  estimated  returns  on  ‘intelligence’,  ‘fine  motor 
skills’,  ‘coordination’,  and  ‘strength’  obtained  via  a  factor  analysis  from  the 
Dictionary  of  occupational  titles  (DOT)  (67)  for  the  years  1971  to  1998.  Results 
show  that  returns  on  intelligence  were  around  12%  during  the  1970s  and 
increased  significantly  during  the  1980s  reaching  a  stable  21%  in  the  1990s. 
Returns  on  motor  skills  (related  to  jobs  requiring  working  with  small  tools  or 
instruments)  were  relatively  small  from  the  beginning  of  the  period,  and  further 
reduced to 4.2% by the 1990s. Returns on coordination rose from 1% in 1971 to 
2.7% in 1998. Returns on strength (ability to perform heavy physical tasks) had 
basically no wage premium in the 1970s, but increased over time reaching a 4% 
earnings  return.  Capatina  (2014),  similar  to  Ingram  and  Neuman  (2006), 
performed a factor analysis to obtain two sets of skills coming from the Dictionary 
of  occupational  titles:  physical  and  cognitive.  Results  show  that  the  wage 
premium associated with one standard deviation in cognitive skills increased from 
6% in 1980 to 22% in 2010. Returns on physical skills remained almost constant 
(around zero) over this period. Abraham and Spetzler (2009) estimate returns on 
skills in the US in 2003 and 2004 using two different datasets (current population 
survey  and  occupational  employment  statistics)  and  using  the  occupational 
information  network  (O*NET) (68)  to  obtain  three  sets  of  skills:  analytical, 
interpersonal,  and  physical.  Estimations  reveal  a  much  higher  wage  premium 
associated with analytical skills than that interpersonal skills and physical activity.  
While most literature reviewed refers to the US, Kelly et al. (2010) focus on 
Ireland and use a graduate follow-up survey to estimate the economic returns on 
field of study and competences among young graduates in 2001 who entered the 
Irish  labour  market  in  spring  2002.  They  considered  five  competences: 
communication,  technical  skills,  team  working,  leadership  and  ability  to  work 
under  pressure.  Results  show  that  only  technical  skills  have  a  positive  wage 
premium  of  4%  and  do  not  vary  much  across  the  earnings  distribution. 
Conversely, ability to work under pressure results in a 2% wage penalty.  

(67) 

Information technology associates: Dictionary of occupational titles:   
http://www.occupationalinfo.org/  
In 1995 it provided 53 job characteristics.  

(68)  The  O*NET  classification  was  first  introduced  in  1998  and  identifies  41  job 

characteristics or activities. 

84 

                                                
Chapter 4. 
The consequences of low skills 

In  conclusion,  regardless  of  the  specific  focus  of  analysis,  evidence  in 
literature suggests an undeniable wage premium associated with higher levels of 
skills and education. 

Impact on individual health  

4.1.3. 
In  addition  to  monetary  benefits  associated  with  higher  levels  of  skills  and 
education, numerous studies (Grossman, 2006; Cutler and Lleras-Muney, 2012) 
have  shown  that  education  strongly  correlates  with  health  and  well-being.  Field 
(2009)  reports  the  positive  effect  of  education  on  health,  in  particularly  mental 
disorders,  while  Lochner  (2011)  reports  improved  health-related  behaviours. 
Hammond  and  Feinstein  (2005)  and  Jenkins  (2011)  find  significant  benefits  of 
participation  in  learning  on  increasing  participants’  self-confidence  and  their 
perceived well-being. 

The  seminal  work  of  Grossman  (1972)  laid  out  theoretical  foundations 
explaining  the  effect  of  education  on  health.  The  author  argues  that  health  is 
partly  the  result  of  choices  and  so  individuals  can  manipulate  certain  factors  to 
affect their health. The more educated will choose different health inputs as they 
face  different  budget  constraints.  This  is  referred  to  as  ‘allocative  efficiency’. 
Education is also expected to improve health even when having the same set of 
health  inputs  at  the  same  prices.  This  is  usually  referred  to  as  ‘productive 
efficiency’. 
While 

level  and  health 
outcomes later in life may be due to lower educational attainment, it could also be 
driven  by  the  fact  that  poor  health may  lead  to  lower  educational  attainment,  or 
education  and  health  could  be  simultaneously  affected  by  other  factors.  For 
instance,  family  background  is  likely  to  drive  both  educational  attainment  and 
health  in  early  life. Whether  education  affects  health  outcomes  is,  therefore,  an 
empirical question. 

the  observed  correlation  between  education 

Several  studies  have  tried  to  identify  the  causal  link  between  the  level  of 
education and health outcomes. Most studies identify the effect of education on 
health using the exogenous increase in the number of years of education caused 
by  changes  in compulsory  school  leaving  age.  Using  this  identification  strategy, 
studies from Germany (Kempter et al., 2011), the Netherlands (Van Kippersluis et 
al.,  2011),  Sweden  (Spasojevic,  2010),  the  UK  (Silles,  2009)  and  a  selected 
number  of  EU  Member  States,  including  south  European  countries  (Brunello  et 
al.,  2015)  show  significant  impacts  of  education  on  health  outcomes.  However, 
Arendt (2005) finds no effect in Denmark, Albouy and Lequien (2009) in France, 
and  Oreopoulos  (2007),  Braakmann  (2011)  and  Clark  and  Royer  (2013)  in  the 

85 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

UK.  Meghir  et  al.  (2012)  find  little  if  no  long-lasting  effects  of  education  in 
Sweden.  

The main limitation of this approach is that the impact of education on health 
is  measured  only  for  the  young  people  who  responded  to the  change  in school 
leaving  age,  which  happened  50  years  ago.  Other  studies  used  surveys  with 
detailed  information  about  a  range  of  individual  characteristics,  including  family 
background, to control for all cofounding factors. For instance, Conti et al. (2010) 
show that in the UK higher educational attainment is associated with better self-
reported health. 

The  health  outcomes  used  vary  widely  across  studies.  Some  focus  on 
mortality  (Van  Kippersluis  et  al.,  2011;  Albouy  and  Lequien,  2009;  Clark  and 
Royer,  2013)  while  other  use  reported  health  –  self-perceived  health,  long-term 
health problems – (Kempter et al., 2011; Brunello et al., 2015) or health-related 
behaviours, such as smoking as in Conti et al., 2010. The main purpose of these 
studies  is  to  establish  the  causal  effects  of  education  on  health,  so  they  do  not 
use  measures  of  health  traditionally  used  in  the  evaluation  of  healthcare 
programmes,  such  as  the  quality-adjusted  life  years  (QALYs).  Groot  and 
Maassen  van  den  Brink’s  study  (2007),  which  estimates  the  effect  of  education 
on health in the Netherlands on QALY, is a notable exception. While QALYs are 
typically used to quantify the impacts of health care programmes, they can also 
consider  health  outcomes  in  cost-benefit  analysis  of  policy  interventions  or 
estimate social costs and could be used to estimate the social costs associated 
with low-skilled adults.  

for  achievers  of 

In  evaluating  the  impact  of  a  youth  programme  in  the  UK,  Nafilyan  and 
Speckesser  (2014)  estimated  health  benefits 
low  and 
intermediate  skills  levels;  these  were  consistently  around  a  gain  in  0.03  QALY 
weight per year. Based on a monetary value estimate of GBP 30 000 per QALY 
applied in most other studies, the authors estimated lifetime benefits of achieved 
qualifications of GBP 42 000. Most of this benefit affects individuals (due to better 
health/life quality) and, to a lesser extent, healthcare spending in public budgets. 
In this particular study, health benefits of higher level skills were almost as high 
as the individual lifetime net earnings gain. 

Impact on individual criminal behaviour 

4.1.4. 
Crime  statistics  show  a  strong  negative  correlation  between  educational 
attainment and crime, well documented in literature. For instance, in 2001, more 
than  75%  of  convicted  people  in  Italy  had  no  upper  secondary  qualification 
(Buonanno  and Leonida,  2006). In the  UK,  47% of  prisoners  said that they  had 
no  qualifications  in  2012,  compared  to  15%  of  the  general  working  age 

86 

Chapter 4. 
The consequences of low skills 

population in the country (Ministry of Justice, 2012). In the Netherlands, 27% of 
early school leavers were suspected of a crime in comparison to 7% of non-early 
school  leavers  (Ministry  of  Education,  Culture  and  Science,  2010).  Among 
Swedes born between 1943 and 1955, male offenders had spent on average 0.7 
years less in school than those not convicted (Hjalmarsson et al., 2011). 

Following the seminal work of Becker (1968) there is consensus in economic 
literature  that  educational  attainment  is  expected  to  affect  criminal  behaviour 
through  three  channels:  income  effects,  time  availability  and  patience  or  risk 
aversion. Education is expected to discourage participation in criminal activities, 
as  it  improves  labour  market  prospects  and  increases  the  opportunity  cost  of 
crime.  Young  people  who  are  enrolled  in  school  may  have  fewer  opportunities 
(and time) to engage in criminal activities than if they were on the streets: this is 
usually  referred  to  as  the  ‘incapacitation  effect’.  Education  may  also  teach 
individuals to be more patient. This would discourage crime since more emphasis 
would  be  put  on  long-term  outcomes,  which  would  imply  a  greater  deterrent 
effect of long prison sentences. 

Several  empirical  studies  have  tried  to  estimate  the  causal  effect  of 
education on crime. Following Lochner and Moretti (2004), which used changes 
in state compulsory schooling laws in the US, most studies using European data 
use  changes  in  compulsory  school  leaving  age  to  identify  a  causal  impact  of 
educational  attainment  on  criminal  activity;  they  tend  to  find  a  crime-reducing 
effect  of  education.  Lochner  and  Moretti  (2004) find that  a  one-year  increase in 
education is predicted to reduce male arrest rates by 11%. 

Estimating the effect of educational attainment on youth conviction rates for 
three cohorts born between 1981 and 1983 in England, Sabates (2008) finds that 
the  increase  in  educational  attainment  between  cohorts  is  associated  with 
reductions in conviction rates for most offences (burglary, theft, criminal damage 
and drug-related offences) but not for violent crime. Machin et al. (2011) find that, 
among  males,  a  one-year  increase  in  average  schooling  levels  reduces 
conviction rate for property crime by 20% to 30% in England and Wales.  

Hjalmarsson et al. (2011) show that among males in Sweden, one additional 
year of education reduces the probability of conviction by 7.5%, of incarceration 
by 16%, and the number of crimes by 0.4 per male. Among  females, it reduces 
the probability of conviction by 11% and the number of crimes by 0.09. Buonanno 
and Leonida (2006) use a panel of 20 Italian regions between 1980 and 1995 to 
show that a 10 percentage point increase in high school graduation rates results 
in a 4% decrease in property crime rates and a 3% decrease in total crime rates. 
There  is  no  evidence  that  university  completion  reduces  crime.  Groot  and 
Maassen  van  der  Brink  (2010)  use  a  Dutch  dataset  on  criminal  behaviour  to 

87 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

estimate  the  effect  of  education  on  five  types  of  criminal  offence.  The  results 
show that the number of years of education significantly reduces shoplifting and 
violence, but increases tax fraud offences. 

Finally, Meghir et al. (2011), using Swedish data, show that there are inter-
generational  effects  of  education  on  crime.  Young  men  whose  fathers  were 
affected  by  an  increase  in  the  compulsory  schooling  age  have  a  2.5%  lower 
probability of being convicted. 

4.2.  Benefits of higher skills levels for society 

Further impacts of higher levels of skills and education affect not only individuals 
but  also  outcomes  such  as  civic  engagement,  participation  in  elections  and  the 
general  functioning  of  society.  Growing  literature  examines  these  effects  of 
education, though much of this research is descriptive.  

Cedefop  (2011)  shows  that  initial  vocational  education  and  training  is 
positively  associated  with  several  social  outcomes:  individuals  who  completed 
vocational  education  and  training  report  significantly  higher  levels  of  civic 
participation  and  self-rated  satisfaction  with  their  job  and  financial  situation. 
Hoskinks  et  al.  (2008)  suggest  that  there  is  a  significant  democratic  return 
associated with formal education while Kuppens et al. (2015) show that in the UK 
higher levels of education are associated with a wide range of positive outcomes: 
better  health  and  well-being,  higher  social  trust,  greater  political  interest,  lower 
political cynicism, and less hostile attitudes towards immigrants.  

While  the  relationship  between  education  and  civic  and  social  engagement 
has  long  been  acknowledged  (See  Campbell,  2006,  for  a  review),  there  is  little 
empirical  evidence  of  whether  the  relationship  is  causal.  Dee  (2004),  finds  that 
graduating from high school increases voter turnout and tolerance; he also finds 
a  positive  effect  on  newspaper  reading  and  group  memberships.  Milligan  et  al. 
(2003)  reach  a  similar  conclusion  for  the  UK,  where  more  years  of  schooling 
increase voter turnout, although the effect is smaller than in the  US. They show 
that  increased  level  of  education  has  a  positive  effect  on  the  likelihood  of 
following  political  campaigns,  attending  political  meetings  (in  the  US)  and 
developing  interests  in  politics  in  the  UK.  Using  a  similar  approach,  Siedler 
(2010) fails to find any causal effect of education on voting turnout in Germany. In 
a  study  using  Norwegian  data  for  secondary  school  students,  Lauglo  and  Øia 
(2007)  find  that  doing  well  in  school  and,  especially,  expecting  to  continue  to 
higher  education,  are  positively  associated  with  civic  engagement,  both  interest 
and participation.  

88 

Chapter 4. 
The consequences of low skills 

Results from Schuller et al. (2004) which mix case study analysis based on 
145  interviews  among  adults  aged  between  16  and  70  in  the  UK,  with  data 
analysis drawn from the 1958 British cohort study or  national child development 
study  (NCDS),  show  that  learning  generally  leads  to  a  wide  range  of  benefits. 
Participants  in  adult  learning  are  more  likely  to  give  up  smoking  and  increase 
their  level  of  exercise  and life satisfaction. The quantitative analysis  also shows 
clear  evidence  of  the  effects  of  learning  in  increasing  tolerance,  decreasing 
political  cynicism,  less  authoritarian  attitudes  and  heightened  political  interest. 
Results also report an increase in membership of organisations and tendency to 
vote in the 1997 election compared to 1987. Overall, results suggest that learning 
encourages more democratic value orientations and stimulates an interest in civic 
engagement.  While  the  quantitative  analysis  cannot  unequivocally  establish  the 
sequencing  of  participation  in  learning  and  its  possible  benefits,  biographical 
analysis of the case study data demonstrated the accumulative effects of learning 
in  reinforcing  sequences:  because  I  learn  I  develop  these  benefits  and  not  the 
other way around.  

Taken  together,  evidence  from  the  case  study  and  the  longitudinal  survey 
data converge on the same conclusions: participation in education has a range of 
non-market benefits that extend beyond the classroom into personal life and the 
community.  

4.3.  Benefits of higher skills levels for employers/firms 

While  there  is  substantial  evidence  on  the  engagement  of  employers  in 
vocational  education  and  training  and  investment  in  skills,  we know  surprisingly 
little about  the  impact  of  skills  on firms (Conlon  et  al.,  2012). The  investment  in 
skills could be repaid by increased labour productivity, a reduction in recruitment 
costs  for  higher  skilled  workers,  saving  of  downtime  due  to  lack  of  skilled  staff, 
improved  employer  attractiveness  for  better  talent  because  of  offering  training 
opportunities,  and  positive  internal  diffusion  of  knowledge.  Although  these  are 
probably the main reasons why firms engage in training, empirical evidence of a 
long-term  return  on  skills  investment  for  employers,  with  the  exception  of 
apprenticeships  (e.g.  Hogarth  et  al.,  2012;  Pfeiffer  et  al.,  2009),  is  limited  and 
often inconclusive (e.g. Conlon et al., 2011).  

A  review  of  literature  on  firm-level  effects  of  skills,  related  to  different 
measures of skills and their impact on firm profitability, uncovered a few rigorous 
studies on firm-level returns on education compared to a rich evidence body on 
the private and aggregate returns on education. Most of the evidence for Europe 
comes from studies carried out using UK data, but there are also examples from 

89 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Belgium, Portugal and the US. While they all find positive impacts on productivity, 
they  also  highlight  the  potential  endogenous  bias  arising  from  more  productive 
firms attracting higher educated workers.  

Disney et al. (2003) analysed productivity growth in UK manufacturing (1980 
to  1992)  using  measures  on  the  adoption  of  new  technologies,  which  explains 
about  5%-18%  of  the  productivity  growth.  Dearden  et  al.  (2005)  suggest  that 
about  half  of  the  productivity  gain  brought  about  by  training  results  in  an 
‘employer return on skills investment’ (based on vocational education and training 
spending).  Galindo-Rueda  and  Haskel  (2005)  study  the  impact  of  educational 
attainment of the local labour force on firm-level productivity in the UK. They use 
merged  data  from  the  annual  business  inquiry  and  employer  skill  survey  to 
estimate  the  effect  of  the  share  of  workers  with  different  qualification  levels  on 
company  productivity,  measured  as  the  logarithm  of  gross  value-added.  They 
find  that  companies  with  larger  shares  of  highly  educated,  male  and  full-time 
workers tend to be more productive.  

Martins and Jin (2010) suggest that workers can learn from their colleagues 
and  that  this  positive  externality  is  reflected  at  firm-level.  Using  the  Portuguese 
dataset  quadros  de  pessoal  and  an  aggregated  version  of  individual  Mincer 
equations, they estimate the effect of average schooling years of a firm’s workers 
on  its  productivity,  measured  as  the  log  of  average  real  hourly  earnings  of  that 
particular  firm.  The  results  indicate  that  profitability  returns  on  education  are 
between 14.2 and 18%. 

Moretti  (2004)  uses  data  from  the  American  census  of  manufacturers 
merged  with  the  census  of  population  to  calculate  the  magnitude  of  education 
spillovers  on  manufacturing  plant  productivity.  He  estimates  a  Cobb-Douglas 
production function, where the dependent variable is the output of each plant and 
the explanatory variable of interest is the fraction of college graduate workers. By 
using  a  panel  dataset,  he  is  able  to  include  time-interactions  to  control  for 
potential  sources  of  bias.  The  results  show  that  a  1%  increase  in  the  share  of 
college graduates in the city where the plant is located is associated with a 0.5-
0.6 percentage point increase in output.  

Riley and Robinson (2011) estimate the effect of the intangible capital stock 
on  labour  productivity  growth  at  firm-level  using  the  annual  business  inquiry 
(using  labour  costs  for  ICT,  R&D  and  organisational  capital  as  investment  in 
intangibles)  and  find  significant  impacts  of  human  capital  variables  on  firm 
profitability. 

Lebedinski  and  Vandenberghe  (2013)  use  the  Belgian  dataset  Bel-first  to 
assess  education’s  contribution  to  Belgian  companies’  productivity  levels.  They 
estimate a fixed effects model based on a Cobb-Douglas production function that 

90 

Chapter 4. 
The consequences of low skills 

allows  for  labour  heterogeneity.  The  companies’  productivity  is  proxied  by  the 
value-added  per  worker  and  they  focus  on  the  effect  of  the  composition  of 
employees’ educational attainment. They also control for the number of workers, 
capital,  share  of  females,  number  of  hours  worked  annually  per  employee  and 
proportion of workers with open-ended contracts. The results of the fixed effects 
model  suggest  that  university  graduates  are  23%  more  productive  than  school 
graduates.  While  the  generalised  method  of  moments  (GMM)  model  indicates 
that  university  graduates  exhibit  a  productivity  advantage  of  1.42  (42%)  relative 
to primary graduates.  

Literature suggests positive effects of human capital investment at company 
level  in  terms  of  productivity.  Education  equips  people  with  the  skills  and 
competences  that  enable  them  to  be  more  productive  at  work.  It  also  equips 
people with the knowledge and competences which enable them to generate and 
adopt  new  ideas  that  spur  innovation  and  technological  progress  (Woessman, 
2014). 

4.4.  Benefits of higher skills levels for the economy 

Over  recent  decades  a  large  body  of  literature  emerged  examining  the  role  of 
human capital in determining the level and growth of GDP per capita. Two broad 
sets of theoretical models have been developed to explain how education affects 
long-term  growth.  The  classical  growth  models  developed  in  the  1950s  which 
build  directly  on  the  microeconomic  theory  of  human  capital;  and  the  so-called 
endogenous growth models, which highlight the role of education in diffusing new 
technologies and new ideas. 

Mankiw et al. (1992) estimate a Solow growth model augmented with human 
capital,  measured  as  the  average  percentage  of  working-age  population  in 
secondary  school  during  1960-85.  The  estimation  results  suggest  that  a  1% 
increase  in  the  average  share  of  working-age  population  enrolled  in  secondary 
education  translates  into  a  0.7%  increase  in  GDP  per  working-age  person. 
Conversely, Barro and Lee (1994) estimate an endogenous growth model using 
a panel dataset with a wide set of explanatory regressors, which include average 
years of secondary education of the adult male population at the beginning of the 
period  studied.  The  results  show  that  an  increase  in  one  year  of  secondary 
schooling  translates  into  a  1.4%  increase  in  per  worker  GDP  growth.  Benhabib 
and  Spiegel  (1994)  estimate  four  different  cross-country  growth  regressions 
based  on  a  Cobb-Douglas  aggregate  production  function.  Human  capital 
investment is proxied by the average level of log average human capital stock as 
measured by Kyriacou (1991). They do not find a significant impact of growth in 

91 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

human capital on economic growth but they find that a 1% increase in the stock 
of human capital increases per capita GDP growth between 12% and 17%.  

Fundamentally,  neoclassical  models  imply  that  a  one-off  increase  in  the 
stock  of  human  capital  leads  to  a  one-off  increase  in  productivity  growth,  while 
endogenous models suggest that the same one-off increase in human capital can 
lead  to  a  permanent  increase  in  productivity  growth.  In  the  short  term  both 
models produce similar results, each dependent on their specifications, but in the 
imply  significantly  higher  returns  on 
long 
investment  in  human  capital  (Wilson  and  Briscoe,  2004).  Regardless  of  the 
specific  models  adopted,  there  is  strong  evidence  that  higher  education 
increases productivity and higher levels of national growth.  

term  the  endogenous  models 

Empirical research in recent years has shown that if education is measured 
by the skills learned, the education of a population is very closely linked to a its 
nation’s  long-term  growth  rate.  However,  in  these  analyses  education  is 
measured by actual achievement (test scores as proxies) rather than educational 
attainment.  Studies  adopting  years  of  education  as  proxy  for  the  education 
variable report a much weaker association with economic growth, suggesting that 
it  is  learning  outcomes  rather  than  attainment  which  matter  (Woessman,  2014). 
At EU level this calls for continued focus on mutual learning and cooperation in 
education and training policies across Member States. Regardless of the models 
adopted,  the  macroeconomic  benefits  of  education  are  undeniable:  for  example 
Woessman  (2014)  reports  that  an  increase  in  educational  achievement  by  50 
PISA  (69)  points  translates  into  1  percentage  point  higher  rates  of  economic 
growth  in  the  long  term.  According  to  the  author,  if  the  EU  was  successful  at 
improving an average student’s achievement by the equivalent of 25 PISA points, 
this would result in an economic gain of a staggering EUR 35 trillion.  

4.5.  Conclusions 

As  Dickson  and  Harmon  (2011)  put  it,  at  individual  level  education  can  define 
major  life  outcomes  (such  as  occupation  and  wage)  and  also  contribute  to 
improving  one’s  individual  satisfaction,  well-being,  self-esteem,  self-awareness 
and  consideration  of  the  future.  It  may  promote  trust,  civic  engagement,  active 

(69)  The  programme 

for 

international  student  assessment  (PISA) 

triennial 
international survey which aims to evaluate education systems worldwide by testing 
the  skills  and  knowledge  of  15-year-old  students.  To  date,  students  representing 
more than 80 economies have participated in the assessment 
(https://www.oecd.org/pisa/aboutpisa/pisa-participants.htm). 

is  a 

92 

                                                
 
Chapter 4. 
The consequences of low skills 

citizenship  and  social  inclusion.  Investment  in  human  capital  also  affects 
economic growth through innovation processes: investment in education leads to 
a  more  skilled  and  competent  population,  which  is  able  to  generate  and  adopt 
new  ideas  that  spur  innovation  and  technological  progress  (Heinrich  and 
Hildebrand, 2005).  

While this chapter has explored the benefits of education and higher levels 
of skills in terms of benefits to individuals, employers, society and the economy, 
all  of  these  benefits  are  interlinked  and  spill  in  all  of  the  four  dimensions 
analysed.  For  example,  higher  employability  and  higher  returns  also  lead  to 
higher  revenues  for  governments  in  increased  returns  from  taxes,  as  well  as 
reduced spending  on  benefits  such  as  income  support. While education  has  an 
impact on improved individual health and lower involvement in criminal activities, 
these  also  impact  the  public  purse  in  terms  of  public  expenditure  in  healthcare 
and  on  prevention  and  prosecution  of  crime.  Therefore,  education  is  not  only  a 
private  good  associated  with  private  benefits,  but  also  a  ‘public  good’  (Heinrich 
and Hildebrand, 2005) which is associated with large gains to our economies and 
societies. 

93 

Chapter 5.
The costs of low-skilled adults  

This chapter provides an estimate of the costs (and/or foregone benefits) of low 
skills  to individuals,  businesses,  the  economy  and  society  at  large. The general 
framework  is  largely  similar  to  measuring  the  outcome  of  a  (possible)  public 
policy aimed at reducing the share of low-skilled adults in the EU. The valuation 
process  is  based  on  robust methodological  approaches  using  empirical data for 
all  EU-28  Member  States  and  –  where  data  are  not  available  –  building  on 
findings  from  literature  research  on  the  impact  of  skills  on  main  socioeconomic 
variables.  The  ultimate aim  is  to  estimate  –  as much  as  this can  be  achieved  – 
the individual and social value in monetary terms of a faster increase in the level 
of skills in Member States compared to the current trend. 

5.1.  Theoretical background 

In  economic  theory,  the  human  capital  approach  stresses  that  educational  and 
training decisions play an important role in determining lifetime earnings (Becker, 
1964; Mincer, 1974). However, the level of investment in education and training 
depends  on  how  different  people  feel  about  giving  up  some  of  today’s 
consumption  in  return  for  future  rewards  (Borjas,  2010)  and  on  expectations 
about  the  lifetime  earnings  premium  associated  with  employment  at  higher 
qualification/skill  levels.  Following  a  cost-benefit  approach,  rational  individuals 
are expected to compare the anticipated net present value of the gain in lifetime 
earnings with higher qualification/skills (higher wage and employment probability) 
to the costs of education/training (both direct costs and opportunity costs in terms 
of foregone earning during the education/training spell).  

Following  standard  microeconomic 

(neoclassical)  principles,  wages 
correspond  to  marginal  productivity  and  so  include  microeconomic  returns  on 
education/training.  The  individual  earnings  return  associated  with  higher  level 
skills  can  be  straightforwardly  interpreted  as  the  additional  gross  value-added 
(GVA) resulting from higher skills, so that the ‘costs of low-skilled adults’ can be 
estimated  by  comparing  counterfactually  higher  level  earnings  to  earnings 
observed for low-skilled adults today. In turn, this means that the social costs of 
low-skilled  adults  can  be  derived  aggregating  the  counterfactual  increase  in 
individual lifetime earnings for low-skilled adults due to higher level skills (subject 
to discounting and further adjustments). 

94 

 
   
 
 
Chapter 5. 
The costs of low-skilled adults 

However,  the  economic  benefit  of  higher-level  skills  to  society  at  large 
(social  benefit)  is  likely  to  differ  from  mere  aggregation  of  individual  lifetime 
earnings in various ways: 
(a)  extensions  to  standard  microeconomic  models,  which  allow  for  market 
imperfections (mobility barriers, firm-specific human capital, and non-perfect 
competition) suggest that individual wages may understate the full return on 
skills  investment.  First,  since  employers  capture  part  of  the  increases  in 
productivity,  the  impact  of  investment  in  skills  on  productivity  usually 
exceeds the impact on wages (McIntosh, 2007; Hogarth et al., 2012; Pfeiffer 
et al., 2009) (70). In addition, a microeconomic analysis of implications for all 
stakeholders  should  also  consider  the  perspective  of  the  public  sector  in 
terms of relevant public expenditure and revenues; 
further,  non-individual  benefits  would  not  be  captured  in  aggregations 
without  further  assumptions.  While  skills  investment  may  generate  positive 
externalities and spillovers both within organisations and in the economy at 
large, a microeconomic analysis should also consider the impact of existing 
skills on returns since it may not be a realistic assumption at aggregate level 
to expect constant returns on skills (e.g. Cedefop, 2014); 

(b) 

(c)  more  generally,  understanding  the  net  benefits  of  higher  level  skills  would 
require  a  macroeconomic  approach  suitable  to  considering  deadweight 
losses, substitution and displacement effects at the system level. 

The main obstacle in estimating the full extent of social costs of low-skilled 
adults  results  from  lack  of  suitable  research  data,  to  capture  returns  beyond 
individual  workers  in  terms  of  skills  composition  of  the  workforce,  and  the 
availability  of  unbiased  case-control  groups.  Macroeconomic  approaches  also 
suffer  from  limitations:  the  lack  of  consideration  of  non-market  values  and 
distributional effects which estimates based on national accounts cannot include. 
Data  available  for  the  estimate  in  this  specific  exercise,  which  includes  an 
unprecedented period of economic downturn, reduces the ability of the models to 
evaluate  fully  the  role  of  spillovers  and  externalities  which  arise  from  public 
investments in human capital.  

This  is  why  we  present  results  from  both  microeconomic  and  macroeconomic 

approaches, their explanatory value as well as their limitations. 

(70)  Basically, all these papers refer to Dearden et al., 2005.  

95 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Table 15.  Approaches used to estimate the cost of low skills 

Microeconomic analysis 

Individuals/ 
families 

Employers/ 
firms 

Fiscal/public 
sector  

Benefits/reduced costs 

  individual wage return 
  higher probability (odds ratio) to be 

employed and/or to participate in the 
labour market 

  improved health, reduced crime 
  other intangibles such as well-being, 

social inclusion 

Costs/reduced benefits 
  direct monetary costs such as 

education/training fees, learning 
materials) 

  opportunity costs (often not 

accounted in literature) such as 
lower or no earnings when 
acquiring skills, loss of 
unemployment benefits, 
family/leisure time) 

  productivity gains and higher returns 

  staff costs of individuals (when 

on investment 

  saving of downtime due to lack of 

skilled staff and saving of recruitment 
costs 

  higher activity rate and lower 

unemployment reduce unemployment 
and out-of-work social benefits, ALMP 
public expenditure 

  higher wages increase tax revenues 
  better health reduces healthcare 

spending 

  reduced crime/positive effects on 

communities – reduce costs to legal 
and social assistance systems 

people engage in training, but do 
not contribute such as in 
apprenticeships) 

  course fees and/or staff costs of 

supervising staff on training; other 
direct costs for workshops, learning 
materials 

  costs for education/training 

spending (equivalent to upper 
secondary qualifications) 
  reduced revenues for people 
participating in education 

Costs 
  increase in aggregate spending on 
education and training (accounting 
problems) 

Macroeconomic analysis 

Society at 
large 

Benefits 

  higher productivity and economic 

growth  no simple aggregation of 
individual benefits (deadweight loss, 
displacement and substitution effects) 
  merit wants, positive externalities, and 

distributional objectives, such as 
reduction of inequalities, social 
exclusion, gender gaps, crime; 
increase in non-monetary well-being, 
citizenship.; reduction in the 
black/informal economy 

Source: Cedefop elaboration. 

5.2.  The impact of skills at microeconomic level 

Individual costs of low skills for young adults 

5.2.1. 
Empirical estimates of the incremental benefit (wage returns) that individuals can 
achieve  through  further  investment  in  skills  beyond  ISCED  2  are  based  on  the 
Mincer  earning  function,  one  of  the  most  widely  used  models  in  empirical 
economics. 

96 

 
 
Chapter 5. 
The costs of low-skilled adults 

5.2.1.1.  Relevant definitions of low-skilled adults 

While  low-skill  is  a  multidimensional  phenomenon,  available  data  sources  limit 
estimates of the costs of adult low skills to three main definitions: 
(a) 

low-skilled people: people with low educational attainment, at or below lower 
secondary school (ISCED 0-2) (71); 
low-skilled jobs: people working in elementary occupations (major group 9 in 
ISCO-08) (72); 

(b) 

(c)  skills  obsolescence:  people  with  educational  attainment  at  ISCED  3  or 
above, who work in either elementary occupations (S1, ISCO major group 9) 
or  in  elementary  occupations,  low-skilled  employment,  craft  and  related 
trades  workers  or  in  skilled  employment  in  agriculture  (S2,  ISCO-08  major 
groups 6-9). 

5.2.1.2.  Data: EU-SILC versus EU-LFS 

(73).  Data 

from  2011  EU-SILC (74)includes 

Both,  the  EU-LFS  and  the  EU-SILC  micro  data  contain  consistent  codes  for 
occupation  (major  groups  of  ISCO-08)  and  education  concepts  (ISCED  levels). 
We use EU-SILC data because of the superior quality of earnings data compared 
important 
to  EU-LFS  data 
characteristics  which  allow  mitigation  of  sources  of  bias  in  empirical  earnings 
functions,  particularly  background  characteristics  of  the  parental  household  and 
the parents’ education levels at age 14. Although the overall sample of EU-SILC, 
which varies between about 8 000 in Iceland and 44 000 in Italy, is smaller than 
EU-LFS, sample sizes are large enough to estimate models separately for every 
EU-28  Member  State  using  a  consistent  concept  of  low  skills  and  further 
variables required in Mincer models (such as work experience and gender). 

(71)  A  similar  approach  is  followed  in  other  studies  previously  mentioned  (Davia  et  al., 
2009;  Quintini,  2014)  who  focus  on  qualifications  attainment  as  the  key  skills 
variable.  EU-SILC  refers  to  the  'highest  level  of  qualifications  attained'  (in  ISCED 
levels)  and  we  specifically  focus  on  the  returns  on  upper  secondary  education 
relative to a base category combining ‘lower secondary education’ and lower levels 
(primary/pre-primary). 

(72)  Consistent with evidence reviewed in previous section of this study (e.g. Machin and 

Van Reenen, 1998). 

(73)  EU-LFS only includes monthly take home pay in deciles, and is not available for all 

countries. 

(74)  At  the  time  the  study  was  carried  out,  more  recent  data  were  available  from  EU-

SILC for 2012-13. 

97 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

5.2.1.3.  Earnings and skills: the role of contextual factors 

This analysis uses the EU-SILC variable cash and near-cash income (PY010G) 
in euros per year adjusted to the working hours (PL060). The resulting earnings 
variable  is  unaffected  by  part-time  effects  and  comparable  for  people  in 
dependent  employment  and  self-employment  (75).  To  account  for  the  effect  of 
unemployment  on  returns  on  skills,  the  earnings  variable  is  set  to  zero  for  the 
unemployed. Wage returns can be estimated straightforwardly also for low-skilled 
jobs  (as  all  people  working  in  low-skilled  jobs  are  observed  with  a  wage)  or 
skilled people working in low-skilled employment.  

In  the  Mincer  model,  a  causal  relationship  between  education  level  and 
earnings  is  identified  only  if  all  factors  that  influence  both  the  probability  to 
achieve particular education qualifications and earnings in adulthood are included 
in  the  regression  model.  If  factors  such  as  parental  background  (76)  are  not 
accounted for, the estimated coefficients are likely to be biased. For this reason 
the  analysis  uses  a  two-step  Heckman  selection  model,  including  a  number  of 
further  observable  characteristics  affecting  labour  supply,  such  as  gender,  age, 
marital  status,  whether  there  are  any  children  under  the  age  of  18  in  the 
household, the use of paid childcare and childcare by relatives and the spouse’s 
employment  status  and  level  of  education,  characteristics  of  parental  education 
background, and household characteristics. Results from the  probit model show 
that  significant  selection  bias  can  be  observed  in  most  countries  (except  in 
Belgium and Germany). 

5.2.1.4.  Earnings and skills: more than just education and training 

To  avoid  overestimating  the  value  of  formal  education  and  training,  the 
specification adopted explains earnings in two main ways:  
(a)  as a function of education and training experience;  
(b)  as a function of work experience (as a second order term, with a flattening 

profile over the life-course). 

Inclusion of work experience is important as it recognises that skills are not 
only  acquired  in  formal  education  but  also  on  the  labour  market  as  individuals 

(75)  We further adjust this hourly variable by removing observations with extreme values 
of observed earnings, at the 99-percentile and the 1-percentile of the distribution. 

(76) 

If children from more privileged family backgrounds tend systematically to earn more 
at any level of education (because, for instance, they have access to their parents’ 
network),  then  omitting  information  about  parental  background  would  overestimate 
the role of education. 

98 

                                                
Chapter 5. 
The costs of low-skilled adults 

continually  invest in  improving their human capital  while  working. Therefore, the 
inclusion of work experience contributes to robust and realistic estimations.  

Box 8. 

Specification adopted to estimate earnings differentials due to 
differences in skills 

The general specification of the Mincer equation adopted in this study is as follows:  

ln 𝑦 = ln 𝑦0 + 𝑟𝑆 + 𝛽1𝐸𝑋 + 𝛽1𝐸𝑋2  

where,  y0  represents  hourly  earnings  of  someone  without  education  and  work 
experience. S is a measure for education (such as years of education completed or a 
set of dummy variables to identify the type and level of highest qualification attained 
by  individuals);  EX  shows  the  number  of  years  of  (potential)  labour  market 
experience.  
In  line  with  the  scope  of  this  study,  the  Mincer  model  is  estimated  separately  for  all 
EU-28  Member  States  and  for  the  adult  population  (age  25  to  65),  making  use  of 
cross-section  data  from  EU-SILC.  We  estimate  these  models  in  samples  of  i 
individuals  in  the  different  Member  States  with  k  discrete  categories  of  educational 
attainment (with ISCED 0-2 representing the base category of low education): 

ln 𝑦𝑖 = ln 𝑦0 + 𝑟𝑘𝑆𝑘,𝑖 + 𝛽1𝐸𝑋𝑖 + 𝛽1𝐸𝑋𝑖

2 + 𝜀𝑖  

where  represents  the  error  term  of  the  empirical  model.  The  empirical  estimate 
estimates of the coefficient at the margin of low and intermediate skill-levels (ISCED 3 
over  ISCED 2)  can  be  interpreted  as  the  incremental  wage  increase  intermediate 
versus low skills. 

5.2.1.5.  Findings 

Table 16 shows estimated coefficients of ISCED 3 compared to a base category 
including  the  adult  population  ‘below  upper  secondary  education’  (ISCED  0-2). 
While  the  findings  of  a  naïve  OLS  model  suggest  substantial  and  significant 
earning  premiums  of  ISCED  3  for  all  Member  States,  the  Heckman  selection 
model (77) shows more realistic estimates of the wage premium as the differential 
risk  of  unemployment  by  skills  groups  and  parental  background  characteristics 
are  included  (78).  Nonetheless,  return  on  skills  is  above  20%  in  most  countries 
presenting significant coefficient estimates. 

(77)  Heckman  selection  model  has  been  used  to  address  the  selectivity  bias  of 
participation in employment (e.g. Hanusheck and Schwerdt, 2013; Mendolicchio and 
Rhein, 2011). 

(78)  Differential  risk  of  unemployment  by  skills  groups  and  parental  background 
characteristics are  included to address the issue  of endogeneity  of the educational 
variable. 

99 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Table 16. 

Incremental returns on skills  

ISCED 3 over ISCED 0-2 

ISCO 8 
(over ISCO 9) 

(1) 
OLS 
0.162*** 
0.299*** 
0.274*** 
0.132*** 
0.201*** 
0.142*** 
0.148*** 
0.259*** 
0.233*** 
0.145*** 
0.313*** 
0.354*** 
0.353*** 
0.234*** 
0.107* 
0.379*** 
0.252*** 
0.305*** 
0.222*** 
0.311*** 
0.268*** 
0.371*** 
0.240*** 
0.257*** 
0.233*** 
0.100*** 
0.392*** 
0.186*** 

(2) 
Heckman model 
0.135*** 
0.388*** 
0.163*** 
-0.047 
0.174*** 
0.099* 
0.199*** 
0.104** 
0.113*** 
0.071** 
0.171*** 
0.208*** 
0.214*** 
0.095** 
-0.108 
0.314*** 
0.160*** 
0.215*** 
0.170*** 
0.189*** 
0.102*** 
0.367*** 
0.013 
0.263*** 
0.159*** 
0.013 
0.080 
0.222*** 

(3) 
OLS 
0.169*** 
0.282*** 
0.254*** 
0.118*** 
0.120*** 
0.224*** 
0.068 
0.291*** 
0.231*** 
0.113*** 
0.159*** 
0.261*** 
0.542*** 
0.411*** 
0.203*** 
0.241*** 
0.213*** 
0.249*** 
0.241*** 
0.167*** 
0.326*** 
0.090* 
0.297*** 
0.165*** 
0.203*** 
0.198*** 
0.146* 
0.054 

BE 
BG 
CZ 
DK 
DE 
EE 
IE 
EL 
ES 
FR 
HR 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
RO 
SI 
SK 
FI 
SE 
UK 

Working ISCO 9 
ISCED 3- 
(over all ISCED 3) 
(4) 
OLS 
-0.141*** 
-0.154*** 
-0.296*** 
-0.191*** 
-0.434*** 
-0.302*** 
-0.295*** 
-0.153*** 
-0.294*** 
-0.094*** 
-0.145*** 
-0.260*** 
-0.568*** 
-0.348*** 
-0.269*** 
-0.455*** 
-0.198*** 
-0.242 
-0.162*** 
-0.271*** 
-0.227*** 
-0.218*** 
-0.158*** 
-0.244*** 
-0.236*** 
-0.201*** 
-0.098* 
-0.194*** 

* p<0.05; **p<0.01; ***p<0.001 

NB:  Analysis: Linear regression model with Heckman correction (ISCED) or OLS (ISCO). 

  Weight: pb040/personal cross-sectional weight. 

Selection: EU-28 (pb020/country); age range 25-65 (derived combining PB130/month of birth, 
PB140/year of birth, PB100/month of personal interview, PB110/year of personal interview, impute 
month of birth where unobserved); outside education pe010/current education activity; selection in labour 
force (pl031/self-defined economic status). 

Dependent variable: hourly wages (derived combining pl060/number of hours usually worked in main 
job; py010g employee cash or near case income; truncated 1/99%iles; logged). 

Variables included: Mincer variables: level of education pe040 ‘highest ISCED level attained’; occupation 
pl050/ISCO-88 major group; work experience (derived combining pb110/year of survey, pe030/year 
when highest level of education was attained); pb150/sex. 

Background characteristics: children under 18 years (derived from personal register based on rb010/year 
of survey, rb080/year of birth, rb220/father id, rb230/mother id, rl030 childcare at centre, rl040 childcare 
day-care, childcare childminder/rl050), spouses economic status (derived from pl031/self-defined 
economic status) 

Source:  Cedefop estimate on EU-SILC, 2011. 

100 

 
 
 
 
 
 
Chapter 5. 
The costs of low-skilled adults 

The  estimated  wage  returns  for  people  working  in  better  jobs  than 
elementary  occupations  (ISCO  8  over  ISCO  9)  are  also  significant  in  almost  all 
countries,  ranging  between  9%  in  Portugal  and  54%  in  Cyprus  (column  3  of 
Table 16. 

Column  4  of  Table  16  presents  estimates  for  mismatch,  i.e.  workers  with 
ISCED  3  or  above  employed  in  elementary  occupations.  The  estimated 
coefficients  are  consistently  negative  and  significant  in  most  Member  States 
(except in  Malta) and show that positive wage returns associated with ISCED 3 
or  above  qualifications  are,  on  average,  lower  than  of  people  with  these 
qualifications working in higher skilled jobs. In most countries the wage penalty is 
around  20%  to  30%,  although  there  are  countries  where  it  is  much  higher, 
including Germany (-43.4%), Estonia (-30.2%), Cyprus (-56.8%) and Luxembourg 
(-45.5%).  

5.2.1.6.  Cumulative (foregone) earnings for individuals aged 25 to 29 
To estimate monetary values of returns on skills for individuals aged 25 to 29 (79), 
we use the observed cash earning and the estimated coefficients from the Mincer 
models to derive the full lifetime earnings gains over the 40 years post-25 in the 
labour force, subject to average trend growth and discounting (80). We focus on 
cumulative returns of investing in ISCED 3 qualifications as opposed to ISCED 0-
2  by  using  the  salary  of  individuals  aged  25  to  29  (81).  As  shown  in  the  first 
column of Table 17, cumulative returns vary widely across Member States. 

5.2.1.7.  Opportunity costs: education and training come to a cost 

To derive net costs of low skills at the micro/individual level, we parameterise the 
opportunity cost of investing in an ISCED 3 qualification as the forgone income of the 
years spent studying, i.e. the income which would have resulted from working with a 
lower secondary qualification right after leaving education. Based on EU-SILC  

(79)  While we used EU-SILC data from 2011 to estimate costs of low skills to individuals 
based on the different demand and supply concepts of low-skills concepts set out in 
Chapter  1,  we  confined  the  valuation  of  the  monetary  costs  of  low  skills  to  ISCED 
education  variables:  first  because  an  estimate  of  the  time  spent  to  acquire  higher 
level  of  skills  is  possible  only  for  ISCED  levels;  and  second,  because  demand-
orientated  concepts  of  low  skills  can  all  only  be  applied  to  employed  people  while 
the analysis is to involve the whole population. 

(80)  Assuming an average trend in economic growth of 2% and a discount rate of 3.5%. 
(81)  Obtained from EU-SILC data 2012. Estimated earnings are independent of people's 
employment  probabilities  since  we  considered  both  employed  and  unemployed 
individuals (the latter with wages set to 0). 

101 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Table 17.  Costs of low level skills to the individual  

Country 

Cumulative  
earnings from 
higher skills (EUR) 

Investment 
costs to  
individual (EUR) 

Net benefit of higher 
skills in (EUR) 
(=costs of low skills) 

BE 
BG 
CZ 
DK 
DE 
EE 
IE 
EL 
ES 
FR 
HR 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
RO 
SI 
SK 
FI 
SE 
UK 

75 200 
14 900 
24 900 
– 
53 400 
18 500 
68 500 
22 200 
30 900 
31 000 
13 500 
96 200 
55 900 
8 700 
– 
277 500 
10 800 
84 800 
147 000 
82 700 
12 500 
90 300 
– 
74 200 
13 700 
– 
– 
76 000 

21 400 
1 900 
11 500 
44 800 
15 600 
16 200 
10 500 
5 900 
11 900 
17 200 
5 600 
20 900 
15 600 
7 500 
2 500 
34 500 
6 100 
13 700 
52 700 
23 700 
5 900 
12 200 
5 500 
27 700 
6 000 
38 200 
24 100 
33 400 

53 800 
13 000 
13 400 
– 
37 800 
2 300 
58 000 
16 300 
19 000 
13 800 
7 900 
75 300 
40 300 
1 200 
– 
243 000 
4 700 
71 100 
94 300 
59 000 
6 600 
78 100 
– 
46 500 
7 700 
– 
– 
42 600 

Cumulative 
earnings/ 
investment 
costs ratio 
3.5 
7.8 
2.2 
– 
3.4 
1.1 
6.5 
3.8 
2.6 
1.8 
2.4 
4.6 
3.6 
1.2 
– 
8.0 
1.8 
6.2 
2.8 
3.5 
2.1 
7.4 
– 
2.7 
2.3 
– 
– 
2.3 

NB:  Values rounded to nearest EUR 100 and only for countries where the return on ISCED 3 qualifications 

(compared to ISCED 0-2) was statistically significant at 95% level. 

Analysis: valuation. 

  Weight: none/individual. 

Selection: EU-28 (pb020/country); age range 25-65 (derived combining PB130/month of birth, 
PB140/year of birth, PB100/month of personal interview, PB110/year of personal interview, impute 
month of birth where unobserved); outside education pe010/Current education activity; selection in 
labour force (pl031/self-defined economic status). 

Valuation parameters:  

  life-course income differential at age 25 – derived from Table 16/country-specific returns on ISCED 3 
and work experience coefficients [Heckman models]; py010g_2/employee cash or near-cash income 
[zero for unemployment]; period: 40 years; discount rate: 3.5% p.a.; growth rate: 2% p.a.;  

  opportunity costs: income of low skills aged 15-24 assuming that most people take their qualification 
at this age – derived from pe040/level of education, py010g_2/employee cash or near-cash income 
[zero for unemployment]; and duration to acquire ISCED 3 (derived from pe040/level of education, 
pe030/year when highest level of education was attained); duration changed in Portugal, Finland and 
Sweden to officially reported figures due to small cells; discount rate/growth rate in opportunity costs: 
assume none. 

Source:  EU-SILC 2011 (impact)/EU-SILC 2012 (valuation) and Cedefop calculations. 

102 

 
 
 
Chapter 5. 
The costs of low-skilled adults 

data,  observed  earnings  are  multiplied  by  the  average  duration  needed  to 
achieve  upper  secondary  education  in  the  different  Member  States  (82).  The 
result of these calculations is reported in the second column of Table 17. 

5.2.1.8.  Net benefit of skills investment for the average young adult 

As a result of both the incremental return on lower skills and the opportunity costs 
of  skills  acquisition  (excluding  direct  costs),  we  derive  net  benefits  of  skills 
investment in terms of learning returns. These (foregone) net benefits are usually 
highest in high-income economies, particularly Luxembourg and the Netherlands, 
and lower in countries such as Estonia, Latvia, Hungary, and Poland (83). 

The average net benefit of higher skills is about  EUR 44 000. Although few 
reference  points  are  available  to  validate  our  findings,  our  estimate  is  largely 
consistent with results obtained for individual countries where similar approaches 
have been applied (Colon et al., 2011). 

One  of  the  strengths  of  this  approach  is  that  we  account  for  the 
individual’s opportunity costs, that is the foregone earnings that individuals could 
have  earned  working  in  lower  skill  regular  employment  during  the  period  of 
further  education/training.  Opportunity  costs  are  often  not  accounted  for  in 
existing  analyses  of  returns  on  further  education  and  training  (e.g.  McIntosh, 
2007). Other types of costs and benefits, both monetary and non-monetary, are 
not  considered  at  this  stage  due  to  lack  of  reliable  information  and/or  the  high 
degree of subjectivity that would need to be introduced in the analysis (84). 

Empirical estimates of the impact of skills on health 

5.2.2. 
Several studies have shown that health is one of the strongest predictors of well-
being and one of the main economic benefits of higher levels of basic skills and 
key competences in the population. In this section we compare health outcomes 
between the  low-skilled  (ISCED  0-2)  and the group  with  marginally  higher  skills 
(ISCED 3).  

(82)  According  to  EU-SILC,  across  the  EU  between  two  and  four  years  are  typically 

needed to acquire an upper secondary degree.  

(83)  An  adjustment  in  purchasing  power  standards  (PPS)  would  be  likely  to  reduce 

differences between countries.  

(84)  Such as intangible costs related to the loss of leisure time, and the effort of learning; 
possible  wages  and  other  monetary  benefits  gained  during  an  apprenticeship  or 
other  vocational  training  experience;  intangible  benefits  in  terms  of  happiness  and 
satisfaction  because  of  a  positive  learning  experience.  Direct  costs  (tuition  fees, 
learning  materials,  or  travel  and  childcare  costs)  may  also  affect  individuals  when 
they are not supported by employers or the government. 

103 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

5.2.2.1.  Observed health differentials (ISCED-0-2 versus ISCED 3) 

long-lasting/chronic illness or condition; 

EU-SILC  contains  several  health-related  questions.  Table  18  shows  the 
difference in the proportion of respondents with ISCED 2 and 3 reporting:  
(a) 
(b)  good or very good health; 
(c) 
(d)  unmet medical needs or treatment. 

limitation in activities because of health problems; 

Table 18  shows  a  wide  cross-country  variation  in  the  proportion  of 
respondents to the different questions, partly due to structural differences across 
the  countries  (age  structure,  quality  of  the  health  care  system,  expectations 
towards  health  and  tolerance  of  illness).  However,  statistically  significant 
differences  in  the  proportion  of  respondents  can  be  observed  between  the  low-
skilled and those with upper secondary education (ISCED 3).  

For instance, differences between ISCED 0-2 and ISCED 3 can be observed 
for  respondents  reporting  that  they  suffer  from  a  long-lasting/chronic  illness  or 
condition. The incidence of long-lasting/chronic illness is twice as high among the 
low-skilled  as  for  those  with  upper  secondary  education  in  Croatia,  the  Czech 
Republic  and  Malta.  In  Malta  for  example,  20.8%  of  low-skilled  adults  report 
suffering  from  long-lasting  health  problems,  compared  to  9.6%  of  those  with 
upper  secondary  education,  a  difference  of  11.2  percentage  points.  Substantial 
differences also are observed in Belgium, Cyprus, Finland, Ireland, Portugal and 
the UK. Similar results can be observed for the other questionnaire variables. 

5.2.2.2. 

Impact of skills on quality-adjusted life years (QALYs) 

Health differentials between groups are typically measured in quality-adjusted life 
years  (QALY)  weights,  an  index  scale  ranging  from  0  to  1,  where  1  refers  to 
perfect  health  and  0  to  near  death  (85).  QALY  weights  can  be  derived  directly 
from health questionnaires or can be estimated through probit regressions using 
information  on  self-perceived  health  (Cutler  and  Richardson,  1997).  EU-SILC 
includes  self-perceived  health  questions  which  appear  to  be  strongly  correlated 
with the prevalence of long-term and activity-limiting health problems  

(85)  For  instance,  a  value  of  0.8  means  that  the  present  year  of  life  is  estimated  to  be 

worth 0.8 year of life in perfect health. 

104 

 
                                                
Chapter 5. 
The costs of low-skilled adults 

Table 18.  Skills and health: systematic differences in the proportion of ISCED 2 

and 3 respondents (ISCED 3 – ISCED 0-2) 

Long-lasting/ 
chronic illness  
or condition 

Very good or  
good health 

Limitation in 
activities because 
of health problems 

Unmet need for 
medical 
examination 

BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

UK 

-0.089*** 

0.000 

-0.144*** 

-0.016 

-0.069*** 

-0.015 

-0.097*** 

-0.075*** 

-0.04*** 

-0.076*** 

-0.117*** 

-0.038*** 

-0.117*** 

-0.002 

-0.062* 

-0.068*** 

-0.077*** 

-0.112*** 

-0.017 

-0.079*** 

-0.039** 

-0.114*** 

-0.027*** 

-0.08*** 

-0.047* 

-0.143*** 

-0.071** 

-0.113*** 

0.097*** 

0.061*** 

0.165*** 

0.053 

0.139*** 

0.004 

0.09*** 

0.095*** 

0.074*** 

0.096*** 

0.231*** 

0.093*** 

0.127*** 

-0.019 

-0.004 

0.115*** 

0.146*** 

0.123*** 

0.067*** 

0.191*** 

0.115*** 

0.207*** 

0.043*** 

0.164*** 

0.12*** 

0.105*** 

0.039 

0.151*** 

-0.106*** 

-0.001 

-0.147*** 

0.035 

-0.136*** 

-0.018 

-0.069*** 

-0.081*** 

-0.042*** 

-0.061*** 

-0.099*** 

-0.071*** 

-0.055*** 

-0.007 

-0.030 

-0.061*** 

-0.083*** 

-0.032*** 

-0.025 

-0.099*** 

-0.043*** 

-0.074*** 

-0.044*** 

-0.091*** 

-0.076** 

-0.042 

-0.067** 

-0.088*** 

-0.032*** 

-0.089*** 

-0.03* 

-0.017 

-0.054*** 

-0.034 

-0.027** 

-0.019 

0.000 

-0.013 

-0.059*** 

-0.043*** 

0.017* 

-0.058*** 

-0.016 

0.002 

-0.084*** 

-0.021*** 

-0.003 

-0.008 

-0.046*** 

-0.046*** 

-0.05*** 

-0.002 

-0.075*** 

-0.007 

-0.049* 

0.001 

* p<0.1 **p<0.05 ***p<0.01 
Weight: pb040/personal cross-sectional weight 
Selection: EU-28 (pb020/country); Age range 25-65 (derived combining PB130/month of birth, PB140/year of 
birth, PB100/Month of personal interview, PB110/year of personal interview, impute month of birth where 
unobserved); Outside education pe010/Current education activity. 

Dependent variable column 1: ph020/Long-term health problems. 
Dependent variable column 2: ph010/Very good or good health. 
Dependent variable column 3: ph030/Limitation in activities because of health problems. 
Dependent variable column 4: ph040/Unmet need for medical examination. 
Variables included: Level of education pe040 ‘Highest ISCED level attained’ 

Source:  EU-SILC, Cedefop estimate. 

105 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

The probit approach is used by Groot and Maassen van den Brink (2007) to 
estimate  the  effect  of  education  on  health  in  the  Netherlands.  In  the  empirical 
modelling we have to distinguish between the true quality of health, 𝐻∗, a latent 
variable which cannot be directly observed, and the subjective measure of health, 
𝐻𝑆, which is measured by the self-perceived health question. The latent quality of 
health variable is assumed to be related to educational attainment in the following 
way: 

6
H∗ = ∑ βiISCEDi
i=3

+ 𝐱γ + ε 

Where 𝐼𝑆𝐶𝐸𝐷𝑖 is equal to 1 if the respondent’s highest qualification is of level 
i.  ISCED  0-2  is  the  excluded  category,  so  that  𝛽𝑖  measures  the  differential  in 
latent health associated with 𝐼𝑆𝐶𝐸𝐷𝑖 qualification, compared to low-skilled adults. 
x is a vector of individual characteristics (including parental backgrounds) and 𝜀 
is an error term capturing unmeasured factors.  

The  observed  health  status  𝐻𝑆  is  a  categorical  ordered  response  variable 

and is assumed to be related to the latent variable in the following way: 

HS = k  ↔ αk−1 < H∗ ≤ αk, k = 1, … ,5 

Here  n  is  the  number  of  response  categories  (5  for  the  general  health 
question  in  EU-SILC)  and  𝛼𝑖  are  threshold  levels.  The  coefficients  can  be 
estimated using ordered probit models. 

Cutler and Richardson (1997) provided an easy way to calculate the QALY 
weight based on coefficients from ordered probit regressions. The 𝛽 coefficients 
are  not  scaled  (in  theory  they  can  range  from  −∞  to  +∞)  and  need  to  be 
normalised  to  derive  a  QALY  weight  (which  ranges  between  0  and  1). 
Normalisation  is  obtained  by  dividing  the  coefficient  by  the  difference  in  the 
thresholds of the upper and lower category. The QALY weight for qualification of 
level i is given by: 

QALYwi =

βi
α4 − α1

The  QALY  weight  measured  by  this  approach  can  be  interpreted  as  the 
those  with 

difference 
low-skilled  adults  and 
qualification 𝐼𝑆𝐶𝐸𝐷𝑖, controlling for relevant individual characteristics. 

in  QALY  weight  between 

The  main  issue  is  that  differences  in  QALY  weight  across  education  may 
also be affected by other factors such the level of parental resources (Cutler and 
Lleras-Muney,  2012).  This  is  why  we  use  data  from  the  2011  EU-SILC,  which 

106 

 
Chapter 5. 
The costs of low-skilled adults 

contains  an  ad  hoc  module  on  intergenerational  transmission  of  disadvantages. 
This  module  asks  people  aged  25  to  59  questions  about  their  parental 
background  when  they  were  around  14  and  allow  us  to  include  in  the  model 
specification the following characteristics of the individuals (86): 
(a)  gender; 
(b)  age (five-year bands); 
(c) 
(d)  migration status; 
(e)  region (NUTs 1); 
(f) 
(g) 
(h) 
(i) 

father’s and mother’s education level; 
father’s and mother’s activity status; 
financial situation of the household; 
tenancy status. 

time since highest qualification was obtained (five-year bands); 

5.2.2.3.  Valuation of individual health differentials 

The  differential  in  QALY  weight  can  be  used  to  derive  monetary  values  of  the 
health  cost  of  low  skills.  The  EuroVaQ  project  developed  different  methods  to 
the  monetary  value  of  a  QALY  across  several  Member 
determine 
States (Donaldson et al., 2010). For this study, following a conservative approach 
we take the mid-point of the different estimates to derive the health cost of low-
skilled  adults  in  Member  States  (87)  We  also  show  the  cumulative  difference  in 
QALYs under the conservative assumption that the life expectancy at age 25 is 
the  same  for  low-skilled  adults  as  for  the  average  individual.  Appropriate 
discounting is used to account for the fact that individuals tend to value benefits 
today more than benefits expected to occur in the future (88). 

Table  19  shows  the  cumulative  difference  in  QALYs  between  low-skilled 
adults  and those with upper  secondary  education,  plus  estimates of  the lifetime 

(86) 

Individual characteristics such as activity status, marital status, and income were not 
included in the model,  as  part of the  effect of education on health may  go  through 
these channels (they are considered to be ‘bad’ controls). It must be considered that 
there are other factors that may drive the correlation between health and education 
that  we  could  not  capture,  such  as  intertemporal  preferences  of  individuals.  Since 
information  such  as  health  status  in  childhood  was  not  available,  we  were  also 
unable  to  address  potential  reverse  causality  (effect  of  health  on  educational 
attainment). 

(87)  Although the EuroVaq final report (Donaldson et al., 2010) did not present estimate 
of value per QALY for France, it presents estimates of value per life year. Value per 
QALY in France was imputed based on the ratio of value per QALY to value per life 
year in the other countries. 

(88)  We used a 3.5% discount rate, and 2% expected growth. 

107 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

health  costs  associated  with  low-skilled  adults  in  the  countries  for  which 
estimates  of  value  per QALY  are  available.  In most  countries,  low-skilled  adults 
are expected to enjoy significantly less QALYs than those with upper secondary 
education,  although  significant  cross-country  variation  can  be  observed.  In 
addition  to  QALY  differentials,  estimates  of  lifetime  health  costs  of  being  low-
skilled  are  also  considered  in  the  analysis.  Although  these  estimates  are 
available  in  seven  Member  States  only,  this  permits  an  estimate  of  the  lifetime 
health costs associated with low skills. 

Table 19.  Lifetime health costs of low skills 

QALY weight 
differentials 
ISCED 3 over 0-2 
(A) 
0.129*** 
0.025** 
0.072*** 
0.092*** 
0.084*** 
0.089* 
0.111*** 

Life 
expectancy  
at age 25 
(B) 
55.9 
58.6 
58.0 
51.5 
57.0 
57.5 
56.7 

Lifetime 
differences 
in QALYs 
(C=A*B) 
7.2  
1.5  
4.2  
4.7  
4.8  
5.1  
6.3  

QALY 
value 
(D) 
36 983 
79 459 
42 476 
31 744 
56 789 
51 054 
60 032 

Lifetime health 
costs (not 
discounted) 
(E=C*D) 
266 278 
119 189 
178 399 
149 197 
272 587 
260 375 
378 202 

Lifetime 
health costs 
(discounted) 
F=E (NPV)a 
177 532 
79 465 
118 942 
99 472 
181 738 
173 596 
252 153 

DK 
ES 
FR 
HU 
NL 
SE 
UK 

* p<0.1 **p<0.05 ***p<0.01 
(a) net present value 
NB:  Values per QALY refer to the mid-point values from the three different estimates in the EuroVaQ report 

(Donaldson et al., 2010). 

Analysis: valuation. 

  Weight: none/individual. 

Selection: EU-28 (pb020/country); age range 25-65 (derived combining PB130/month of birth, 
PB140/year of birth, PB100/month of personal interview, PB110/year of personal interview, impute 
month of birth where unobserved); outside education pe010/Current education activity. 

Valuation parameters: 

  quality-adjusted life years (QALYs) monetary value – derived from ordered probit analysis of EU SILC 

2011 ad hoc module (intergenerational transmission of disadvantages, life expectancy at age 25 
Eurostat [series name]; per-QALY monetary values (Donaldson et al., 2010); period: 40 years; 
discount rate: 3.5% p.a.; growth rate: 2% p.a.; 

  quality-adjusted life years (QALYs) monetary value – alternative derived from ordered probit analysis 
of EU SILC 2011 ad hoc module (intergenerational transmission of disadvantages, life expectancy at 
age 25 Eurostat [series name]; per-QALY monetary value changed to EUR 31 250 (based on National 
Institute for Health and Care Excellence in the UK/midpoint (NICE, 2013)); adjusted to other countries 
based on GDP per capita differentials [series nama_10_pc]. 

Source: EU-SILC 2011 (impact)/Cedefop calculations; life expectancy at age 25: Eurostat. 

Results  from  countries  where  full  information  is  available  confirm  that  low 
levels  of  skills  are  associated  with  substantial  health costs. The  lifetime present 
value (89) of health costs for the average low-skilled would be about EUR 150 000 
and a corresponding average QALY value of about EUR 50 000.  

(89)  Under the usual assumptions of 3.5% discount rate and 2% growth rate. 

108 

 
 
 
 
 
 
                                                
Chapter 5. 
The costs of low-skilled adults 

As  figures  acquired  from  studies  such  as  the  EuroVaQ  tend  to  exceed 
national sources, towards the end of the final estimates provided in this study we 
assumed a very conservative QALY value of EUR 31 250 (midpoint of estimates 
provided in the guidelines used by the NICE-UK (NICE, 2013)). The estimates for 
other  countries  are  calculated  weighting  the  QALY  value  for  the  GDP  of  the 
country concerned (GDP ratio). 

Valuation of the impact of skills on crime 

5.2.3. 
It is widely recognised in specialised literature that there are benefits to education 
and  training  in  relation  to  crime.  These  are  not  considered  by  individuals 
themselves  and  so  help  increase  the  social  return  on  education  and  training 
distinct from the private return. Five possible channels can be identified. 
(a) 

income  effects,  since  education  and  training  raise  the  opportunity  cost  of 
time spent engaged in criminal activity or in subsequent incarceration; 

(b)  direct  effects  on  risk  aversion,  since  education  and  training  may  reduce 
discount  rates  of  individuals  and,  therefore,  the propensity  to commit  crime 
because of the associate risk of future punishments; 

(c)  direct  (positive)  effects  on  the  return  on  crime  may  also  occur  if  education 
and  training  may  increase  the  earnings  one  can  derive  from  crime,  the 
association of crime and education is positively influenced; 

(d)  direct effects on reducing motivation to commit crime, relevant especially for 
teenagers who tend to participate in criminal activities generally for futile and 
hedonic reasons. 

However,  only  a  few  studies  provide  concrete  monetary  measures  of  the 
benefits  arising  from  the  crime-reduction  effect  of  education  and  training;  they 
generally  do  not  identify  the  magnitude  of  the  benefit  separately  for  individuals 
and  society  at  large  as  this  is  particularly  difficult  to  measure.  The  available 
estimates  can  be  interpreted  as  social  savings  from  crime reduction  associated 
with higher level of education/skills. 

For this study, the best estimates of the effects of education and training in 
monetary terms are provided by Lochner and Moretti (2001; 2004) and Machin et 
al. (2011).  
(a)  Lochner  and  Moretti  (2004)  estimate  the  effect  of  education  in  the  US  on 
participation of criminal activity and find that schooling significantly changes 
criminal  behaviours.  They  distinguish  between  social  and  private  return  on 
education  and  estimate  that  social  savings  is  about  20%  of  private  returns 
associated  to  with  high  school  graduation.  From  this  it  is  estimated  that  a 
one  percentage  point  increase  in  male  high  school  graduation  rate  would 
have  resulted  in  around  95 000  fewer  crimes  taking  place  in  1990  for  a 

109 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

social  benefit  of  USD  1.4  billion  (at  1993  price),  leaving  aside  many  of  the 
costs associated with crime and including only a partial list of all crime. On 
this  basis,  an  underestimate  of  the  social  benefits  per  additional  male 
graduate  would  be  about  EUR  5 000  at  current  price  (USD 3 000  in  2008 
(90)); 

(b)  Machin et  al.  (2011)  study  the  crime-reducing  potential  of  learning, framing 
the analysis in a regression-discontinuity setting under the assumption of an 
increase  in  the  age  of  leaving  from  education  and  training.  The  empirical 
analysis  identifies  a  robust,  causal  impact  of  education  on  property  crime. 
They estimate the social benefits from crime reduction that would follow from 
a  1%  reduction  in  the  percentage  of  individuals  with  no  qualifications:  this 
would  result  in  about  0.9%  fall  in  property  crime  convictions,  producing  a 
total  net  social  benefit  of  30  million  GBP  (in  2007  prices)  over  a  10-year 
span.  The  study  suggests  that  the  social  benefits  from  the  crime-reducing 
effect  of  education  are  EUR  16 000  at  current  price  (GBP  10 000  in  2007 
prices) per additional male qualification. 

In  their  empirical  analysis,  Machin  et  al.  (2011)  identify  a  robust,  causal 
impact of education only on property crimes, while results on violent crimes were 
not significant. If their estimates were to be robust and significant also for violent 
crimes,  as for  Lochner  and  Moretti  (2004),  the  full  impact  of  the  crime-reducing 
effect  of  education  would  be  even  higher  as  the  unit  cost  of  violent  crimes,  as 
estimated  for  instance  by  Dubourg  et  al.  (2005),  is  substantially  higher  than 
property crimes.  

Costs of low skills to firms 

5.2.4. 
The  key  benefits  of  higher  level  skills  to  firms  are  increased  productivity  and 
profitability, which represent a return on investment to the employer, in addition to 
individual  wage  increases  found  in  empirical  studies.  Literature  also  discusses 
the  effect  of  skills  on  further  organisational  outcomes  like  innovation  or  growth. 
Hogarth et al. (2012), Konings and Vanormelingen (2015), Dearden et al. (2005) 
found  a  positive  employer  return  on  skills  investment  with  about  half  of  the 
productivity  gain  brought  about  by  training.  Further  benefits  of  skills  investment 
by  employers  are  lower  recruitment  costs  for  skilled  workers  if  they  provide  in-
house  training  and  achieve  higher  skill  levels,  which  would  also  increase 
profitability.  While 
to 
calculate/estimate. 

they  are  difficult 

tangible  benefits, 

these  are 

(90)  Based on the estimates converted in 2008 dollar by Lochner and Moretti (2001). 

110 

                                                
Chapter 5. 
The costs of low-skilled adults 

Table 20.  Compensation of employees and operating surplus in 2015 

Compensation of 
employees 
(A) 
6 952 106 
206 846 
18 027 
66 549 
140 775 
1 538 440 
9 843 
78 294 
59 021 
509 894 
1 136 828 
20 816 
651 294 
7 690 
10 585 
15 334 
24 847 
45 949 
3 851 
331 798 
163 418 
156 634 
77 546 
51 784 
18 774 
29 942 
102 460 
211 892 
1 280 194 

Gross operating surplus 
and mixed income 
(B) 
5 939 780 
162 821 
20 474 
84 558 
87 205 
1 187 774 
7 947 
157 940 
94 366 
460 156 
757 891 
15 498 
768 014 
7 252 
11 014 
17 887 
21 698 
45 162 
3 884 
274 612 
132 676 
221 550 
78 095 
89 374 
14 503 
40 756 
80 355 
142 434 
982 678 

EU-28 
BE 
BG 
CZ 
DK 
DE 
EE 
IE 
EL 
ES 
FR 
HR 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
RO 
SI 
SK 
FI 
SE 
UK 

Surplus/compensation 
ratio (%) 
(C) 

85 
79 
114 
127 
62 
77 
81 
202 
160 
90 
67 
74 
118 
94 
104 
117 
87 
98 
101 
83 
81 
141 
101 
173 
77 
136 
78 
67 
77 

Source: Eurostat, GDP income components. 

5.2.4.1.  Benefits to employers of higher level of education and skills  
Apart  from  theoretical  reasons  (91),  the  main  difficulty  with  estimating  the 
employer  benefit  of  higher  level  skills  is  the  lack  of  suitable  research  data. 
Although  some  empirical  estimates  are  available  in  literature  (e.g.  Konings  and 
Vanormelingen,  2015;  Crépon  et  al.,  2003;  Hellerstein  et  al.,  1999;  Hellerstein 

(91) 

In  a  hypothetical  perfect  completion  economy  factors  of  production,  capital  and 
labour,  would  be  allocated  and  paid  for  in  accordance  to  their  marginal.  As  a 
consequence,  workers  paid  below  their  true  level  of  productivity  would  find 
alternative  employment  and,  in  the  longer  term,  wages  and  productivity  would  be 
aligned.  However,  more  recent  literature  explains  why  productivity  of  employees  is 
to increase more than their wages in response to training. 

111 

 
                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

and Neumark, 2007), to provide a comprehensive estimate of the overall benefits 
of skills investment at EU level, we will make use of data provided by Eurostat on 
GDP  income  components  (92)  and  specifically  of  the  gross  profit  ratio  of  non-
financial corporations as a proxy for firms’ profits.  

For  the  purpose  of  our  estimate  a  surplus/compensation  ratio  has  been 
calculated to complement the cumulative (foregone) earnings for individuals and 
take  into  account  added  value  created  during  the  production  process  which 
remunerates capital and employers’ work more generally.  

Table 21 shows the ratio between compensation of employees and surplus 
remunerating  capital  and  employers’  work  at  85%  in  2015.  That  is  to  say  that 
about 46% of the added value distributed to employees and ‘employers’ goes to 
the latter. This is a value close to the above empirical findings and is stable over 
time.  

On  this  basis,  in  addition  to  the  cumulative  returns  for  individuals  the 
average further return for employers due to the increased productivity of investing 
in ISCED  3 qualifications  as opposed  to  ISCED 2 is  about  EUR  37  000.  This  is 
calculated applying the surplus/compensation ratio to the average net benefit of 
higher skills for the individual (about EUR 44 000) derived from Table 17.  

Table 21.  GDP components: surplus/compensation ratio 2011-15 

EU-28 

2011 
86% 

2012 
84% 

2013 
85% 

2014 
85% 

2015 
85% 

Source:  Eurostat, GDP income components. 

5.2.4.2.  Further costs and benefits of higher level of education and skills  

In  addition  to  the  direct  productivity  effect  of  higher  level  skills  benefiting  the 
individual  or  firms,  further  positive  returns  at  higher  levels  of  aggregation  are 
possible:  diffusion  processes  within  firms;  productivity  effects  along  the  supply 
chain;  and  industry-wide  ‘upskilling’,  for  example  through  labour  turnover.  This 
suggests that total returns on skills exceed individual returns because of spillover 
effects  both  within  firms  and  in  the  economy  at  large.  For  example,  a  recent 
Conlon  et  al.  (2012)  survey  of  international  empirical  evidence  reported  an 
additional  9%  to  12%  return  resulting  from  intra-firm  diffusion.  These  are 

(92)  GDP  income  components  include:  (i)  compensation  of  employees,  that  is  the  total 
remuneration, in cash or in kind payable by an employer to an employee; (ii) gross 
operating  surplus  and  mixed  income,  that  is  the  surplus  on  production  activities 
before the interest, rents or charges, plus the remuneration for the work carried out 
by the employer; (iii) taxes less subsidies on production and imports.  

112 

 
 
                                                
Chapter 5. 
The costs of low-skilled adults 

increases in wages of workers not directly involved in education and training, but 
which  result  from  diffusion  of  benefits  deriving  from  co-workers  investing  in 
education and training. They are therefore non-private returns on the educational 
investment.  Ignoring  these  externalities  inevitably  understates  the  full  impact 
arising from skills investment. 

However,  direct  costs  for  education/training  have  not  been  considered  so 
far. Usually direct costs to acquire ISCED 3 qualifications are mostly borne by the 
public  sector  and  partly  by  individuals  and  their  families.  Employers  may  also 
face  investment  costs  for  employee  skills  development.  Some  studies  provide 
estimates for such costs, for example in relation to apprenticeships (e.g. Hogarth 
et  al.,  2012;  Pfeiffer  et  al.,  2009):  these  include  staff  costs  for  participants  in 
training/skills  development  (93)  and  set-up  costs, 
learning  materials/other 
consumables,  administrative  costs,  training  workshops  delivered  in-house,  and 
course fees if not paid for by the government. However, systematic information at 
EU  level  on  employer  investment  in skills  suitable  for  empirical  estimates  is  not 
currently available. 

The public budget costs of low skills: a satellite account  

5.2.5. 
While  there  are  additional  benefits  from  higher  level  skills  affecting  the  public 
budget  in  the  long  term,  for  example  via  multiplier  effects  and  higher  economic 
growth,  at  micro  level  we  focus  on  some  tangible  measures  of  public  budget 
effects: 
(a) 

investment for skills acquisition: 
(i) 
(ii) 

education/training spending to facilitate higher level skills; 
relatively  lower  revenues  in  compulsory  insurance  contributions  and 
direct  and  indirect  taxes  due  to  people  engaging  in  skills  investment 
rather than working at lower level skills;  

(b)  benefits of higher levels skills: 

(i) 

increased  employment  probability  and  higher  average  wages  result  in 
net  earnings  increase,  which  feeds  through  to  public  budgets  via 
increased labour taxes; 

(ii)  higher earnings have additional effects on indirect taxes (94); 

(93)  Wages,  compulsory  insurance,  including  social  security  insurance,  discretionary 
payments,  pension  contributions;  course  fees  and/or  staff  costs  of  supervisors 
(wages, related employer compulsory insurance, including social security insurance, 
discretionary payments, pension contributions). 

(94)  Considering  the  life-course  earnings  of  individuals  the  assumption  is  that  all 

disposable income is ultimately (either directly or indirectly) spent on consumption. 

113 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

(iii) 

lower  out-of-work  benefits  (because  of 

the  effect  of 
improved 
employment resulting from higher level skills) for public budgets in the 
longer term, in relation to the life-course labour market trajectory of low-
skilled people compared to the comparatively higher skilled. 

Full valuation of the total costs of low skills on public budgets is not possible 
for  lack  of  suitable  data.  However,  existing  data  allow  us  to  estimate  the  public 
budget  necessary  to  improve  skills  levels  (based  on  data  for  education 
spending),  and  cash-flow  associated  with  higher  labour  and  consumption  taxes 
resulting from increased incomes and higher-level skills. 

5.2.5.1.  Valuation of public budget effects of individual earnings returns  

The  individual  net  benefits  of  higher  level  skills  in  terms  of  increased  earnings 
have  a  direct  link  to  public  budgets  through  increased  labour  taxation  and 
consumption taxes, which can be valued in monetary terms.  

This component is not included in the final estimate as it would largely result 
in a duplication (income tax is included in the gross wage returns of individuals), 
but  it  is  possible  to  estimate  the  effect  on  the  public  budget  using  the  results 
obtained in the Mincer models (where significant): increased individual life course 
earnings can be calculated with a marginal labour tax, including social insurance 
contributions (Table 22, column 2) corresponding to the income tax rate and the 
social security contributions (SSC) for earnings amounting to 67% of the average 
wage paid by a married individual with two children (95).A further effect on public 
budget  results  from  a  potential  return from  value-added  taxes  (VAT)  due  to  the 
impact of net income on consumption.  

For  the  average  individual  in  the  average  country  (arithmetic  average),  the 

potential return for the public budget is about EUR 27 000.  

5.2.5.2.  Valuation of public budget effects created by out-of-work benefits 

To  estimate  the  life-course  differences  in  gross  benefit  payments  between  the 
low-skilled (ISCED 0-2) and the group with marginally higher skills (ISCED 3), we 
look  separately  at  unemployment  benefits  and  other  benefits.  Other  benefits 
include  old  age  benefits  (pensions,  care  and  disability  allowance  if  above 
retirement age), survivor benefits (survivor’s pension and death grant), sickness 
benefits  (paid  sick  leave),  disability  benefits  (disability  pension,  care  allowance) 

(95)  European Commission: Tax and benefits indicators database (2012): 

https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-
databases/tax-and-benefits-indicators-database_en 

114 

                                                
 
Chapter 5. 
The costs of low-skilled adults 

and education allowance (grants, scholarships and other education help received 
by  students).  Benefits  which  are  received  at  the  household  level  (such  as 
housing and child benefits) are excluded from this analysis. 

Table 22. 

Implications for tax revenue 

Net benefit 
(*) 
75 200 
14 900 
24 900 
53 400 
18 500 
68 500 
22 200 
30 900 
31 000 
96 200 
8 700 
277 500 
10 800 
84 800 
147 000 
82 700 
12 500 
90 300 
74 200 
13 700 
76 000 

BE 
BG 
CZ 
DE 
EE 
IE 
EL 
ES 
FR 
IT 
LV 
LU 
HU 
MT 
NL 
AT  
PL 
PT 
SI 
SK 
UK 

Marginal tax rate 
(in %), 2012 (**) 

55 
22 
31 
47 
25 
31 
28 
39 
42 
40 
33 
34 
35 
22 
48 
44 
30 
25 
22 
30 
32 

Net return 
rounded 
41 400 
3 300 
7 700 
25 100 
4 600 
21 200 
6 200 
12 100 
13 000 
38 500 
2 900 
94 400 
3 800 
18 700 
70 600 
36 400 
3 800 
22 600 
16 300 
4 100 
24 300 

Average VAT 
(2012) (***) 

21 
20 
20 
19 
20 
23 
23 
18 
20 
21 
21 
17 
27 
18 
21 
20 
23 
23 
20 
20 
20 

VAT return 
rounded 
8 700 
700 
1 500 
4 800 
900 
4 900 
1 400 
2 200 
2 600 
8 100 
600 
16 000 
1 000 
3 400 
14 800 
7 300 
900 
5 200 
3 300 
800 
4 900 

NB:  (*) Return estimates not significant for DK, LT, RO, SE and FI. (**) marginal tax rate not comparable for 
CY and HR; marginal tax rate (in %) - income tax and social insurance contributions for earnings 
corresponding to two thirds of the average wage (for married individuals with two children), not available 
for some countries. (***) European Commission: Taxation and customs union, 1 July 2011 
https://ec.europa.eu/taxation_customs/home_en [retrieved 29.1.2012]. 

Analysis: valuation. 

  Weight: none/individual 

Selection: EU-28 (pb020/country); age range 25-65 (derived combining PB130/month of birth, 
PB140/year of birth, PB100/month of personal interview, PB110/year of personal interview, impute 
month of birth where unobserved); outside education pe010/Current education activity; selection in 
labour force (pl031/self-defined economic status). 

Valuation parameters: 

   net return from increased labour taxation – derived Table 22, column 4; marginal tax rate in % in 2012 
from European Commission, tax and benefits indicators database, 2012 (income tax and SSC for 
earnings amounting to 67% of the average wage paid by a married individual with two children) 
http://ec.europa.eu/economy_finance/db_indicators/tab/; 

   net return from increased VAT consumption – Derived Table 22, column 4; marginal tax rate in % in 
2012 from European Commission, tax and benefits indicators database, 2012 (income tax and SSC 
for earnings amounting to 67% of the average wage paid by a married individual with two children, 
http://ec.europa.eu/economy_finance/db_indicators/tab/) and VAT rates (European Commission, 
2014).  

Source:  EU-SILC (2012); European Commission, tax and benefits indicators database, 2012; European 

Commission: Taxation and customs union, 1 July 2011. 
https://ec.europa.eu/taxation_customs/home_en [retrieved 29.1. 2012]. 

115 

 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Table 23.  Differences in life course receipt of benefits between ISCED 0-2 and 3 

(based on EU-SILC 2012) 

Unemployment 
benefits (EUR) 

Other benefits  
(EUR) 

Total difference  
(EUR) 

BE 
BG 
CZ 
DK 
DE 
EE 
EL 
ES 
IE 
FR 
HR 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
RO 
SI 
SK 
SE 
FI 
UK 

27 000 
-300 
500 
60 700 
18 800 
-300 
100 
5 900 
36 500 
600 
1 400 
8 100 
9 800 
0 
-500 
16 600 
5 500 
8 200 
4 500 
14 200 
-100 
2 000 
0 
-1 700 
1 200 
2 000 
19 800 
8 200 

1 600 
-2 300 
2 900 
12 100 
-500 
3 000 
600 
1 900 
10 800 
800 
100 
2 000 
-2 600 
-1 500 
-2 000 
3 400 
0 
3 300 
6 500 
-4 600 
-1 800 
900 
400 
600 
600 
11 800 
3 000 
4 900 

28 600 
-2 600 
3 400 
72 800 
18 200 
2 700 
700 
7 800 
47 300 
1 400 
1 600 
10 100 
7 200 
-1 500 
-2 500 
20 000 
5 500 
11 500 
11 000 
9 600 
-1 900 
2 900 
400 
-1 100 
1 900 
13 800 
22 900 
13 100 

NB:  Totals may not add up because of rounding. 

Model type: none/valuation. 

  Weight: pb040/personal cross-sectional weight. 

Selection: EU-28 (pb020/country); age range 25-65 (derived combining PB130/month of birth, 
PB140/year of birth, PB100/month of personal interview, PB110/year of personal interview, impute 
month of birth where unobserved); outside education pe010/current education activity 
Valuation parameters: 
  life-course differential in unemployment benefits at age 25 (py090g/Unemployment benefits; 

pe040/level of education; annual averages in age range 25-65 (derived/see above in four bands); 
period: 40 years; discount rate: 3.5% p.a.; growth rate: 2% p.a.); 

   life-course differential in other benefits at age 25 (sum of py100g/Old-age benefits, py110g/survivor’ 
benefits, py120g/sickness benefits, py130g/disability benefits, py140g/education-related allowances; 
pe040/level of education; annual averages in age range 25-65 (derived/see above in four bands); 
period: 40 years; discount rate: 3.5% p.a.; growth rate: 2% p.a. 

Source:  Cedefop calculations on EU-SILC 2011 (impact)/EU-SILC 2012 (valuation).  

116 

 
 
 
 
 
Chapter 5. 
The costs of low-skilled adults 

First  we  estimate  benefits  by  age  group  (96)  for  people  with  ISCED  0-2 
compared  to  ISCED  3,  setting  benefit  payments  to  zero  for  those  who  do  not 
receive  any.  To  derive  life-course  difference,  for  every  individual  aged  between 
25 and 34 we assume that the observed difference in benefit payment will apply 
for  every  year  between  25  and  34,  subject  to  2%  growth  and  appropriate 
discounting.  Then,  for  every  year  between  35  and  44,  we  assume  that  the 
corresponding differential applies. We apply this method until age 64. Finally we 
sum up differences in benefit payment for every year to estimate the difference in 
life-course  benefit  payment  between  the  low-skilled  and  the  higher  skilled 
individuals. 

Table  23  shows  estimates  of  life-course  differential  in  receipt  of  benefit 
payments  for  all  Member  States  between  the  low-skilled  (ISCED  0-2)  and  the 
group with marginally  higher  skills  (ISCED  3).  In  many  Member  States, the  life-
course difference in benefit payment comes mostly from differences in the receipt 
of  unemployment  benefits  (97).  There  is  a  wide  cross-country  variation:  the 
average  amount  of  unemployment  benefits  depends  on  both  the  incidence  of 
unemployment  and  the  generosity  of  the  unemployment  benefits  system. 
However,  in  most  Member  States,  low-skilled  individuals  tend  to  receive  more 
unemployment  benefits  than  those  with  a  marginally  higher  level  of  skills.  The 
average  amount  of  other  benefits  received  by  the  low-skilled  individual  also 
varies considerably across countries (98).  

5.2.5.3.  Valuation of public budget effects of creating higher skills levels  

To estimate public spending on education and training to achieve qualifications at 
ISCED 3 levels, we use the public education final expenditure in upper secondary 
education  (ISCED  3)  in  2012  (Table  24,  column  1)  and  the  total  number  of 
students  enrolled  in  upper  secondary  education  (99)  during  the  academic  year 
2012/13 (Table 24, column 2). From this, total public spending per participant is 

(96)  25 to 34, 35 to 44, 45 to 54 and 55 to 64. 
(97)  One limitation of this analysis is that estimates are based on observed differences in 
benefit  receipt  and  are  not  further  adjusted  for  differences  in  demographic 
characteristics such as gender and race (although age differences between the two 
groups are partly accounted for since the analysis is based on age groups). 

(98) 

In  almost  all  countries,  larger  benefits  payments  are  observed  for  older  individuals 
(55 to 64) than any  other  age group. This is  due to  pensions being included in the 
other benefit category. In most countries, low-skilled individuals tend to receive, on 
average, larger benefit payments. 

(99)  The figure for France corresponds to the total number of full-time students. 

117 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

estimated  taking  into  account  the  empirically  observed  average  years  spent  in 
upper secondary education in every country (EU-SILC 2012).  

On average across the Member States for which has been possible to get an 
estimate, the public spending on education and training to achieve qualifications 
at ISCED 3 levels is about EUR 18 000. 

Table 24.  Public spending in ISCED 3 qualifications 2012/13 

Public 
expenditure in 
upper secondary 
education in 
million EUR (2012) 
(A) 

BG 
CZ 
DE 
EE 
ES 
FR 
CY 
LV 
LT 
LU 
HU 
AT 
PL 
PT 
RO 
SK 
SE 

295 
1 571 
24 402 
175 
8 535 
24 573 
278 
199 
209 
414 
944 
3 354 
3 310 
1 758 
765 
652 
5 789 

Total students 
enrolled in upper 
secondary 
education (full-time 
equivalents 2013) 
(B) 
270 558 
431 932 
2 557 611 
42 853 
1 477 376 
2 581 511 
32 128 
69 324 
89 677 
24 345 
472 434 
369 842 
1 410 886 
372 708 
832 592 
222 339 
429 414 

Yearly spending 
per participant  
(EUR, rounded) 

Average years 
spent in upper 
secondary 
education 

Total 
spending 
rounded 

(C=A/B) 
1 100 
3 600 
9 500 
4 100 
5 800 
7 100 
8 700 
2 900 
2 300 
17 000 
2 000 
9 100 
2 300 
4 700 
900 
2 900 
13 500 

(D) 
3.86 
4.37 
4.00 
3.56 
2.95 
3.13 
2.97 
3.38 
2.31 
2.82 
4.28 
4.33 
3.33 
3.00 
3.49 
3.74 
3.00 

(E=C*D) 
4 200 
15 900 
38 100 
14 500 
17 000 
12 600 
25 700 
9 700 
5 400 
48 000 
8 600 
39 300 
7 800 
14 200 
3 200 
11 000 
40 400 

NB:  Some countries either do not report data or data not reliable: BE, DK, IE, EL, IT, HR, MT, NL, SI, FI, UK.  

Average years spent in upper secondary: empirically observed from EU-SILC (2012). Exceptions: FI 
(Official Statistics of Finland: Progress of studies [e-publication], 2012); PT (Ministry of Education, 2007); 
SE (Cedefop, 2014)  

Analysis: valuation. 

  Weight: none/individual. 

Selection: N/A 

Valuation parameters:  duration in education from opportunity cost calculation (Table 17) multiplied by 
education expenditure from Eurostat database (derived using education final expenditure in upper 
secondary education (ISCED 3) in 2012 [educ_uo_fine02] and total number of students enrolled in upper 
secondary education during the academic year [educ_uoe_enrs04]. 

Source:  Eurostat database. 

Limitations in the valuation of microeconomic impacts 

5.2.6. 
The  above analysis  provides  estimates of  important  inputs for  a comprehensive 
valuation of cost and benefits of low skills from the point of view of the individual 
actors 
largely  based  on 
microeconomic estimates of the wage impact of skills and other components. The 

(individuals,  companies  and  public  sector), 

118 

 
 
 
 
 
 
 
Chapter 5. 
The costs of low-skilled adults 

substantial net earnings increases from higher level skills show the massive cost 
that  low  skills  represent  for  individuals  in  terms  of  foregone  earnings,  and 
therefore  a  case  for  public  policy  to  increase  educational  attainment  and 
upskilling of the adult population. 

However,  alongside  possible  effects  on  productivity/profitability  for  the 
economy  as  a  whole,  other  costs  associated  with  low  skills  remain  clearly 
excluded  from  the  earnings-based  approach,  including  the  economic  (non-
financial)  costs  of  crime,  and  the  impact  on  civic/community  engagement  and 
social exclusion caused by unemployment, poverty and lack of key competences. 
Although  these  costs  are  substantial,  they  could  be  fully  estimated  at  the  micro 
level  only  by  using  subjective  measures,  such  as  a  reservation  price,  hedonic 
price,  social  valuation  processes  and  similar  approaches;  in  many  cases  these 
require distributional weights and value judgments. 

5.3.  Aggregate economic benefits of reducing the 

share of low-skilled adults by 2025 

Under  standard  assumptions  the  earnings  differential  can  be  used  as  an 
approximation  of  the  output  gap  between  different  levels  of  skills.  However, 
estimating the aggregated economic benefit of reducing the size of the low-skilled 
adult  population  on  the  basis  of  individual  earning  is  likely  to  be  conservative. 
This is why, in the following, we develop a set of aggregate scenarios to include 
some  of  the  values  which  are  causally  affected  by  education  and  skills  levels 
(health and crime), as well as available information about the impact of low skills 
on public spending. 

The  following  valuation  exercise  makes  important  assumptions:  constant 
returns on education, relative stability in the earnings premium, exchange rates, 
and  population  trends.  The  last  of  these  includes  the  assumption  that  today’s 
cohorts of low-skilled people can reasonably represent the adult population in the 
future  and  are  not  considerably  altered  by  migration.  Although  these  are  all 
reasonable  assumptions,  it’s  clear  that  such  estimates  can  provide  only  an 
indication of the potential magnitude of monetary impacts of the reduction of the 
share of low-skilled adults on the EU-28 economy. 
The analysis compares three scenarios:  

(a)  a baseline scenario (business as usual), which assumes that population 15 
to 54 observed in 2015 would age to the relevant adult cohorts in 2025 (25 
to 64 year-olds) and would be affected by the past trend of decreasing levels 
of low skills, gradually reaching a proportion of low-skilled adults of 14.7% by 

119 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

2025.  This  target  was  chosen  in  line  with  Cedefop’s  forecast  scenarios 
(Section 2.3); 

(b)  an  upskilling  scenario,  which  assumes  a  further  decreasing  trend  in  the 
proportion  of  low-skilled  adults  to reach 7.4%  by  2025  (half  of  the  baseline 
target).  A  further  assumption  is  that  the  increased  reduction  is  higher  for 
younger people and gradually lower for the older cohort (100). This scenario 
is  challenging  as  it  assumes  that  is  possible  to  double  the  current  trend  in 
reducing  the  proportion  of  low-skilled  population,  but  not  impossible  when 
considering  that  a  share  of  low-skilled  around  10%  is  already  a  reality  in 
several Member States; 

(c)  a  high  hypothetical  zero  low-skilled  scenario,  which  assumes  that  by  2025 
the  proportion  of  low-skilled  in  the  adult  population  would  be  completely 
eradicated.  This  scenario  is  basically  unrealistic  because  of  the  extensive 
(and  difficult  to  sustain) investment  in  adult  learning facilities and policies it 
would require. However, it was included because it represents an interesting 
reference point for the analysis. 

Income effect from upskilling 

5.3.1. 
The empirical results from the microeconomic analysis are based on the returns 
on  ISCED  3  qualifications  as  presented  in  previous  sections,  which  shows 
significant earnings increases in 25 of the 28 Member States. These returns are 
used to compute the gains in lifetime income resulting from upskilling low-skilled 
adults,  similar  to  the  monetary  value  estimate  of  individual  lifetime  costs  of  low 
skills  presented  in  Table  17.  These  lifetime  benefits  were  based  on  the 
assumption  that  substantial  investment  in  education  usually  takes  place  before 
the  age  of  25  and  the  skills-related  earnings  increase  accrues  over  the  whole 
working life, subject to trend growth and increasing work experience, until people 
retire at (65 years old).  

The aggregation of individual-level impacts/benefits resulting from upskilling 
the  current  low-skilled  population  needs  alternative  assumptions,  since  initial 
skills  levels,  opportunity  costs,  and  residual  working  life  duration  vary  with  age. 
Hence: 
(a)  to  parametrise  the  gross  earnings  impact  for  different  age  groups  we  use 
observed gross earnings from EU-SILC in 2012 for low-skilled adults of the 

(100)  The  share  of  low-skilled  adults  in  10  years’  time  would  fall  to  7.5%  of  the  share  of 
the business as usual scenario for 25 to 34 (15 to 24 today); to 20% for 35 to 39 (25 
to 39 today); to 30% for 40 to 44 (30 to 34 today); to 40% for 45 to 49, 50% for 50 to 
54, 80% for 55 to 59, and 95% for 60 to 64. 

120 

                                                
Chapter 5. 
The costs of low-skilled adults 

different age groups and estimate the monetary impact using the coefficients 
from the Mincer models. The cumulative benefit of the residual working lives 
for the different age groups is then obtained, making the usual adjustment for 
increasing work experience, trend growth and discounting; 

(b)  to  parameterise  the  opportunity  costs,  we  use  the  empirically  observed 
duration  of  acquiring  the  qualifications  and  assume  that  the  full  duration 
would affect new entrants into the labour market,  while older cohorts would 
upskill  in  part-time  programmes.  The  time  spent  outside  employment  to 
acquire higher skills is then valued using observed gross earnings from EU-
SILC  in  2012  for  people  with  low  skills  of  the  different  age  groups  and  is 
discounted and adjusted for growth (101).  

Employer benefits from upskilling  

5.3.2. 
The  employer  cost  associated  with  low  skills  is  calculated  applying  the  surplus/ 
compensation ratio presented in Section 5.2.4 to the aggregate income effect of 
upskilling  presented  above.  This  gives  us  the  aggregated  value-added  created 
during  the  production  process  which  remunerates  capital  and  employers’  work 
more generally. 

Health, crime and fiscal benefits from upskilling 

5.3.3. 
In addition to the conservative effect on income (earnings and opportunity costs), 
further  economic  benefits  are  considered  comparing  the  upskilling  and the  zero 
low-skilled scenario against the baseline/business as usual scenario. 

The  health  cost  associated  with  low  skills  is  based  on  the  differential  in 
QALY  weight  between  low-skilled  adults  and  those  with  upper  secondary 
education  (102)  (Section  5.2.2).  However,  since  health  is  a  cumulative  process 

(101)  We  assume  that  the  lower  share  of  low-skilled  adults  can  be  achieved  within  the 
next 10 years (depending on the country-specific duration of upskilling programmes) 
and  that  young  people  15  to  24  (in  2015)  are  also  upskilled  at  the  same  time. 
Benefits  start  accruing  as  soon  as  the  upskilling  has  taken  place  and  last  until  the 
age of 65 and are calibrated depending on the age. For instance, benefits for those 
aged 15 to 19 today start arising in 2023, since, on average they turn 25 in that year. 
We  assume  that  within  each  five-year  age  band  the  population  is  uniformly 
distributed  so  that  the  average  age  of  the  15  to  19  is  17,  etc.  Returns  on  skills  at 
younger age (e.g. 20 to 24) are not included.  

(102)  We  use  a  QALY  value  of  EUR  31 250  resulting  from  guidelines  for  cost-
effectiveness  used  by  the  National  Institute  for  Health  and  Care  Excellence  in  the 
UK  (corresponding  to  GBP  25 000  as  the  midpoint  for  determining  the  cost 
effectiveness  of  public  health  interventions,  see  NICE  (2013))  as  figures  acquired 
from willingness-to-pay studies such as the EuroVaQ project exceeded the evidence 

121 

                                                
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

and may have a different impact depending on the age at which the higher skills 
are  acquired,  we  discount  the  estimates  of  the  differential  in  QALY  weight 
penalising  older  cohorts  of  the  population  (103).  For  each  age  group,  the  annual 
value  of  the  differential  in  QALY  weight  is  then  summed  across  the  expected 
remaining  years  of  life  and  multiplied  by  the  number  of  people  of  that  age  with 
low skills.  

For crime costs, we use the result from Machin et al. (2011) suggesting that 
the  social  benefits  from  the  crime-reducing  effect  of  education  amount  to  about 
EUR 16 000 per additional qualification for males. In line with this, we make the 
conservative assumption that achieving higher skills levels only reduces criminal 
activity  among  low-skilled  young  males  aged  25  to  34  (in  2025).  Machin  et  al. 
(2011) estimate fiscal costs only on the basis of the total cost of property crime 
(30%); this assumption makes the estimate even more conservative.  

While  low-skilled  individuals  tend  to  receive  more  unemployment  benefits 
than those with higher level of skills, other benefits for low-skilled adults are not 
systematically  higher  across  the  EU-28  (Table  23).  For  earnings  returns,  we 
assume  that  total  fiscal  costs  caused  by  unemployment  benefits  vary  with  the 
age of individuals and are higher for young people whose residual working lives 
are shorter. 

The aggregate monetary  benefit  in  earnings  also  leads  to further  increases 
in  tax  revenues  from  labour  taxes  and  via  increased  consumption.  In  the 
following,  we  apply  marginal  tax  rates  and  VAT  rates  for  earnings  impact  to 
derive  the  total  fiscal  revenue  increases  of  upskilling  above  baseline.  However, 
the  cost  of  producing  higher  level  skills  generates  an  increase  in  the  public 
spending. Although this representation excludes the likely impact of higher skills 
on reduced healthcare and criminal justice costs, the net public spending position 
is  positive  in  most  countries;  the  fiscal  benefits  outweigh  the  spending  almost 
everywhere, with highest gains in large countries and in high income economies  
Increasing  the  educational  level  of  a  substantial  fraction  of  the  low-skilled 
adult  population  would  come  at  a  cost.  In  addition  to  the  opportunity  costs,  to 
estimate  the  fiscal  costs  of  upskilling  low-skilled  adults  we  assume  the  time 
required to achieve ISCED 3 qualifications to be smaller for adults than for young 

from national sources. For the other countries, QALY values are imputed based on 
average QALY and GDP values from the UK and country GDP. 

(103)  For instance, those aged 35 to 39 only benefit from 75% of the differential in QALY 
weight,  to  account  for  the  fact  that  they  get  a  qualification  at  an  older  age.  Those 
aged  60  to  64  see  their  QALY  weight  increased  by  only  12.5%  of  the  estimated 
QALY differential. 

122 

                                                                                                                                 
Chapter 5. 
The costs of low-skilled adults 

people as those of primary age would have to acquire higher-level skills in part-
time programmes, while continuing employment as their main activity. 

Aggregated net effects 

5.3.4. 
Based on the above parameters and estimates, the aggregate earning increases 
from  upskilling  over  baseline  scenario  add  up  to  EUR  904  billion.  Considering 
opportunity  costs  of  about  EUR  288  billion,  the  total  net  benefit  would  be 
EUR 616 billion (Table 25). 

Table 25.  Aggregate cost-benefit for individual agents: upskilling (7.4%) and zero 

low-skilled scenarios (0%) (million EUR) 

(+)  Aggregate net income 
(-)  Opportunity costs (foregone earnings) 

Net benefit (~GVA) 

(+)  Surplus/compensation 

Net benefit including surplus 

(-)  Net public spending 
(+)  Health and crime economic benefits 

Total net benefit (+/-) 

Up-skilling scenario 
(7.4%) 
903 618 
287 936 
615 682 
523 330 
1 139 012 
156 267 
1 030 044 
2 012 789 

Zero low-skilled scenario 
(0%) 
1 614 877 
453 946 
1 160 932 
986 792 
2 147 724 
345 010 
1 725 841 
3 528 554 

NB:  All figures are expressed in net present value. Aggregate GVA is computed as the difference between 

aggregate income increase and costs (foregone earnings). 

Source:  Cedefop estimations. 

Including  net  public  spending  and  health  and  crime  benefits,  the  total 
present value over the 10 years is of EUR 2 013 billion (yearly average value of 
about EUR 200 billion). A similar calculation made for the hypothetical zero low-
skilled scenario led to a  10-year total present value of EUR 3 529 billion  (yearly 
average value of about EUR 350 billion). 

5.4.  Skills impact at macroeconomic level  

Under  standard  assumptions,  an  estimate  of  the  social  return  on  investment  in 
education  and  training  to  the  economy  can  be  obtained  by  aggregation  of 
foregone individual earnings. Similar approaches have been carried out in some 
countries  like  the  UK,  for  example  through  a  cost-benefit  model  of  skills 
investment facilitated by government intervention (Cambridge Econometrics and 
IER,  2013)  or  to derive the  economic  benefits from  investing  in  apprenticeships 
(National Audit Office, 2012).  

However, simple aggregations of individual benefits estimated at micro-level, 

have limitations: 

123 

 
 
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

(a) 

individual-level decision-making following the Mincer framework implies that 
people invest in skills as long as the incremental net returns on qualifications 
exceed the costs of acquisition. While this is related to net earnings, the true 
opportunity  costs  of  skills  investment  for  the  society  to  acquire  higher  level 
skills  (the  foregone  GDP)  would  correspond  to  gross  earnings  and, 
therefore, the social investment in education would be understated; 

(b)  even  assuming  that  education  only  affects  earnings  and  there  are  no 
spillover effects and externalities, because of taxation on labour earnings the 
social  benefits  of  education  investment  are  also  larger  than  the  individual 
returns and so the social benefits would be understated. Similarly, a further 
source  of  social  benefits  underestimation  is  the  existence  of  positive 
employer return on skills investment through productivity gains; 

(c)  some  non-individual  returns  should  be  included  in  the  estimate  to  scale 

individual returns up to social returns: 
spillover effects and externalities of increased skills on productivity; 
(i) 
(ii)  positive  externalities  resulting  from  reduced  crime,  better  health  and 

other intangible social values; 

(iii)  multiplier effects from individually higher earnings. 
Lack  of  consideration  of  these  aspects  inevitably  underestimates  the 
potential social value of the skills investment. 

To  help  reduce  this  type  of  problem,  an  estimation  of  social  benefits  of 
higher  level  skills  needs  should  be  carried  out  at  a  higher  level  of  data 
aggregation. This is why this section aims at providing an alternative estimate of 
the  costs  of  low  skills  to  the  European  economy,  using  data  at  national  level 
rather than aggregating individual returns estimated at micro level. 

Empirical estimates of the impact of skills on growth 

5.4.1. 
To identify the empirical design of the analysis, the most important studies have 
been thoroughly reviewed (104). These are important because they allow capture 
of externalities otherwise omitted in a micro approach (Sianesi and Van Reenen, 
2000).  Literature  generally  point  towards  a  positive  effect  of  education  on 
aggregate  economic  growth,  consistent  with  microeconomic  evidence,  although 
results are dependent on data quality and the sample of countries considered. 

(104)  Barro,  1991;  Barro  and  Lee,  1994;  Barro  and  Sala-i-Martin,  1995;  Mankiw  et  al., 
1992; Benhabib and Spiegel, 1994; Pritchett, 2001; Sianesi and Ven Reenen, 2000; 
Krueger and Lindahl, 2001; Cohen and Soto, 2007; Canton, 2007; Hanusheck and 
Kimko, 2002; Lodde, 2008; Wilson and Briscoe, 2004.  

124 

                                                
Chapter 5. 
The costs of low-skilled adults 

This approach makes use of macroeconomic variables on the skill levels in 
all Member States and macroeconomic output measures, i.e. the gross domestic 
product (GDP). The impact of skills on such aggregates can be best estimated by 
quantifying the foregone output growth due to low skills in models which explain 
GDP  growth  per  capita  based  on  factors  of  production  and  further  variables 
relevant  to  macroeconomic  output  (savings,  depreciation  of  capital,  population 
growth  and  labour  market  participation).  Coefficients  estimated  in  such  models 
can be interpreted as a ceteris paribus effect of an increase of skills on economic 
growth in the EU.  

Because of the explicit modelling of ISCED levels, the dataset available has 
limitations:  it  is  comparatively  small  (about  20  years)  and  includes  almost  a 
decade of unprecedented economic downturn which may tend to understate the 
impact of skills on economic growth.  It is also clear that GDP per capita cannot 
represent the full value of skills for society which includes related unpriced values 
important 
such  as  externalities,  distributional  considerations  and  other 
determinants of social well-being.  

5.4.1.1.  Modelling 

Our analysis follows the main principles of estimating the impact of education on 
economic growth as suggested by Barro (1991), Mankiw et al. (1992) and Barro 
and  Sala-i-Martin  (1995);  these  estimate  ‘neoclassical’  growth  models  in  the 
Solow  tradition,  including  a  set  of  human  capital  variables  (Wilson  and  Briscoe, 
2004; for an overview of several studies with such designs). In implementing the 
empirical model, we estimate the impact of higher level skills (ISCED 3-4 and 5-
8)  on  growth  of  GDP  per  capita.  The  coefficient  magnitude  and  level  of 
significance show the impact of increasing ISCED 3-4 by a percentage point on 
the per capita growth rate. 

The macroeconomic level of analysis aims to obtain a measure of the total 
effect of skills on GDP per capita as a way to estimate of the costs of low skills to 
European  society.  It  implicitly  encompasses  the  costs  of  producing  higher  level 
skills  and,  therefore,  represents  a  net  benefit  of  higher  level  skills.  While  the 
individual-level decision is based on net earnings, including the opportunity costs 
of the skills investment, the actual effect of skills investment on output could be 
better  approximated  by 
the  gross  earnings  under  standard  neoclassical 
assumptions.  There  are  further  social  costs  and  benefits  of  education/training 
investment,  for  example  the  impact  of  higher  skills  on  health,  life  expectancy, 
infant  mortality  and,  therefore,  output,  which  can  be  partly  captured  in  an 
aggregate analysis. From this point of view, a macroeconomic analysis obtains a 

125 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

more comprehensive measure of the impact of higher level skills and the costs of 
low level skills than the microeconomic approach.  

Some studies apply the same variables as in the standard Mincer framework 
– years of education – in macroeconomic data to estimate a social rate of return 
on  education  investment.  The  coefficient  of  such  growth  models  can  be 
interpreted  analogous  to  a  Mincer  individual  return  for  an  incremental  year  of 
schooling on income (see e.g. Wilson and Briscoe, 2004, p. 44). In the presence 
of the externality of skills investments (individuals decide on net incomes, but the 
impact on output refers to gross earnings), total investment required to increase 
skills  and  the  potential  social  returns  are  expected  to  be  higher than suggested 
by  microeconomic  Mincer  models.  However,  as  found  in  Wilson  and  Briscoe 
(2004), some of the studies find returns which are very similar to microeconomic 
returns. 

In  the  following  analyses,  we  make  use  of  models  which  assume  a  steady 
state growth and diminishing returns on investment. Although increasing returns 
on investment in human capital are possible, not least because of the spillover of 
increased  human  capital  (Wilson  and  Briscoe,  2004,  p. 41),  alternative  models 
such as ‘endogenous growth’ models have serious limitations to be applied in this 
context:  possible  endogeneity  (richer  countries  invest  more  in  education  and 
training),  and  problems  with  data  and  measurement  of  education  and  training 
investment. 

5.4.1.2.  Data and implementation 

To estimate the empirical specification of the model, we combine macroeconomic 
data  obtained from  a tailored  request  to  Eurostat  about  the  qualifications  of  the 
population aged 25 to 65 years, with further macroeconomic aggregates from the 
annual  macroeconomic  database  of  the  European  Commission  (AMECO)  and 
the  total  economy  database  of  the  Conference  Board  for  all  28  EU  Member 
States. These data were used to derive dependent and independent variables of 
the model: 
(a)  GDP per capita (2014 price level with 2011 purchasing power parities);  
(b)  savings rate; 
(c)  population growth;  
(d)  qualification of the adult population (ISCED 3-4 and 5-8); 
(e) 
(f)  working time;  
(g) 

further  data  affecting  macroeconomic  aggregates  such  as  the  ‘openness 
ratio’  (sum  of  imports  and  exports  as percentage  of GDP)  and  government 
consumption as a percentage of GDP.  

labour market participation rate of the working-age population; 

126 

Chapter 5. 
The costs of low-skilled adults 

Since  education  and  ISCO  data  identifying  concepts  of  low-skilled  used  in 
this  paper  have  only  been  available  since  1992,  the  estimates  are  based  on  a 
period  of  observation  of  about  20  years.  Due  to  EU  enlargement,  the  nature  of 
the  time  series  dimension  of  this  dataset  is  unbalanced,  which  implies  that  the 
time series is in parts considerably shorter for the Eastern Member States joining 
the union after 2000. 

Extending  from  Mankiw  et  al.  (1992),  we  specify  the  empirical  model  to 
explain  the  growth  rate  in  GDP  per  capita  y  in  countries  i  and  over  time  t 
(modelled  as  log  differences  using  values  referring  to  fixed  price  levels)  as  the 
dependent variable of the following model: 

the  growth  rate  of 

with the following in-period covariates: 
logarithm of the savings rate 
logarithm  of 
the  population  plus  0.05 
representing  the  sum  of  exogenous  technical  change  (g)  and  a 
depreciation rates () (Bond et al., 2001, p. 15) 
  share of the population 25 to 65 with ISCED 3-4 
share of the population 25 to 65 with ISCED 5-8 
ratio of employment to working-age population 

  average hours worked per person employed 

  openness ratio 
  government spending as % of GDP 

The  specification  as  a  panel  data  model  also  allows  us  to  capture  a  time 
invariant  element  𝜂𝑖  on  unobserved  characteristics  that  are  intrinsic  to  the 
different  countries  and  do  not  change  over  time.  𝜐𝑖,𝑡.  is  an  error  term  of  the 
empirical  model.  In  estimating  panel  data  models,  there  is  a  principal  choice  of 
static or dynamic approaches; both aim to remove time invariant level differences 
between  countries,  caused  by  unobservable  or  unobserved  characteristics,  in 
order to obtain unbiased estimates.  

In addition to including time 𝛾𝑡 effects in the equation by a series of dummy 
variables  affecting  all  countries  in  the  same  way,  we  also  included  the  lagged 
dependent  variable;  we  consequentially  estimate  the  relationship  as  dynamic 
panel data (Bond et al., 2001). While such modelling can capture country-specific 
dynamics  better  than static  panel  data models, such  as fixed  or  random  effects 
models,  there  are  econometric  problems  of  estimating  models  with  lagged 
dependent  variables  as  generally  as  models  including  sources  of  endogeneity. 

127 

tiiitititititititittititgcophpllfphsmsnsyy,876543211,itsitnitmsithsitlfpithplitopitgc 
 
 
 
 
 
 
 
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

We address this by estimating the dynamic panel data model using the estimator 
initially put forward by Arellano and Bond (1991) and later improved by Blundell 
and Bond (1998). 

5.4.1.3.  Findings 

The empirical results of the estimates derived from the macroeconomic data are 
shown  in  Table  26.  As  a  model  summary,  we  find  support  for  the  dynamic 
modelling  approach  in  that  both  the  relevant  tests  for  autocorrelation  and  the 
lagged  endogenous  variable  are  statistically  significant.  We  also  see  that  the 
magnitude  of  the  coefficient  of  the  lagged  endogenous  variable  shows  the 
greatest impact on growth of GDP per capita. 

The findings are generally consistent with the predictions of the Solow model 
and  other  empirical  growth  regressions  including  human  capital  variables  as 
summarised  in  Wilson  and  Briscoe  (2004):  the  coefficient  of  the  savings  rate 
shows  the  expected  positive  and  significant  coefficient;  the  rate  of  population 
growth  affects  per  capita  GDP  negatively.  The  models  also  show  that 
employment  rates  do  not  significantly  influence  the  rate  of  growth  of  GDP  per 
capita, while there is a significant impact of working time. Some of the variables 
relating to economic policy  regimes  show  that  economies  with a high degree of 
international trade expressed by the openness ratio are growing at higher rates. 

More  important,  as  suggested  by  Mankiw  et  al.  (1992),  human  capital 
variables  have  been  found  to  show  positive  and  significant  coefficients  in  the 
regressions (105); an increase in intermediate skills (ISCED 3-4) by 10 percentage 
points increases GDP per capita by 0.1 percentage points (106).  

A  baseline  steady  state  growth  can  be  derived  from  existing  estimates  of 
aggregate  output  growth  and  figures  of  population  change.  Assuming  that  the 
economy  is  in  a  steady  state  in  2015,  the  ceteris  paribus  effect  derived  here 
shows increased steady state growth rate with decreasing shares of low skills. 

(105)  Our human capital variables show a differential impact of the share of intermediate 
and high-level skills of the adult population (age 25 to 64), which differs from other 
concepts used in this literature, such as enrolment rates. 

(106)  This coefficient is significant, but shows a smaller impact of skills on growth than, for 
example, the share of the population with high tertiary level skills. A 10 percentage 
points  increase  in  the  share  of  adults  aged  25  to  64  with  the  highest  skills  would 
increase  the  long-term  growth  rate  of  GDP  per  capita  by  0.23  percentage  points, 
twice the size of the coefficient for the intermediate skills. 

128 

 
                                                
Chapter 5. 
The costs of low-skilled adults 

Table 26.  Macroeconomic model estimates: GDP per capita growth rate 

Coefficient 

0.457 
0.008 

Standard 
error 
0.043 
0.004 

z 

10.540 
2.030 

P> z 

0.000 
0.042 

-0.048 

0.008 

-6.100 

0.000 

0.005 
0.009 
0.015 
0.000 
0.001 
0.027 
0.025 

2.160 
2.530 
-0.580 
2.820 
3.360 
-2.380 
-5.760 

0.031 
0.011 
0.562 
0.005 
0.001 
0.017 
0.000 

0.010 
0.023 
-0.009 
0.000 
0.004 
-0.064 
-0.144 
523 
27 
283 
162026  
0.000 

N by G Min. 
Mean. 
Max. 
Pr > z  
Pr > z  

15 
19.37 
22 
0.000 
0.069 
Prob > chi2   0.0000 
Prob > chi2   1.0000 

-3.86 
-1.82 
chi2(252)= 461.62 
chi2(252) = 0.00 

Lagged dependent variable 
Logarithm of the savings rate 
Logarithm of the growth rate of the population 
plus 0.05 (exogenous technical change (g) and 
depreciation d) 
Share of the population 25-65 with ISCED 3-4 
Share of the population 25-65 with ISCED 5-8 
Ratio of employment to working-age population 
Average hours worked per person employed 
Openness ratio 
Government spending as % of GDP 
Constant  
N 
Groups 
Number of instruments 
Wald chi2(30)  
Prob > chi2 
Arellano-Bond test for AR(1) in first differences: z  
Arellano-Bond test for AR(2) in first differences: z  
Sargan test of overid. restrictions 
Hansen test of overid. restrictions 

NB:  Analysis: dynamic panel data (system GMM). 

  Weight: n.a. 

Selection: EU-28, 1992-2014. 

Input variables: 

   highest level of qualification (hatlev1d), ISCO (isco1d) (population 25-65); 

   total population, employment persons: total economy, final consumption expenditure of general 

government at 2010 prices (billion national currencies), gross national savings at 2010 prices: total 
economy, exports of goods and services at 2010 prices, imports of goods and services at 2010 prices 

   total GDP in millions of 2014 USD (2014 price level with 2011 PPPs), mid-year population (in 

thousands of persons) annual hours worked per worker. 

Dependent variable: GDP per capita in 2014 USD (2014 price level with 2011 PPPs). 

Variables included: GDP per capita (2014 price level with 2011 PPPs); savings rate; population growth; 
qualification of the adult population (ISCED 3-4/ISCED 5-8); labour market participation rate of the 
working-age population; working time; openness ratio (sum of imports and exports as percentage of 
GDP) and government consumption as a percentage of the GDP. 

Sources: Eurostat EU-LFS custom aggregation; annual macroeconomic database (AMECO); total economy 

database, Conference Board. 

5.4.2. 

The long-term impact on the steady-state growth of GDP in 2025-50 

5.4.2.1. 

Impact of low skill on steady-state growth 

To determine the possible long-term impact on GDP of reducing the share of low-
skilled  adults,  we  present  a  valuation  exercise  based  on  the  macroeconomic 
approach  discussed  in  the  previous  section.  This  exercise  describes  the 
development  of  GDP  per  capita  once  a  particular  proportion  of  adults  with  low 

129 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

skills  (policy  target)  has  been  reached  and  is  no  longer  changing.  We  interpret 
the coefficient resulting from the macroeconomic estimates as capturing not only 
the  benefit  from  upskilling  (over  a  baseline  of  continuously  decreasing  share  of 
low-skilled  adults),  but  also  long-term  effects  on  the  steady-state growth rate  of 
GDP per capita in the setup of a neoclassical growth model.  

Results from  the  macroeconomic  analysis  show  that  a  10  percentage  point 
reduction in the long-term proportion of low-skilled adults (those with ISCED 0-2) 
would  increase  the  long-term  GDP  per  capita  growth  rate  by  0.1  percentage 
point.  We  assume  that  the  reduction  in  the  proportion  of  low-skilled  adults 
translates into a similar increase in proportion of adults with ISCED 3-4, and has 
no  impact  on  the  proportion  of  adults  with  ISCED  5-8.  Initially,  this  effect  may 
seem  modest  but  since  this  is  a  shift  in  the  GPD  per  capita  growth  rate  it  can 
have not negligible effects on GDP growth in the long term. 

5.4.2.2.  Baseline steady-state GDP per capita growth rate  

We  follow  the  scenarios  presented  before  of  a business  as  usual  situation  of  a 
proportion  of  low-skilled  adults  in  the  population  of  14.7%  in  2025  (baseline), 
7.4%  (upskilling),  and  the  hypothetical  0%  (zero  low-skilled).  These  targets  are 
assumed  as  the  long-term  proportion  of  low-skilled  adults  in  steady  state;  this 
proportion  is  no  longer  changing  as  of  2025  and  the  impact  of  the  steady-state 
growth rate of the GDP per capita is based on the model. 

As  an  estimate  of  the  baseline  steady-state  GDP  growth  rate,  we  use  the 
forecast of GDP growth for the EU-28 in 2021 published in the latest IMF  world 
economic outlook (107). In 2021, GDP, in constant prices, is expected to grow by 
1.801%.  The  GDP  per  capita  growth  rate,  is  calculated  considering  the  annual 
population growth rate based between 2020 and 2030 provided by Eurostat (108). 
Between 2020 and 2030, population is expected to grow by 0.117% annually and 
we assume it as the population growth trend in long-term steady-state. 

5.4.2.3.  Results 

The  solid line  in Figure  37  shows  estimates of  the  steady-state GDP  per  capita 
growth rate in relation to different steady-state proportion of low-skilled adults in 
the  population.  The  dashed  line  displays  the  baseline  steady-state  GDP  per 
capita growth rate (1.72%), which assumes a long-term proportion of low-skilled 
adults of 14.7%. Reducing the proportion of low-skilled adults to 7.4% (upskilling 

(107)  IMF: World economic outlook database: 

https://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx  

(108)  Eurostat population projections, Series proj_13npms. 

130 

                                                
 
Chapter 5. 
The costs of low-skilled adults 

scenario) would result into a steady state growth rate of 1.79%, 0.07 percentage 
points  higher  than  in  the  baseline  scenario;  in  a  hypothetical  zero  low-skilled 
scenario,  the  steady-state  growth  rate  would  reach  1.86%,  0.14  percentage 
points higher than in the baseline. 

Figure 37.  Steady-state GDP per capita growth rate in relation to proportion of low-

skilled adults  

Source:  Cedefop estimation on Eurostat LFS, AMECO and total economy database. 

Given the characteristics of this exercise, one implication is that the potential 
increase in the steady-state growth rate is larger for countries that currently have 
a larger low-skilled population. 

Applying  the  steady-state  growth  rate  to  the  2015  GDP  per  capita (109) 
(EUR 27 723), we obtain the expected GDP per capita in 2025 (EUR 33 937). On 
this basis, after 25 years the GDP per capita in the upskilling scenario would be 
EUR 52 909,  to  EUR 52 007  for  the  baseline  scenario.  With  an  expected 
population  in  the  EU-28  increasing  by  about  15  million  by  2050,  the  long-term 
annual GDP for the EU-28 would be about EUR 480 billion larger in the upskilling 
scenario  than  in  the  baseline  (965  billion  larger  in  the  hypothetical  zero  low-
skilled scenario). 

Across  the  average  of  the  25-year  period  (2025-50)  the  increase  in  the 
yearly  GDP  due  to  the  reduction  of  the  share  of  low-skilled  adults  set  in  the 
upskilling and the zero low-skilled scenarios  would be over  EUR 200 billion and 
EUR 410 billion respectively. 

(109)  Eurostat, Series nama_10_pc  

131 

 
 
                                                
Conclusions  

The  economic  and  financial  crisis  has  profoundly  affected  labour  market 
dynamics in the Member States. Soaring unemployment rates, especially among 
certain groups of the population such as low-skilled adults, may lead to serious, 
long-term  consequences.  Growing  social  exclusion,  disengagement  from  the 
labour market and persistent underuse of an important stratum of the population 
can lead to hysteresis and permanently lower potential growth. 

While future trends in low skills suggest that shares of low-skilled adults will 
continue  to  decrease,  current  trends  also  indicate  how  low-skilled  people  are 
particularly  disadvantaged  and  vulnerable  on  the  labour  market.  For  policy-
makers to design and implement policies tailored to this particular group there is 
a need to develop a comprehensive and robust evidence base in relation to low-
skilled  adults  in  the  EU,  their  volume  and  characteristics,  as  well  as  their 
economic and social costs. 

While the ultimate aim of this study was to estimate the individual and social 
value  in  monetary  terms  of  a  faster  increase  in  skills  levels  in  Member  States 
compared  to  the  current  trend,  outcomes  from  the  study  allow  us  to  identify 
several conclusions and also recommendations for future work in this area: 
(a) 

low-skilled  status  should  be  conceptualised  as  a 
the  concept  of 
multidimensional  and  dynamic  phenomenon,  which  goes  beyond 
educational attainment level and considers both its determinants and effects. 
It  should  also  include  a  wider  typology  of  people  with  low-skills,  such  as 
those with obsolete skills and mismatched overqualified workers; 
low-skilled  adults  should  be  recognised  as  an  increasingly  vulnerable 
segment  of  the  population  in  most  Member  States.  Evidence  shows  that 
being  low-skilled  is  associated  with  negative  consequences  for  individuals, 
companies and society as a whole. Empowering low-skilled adults by means 
of promoting their up-skilling and/or reskilling is clearly associated with large 
social and economic incentives;  
the low-skilled population is a heterogeneous group with different needs and 
characteristics. Return on investment in skills varies for different groups and 
is diverse in different Member States. Effective policy actions must recognise 
this diversity, targeting the different needs and characteristics of low-skilled 
subpopulations and taking into account national contexts; 

(b) 

(c) 

(d)  policy actions to improve basic skills require an integrated policy approach to 
designing comprehensive and flexible education and training programmes to 
meet  the  needs  of  the  different  low-skilled  subpopulations.  Identifying 

132 

 
 
 
Conclusions 

obstacles to implementation of policy and programmes, careful monitoring of 
responsiveness  and 
progress 
adaptability  in  light  of  experience  and  changing  circumstances,  are  key 
elements of effective policy action; 

towards  established  objectives,  and 

(e)  while analysis provides robust estimates of the potential benefits of a faster 
increase  on  skills  levels  in  Member  States  compared  to  the  current  trend, 
lack of exhaustive data prevents determining a comprehensive figure for the 
cost  of  low-skills.  Outcomes  from  the  study,  while  alarming,  should  be 
considered  as  underestimating  the  real  economic  and  social  cost  of  low 
skills in Europe and call for immediate action. 

133 

List of abbreviations 

ALMP 

AMECO 
EU 
EU-LFS 
Eurofound 
EU-SILC 
EWCS 
GDP 
GVA 
IALS 
ICT 
IER 
ILO 
ISCED 
ISCO 
LFS 
HSJ 
LMP 
LSJ 
NEET 
OECD 
OLS 
PIAAC 
PPP 
QALY 
SSC 
SSJ 
SSMJ 

active labour market policy 

annual macroeconomic database of the European Commission 
European Union 
European Union labour force survey 
European Foundation for the Improvement of Living and Working Conditions 
European Union statistics on income and living conditions 
European working conditions surveys 
gross domestic product 
gross value-added 
international adult literacy survey 
information and communications technology 
Warwick Institute for Employment Research (UK) 
International Labour Organisation 
international standard classification of education 
international standard classification of occupations 
labour force survey 
Highly skilled job 
labour market policy 
low-skilled jobs 
not in education, employment, or training 
Organisation for Economic Cooperation and Development 
ordinary least squares 
programme for the international assessment of adult competencies 
purchasing power parity 
quality-adjusted life year 
social security contributions  
semi-skilled non-manual jobs 
semi-skilled manual job 

134 

 
 
 
References  

Abraham, K.G.; Spletzer, J.R. (2009). New evidence on the returns to job skills. 

American economic review, Vol. 99; No 2, pp. 52-57. 

Abrassart, A. (2011). Cognitive skills matter: the employment disadvantage of the 
low-educated  in  international  comparison.  REC-WP,  working  paper  on  the 
reconciliation of work and welfare in Europe; No 4/11. 

Albouy,  V.;  Lequien,  L.  (2009).  Does  compulsory  education  lower  mortality? 

Journal of health economics, Vol. 28; No 1, pp. 155-168. 

Arellano,  M.;  Bond S.  (1991).  Some tests  of  specification for  panel  data:  Monte 
Carlo  evidence  and  an  application  to  employment  equations.  Review  of 
economic studies, No 58, pp. 277-297. 

Arendt, J.N. (2005). Does education cause better health? A panel data analysis 
using school reforms for identification. Economics of education review, Vol. 
24; No 2, pp. 149-160. 

Arthur,  W.J.  et  al.  (1998).  Factors  that  influence  skill  decay  and  retention:  a 
quantitative review and analysis. Human performance, Vol. 11, No 1, pp. 57-
101. 

Autor, D.H.; Dorn, D. (2009). Inequality and specialisation: the growth of low-skill 

service jobs in the United States. IZA discussion paper; No 4290. 

Autor,  D.H.  et  al.  (2003).  The  skill  content  of  recent  technological  change:  an 
empirical  exploration.  Quarterly  journal  of  economics,  Vol. 118,  No 4, 
pp. 1279-1333. http://www.frbsf.org/economic-research/files/alm-skillcontent-
qje.pdf  

Bachmann,  R.  et  al.  (2014).  Labour market  dynamics and  worker  heterogeneity 
during  the  great  recession:  evidence  from  Europe.  Ruhr  economic  papers; 
No 499.  

Badescu, M. et al. (2011). Returns to education in European countries: evidence 
from  the  European  Community  statistics  on  income  and  living  conditions 
(EU-SILC). JRC scientific and technical reports. 

Banks,  J.;  Mazzonna,  F.  (2012).  The  effect  of  education  on  old  age  cognitive 
abilities: evidence from a regression discontinuity design. Economic journal, 
Vol. 122, No 560, pp. 418-448. 

Barro,  R.  (1991).  Economic  growth  in  a  cross-section  of  countries.  Quarterly 

journal of economics, Vol. 106; No 2, pp. 407-443. 

Barro,  R.;  Lee,  J.  (1994).  Sources  of  economic  growth.  Carnegie  Rochester 

Conference on public policy, Vol. 40, pp. 1-46. 

Barro, R.; Sala-i-Martin, X. (1995). Economic growth. New York: McGraw Hill. 

Bassanini, A. et al. (2005). Workplace training in Europe. IZA discussion papers; 

No 1640. http://ftp.iza.org/dp1640.pdf 

135 

 
 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Becker,  G.  (1964).  Human  capital.  Second  edition.  New  York:  Columbia 

University Press. 

Becker,  G.  (1968).  Crime  and  punishment:  an  economic  approach.  Journal  of 

political economy, Vol. 76, No 2, pp. 169-217. 

Becker,  G.;  Chiswick,  B.R.  (1966).  Education  and  the  distribution  of  earning 

American economic review, Vol. 56, No 1/2; pp. 358-369. 

Behrman J.R. et al.(2014). What determines adult cognitive skills? Influences of 
pre-school,  school,  and  post-school  experiences  in  Guatemala.  Latin 
American economic review, Vol. 23; No 1, pp. 1-32.  

Beirne,  J.;  Campos,  N.F.  (2006).  Educational  inputs  and  outcomes  before  the 
IZA  discussion  paper  series,  No  2502, 

from  communism. 

transition 
December 2006. http://ftp.iza.org/dp2502.pdf 

Benhabib,  J.;  Spiegel,  M.M.  (1994).  The  role  of  human  capital  in  economic 
development:  evidence  from  aggregate  cross-country  data.  Journal  of 
monetary economics, Vol. 33, pp. 147-173. 

Bernardi, F.; Garrido, L. (2008). Is there a new service proletariat? Post-industrial 
employment  growth  and  social  inequality  in  Spain.  European  sociological 
review, Vol. 24, No 3, p. 299-313. 

Blanden,  J.;  McNally,  S.  (2014).  Reducing  inequality  in  education  and  skills: 

implications for economic growth. EENEE analytical reports. 

Blundell,  R.;  Bond,  S.  (1998).  Initial  conditions  and  moment  restrictions  in 
dynamic panel data models. Journal of econometrics, No 87, pp. 115-143. 

Boeren,  E.  et  al.  (2010).  Theoretical  models  of  participation  in  adult  education: 
the need for an integrated model. International journal of lifelong education, 
Vol. 29, No 1, pp. 45-61.  

Bond,  S.  et  al.  (2001).  GMM  estimation  of  empirical  growth  models.  CEPR 

discussion papers; No 3048.  

Borghans,  L.;  ter  Weel,  B.  (2006).  Do  we  need  computer  skills  to  use  a 

computer? Evidence from Britain. Labour, Vol. 20, No 3, pp. 505-532. 

Borjas, G. (2010). Labour economics (Sixth edition). London: McGraw-Hill Higher 

Education. 

Braakmann,  N.  (2011).  The  causal  relationship  between  education,  health  and 
health  related  behaviour:  evidence  from  a  natural  experiment  in  England. 
Journal of health economics, Vol. 30, pp. 753-763. 

Brozovicova,  K.  et  al.  (2012).  Overview  of  the  labour  market  situation  of  low-
educated and Roma population and regulations affecting their employment. 
Neujobs state of the art report; N.D 19.1. 

Brunello,  G.  et  al.  (2009).  Changes in compulsory  schooling,  education  and the 
distribution  of  wages  in  Europe,  Economic  journal,  Vol.  119,  No  536,  pp. 
516-539. 

136 

References 

Brunello, G. et al. (2012). Training subsidies and the wage returns to continuing 
vocational  training:  evidence  from  Italian  regions.  Labour  economics, 
Vol. 19, No 3, pp. 361-372. http://doi.org/10.1016/j.labeco.2012.03.002 

Brunello,  G.  et  al.  (2015).  The  causal  effect  of  education  on  health:  what  is  the 

role of health behaviors? Health economics, pp. 1099-1050. 

Bukowski,  M.;  Lewandowski,  P.  (2005).  Assessing  flows  out  of  employment  in 

Poland: evidence from multinomial logit analysis. EconWPA.   
http://econwpa.repec.org/eps/lab/papers/0511/0511007.pdf 

Buonanno,  P.;  Leonida,  L.  (2006).  Education  and  crime:  evidence  from  Italian 

regions. Applied economic letters, Vol. 13, No 11, pp. 709-713. 

Calero,  J.  and  Escardíbul,  J.O.  (2014).  Barriers  to  non-formal  professional 
training in Spain: an analysis of their evolution in periods of economic growth 
and crisis, with special attention to the effect of the previous human capital 
of workers. Institut d'Economia de Barcelona. 

Cambridge  Econometrics  and  IER  (2013).  Review  of  the  economic  benefits  of 
training  and  qualifications  as  shown  by  research  based  on  cross-sectional 
and administrative data. BIS research paper; No 105. 

Cameron,  A.C.;  Trivedi,  P.K.  (2010).  Microeconometrics  using  Stata  (revised 

edition). Stata Press. 

Campbell,  D.E.  (2006).  What  is  education’s  impact  on  civic  and  social 
engagement?  In:  Measuring  the  effects  of  education  on  health  and  civic 
engagement. Proceedings of the Copenhagen Symposium. 
http://www.oecd.org/edu/innovation-
education/measuringtheeffectsofeducationonhealthandcivicengagement.htm  

Canton, E. (2007). Social returns to education: macro-evidence. Industrial policy 

and economic reforms paper; No 9.  

Capatina,  E.  (2014).  Skills  and  the  evolution  of  wage  inequality.  Labour 

economics, Vol. 28, pp. 41-57. 

Cappellari,  L.  et  al.  (2015).  Skilled  or  educated?  Educational  reforms,  human 
capital  and  earnings.  Paper  presented  at  the  Cedefop/IZA  workshop  on 
Skills and skill mismatch, Thessaloniki, 29 and 30 October 2015. 
http://www.iza.org/conference_files/2015_Skill_Mismatch/cappellari_l1064.pdf 

Carbonaro,  W. 

(2006).  Cross-national  differences 

the  skills-earnings 
relationship: the role of labor market institutions. Social forces, Vol. 84, No 3, 
pp. 1820-1842. 

in 

Card, D. (1999). The causal effect of education on earnings. In: Ashenfelter, O.; 
Card,  D.  (eds).  Handbook  of  labor  economics  volume  3A.  Amsterdam: 
Elsevier, 1999. 

Card,  D.;  Lemieux,  T.  (1994).  Changing  wage  structure  and  black-white  wage 

differentials. AEA papers and proceedings, No 84, No 2, pp. 29-33. 

Carlsson M. et al. (2012). The effect of schooling on cognitive skills. Cambridge: 
National Bureau of Economic Research. NBER working paper; No 18484.  

137 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Cedefop  (2011).  Vocational  education  and  training  is  good  for  you:  the  social 
benefits  of  VET  for  individuals.  Luxembourg:  Publications  Office.  Cedefop 
research paper; No 17.   
http://www.cedefop.europa.eu/EN/Files/5517_en.pdf  

Cedefop  (2013).  Benefits  of  vocational  education  and  training  in  Europe  for 
people, organisations and countries. Luxembourg: Publications Office. 
http://www.cedefop.europa.eu/en/publications-and-
resources/publications/4121  

Cedefop  (2014).  Macroeconomic  benefits  of  vocational  education  and  training. 

Cedefop research paper; No 40.  
http://www.cedefop.europa.eu/en/publications-and-
resources/publications/5540  

Cedefop (2015). Europe’s uneven return to job growth. Briefing note; June 2015. 

http://www.cedefop.europa.eu/en/publications-and-
resources/publications/9098 

Cedefop (2016).  Leaving education  early: putting  vocational education  and training 
centre stage. Volume II: evaluating policy impact. Luxembourg: Publications 
Office. Cedefop research paper; No 58.  
http://www.cedefop.europa.eu/en/publications-and-
resources/publications/5558 

Clark, D.; Royer, H. (2013). The effect of education on adult mortality and health: 
evidence from Britain. American economic review, Vol. 103, No 6, pp. 2087-
2120. 

Cohen, D.; Soto, M. (2007). Growth and human capital: good data, good results. 

Journal of economic growth, Vol. 12, pp. 51-76. 

Conlon,  G.  et  al.  (2011).  Returns  to  intermediate  and  low  level  vocational 

qualifications. BIS research paper, No 53. 

Conlon,  G.  et  al.  (2012).  The  impact  of  investment  in  intangible  assets  on 

productivity spillovers. BIS research paper; No 74. 

Conti,  G.  et  al.  (2010).  The  education-health  gradient.  American  economic 

review: papers and proceedings, Vol. 100, pp. 234-238. 

Council of the European Union (2016). Council recommendation of 19 December 
2016 on upskilling pathways: new opportunities for adults. Official Journal of 
the European Union, C 484, 24.12.2016. 
http://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32016H1224(01)&from=EN  

Crépon, B. et al. (2003). Wages, productivity and worker characteristics: a French 
perspective.  Centre  de  Recherche  en  Economie  et  Statistique.  Working 
papers 2003-04.   
http://www.crest.fr/ckfinder/userfiles/files/Pageperso/crepon/CreponDeniauP
erezDuarte2002.pdf 

Cutler,  D.;  Lleras-Muney,  A.  (2012).  Education  and  health:  insights  from 

international comparisons. NBER working paper, No 17738. 

138 

 
References 

Cutler,  D.;  Richardson,  E.  (1997).  Measuring  the  health  of  the  US  population. 

Brookings paper on economic activity, pp. 217-271.  

Daehlen, M.; Ure, O.B. (2009). Low‐skilled adults in formal continuing education: 
does  their  motivation  differ  from  other  learners?  International  journal  of 
lifelong education, Vol. 28, No 5, pp. 661-674. 

Davia, M.A. et al. (2009). Exploring international differences in rates of return to 

education: evidence from EU-SILC. ESRI working paper, No 311. 

De  Anda,  R.M.;  Hernandez,  P.M.  (2007).  Literacy  skills  and  earnings:  race  and 
gender  differences.  Review  of  black  political  economy,  Vol.  34,  No 3-4, 
pp. 231-243. 

De  Grip,  A.;  Zwick,  T.  (2004).  The  employability  of  low-skilled  workers  in  the 
knowledge  economy.  Paper  presented  at  the  LOWER  2004  conference. 
London: London School of Economics.  

De  Grip,  A.  et  al.  (2008).  Job-worker  mismatch  and  cognitive  decline.  Oxford 

economics papers, Vol. 60, No 2, pp. 237-253. 

Dearden,  L.  et  al.  (2005).  The  impact  of  training  on  productivity  and  wages: 
evidence  from  British  panel  data.  London  School  of  Economics.  CEP 
discussion paper; No 674. http://cep.lse.ac.uk/pubs/download/dp0674.pdf 

Dee,  T.S.  (2004).  Are  there  civic  returns  to  education?  Journal  of  public 

economics, Vol. 88, pp. 1697-1720. 

Dench,  S.  et  al.  (2006).  The  impact  of  learning  on  unemployed,  low-qualified 
adults:  a  systematic  review.  Department  for  Work  and  Pensions  research 
report, No 375.  

Desjardins,  R.  (2003).  Determinants  of  literacy  proficiency:  a  lifelong-lifewide 
learning  perspective.  International  journal  of  educational  research,  Vol. 39, 
pp. 205-245. 

Desjardins,  R.;  Warnke,  A.  (2012).  Ageing  and  skills:  a  review  and  analysis  of 
skill  gain  and  skill  loss  over  the  lifespan  and  over  time.  OECD  education 
working papers, No 72, http://dx.doi.org/10.1787/5k9csvw87ckh-en  

Dickson, M.; Harmon, C. (2011). Economic returns to education: what we know, 
what  we  don’t  know,  and  where  we  are  going  –  Some  brief  pointers. 
Economics of  Education  Review,  Volume  30, Issue  6,  December  2011,  pp. 
1118–1122. http://www.sciencedirect.com/science/journal/02727757/30/6 

Dickson, M.; Smith, S. (2011). What determines the return to education: an extra 
year  or  a  hurdle  cleared?  Economics  of  education  review,  Vol. 30,  No 6, 
pp. 1167-1176. 

Dickerson,  A.;  Green,  F.  (2004).  The  growth  and  valuation  of  computing  and 
other generic skills. Oxford economic papers, Vol. 56, No 3, pp. 371-406. 

Dieckhoff, M. (2008). Skills and occupational attainment: a comparative study of 
Germany,  Denmark  and  the  UK.  Work,  employment  and  society,  Vol. 22, 
No 1, pp. 89-108. 

139 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Disney, R. et al. (2003). Restructuring and productivity growth in UK manufacturing. 

Economic journal, Vol. 113, No 489, pp. 666-694. 

Dolton,  P.;  Pelkonen,  P.  (2004).  The  wage  effects  of  computer  use:  evidence 
from  WERS  2004.  British  journal  of  industrial  relations,  Vol. 46,  No 4,  pp. 
587-630. 

Donaldson, C. et al. (2010). European value of a quality adjusted life year. Newcastle 

University  
http://research.ncl.ac.uk/eurovaq/EuroVaQ_Final_Publishable_Report_and_
Appendices.pdf 

Dorsett, R. et al. (2010). Economic benefits of lifelong learning. London: National 
Institute of Economic and Social Research. NIESR discussion paper, No 352. 

Dubourg,  J.  et  al.  (2005).  The  economic  and  social  costs  of  crime  against 
individuals  and  households  2003/04.  Home  Office  online  report,  No 30/05 
http://webarchive.nationalarchives.gov.uk/20100413151441/http://www.hom
eoffice.gov.uk/rds/pdfs05/rdsolr3005.pdf  

Eurofound  (2008).  Who  needs  upskilling?  Low-skilled  and  low-qualified  workers 

in the European Union.   
https://www.eurofound.europa.eu/sites/default/files/ef_publication/field_ef_d
ocument/ef0897en.pdf 

European Commission (2008). New skills for new jobs: anticipating and matching 
labour  market  and  skills  needs.  Commission  staff  working  document. 
SEC(2008) 3058.  http://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:52008SC3058&from=EN  

European  Commission  (2013a).  Towards  social  investment  for  the  growth  and 
cohesion  –  Including  implementing  the  European  Social  Fund  2014-20: 
communication  from  the  Commission  to  the  European  Parliament,  the 
Council,  the  European  Economic  and  Social  Committee  of  the  Regions. 
COM(2013) 83 final. 
http://ec.europa.eu/social/BlobServlet?docId=9761&langId=en 

European  Commission  (2013b).  Investing  in  children:  breaking  the  cycle  of 
disadvantage:  Commission  recommendation  of  20  February  2013.  Official 
Journal of the European Union, L 59, 2 March 2013.   http://eur-
lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32013H0112 

European  Commission 

(2014a).  Education  and 

training  monitor  2014. 

Luxembourg: Publications Office. 
http://ec.europa.eu/dgs/education_culture/repository/education/library/public
ations/monitor14_en.pdf 

European Commission (2014b). Taxation trends in the European Union: data for 
the  EU  Member  States,  Iceland  and  Norway.  Luxembourg:  Publications 
Office.  Eurostat  statistical  books.  http://bookshop.europa.eu/en/taxation-
trends-in-the-european-union-pbKSDU14001/ 

140 

 
 
References 

European Commission (2015). Secondary earners and fiscal policies in Europe.   

http://ec.europa.eu/justice/gender-
equality/files/documents/150511_secondary_earners_en.pdf  

European  Commission  (2016a).  A  new  skills  agenda  for  Europe.  Working 
together  to  strengthen  human  capital,  employability  and  competitiveness: 
communication  from  the  Commission  to  the  European  Parliament,  the 
Council, the European Economic and Social Committee and the Committee 
of the Regions. COM(2016) 381 final. 
content/EN/TXT/PDF/?uri=CELEX:52016DC0381&qid=1487328805971&fro
m=EN  

 http://eur-lex.europa.eu/legal-

European  Commission  (2016b).  Proposal  for  a  Council  recommendation  on 

establishing a skills guarantee. COM(2016) 382 final.   http://eur-
lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52016DC0382&from=en  

Field,  J.  (2009). Well-being  and  happiness.  Leicester:  National  Institute  of  Adult 

Continuing Education. IFLL thematic papers; No 4. 

Fields, G.S. (2004). Regression-based decompositions: a new tool for managerial 

decision-making. Cornel University: Mimeo.  

Flores,  M.  et  al.  (2013).  Earnings  returns  to  education,  experience  and  health: 
evidence  from  EU-SILC.  ERSA  conference  papers;  No  ersa13p1169. 
http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_01169.pdf  

Fritsche, S. (2012). Determinants of training participation: a literature review and 
empirical support from personal-, job-, employer- and health related factors 
in Germany. NETSPAR theses, No 2012-025.   
http://arno.uvt.nl/show.cgi?fid=128033  

Galindo-Rueda,  F.;  Haskel,  J.  (2005).  Skills,  workforce  characteristics  and  firm-
level  productivity:  evidence  from  the  matched  ABI/employer  skills  survey. 
IZA discussion paper series; No 1542. 

Gesthuizen,  M.;  Scheepers,  P.  (2010).  Economic  vulnerability  among  low-
educated  Europeans:  the  impact  of  resources,  the  group's  position,  labour 
market conditions and welfare state arrangements. Acta sociologica, Vol. 53, 
No 30, pp. 247-267. 

Goldin,  C.  (2006).  The  quiet  revolution  that  transformed  women's  employment, 
education, and family. American economic review, Vol. 96, No 2, pp. 1-21. 

Goldin, C.; Olivetti, C. (2013). Shocking labor supply: a reassessment of the role 
of  World  War  II  on  women's  labor  supply.  American  economic  review, 
Vol. 103, No 3, pp. 257-62. 

Goldring, I.; Yamina, G. (2013). Addressing overqualification of skilled immigrants in 
the European Union: situations and approaches from the local level. Realise 
project. 
publication-for-partners.pdf  

 https://realise2020.files.wordpress.com/2011/09/realise-final-

Green, F. (2012). What is skill? An inter-disciplinary synthesis. London: Institute 

of Education, University of London. LLAKES research paper; No 20.   

141 

Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

http://www.llakes.org/wp-content/uploads/2011/02/Green-What-is-Skill-
reduced.pdf  

Green, D.A.; Riddell, C.W. (2015). Understanding educational impacts: the role of 
literacy and numeracy skills. Paper presented at the Cedefop/IZA workshop on 
skills and skill mismatch, Thessaloniki, 29 and 30 October 2015. 
http://www.iza.org/conference_files/2015_Skill_Mismatch/riddell_w5670.pdf 

Groot,  W.;  Maassen  van  den  Brink,  H.  (2007).  The  health  effects  of  education. 

Economics of education review, Vol. 26, pp. 186-200. 

Groot, W.; Maassen van der Brinck, H. (2010). The effects of education on crime. 

Applied economics, Vol. 42, pp. 279-289. 

Grossman,  M.  (1972).  The  demand  for  health:  a  theoretical  and  empirical 

investigation. New York: National Bureau of Economic Research. 

Grossman,  M.  (2006).  Education  and  non-market  outcomes.  In:  Hanushek,  E.; 

Welch, F. (eds). Handbook of the economics of education. Elsevier. 

Gvaramadze, I. (2010). Low-skilled workers and adult vocational skills-upgrading 
strategies in Denmark and South Korea. Journal of vocational education and 
training, Vol. 62, No 1, pp. 51-61. 

Hammond, C.; Feinstein, L. (2005). The effects of adult learning on self‐efficacy. 

London review of education, Vol. 3, No 3, pp. 265-287. 

Hanushek,  E.A.;  Kimko,  D.D.  (2010).  Schooling,  labour  force  quality,  and  the 
growth of nations. American economic review, Vol. 90; No 5, pp. 1184-1208. 

Hanushek,  E.A.;  Schwerdt,  G.  (2013).  Returns  to  skills  around  the  world: 

evidence from PIAAC. IZA discussion paper; No 7850.  

Harmon,  C.  et  al.  (2003).  The  returns  to  education:  microeconomics.  Journal  of 

economic surveys, Vol. 17, No 2, pp. 115-156.  

Hausman  J.A.;  Taylor,  W.E.  (1981).  Panel  data  and  unobservable  individual 

effects. Econometrica, Vol. 49, No 6, pp. 1377-1398. 

Heckman, J.J. et al. (2006). The effects of cognitive and non-cognitive abilities on 
labour market outcomes and social behaviour. Journal of labour economics, 
Vol. 24, No 3. 

Heinrich, G.; Hildebrand, V (2005). Returns to education in the European Union: 
a  reassessment  from  comparative  data.  European  journal  of  education, 
Vol. 40, No 1, pp. 13-34. 

Hellerstein,  J.  et  al.  (1999).  Wages,  productivity,  and  worker  characteristics: 
evidence from plant-level production functions and wage equations. Journal 
of labor economics, Vol. 17, pp. 409-446. 

Hellerstein,  J.;  Neumark,  D.  (2007).  Production  function  and  wage  equation 
estimation  with  heterogeneous  labour:  evidence  from  a  new  matched 
employer-employee  data  set.  In:  Hard-to-measure  goods  and  services: 
essays  in  honor  of  Zvi  Griliches.  National  Bureau  of  Economic  Research 
pp. 31-71. 

142 

 
References 

Hjalmarsson, R. et al. (2011). The effect of education on criminal convictions and 
incarceration: causal evidence from micro-data. Centre for Economic Policy 
Research discussion paper; No 8646. 

Hogarth, T. et al. (2012). Employer investment in apprenticeships and workplace 
learning: the fifth net benefits of training to employers study. Department for 
Business, Innovation and Skills. Research paper; No 67. 

Hoskins,  B.  et  al.  (2008).  Does  formal  education  have  an  impact  on  active 
citizenship behaviour? European educational research journal, Vol. 7, No 3, 
pp. 386-402. http://journals.sagepub.com/doi/abs/10.2304/eerj.2008.7.3.386  

Ingram,  B.F;  Neumann,  G.R.  (2006).  The  returns  to  skill.  Labour  economics, 

Vol. 13, pp. 35-59. 

Jackson,  M.  (2001).  Non-meritocratic  job  requirements  and  the  reproduction  of 
class  inequality:  an  investigation.  Work,  employment  and  society,  Vol.  15, 
No 3, pp. 619-630. 

Jackson, M. et al. (2005). Education, employers and class mobility. Research in 

social stratification and mobility, Vol. 23, pp. 3-33. 

Jenkins,  A.  (2011).  Participation  in  learning  and  well-being  among  older  adults. 

International journal of lifelong education, Vol. 30, No 3, pp. 403-420. 

Jaumotte,  F.  (2003).  Female  labour  force  participation:  past  trends  and  main 
in  OECD  countries.  Paris:  OECD  Publishing.  OECD 

determinants 
Economics Department working papers, No 376. 
http://dx.doi.org/10.1787/082872464507 

Jones, M.K. et al. (2008). Crossing the tracks? Trends in the training of male and 
female workers in Great Britain. British journal of industrial relations, Vol. 46, 
No 2, pp. 268-282. 

Kaufman,  H.G.  (1995).  Salvaging  displaced  employees:  job  obsolescence, 
retraining  and  redeployment.  In:  London,  M.  (ed.).  Employees,  careers and 
job creation. San Francisco, CA: Jossey-Bass, pp. 105-120. 

Kelly, E. et al. (2010). The economic returns to field of study and competencies 
among  higher  education  graduates  in  Ireland.  Economics  of  education 
review, Vol. 29, pp. 650-657. 

Kempter, D. et al. (2011). Changes in compulsory schooling and the causal effect 
of  education  on  health:  evidence  from  Germany.  Journal  of  health 
economics, Vol. 30, No 2, pp. 340-354. 

Konings, J.; Vanormelingen, S. (2015). The impact of training on productivity and 
wages:  firm-level  evidence.  Review  of  economics  and  statistics,  Vol. 97, 
No 2, pp. 485-497. 
http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00460  

Krause, M.; Lubik, T. (2007). On-the-job search and the cyclical dynamics of the 
labor  market.  Deutsche  Bundesbank,  discussion  paper  series  1:  economic 
studies; No 15/2007. 

143 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Krueger,  A.B.;  Lindahl,  M.  (2001).  Education  for  growth:  why  and  for  whom? 

Journal of economic literature, Vol. 39, No 4, pp. 1101-1136. 

Kuppens,  T.  et  al  (2015).  Life  at  both  ends  of  the  ladder:  education-based 
identification  and  its  association  with  well-being  and  social  attitudes. 
Personality and social psychology bulletin, Vol. 41, No 9, pp. 1260-1275. 

Kureková, L. et al. (2012). Qualifications or soft skills? Studying job advertisements 
for demand for low-skilled staff in Slovakia. NEUJOBS working paper; No D. 
4.3.3.  

Kureková,  L.  et  al.  (2013a).  Being  and  becoming  low-skilled:  a  comprehensive 
approach to studying lowskillness. NEUJOBS, working paper; No 4.3.1. 

Kureková, L. et al. (2013b). Demand for low- and medium-skilled workers across 
Europe:  between  formal  qualifications  and  non-cognitive  skills.  NEUJOBS 
working paper; No 4.3.3.  
http://biblioteka-krk.ibe.edu.pl/opac_css/doc_num.php?explnum_id=592  

Kyndt,  E.  et  al.  (2013).  Examining  the  learning  intentions  of  low‐qualified 
employees:  a  mixed  method  study.  Journal  of  workplace  learning,  Vol. 25, 
No 3, pp. 178-197. 

Kyriacou,  G.A.  (1991).  Level  and  growth  effects  of  human  capital.  Economic 

research reports, pp. 91-26.  

Lauglo,  J.;  Øia,  T.  (2007).  Education  and  civic  engagement:  review  of  research 
and  a  study  on  Norwegian  youths.  Paris:  OECD  Publishing.  OECD 
education working papers; No 12. 
http://dx.doi.org/10.1787/050484723262OECD 

Lebedinski,  L.;  Vandenberghe,  V.  (2013).  Assessing  education’s  contribution  to 
productivity  using  firm-level  evidence.  International  journal  of  manpower, 
Vol. 35, No 8, pp. 1116-1139. 

Leuven,  E.  et  al.  (2010).  Expanding  schooling  opportunities  for  4-year-olds. 

Economics of education review, Vol. 29, No 3, pp. 319-328.  

Lloyd,  C.;  Mayhew,  K.  (2010).  Skill:  the  solution  to  low  wage  work?  Industrial 

relations journal, Vol. 41, No 5, pp. 429-445. 

Lochner,  L.  (2011).  Non-production  benefits  of  education:  crime,  health,  and 
good citizenship. In: Hanushek, E.A et al. (eds). Handbook of the economics 
of education, Vol. 4. Amsterdam: North-Holland, p. 183-282. 

Lochner, L.; Moretti, E. (2001). The effect of education on crime: evidence from 
prison  inmates.  National  Bureau  of  Economic  Research  working  paper; 
No 8605. 

Lochner, L.; Moretti, E. (2004). The  effect of education on crime: evidence from 
prison  inmates,  arrests,  and  self-reports.  American  economic  review, 
Vol. 94, pp. 155-189. 

Lodde,  S.  (2008).  Human  capital  and  productivity  growth  in  the  Italian  regional 
economies.  A  sectoral  analysis.  Rivista  internazionale  di  scienze  sociali, 
Vol. 116, No 2, pp. 211-233. 

144 

 
References 

Machin,  S.;  Van  Reenen,  J.  (1998).  Technology  and  changes  in  skill  structure: 
evidence  from  seven  OECD  countries.  Quarterly  journal  of  economics, 
Vol. 113, pp. 1215-1244. 

Machin,  S.  et  al.  (2011).  The  crime  reducing  effect  of  education.  Economic 

journal, Vol. 121, pp. 463-484. 

Mankiw,  N.  et  al.  (1992).  A  contribution  to  the  empirics  of  economic  growth. 

Quarterly journal of economics, pp. 407-438. 

Manning,  A.  (2004). We  can  work  it  out:  the  impact  of  technological  change  on 
the  demand  for  low-skill  workers.  Scottish  journal  of  political  economy, 
Vol. 51, No 5, pp. 581-608. 

Martins,  P.S.;  Jin,  J.Y.  (2010).  Firm-level  social  returns to  education.  Journal  of 

population economics, Vol. 23, No 2, pp. 539-558. 

McIntosh,  S.  (2007).  A  cost-benefit  analysis  of  apprenticeships  and  other 

vocational qualifications. DfES research report; No 834. 

Meghir, C. et al. (2011). The effect of education policy on crime: an intergenerational 
perspective. Cambridge (MA): NBER. NBER working paper; No 18145. 

Meghir, C. et al. (2012). Education, health and mortality: evidence from a social 
experiment. Cambridge (MA): NBER. NBER working paper; No 17932.  

Mendolicchio,  C.;  Rhein,  T.  (2011).  The  gender  gap  of  returns  on  education 

across west European countries. IAB discussion paper; No 20.  

Milligan,  K.  (2003).  Does  education  improve  citizenship?  Evidence  from  the  US 
and  the  UK.  National  Bureau  of  Economic  Research  working  paper; 
No 9584. 

Mincer, J. (1958). Investment in human capital and personal income distribution. 

Journal of political economy, Vol. 66, No 4, pp. 281-302. 

Mincer, J. (1974). Schooling, experience, and earnings. Cambridge, MA: National 

Bureau of Economic Research. 

Ministry of Education (2007). Education and training in Portugal. 

http://www.dgeec.mec.pt/np4/97/%7B$clientServletPath%7D/?newsId=147&
fileName=education_training_portugal.pdf 

Ministry  of  Education,  Culture  and  Science  (2010).  The  approach  to  school 
dropout: policy in the Netherlands and the provisional figures of the 2008-09 
performance agreements.  

Ministry  of  Justice  (2012).  The  pre-custody  employment,  training  and  education 

status of newly sentenced prisoners. London: Ministry of Justice. 

Montenegro,  C.E.;  Patrinos,  H.A.  (2014).  Comparable  estimates  of  returns  to 
schooling  around  the  world.  Washington,  DC:  World  Bank  Group.  Policy 
research working paper; No WPS 7020. 
http://documents.worldbank.org/curated/en/2014/09/20173085/comparable-
estimates-returns-schooling-around-world  

145 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Moretti, E. (2004). Workers’ education, spillovers, and productivity: evidence from 
plant-level  production  functions.  American  economic  review,  Vol. 94,  No 3, 
pp. 656-690. 

Morrison  Gutman,  L.;  Schoon,  I.  (2013).  The  impact  of  non-cognitive  skills  on 
outcomes  for  young  people.  London:  Institute  for  Education,  University  of 
London. 
https://educationendowmentfoundation.org.uk/public/files/Publications/EEF_
Lit_Review_Non-CognitiveSkills.pdf  

Nafilyan, V.; Speckesser, S. (2014). The youth contract provision for 16- and 17-
year-olds  not  in  education,  employment  or  training  evaluation,  econometric 
estimates  of  programme 
impacts  and  net  social  benefits.  London: 
Department for Education Research report; No 318. 

National  Audit  Office  (2012).  Adult  apprenticeships.  Report  by  the  Comptroller 

and Auditor General. London: The Stationery Office. 
https://www.nao.org.uk/wp-content/uploads/2012/02/10121787.pdf 

Nelson,  M.  (2010).  The  adjustment  of  national  education  systems  to  a 
knowledge-based  economy:  a  new  approach.  Comparative  education, 
Vol. 46, No 4, pp. 463-486. 

NICE (2013). Judging whether public health interventions offer value for money. 
NICE,  National  Institute for  Health  and  Care  Excellence.  Local  government 
briefing [LGB10], September 2013. 
https://www.nice.org.uk/advice/lgb10/chapter/About-this-briefing  

OECD  (2011).  PIAAC  conceptual  framework  of  the  background  questionnaire 

main survey. 
http://www.oecd.org/site/piaac/PIAAC(2011_11)MS_BQ_ConceptualFramew
ork_1%20Dec%202011.pdf  

OECD  (2013a).  OECD  skills  outlook  2013:  first  results  from  the  survey  of  adult 
skills. OECD Publishing. http://dx.doi.org/10.1787/9789264204256-en  

OECD (2013b). Technical report of the survey of adult skills (PIAAC). 

OECD (2013c). The survey of adult skills: reader’s companion. OECD Publishing. 

OECD (2014). OECD employment outlook 2014. OECD Publishing. 

http://dx.doi.org/10.1787/empl_outlook-2014-en  

OECD  (2016).  The  survey  of  adult  skills:  reader's  companion  (second  edition). 

Paris: OECD Publishing. http://dx.doi.org/10.1787/9789264258075-en 

OECD;  Statistics  Canada  (2000).  Literacy  in  the  information  age:  final  report  of 
the  international  adult  literacy  survey.  Paris:  OECD  Publications  Service. 
http://www.oecd.org/edu/skills-beyond-school/41529765.pdf  

Oesch, D. (2010). What explains high unemployment among low-skilled workers? 
Evidence from 21 OECD countries. European journal of industrial relations, 
Vol. 16, No 1, pp. 39-55.  
http://journals.sagepub.com/doi/pdf/10.1177/0959680109355307  

146 

 
 
 
 
References 

Oreopoulos,  P.  (2007).  Do  dropouts  drop  out  too  soon?  Wealth,  health,  and 
happiness from compulsory schooling. Journal of public economics, Vol. 91, 
No 1112, pp. 2213-2229. 

Pfeiffer, H. et al. (2009). Kosten und Nutzen der betrieblichen Berufsausbildung. 
[Costs  and  benefits  of  company-based  apprenticeships].  Bonn:  Federal 
Institute  for  Vocational  Education  and  Training.  BiBB  research  report; 
No 2.1.203. 

Pritchett,  L.  (2001).  Where  has  all  the  education  gone?  World  Bank  economic 

review, Vol. 15, No 3, pp. 367-391.  

Psacharopoulos, G.; Patrinos, H.A. (2004). Returns to investment in education: a 

further update. Education economics Vol. 12, No 2, pp. 111-134. 

Quintini, G. (2014). The effects of vocational education on adult skills and wages: 
what can we learn from PIAAC? OECD, Directorate for Employment, Labour 
and Social Affairs.  

Riley, R.; Robinson, C. (2011). UK economic performance: how far do intangibles 

count? Innodrive working paper; No 14. 

Rocco, T.S.; Thijssen, J.G.L. (2006). Older workers, new directions: employment 
and development in an aging labour market. Miami, FL: Florida International 
University, Center for Labour Research and Studies. 

Roosmaa,  E.L.;  Saar,  E.  (2012).  Participation  in  non-formal  learning  in  EU-15 
and EU-8 countries: demand and supply side factors. International journal of 
lifelong education, Vol. 31, N 4, pp. 477-501. 
http://dx.doi.org/10.1080/02601370.2012.689376 

Rovny, A.E. (2014). The capacity of social policies to combat poverty among new 
social risk groups. Journal of European social policy, Vol. 24, No 5, pp. 405-
423. 

Sabates,  R.  (2008).  Education  attainment  and  juvenile  crime.  British  journal  of 

criminology, Vol. 48, pp. 395-409. 

Sakellariou,  C.  (2009).  Endogeneity,  computers,  language  skills  and  wages 
among  university  graduates  in  Vietnam.  Applied  economics,  Vol. 41, 
pp. 653-663. 

Schuller,  T.  et  al.  (2004).  The  benefits  of  learning:  the  impact  of  education  on 

health, family life and social capital. New York: Routledge Falmer. 

Sianesi,  B.;  Van  Reenen,  J.  (2000).  The  returns  to  education:  a  review  of  the 

macro-economic literature. A report to the DfEE. 

Siedler,  T.  (2010).  Schooling  and  citizenship  in  a  young  democracy:  evidence 
from post-war Germany. Scandinavian journal of economics, Vol. 112, No 2, 
pp. 315-338. 

Silles,  M.A. (2009). The causal effect of education on health: evidence from the 
United Kingdom. Economics of education review, Vol. 28, No 1, pp. 122-128. 

147 

 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

Smits,  W.;  Zwick,  T.  (2004).  Why  do  business  service  firms  employ  fewer 
apprentices?  A  comparison  between  Germany  and  the  Netherlands. 
International journal of manpower, Vol. 25, No 1, pp. 36-54. 

Spasojevic,  J.  (2010).  Effects  of  education  on  adult  health  in  Sweden:  results 
from a natural experiment. In: Slottje, D.; Tchernis, R. (eds). Current issues 
in  health  economics.  Emerald  Group  Publishing,  pp.  179-199,  Chapter  9. 
Contributions to economic analysis series, No 290. 
http://www.emeraldinsight.com/doi/pdfplus/10.1108/S0573-
8555%282010%290000290012  

Statistics Canada (2005). Building on our competencies: Canadian results of the 
international adult literacy and skills survey. Ottawa: Statistics Canada.  

Steedman, H., McIntosh, S. (2001). Measuring low skills in Europe: how useful is 
the ISCED framework? Oxford economic papers, Vol. 53, No 3, pp. 564-581. 

Tyler,  J.H.  (2004).  Basic  skills  and  the  earnings  of  dropouts.  Economics  of 

education review, Vol. 23, No 3, pp. 221-235. 

Van Kippersluis, H. et al. (2011). Long run returns to education: does schooling 
lead  to  an  extended  old  age?  Journal  of  human  resources,  Vol.  46,  No 4, 
pp. 695-721. 

Vignoles,  A.  et  al.  (2004).  The  labour  market  impact  of  adult  education  and 
training:  a  cohort  analysis.  Scottish  journal  of  political  economy,  Vol. 51, 
No 2, pp. 266-280. 

Vignoles,  A.  et  al.  (2010).  The  value  of  basic  skills  in  the  British  labour  market. 

Oxford economic papers, Vol. 63, pp. 27-48. 

Williams,  R.  (2012).  Using  the  margins  command  to  estimate  and  interpret 
adjusted  predictions  and  marginal  effects.  Stata  journal,  Vol. 12,  No 2, 
pp. 308-331. 

Willms,  J.D.;  Murray,  T.S.  (2007).  Gaining  and  losing  literacy  skills  over  the 

lifecourse. Ottawa: Statistics Canada. 

Wilson. R.; Briscoe, G. (2004). The impact of human capital on economic growth: 
a  review.  In:  Cedefop  et  al.  (eds).  Impact  of  education  and  training:  third 
report  on  vocational  training  research  in  Europe:  background  report. 
Luxembourg:  Publications  Office.  Cedefop  Reference  series;  No 54, 
pp. 9-70. http://www.cedefop.europa.eu/EN/Files/3036_en.pdf  

Woessmann,  L.  (2014).  The  economic  case  for  education.  EENEE  analytical 

report; No 20. 

148 

 
ANNEXES 

ANNEX 1.   
Low skills definitions used in this report: by 
chapter 

Table A1.  Low skill definition used for Chapter 2 and Chapter 3  

Formal education (Eurostat LFS) 

Qualification level  

Low-qualified  
Medium-qualified  

High-qualified  

Description  

ISCED-2011 

Up to lower secondary education  
Upper secondary and post-secondary 
non-tertiary education  
Tertiary education  

(0-2)  
(3, 4)  

(5-8)  

Information processing (or cognitive) skills (OECD PIAAC) 

Skills level (cognitive)  

Description  

OECD-PIAAC 2011  

Low skills in literacy or numeracy   Proficiency score less than 226 points  
Proficiency score less than 241 points  
Low skills in problem solving  

(below 1 or level 1)  
(below level 1)  

Job/occupation skill level  

Occupation category  

Description  

Highly skilled  

Semi-skilled non-manual  

Semi-skilled manual  

Low-skilled  

Managers; professionals, technicians 
and associate professionals  
Clerical support workers; service and 
sales workers  
Skilled agricultural, forestry and fishery 
workers; craft and related trades 
workers; plant and machine operators, 
and assemblers  
Elementary occupations  

ISCO- 08  

(groups 1-3)  

(groups 4-5)  

(groups 6-8) 

(group 9) 

Due  to  data  limitation,  in  Section  2.3  (future  demand  and  supply  scenarios 
for  low-skilled  adults),  low-skilled  are  referred  only  using  the  narrow  ISCED 
definition (ISCED 0-2). 

150 

 
 
 
 
 
 
 
ANNEX 1. 
Low skills definitions used in this report: by chapter 

Table A2.  Low skill definition used for Chapter 5, the cost of low skills  

Formal education (Eurostat LFS) 

Qualification level  

Description  

Low-qualified  

Medium-qualified  

High-qualified  

Skills obsolescence 

Up to lower secondary education  
Upper secondary and post-secondary non-
tertiary education  
Tertiary education  

ISCED-2011 

(0-2)  

(3, 4)  

(5-8)  

Description  

People with educational attainment at ISCED 3 
or above, who work in either elementary 
occupations (S1, ISCO major group 9) or in 
elementary occupations, low-skilled 
employment, craft and related trades workers or 
in skilled employment in agriculture (S2, major 
groups 6-9 in ISCO-08) 

Job/occupation skill level  

Occupation category  

Description  

Highly skilled  

Semi-skilled non-manual  

Semi-skilled manual  

Low-skilled  

Managers; professionals, technicians and 
associate professionals  
Clerical support workers; service and sales 
workers  
Skilled agricultural, forestry and fishery workers; 
craft and related trades workers; plant and 
machine operators, and assemblers  
Elementary occupations  

ISCO- 08  

(groups 1-3)  

(groups 4-5)  

(groups 6-8) 

(group 9) 

151 

 
 
ANNEX 2.   
Cluster analysis 

Methodological approach 

The cluster analysis was based on an original dataset of policy indicators for 27 
Member States over the 2003-13 period. The main sources of data are Eurostat 
and OECD (Table A3). Croatia was not included in the analysis, since data were 
not available for the most of the policy indicators considered. 

To  cluster  Member  States  we  have  considered  the  2003-07  average  (pre-

crisis period) for the following policy indicators: 
(a) 

labour market policies (LMP) expenditure, distinguishing between active and 
passive measures (as % of GDP); 

(b)  education  system  in  terms  of  (i)  public  expenditure  on  education  (as  %  of 
GDP)  and  (ii)  adult  lifelong  learning  (participation  of  people  aged  25  to  64 
years  old  in  formal  and  non-formal  education  and  training  in  the  last  four 
weeks); 

(c)  market  regulation  (both  product  and  labour  markets,  including  unions  –  as 

(d) 

representation may be higher for low-skilled adults – and minimum wages); 
taxation on second earner, since empirical literature has shown that this is a 
key factor in deciding to participate in the labour market;  

(e)  work-life balance policies in terms of (i) public expenditure in maternity and 
paternal leave as a percentage of GDP, and (ii) formal childcare (coverage 
rate for children aged 0-2 years).  

We  used  a  hierarchical  cluster  technique,  with  the  method  of  complete 
linkage, also known as farthest neighbour clustering, which tends to find compact 
clusters of approximately equal diameters. 

152 

 
 
 
ANNEX 2. 
Cluster analysis 

Table A3.  Description of variables used in the cluster analysis 

Variable 

LMP expenditure 

LMP expenditure as 
% of GDP 

Education system 

Expenditure on 
education as % of 
GDP 
Lifelong learning 

Market regulation 

Product market 
regulation (PMR) 

Employment 
protection legislation 
(EPL) 

Union density (UD) 

Description 

Source 

% of expenditure in each type of measure and support; 
The indicator is also available for overall LMP, total 
measures LMP (category 2-7); and total supports LMP 
(category 8-9). 

Eurostat, LM 
policy database  

Public expenditure on overall education (all ISCED) as 
% of GDP. 

Eurostat 
database 

Participation rate of 25 to 64 years old people in formal 
and non-formal education and training in the last four 
weeks  

Eurostat 
database 

Integrated indicator which measures the degree to 
which policies promote or inhibit competition in areas of 
the product market where competition is viable. Higher 
values of PMR are assigned to those countries with 
more pervasive state regulation. 
A measure of all types of employment protection, 
whether grounded primarily in legislation, court rulings, 
collectively bargained conditions of employment or 
customary practice. The indicator ranges from 0 to 6, 
with higher scores representing stricter regulation. 
UD (0-100) = net union membership as a proportion of 
all wage and salary earners in employment; quadratic 
form used in regressions 

Adjusted union 
coverage 

Minimum wages 

Adjusted union coverage (0-100) = employees covered 
by wage bargaining agreements as a % of all wage and 
salary earners in employment with the right to 
bargaining, adjusted for the possibility that some 
sectors or occupations are excluded from the right to 
bargain 
Proportion of minimum relative to median monthly 
wages of full-time workers. 

OECD database 

OECD database 

Visser, J. (2013), 
ICTWSS 
database, 
Version 4.0 
Visser, J. (2013), 
ICTWSS 
database, 
Version 4.0 

Visser, J. (2013), 
ICTWSS 
database, 
Version 4.0 

Family-related taxation 

Tax rate on second 
earner 

Taxation on second earner = 

OECD database 

1 −

(𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒)𝐴 − (𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒)𝐵
(𝐺𝑟𝑜𝑠𝑠 𝑖𝑛𝑐𝑜𝑚𝑒)𝐴 − (𝐺𝑟𝑜𝑠𝑠 𝑖𝑛𝑐𝑜𝑚𝑒)𝐵

where A denotes married couples with no children and 
only one earner (100% or average wage), and B refers 
to two-earner married couples with no children (67% of 
average wage). 

Work-life balance policies 

Parental leave  
(% GDP) 
Formal child care 
(0-2) 

Public expenditure in maternity and paternal leave as  
% of GDP 
Children aged less than 3 years cared for (by formal 
arrangements other than by the family) 30 hours or 
more a week, as a proportion of all children in the same 
age group. 

OECD database 

Eurostat 
database 

153 

 
ANNEX 3.   
Characteristics, determinants and risks of 
being low-skilled among adults in the EU  

Methodological approach 

To  assess  the  relationships  between  the  level  of  cognitive  skills  and  the 
characteristics  and  family  background  of  individuals,  as  well  as  other  factors 
influencing the accumulation or maintenance of skills, we estimated a multivariate 
econometric model, using an OLS regression of proficiency scores in literacy and 
numeracy  (separately)  on  several  factors  possibly  influencing  cognitive  skills 
level, controlling for country fixed effects. The model specification is:  

𝑌𝑖 =  𝛼 + 𝛽𝑃𝑖 + 𝛾𝐸𝑖 + 𝛿𝑇𝑖 + 𝜌𝐶𝑖 + 𝜗𝑈𝑖 + 𝜃𝑊𝑖 + 𝜑𝑋𝑖 + 𝜀𝑖  

[equation 1] 

Where:  
  Y is the proficiency score in numeracy (or literacy) measured on a 500-

point scale;  

  P  is  a  vector  of  variables  reporting  personal  and  family  background 
characteristics:  gender,  age,  perceived  general  health,  migrant 
background, parental education, number of books available at home (110). 
Family  background  characteristics 
influences  both  schooling  and 
cognitive skills and serves as a proxy to innate learning abilities;  

  E  is  a  vector  of  dummy  variables  on  the  highest  level  of  education 
attained (low, medium and high). In the empirical literature, the achieved 
level of education is found to be the most important factor affecting skills 
levels;  

  T is a dummy variable indicating if the individual has participated to any 
formal  or  non-formal  education  or  training  course  during  the  12  months 
preceding  participation  in  the  survey.  It  seeks  to  capture  the  fact  that 
people do not only learn during initial education, but also later in life;  
  C  is  a  dummy  variable  indicating  whether  or  not  the  individual  has 

computer experience;  

(110)  This is a widely used measure of cultural capital used in international surveys (e.g. 

PISA) (OECD, 2011). 

154 

 
                                                
ANNEX 3. 
Characteristics, determinants and risks of being low-skilled among adults in the EU 

  U identifies a set of dummy variables related to the frequency of use of 
(111): 
cognitive  skills 
engagement  in  literacy  and  numeracy  practices  and  the  use  of  ICTs  at 
work  and  in  everyday  life  is  one  way  adults  enhance  or  maintain  their 
skills (OECD, 2013c);  

(reading,  writing  and  numeracy)  at  home 

  W  is  a  set  of  dummy  variables  reporting  the  work  situation  of  the 
individual  distinguishing  according  to  the  duration  of  not  being  in 
employment:  never  worked  in  life;  experiencing  a  very  long,  long  and 
short unemployment or inactivity spell. Inactivity and unemployment may 
reduce  the  use  of  skills  with  a  detrimental  effect  on  information 
processing skills;  

  X are country fixed effects to control for institutional features and policies.  

This  general  specification  was  applied  to  perform  OLS  regressions  on 
numeracy and on literacy proficiency scores on the PIAAC full set of adults aged 
25  to  65,  excluding  retired  people,  students,  permanently  disabled  people  and 
individuals in compulsory military or community service. 

Along  with  the  pooled  regressions,  a  set  of  categorical  regressions  have 
been  estimated  to  assess  differences  in  the  relationships  with  cognitive  skills 
related to age, level of education and employment status.  

For  the  employment  status  of  participants,  the  OLS  regression  has  been 
estimated on a sample of workers with a different specification model to capture 

(111)  The  PIAAC  dataset  contains  indices  to  describe  the  use  of  information  processing 
skills  at  work  and  at  home.  These  indices  were  created  from  multiple  questions 
included in the background questionnaire and cover several information processing 
skills:  numeracy  (calculating  prices,  costs  or  budgets;  use  of fractions,  decimals  or 
percentages; use of calculators; preparing graphs or tables; algebra or formulas; use 
of advanced math or statistics); reading (reading documents: directions, instructions, 
letters,  memos,  emails,  articles,  books,  manuals,  bills,  invoices,  diagrams,  maps); 
writing  (writing  documents:  letters,  memos,  emails,  articles,  reports,  forms),  ICT 
skills  (using  Internet,  spreadsheets,  word  processors,  programming  languages; 
conducting transactions online; participating in online discussions); problem solving 
(facing hard problems: at least 30 minutes of thinking to find a solution). Each skill 
index  has  been  produced  by  using  the  item  response  theory  (IRT)  and  provides  a 
measure  of  how  often  the  tasks  making  up  the  index  were  carried  out  by  a 
participant.  Internationally,  participants  were  grouped  into  quintiles,  which  give  an 
indication of how often they perform these tasks. For instance, participants who fall 
in the lowest 20% on the index internationally will tend to perform some or all of the 
tasks  infrequently,  while  participants  who  fall  in  the  more  than  80%  group  will 
frequently  perform  many  of  the  tasks.  Respondents  who  answered  ‘never’  to  all 
questions in the index appear in an ‘all zero response’ category. For more details on 
how the indices were created see the OECD reader companion (OECD, 2013c) and 
the PIAAC technical report (OECD, 2013b).  

155 

                                                
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

the  effect  of  work  experience  and  to  control  for  job-related  characteristics.  The 
model specification is:  

𝑌𝑖 =  𝛼 + 𝜃 𝐽𝑖 + 𝜔 𝑍𝑖 + 𝛽𝑃𝑖 + 𝛾𝐸𝑖 + 𝛿𝑇𝑖 + 𝜌𝐶𝑖 + 𝜗𝑈𝑖 + 𝜑𝑋𝑖 + 𝜀𝑖 

[equation 2] 

where  
  Y is the proficiency score in numeracy (or literacy) measured on a 500-

point scale;  

  J is a set of variables related to the job held (occupation, type of contract 

and hours of work) and the employer (firm size, sector);  

  Z  is  a  vector  reporting  a  scalar  of  the  years  of  paid  work  during  one’s 

lifetime and its squared term.  

The  other  regressors  are  the  same  as  equation  1,  apart from  the  variables 
relating  to  training  in  the  previous  12  months  which,  in  this  specification, 
distinguish between job-related training and non-job-related training.  

This  model  specification  was  estimated  on  a  sample  of  dependent  workers 
aged  25  to  65;  the  sample  is  restricted  to  employees  with  one  job  only  and 
excludes workers employed in non-profit organisations, in the armed forces, and 
skilled agricultural, forestry and fishery workers. 

family  background,  highest 

To present in a synthetic way the contribution of group of variables (personal 
level  of  education  attained, 
characteristics, 
participation  in  training,  use  of  cognitive  skills  at  home,  work  experience  and 
unemployment)  in  explaining  the  scores  in  cognitive  skills  we  conducted  a 
variance  decomposition  analysis.  This  allows  assessment  of  how  much  of  the 
observed variance in the outcome variable of the OLS regression (R2) – the score 
in  cognitive  skills  –  is  explained  by  the  dependent  variables.  Like  the  OECD 
(2014) we used Fields’ (2004) regression-based decomposition technique, which 
performs  an  exact  decomposition  of  the  outcome  variable  variance  into  the 
variance  attributable  to  each  explanatory  variable  and  the  residual.  Fields’ 
approach  consists  in  decomposing  the  explained  portion  of  the  regression  (R-
squared)  into  weights  for  each  of  the  factors  (for  more  details  see  Fields,  2004 
and OECD, 2014).  

To  assess  how  the  risk  of  being  low-skilled  in  numeracy  (112)  changes  in 
relation  to  the  personal  background  and  other  factors  associated  with  the 
acquisition,  maintenance  and  development  of  this  skill,  we  performed  a 
probabilistic regression using the following specification:  

(112)  In  this  part  of  the  analysis  we  concentrate,  and  present  results,  only  on  numeracy 
skills,  since  literacy  and  numeracy  proficiency  scores  are  highly  correlated  (the 
correlation coefficient being 0.86 in our sample) and produce similar results.  

156 

                                                
ANNEX 3. 
Characteristics, determinants and risks of being low-skilled among adults in the EU 

𝑍𝑖 =  𝛼 + 𝛽𝑃𝑖 + 𝛾𝐸𝑖 + 𝛿𝑇𝑖 + 𝜌𝐶𝑖 + 𝜗𝑈𝑖 + 𝜃𝑊𝑖 + 𝜑𝑋𝑖 + 𝜀𝑖  

[equation 3] 

where  
  Z  is  a  variable  taking  value  1  for  individuals  scoring  at  or  below 
proficiency  level  1  (i.e.  scores  less  than  226  points)  (113)  in  numeracy, 
and 0 otherwise. 

Explanatory  variables are the same as those used in  equation 1 and relate 
to  personal  and  family  background  characteristics,  highest  level  of  education 
attained and training in the previous 12 months, computer experience, frequency 
of use of cognitive skills at home, work experience and unemployment spells and 
country fixed effects.  

The probit regression was calculated on the PIAAC full set of adults aged 25 
to  65,  excluding  retired  people,  students,  permanently  disabled  people  and 
individuals in compulsory military or community service. 

Predicted  (or  expected)  probabilities  for  hypothetical  or  prototypical  cases 
were  calculated  using  Stata  ‘margins’  command.  Expected  probabilities  have 
been  estimated  using  average  adjusted  predictions  and  adjusted  predictions  at 
representative values (Williams, 2012).  

(113)  For more details see Box 2.  

157 

                                                
ANNEX 4.   
Effects of low skills on employability 

Methodological approach 

To  analyse  the  effects  of  low-skills  on  employability  we  performed  two  distinct 
analyses.  

The first  is  a  logistic  regression  on  PIAAC  data to assess  the  effect  on  the 
employment status of different types of skills (formal education, training, cognitive 
skills, computer experience), controlling for the socio-demographic characteristics 
of individuals, and for economic and institutional factors. The model specification 
is:  

𝑌𝑖 =  𝛼 + 𝛽𝑃𝑖 + 𝛾𝐸𝑖 + 𝛿𝑇𝑖 + 𝜌𝐶𝑖 + 𝜑𝑋𝑖 + 𝜀𝑖  

[equation 4] 

where  
  Y is a variable taking value 1 employed people and 0 for unemployed or 

inactive people;  

  P  is  a  vector  of  variables  reporting  personal  and  family  characteristics: 
gender,  age,  perceived  general  health,  migrant  background,  parental 
education,  having  children  and  an  interaction  variable  for  women  with 
children (114);  

  E  is  a  vector  of  dummy  variables  on  the  highest  level  of  education 

attained (low, medium and high);  

  T  is  a  dummy  variable  indicating  if  the  individual  has  obtained  a 
proficiency  score  in  numeracy  below  level  1  of  OECD  classification 
(lower than 226 points), low-skilled in numeracy;  

  C  is  a  dummy  variable  indicating  whether  or  not  the  individual  has 

computer experience;  

  X are country fixed effects to control for institutional features and policies.  

This  specification  has  been  used  to  perform  a  logistic  regression  on  a 
sample of adults aged 25 to 65 surveyed by PIAAC. The sample does not include 
retired  people,  students,  permanently  disabled  people  and  individuals  in 
compulsory military or community service.  

(114)  Since, in contrast with men, women with children present a lower participation to the 

labour market.  

158 

 
                                                
ANNEX 4. 
Effects of low skills on employability 

Predicted  (or  expected)  probabilities  for  hypothetical  or  prototypical  cases 
were  calculated  using  Stata  ‘margins’  command.  Expected  probabilities  were 
estimated  using  average  adjusted  predictions  and  adjusted  predictions  at 
representative values (see Williams, 2012). 

The  second  analysis  uses  a  multinomial  logit  econometric  specification  to 

investigate the determinants of transitions across labour market statuses.  

Multinomial  logit  is  based  on  a  nominal  multi-response  model  where  the 

dependent variable has more than two categories.  

We consider  as dependent  variable the transitions from  a low-skilled  job  to 

four possible outcomes:  
(a) 

transition  to  a  high-skilled  or  semi-skilled  manual  or  non-manual  jobs 
(HSJ/SSJ/SSMJ), that we select as reference state 𝑗∗ ;  

(b)  permanence in a low-skilled job (LSJ); 
(c)  movement to unemployment (U); 
(d)  movement to inactivity (N). 

As covariates we have used:  
level of education (low, medium and high) as proxy of skills;  

(a) 
(b)  personal  and  household  variables  such  as  gender  (a  dummy  for  female), 
marital  status,  age,  a  variable  indicating  the  number  of  children  aged  four 
years and below (and an interaction term between female and the number of 
children),  household  size  (in  terms  of  persons),  a  dummy  (bad  health) 
indicating if the perceived health of the individual is bad or very bad;  

(c)  other  job  related  variables  such  as  years  of  work  experience,  the  type  of 
contract/employment (dependent  worker  with a  permanent  contract,  or  with 
a  fixed-term  contract  or  self-employed)  and  a  dummy  with  value  1  if  the 
individual has had an unemployment spell between the initial labour market 
status and the current one.  

This  specification  was  adopted  for  the  three-year  transition  using  the  2011 

longitudinal EU-SILC dataset, on a sample of people aged 25 to 65 in 2011. 
The conditional individual transition probabilities can be defined as: 

𝑃𝑖𝑗,𝑡(ℎ) = Pr(𝑋𝑡,ℎ = 𝑗|𝑋𝑡−1,ℎ = 𝑖, 𝑧𝑡,ℎ) 

 [equation 5] 

where  𝑋𝑡,ℎ  is  the  labour  market  state  of  individual  ℎ at  time  𝑡,  and  𝑧𝑡,ℎ  is  a 

vector of covariates.  

The multinomial logit model is specified as follows: 

𝑝𝑖𝑗,𝑡(ℎ) =

exp[𝑔𝑖(𝑧𝑡,ℎ)]

∑

𝑘
𝑗=1

exp[𝑔𝑖(𝑧𝑡,ℎ)]

[equation 6] 

159 

 
 
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

where 

𝑔𝑗(𝑧𝑡,ℎ) = log [

Pr(𝑋𝑡,ℎ = 𝑗|𝑋𝑡−1,ℎ = 𝑖, 𝑧𝑡,ℎ)
] 
Pr(𝑋𝑡,ℎ = 𝑗∗|𝑋𝑡−1,ℎ = 𝑖, 𝑧𝑡,ℎ)

 = 𝛽𝑗0 + 𝛽𝑗1𝑧1ℎ + ⋯ + 𝛽𝑗𝑝𝑍𝑝,ℎ  

[equation 7] 

with 𝑔𝑗∗( 𝑧𝑡,ℎ) = 0.  
We  do  not  take  permanence  in  the  initial  labour  market  status  as  the 
reference  category  ( 𝑗∗ ≠ 𝑖),  since  we  are  interested  in  the  determinants  of 
permanence in a low-skilled job.  

The  coefficients  of  multinomial  models  are  generally  not  directly 
interpretable. Instead, the marginal effects of the explanatory variables are given 
by the following equation (115): 

𝜕 Pr(𝑋𝑡,ℎ=𝑗) 
𝜕𝑧𝑚

= Pr(𝑋𝑡,ℎ = 𝑗|𝑍) × [𝛽𝑚

𝑗 − ∑
𝐾
𝑗=1

𝑗 Pr ( 𝑋𝑡,ℎ = 𝑗|𝑍)
] 
𝛽𝑚

[equation 8] 

the marginal effect of a change in the  mth regressor on the probability that 

alternative j is the outcome.  

It  is  also  helpful  to  transform  the  model  coefficient  in  the  odds  ratio  or 
relative  risk  ratio,  because,  with  interaction  term,  marginal  effects  cannot  be 
computed.  The  relative-risk  ratio  of  choosing  alternative  j  rather  than  the 
reference category j* is given by: 

Pr(𝑋𝑡,ℎ=𝑗 )
Pr(𝑋𝑡,ℎ=j∗)

= exp(z𝑚

′ βj) 

[equation 9] 

the proportionate change in the probability of occurrence of outcome j rather 

than the reference category j* for one unit change of explanatory variables. 

(115)  As a reference for this multinomial logit framework and interpretation of the marginal 
effects see Bukowski and Lewandowski (2005) and Cameron and Trivedi (2010). 

160 

 
                                                
ANNEX 5.   
Cost of low skills: empirical specification for 
the macroeconomic approach  

Starting  with  the  standard  version  of  a  ‘neoclassical’  growth model,  we  assume 
production output Y to result from a production function F, depending on a level 
parameter  of  productivity  A,  capital  K  and  labour  L  and  a  given  production 
technology, for example Cobb Douglas, such as: 

[equation 10] 

Following  the  basic  assumption  of  a  neoclassical  production  function,  this 
function increases from the origin with decreasing returns. The marginal products 
–  the  change  of  output  based  on  an  increase  of  a  unit  in  either  one  factor  of 
production  –  can  be  obtained  by  the  partial  of  the  production  function  by  the 
relevant factor;  

[equation 11] 

Under standard neoclassical assumptions, these marginal products exhaust 
the  production  result  and  correspond  to  wages  and  the  costs  of  capital. 
Assuming,  in the  case  of  Cobb Douglas,  that output  per  capita  can  be 
related to a per capita capital stock and a production technology as follows: 

[equation 12] 

This  proposes  a  per  capita  representation  of  the  production  output,  with  a 
per capita capital stock as the sole factor of production. Exogenous variables to 
this  relationship  are  the  rate  of  depreciation  ,  the  savings  rate  s,  the  rate  of 
population  growth  n  all  affecting  the  intensity  and  the  rate  of  the  technological 
change g affecting the level parameter  A. The comparative statics of this model 
suggest that – provided other parameters remain unchanged – an increase in the 

161 

LKALKFY,LYLLKALKALLKFY1,KYLKAKLKFY1,kfkALKALLKALLKALY1 
 
 
 
 
 
 
  
Investing in skills pays off 
The economic and social cost of low-skilled adults in the EU 

savings  rate  would  increase  the  per  capita  capital  stock,  the  per  capita  output 
and the overall output in the economy. However, without an exogenous change 
to  technological  progress,  long-term  growth  as  well  as  the  per  capita  output 
would result in a steady state and convergence to a steady state per capita stock 
k*. 

[equation 13] 

In contrast, a positive productivity increase by some magnitude would, in the 
long-term,  increase  output  by  more  than  this  quantity  as  there  would  be  further 
effects via increased savings from the higher output.  

A  log  specification  of  the  production  function  to  show  growth  over  time  t 
relative to a base level in period 0 incorporating this convergence property shows 
the main properties of the standard neoclassical growth model. 

[equation 14] 

As described in Mankiw et al. (1992, p. 410), the assumption that the factors 
of production are rewarded based on their marginal products makes it possible to 
derive  both  the  sign  and  the  magnitude  of  changes  in  savings  and  population 
growth. The empirical specification of such a model, such as to test the prediction 
of the Solow growth model using a dynamic panel data model for a total number 
of i countries and t time periods observed is straightforward, by simply adding a 
further error term to this and estimating OLS. 

The  augmented  growth  model  following  Mankiw  et  al.  (1992)  includes 
measures  for  skills/human  capital  in  the  economy  in  the  model,  which  are 
theoretically suggesting a positive impact on production out (Mankiw et al., 1992, 
p. 418).  In  empirical  tests,  this  can  be  the  logarithm  of  the  secondary-school 
enrolment rate (enrit). We follow this basic set-up and specify our human capital 
augmented  growth  model  by  including  the  percentage  shares  of  people  of  the 
population  of  aged  25  to  65,  who  achieved  at  ISCED  3-4  and  an  equivalent 
variable for the proportion of people with ISCED 5-8 attainment. 

In addition to differential levels in human capital, we include further variables 
to  account  for  differences  in  growth  caused  by  country-specific  labour  market 
participation patterns. This is especially relevant in the context of Europe, where 
employment  and  working  time  differs  markedly,  with  higher  working  time  in  the 
formerly communist Central and Eastern Member States and lower working time 
in the continental economies of the West (in particular, Germany, France and the 
Netherlands).  

162 

)1/(1*)/(gnskgnsgALYttln1ln1lnln0 
 
Investing in skills pays off: 
the economic and social cost 
of low-skilled adults in the EU

This  Cedefop  study  seeks  to  provide  a  comprehensive  and 
robust  evidence  base  on  low-skilled  adults  in  the  European 
Union. It analyses trends in low skills among adults as well as 
characteristics, determinants and risks of being low-skilled. The 
study  also  provides  an  estimation  of  the  individual  and  social 
value in monetary terms of a faster increase in the general level 
of skills in Member States compared to the current trend. Fully 
appreciating  the  benefits  of  updating  the  skills  of  individuals 
through adult and lifelong learning is essential to strengthening 
the rationale for public intervention in this area and to designing 
and  implementing  effective  policies  tailored  to  this  particular 
target group.

5
5
6
0
Ε
Ν

-
T
I
-

B
C
-
1
7
-
0
0
2
-
E
N
N

-

-

d
o
i
:
1
0
.
2
8
0
1
/
2
3
2
5
0

Europe 123, 570 01 Thessaloniki (Pylea), GREECE
Postal address: PO Box 22427, 551 02 Thessaloniki, GREECE
Tel. +30 2310490111, Fax +30 2310490020
E-mail: info@cedefop.europa.eu
www.cedefop.europa.eu

Investing in skills pays off: 
the economic and social 
cost of low-skilled adults 
in the EU